practice.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from scipy.ndimage.interpolation import shift"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_excel('practice/2017_PRACTICE_IMPACT_MASTER.xlsx', sheet_name=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "\"['Unnamed: 8'] not found in axis\"",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-222-c2361d25f1b0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolumns\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'Unnamed: 8'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[1;32m   3695\u001b[0m                                            \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3696\u001b[0m                                            \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minplace\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3697\u001b[0;31m                                            errors=errors)\n\u001b[0m\u001b[1;32m   3698\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3699\u001b[0m     @rewrite_axis_style_signature('mapper', [('copy', True),\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[1;32m   3109\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;32min\u001b[0m \u001b[0maxes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3110\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3111\u001b[0;31m                 \u001b[0mobj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_drop_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3112\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3113\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m_drop_axis\u001b[0;34m(self, labels, axis, level, errors)\u001b[0m\n\u001b[1;32m   3141\u001b[0m                 \u001b[0mnew_axis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3142\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3143\u001b[0;31m                 \u001b[0mnew_axis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3144\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0maxis_name\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mnew_axis\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3145\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, errors)\u001b[0m\n\u001b[1;32m   4402\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0merrors\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m'ignore'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4403\u001b[0m                 raise KeyError(\n\u001b[0;32m-> 4404\u001b[0;31m                     '{} not found in axis'.format(labels[mask]))\n\u001b[0m\u001b[1;32m   4405\u001b[0m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m~\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4406\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdelete\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: \"['Unnamed: 8'] not found in axis\""
     ]
    }
   ],
   "source": [
    "data = data.drop(columns = ['Unnamed: 8'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "practice_dates = data['date'].unique()\n",
    "players = data['PLAYER NAME - CORRECTED'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['  09/26/2017', '  09/27/2017', '  10/03/2017', '  10/04/2017',\n",
       "       '  10/05/2017', '  10/07/2017', '  10/10/2017', '  10/11/2017',\n",
       "       '  10/12/2017', '  10/14/2017', '  10/17/2017', '  10/18/2017',\n",
       "       '  10/19/2017', '  10/21/2017', '  10/24/2017', '  10/25/2017',\n",
       "       '  10/26/2017', '  10/28/2017', '  10/31/2017', '  11/01/2017',\n",
       "       '  11/02/2017', '  11/05/2017', '  11/07/2017', '  11/08/2017',\n",
       "       '  11/10/2017', '  11/14/2017', '  11/17/2017', '  11/18/2017',\n",
       "       '  11/20/2017', '  11/21/2017'], dtype=object)"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "practice_dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Sydney Wilson', 'Gabby Benda', 'Alessia Del Romano',\n",
       "       'Nicole Alford', 'Cori Clifton', 'Grace Rigsbee', 'Ashley Askin',\n",
       "       'Lauren Frerking', 'Kendall Wilson', 'Gabriela Stavnetchei',\n",
       "       'Kodie Comby', 'Sam Knapp', 'Emily Becker', 'Simone Spencer'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "players"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Sydney', 'Wilson']\n"
     ]
    }
   ],
   "source": [
    "x = 'sydney wilson'\n",
    "y = x.split(' ')\n",
    "y[0] = y[0][0].upper() + y[0][1:]\n",
    "y[1] = y[1][0].upper() + y[1][1:]\n",
    "\n",
    "print(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "sydney = data.loc[np.bitwise_and(data['date'] == '  09/26/2017',\n",
    "                        data['PLAYER NAME - CORRECTED'] == 'Sydney Wilson')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f21356ce390>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 2520x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, figsize = (35, 4))\n",
    "sydney['gforce (G)'].plot(ax = ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "sydney['diff'] = abs(sydney['gforce (G)'].shift(-1) - sydney['gforce (G)'])\n",
    "sydney.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sydney['datetime'] = sydney.apply(func = lambda x: x['time (s)'].split(' ')[0], axis = 1)\n",
    "sydney['datetime'] = pd.to_datetime(sydney['datetime'], format='%H:%M:%S.%f')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sydney['datetime_diff'] = abs(sydney['datetime'].shift(-1) - sydney['datetime'])\n",
    "sydney.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sydney['datetime_diff'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, figsize =  (20, 5))\n",
    "\n",
    "plt.scatter(sydney.index, sydney['gforce (G)'])\n",
    "\n",
    "from io import BytesIO\n",
    "import base64\n",
    "\n",
    "buf = BytesIO()\n",
    "plt.savefig(buf, format='png')\n",
    "def convertback(buffer):\n",
    "    img_str = base64.b64encode(buffer.getvalue())\n",
    "    return img_str.decode('utf-8')\n",
    "\n",
    "returnval = convertback(buf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [],
   "source": [
    "i = np.array(sydney.index)\n",
    "\n",
    "sydney['index_diff'] = i - shift(i, -1, cval=np.NaN)\n",
    "sydney = sydney[0:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [],
   "source": [
    "sydney['stop'] = (sydney['index_diff'] <= -200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    1471\n",
       "True       14\n",
       "Name: index_diff, dtype: int64"
      ]
     },
     "execution_count": 249,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(sydney['index_diff'] <= -200).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gforce avg for 1 jump: 3.708701\n",
      "Max Gforce for 1 jump: 9.986491\n",
      "******************\n",
      "Gforce avg for 2 jump: 3.524912\n",
      "Max Gforce for 2 jump: 11.153923\n",
      "******************\n",
      "Gforce avg for 3 jump: 3.943442\n",
      "Max Gforce for 3 jump: 18.782438\n",
      "******************\n",
      "Gforce avg for 4 jump: 2.446080\n",
      "Max Gforce for 4 jump: 3.144837\n",
      "******************\n",
      "Gforce avg for 5 jump: 3.617807\n",
      "Max Gforce for 5 jump: 13.164725\n",
      "******************\n",
      "Gforce avg for 6 jump: 3.757897\n",
      "Max Gforce for 6 jump: 11.552056\n",
      "******************\n",
      "Gforce avg for 7 jump: 3.626748\n",
      "Max Gforce for 7 jump: 11.903782\n",
      "******************\n",
      "Gforce avg for 8 jump: 3.929898\n",
      "Max Gforce for 8 jump: 33.657688\n",
      "******************\n",
      "Gforce avg for 9 jump: 3.908248\n",
      "Max Gforce for 9 jump: 11.488255\n",
      "******************\n",
      "Gforce avg for 10 jump: 3.863866\n",
      "Max Gforce for 10 jump: 13.843049\n",
      "******************\n",
      "Gforce avg for 11 jump: 3.559342\n",
      "Max Gforce for 11 jump: 9.565041\n",
      "******************\n",
      "Gforce avg for 12 jump: 3.534439\n",
      "Max Gforce for 12 jump: 15.173002\n",
      "******************\n",
      "Gforce avg for 13 jump: 2.711740\n",
      "Max Gforce for 13 jump: 3.839271\n",
      "******************\n",
      "Gforce avg for 14 jump: 4.311932\n",
      "Max Gforce for 14 jump: 21.567104\n",
      "******************\n",
      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\n"
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    {
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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "indeces = sydney[sydney['stop']].index\n",
    "\n",
    "fig = plt.figure(figsize = (20,5))\n",
    "\n",
    "plt.scatter(sydney.index, sydney['gforce (G)'])\n",
    "for i in range(0, len(indeces)):\n",
    "    partial = None\n",
    "    if(i == 0):\n",
    "        partial = sydney.loc[0:indeces[i]]\n",
    "    elif(i == len(indeces) - 1):\n",
    "        partial = sydney.loc[indeces[i]:]\n",
    "    else:\n",
    "        partial = sydney.loc[indeces[i]:indeces[i+1]]\n",
    "    if(partial['gforce (G)'].max() > 27.39012432):\n",
    "        plt.scatter(partial['gforce (G)'].idxmax(), partial['gforce (G)'].max(),  linewidths=100, marker = '+', color = 'green', s = 500)\n",
    "    else:\n",
    "        pass\n",
    "    print('Gforce avg for %d jump: %f' % (i+1, partial['gforce (G)'].mean()))\n",
    "    print('Max Gforce for %d jump: %f' % (i+1, partial['gforce (G)'].max()))\n",
    "    print('******************')\n",
    "    plt.axvline(indeces[i], linestyle = '--', color = 'r')\n",
    "\n",
    "plt.axhline(sydney['gforce (G)'].mean(), linestyle = '--', color  = 'black')\n",
    "plt.xlabel('Index of Jump')\n",
    "plt.ylabel('GForce of Jump (G)')\n",
    "plt.title('Data of Blank')\n",
    "plt.legend()\n",
    "\n",
    "from io import BytesIO\n",
    "import base64\n",
    "\n",
    "buf = BytesIO()\n",
    "plt.savefig(buf, format='png')\n",
    "def convertback(buffer):\n",
    "    img_str = base64.b64encode(buffer.getvalue())\n",
    "    return img_str.decode('utf-8')\n",
    "\n",
    "returnval = convertback(buf)\n",
    "\n",
    "print(returnval)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  11/21/2017*************************\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/sachk/.local/lib/python3.6/site-packages/ipykernel_launcher.py:19: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "/home/sachk/.local/lib/python3.6/site-packages/ipykernel_launcher.py:21: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  11/21/2017*************************\n",
      "  11/21/2017*************************\n",
      "  11/21/2017*************************\n",
      "  11/21/2017*************************\n",
      "  11/21/2017*************************\n",
      "  11/21/2017*************************\n",
      "  11/21/2017*************************\n",
      "  11/21/2017*************************\n",
      "  11/21/2017*************************\n",
      "  11/21/2017*************************\n",
      "  11/21/2017*************************\n",
      "  11/21/2017*************************\n",
      "  11/21/2017*************************\n"
     ]
    }
   ],
   "source": [
    "from collections import defaultdict\n",
    "\n",
    "\"\"\"\n",
    "x = np.array(max_vals)\n",
    "x = x[~np.isnan(x)]\n",
    "q99 = np.percentile(x, [99])\n",
    "array([38.55761719])\n",
    "\n",
    "\"\"\"\n",
    "total_data = defaultdict(lambda : defaultdict(list))\n",
    "max_vals = []\n",
    "for player in players:\n",
    "    print(practice + \"*************************\")\n",
    "    for practice in practice_dates:\n",
    "        current_player = data.loc[np.bitwise_and(data['date'] == practice,\n",
    "                        data['PLAYER NAME - CORRECTED'] == player)]\n",
    "        i = np.array(current_player.index)\n",
    "\n",
    "        current_player['diff'] = abs(current_player['gforce (G)'].shift(-1) - current_player['gforce (G)'])\n",
    "        sydney.dropna()\n",
    "        current_player['index_diff'] = i - shift(i, -1, cval=np.NaN)\n",
    "        current_player = current_player[0:-1]\n",
    "        current_player['stop'] = (current_player['index_diff'] <= -350)\n",
    "\n",
    "        l = {'bad_jump': 0, 'good_jump': 0}\n",
    "\n",
    "        indeces = current_player[current_player['stop']].index\n",
    "        for i in range(0, len(indeces)):\n",
    "            partial = None\n",
    "            if(i == 0):\n",
    "                partial = current_player.loc[0:indeces[i]]\n",
    "            elif(i == len(indeces) - 1):\n",
    "                partial = current_player.loc[indeces[i]:]\n",
    "            else:\n",
    "                partial = current_player.loc[indeces[i]:indeces[i+1]]\n",
    "            max_vals.append(partial['gforce (G)'].max())\n",
    "            #l.append([current_player['gforce (G)'].mean(), current_player['gforce (G)'].max()])\n",
    "            if(partial['gforce (G)'].max() > 27.39012432):\n",
    "                l['bad_jump']+=1\n",
    "            else:\n",
    "                l['good_jump']+=1\n",
    "        total_data[player][practice] = l\n",
    "            #print('Max Slope for jump: %d ' % (partial['diff'].max()) )\n",
    "            #print('Gforce avg for %d jump: %f' % (i+1, partial['gforce (G)'].mean()))\n",
    "            #print('Max Gforce for %d jump: %f' % (i+1, partial['gforce (G)'].max()))\n",
    "            #print('******************')\n",
    "        #plt.plot(current_player['index_diff'])\n",
    "        #plt.plot(current_player.index, current_player['gforce (G)'])\n",
    "        #plt.scatter(current_player.index, current_player['gforce (G)'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "defaultdict(<function __main__.<lambda>()>,\n",
       "            {'Sydney Wilson': defaultdict(list,\n",
       "                         {'  09/26/2017': {'bad_jump': 1, 'good_jump': 9},\n",
       "                          '  09/27/2017': {'bad_jump': 0, 'good_jump': 5},\n",
       "                          '  10/03/2017': {'bad_jump': 1, 'good_jump': 10},\n",
       "                          '  10/04/2017': {'bad_jump': 0, 'good_jump': 14},\n",
       "                          '  10/05/2017': {'bad_jump': 0, 'good_jump': 8},\n",
       "                          '  10/07/2017': {'bad_jump': 0, 'good_jump': 11},\n",
       "                          '  10/10/2017': {'bad_jump': 0, 'good_jump': 10},\n",
       "                          '  10/11/2017': {'bad_jump': 0, 'good_jump': 10},\n",
       "                          '  10/12/2017': {'bad_jump': 0, 'good_jump': 7},\n",
       "                          '  10/14/2017': {'bad_jump': 1, 'good_jump': 4},\n",
       "                          '  10/17/2017': {'bad_jump': 1, 'good_jump': 6},\n",
       "                          '  10/18/2017': {'bad_jump': 0, 'good_jump': 12},\n",
       "                          '  10/19/2017': {'bad_jump': 1, 'good_jump': 11},\n",
       "                          '  10/21/2017': {'bad_jump': 0, 'good_jump': 8},\n",
       "                          '  10/24/2017': {'bad_jump': 2, 'good_jump': 3},\n",
       "                          '  10/25/2017': {'bad_jump': 0, 'good_jump': 10},\n",
       "                          '  10/26/2017': {'bad_jump': 1, 'good_jump': 8},\n",
       "                          '  10/28/2017': {'bad_jump': 2, 'good_jump': 4},\n",
       "                          '  10/31/2017': {'bad_jump': 0, 'good_jump': 11},\n",
       "                          '  11/01/2017': {'bad_jump': 0, 'good_jump': 0},\n",
       "                          '  11/02/2017': {'bad_jump': 0, 'good_jump': 6},\n",
       "                          '  11/05/2017': {'bad_jump': 0, 'good_jump': 4},\n",
       "                          '  11/07/2017': {'bad_jump': 0, 'good_jump': 5},\n",
       "                          '  11/08/2017': {'bad_jump': 0, 'good_jump': 6},\n",
       "                          '  11/10/2017': {'bad_jump': 0, 'good_jump': 2},\n",
       "                          '  11/14/2017': {'bad_jump': 0, 'good_jump': 0},\n",
       "                          '  11/17/2017': {'bad_jump': 1, 'good_jump': 4},\n",
       "                          '  11/18/2017': {'bad_jump': 2, 'good_jump': 2},\n",
       "                          '  11/20/2017': {'bad_jump': 1, 'good_jump': 3},\n",
       "                          '  11/21/2017': {'bad_jump': 0, 'good_jump': 1}}),\n",
       "             'Gabby Benda': defaultdict(list,\n",
       "                         {'  09/26/2017': {'bad_jump': 1, 'good_jump': 3},\n",
       "                          '  09/27/2017': {'bad_jump': 2, 'good_jump': 0},\n",
       "                          '  10/03/2017': {'bad_jump': 0, 'good_jump': 3},\n",
       "                          '  10/04/2017': {'bad_jump': 1, 'good_jump': 5},\n",
       "                          '  10/05/2017': {'bad_jump': 0, 'good_jump': 1},\n",
       "                          '  10/07/2017': {'bad_jump': 0, 'good_jump': 1},\n",
       "                          '  10/10/2017': {'bad_jump': 0, 'good_jump': 0},\n",
       "                          '  10/11/2017': {'bad_jump': 1, 'good_jump': 1},\n",
       "                          '  10/12/2017': {'bad_jump': 0, 'good_jump': 0},\n",
       "                          '  10/14/2017': {'bad_jump': 0, 'good_jump': 0},\n",
       "                          '  10/17/2017': {'bad_jump': 0, 'good_jump': 3},\n",
       "                          '  10/18/2017': {'bad_jump': 0, 'good_jump': 2},\n",
       "                          '  10/19/2017': {'bad_jump': 0, 'good_jump': 1},\n",
       "                          '  10/21/2017': {'bad_jump': 0, 'good_jump': 0},\n",
       "                          '  10/24/2017': {'bad_jump': 0, 'good_jump': 1},\n",
       "                          '  10/25/2017': {'bad_jump': 0, 'good_jump': 0},\n",
       "                          '  10/26/2017': {'bad_jump': 1, 'good_jump': 0},\n",
       "                          '  10/28/2017': {'bad_jump': 1, 'good_jump': 0},\n",
       "                          '  10/31/2017': {'bad_jump': 0, 'good_jump': 1},\n",
       "                          '  11/01/2017': {'bad_jump': 0, 'good_jump': 0},\n",
       "                          '  11/02/2017': {'bad_jump': 0, 'good_jump': 4},\n",
       "                          '  11/05/2017': {'bad_jump': 0, 'good_jump': 0},\n",
       "                          '  11/07/2017': {'bad_jump': 0, 'good_jump': 1},\n",
       "                          '  11/08/2017': {'bad_jump': 1, 'good_jump': 3},\n",
       "                          '  11/10/2017': {'bad_jump': 0, 'good_jump': 0},\n",
       "                          '  11/14/2017': {'bad_jump': 0, 'good_jump': 1},\n",
       "                          '  11/17/2017': {'bad_jump': 0, 'good_jump': 0},\n",
       "                          '  11/18/2017': {'bad_jump': 0, 'good_jump': 0},\n",
       "                          '  11/20/2017': {'bad_jump': 0, 'good_jump': 5},\n",
       "                          '  11/21/2017': {'bad_jump': 0, 'good_jump': 0}}),\n",
       "             'Alessia Del Romano': defaultdict(list,\n",
       "                         {'  09/26/2017': {'bad_jump': 1, 'good_jump': 5},\n",
       "                          '  09/27/2017': {'bad_jump': 0, 'good_jump': 2},\n",
       "                          '  10/03/2017': {'bad_jump': 1, 'good_jump': 6},\n",
       "                          '  10/04/2017': {'bad_jump': 1, 'good_jump': 5},\n",
       "                          '  10/05/2017': {'bad_jump': 1, 'good_jump': 3},\n",
       "                          '  10/07/2017': {'bad_jump': 0, 'good_jump': 0},\n",
       "                          '  10/10/2017': {'bad_jump': 1, 'good_jump': 3},\n",
       "                          '  10/11/2017': {'bad_jump': 0, 'good_jump': 6},\n",
       "                          '  10/12/2017': {'bad_jump': 1, 'good_jump': 3},\n",
       "                          '  10/14/2017': {'bad_jump': 0, 'good_jump': 2},\n",
       "                          '  10/17/2017': {'bad_jump': 0, 'good_jump': 5},\n",
       "                          '  10/18/2017': {'bad_jump': 1, 'good_jump': 5},\n",
       "                          '  10/19/2017': {'bad_jump': 1, 'good_jump': 4},\n",
       "                          '  10/21/2017': {'bad_jump': 0, 'good_jump': 0},\n",
       "                          '  10/24/2017': {'bad_jump': 1, 'good_jump': 2},\n",
       "                          '  10/25/2017': {'bad_jump': 0, 'good_jump': 2},\n",
       "                          '  10/26/2017': {'bad_jump': 1, 'good_jump': 5},\n",
       "                          '  10/28/2017': {'bad_jump': 1, 'good_jump': 2},\n",
       "                          '  10/31/2017': {'bad_jump': 0, 'good_jump': 0},\n",
       "                          '  11/01/2017': {'bad_jump': 0, 'good_jump': 0},\n",
       "                          '  11/02/2017': {'bad_jump': 0, 'good_jump': 1},\n",
       "                          '  11/05/2017': {'bad_jump': 1, 'good_jump': 0},\n",
       "                          '  11/07/2017': {'bad_jump': 1, 'good_jump': 0},\n",
       "                          '  11/08/2017': {'bad_jump': 2, 'good_jump': 2},\n",
       "                          '  11/10/2017': {'bad_jump': 1, 'good_jump': 2},\n",
       "                          '  11/14/2017': {'bad_jump': 0, 'good_jump': 1},\n",
       "                          '  11/17/2017': {'bad_jump': 0, 'good_jump': 4},\n",
       "                          '  11/18/2017': {'bad_jump': 1, 'good_jump': 0},\n",
       "                          '  11/20/2017': {'bad_jump': 2, 'good_jump': 1},\n",
       "                          '  11/21/2017': {'bad_jump': 0, 'good_jump': 4}}),\n",
       "             'Nicole Alford': defaultdict(list,\n",
       "                         {'  09/26/2017': {'bad_jump': 1, 'good_jump': 3},\n",
       "                          '  09/27/2017': {'bad_jump': 0, 'good_jump': 3},\n",
       "                          '  10/03/2017': {'bad_jump': 0, 'good_jump': 4},\n",
       "                          '  10/04/2017': {'bad_jump': 1, 'good_jump': 9},\n",
       "                          '  10/05/2017': {'bad_jump': 1, 'good_jump': 1},\n",
       "                          '  10/07/2017': {'bad_jump': 0, 'good_jump': 1},\n",
       "                          '  10/10/2017': {'bad_jump': 0, 'good_jump': 0},\n",
       "                          '  10/11/2017': {'bad_jump': 0, 'good_jump': 0},\n",
       "                          '  10/12/2017': {'bad_jump': 0, 'good_jump': 0},\n",
       "                          '  10/14/2017': {'bad_jump': 0, 'good_jump': 0},\n",
       "                          '  10/17/2017': {'bad_jump': 0, 'good_jump': 0},\n",
       "                          '  10/18/2017': {'bad_jump': 0, 'good_jump': 0},\n",
       "                          '  10/19/2017': {'bad_jump': 0, 'good_jump': 0},\n",
       "                          '  10/21/2017': {'bad_jump': 0, 'good_jump': 0},\n",
       "                          '  10/24/2017': {'bad_jump': 0, 'good_jump': 0},\n",
       "                          '  10/25/2017': {'bad_jump': 1, 'good_jump': 5},\n",
       "                          '  10/26/2017': {'bad_jump': 0, 'good_jump': 2},\n",
       "                          '  10/28/2017': {'bad_jump': 2, 'good_jump': 1},\n",
       "                          '  10/31/2017': {'bad_jump': 0, 'good_jump': 1},\n",
       "                          '  11/01/2017': {'bad_jump': 0, 'good_jump': 0},\n",
       "                          '  11/02/2017': {'bad_jump': 0, 'good_jump': 4},\n",
       "                          '  11/05/2017': {'bad_jump': 0, 'good_jump': 1},\n",
       "                          '  11/07/2017': {'bad_jump': 1, 'good_jump': 2},\n",
       "                          '  11/08/2017': {'bad_jump': 1, 'good_jump': 2},\n",
       "                          '  11/10/2017': {'bad_jump': 0, 'good_jump': 0},\n",
       "                          '  11/14/2017': {'bad_jump': 1, 'good_jump': 6},\n",
       "                          '  11/17/2017': {'bad_jump': 1, 'good_jump': 3},\n",
       "                          '  11/18/2017': {'bad_jump': 0, 'good_jump': 13},\n",
       "                          '  11/20/2017': {'bad_jump': 1, 'good_jump': 5},\n",
       "                          '  11/21/2017': {'bad_jump': 1, 'good_jump': 8}}),\n",
       "             'Cori Clifton': defaultdict(list,\n",
       "                         {'  09/26/2017': {'bad_jump': 0, 'good_jump': 2},\n",
       "                          '  09/27/2017': {'bad_jump': 0, 'good_jump': 3},\n",
       "                          '  10/03/2017': {'bad_jump': 2, 'good_jump': 12},\n",
       "                          '  10/04/2017': {'bad_jump': 0, 'good_jump': 12},\n",
       "                          '  10/05/2017': {'bad_jump': 1, 'good_jump': 5},\n",
       "                          '  10/07/2017': {'bad_jump': 3, 'good_jump': 7},\n",
       "                          '  10/10/2017': {'bad_jump': 3, 'good_jump': 9},\n",
       "                          '  10/11/2017': {'bad_jump': 1, 'good_jump': 2},\n",
       "                          '  10/12/2017': {'bad_jump': 0, 'good_jump': 11},\n",
       "                          '  10/14/2017': {'bad_jump': 0, 'good_jump': 3},\n",
       "                          '  10/17/2017': {'bad_jump': 0, 'good_jump': 5},\n",
       "                          '  10/18/2017': {'bad_jump': 1, 'good_jump': 11},\n",
       "                          '  10/19/2017': {'bad_jump': 0, 'good_jump': 15},\n",
       "                          '  10/21/2017': {'bad_jump': 0, 'good_jump': 8},\n",
       "                          '  10/24/2017': {'bad_jump': 0, 'good_jump': 9},\n",
       "                          '  10/25/2017': {'bad_jump': 0, 'good_jump': 9},\n",
       "                          '  10/26/2017': {'bad_jump': 1, 'good_jump': 6},\n",
       "                          '  10/28/2017': {'bad_jump': 3, 'good_jump': 5},\n",
       "                          '  10/31/2017': {'bad_jump': 2, 'good_jump': 10},\n",
       "                          '  11/01/2017': {'bad_jump': 0, 'good_jump': 0},\n",
       "                          '  11/02/2017': {'bad_jump': 1, 'good_jump': 7},\n",
       "                          '  11/05/2017': {'bad_jump': 0, 'good_jump': 8},\n",
       "                          '  11/07/2017': {'bad_jump': 1, 'good_jump': 16},\n",
       "                          '  11/08/2017': {'bad_jump': 0, 'good_jump': 8},\n",
       "                          '  11/10/2017': {'bad_jump': 0, 'good_jump': 9},\n",
       "                          '  11/14/2017': {'bad_jump': 0, 'good_jump': 11},\n",
       "                          '  11/17/2017': {'bad_jump': 1, 'good_jump': 0},\n",
       "                          '  11/18/2017': {'bad_jump': 0, 'good_jump': 3},\n",
       "                          '  11/20/2017': {'bad_jump': 1, 'good_jump': 1},\n",
       "                          '  11/21/2017': {'bad_jump': 0, 'good_jump': 0}}),\n",
       "             'Grace Rigsbee': defaultdict(list,\n",
       "                         {'  09/26/2017': {'bad_jump': 0, 'good_jump': 5},\n",
       "                          '  09/27/2017': {'bad_jump': 0, 'good_jump': 2},\n",
       "                          '  10/03/2017': {'bad_jump': 0, 'good_jump': 12},\n",
       "                          '  10/04/2017': {'bad_jump': 1, 'good_jump': 15},\n",
       "                          '  10/05/2017': {'bad_jump': 0, 'good_jump': 1},\n",
       "                          '  10/07/2017': {'bad_jump': 0, 'good_jump': 7},\n",
       "                          '  10/10/2017': {'bad_jump': 0, 'good_jump': 14},\n",
       "                          '  10/11/2017': {'bad_jump': 1, 'good_jump': 9},\n",
       "                          '  10/12/2017': {'bad_jump': 1, 'good_jump': 12},\n",
       "                          '  10/14/2017': {'bad_jump': 0, 'good_jump': 4},\n",
       "                          '  10/17/2017': {'bad_jump': 1, 'good_jump': 7},\n",
       "                          '  10/18/2017': {'bad_jump': 0, 'good_jump': 11},\n",
       "                          '  10/19/2017': {'bad_jump': 1, 'good_jump': 10},\n",
       "                          '  10/21/2017': {'bad_jump': 0, 'good_jump': 7},\n",
       "                          '  10/24/2017': {'bad_jump': 2, 'good_jump': 11},\n",
       "                          '  10/25/2017': {'bad_jump': 0, 'good_jump': 12},\n",
       "                          '  10/26/2017': {'bad_jump': 0, 'good_jump': 9},\n",
       "                          '  10/28/2017': {'bad_jump': 1, 'good_jump': 10},\n",
       "                          '  10/31/2017': {'bad_jump': 0, 'good_jump': 12},\n",
       "                          '  11/01/2017': {'bad_jump': 0, 'good_jump': 0},\n",
       "                          '  11/02/2017': {'bad_jump': 0, 'good_jump': 5},\n",
       "                          '  11/05/2017': {'bad_jump': 0, 'good_jump': 1},\n",
       "                          '  11/07/2017': {'bad_jump': 0, 'good_jump': 8},\n",
       "                          '  11/08/2017': {'bad_jump': 1, 'good_jump': 6},\n",
       "                          '  11/10/2017': {'bad_jump': 1, 'good_jump': 5},\n",
       "                          '  11/14/2017': {'bad_jump': 1, 'good_jump': 8},\n",
       "                          '  11/17/2017': {'bad_jump': 0, 'good_jump': 3},\n",
       "                          '  11/18/2017': {'bad_jump': 0, 'good_jump': 4},\n",
       "                          '  11/20/2017': {'bad_jump': 1, 'good_jump': 7},\n",
       "                          '  11/21/2017': {'bad_jump': 0, 'good_jump': 4}}),\n",
       "             'Ashley Askin': defaultdict(list,\n",
       "                         {'  09/26/2017': {'bad_jump': 1, 'good_jump': 8},\n",
       "                          '  09/27/2017': {'bad_jump': 0, 'good_jump': 5},\n",
       "                          '  10/03/2017': {'bad_jump': 0, 'good_jump': 6},\n",
       "                          '  10/04/2017': {'bad_jump': 0, 'good_jump': 5},\n",
       "                          '  10/05/2017': {'bad_jump': 1, 'good_jump': 0},\n",
       "                          '  10/07/2017': {'bad_jump': 0, 'good_jump': 3},\n",
       "                          '  10/10/2017': {'bad_jump': 0, 'good_jump': 1},\n",
       "                          '  10/11/2017': {'bad_jump': 1, 'good_jump': 4},\n",
       "                          '  10/12/2017': {'bad_jump': 0, 'good_jump': 1},\n",
       "                          '  10/14/2017': {'bad_jump': 0, 'good_jump': 3},\n",
       "                          '  10/17/2017': {'bad_jump': 1, 'good_jump': 1},\n",
       "                          '  10/18/2017': {'bad_jump': 2, 'good_jump': 2},\n",
       "                          '  10/19/2017': {'bad_jump': 1, 'good_jump': 0},\n",
       "                          '  10/21/2017': {'bad_jump': 0, 'good_jump': 3},\n",
       "                          '  10/24/2017': {'bad_jump': 2, 'good_jump': 4},\n",
       "                          '  10/25/2017': {'bad_jump': 2, 'good_jump': 1},\n",
       "                          '  10/26/2017': {'bad_jump': 2, 'good_jump': 1},\n",
       "                          '  10/28/2017': {'bad_jump': 1, 'good_jump': 5},\n",
       "                          '  10/31/2017': {'bad_jump': 0, 'good_jump': 0},\n",
       "                          '  11/01/2017': {'bad_jump': 0, 'good_jump': 0},\n",
       "                          '  11/02/2017': {'bad_jump': 1, 'good_jump': 2},\n",
       "                          '  11/05/2017': {'bad_jump': 0, 'good_jump': 0},\n",
       "                          '  11/07/2017': {'bad_jump': 0, 'good_jump': 1},\n",
       "                          '  11/08/2017': {'bad_jump': 0, 'good_jump': 5},\n",
       "                          '  11/10/2017': {'bad_jump': 1, 'good_jump': 1},\n",
       "                          '  11/14/2017': {'bad_jump': 1, 'good_jump': 2},\n",
       "                          '  11/17/2017': {'bad_jump': 1, 'good_jump': 1},\n",
       "                          '  11/18/2017': {'bad_jump': 2, 'good_jump': 0},\n",
       "                          '  11/20/2017': {'bad_jump': 0, 'good_jump': 4},\n",
       "                          '  11/21/2017': {'bad_jump': 0, 'good_jump': 0}}),\n",
       "             'Lauren Frerking': defaultdict(list,\n",
       "                         {'  09/26/2017': {'bad_jump': 0, 'good_jump': 12},\n",
       "                          '  09/27/2017': {'bad_jump': 0, 'good_jump': 3},\n",
       "                          '  10/03/2017': {'bad_jump': 1, 'good_jump': 6},\n",
       "                          '  10/04/2017': {'bad_jump': 0, 'good_jump': 7},\n",
       "                          '  10/05/2017': {'bad_jump': 0, 'good_jump': 9},\n",
       "                          '  10/07/2017': {'bad_jump': 0, 'good_jump': 10},\n",
       "                          '  10/10/2017': {'bad_jump': 0, 'good_jump': 7},\n",
       "                          '  10/11/2017': {'bad_jump': 0, 'good_jump': 6},\n",
       "                          '  10/12/2017': {'bad_jump': 0, 'good_jump': 8},\n",
       "                          '  10/14/2017': {'bad_jump': 0, 'good_jump': 6},\n",
       "                          '  10/17/2017': {'bad_jump': 2, 'good_jump': 3},\n",
       "                          '  10/18/2017': {'bad_jump': 1, 'good_jump': 9},\n",
       "                          '  10/19/2017': {'bad_jump': 0, 'good_jump': 12},\n",
       "                          '  10/21/2017': {'bad_jump': 0, 'good_jump': 9},\n",
       "                          '  10/24/2017': {'bad_jump': 0, 'good_jump': 5},\n",
       "                          '  10/25/2017': {'bad_jump': 0, 'good_jump': 11},\n",
       "                          '  10/26/2017': {'bad_jump': 0, 'good_jump': 11},\n",
       "                          '  10/28/2017': {'bad_jump': 0, 'good_jump': 6},\n",
       "                          '  10/31/2017': {'bad_jump': 1, 'good_jump': 7},\n",
       "                          '  11/01/2017': {'bad_jump': 0, 'good_jump': 0},\n",
       "                          '  11/02/2017': {'bad_jump': 0, 'good_jump': 7},\n",
       "                          '  11/05/2017': {'bad_jump': 1, 'good_jump': 7},\n",
       "                          '  11/07/2017': {'bad_jump': 1, 'good_jump': 7},\n",
       "                          '  11/08/2017': {'bad_jump': 0, 'good_jump': 11},\n",
       "                          '  11/10/2017': {'bad_jump': 0, 'good_jump': 4},\n",
       "                          '  11/14/2017': {'bad_jump': 0, 'good_jump': 4},\n",
       "                          '  11/17/2017': {'bad_jump': 0, 'good_jump': 6},\n",
       "                          '  11/18/2017': {'bad_jump': 0, 'good_jump': 4},\n",
       "                          '  11/20/2017': {'bad_jump': 0, 'good_jump': 3},\n",
       "                          '  11/21/2017': {'bad_jump': 1, 'good_jump': 4}}),\n",
       "             'Kendall Wilson': defaultdict(list,\n",
       "                         {'  09/26/2017': {'bad_jump': 0, 'good_jump': 10},\n",
       "                          '  09/27/2017': {'bad_jump': 0, 'good_jump': 4},\n",
       "                          '  10/03/2017': {'bad_jump': 0, 'good_jump': 10},\n",
       "                          '  10/04/2017': {'bad_jump': 0, 'good_jump': 8},\n",
       "                          '  10/05/2017': {'bad_jump': 0, 'good_jump': 4},\n",
       "                          '  10/07/2017': {'bad_jump': 1, 'good_jump': 8},\n",
       "                          '  10/10/2017': {'bad_jump': 0, 'good_jump': 10},\n",
       "                          '  10/11/2017': {'bad_jump': 0, 'good_jump': 14},\n",
       "                          '  10/12/2017': {'bad_jump': 1, 'good_jump': 10},\n",
       "                          '  10/14/2017': {'bad_jump': 1, 'good_jump': 0},\n",
       "                          '  10/17/2017': {'bad_jump': 0, 'good_jump': 5},\n",
       "                          '  10/18/2017': {'bad_jump': 0, 'good_jump': 11},\n",
       "                          '  10/19/2017': {'bad_jump': 0, 'good_jump': 12},\n",
       "                          '  10/21/2017': {'bad_jump': 0, 'good_jump': 10},\n",
       "                          '  10/24/2017': {'bad_jump': 0, 'good_jump': 4},\n",
       "                          '  10/25/2017': {'bad_jump': 0, 'good_jump': 10},\n",
       "                          '  10/26/2017': {'bad_jump': 2, 'good_jump': 10},\n",
       "                          '  10/28/2017': {'bad_jump': 0, 'good_jump': 7},\n",
       "                          '  10/31/2017': {'bad_jump': 1, 'good_jump': 7},\n",
       "                          '  11/01/2017': {'bad_jump': 0, 'good_jump': 0},\n",
       "                          '  11/02/2017': {'bad_jump': 0, 'good_jump': 0},\n",
       "                          '  11/05/2017': {'bad_jump': 0, 'good_jump': 0},\n",
       "                          '  11/07/2017': {'bad_jump': 0, 'good_jump': 0},\n",
       "                          '  11/08/2017': {'bad_jump': 0, 'good_jump': 0},\n",
       "                          '  11/10/2017': {'bad_jump': 0, 'good_jump': 0},\n",
       "                          '  11/14/2017': {'bad_jump': 0, 'good_jump': 0},\n",
       "                          '  11/17/2017': {'bad_jump': 0, 'good_jump': 0},\n",
       "                          '  11/18/2017': {'bad_jump': 0, 'good_jump': 0},\n",
       "                          '  11/20/2017': {'bad_jump': 0, 'good_jump': 0},\n",
       "                          '  11/21/2017': {'bad_jump': 0, 'good_jump': 0}}),\n",
       "             'Gabriela Stavnetchei': defaultdict(list,\n",
       "                         {'  09/26/2017': {'bad_jump': 2, 'good_jump': 7},\n",
       "                          '  09/27/2017': {'bad_jump': 0, 'good_jump': 1},\n",
       "                          '  10/03/2017': {'bad_jump': 1, 'good_jump': 0},\n",
       "                          '  10/04/2017': {'bad_jump': 1, 'good_jump': 2},\n",
       "                          '  10/05/2017': {'bad_jump': 0, 'good_jump': 1},\n",
       "                          '  10/07/2017': {'bad_jump': 1, 'good_jump': 0},\n",
       "                          '  10/10/2017': {'bad_jump': 0, 'good_jump': 3},\n",
       "                          '  10/11/2017': {'bad_jump': 1, 'good_jump': 2},\n",
       "                          '  10/12/2017': {'bad_jump': 0, 'good_jump': 1},\n",
       "                          '  10/14/2017': {'bad_jump': 1, 'good_jump': 0},\n",
       "                          '  10/17/2017': {'bad_jump': 0, 'good_jump': 0},\n",
       "                          '  10/18/2017': {'bad_jump': 0, 'good_jump': 3},\n",
       "                          '  10/19/2017': {'bad_jump': 0, 'good_jump': 2},\n",
       "                          '  10/21/2017': {'bad_jump': 0, 'good_jump': 1},\n",
       "                          '  10/24/2017': {'bad_jump': 2, 'good_jump': 2},\n",
       "                          '  10/25/2017': {'bad_jump': 2, 'good_jump': 0},\n",
       "                          '  10/26/2017': {'bad_jump': 0, 'good_jump': 1},\n",
       "                          '  10/28/2017': {'bad_jump': 0, 'good_jump': 2},\n",
       "                          '  10/31/2017': {'bad_jump': 0, 'good_jump': 1},\n",
       "                          '  11/01/2017': {'bad_jump': 0, 'good_jump': 0},\n",
       "                          '  11/02/2017': {'bad_jump': 0, 'good_jump': 1},\n",
       "                          '  11/05/2017': {'bad_jump': 0, 'good_jump': 1},\n",
       "                          '  11/07/2017': {'bad_jump': 1, 'good_jump': 2},\n",
       "                          '  11/08/2017': {'bad_jump': 1, 'good_jump': 2},\n",
       "                          '  11/10/2017': {'bad_jump': 1, 'good_jump': 1},\n",
       "                          '  11/14/2017': {'bad_jump': 1, 'good_jump': 3},\n",
       "                          '  11/17/2017': {'bad_jump': 0, 'good_jump': 2},\n",
       "                          '  11/18/2017': {'bad_jump': 1, 'good_jump': 1},\n",
       "                          '  11/20/2017': {'bad_jump': 2, 'good_jump': 0},\n",
       "                          '  11/21/2017': {'bad_jump': 0, 'good_jump': 0}}),\n",
       "             'Kodie Comby': defaultdict(list,\n",
       "                         {'  09/26/2017': {'bad_jump': 0, 'good_jump': 10},\n",
       "                          '  09/27/2017': {'bad_jump': 1, 'good_jump': 4},\n",
       "                          '  10/03/2017': {'bad_jump': 2, 'good_jump': 8},\n",
       "                          '  10/04/2017': {'bad_jump': 0, 'good_jump': 13},\n",
       "                          '  10/05/2017': {'bad_jump': 0, 'good_jump': 7},\n",
       "                          '  10/07/2017': {'bad_jump': 0, 'good_jump': 10},\n",
       "                          '  10/10/2017': {'bad_jump': 1, 'good_jump': 2},\n",
       "                          '  10/11/2017': {'bad_jump': 1, 'good_jump': 10},\n",
       "                          '  10/12/2017': {'bad_jump': 0, 'good_jump': 6},\n",
       "                          '  10/14/2017': {'bad_jump': 1, 'good_jump': 6},\n",
       "                          '  10/17/2017': {'bad_jump': 2, 'good_jump': 6},\n",
       "                          '  10/18/2017': {'bad_jump': 0, 'good_jump': 13},\n",
       "                          '  10/19/2017': {'bad_jump': 1, 'good_jump': 16},\n",
       "                          '  10/21/2017': {'bad_jump': 0, 'good_jump': 7},\n",
       "                          '  10/24/2017': {'bad_jump': 1, 'good_jump': 6},\n",
       "                          '  10/25/2017': {'bad_jump': 0, 'good_jump': 11},\n",
       "                          '  10/26/2017': {'bad_jump': 0, 'good_jump': 10},\n",
       "                          '  10/28/2017': {'bad_jump': 0, 'good_jump': 7},\n",
       "                          '  10/31/2017': {'bad_jump': 0, 'good_jump': 12},\n",
       "                          '  11/01/2017': {'bad_jump': 0, 'good_jump': 0},\n",
       "                          '  11/02/2017': {'bad_jump': 1, 'good_jump': 3},\n",
       "                          '  11/05/2017': {'bad_jump': 0, 'good_jump': 2},\n",
       "                          '  11/07/2017': {'bad_jump': 1, 'good_jump': 6},\n",
       "                          '  11/08/2017': {'bad_jump': 1, 'good_jump': 7},\n",
       "                          '  11/10/2017': {'bad_jump': 0, 'good_jump': 1},\n",
       "                          '  11/14/2017': {'bad_jump': 1, 'good_jump': 0},\n",
       "                          '  11/17/2017': {'bad_jump': 1, 'good_jump': 2},\n",
       "                          '  11/18/2017': {'bad_jump': 1, 'good_jump': 4},\n",
       "                          '  11/20/2017': {'bad_jump': 0, 'good_jump': 3},\n",
       "                          '  11/21/2017': {'bad_jump': 1, 'good_jump': 0}}),\n",
       "             'Sam Knapp': defaultdict(list,\n",
       "                         {'  09/26/2017': {'bad_jump': 0, 'good_jump': 0},\n",
       "                          '  09/27/2017': {'bad_jump': 0, 'good_jump': 10},\n",
       "                          '  10/03/2017': {'bad_jump': 1, 'good_jump': 9},\n",
       "                          '  10/04/2017': {'bad_jump': 2, 'good_jump': 13},\n",
       "                          '  10/05/2017': {'bad_jump': 1, 'good_jump': 4},\n",
       "                          '  10/07/2017': {'bad_jump': 0, 'good_jump': 0},\n",
       "                          '  10/10/2017': {'bad_jump': 0, 'good_jump': 0},\n",
       "                          '  10/11/2017': {'bad_jump': 1, 'good_jump': 4},\n",
       "                          '  10/12/2017': {'bad_jump': 1, 'good_jump': 3},\n",
       "                          '  10/14/2017': {'bad_jump': 0, 'good_jump': 6},\n",
       "                          '  10/17/2017': {'bad_jump': 1, 'good_jump': 5},\n",
       "                          '  10/18/2017': {'bad_jump': 1, 'good_jump': 6},\n",
       "                          '  10/19/2017': {'bad_jump': 0, 'good_jump': 9},\n",
       "                          '  10/21/2017': {'bad_jump': 0, 'good_jump': 5},\n",
       "                          '  10/24/2017': {'bad_jump': 3, 'good_jump': 2},\n",
       "                          '  10/25/2017': {'bad_jump': 0, 'good_jump': 7},\n",
       "                          '  10/26/2017': {'bad_jump': 1, 'good_jump': 4},\n",
       "                          '  10/28/2017': {'bad_jump': 0, 'good_jump': 4},\n",
       "                          '  10/31/2017': {'bad_jump': 0, 'good_jump': 0},\n",
       "                          '  11/01/2017': {'bad_jump': 0, 'good_jump': 0},\n",
       "                          '  11/02/2017': {'bad_jump': 0, 'good_jump': 0},\n",
       "                          '  11/05/2017': {'bad_jump': 0, 'good_jump': 0},\n",
       "                          '  11/07/2017': {'bad_jump': 2, 'good_jump': 5},\n",
       "                          '  11/08/2017': {'bad_jump': 1, 'good_jump': 8},\n",
       "                          '  11/10/2017': {'bad_jump': 0, 'good_jump': 7},\n",
       "                          '  11/14/2017': {'bad_jump': 0, 'good_jump': 8},\n",
       "                          '  11/17/2017': {'bad_jump': 0, 'good_jump': 5},\n",
       "                          '  11/18/2017': {'bad_jump': 0, 'good_jump': 2},\n",
       "                          '  11/20/2017': {'bad_jump': 0, 'good_jump': 11},\n",
       "                          '  11/21/2017': {'bad_jump': 0, 'good_jump': 7}}),\n",
       "             'Emily Becker': defaultdict(list,\n",
       "                         {'  09/26/2017': {'bad_jump': 0, 'good_jump': 0},\n",
       "                          '  09/27/2017': {'bad_jump': 0, 'good_jump': 0},\n",
       "                          '  10/03/2017': {'bad_jump': 0, 'good_jump': 0},\n",
       "                          '  10/04/2017': {'bad_jump': 0, 'good_jump': 0},\n",
       "                          '  10/05/2017': {'bad_jump': 0, 'good_jump': 0},\n",
       "                          '  10/07/2017': {'bad_jump': 0, 'good_jump': 0},\n",
       "                          '  10/10/2017': {'bad_jump': 0, 'good_jump': 0},\n",
       "                          '  10/11/2017': {'bad_jump': 0, 'good_jump': 0},\n",
       "                          '  10/12/2017': {'bad_jump': 0, 'good_jump': 0},\n",
       "                          '  10/14/2017': {'bad_jump': 0, 'good_jump': 0},\n",
       "                          '  10/17/2017': {'bad_jump': 0, 'good_jump': 0},\n",
       "                          '  10/18/2017': {'bad_jump': 0, 'good_jump': 0},\n",
       "                          '  10/19/2017': {'bad_jump': 0, 'good_jump': 0},\n",
       "                          '  10/21/2017': {'bad_jump': 0, 'good_jump': 0},\n",
       "                          '  10/24/2017': {'bad_jump': 0, 'good_jump': 0},\n",
       "                          '  10/25/2017': {'bad_jump': 0, 'good_jump': 0},\n",
       "                          '  10/26/2017': {'bad_jump': 0, 'good_jump': 0},\n",
       "                          '  10/28/2017': {'bad_jump': 0, 'good_jump': 0},\n",
       "                          '  10/31/2017': {'bad_jump': 0, 'good_jump': 3},\n",
       "                          '  11/01/2017': {'bad_jump': 0, 'good_jump': 0},\n",
       "                          '  11/02/2017': {'bad_jump': 1, 'good_jump': 1},\n",
       "                          '  11/05/2017': {'bad_jump': 0, 'good_jump': 7},\n",
       "                          '  11/07/2017': {'bad_jump': 3, 'good_jump': 9},\n",
       "                          '  11/08/2017': {'bad_jump': 0, 'good_jump': 9},\n",
       "                          '  11/10/2017': {'bad_jump': 2, 'good_jump': 1},\n",
       "                          '  11/14/2017': {'bad_jump': 3, 'good_jump': 4},\n",
       "                          '  11/17/2017': {'bad_jump': 0, 'good_jump': 2},\n",
       "                          '  11/18/2017': {'bad_jump': 0, 'good_jump': 1},\n",
       "                          '  11/20/2017': {'bad_jump': 1, 'good_jump': 3},\n",
       "                          '  11/21/2017': {'bad_jump': 2, 'good_jump': 3}}),\n",
       "             'Simone Spencer': defaultdict(list,\n",
       "                         {'  09/26/2017': {'bad_jump': 0, 'good_jump': 0},\n",
       "                          '  09/27/2017': {'bad_jump': 0, 'good_jump': 0},\n",
       "                          '  10/03/2017': {'bad_jump': 0, 'good_jump': 0},\n",
       "                          '  10/04/2017': {'bad_jump': 0, 'good_jump': 0},\n",
       "                          '  10/05/2017': {'bad_jump': 0, 'good_jump': 0},\n",
       "                          '  10/07/2017': {'bad_jump': 0, 'good_jump': 0},\n",
       "                          '  10/10/2017': {'bad_jump': 0, 'good_jump': 0},\n",
       "                          '  10/11/2017': {'bad_jump': 0, 'good_jump': 0},\n",
       "                          '  10/12/2017': {'bad_jump': 0, 'good_jump': 0},\n",
       "                          '  10/14/2017': {'bad_jump': 0, 'good_jump': 0},\n",
       "                          '  10/17/2017': {'bad_jump': 0, 'good_jump': 0},\n",
       "                          '  10/18/2017': {'bad_jump': 0, 'good_jump': 0},\n",
       "                          '  10/19/2017': {'bad_jump': 0, 'good_jump': 0},\n",
       "                          '  10/21/2017': {'bad_jump': 0, 'good_jump': 0},\n",
       "                          '  10/24/2017': {'bad_jump': 0, 'good_jump': 0},\n",
       "                          '  10/25/2017': {'bad_jump': 0, 'good_jump': 0},\n",
       "                          '  10/26/2017': {'bad_jump': 0, 'good_jump': 0},\n",
       "                          '  10/28/2017': {'bad_jump': 0, 'good_jump': 0},\n",
       "                          '  10/31/2017': {'bad_jump': 0, 'good_jump': 0},\n",
       "                          '  11/01/2017': {'bad_jump': 0, 'good_jump': 0},\n",
       "                          '  11/02/2017': {'bad_jump': 2, 'good_jump': 1},\n",
       "                          '  11/05/2017': {'bad_jump': 0, 'good_jump': 0},\n",
       "                          '  11/07/2017': {'bad_jump': 0, 'good_jump': 0},\n",
       "                          '  11/08/2017': {'bad_jump': 0, 'good_jump': 0},\n",
       "                          '  11/10/2017': {'bad_jump': 0, 'good_jump': 0},\n",
       "                          '  11/14/2017': {'bad_jump': 0, 'good_jump': 0},\n",
       "                          '  11/17/2017': {'bad_jump': 0, 'good_jump': 0},\n",
       "                          '  11/18/2017': {'bad_jump': 0, 'good_jump': 0},\n",
       "                          '  11/20/2017': {'bad_jump': 0, 'good_jump': 0},\n",
       "                          '  11/21/2017': {'bad_jump': 0, 'good_jump': 0}})})"
      ]
     },
     "execution_count": 301,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getPlayerJumps(d, name):\n",
    "    badJumpsMean = 0\n",
    "    goodJumpsMean =0\n",
    "    counter = 0\n",
    "    for practices in d[name].keys():\n",
    "        badJumpsMean += d[name][practices]['bad_jump']\n",
    "        goodJumpsMean += d[name][practices]['good_jump']\n",
    "        counter+=1\n",
    "    return badJumpsMean, goodJumpsMean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getPlayer(d):\n",
    "    e = {}\n",
    "    for name in d.keys():\n",
    "        badJumpsMean = 0\n",
    "        goodJumpsMean =0\n",
    "        counter = 0\n",
    "        for practices in d[name].keys():\n",
    "            badJumpsMean += d[name][practices]['bad_jump']\n",
    "            goodJumpsMean += d[name][practices]['good_jump']\n",
    "            counter+=1\n",
    "        e[name] = (badJumpsMean, goodJumpsMean)\n",
    "    return e"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 360,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Frequency')"
      ]
     },
     "execution_count": 360,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "output = getPlayer(total_data)\n",
    "df = pd.DataFrame(output)\n",
    "df = df.T\n",
    "df.columns = ['BadJumps', 'GoodJumps']\n",
    "\n",
    "fig, ax = plt.subplots(figsize = (20, 5))\n",
    "df['GoodJumps'].plot.bar(ax = ax)\n",
    "ax.set_title(\"Mean Number of Good Jumps of Players Over Season\")\n",
    "ax.set_ylabel('Frequency')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[27.39012432]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.array(max_vals)\n",
    "x = x[~np.isnan(x)]\n",
    "q = np.percentile(x, [90])\n",
    "plt.plot(x)\n",
    "plt.axhline(y=q, color='r', linestyle='-')\n",
    "print(q)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/sachk/.local/lib/python3.6/site-packages/ipykernel_launcher.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n",
      "/home/sachk/.local/lib/python3.6/site-packages/ipykernel_launcher.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max Slope for jump: 7 \n",
      "Gforce avg for 1 jump: 2.985853\n",
      "Max Gforce for 1 jump: 9.698454\n",
      "******************\n",
      "Max Slope for jump: 21 \n",
      "Gforce avg for 2 jump: 3.091039\n",
      "Max Gforce for 2 jump: 23.944937\n",
      "******************\n",
      "Max Slope for jump: 23 \n",
      "Gforce avg for 3 jump: 3.276436\n",
      "Max Gforce for 3 jump: 26.491320\n",
      "******************\n",
      "Max Slope for jump: 0 \n",
      "Gforce avg for 4 jump: 2.409602\n",
      "Max Gforce for 4 jump: 2.632489\n",
      "******************\n",
      "Max Slope for jump: 10 \n",
      "Gforce avg for 5 jump: 3.234602\n",
      "Max Gforce for 5 jump: 12.248265\n",
      "******************\n",
      "Max Slope for jump: 1 \n",
      "Gforce avg for 6 jump: 2.676678\n",
      "Max Gforce for 6 jump: 3.734970\n",
      "******************\n",
      "Max Slope for jump: 18 \n",
      "Gforce avg for 7 jump: 3.207013\n",
      "Max Gforce for 7 jump: 25.972679\n",
      "******************\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "person('  10/25/2017', 'Sam Knapp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "def person(practice, player):\n",
    "    current_player = data.loc[np.bitwise_and(data['date'] == practice,\n",
    "                        data['PLAYER NAME - CORRECTED'] == player)]\n",
    "    i = np.array(current_player.index)\n",
    "    \n",
    "    current_player['diff'] = abs(current_player['gforce (G)'].shift(-1) - current_player['gforce (G)'])\n",
    "    sydney.dropna()\n",
    "    current_player['index_diff'] = i - shift(i, -1, cval=np.NaN)\n",
    "    current_player = current_player[0:-1]\n",
    "    current_player['stop'] = (current_player['index_diff'] <= -350)\n",
    "    \n",
    "    l = {'bad_jump': 0, 'good_jump': 0}\n",
    "\n",
    "    indeces = current_player[current_player['stop']].index\n",
    "    for i in range(0, len(indeces)):\n",
    "        partial = None\n",
    "        if(i == 0):\n",
    "            partial = current_player.loc[0:indeces[i]]\n",
    "        elif(i == len(indeces) - 1):\n",
    "            partial = current_player.loc[indeces[i]:]\n",
    "        else:\n",
    "            partial = current_player.loc[indeces[i]:indeces[i+1]]\n",
    "        #max_vals.append(current_player['gforce (G)'].max())\n",
    "        #l.append([current_player['gforce (G)'].mean(), current_player['gforce (G)'].max()])\n",
    "        if(current_player['gforce (G)'].max() > 28):\n",
    "            l['bad_jump']+=1\n",
    "        else:\n",
    "            l['good_jump']+=1\n",
    "        print('Max Slope for jump: %d ' % (partial['diff'].max()) )\n",
    "        print('Gforce avg for %d jump: %f' % (i+1, partial['gforce (G)'].mean()))\n",
    "        print('Max Gforce for %d jump: %f' % (i+1, partial['gforce (G)'].max()))\n",
    "        print('******************')\n",
    "    return \n",
    "    #plt.plot(current_player['index_diff'])\n",
    "    #plt.plot(current_player.index, current_player['gforce (G)'])\n",
    "    #plt.scatter(current_player.index, current_player['gforce (G)'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 443,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>UNIQUE_ID</th>\n",
       "      <th>DATE</th>\n",
       "      <th>PLAYER ##</th>\n",
       "      <th>GAME_ID</th>\n",
       "      <th>OPPONENT</th>\n",
       "      <th>Player Name</th>\n",
       "      <th>Team Rank</th>\n",
       "      <th>Load Sum</th>\n",
       "      <th>Movements</th>\n",
       "      <th>Movement Category</th>\n",
       "      <th>...</th>\n",
       "      <th>High Surges Per Minute</th>\n",
       "      <th>Kinetic Energy (Joules/Pound)</th>\n",
       "      <th>Avg Intensity (Watts/Pound)</th>\n",
       "      <th>Avg Burst (Feet/Sec)</th>\n",
       "      <th>Jumps</th>\n",
       "      <th>Avg Jump (Inches)</th>\n",
       "      <th>High Jumps (20+ Inches)</th>\n",
       "      <th>Avg High Jumps (20+ Inches)</th>\n",
       "      <th>Highest Jump (inches)</th>\n",
       "      <th>Point Differential</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>101211.0</td>\n",
       "      <td>2017-10-01</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1012.0</td>\n",
       "      <td>North Carolina</td>\n",
       "      <td>Gabriela Stavnetchei</td>\n",
       "      <td>1.0</td>\n",
       "      <td>537.0</td>\n",
       "      <td>3980.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.46</td>\n",
       "      <td>4592.89</td>\n",
       "      <td>32.80</td>\n",
       "      <td>1.69</td>\n",
       "      <td>107.0</td>\n",
       "      <td>18.90</td>\n",
       "      <td>55.0</td>\n",
       "      <td>23.27</td>\n",
       "      <td>44.94</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>101233.0</td>\n",
       "      <td>2017-10-01</td>\n",
       "      <td>33.0</td>\n",
       "      <td>1012.0</td>\n",
       "      <td>North Carolina</td>\n",
       "      <td>Ashley Askin</td>\n",
       "      <td>2.0</td>\n",
       "      <td>528.0</td>\n",
       "      <td>3853.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.88</td>\n",
       "      <td>3981.07</td>\n",
       "      <td>27.83</td>\n",
       "      <td>1.76</td>\n",
       "      <td>88.0</td>\n",
       "      <td>15.04</td>\n",
       "      <td>14.0</td>\n",
       "      <td>22.54</td>\n",
       "      <td>25.03</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10121.0</td>\n",
       "      <td>2017-10-01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1012.0</td>\n",
       "      <td>North Carolina</td>\n",
       "      <td>Kodie Comby</td>\n",
       "      <td>3.0</td>\n",
       "      <td>475.0</td>\n",
       "      <td>2729.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.76</td>\n",
       "      <td>2891.47</td>\n",
       "      <td>29.20</td>\n",
       "      <td>1.73</td>\n",
       "      <td>132.0</td>\n",
       "      <td>16.21</td>\n",
       "      <td>20.0</td>\n",
       "      <td>21.33</td>\n",
       "      <td>23.61</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>101297.0</td>\n",
       "      <td>2017-10-01</td>\n",
       "      <td>97.0</td>\n",
       "      <td>1012.0</td>\n",
       "      <td>North Carolina</td>\n",
       "      <td>Grace Rigsbee</td>\n",
       "      <td>4.0</td>\n",
       "      <td>402.0</td>\n",
       "      <td>2396.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.04</td>\n",
       "      <td>2103.53</td>\n",
       "      <td>19.84</td>\n",
       "      <td>1.57</td>\n",
       "      <td>72.0</td>\n",
       "      <td>15.02</td>\n",
       "      <td>12.0</td>\n",
       "      <td>22.63</td>\n",
       "      <td>25.56</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10129.0</td>\n",
       "      <td>2017-10-01</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1012.0</td>\n",
       "      <td>North Carolina</td>\n",
       "      <td>Gabby Benda</td>\n",
       "      <td>4.0</td>\n",
       "      <td>402.0</td>\n",
       "      <td>2870.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7.94</td>\n",
       "      <td>1867.09</td>\n",
       "      <td>16.97</td>\n",
       "      <td>1.35</td>\n",
       "      <td>76.0</td>\n",
       "      <td>10.88</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>14.99</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>101214.0</td>\n",
       "      <td>2017-10-01</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1012.0</td>\n",
       "      <td>North Carolina</td>\n",
       "      <td>Cori Clifton</td>\n",
       "      <td>6.0</td>\n",
       "      <td>382.0</td>\n",
       "      <td>2390.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.70</td>\n",
       "      <td>2305.34</td>\n",
       "      <td>22.60</td>\n",
       "      <td>1.54</td>\n",
       "      <td>63.0</td>\n",
       "      <td>18.92</td>\n",
       "      <td>15.0</td>\n",
       "      <td>22.40</td>\n",
       "      <td>24.90</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>10124.0</td>\n",
       "      <td>2017-10-01</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1012.0</td>\n",
       "      <td>North Carolina</td>\n",
       "      <td>Sydney Wilson</td>\n",
       "      <td>7.0</td>\n",
       "      <td>334.0</td>\n",
       "      <td>1825.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.88</td>\n",
       "      <td>1624.14</td>\n",
       "      <td>16.40</td>\n",
       "      <td>1.55</td>\n",
       "      <td>87.0</td>\n",
       "      <td>18.45</td>\n",
       "      <td>35.0</td>\n",
       "      <td>22.72</td>\n",
       "      <td>27.41</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>101212.0</td>\n",
       "      <td>2017-10-01</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1012.0</td>\n",
       "      <td>North Carolina</td>\n",
       "      <td>Nicole Alford</td>\n",
       "      <td>8.0</td>\n",
       "      <td>286.0</td>\n",
       "      <td>2032.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.10</td>\n",
       "      <td>1430.78</td>\n",
       "      <td>14.45</td>\n",
       "      <td>1.42</td>\n",
       "      <td>73.0</td>\n",
       "      <td>13.03</td>\n",
       "      <td>2.0</td>\n",
       "      <td>21.06</td>\n",
       "      <td>21.48</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>10128.0</td>\n",
       "      <td>2017-10-01</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1012.0</td>\n",
       "      <td>North Carolina</td>\n",
       "      <td>Sam Knapp</td>\n",
       "      <td>9.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>2045.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.15</td>\n",
       "      <td>1176.74</td>\n",
       "      <td>10.14</td>\n",
       "      <td>1.39</td>\n",
       "      <td>44.0</td>\n",
       "      <td>10.73</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>16.05</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>101223.0</td>\n",
       "      <td>2017-10-01</td>\n",
       "      <td>23.0</td>\n",
       "      <td>1012.0</td>\n",
       "      <td>North Carolina</td>\n",
       "      <td>Alessia Del Romano</td>\n",
       "      <td>10.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>357.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.93</td>\n",
       "      <td>251.78</td>\n",
       "      <td>5.85</td>\n",
       "      <td>1.19</td>\n",
       "      <td>13.0</td>\n",
       "      <td>10.29</td>\n",
       "      <td>1.0</td>\n",
       "      <td>20.91</td>\n",
       "      <td>20.91</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10123.0</td>\n",
       "      <td>2017-10-01</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1012.0</td>\n",
       "      <td>North Carolina</td>\n",
       "      <td>Lauren Frerking</td>\n",
       "      <td>11.0</td>\n",
       "      <td>109.0</td>\n",
       "      <td>159.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.26</td>\n",
       "      <td>5.62</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>10125.0</td>\n",
       "      <td>2017-10-01</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1012.0</td>\n",
       "      <td>North Carolina</td>\n",
       "      <td>Kendall Wilson</td>\n",
       "      <td>12.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>101311.0</td>\n",
       "      <td>2017-10-06</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1013.0</td>\n",
       "      <td>Boston College</td>\n",
       "      <td>Gabriela Stavnetchei</td>\n",
       "      <td>1.0</td>\n",
       "      <td>556.0</td>\n",
       "      <td>3431.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.35</td>\n",
       "      <td>3995.33</td>\n",
       "      <td>38.41</td>\n",
       "      <td>1.71</td>\n",
       "      <td>102.0</td>\n",
       "      <td>20.46</td>\n",
       "      <td>63.0</td>\n",
       "      <td>22.66</td>\n",
       "      <td>26.73</td>\n",
       "      <td>-2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>101333.0</td>\n",
       "      <td>2017-10-06</td>\n",
       "      <td>33.0</td>\n",
       "      <td>1013.0</td>\n",
       "      <td>Boston College</td>\n",
       "      <td>Ashley Askin</td>\n",
       "      <td>2.0</td>\n",
       "      <td>505.0</td>\n",
       "      <td>2752.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.21</td>\n",
       "      <td>2422.99</td>\n",
       "      <td>26.05</td>\n",
       "      <td>1.60</td>\n",
       "      <td>65.0</td>\n",
       "      <td>17.17</td>\n",
       "      <td>15.0</td>\n",
       "      <td>22.67</td>\n",
       "      <td>26.92</td>\n",
       "      <td>-2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>10131.0</td>\n",
       "      <td>2017-10-06</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1013.0</td>\n",
       "      <td>Boston College</td>\n",
       "      <td>Kodie Comby</td>\n",
       "      <td>3.0</td>\n",
       "      <td>454.0</td>\n",
       "      <td>2326.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.26</td>\n",
       "      <td>2407.59</td>\n",
       "      <td>29.00</td>\n",
       "      <td>1.66</td>\n",
       "      <td>110.0</td>\n",
       "      <td>17.13</td>\n",
       "      <td>23.0</td>\n",
       "      <td>21.54</td>\n",
       "      <td>24.29</td>\n",
       "      <td>-2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>10134.0</td>\n",
       "      <td>2017-10-06</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1013.0</td>\n",
       "      <td>Boston College</td>\n",
       "      <td>Sydney Wilson</td>\n",
       "      <td>4.0</td>\n",
       "      <td>395.0</td>\n",
       "      <td>1667.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.46</td>\n",
       "      <td>1639.71</td>\n",
       "      <td>20.49</td>\n",
       "      <td>1.65</td>\n",
       "      <td>82.0</td>\n",
       "      <td>18.63</td>\n",
       "      <td>26.0</td>\n",
       "      <td>21.65</td>\n",
       "      <td>25.26</td>\n",
       "      <td>-2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>10139.0</td>\n",
       "      <td>2017-10-06</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1013.0</td>\n",
       "      <td>Boston College</td>\n",
       "      <td>Gabby Benda</td>\n",
       "      <td>5.0</td>\n",
       "      <td>380.0</td>\n",
       "      <td>2241.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>9.17</td>\n",
       "      <td>1387.31</td>\n",
       "      <td>18.25</td>\n",
       "      <td>1.30</td>\n",
       "      <td>56.0</td>\n",
       "      <td>10.35</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>13.75</td>\n",
       "      <td>-2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>101312.0</td>\n",
       "      <td>2017-10-06</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1013.0</td>\n",
       "      <td>Boston College</td>\n",
       "      <td>Nicole Alford</td>\n",
       "      <td>6.0</td>\n",
       "      <td>355.0</td>\n",
       "      <td>1827.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.09</td>\n",
       "      <td>1432.51</td>\n",
       "      <td>17.68</td>\n",
       "      <td>1.51</td>\n",
       "      <td>83.0</td>\n",
       "      <td>14.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>19.28</td>\n",
       "      <td>-2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>101397.0</td>\n",
       "      <td>2017-10-06</td>\n",
       "      <td>97.0</td>\n",
       "      <td>1013.0</td>\n",
       "      <td>Boston College</td>\n",
       "      <td>Grace Rigsbee</td>\n",
       "      <td>7.0</td>\n",
       "      <td>271.0</td>\n",
       "      <td>1128.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.05</td>\n",
       "      <td>972.40</td>\n",
       "      <td>18.00</td>\n",
       "      <td>1.61</td>\n",
       "      <td>36.0</td>\n",
       "      <td>16.24</td>\n",
       "      <td>5.0</td>\n",
       "      <td>21.38</td>\n",
       "      <td>21.90</td>\n",
       "      <td>-2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>10133.0</td>\n",
       "      <td>2017-10-06</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1013.0</td>\n",
       "      <td>Boston College</td>\n",
       "      <td>Lauren Frerking</td>\n",
       "      <td>8.0</td>\n",
       "      <td>258.0</td>\n",
       "      <td>977.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.55</td>\n",
       "      <td>961.86</td>\n",
       "      <td>16.30</td>\n",
       "      <td>1.63</td>\n",
       "      <td>61.0</td>\n",
       "      <td>12.19</td>\n",
       "      <td>1.0</td>\n",
       "      <td>47.11</td>\n",
       "      <td>47.11</td>\n",
       "      <td>-2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>101323.0</td>\n",
       "      <td>2017-10-06</td>\n",
       "      <td>23.0</td>\n",
       "      <td>1013.0</td>\n",
       "      <td>Boston College</td>\n",
       "      <td>Alessia Del Romano</td>\n",
       "      <td>9.0</td>\n",
       "      <td>247.0</td>\n",
       "      <td>1289.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.59</td>\n",
       "      <td>997.06</td>\n",
       "      <td>16.89</td>\n",
       "      <td>1.53</td>\n",
       "      <td>21.0</td>\n",
       "      <td>16.43</td>\n",
       "      <td>1.0</td>\n",
       "      <td>20.85</td>\n",
       "      <td>20.85</td>\n",
       "      <td>-2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>101314.0</td>\n",
       "      <td>2017-10-06</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1013.0</td>\n",
       "      <td>Boston College</td>\n",
       "      <td>Cori Clifton</td>\n",
       "      <td>10.0</td>\n",
       "      <td>171.0</td>\n",
       "      <td>887.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.36</td>\n",
       "      <td>594.75</td>\n",
       "      <td>13.51</td>\n",
       "      <td>1.30</td>\n",
       "      <td>21.0</td>\n",
       "      <td>18.27</td>\n",
       "      <td>3.0</td>\n",
       "      <td>21.45</td>\n",
       "      <td>22.39</td>\n",
       "      <td>-2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>10138.0</td>\n",
       "      <td>2017-10-06</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1013.0</td>\n",
       "      <td>Boston College</td>\n",
       "      <td>Sam Knapp</td>\n",
       "      <td>11.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>1791.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.43</td>\n",
       "      <td>947.17</td>\n",
       "      <td>9.76</td>\n",
       "      <td>1.29</td>\n",
       "      <td>17.0</td>\n",
       "      <td>9.81</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>12.48</td>\n",
       "      <td>-2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>10135.0</td>\n",
       "      <td>2017-10-06</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1013.0</td>\n",
       "      <td>Boston College</td>\n",
       "      <td>Kendall Wilson</td>\n",
       "      <td>12.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>391.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.27</td>\n",
       "      <td>67.39</td>\n",
       "      <td>2.04</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>10149.0</td>\n",
       "      <td>2017-10-08</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1014.0</td>\n",
       "      <td>Syracuse</td>\n",
       "      <td>Gabby Benda</td>\n",
       "      <td>1.0</td>\n",
       "      <td>501.0</td>\n",
       "      <td>3356.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>11.53</td>\n",
       "      <td>2721.25</td>\n",
       "      <td>30.57</td>\n",
       "      <td>1.46</td>\n",
       "      <td>110.0</td>\n",
       "      <td>12.35</td>\n",
       "      <td>1.0</td>\n",
       "      <td>20.77</td>\n",
       "      <td>20.77</td>\n",
       "      <td>-3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>101433.0</td>\n",
       "      <td>2017-10-08</td>\n",
       "      <td>33.0</td>\n",
       "      <td>1014.0</td>\n",
       "      <td>Syracuse</td>\n",
       "      <td>Ashley Askin</td>\n",
       "      <td>2.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>2915.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.48</td>\n",
       "      <td>2870.71</td>\n",
       "      <td>28.42</td>\n",
       "      <td>1.69</td>\n",
       "      <td>64.0</td>\n",
       "      <td>16.41</td>\n",
       "      <td>9.0</td>\n",
       "      <td>22.97</td>\n",
       "      <td>26.42</td>\n",
       "      <td>-3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>101411.0</td>\n",
       "      <td>2017-10-08</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1014.0</td>\n",
       "      <td>Syracuse</td>\n",
       "      <td>Gabriela Stavnetchei</td>\n",
       "      <td>3.0</td>\n",
       "      <td>497.0</td>\n",
       "      <td>3102.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.03</td>\n",
       "      <td>3274.74</td>\n",
       "      <td>33.76</td>\n",
       "      <td>1.60</td>\n",
       "      <td>74.0</td>\n",
       "      <td>19.39</td>\n",
       "      <td>37.0</td>\n",
       "      <td>23.06</td>\n",
       "      <td>27.73</td>\n",
       "      <td>-3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>10141.0</td>\n",
       "      <td>2017-10-08</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1014.0</td>\n",
       "      <td>Syracuse</td>\n",
       "      <td>Kodie Comby</td>\n",
       "      <td>4.0</td>\n",
       "      <td>446.0</td>\n",
       "      <td>1917.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.98</td>\n",
       "      <td>2037.91</td>\n",
       "      <td>30.87</td>\n",
       "      <td>1.74</td>\n",
       "      <td>87.0</td>\n",
       "      <td>17.90</td>\n",
       "      <td>26.0</td>\n",
       "      <td>22.02</td>\n",
       "      <td>25.46</td>\n",
       "      <td>-3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>10144.0</td>\n",
       "      <td>2017-10-08</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1014.0</td>\n",
       "      <td>Syracuse</td>\n",
       "      <td>Sydney Wilson</td>\n",
       "      <td>5.0</td>\n",
       "      <td>406.0</td>\n",
       "      <td>1558.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.72</td>\n",
       "      <td>1757.44</td>\n",
       "      <td>23.74</td>\n",
       "      <td>1.73</td>\n",
       "      <td>78.0</td>\n",
       "      <td>18.58</td>\n",
       "      <td>27.0</td>\n",
       "      <td>21.97</td>\n",
       "      <td>26.34</td>\n",
       "      <td>-3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>101497.0</td>\n",
       "      <td>2017-10-08</td>\n",
       "      <td>97.0</td>\n",
       "      <td>1014.0</td>\n",
       "      <td>Syracuse</td>\n",
       "      <td>Grace Rigsbee</td>\n",
       "      <td>6.0</td>\n",
       "      <td>344.0</td>\n",
       "      <td>1725.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.11</td>\n",
       "      <td>1459.69</td>\n",
       "      <td>19.20</td>\n",
       "      <td>1.60</td>\n",
       "      <td>53.0</td>\n",
       "      <td>15.47</td>\n",
       "      <td>7.0</td>\n",
       "      <td>21.80</td>\n",
       "      <td>23.14</td>\n",
       "      <td>-3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>102614.0</td>\n",
       "      <td>2017-11-19</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1026.0</td>\n",
       "      <td>Pittsburgh</td>\n",
       "      <td>Cori Clifton</td>\n",
       "      <td>7.0</td>\n",
       "      <td>305.0</td>\n",
       "      <td>895.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.23</td>\n",
       "      <td>938.94</td>\n",
       "      <td>24.70</td>\n",
       "      <td>1.57</td>\n",
       "      <td>26.0</td>\n",
       "      <td>19.60</td>\n",
       "      <td>10.0</td>\n",
       "      <td>23.50</td>\n",
       "      <td>23.80</td>\n",
       "      <td>-3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>102697.0</td>\n",
       "      <td>2017-11-19</td>\n",
       "      <td>97.0</td>\n",
       "      <td>1026.0</td>\n",
       "      <td>Pittsburgh</td>\n",
       "      <td>Grace Rigsbee</td>\n",
       "      <td>8.0</td>\n",
       "      <td>301.0</td>\n",
       "      <td>816.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.63</td>\n",
       "      <td>645.40</td>\n",
       "      <td>17.92</td>\n",
       "      <td>1.30</td>\n",
       "      <td>18.0</td>\n",
       "      <td>21.60</td>\n",
       "      <td>12.0</td>\n",
       "      <td>23.60</td>\n",
       "      <td>72.70</td>\n",
       "      <td>-3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>102623.0</td>\n",
       "      <td>2017-11-19</td>\n",
       "      <td>23.0</td>\n",
       "      <td>1026.0</td>\n",
       "      <td>Pittsburgh</td>\n",
       "      <td>Alessia Del Romano</td>\n",
       "      <td>9.0</td>\n",
       "      <td>224.0</td>\n",
       "      <td>1148.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.51</td>\n",
       "      <td>686.12</td>\n",
       "      <td>15.95</td>\n",
       "      <td>1.25</td>\n",
       "      <td>26.0</td>\n",
       "      <td>15.60</td>\n",
       "      <td>4.0</td>\n",
       "      <td>20.30</td>\n",
       "      <td>21.70</td>\n",
       "      <td>-3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>102610.0</td>\n",
       "      <td>2017-11-19</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1026.0</td>\n",
       "      <td>Pittsburgh</td>\n",
       "      <td>Emily Becker</td>\n",
       "      <td>10.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.31</td>\n",
       "      <td>1177.17</td>\n",
       "      <td>12.52</td>\n",
       "      <td>1.27</td>\n",
       "      <td>22.0</td>\n",
       "      <td>11.70</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.60</td>\n",
       "      <td>13.60</td>\n",
       "      <td>-3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>102612.0</td>\n",
       "      <td>2017-11-19</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1026.0</td>\n",
       "      <td>Pittsburgh</td>\n",
       "      <td>Nicole Alford</td>\n",
       "      <td>11.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>263.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.30</td>\n",
       "      <td>46.05</td>\n",
       "      <td>1.39</td>\n",
       "      <td>0.62</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.00</td>\n",
       "      <td>9.00</td>\n",
       "      <td>-3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>10268.0</td>\n",
       "      <td>2017-11-19</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1026.0</td>\n",
       "      <td>Pittsburgh</td>\n",
       "      <td>Sam Knapp</td>\n",
       "      <td>12.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.03</td>\n",
       "      <td>2.03</td>\n",
       "      <td>1.33</td>\n",
       "      <td>25.0</td>\n",
       "      <td>11.40</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.80</td>\n",
       "      <td>16.70</td>\n",
       "      <td>-3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>10279.0</td>\n",
       "      <td>2017-11-22</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1027.0</td>\n",
       "      <td>Miami</td>\n",
       "      <td>Gabby Benda</td>\n",
       "      <td>1.0</td>\n",
       "      <td>511.0</td>\n",
       "      <td>5004.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>12.61</td>\n",
       "      <td>3646.57</td>\n",
       "      <td>30.90</td>\n",
       "      <td>1.39</td>\n",
       "      <td>157.0</td>\n",
       "      <td>13.00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>14.90</td>\n",
       "      <td>52.40</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>102711.0</td>\n",
       "      <td>2017-11-22</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1027.0</td>\n",
       "      <td>Miami</td>\n",
       "      <td>Gabriela Stavnetchei</td>\n",
       "      <td>2.0</td>\n",
       "      <td>498.0</td>\n",
       "      <td>3431.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.59</td>\n",
       "      <td>4052.13</td>\n",
       "      <td>31.65</td>\n",
       "      <td>1.66</td>\n",
       "      <td>104.0</td>\n",
       "      <td>19.30</td>\n",
       "      <td>47.0</td>\n",
       "      <td>24.40</td>\n",
       "      <td>28.00</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>10271.0</td>\n",
       "      <td>2017-11-22</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1027.0</td>\n",
       "      <td>Miami</td>\n",
       "      <td>Kodie Comby</td>\n",
       "      <td>3.0</td>\n",
       "      <td>463.0</td>\n",
       "      <td>2756.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.71</td>\n",
       "      <td>2866.05</td>\n",
       "      <td>31.15</td>\n",
       "      <td>1.70</td>\n",
       "      <td>123.0</td>\n",
       "      <td>17.50</td>\n",
       "      <td>41.0</td>\n",
       "      <td>22.80</td>\n",
       "      <td>25.40</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>102733.0</td>\n",
       "      <td>2017-11-22</td>\n",
       "      <td>33.0</td>\n",
       "      <td>1027.0</td>\n",
       "      <td>Miami</td>\n",
       "      <td>Ashley Askin</td>\n",
       "      <td>4.0</td>\n",
       "      <td>457.0</td>\n",
       "      <td>3246.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.50</td>\n",
       "      <td>3370.05</td>\n",
       "      <td>28.31</td>\n",
       "      <td>1.75</td>\n",
       "      <td>84.0</td>\n",
       "      <td>17.80</td>\n",
       "      <td>24.0</td>\n",
       "      <td>23.90</td>\n",
       "      <td>27.20</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>10274.0</td>\n",
       "      <td>2017-11-22</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1027.0</td>\n",
       "      <td>Miami</td>\n",
       "      <td>Sydney Wilson</td>\n",
       "      <td>5.0</td>\n",
       "      <td>376.0</td>\n",
       "      <td>2303.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.12</td>\n",
       "      <td>2384.35</td>\n",
       "      <td>24.58</td>\n",
       "      <td>1.67</td>\n",
       "      <td>114.0</td>\n",
       "      <td>18.70</td>\n",
       "      <td>40.0</td>\n",
       "      <td>24.20</td>\n",
       "      <td>27.50</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>10273.0</td>\n",
       "      <td>2017-11-22</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1027.0</td>\n",
       "      <td>Miami</td>\n",
       "      <td>Lauren Frerking</td>\n",
       "      <td>6.0</td>\n",
       "      <td>340.0</td>\n",
       "      <td>1249.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.25</td>\n",
       "      <td>1349.26</td>\n",
       "      <td>24.09</td>\n",
       "      <td>1.72</td>\n",
       "      <td>46.0</td>\n",
       "      <td>19.70</td>\n",
       "      <td>19.0</td>\n",
       "      <td>23.50</td>\n",
       "      <td>25.20</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>102714.0</td>\n",
       "      <td>2017-11-22</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1027.0</td>\n",
       "      <td>Miami</td>\n",
       "      <td>Cori Clifton</td>\n",
       "      <td>7.0</td>\n",
       "      <td>310.0</td>\n",
       "      <td>1187.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.82</td>\n",
       "      <td>1194.41</td>\n",
       "      <td>26.54</td>\n",
       "      <td>1.59</td>\n",
       "      <td>52.0</td>\n",
       "      <td>18.90</td>\n",
       "      <td>17.0</td>\n",
       "      <td>21.70</td>\n",
       "      <td>23.70</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>102797.0</td>\n",
       "      <td>2017-11-22</td>\n",
       "      <td>97.0</td>\n",
       "      <td>1027.0</td>\n",
       "      <td>Miami</td>\n",
       "      <td>Grace Rigsbee</td>\n",
       "      <td>8.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>746.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.28</td>\n",
       "      <td>773.64</td>\n",
       "      <td>20.35</td>\n",
       "      <td>1.67</td>\n",
       "      <td>25.0</td>\n",
       "      <td>17.50</td>\n",
       "      <td>12.0</td>\n",
       "      <td>22.80</td>\n",
       "      <td>24.20</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>102710.0</td>\n",
       "      <td>2017-11-22</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1027.0</td>\n",
       "      <td>Miami</td>\n",
       "      <td>Emily Becker</td>\n",
       "      <td>9.0</td>\n",
       "      <td>233.0</td>\n",
       "      <td>2671.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.46</td>\n",
       "      <td>1814.16</td>\n",
       "      <td>16.49</td>\n",
       "      <td>1.47</td>\n",
       "      <td>20.0</td>\n",
       "      <td>9.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.50</td>\n",
       "      <td>12.10</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>10278.0</td>\n",
       "      <td>2017-11-22</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1027.0</td>\n",
       "      <td>Miami</td>\n",
       "      <td>Sam Knapp</td>\n",
       "      <td>10.0</td>\n",
       "      <td>174.0</td>\n",
       "      <td>2125.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.51</td>\n",
       "      <td>1208.36</td>\n",
       "      <td>11.96</td>\n",
       "      <td>1.37</td>\n",
       "      <td>43.0</td>\n",
       "      <td>10.20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.50</td>\n",
       "      <td>16.00</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>102723.0</td>\n",
       "      <td>2017-11-22</td>\n",
       "      <td>23.0</td>\n",
       "      <td>1027.0</td>\n",
       "      <td>Miami</td>\n",
       "      <td>Alessia Del Romano</td>\n",
       "      <td>11.0</td>\n",
       "      <td>153.0</td>\n",
       "      <td>663.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.57</td>\n",
       "      <td>531.37</td>\n",
       "      <td>13.98</td>\n",
       "      <td>1.46</td>\n",
       "      <td>10.0</td>\n",
       "      <td>19.90</td>\n",
       "      <td>8.0</td>\n",
       "      <td>21.70</td>\n",
       "      <td>21.80</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>102712.0</td>\n",
       "      <td>2017-11-22</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1027.0</td>\n",
       "      <td>Miami</td>\n",
       "      <td>Nicole Alford</td>\n",
       "      <td>12.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>302.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.25</td>\n",
       "      <td>133.34</td>\n",
       "      <td>3.10</td>\n",
       "      <td>0.98</td>\n",
       "      <td>20.0</td>\n",
       "      <td>8.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.80</td>\n",
       "      <td>12.00</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>10281.0</td>\n",
       "      <td>2017-11-24</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1028.0</td>\n",
       "      <td>Florida State</td>\n",
       "      <td>Kodie Comby</td>\n",
       "      <td>1.0</td>\n",
       "      <td>512.0</td>\n",
       "      <td>1042.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>8.68</td>\n",
       "      <td>1529.80</td>\n",
       "      <td>52.75</td>\n",
       "      <td>1.83</td>\n",
       "      <td>40.0</td>\n",
       "      <td>19.90</td>\n",
       "      <td>25.0</td>\n",
       "      <td>24.10</td>\n",
       "      <td>25.90</td>\n",
       "      <td>-3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>102811.0</td>\n",
       "      <td>2017-11-24</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1028.0</td>\n",
       "      <td>Florida State</td>\n",
       "      <td>Gabriela Stavnetchei</td>\n",
       "      <td>2.0</td>\n",
       "      <td>457.0</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.80</td>\n",
       "      <td>1466.34</td>\n",
       "      <td>40.73</td>\n",
       "      <td>1.65</td>\n",
       "      <td>29.0</td>\n",
       "      <td>20.40</td>\n",
       "      <td>15.0</td>\n",
       "      <td>24.00</td>\n",
       "      <td>28.30</td>\n",
       "      <td>-3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>10289.0</td>\n",
       "      <td>2017-11-24</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1028.0</td>\n",
       "      <td>Florida State</td>\n",
       "      <td>Gabby Benda</td>\n",
       "      <td>3.0</td>\n",
       "      <td>429.0</td>\n",
       "      <td>898.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>11.59</td>\n",
       "      <td>1248.25</td>\n",
       "      <td>46.23</td>\n",
       "      <td>1.58</td>\n",
       "      <td>46.0</td>\n",
       "      <td>12.40</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.60</td>\n",
       "      <td>17.00</td>\n",
       "      <td>-3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>10284.0</td>\n",
       "      <td>2017-11-24</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1028.0</td>\n",
       "      <td>Florida State</td>\n",
       "      <td>Sydney Wilson</td>\n",
       "      <td>4.0</td>\n",
       "      <td>427.0</td>\n",
       "      <td>799.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.15</td>\n",
       "      <td>1087.62</td>\n",
       "      <td>33.98</td>\n",
       "      <td>1.80</td>\n",
       "      <td>39.0</td>\n",
       "      <td>20.50</td>\n",
       "      <td>28.0</td>\n",
       "      <td>24.50</td>\n",
       "      <td>26.30</td>\n",
       "      <td>-3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>10283.0</td>\n",
       "      <td>2017-11-24</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1028.0</td>\n",
       "      <td>Florida State</td>\n",
       "      <td>Lauren Frerking</td>\n",
       "      <td>5.0</td>\n",
       "      <td>324.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>8.00</td>\n",
       "      <td>651.69</td>\n",
       "      <td>162.92</td>\n",
       "      <td>3.00</td>\n",
       "      <td>4.0</td>\n",
       "      <td>17.40</td>\n",
       "      <td>1.0</td>\n",
       "      <td>25.40</td>\n",
       "      <td>29.80</td>\n",
       "      <td>-3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>10288.0</td>\n",
       "      <td>2017-11-24</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1028.0</td>\n",
       "      <td>Florida State</td>\n",
       "      <td>Sam Knapp</td>\n",
       "      <td>6.0</td>\n",
       "      <td>265.0</td>\n",
       "      <td>821.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.09</td>\n",
       "      <td>772.52</td>\n",
       "      <td>24.14</td>\n",
       "      <td>1.51</td>\n",
       "      <td>12.0</td>\n",
       "      <td>10.30</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.60</td>\n",
       "      <td>15.40</td>\n",
       "      <td>-3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>102823.0</td>\n",
       "      <td>2017-11-24</td>\n",
       "      <td>23.0</td>\n",
       "      <td>1028.0</td>\n",
       "      <td>Florida State</td>\n",
       "      <td>Alessia Del Romano</td>\n",
       "      <td>7.0</td>\n",
       "      <td>248.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7.60</td>\n",
       "      <td>740.41</td>\n",
       "      <td>148.08</td>\n",
       "      <td>2.66</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>102810.0</td>\n",
       "      <td>2017-11-24</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1028.0</td>\n",
       "      <td>Florida State</td>\n",
       "      <td>Emily Becker</td>\n",
       "      <td>8.0</td>\n",
       "      <td>238.0</td>\n",
       "      <td>702.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.57</td>\n",
       "      <td>607.46</td>\n",
       "      <td>15.18</td>\n",
       "      <td>1.48</td>\n",
       "      <td>6.0</td>\n",
       "      <td>10.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.10</td>\n",
       "      <td>11.50</td>\n",
       "      <td>-3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>102812.0</td>\n",
       "      <td>2017-11-24</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1028.0</td>\n",
       "      <td>Florida State</td>\n",
       "      <td>Nicole Alford</td>\n",
       "      <td>9.0</td>\n",
       "      <td>228.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.52</td>\n",
       "      <td>469.33</td>\n",
       "      <td>24.70</td>\n",
       "      <td>1.56</td>\n",
       "      <td>15.0</td>\n",
       "      <td>8.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.20</td>\n",
       "      <td>10.40</td>\n",
       "      <td>-3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>102833.0</td>\n",
       "      <td>2017-11-24</td>\n",
       "      <td>33.0</td>\n",
       "      <td>1028.0</td>\n",
       "      <td>Florida State</td>\n",
       "      <td>Ashley Askin</td>\n",
       "      <td>10.0</td>\n",
       "      <td>212.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>10.00</td>\n",
       "      <td>372.07</td>\n",
       "      <td>124.02</td>\n",
       "      <td>3.72</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>102814.0</td>\n",
       "      <td>2017-11-24</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1028.0</td>\n",
       "      <td>Florida State</td>\n",
       "      <td>Cori Clifton</td>\n",
       "      <td>11.0</td>\n",
       "      <td>208.0</td>\n",
       "      <td>212.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.00</td>\n",
       "      <td>184.97</td>\n",
       "      <td>20.55</td>\n",
       "      <td>1.46</td>\n",
       "      <td>4.0</td>\n",
       "      <td>19.40</td>\n",
       "      <td>2.0</td>\n",
       "      <td>21.40</td>\n",
       "      <td>21.80</td>\n",
       "      <td>-3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>102897.0</td>\n",
       "      <td>2017-11-24</td>\n",
       "      <td>97.0</td>\n",
       "      <td>1028.0</td>\n",
       "      <td>Florida State</td>\n",
       "      <td>Grace Rigsbee</td>\n",
       "      <td>12.0</td>\n",
       "      <td>181.0</td>\n",
       "      <td>123.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>6.14</td>\n",
       "      <td>187.67</td>\n",
       "      <td>26.81</td>\n",
       "      <td>1.42</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>192 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     UNIQUE_ID       DATE  PLAYER ##  GAME_ID        OPPONENT  \\\n",
       "0     101211.0 2017-10-01       11.0   1012.0  North Carolina   \n",
       "1     101233.0 2017-10-01       33.0   1012.0  North Carolina   \n",
       "2      10121.0 2017-10-01        1.0   1012.0  North Carolina   \n",
       "3     101297.0 2017-10-01       97.0   1012.0  North Carolina   \n",
       "4      10129.0 2017-10-01        9.0   1012.0  North Carolina   \n",
       "5     101214.0 2017-10-01       14.0   1012.0  North Carolina   \n",
       "6      10124.0 2017-10-01        4.0   1012.0  North Carolina   \n",
       "7     101212.0 2017-10-01       12.0   1012.0  North Carolina   \n",
       "8      10128.0 2017-10-01        8.0   1012.0  North Carolina   \n",
       "9     101223.0 2017-10-01       23.0   1012.0  North Carolina   \n",
       "10     10123.0 2017-10-01        3.0   1012.0  North Carolina   \n",
       "11     10125.0 2017-10-01        5.0   1012.0  North Carolina   \n",
       "13    101311.0 2017-10-06       11.0   1013.0  Boston College   \n",
       "14    101333.0 2017-10-06       33.0   1013.0  Boston College   \n",
       "15     10131.0 2017-10-06        1.0   1013.0  Boston College   \n",
       "16     10134.0 2017-10-06        4.0   1013.0  Boston College   \n",
       "17     10139.0 2017-10-06        9.0   1013.0  Boston College   \n",
       "18    101312.0 2017-10-06       12.0   1013.0  Boston College   \n",
       "19    101397.0 2017-10-06       97.0   1013.0  Boston College   \n",
       "20     10133.0 2017-10-06        3.0   1013.0  Boston College   \n",
       "21    101323.0 2017-10-06       23.0   1013.0  Boston College   \n",
       "22    101314.0 2017-10-06       14.0   1013.0  Boston College   \n",
       "23     10138.0 2017-10-06        8.0   1013.0  Boston College   \n",
       "24     10135.0 2017-10-06        5.0   1013.0  Boston College   \n",
       "26     10149.0 2017-10-08        9.0   1014.0        Syracuse   \n",
       "27    101433.0 2017-10-08       33.0   1014.0        Syracuse   \n",
       "28    101411.0 2017-10-08       11.0   1014.0        Syracuse   \n",
       "29     10141.0 2017-10-08        1.0   1014.0        Syracuse   \n",
       "30     10144.0 2017-10-08        4.0   1014.0        Syracuse   \n",
       "31    101497.0 2017-10-08       97.0   1014.0        Syracuse   \n",
       "..         ...        ...        ...      ...             ...   \n",
       "175   102614.0 2017-11-19       14.0   1026.0      Pittsburgh   \n",
       "176   102697.0 2017-11-19       97.0   1026.0      Pittsburgh   \n",
       "177   102623.0 2017-11-19       23.0   1026.0      Pittsburgh   \n",
       "178   102610.0 2017-11-19       10.0   1026.0      Pittsburgh   \n",
       "179   102612.0 2017-11-19       12.0   1026.0      Pittsburgh   \n",
       "180    10268.0 2017-11-19        8.0   1026.0      Pittsburgh   \n",
       "182    10279.0 2017-11-22        9.0   1027.0           Miami   \n",
       "183   102711.0 2017-11-22       11.0   1027.0           Miami   \n",
       "184    10271.0 2017-11-22        1.0   1027.0           Miami   \n",
       "185   102733.0 2017-11-22       33.0   1027.0           Miami   \n",
       "186    10274.0 2017-11-22        4.0   1027.0           Miami   \n",
       "187    10273.0 2017-11-22        3.0   1027.0           Miami   \n",
       "188   102714.0 2017-11-22       14.0   1027.0           Miami   \n",
       "189   102797.0 2017-11-22       97.0   1027.0           Miami   \n",
       "190   102710.0 2017-11-22       10.0   1027.0           Miami   \n",
       "191    10278.0 2017-11-22        8.0   1027.0           Miami   \n",
       "192   102723.0 2017-11-22       23.0   1027.0           Miami   \n",
       "193   102712.0 2017-11-22       12.0   1027.0           Miami   \n",
       "195    10281.0 2017-11-24        1.0   1028.0   Florida State   \n",
       "196   102811.0 2017-11-24       11.0   1028.0   Florida State   \n",
       "197    10289.0 2017-11-24        9.0   1028.0   Florida State   \n",
       "198    10284.0 2017-11-24        4.0   1028.0   Florida State   \n",
       "199    10283.0 2017-11-24        3.0   1028.0   Florida State   \n",
       "200    10288.0 2017-11-24        8.0   1028.0   Florida State   \n",
       "201   102823.0 2017-11-24       23.0   1028.0   Florida State   \n",
       "202   102810.0 2017-11-24       10.0   1028.0   Florida State   \n",
       "203   102812.0 2017-11-24       12.0   1028.0   Florida State   \n",
       "204   102833.0 2017-11-24       33.0   1028.0   Florida State   \n",
       "205   102814.0 2017-11-24       14.0   1028.0   Florida State   \n",
       "206   102897.0 2017-11-24       97.0   1028.0   Florida State   \n",
       "\n",
       "              Player Name  Team Rank  Load Sum  Movements  Movement Category  \\\n",
       "0    Gabriela Stavnetchei        1.0     537.0     3980.0                2.0   \n",
       "1            Ashley Askin        2.0     528.0     3853.0                2.0   \n",
       "2             Kodie Comby        3.0     475.0     2729.0                2.0   \n",
       "3           Grace Rigsbee        4.0     402.0     2396.0                2.0   \n",
       "4             Gabby Benda        4.0     402.0     2870.0                2.0   \n",
       "5            Cori Clifton        6.0     382.0     2390.0                2.0   \n",
       "6           Sydney Wilson        7.0     334.0     1825.0                2.0   \n",
       "7           Nicole Alford        8.0     286.0     2032.0                2.0   \n",
       "8               Sam Knapp        9.0     185.0     2045.0                2.0   \n",
       "9      Alessia Del Romano       10.0     156.0      357.0                0.0   \n",
       "10        Lauren Frerking       11.0     109.0      159.0                0.0   \n",
       "11         Kendall Wilson       12.0      50.0        0.0                0.0   \n",
       "13   Gabriela Stavnetchei        1.0     556.0     3431.0                2.0   \n",
       "14           Ashley Askin        2.0     505.0     2752.0                2.0   \n",
       "15            Kodie Comby        3.0     454.0     2326.0                2.0   \n",
       "16          Sydney Wilson        4.0     395.0     1667.0                2.0   \n",
       "17            Gabby Benda        5.0     380.0     2241.0                2.0   \n",
       "18          Nicole Alford        6.0     355.0     1827.0                2.0   \n",
       "19          Grace Rigsbee        7.0     271.0     1128.0                1.0   \n",
       "20        Lauren Frerking        8.0     258.0      977.0                0.0   \n",
       "21     Alessia Del Romano        9.0     247.0     1289.0                1.0   \n",
       "22           Cori Clifton       10.0     171.0      887.0                0.0   \n",
       "23              Sam Knapp       11.0     152.0     1791.0                2.0   \n",
       "24         Kendall Wilson       12.0     104.0      391.0                0.0   \n",
       "26            Gabby Benda        1.0     501.0     3356.0                2.0   \n",
       "27           Ashley Askin        2.0     500.0     2915.0                2.0   \n",
       "28   Gabriela Stavnetchei        3.0     497.0     3102.0                2.0   \n",
       "29            Kodie Comby        4.0     446.0     1917.0                2.0   \n",
       "30          Sydney Wilson        5.0     406.0     1558.0                2.0   \n",
       "31          Grace Rigsbee        6.0     344.0     1725.0                2.0   \n",
       "..                    ...        ...       ...        ...                ...   \n",
       "175          Cori Clifton        7.0     305.0      895.0                0.0   \n",
       "176         Grace Rigsbee        8.0     301.0      816.0                0.0   \n",
       "177    Alessia Del Romano        9.0     224.0     1148.0                1.0   \n",
       "178          Emily Becker       10.0     219.0     2015.0                2.0   \n",
       "179         Nicole Alford       11.0      98.0      263.0                0.0   \n",
       "180             Sam Knapp       12.0      68.0        5.0                0.0   \n",
       "182           Gabby Benda        1.0     511.0     5004.0                2.0   \n",
       "183  Gabriela Stavnetchei        2.0     498.0     3431.0                2.0   \n",
       "184           Kodie Comby        3.0     463.0     2756.0                2.0   \n",
       "185          Ashley Askin        4.0     457.0     3246.0                2.0   \n",
       "186         Sydney Wilson        5.0     376.0     2303.0                2.0   \n",
       "187       Lauren Frerking        6.0     340.0     1249.0                1.0   \n",
       "188          Cori Clifton        7.0     310.0     1187.0                1.0   \n",
       "189         Grace Rigsbee        8.0     270.0      746.0                0.0   \n",
       "190          Emily Becker        9.0     233.0     2671.0                2.0   \n",
       "191             Sam Knapp       10.0     174.0     2125.0                2.0   \n",
       "192    Alessia Del Romano       11.0     153.0      663.0                0.0   \n",
       "193         Nicole Alford       12.0      88.0      302.0                0.0   \n",
       "195           Kodie Comby        1.0     512.0     1042.0                1.0   \n",
       "196  Gabriela Stavnetchei        2.0     457.0     1138.0                1.0   \n",
       "197           Gabby Benda        3.0     429.0      898.0                0.0   \n",
       "198         Sydney Wilson        4.0     427.0      799.0                0.0   \n",
       "199       Lauren Frerking        5.0     324.0       78.0                0.0   \n",
       "200             Sam Knapp        6.0     265.0      821.0                0.0   \n",
       "201    Alessia Del Romano        7.0     248.0       99.0                0.0   \n",
       "202          Emily Becker        8.0     238.0      702.0                0.0   \n",
       "203         Nicole Alford        9.0     228.0      242.0                0.0   \n",
       "204          Ashley Askin       10.0     212.0       52.0                0.0   \n",
       "205          Cori Clifton       11.0     208.0      212.0                0.0   \n",
       "206         Grace Rigsbee       12.0     181.0      123.0                0.0   \n",
       "\n",
       "            ...          High Surges Per Minute  \\\n",
       "0           ...                            4.46   \n",
       "1           ...                            4.88   \n",
       "2           ...                            4.76   \n",
       "3           ...                            4.04   \n",
       "4           ...                            7.94   \n",
       "5           ...                            3.70   \n",
       "6           ...                            2.88   \n",
       "7           ...                            3.10   \n",
       "8           ...                            1.15   \n",
       "9           ...                            0.93   \n",
       "10          ...                            1.26   \n",
       "11          ...                            0.00   \n",
       "13          ...                            5.35   \n",
       "14          ...                            5.21   \n",
       "15          ...                            4.26   \n",
       "16          ...                            3.46   \n",
       "17          ...                            9.17   \n",
       "18          ...                            4.09   \n",
       "19          ...                            3.05   \n",
       "20          ...                            2.55   \n",
       "21          ...                            2.59   \n",
       "22          ...                            2.36   \n",
       "23          ...                            1.43   \n",
       "24          ...                            2.27   \n",
       "26          ...                           11.53   \n",
       "27          ...                            5.48   \n",
       "28          ...                            5.03   \n",
       "29          ...                            4.98   \n",
       "30          ...                            3.72   \n",
       "31          ...                            3.11   \n",
       "..          ...                             ...   \n",
       "175         ...                            4.23   \n",
       "176         ...                            4.63   \n",
       "177         ...                            3.51   \n",
       "178         ...                            3.31   \n",
       "179         ...                            1.30   \n",
       "180         ...                            0.00   \n",
       "182         ...                           12.61   \n",
       "183         ...                            4.59   \n",
       "184         ...                            5.71   \n",
       "185         ...                            4.50   \n",
       "186         ...                            4.12   \n",
       "187         ...                            4.25   \n",
       "188         ...                            4.82   \n",
       "189         ...                            4.28   \n",
       "190         ...                            3.46   \n",
       "191         ...                            1.51   \n",
       "192         ...                            2.57   \n",
       "193         ...                            1.25   \n",
       "195         ...                            8.68   \n",
       "196         ...                            5.80   \n",
       "197         ...                           11.59   \n",
       "198         ...                            5.15   \n",
       "199         ...                            8.00   \n",
       "200         ...                            3.09   \n",
       "201         ...                            7.60   \n",
       "202         ...                            3.57   \n",
       "203         ...                            3.52   \n",
       "204         ...                           10.00   \n",
       "205         ...                            3.00   \n",
       "206         ...                            6.14   \n",
       "\n",
       "     Kinetic Energy (Joules/Pound)  Avg Intensity (Watts/Pound)  \\\n",
       "0                          4592.89                        32.80   \n",
       "1                          3981.07                        27.83   \n",
       "2                          2891.47                        29.20   \n",
       "3                          2103.53                        19.84   \n",
       "4                          1867.09                        16.97   \n",
       "5                          2305.34                        22.60   \n",
       "6                          1624.14                        16.40   \n",
       "7                          1430.78                        14.45   \n",
       "8                          1176.74                        10.14   \n",
       "9                           251.78                         5.85   \n",
       "10                            5.62                         0.37   \n",
       "11                            0.00                         0.00   \n",
       "13                         3995.33                        38.41   \n",
       "14                         2422.99                        26.05   \n",
       "15                         2407.59                        29.00   \n",
       "16                         1639.71                        20.49   \n",
       "17                         1387.31                        18.25   \n",
       "18                         1432.51                        17.68   \n",
       "19                          972.40                        18.00   \n",
       "20                          961.86                        16.30   \n",
       "21                          997.06                        16.89   \n",
       "22                          594.75                        13.51   \n",
       "23                          947.17                         9.76   \n",
       "24                           67.39                         2.04   \n",
       "26                         2721.25                        30.57   \n",
       "27                         2870.71                        28.42   \n",
       "28                         3274.74                        33.76   \n",
       "29                         2037.91                        30.87   \n",
       "30                         1757.44                        23.74   \n",
       "31                         1459.69                        19.20   \n",
       "..                             ...                          ...   \n",
       "175                         938.94                        24.70   \n",
       "176                         645.40                        17.92   \n",
       "177                         686.12                        15.95   \n",
       "178                        1177.17                        12.52   \n",
       "179                          46.05                         1.39   \n",
       "180                           2.03                         2.03   \n",
       "182                        3646.57                        30.90   \n",
       "183                        4052.13                        31.65   \n",
       "184                        2866.05                        31.15   \n",
       "185                        3370.05                        28.31   \n",
       "186                        2384.35                        24.58   \n",
       "187                        1349.26                        24.09   \n",
       "188                        1194.41                        26.54   \n",
       "189                         773.64                        20.35   \n",
       "190                        1814.16                        16.49   \n",
       "191                        1208.36                        11.96   \n",
       "192                         531.37                        13.98   \n",
       "193                         133.34                         3.10   \n",
       "195                        1529.80                        52.75   \n",
       "196                        1466.34                        40.73   \n",
       "197                        1248.25                        46.23   \n",
       "198                        1087.62                        33.98   \n",
       "199                         651.69                       162.92   \n",
       "200                         772.52                        24.14   \n",
       "201                         740.41                       148.08   \n",
       "202                         607.46                        15.18   \n",
       "203                         469.33                        24.70   \n",
       "204                         372.07                       124.02   \n",
       "205                         184.97                        20.55   \n",
       "206                         187.67                        26.81   \n",
       "\n",
       "     Avg Burst (Feet/Sec)  Jumps  Avg Jump (Inches)  High Jumps (20+ Inches)  \\\n",
       "0                    1.69  107.0              18.90                     55.0   \n",
       "1                    1.76   88.0              15.04                     14.0   \n",
       "2                    1.73  132.0              16.21                     20.0   \n",
       "3                    1.57   72.0              15.02                     12.0   \n",
       "4                    1.35   76.0              10.88                      0.0   \n",
       "5                    1.54   63.0              18.92                     15.0   \n",
       "6                    1.55   87.0              18.45                     35.0   \n",
       "7                    1.42   73.0              13.03                      2.0   \n",
       "8                    1.39   44.0              10.73                      0.0   \n",
       "9                    1.19   13.0              10.29                      1.0   \n",
       "10                   0.25    0.0               0.00                      0.0   \n",
       "11                   0.00    0.0               0.00                      0.0   \n",
       "13                   1.71  102.0              20.46                     63.0   \n",
       "14                   1.60   65.0              17.17                     15.0   \n",
       "15                   1.66  110.0              17.13                     23.0   \n",
       "16                   1.65   82.0              18.63                     26.0   \n",
       "17                   1.30   56.0              10.35                      0.0   \n",
       "18                   1.51   83.0              14.00                      0.0   \n",
       "19                   1.61   36.0              16.24                      5.0   \n",
       "20                   1.63   61.0              12.19                      1.0   \n",
       "21                   1.53   21.0              16.43                      1.0   \n",
       "22                   1.30   21.0              18.27                      3.0   \n",
       "23                   1.29   17.0               9.81                      0.0   \n",
       "24                   0.39    0.0               0.00                      0.0   \n",
       "26                   1.46  110.0              12.35                      1.0   \n",
       "27                   1.69   64.0              16.41                      9.0   \n",
       "28                   1.60   74.0              19.39                     37.0   \n",
       "29                   1.74   87.0              17.90                     26.0   \n",
       "30                   1.73   78.0              18.58                     27.0   \n",
       "31                   1.60   53.0              15.47                      7.0   \n",
       "..                    ...    ...                ...                      ...   \n",
       "175                  1.57   26.0              19.60                     10.0   \n",
       "176                  1.30   18.0              21.60                     12.0   \n",
       "177                  1.25   26.0              15.60                      4.0   \n",
       "178                  1.27   22.0              11.70                      0.0   \n",
       "179                  0.62    1.0               9.00                      0.0   \n",
       "180                  1.33   25.0              11.40                      0.0   \n",
       "182                  1.39  157.0              13.00                      2.0   \n",
       "183                  1.66  104.0              19.30                     47.0   \n",
       "184                  1.70  123.0              17.50                     41.0   \n",
       "185                  1.75   84.0              17.80                     24.0   \n",
       "186                  1.67  114.0              18.70                     40.0   \n",
       "187                  1.72   46.0              19.70                     19.0   \n",
       "188                  1.59   52.0              18.90                     17.0   \n",
       "189                  1.67   25.0              17.50                     12.0   \n",
       "190                  1.47   20.0               9.80                      0.0   \n",
       "191                  1.37   43.0              10.20                      0.0   \n",
       "192                  1.46   10.0              19.90                      8.0   \n",
       "193                  0.98   20.0               8.80                      0.0   \n",
       "195                  1.83   40.0              19.90                     25.0   \n",
       "196                  1.65   29.0              20.40                     15.0   \n",
       "197                  1.58   46.0              12.40                      0.0   \n",
       "198                  1.80   39.0              20.50                     28.0   \n",
       "199                  3.00    4.0              17.40                      1.0   \n",
       "200                  1.51   12.0              10.30                      0.0   \n",
       "201                  2.66    0.0               0.00                      0.0   \n",
       "202                  1.48    6.0              10.00                      0.0   \n",
       "203                  1.56   15.0               8.80                      0.0   \n",
       "204                  3.72    0.0               0.00                      0.0   \n",
       "205                  1.46    4.0              19.40                      2.0   \n",
       "206                  1.42    0.0               0.00                      0.0   \n",
       "\n",
       "     Avg High Jumps (20+ Inches)  Highest Jump (inches)  Point Differential  \n",
       "0                          23.27                  44.94                   2  \n",
       "1                          22.54                  25.03                   2  \n",
       "2                          21.33                  23.61                   2  \n",
       "3                          22.63                  25.56                   2  \n",
       "4                           0.00                  14.99                   2  \n",
       "5                          22.40                  24.90                   2  \n",
       "6                          22.72                  27.41                   2  \n",
       "7                          21.06                  21.48                   2  \n",
       "8                           0.00                  16.05                   2  \n",
       "9                          20.91                  20.91                   2  \n",
       "10                          0.00                   0.00                   2  \n",
       "11                          0.00                   0.00                   2  \n",
       "13                         22.66                  26.73                  -2  \n",
       "14                         22.67                  26.92                  -2  \n",
       "15                         21.54                  24.29                  -2  \n",
       "16                         21.65                  25.26                  -2  \n",
       "17                          0.00                  13.75                  -2  \n",
       "18                          0.00                  19.28                  -2  \n",
       "19                         21.38                  21.90                  -2  \n",
       "20                         47.11                  47.11                  -2  \n",
       "21                         20.85                  20.85                  -2  \n",
       "22                         21.45                  22.39                  -2  \n",
       "23                          0.00                  12.48                  -2  \n",
       "24                          0.00                   0.00                  -2  \n",
       "26                         20.77                  20.77                  -3  \n",
       "27                         22.97                  26.42                  -3  \n",
       "28                         23.06                  27.73                  -3  \n",
       "29                         22.02                  25.46                  -3  \n",
       "30                         21.97                  26.34                  -3  \n",
       "31                         21.80                  23.14                  -3  \n",
       "..                           ...                    ...                 ...  \n",
       "175                        23.50                  23.80                  -3  \n",
       "176                        23.60                  72.70                  -3  \n",
       "177                        20.30                  21.70                  -3  \n",
       "178                        12.60                  13.60                  -3  \n",
       "179                         9.00                   9.00                  -3  \n",
       "180                        14.80                  16.70                  -3  \n",
       "182                        14.90                  52.40                  -1  \n",
       "183                        24.40                  28.00                  -1  \n",
       "184                        22.80                  25.40                  -1  \n",
       "185                        23.90                  27.20                  -1  \n",
       "186                        24.20                  27.50                  -1  \n",
       "187                        23.50                  25.20                  -1  \n",
       "188                        21.70                  23.70                  -1  \n",
       "189                        22.80                  24.20                  -1  \n",
       "190                        11.50                  12.10                  -1  \n",
       "191                        12.50                  16.00                  -1  \n",
       "192                        21.70                  21.80                  -1  \n",
       "193                        10.80                  12.00                  -1  \n",
       "195                        24.10                  25.90                  -3  \n",
       "196                        24.00                  28.30                  -3  \n",
       "197                        14.60                  17.00                  -3  \n",
       "198                        24.50                  26.30                  -3  \n",
       "199                        25.40                  29.80                  -3  \n",
       "200                        12.60                  15.40                  -3  \n",
       "201                         0.00                   0.00                  -3  \n",
       "202                        11.10                  11.50                  -3  \n",
       "203                        10.20                  10.40                  -3  \n",
       "204                         0.00                   0.00                  -3  \n",
       "205                        21.40                  21.80                  -3  \n",
       "206                         0.00                   0.00                  -3  \n",
       "\n",
       "[192 rows x 26 columns]"
      ]
     },
     "execution_count": 443,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "games = pd.read_excel('games/gt-volleyball-game-data.xlsx', sheet_name =0).dropna()\n",
    "games"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 454,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = games.loc[:, \"Movements\":\"Point Differential\"].values\n",
    "y = games.loc[:, 'Point Differential'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 455,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.9645569]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "pca = PCA(n_components=1, svd_solver='full')\n",
    "pca.fit(x, y)                 \n",
    "PCA(copy=True, iterated_power='auto', n_components=1, random_state=None,\n",
    "svd_solver='full', tol=0.0, whiten=False)\n",
    "print(pca.explained_variance_ratio_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 457,
   "metadata": {},
   "outputs": [],
   "source": [
    "reduced = pca.fit_transform(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 458,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f2122d87e48>"
      ]
     },
     "execution_count": 458,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(reduced, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 372,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['UNIQUE_ID', 'DATE', 'PLAYER ##', 'GAME_ID', 'OPPONENT',\n",
       "       '    Player Name', 'Team Rank', 'Load Sum', 'Movements',\n",
       "       'Movement Category', 'High Surges', 'Surge Category',\n",
       "       'Stress Percentage', 'Stress Category', 'Active Minutes',\n",
       "       'Movements Per Minute', 'High Surges Per Minute',\n",
       "       'Kinetic Energy (Joules/Pound)', 'Avg Intensity (Watts/Pound)',\n",
       "       'Avg Burst (Feet/Sec)', 'Jumps', 'Avg Jump (Inches)',\n",
       "       'High Jumps (20+ Inches)', 'Avg High Jumps (20+ Inches)',\n",
       "       'Highest Jump (inches)'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 372,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "games.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 386,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f212fa83160>"
      ]
     },
     "execution_count": 386,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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44YcnlUMI+9xXWVGcLdnrAuuA50II74YQng4hHA5UizHu3FPgc6BaUReHEK4LIcwLIcxbt25dMcKQpAPPm1PqJ70kSZIkSZIkSdLB55hjjuGXv/wlzzzzTKFzRxxxBLVq1eLVV18F4Ntvv+Xrr7/mjDPOYMyYMXz33XesW7eOt956izZt2iRde+655/LEE0+wdetWAD744AM2bdqU1GbcuHFcddVVfPzxx+Tl5bF69Wrq1q3LjBkzdhtv5cqVOfrooxNtXnjhhcRq8+/r0KFD4rnoo0aN2st3pOwpzpbs5YAWwI0xxr+FEB5mx/brCTHGGEKIRV0cYxwODAdo1apVkW0kSZIkSZIkSZIkpbfPz2qW0vFvvfVWHnvssSLPvfDCC/z617/mrrvu4pBDDuHll1/mwgsvZPbs2TRt2pQQAn/4wx+oXr06eXl5iev69OlDXl4eLVq0IMZI1apVE4n3nUaPHl1om/SLL764yPpdjRw5kr59+/L1119Tr149nnvuuSLbPfzww1x++eXcf//9dOvWbS/fjbKnOAnzT4FPY4x/KyiPY0fCfG0IoUaMcU0IoQbwj+IGKRVXnUETk8p5FVIUCBRebRxeSU0gkiRJkiRJkiRJKlJ+fn7iuFq1anz99deJck5OTuL4pJNOYsqUKYWuHzJkSKGt0+vUqcPixYsByMjI4N577+Xee+/dbQxTp04tVNe/f//E8c6+du0XoFmzZrzzzjuFrp02bVpSuW7dusyePTtRHjx48G5jKcv2eUv2GOPnwOoQwskFVZ2BpcDrwNUFdVcDrxUrQkmSJEmSJEmSJEmSSkFxVpgD3AiMCiEcCnwE9GZHEn5sCOFXwMfAL4s5hiRJkiRJkiRJkiRJJa5YCfMYYy7QqohTnYvTryTtiwNp631JaS6n8vfKG1MThyRJkiRJkiTpB+3zluySJEmSJEmSJEmSJB3MirsluyRJkiRJkiRJkqQy4s0p9QvVde60MgWRSPuHK8wlSZIkSZIkSZIkSWnJFeaSJEmSJEmSDhjVp+YmlSukKA5JknTgqDNoYon2l3ffz/fY5p577uHFF18kMzOTjIwMhg0bRtu2bUs0jjp16jBv3jyqVKnC/PnzueSSSxg/fjzNmzcv0XH0w0yYS8XwQI/zk8q3jpmQokh0oPOXfUnaN1kjs5LKY1MUhyRJKjuWNTwlqXzK8mUpikSSJEkHqtmzZzNhwgQWLFhA+fLlWb9+PVu2bCm18RYuXMgll1zCmDFjTJangFuyS5IkSZIkSZIkSVKBNWvWUKVKFcqXLw9AlSpVqFmzJgD/+Z//SevWrWncuDHXXXcdMUYAsrOzufnmm2nVqhWnnHIKc+fO5aKLLuKkk07ijjvu2O1Yy5Yt44ILLuCFF16gTZs2AOTk5HDNNdeQnZ1NvXr1eOSRRxLtL7jgAlq2bEmjRo0YPnx4or5SpUrcfPPNNGrUiM6dO7Nu3bpEXAMGDKBZs2Y0btyYOXPmlOybVQaYMJckSZIkSdJBofrU3KSXJEmSVBq6dOnC6tWradCgAf369WP69OmJczfccANz585l8eLFfPPNN0yY8O/dhw899FDmzZtH37596datG48//jiLFy9mxIgRfPHFF0WO1a1bNx577DFOP/30pPrly5fzxhtvMGfOHH7/+9+zdetWAJ599lnmz5/PvHnzeOSRRxL9btq0iVatWrFkyRI6duzI73//+0RfX3/9Nbm5ufzpT3/immuuKbH3qawwYS5JkiRJkiRJkiRJBSpVqsT8+fMZPnw4VatWpUePHowYMQKAqVOn0rZtW7KyspgyZQpLlixJXNe1a1cAsrKyaNSoETVq1KB8+fLUq1eP1atXFznW2WefzdNPP813332XVP/zn/+c8uXLU6VKFY477jjWrl0LwCOPPELTpk1p164dq1evZsWKFQBkZGTQo0cPAK688kpmzpyZ6Ouyyy4D4Mwzz+TLL79kw4YNJfAulR0+w1ySJEmSJEmSJP0oOTk5P1iWpINdZmYm2dnZZGdnk5WVxciRI7n00kvp168f8+bN4/jjjycnJ4fNmzcnrtm5hXtGRkbieGd527ZtRY7z2GOP0bdvX/r168ewYcMK9bUzlm3btjFt2jQmT57M7NmzqVixItnZ2Unj7yqEUORxUeV05wpzSZIkSZIkSZIkSSrw/vvvJ1ZuA+Tm5nLCCSckktNVqlQhPz+fcePGFXusjIwMXnzxRZYvX85dd931g203btzI0UcfTcWKFVm+fDnvvPNO4tz27dsT8bz44otJW7yPGTMGgJkzZ1K5cmUqV65c7LjLEleYS5JUhE8HzUiuqJCaOKS0klPED+p1a+//OCQdMB7ocX5S+dYxE3bTUpIkSZJUluXd9/P9Ol5+fj433ngjGzZsoFy5cpx44okMHz6co446imuvvZbGjRtTvXp1WrduXSLjVahQgddff52OHTtSrVq13bY777zzePLJJznllFM4+eSTadeuXeLc4Ycfzpw5cxg8eDDHHXdcIkm+s//mzZuzdetWnn322RKJuSwxYS5JEoVvyPeoe1uKIpEkSZLKHj8AI0ll35tT6heq69xpZQoikaTia9myJbNmzSry3ODBgxk8eHCh+mnTpiWOd27lXtS5XeXl5SWOK1euTG5ubpHtFi9enDj+f//v/+027gcffLDI+iuvvJKHHnpot9elO7dklyRJkiRJkiRJkiSlJVeYS5KktOHKJpWo728hn7MxNXFIkiRJ0kGizqCJSeU8H4EnSSUmPz+/yPrdrW7Xv7nCXJIkSZIkSZIkSZKUlkyYS5IkSZIkSZIkSZLSkglzSZIkSZIkSZIkSVJa8hnmkiRJkiTpoPLmlPpJ5c6dVqYoEqnsyhqZlVQem6I4JEmSpNJmwlySJEmSJEmSJEnSgSuncgn3t3GPTe655x5efPFFMjMzycjIYNiwYbRt23avuh8xYgTz5s3jscceK26kRbr55ps54YQTuOmmmwA499xzOf7443n66acBuPXWW/nJT37CpZdeSv/+/Rk3bhzTpk1j6NChTJgwoVRiOpiZMJckSZIkHRQ+HTSjcGWF/R+Hyq5lDU9JKp+yfFmKIpEkSZKUSrNnz2bChAksWLCA8uXLs379erZs2ZLqsBI6dOjA2LFjuemmm9i+fTvr16/nyy+/TJyfNWsWf/zjH6lZsybjxo1LYaQHB59hLkmSJEmSJEmSJB3gskZmFXqpdKxZs4YqVapQvnx5AKpUqULNmjWZMmUKF1xwQaLdX//6Vy688EIAnnvuORo0aECbNm14++23E2169epF//79Oe2006hXr15SAnvIkCG0bt2aJk2acPfddwNw11138dBDDyXa/O53v+Phhx9Oiu+0005j9uzZACxZsoTGjRtzxBFH8K9//Ytvv/2WZcuW0aJFC/Ly8mjcuHGhr2/69Ok0a9aMZs2a0bx5c7766itijAwcOJDGjRuTlZXFmDFjAJg2bRrZ2dlccsklNGzYkCuuuIIYY7He3wONCXNJkiRJkiRJkiRJKtClSxdWr15NgwYN6NevH9OnTwfgrLPOYvny5axbtw7YkSS/5pprWLNmDXfffTdvv/02M2fOZOnSpUn9rVmzhpkzZzJhwgQGDRoEwKRJk1ixYgVz5swhNzeX+fPn89Zbb3HNNdfw/PPPA7B9+3ZeeuklrrzyyqT+atasSbly5fjkk0+YNWsW7du3p23btsyePZt58+aRlZXFoYceutuvb+jQoTz++OPk5uYyY8YMDjvsMMaPH09ubi7vvfcekydPZuDAgaxZswaAd999l4ceeoilS5fy0UcfJX0goCxwS3YJivwU1tgUxCFJkiRJkiSVNW9OqV+o7orwSlLZp6xIkg4klSpVYv78+cyYMYOpU6fSo0cP7rvvPnr16sVVV13Fn//8Z3r37s3s2bN5/vnnmTBhAtnZ2VStWhWAHj168MEHHyT6u+CCC8jIyODUU09l7dq1wI6E+aRJk2jevDkA+fn5rFixgjPPPJNjjz2Wd999l7Vr19K8eXOOPfbYQjGedtppzJo1i1mzZnHLLbfw2WefMWvWLCpXrkyHDh1+8Ovr0KEDt9xyC1dccQUXXXQRtWrVYubMmVx22WVkZmZSrVo1OnbsyNy5cznyyCNp06YNtWrVAqBZs2bk5eVx+umnl8h7fSAwYS5JkiRJkiRJkiRJu8jMzCQ7O5vs7GyysrIYOXIkvXr1onfv3vziF7+gQoUKdO/enXLl9pxu3bm1O5DYzjzGyO23386vf/3rQu379OnDiBEj+Pzzz7nmmmuK7LNDhw7MmjWLRYsW0bhxY44//ngeeOABjjzySHr37v2D8QwaNIif//zn/M///A8dOnTgjTfe2Ov4MzMz2bZt2w+2P9ikfcK8zqCJSeU8P8ooSZIkHXQ+HTSjcKU/20uSJEkqI8xlKNWqT81NKpf1v4Lvv/8+GRkZnHTSSQDk5uZywgknADu2Q69ZsyaDBw9m8uTJALRt25YBAwbwxRdfcOSRR/Lyyy/TtGnTHxzj3HPP5c477+SKK66gUqVKfPbZZxxyyCEcd9xxXHjhhdx1111s3bqVF198scjrTzvtNIYOHUq9evXIzMzkmGOOYcOGDSxZsoSnnnrqB8deuXIlWVlZZGVlMXfuXJYvX84ZZ5zBsGHDuPrqq/nnP//JW2+9xZAhQ1i+fPmPffsOOmmfMJckSZIkSZIkSZJ0AMvZuF+Hy8/P58Ybb2TDhg2UK1eOE088keHDhyfOX3HFFaxbt45TTjkFgBo1apCTk0P79u056qijaNas2R7H6NKlC8uWLaN9+/bAjm3g//znP3Pcccdx6KGHctZZZ3HUUUeRmZlZ5PVZWVmsX7+eyy+/PKkuPz+fKlWq/ODYDz30EFOnTiUjI4NGjRrx05/+lEMPPZTZs2fTtGlTQgj84Q9/oHr16ibM90YIIROYB3wWYzw/hFAXeAk4FpgPXBVj3FLccSRJkiRJUtnzQI/zk8q3jpmQokgkSZIkaYeWLVsya9as3Z6fOXMm1157bVJd7969i9wKfcSIEUnl/Pz8xPGAAQMYMGBAoWu2b9/OO++8w8svv7zbGDIzM/nyyy9/cKw6deqwePFigMT28gCPPvpokX0OGTKEIUOGJNXteh3AY489ttuYDlYZJdDHAGDZLuX7gT/GGE8E/gX8qgTGkCRJkiRJkiRJkqSUatmyJQsXLuTKK68slf6XLl3KiSeeSOfOnRNbwqt0FWuFeQihFvBz4B7glhBCADoBO9f+jwRygCeKM44kSZIkSZIkSZIkpdr8+fNLtf9TTz2Vjz76qFTHULLibsn+EPBb4IiC8rHAhhjjtoLyp8BPirowhHAdcB1A7dq1ixmGJEmSJEmSJEmSJGl/WrJ+SVK5UZVGKYpk3+3zluwhhPOBf8QY9+ljFDHG4THGVjHGVlWrVt3XMCRJkiRJkiRJkiRJ2ifFWWHeAegaQvgZUAE4EngYOCqEUK5glXkt4LPihylJkiRJkiRJkiRJUsna5xXmMcbbY4y1Yox1gEuBKTHGK4CpwCUFza4GXit2lJIkSZIkSZIkSZIklbDiPsO8KLcBL4UQBgPvAs+UwhiSpDRVZ9DEpHJehRQFolLlPEuSJEmSJO29rJFZherGpiAOFU9p3BN7oMf5hepuHTOh+B3vZ0X9HS+ORVcv2mObzMxMsrL+Pe6ll17KoEGD9qr/v//97/Tv359x48Yxbdo0hg4dyoQJe/e+jxgxgoEDB/KTn/yErVu3csopp/D8889TsWLFvbp+V5UqVSI/P/9HX5duSiRhHmOcBkwrOP4IaFMS/UqSJEmSJEmSJEnS/nbYYYeRm5u7T9fWrFmTcePG7fPYPXr04LHHHgPg8ssvZ8yYMfTu3Xuf+/uxYozEGMnI2OfNyg8q6fFVSpIkSZIkSZIkSVIx1alTh9tvv51mzZrRqlUrFixYwLnnnkv9+vV58sknAcjLy6Nx48ZJ123fvp2TTjqJdevWJconnnhiolyUbdu2sWnTJo4++mgA1q1bx8UXX0zr1q1p3bo1b7/9NgD5+fn07t2brKwsmjRpwiuvvJLUz/r162nfvj0TJ+7YxWDIkCG0bt2aJk2acPfddydiPvnkk+nZsyeNGzdm9erVJfBuHRxKY0t2SZIkSZIkSZIkHcDKylbdUmn55ptvaNasWaJ8++2306NHDwBq165Nbm4uN998M7169eLtt99m8+bNNG7cmL59+xbZX0ZGBldeeSWjRo3ipptuYvLkyTRt2pSqVasWajtmzBhmzpzJmjVraNCgAb/4xS8AGDBgADfffDMNjm/Cp5+t5tKeFzHzzbk88Kd7qFy5MosW7dhq/l//+leir7Vr19K1a1cGDx7MOeecw6RJk1ixYgVz5swhxkjXrl156623qF27NitWrGDkyJG0a9euxN7Hg4EJc0mSJEmSJEmSJEnaxQ9tyd61a1cAsrKyyM/P54gjjuCII46gfPnybNiwYbd9XnPNNXTr1o2bbrqJZ599drfbrO/ckj3GyPXXX8+QIUMYNGgQkydPZunSpWzbsh2Ar/K/YtOmfCZPnsxLL72UuH7nivStW7fSuXNnHn/8cTp27AjApEmTmDRpEs2bNwd2rE5fsWIFtWvX5oQTTki7ZDmYMJckSZIkSZIkSZKkvVa+fHlgx6rxncc7y9u2bdvtdccffzzVqlVjypQpzJkzh1GjRv3gOCEEfvGLX/Doo48yaNAgtm/fzjvvvMOXa7fsVZzlypWjZcuWvPHGG4mEeYyR22+/nV//+tdJbfPy8jj88MP3qt+yxmeYS5IkSZIkSZIkSdJ+0KdPH6688kq6d+9OZmbmHtvPnDmT+vXrA9ClSxceffTRxLnFSxYCcM455/D4448n6nduyR5C4Nlnn2X58uXcf//9AJx77rk8++yz5OfnA/DZZ5/xj3/8o2S+uIOUK8wlSZIkSZIkKQ1ljcxKKo9NURySJO3JoqsX7fcxv/8M8/POO4/77ruv2P127dqV3r1773Y7dvj3M8y3b99OrVq1GDFiBACPPPII119/Pc89O5Lvtm2jXZvTGHLvQ9xxxx1cf/31NG7cmMzMTO6++24uuugiADIzMxk9ejRdu3bliCOOoF+/fixbtoz27dsDUKlSJf785z/vVfK+rDJhLpWgTwfNSK6okJo4JEmSJEmSJEnaH6pPTX7G8+dnNdtNS+ng8t133xVZn5eXlzju1asXvXr1KnSuSpUqLF68GIDs7Gyys7MTbd577z2aNm1Kw4YNi+z/+33uqkqVKowZM4Z/fPxlUn2lSpUYOXJkofY7V5GXL1+eN954I1E/YMAABgwYUKj9zpjTjQlzSZIkSZIkSZIkSSpl9913H0888cQen12u/cuEuSRJkiRJkiRJkiSVskGDBjFo0KBUh7HXFn66oVBdk4xVyRWHHrqfoik9GakOQJIkSZIkSZIkSZKkVDBhLkmSJEmSJEmSJElKSybMJUmSJEmSJEmSJElpyWeYS5IkKe29OaV+Urlzp5UpikSSJEmSJEnS/mTCXJIkSZIkSZIkSdIBa1nDU0q0v1OWL9tjm3vuuYcXX3yRzMxMMjIyGDZsGG3btqVPnz7ccsstnHrqqSUa0481b8Fc7vj9bWzZsoXv4lZ69OhBTk7Obtv//e9/L1RXs2bNUozw4GHCXJIkSZIkSZIkSZIKzJ49mwkTJrBgwQLKly/P+vXr2bJlCwBPP/10iqPbof9/9OWpx0bQ6NQsjq11OO+//36qQ0qIMRJjJCPj4Hg6+MERpSRJkiRJ0m5Un5pb6CVJklLP/58lHazWrFlDlSpVKF++PABVqlRJrMbOzs5m3rx5AFSqVImBAwfSqFEjzj77bObMmUN2djb16tXj9ddfB2Dz5s307t2brKwsmjdvztSpUwEYMWIEF110Eeeddx4nnXQSv/3tbxPjT5o0ifbt29OiRQu6d+9Ofn5+oRjXr1/HccdVByAzMzOx4j0nJ4errrqK9u3bc9JJaN3zcQAAGH1JREFUJ/HUU08lrnniiSf42c9+xtlnn83QoUMByMvL45RTTuHaa6+lUaNGdOnShW+++QaAT1Z9xHWXXUD3LqfT46cdWZ23CoAhT4yk9c+upMnZv+Sx+x8D4LNPPuP8dufTs2dPGjduzOrVq0tiKvYLE+aSJEmSJEmSJEmSVKBLly6sXr2aBg0a0K9fP6ZPn15ku02bNtGpUyeWLFnCEUccwR133MFf//pX/vKXv3DXXXcB8PjjjxNCYNGiRYwePZqrr76azZs3A5Cbm8uYMWNYtGgRY8aMYfXq1axfv57BgwczefJkFixYQKtWrXjwwQcLjf3rX11Ph04t6XXdFQwbNizRJ8DChQuZMmUKs2fP5j//8z/5+9//zvTp01m1ahUTJ05k0qRJLFy4kLfeeguAFStWcP3117NkyRKOOuooXnnlFQBu738dPXr24eVJM3n+L29QpVo1Jk2fzYpVnzBn4gvkTnqJpe8tZd6sHR8g+Pijj+nXrx9LlizhhBNOKLkJKWVuyS5JkvQD3pxSP6ncudPKFEUiSZIkSZIkaX+oVKkS8+fPZ8aMGUydOpUePXpw33330atXr6R2hx56KOeddx4AWVlZlC9fnkMOOYSsrCzy8vIAmDlzJjfeeCMADRs25IQTTuCDDz4AoHPnzlSuXBmAU089lY8//pgNGzawdOlSOnToAMCWLVto3bAh3yxe/O+Bj6jNrQNu4+ILujPtrSm8+OKLjB49mmnTpgHQrVs3DjvsMA477DDOOuss5syZw/Tp05k+fTpdunQB4Ouvv2bFihXUrl2bunXr0qxZMwBatmxJXl4eX331Ff/4fA2df3o+AOUrVADg5envMGn6OzTvchkA//z6Gz7+6GNq1KpBzeNr0q5du5Kahv3GhLkkSZIkSZIkSZIk7SIzM5Ps7Gyys7PJyspi5MiRhRLmhxxyCCEEADIyMhJbuGdkZLBt27Y9jrGz/c7xtm3bRoyRc845h9GjRyfOJSXLd1HnhHr0uqoetwzqT9WqVfniiy8AEjHtFEIgxsgNN9zAVVddlaivWbMmeXl5heLYuSV7UWKM3H5Db3591SUALDn0UGDHluyHVTxsj1/zgcgt2SVJZU7WyKyklyRJkiRJkiRJe+v9999nxYoViXJubu4+bzF+xhlnMGrUKAA++OADPvnkE04++eTdtm/Xrh1vv/02H374IbBj2/cVBavVd/XXKW8QYwR2bKmemZnJUUcdBcBrr73G5s2b+eKLL5g2bRqtW7cmOzubMWPGsGnTJmDHc9r/8Y9/7DaOI444gmo1ajLlfycCsOXbb/nmm685N7s9z455nfxNXwOwds1avlj3xY98Vw4srjCXJEmSJEmSJEmSdMA6Zfmy/Tpefn4+N954Ixs2bKBcuXKceOKJDB8+fJ/66tevH7/5zW/IysqiXLlyjBgxImlF9/dVrVqVESNGcNlll/Htt98CcOe113JSnTpJ7V4e/xJ3/dftHFahIhUqHsqoUaPIzMwEoEmTJpx11lmsX7+eO++8k5o1a9KxY0dWrFhB165dAahYsSJjx45NXFOUex5+kv8adDN/euBeyh1yCEOfGMHPOrZn2YpVtO/aC4DMSofzf//0f3+wnwOdCXNJkiRJkiRJkiRJKtCyZUtmzZpV5LmdzwmHHYn1nXJycpLa7TxXoUIFnnvuuUL99OrVK2mL9wkTJiSOO3XqxNy5cxPlorZkH/7Yv/s87oQjk841adKE559/vtA1ffr0oU+fPolyzZo1AVi8S///8R//kTg+oW59nh7z+vd6WcWAPpczoM/lwL+3ZAd4dcarhcY8GLgluyRJkiRJkiRJkiQpLbnCXJIkSZIkSZIkSZLKgO+vdNeemTCXpB/p00EzkisqpCYOSZIkSZIkSZLKqhgjIYRUh6GDQIyxWNe7JbskSZIkSZIkSZKkA0aFChX44osvip0IVdkXY+SLL76gQoV9X924zyvMQwjHA88D1YAIDI8xPhxCOAYYA9QB8oBfxhj/tc8RSlIJyhqZlVQem6I4JEmSJEmSJElS0WrVqsWnn37KunXrUh3KAWHr2rVJ5c0btiaVv/h6z8niDRs2FKrbuHHjD16z9l/fFKpbFpLn5PNyyenmjHX7f712hQoVqFWr1j5fX5wt2bcBt8YYF4QQjgDmhxD+CvQC3owx3hdCGAQMAm4rxjiSJEmSJEmSJEmS0sQhhxxC3bp1Ux3GAWPZhRcllWdkP55Uvv7JTnvso6hnm+/peec/HTSxUF1ehcuTyr+sWzupvOjqRXuM5UCzzwnzGOMaYE3B8VchhGXAT4BuQHZBs5HANEyYS5IkaT95oMf5SeVbx0xIUSSSJEmSJEmSDnTFWWGeEEKoAzQH/gZUK0imA3zOji3bi7rmOuA6gNq1axfVRJIkqVR9OmhGcsW+P+ZGkiRJkiRJknQQKvYm8iGESsArwE0xxi93PRdjjOx4vnkhMcbhMcZWMcZWVatWLW4YkiRJkiRJkiRJkiT9KMVaYR5COIQdyfJRMcbxBdVrQwg1YoxrQgg1gH8UN0hJkiRJkkpNTuXvlTemJg5JkiRJklKo+tTcpHK6bMi5zyvMQwgBeAZYFmN8cJdTrwNXFxxfDby27+FJkiRJkiRJkiRJklQ6irPCvANwFbAohLDz4wb/B7gPGBtC+BXwMfDL4oUoSZIkScke6HF+UrlH3dtSFIkkSZIkSZIOZvucMI8xzgTCbk533td+JUmSJEmSJEmSJEnaH4r1DHNJkiRJOli8OaV+Urlzp5UpikSSJEmSJEkHin1+hrkkSZIkSZIkSZIkSQczE+aSJEmSJEmSJEmSpLTkluwHkO9vEUl4JTWBSJIk7Qd1Bk1MKudVSFEgkiRJkqQkD/Q4v1DdrWMmpCASSZJKnyvMJUmSJEmSJEmSJElpyYS5JEmSJEmSJEmSJCktmTCXJEmSJEmSJEmSJKUlE+aSJEmSJEmSJEmSpLRkwlySJEmSJEmSJEmSlJZMmEuSJEmSJEmSJEmS0lK5VAcgSZIkSZIkSZJKR9bIrEJ1Y1MQhyRJBypXmEuSJEmSJEmSJEmS0pIJc0mSJEmSJEmSJElSWjJhLkmSJEmSJEmSJElKSz7DXJIkHZS+/ww2n78mSZIkSZIk6WD3/fue4L3P0uYKc0mSJEmSJEmSJElSWjJhLkmSJEmSJEmSJElKS27JLkmSpDLt00EzCldW2P9xSJIkSZIkSaXhgR7nF6q7dcyEFERycHKFuSRJkiRJkiRJkiQpLZkwlyRJkiRJkiRJkiSlJRPmkiRJkiRJkiRJkqS05DPMJUmSJEmSJEmSJGk/qDNoYlI5776fpygS7WTCXJIkSZIkSZIkSZLSzJtT6idXhFdSE0iKuSW7JEmSJEmSJEmSJCktucJckiRJkiRJkiRJklIhp3JyuW7t1MSRxlxhLkmSJEmSJEmSJElKS6W2wjyEcB7wMJAJPB1jvK+0xpIkSZIkaW/VGTQxqZxXIUWBSJIkSZKklCuVFeYhhEzgceCnwKnAZSGEU0tjLEmSJEmSJEmSJEmS9kVprTBvA3wYY/wIIITwEtANWFpK40mSJEmSJEmSJCnF3NFJ0sGmtJ5h/hNg9S7lTwvqJEmSJEmSJEmSJEk6IIQYY8l3GsIlwHkxxj4F5auAtjHGG3Zpcx1wXUHxZOD9Eg/kwFYFWJ/qIFTqnOf04DynB+c5PTjP6cO5Tg/Oc3pwntOD85wenOf04DynB+c5PTjP6cF5Tg/Oc3pIx3k+IcZYdU+NSmtL9s+A43cp1yqoS4gxDgeGl9L4B7wQwrwYY6tUx6HS5TynB+c5PTjP6cF5Th/OdXpwntOD85wenOf04DynB+c5PTjP6cF5Tg/Oc3pwntOD87x7pbUl+1zgpBBC3RDCocClwOulNJYkSZIkSZIkSZIkST9aqawwjzFuCyHcALwBZALPxhiXlMZYkiRJkiRJkiRJkiTti9Lakp0Y4/8A/1Na/ZcBabsdfZpxntOD85wenOf04DynD+c6PTjP6cF5Tg/Oc3pwntOD85wenOf04DynB+c5PTjP6cF53o0QY0x1DJIkSZIkSZIkSZIk7Xel9QxzSZIkSZIkSZIkSZIOaCbMJUmSJEmSJEmSJElpyYS5JEmSJEmSJEmSJCktmTCXJEmSJEmSJEmSJKUlE+aSJEmSJEmSJEmSpLRkwnw/CSE0DCF0DiFU+l79eamKSSUvhNAmhNC64PjUEMItIYSfpToula4QwvOpjkGlL4RwesG/6S6pjkUlJ4TQNoRwZMHxYSGE34cQ/juEcH8IoXKq41PJCCEcGkLoGUI4u6B8eQjhsRDC9SGEQ1Idn0pOCKFeCOE/QggPhxAeDCH03flvXGVHwe9Wt4UQHil43RZCOCXVcWn/CCH0TnUMKjneK0kP3itJT94rKfu8T1I2eZ8kPYQQ+ocQjk91HCpd3g/7cUKMMdUxlHkhhP7A9cAyoBkwIMb4WsG5BTHGFqmMTyUjhHA38FOgHPBXoC0wFTgHeCPGeE8Kw1MJCSG8/v0q4CxgCkCMset+D0qlIoQwJ8bYpuD4WnZ8H/8L0AX47xjjfamMTyUjhLAEaBpj3BZCGA58DYwDOhfUX5TSAFUiQgij2PH/c0VgA1AJGM+OeQ4xxqtTGJ5KSMHP3OcDbwE/A95lx3xfCPSLMU5LXXQqKSGE24DLgJeATwuqawGXAi/5/3PZF0L4JMZYO9VxqPi8V5IevFeSHrxXkh68T5IevE+SHkIIG4FNwEpgNPByjHFdaqNSSfN+2I9jwnw/CCEsAtrHGPNDCHXY8R/MCzHGh0MI78YYm6c0QJWIgnluBpQHPgdqxRi/DCEcBvwtxtgkpQGqRIQQFgBLgaeByI5fAkez4yYtMcbpqYtOJWnX788hhLnAz2KM60IIhwPvxBizUhuhSkIIYVmM8ZSC46QbsyGE3Bhjs9RFp5ISQlgYY2wSQigHfAbUjDF+F0IIwHv+H1027PxZrGBuKwL/E2PMDiHUBl7zZ+6yIYTwAdAoxrj1e/WHAktijCelJjKVpBDCwt2dAhrEGMvvz3hUOrxXkh68V5IevFeSHrxPkh68T5IeQgjvAi2Bs4EeQFdgPju+d4+PMX6VwvBUQrwf9uO4Jfv+kRFjzAeIMeYB2cBPQwgPsuMHSJUN22KM38UYvwZWxhi/BIgxfgNsT21oKkGt2PHDw++AjQWr1b6JMU73F8AyJyOEcHQI4Vh2fMBsHUCMcROwLbWhqQQt3mVr1/dCCK0AQggNgK27v0wHmYyCZNoR7PhU7c5t5MoDbkFVtpQr+LM8Oz45TYzxE5znsmQ7ULOI+hr4M3dZUg3oCfyiiNcXKYxLJct7JenBeyXpwXsl6cH7JOnB+yTpIcYYt8cYJ8UYf8WO37H+BJwHfJTa0FSCvB/2I5TbcxOVgLUhhGYxxlyAgk9Pnw88C/jJu7JjSwihYsEvgS13VhY828VfAsuIGON24I8hhJcL/lyL30vLqsrs+IU/ADGEUCPGuKbg+YrewCs7+gAPhxDuANYDs0MIq4HVBedUNjwDLAcy2XET7+UQwkdAO3Zs66yy4Wlgbgjhb8AZwP0AIYSqwD9TGZhK1E3AmyGEFez4Xg1QGzgRuCFlUamkTQAq7fwdelchhGn7PxyVEu+VpAfvlaQB75WkDe+TpAfvk6SHpH+zBTt4vQ68XrBjm8oG74f9CG7Jvh+EEGqx4xO1nxdxrkOM8e0UhKUSFkIoH2P8toj6KkCNGOOiFISlUhZC+DnQIcb4f1Idi/aPgh8aq8UYV6U6FpWcEMKRQF123NT5NMa4NsUhqYSFEGr+//buLdTSuozj+O8ZNEImNA2GSMIiMhE7UHmrdsAOVIpKSggJ3kTQRWRFXeRNURgURBnddIAO2EnCyAtTKynTNA9ZOlCig9FNSkEFUjxd7DWw2TriYvZay9nP5wOb2et911r7Gb68G2b9533fJOnuv1bVSdm67Nij3X3HZidjN1XVmUnOSPKH7n5w0/OwGlW1L8nZSV6y2PRYkju7+3+bmwpYls9KZvBZyUw+K5nF5yR7k89J9raqemV3H9z0HKyez8OePQvmG1ZV+w9fgoy9S+cZdJ5D6xl0nkHnGXQGAAAA4Ejcw3zz/rjpAVgLnWfQeQ6tZ9B5Bp1n0HmPqKpXV9XtVXWoqr5WVS/cts//kN8jdJ6hqs7See9zPM/geJ7B8TyDzjP4vT2D43k57iWzBlX14SPtSrJ/nbOwOjrPoPMcWs+g8ww6z6DzGF9JcnWS27N1D8Xbqurd3f3nJMdvcjB2lc4zXBudJ3A8z+B4nsHxPIPOM/i9PYPjeQkWzNfjM0muSfLfp9nnLP+9Q+cZdJ5D6xl0nkHnGXSe4QXdfePi+89X1V1Jbqyqy5O439jeofMMOs+g8ww6z6DzDDrPoPMMOi/Bgvl63J3k+u6+a+eOqrpyA/OwGjrPoPMcWs+g8ww6z6DzEFV1Ynf/I0m6+5aquijJD5OcvNnJ2E06z6DzDDrPoPMMOs+g8ww6z6Dzs+dMi/W4IsmjR9j3hnUOwkrpPIPOc2g9g84z6DyDzjN8LskZ2zd0931J3pzkRxuZiFXQeQadZ9B5Bp1n0HkGnWfQeQadl1DdzroHAAAAAAAAYB5nmK9BVZ1YVZ+tqger6vGq+ntV/Wmx7aRNz8fu0HkGnefQegadZ9B5Bp1n0HkGnWfQeQadZ9B5Bp1n0HkGnWfQeTkWzNfjuiRPJDm3u0/u7lOSnLfYdt1GJ2M36TyDznNoPYPOM+g8g84z6DyDzjPoPIPOM+g8g84z6DyDzjPovASXZF+Dqnqou09fdh/HFp1n0HkOrWfQeQadZ9B5Bp1n0HkGnWfQeQadZ9B5Bp1n0HkGnZfjDPP1eKSqPlpVBw5vqKoDVfWxJIc2OBe7S+cZdJ5D6xl0nkHnGXSeQecZdJ5B5xl0nkHnGXSeQecZdJ5B5yVYMF+P9yY5JckvFvcJeDzJrUlOTnLJJgdjV+k8g85zaD2DzjPoPIPOM+g8g84z6DyDzjPoPIPOM+g8g84z6LwEl2TfsKq6oru/vuk5WC2dZ9B5Dq1n0HkGnWfQeQadZ9B5Bp1n0HkGnWfQeQadZ9B5Bp2fyoL5hlXVo9390k3PwWrpPIPOc2g9g84z6DyDzjPoPIPOM+g8g84z6DyDzjPoPIPOM+j8VMdteoAJquq+I+1KcuAI+zjG6DyDznNoPYPOM+g8g84z6DyDzjPoPIPOM+g8g84z6DyDzjPovBwL5utxIMn5SZ7Ysb2S/Hr947AiOs+g8xxaz6DzDDrPoPMMOs+g8ww6z6DzDDrPoPMMOs+g8ww6L8GC+XrckGR/d9+zc0dV3br+cVgRnWfQeQ6tZ9B5Bp1n0HkGnWfQeQadZ9B5Bp1n0HkGnWfQeQadl+Ae5gAAAAAAAACMtG/TAwAAAAAAAADAJlgwBwAAAAAAAGAkC+YAAAAAAAAAjGTBHAAAAFakqg5U1Xeq6i9VdVdV/aaqLty2/4tV9VhV7du27f1V1VX1lm3bLlhsu3jx+Naqeqiq7ll8/eAZZri6qj6y+P4bVfVwVd1bVQer6ltVdepq/vYAAADw3GfBHAAAAFagqirJ9Ul+2d0v7+7XJ7k0yamL/fuSXJjkUJJzdrz8/sVzD7ssyb07nvO+7n7t4uviJUa7qrtfk+T0JL9PcnNVPW+J1wMAAMCeYcEcAAAAVuNNSZ7s7q8e3tDdj3T3lxYPz03yQJJrs7Ugvt2vkpxdVcdX1f4kr0hyz24O11u+kORvSd6+m+8NAAAAxwoL5gAAALAaZya5+xn2X5bku0l+nOSdVXX8tn2d5KYk5yd5T5KfPM3rv73tkuzXHMWcdyd51VG8HgAAAI5ZFswBAABgDarqy4t7h9+5uAT6O5Jc393/TPLbbC2Ob/e9bF2W/dJsLazvtP2S7FcdzWhH8VoAAAA4ph236QEAAABgj3ogyUWHH3T3B6vqRUl+l63F8ZOS3L91q/OckOQ/SW7Y9vw7quqsJP/u7oOL563C65L8fFVvDgAAAM9lzjAHAACA1bg5yfOr6gPbtp2w+POyJFd292ndfVqSlyV5a1WdsOM9Pp7kE6sYrrZ8KMmLk9y4ip8BAAAAz3UWzAEAAGAFuruTXJDknKp6uKruSPLNJJ9K8rYkP9323H8luS3Ju3a8x8+6+5Yj/Ijt9zC/aYnRrqmqe5McTPLGJOd195NLvB4AAAD2jNr69zsAAAAAAAAAzOIMcwAAAAAAAABGOm7TAwAAAABHr6o+meSSHZu/392f3sQ8AAAAcCxwSXYAAAAAAAAARnJJdgAAAAAAAABGsmAOAAAAAAAAwEgWzAEAAAAAAAAYyYI5AAAAAAAAACP9H12bFXUEeMq3AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 2520x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (35,5))\n",
    "\n",
    "games.groupby(by = ['GAME_ID', '    Player Name']).mean()['Active Minutes'].unstack().plot(kind='bar', ax = ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 401,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Team Rank</th>\n",
       "      <th>Load Sum</th>\n",
       "      <th>Movements</th>\n",
       "      <th>Movement Category</th>\n",
       "      <th>High Surges</th>\n",
       "      <th>Surge Category</th>\n",
       "      <th>Stress Percentage</th>\n",
       "      <th>Stress Category</th>\n",
       "      <th>Active Minutes</th>\n",
       "      <th>Movements Per Minute</th>\n",
       "      <th>High Surges Per Minute</th>\n",
       "      <th>Kinetic Energy (Joules/Pound)</th>\n",
       "      <th>Avg Intensity (Watts/Pound)</th>\n",
       "      <th>Avg Burst (Feet/Sec)</th>\n",
       "      <th>Jumps</th>\n",
       "      <th>Avg Jump (Inches)</th>\n",
       "      <th>High Jumps (20+ Inches)</th>\n",
       "      <th>Avg High Jumps (20+ Inches)</th>\n",
       "      <th>Highest Jump (inches)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GAME_ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1012</th>\n",
       "      <td>6.416667</td>\n",
       "      <td>320.500000</td>\n",
       "      <td>2053.000000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>355.250000</td>\n",
       "      <td>1.083333</td>\n",
       "      <td>14.645833</td>\n",
       "      <td>0.416667</td>\n",
       "      <td>89.333333</td>\n",
       "      <td>19.209167</td>\n",
       "      <td>3.258333</td>\n",
       "      <td>1852.537500</td>\n",
       "      <td>16.370833</td>\n",
       "      <td>1.286667</td>\n",
       "      <td>62.916667</td>\n",
       "      <td>12.289167</td>\n",
       "      <td>12.833333</td>\n",
       "      <td>14.738333</td>\n",
       "      <td>20.406667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1013</th>\n",
       "      <td>6.500000</td>\n",
       "      <td>320.666667</td>\n",
       "      <td>1725.583333</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>290.750000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>16.294167</td>\n",
       "      <td>0.583333</td>\n",
       "      <td>71.916667</td>\n",
       "      <td>22.765000</td>\n",
       "      <td>3.815833</td>\n",
       "      <td>1485.505833</td>\n",
       "      <td>18.865000</td>\n",
       "      <td>1.431667</td>\n",
       "      <td>54.500000</td>\n",
       "      <td>14.223333</td>\n",
       "      <td>11.416667</td>\n",
       "      <td>16.609167</td>\n",
       "      <td>21.746667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1014</th>\n",
       "      <td>6.500000</td>\n",
       "      <td>315.916667</td>\n",
       "      <td>1573.666667</td>\n",
       "      <td>1.166667</td>\n",
       "      <td>289.250000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>17.609167</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>67.250000</td>\n",
       "      <td>21.021667</td>\n",
       "      <td>3.748333</td>\n",
       "      <td>1502.359167</td>\n",
       "      <td>19.962500</td>\n",
       "      <td>1.517500</td>\n",
       "      <td>51.250000</td>\n",
       "      <td>14.018333</td>\n",
       "      <td>8.916667</td>\n",
       "      <td>11.049167</td>\n",
       "      <td>20.067500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1015</th>\n",
       "      <td>6.500000</td>\n",
       "      <td>319.000000</td>\n",
       "      <td>2110.666667</td>\n",
       "      <td>1.166667</td>\n",
       "      <td>399.833333</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>15.435000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>76.166667</td>\n",
       "      <td>22.641667</td>\n",
       "      <td>4.010833</td>\n",
       "      <td>2054.830000</td>\n",
       "      <td>21.800000</td>\n",
       "      <td>1.488333</td>\n",
       "      <td>64.500000</td>\n",
       "      <td>13.464167</td>\n",
       "      <td>11.166667</td>\n",
       "      <td>12.823333</td>\n",
       "      <td>19.259167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1016</th>\n",
       "      <td>6.500000</td>\n",
       "      <td>315.750000</td>\n",
       "      <td>1262.666667</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>251.333333</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>13.469167</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>20.826667</td>\n",
       "      <td>3.705833</td>\n",
       "      <td>1159.735833</td>\n",
       "      <td>18.195833</td>\n",
       "      <td>1.382500</td>\n",
       "      <td>36.916667</td>\n",
       "      <td>11.641667</td>\n",
       "      <td>7.166667</td>\n",
       "      <td>10.777500</td>\n",
       "      <td>16.217500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1018</th>\n",
       "      <td>6.500000</td>\n",
       "      <td>314.750000</td>\n",
       "      <td>1100.083333</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>210.333333</td>\n",
       "      <td>0.583333</td>\n",
       "      <td>13.244167</td>\n",
       "      <td>0.416667</td>\n",
       "      <td>42.083333</td>\n",
       "      <td>19.332500</td>\n",
       "      <td>3.408333</td>\n",
       "      <td>1030.423333</td>\n",
       "      <td>17.792500</td>\n",
       "      <td>1.371667</td>\n",
       "      <td>34.833333</td>\n",
       "      <td>11.297500</td>\n",
       "      <td>7.333333</td>\n",
       "      <td>11.035833</td>\n",
       "      <td>16.660000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1019</th>\n",
       "      <td>6.500000</td>\n",
       "      <td>320.333333</td>\n",
       "      <td>1841.666667</td>\n",
       "      <td>1.166667</td>\n",
       "      <td>351.333333</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>16.350833</td>\n",
       "      <td>0.583333</td>\n",
       "      <td>72.250000</td>\n",
       "      <td>21.413333</td>\n",
       "      <td>4.065000</td>\n",
       "      <td>1575.771667</td>\n",
       "      <td>15.710833</td>\n",
       "      <td>1.076667</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>14.483333</td>\n",
       "      <td>12.500000</td>\n",
       "      <td>15.971667</td>\n",
       "      <td>21.277500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1020</th>\n",
       "      <td>6.500000</td>\n",
       "      <td>323.500000</td>\n",
       "      <td>1859.916667</td>\n",
       "      <td>1.083333</td>\n",
       "      <td>359.333333</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>18.773333</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>76.333333</td>\n",
       "      <td>21.961667</td>\n",
       "      <td>4.270000</td>\n",
       "      <td>1787.884167</td>\n",
       "      <td>21.149167</td>\n",
       "      <td>1.515000</td>\n",
       "      <td>66.166667</td>\n",
       "      <td>16.464167</td>\n",
       "      <td>12.916667</td>\n",
       "      <td>20.512500</td>\n",
       "      <td>24.951667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1021</th>\n",
       "      <td>6.500000</td>\n",
       "      <td>323.250000</td>\n",
       "      <td>1887.750000</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>360.166667</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>17.729167</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>73.916667</td>\n",
       "      <td>23.298333</td>\n",
       "      <td>4.350833</td>\n",
       "      <td>1702.348333</td>\n",
       "      <td>20.648333</td>\n",
       "      <td>1.435000</td>\n",
       "      <td>56.250000</td>\n",
       "      <td>16.514167</td>\n",
       "      <td>15.416667</td>\n",
       "      <td>16.649167</td>\n",
       "      <td>22.422500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1022</th>\n",
       "      <td>6.500000</td>\n",
       "      <td>322.916667</td>\n",
       "      <td>2162.583333</td>\n",
       "      <td>1.416667</td>\n",
       "      <td>409.916667</td>\n",
       "      <td>1.083333</td>\n",
       "      <td>17.669167</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>84.083333</td>\n",
       "      <td>23.663333</td>\n",
       "      <td>4.365833</td>\n",
       "      <td>2066.409167</td>\n",
       "      <td>22.449167</td>\n",
       "      <td>1.461667</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>16.100000</td>\n",
       "      <td>18.166667</td>\n",
       "      <td>16.949167</td>\n",
       "      <td>23.796667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1023</th>\n",
       "      <td>6.500000</td>\n",
       "      <td>322.583333</td>\n",
       "      <td>2641.166667</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>488.416667</td>\n",
       "      <td>1.166667</td>\n",
       "      <td>16.597500</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>103.416667</td>\n",
       "      <td>23.923333</td>\n",
       "      <td>4.213333</td>\n",
       "      <td>2539.574167</td>\n",
       "      <td>22.372500</td>\n",
       "      <td>1.541667</td>\n",
       "      <td>90.750000</td>\n",
       "      <td>15.633333</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>19.416667</td>\n",
       "      <td>33.150000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1024</th>\n",
       "      <td>6.500000</td>\n",
       "      <td>322.583333</td>\n",
       "      <td>1789.250000</td>\n",
       "      <td>1.416667</td>\n",
       "      <td>337.166667</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>16.996667</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>71.583333</td>\n",
       "      <td>23.335000</td>\n",
       "      <td>4.228333</td>\n",
       "      <td>1670.181667</td>\n",
       "      <td>21.672500</td>\n",
       "      <td>1.508333</td>\n",
       "      <td>58.583333</td>\n",
       "      <td>15.983333</td>\n",
       "      <td>16.916667</td>\n",
       "      <td>20.133333</td>\n",
       "      <td>25.841667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1025</th>\n",
       "      <td>6.500000</td>\n",
       "      <td>316.333333</td>\n",
       "      <td>1306.833333</td>\n",
       "      <td>1.166667</td>\n",
       "      <td>279.000000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>16.714167</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>50.166667</td>\n",
       "      <td>20.350833</td>\n",
       "      <td>4.119167</td>\n",
       "      <td>1287.460000</td>\n",
       "      <td>19.029167</td>\n",
       "      <td>1.280000</td>\n",
       "      <td>40.583333</td>\n",
       "      <td>11.266667</td>\n",
       "      <td>8.333333</td>\n",
       "      <td>13.333333</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1026</th>\n",
       "      <td>6.500000</td>\n",
       "      <td>321.000000</td>\n",
       "      <td>1697.583333</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>341.416667</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>16.914167</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>66.666667</td>\n",
       "      <td>22.360833</td>\n",
       "      <td>4.199167</td>\n",
       "      <td>1618.773333</td>\n",
       "      <td>20.344167</td>\n",
       "      <td>1.440833</td>\n",
       "      <td>59.583333</td>\n",
       "      <td>15.958333</td>\n",
       "      <td>14.750000</td>\n",
       "      <td>19.666667</td>\n",
       "      <td>25.208333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1027</th>\n",
       "      <td>6.500000</td>\n",
       "      <td>322.750000</td>\n",
       "      <td>2140.250000</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>403.583333</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>17.760000</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>82.083333</td>\n",
       "      <td>24.019167</td>\n",
       "      <td>4.472500</td>\n",
       "      <td>1943.640833</td>\n",
       "      <td>21.925000</td>\n",
       "      <td>1.535833</td>\n",
       "      <td>66.500000</td>\n",
       "      <td>15.925000</td>\n",
       "      <td>17.500000</td>\n",
       "      <td>19.558333</td>\n",
       "      <td>24.625000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1028</th>\n",
       "      <td>6.500000</td>\n",
       "      <td>310.750000</td>\n",
       "      <td>517.166667</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>118.166667</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>28.575833</td>\n",
       "      <td>1.416667</td>\n",
       "      <td>20.250000</td>\n",
       "      <td>23.285833</td>\n",
       "      <td>6.345000</td>\n",
       "      <td>776.510833</td>\n",
       "      <td>60.007500</td>\n",
       "      <td>1.972500</td>\n",
       "      <td>16.250000</td>\n",
       "      <td>11.591667</td>\n",
       "      <td>5.916667</td>\n",
       "      <td>13.991667</td>\n",
       "      <td>15.533333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Team Rank    Load Sum    Movements  Movement Category  High Surges  \\\n",
       "GAME_ID                                                                       \n",
       "1012      6.416667  320.500000  2053.000000           1.500000   355.250000   \n",
       "1013      6.500000  320.666667  1725.583333           1.333333   290.750000   \n",
       "1014      6.500000  315.916667  1573.666667           1.166667   289.250000   \n",
       "1015      6.500000  319.000000  2110.666667           1.166667   399.833333   \n",
       "1016      6.500000  315.750000  1262.666667           0.916667   251.333333   \n",
       "1018      6.500000  314.750000  1100.083333           0.916667   210.333333   \n",
       "1019      6.500000  320.333333  1841.666667           1.166667   351.333333   \n",
       "1020      6.500000  323.500000  1859.916667           1.083333   359.333333   \n",
       "1021      6.500000  323.250000  1887.750000           1.250000   360.166667   \n",
       "1022      6.500000  322.916667  2162.583333           1.416667   409.916667   \n",
       "1023      6.500000  322.583333  2641.166667           1.500000   488.416667   \n",
       "1024      6.500000  322.583333  1789.250000           1.416667   337.166667   \n",
       "1025      6.500000  316.333333  1306.833333           1.166667   279.000000   \n",
       "1026      6.500000  321.000000  1697.583333           1.250000   341.416667   \n",
       "1027      6.500000  322.750000  2140.250000           1.333333   403.583333   \n",
       "1028      6.500000  310.750000   517.166667           0.166667   118.166667   \n",
       "\n",
       "         Surge Category  Stress Percentage  Stress Category  Active Minutes  \\\n",
       "GAME_ID                                                                       \n",
       "1012           1.083333          14.645833         0.416667       89.333333   \n",
       "1013           0.750000          16.294167         0.583333       71.916667   \n",
       "1014           0.750000          17.609167         0.833333       67.250000   \n",
       "1015           0.833333          15.435000         0.750000       76.166667   \n",
       "1016           0.750000          13.469167         0.500000       48.000000   \n",
       "1018           0.583333          13.244167         0.416667       42.083333   \n",
       "1019           0.916667          16.350833         0.583333       72.250000   \n",
       "1020           0.833333          18.773333         1.000000       76.333333   \n",
       "1021           0.916667          17.729167         0.833333       73.916667   \n",
       "1022           1.083333          17.669167         0.750000       84.083333   \n",
       "1023           1.166667          16.597500         0.666667      103.416667   \n",
       "1024           0.750000          16.996667         0.916667       71.583333   \n",
       "1025           0.750000          16.714167         0.666667       50.166667   \n",
       "1026           0.833333          16.914167         0.750000       66.666667   \n",
       "1027           1.000000          17.760000         0.833333       82.083333   \n",
       "1028           0.250000          28.575833         1.416667       20.250000   \n",
       "\n",
       "         Movements Per Minute  High Surges Per Minute  \\\n",
       "GAME_ID                                                 \n",
       "1012                19.209167                3.258333   \n",
       "1013                22.765000                3.815833   \n",
       "1014                21.021667                3.748333   \n",
       "1015                22.641667                4.010833   \n",
       "1016                20.826667                3.705833   \n",
       "1018                19.332500                3.408333   \n",
       "1019                21.413333                4.065000   \n",
       "1020                21.961667                4.270000   \n",
       "1021                23.298333                4.350833   \n",
       "1022                23.663333                4.365833   \n",
       "1023                23.923333                4.213333   \n",
       "1024                23.335000                4.228333   \n",
       "1025                20.350833                4.119167   \n",
       "1026                22.360833                4.199167   \n",
       "1027                24.019167                4.472500   \n",
       "1028                23.285833                6.345000   \n",
       "\n",
       "         Kinetic Energy (Joules/Pound)  Avg Intensity (Watts/Pound)  \\\n",
       "GAME_ID                                                               \n",
       "1012                       1852.537500                    16.370833   \n",
       "1013                       1485.505833                    18.865000   \n",
       "1014                       1502.359167                    19.962500   \n",
       "1015                       2054.830000                    21.800000   \n",
       "1016                       1159.735833                    18.195833   \n",
       "1018                       1030.423333                    17.792500   \n",
       "1019                       1575.771667                    15.710833   \n",
       "1020                       1787.884167                    21.149167   \n",
       "1021                       1702.348333                    20.648333   \n",
       "1022                       2066.409167                    22.449167   \n",
       "1023                       2539.574167                    22.372500   \n",
       "1024                       1670.181667                    21.672500   \n",
       "1025                       1287.460000                    19.029167   \n",
       "1026                       1618.773333                    20.344167   \n",
       "1027                       1943.640833                    21.925000   \n",
       "1028                        776.510833                    60.007500   \n",
       "\n",
       "         Avg Burst (Feet/Sec)      Jumps  Avg Jump (Inches)  \\\n",
       "GAME_ID                                                       \n",
       "1012                 1.286667  62.916667          12.289167   \n",
       "1013                 1.431667  54.500000          14.223333   \n",
       "1014                 1.517500  51.250000          14.018333   \n",
       "1015                 1.488333  64.500000          13.464167   \n",
       "1016                 1.382500  36.916667          11.641667   \n",
       "1018                 1.371667  34.833333          11.297500   \n",
       "1019                 1.076667  47.000000          14.483333   \n",
       "1020                 1.515000  66.166667          16.464167   \n",
       "1021                 1.435000  56.250000          16.514167   \n",
       "1022                 1.461667  74.000000          16.100000   \n",
       "1023                 1.541667  90.750000          15.633333   \n",
       "1024                 1.508333  58.583333          15.983333   \n",
       "1025                 1.280000  40.583333          11.266667   \n",
       "1026                 1.440833  59.583333          15.958333   \n",
       "1027                 1.535833  66.500000          15.925000   \n",
       "1028                 1.972500  16.250000          11.591667   \n",
       "\n",
       "         High Jumps (20+ Inches)  Avg High Jumps (20+ Inches)  \\\n",
       "GAME_ID                                                         \n",
       "1012                   12.833333                    14.738333   \n",
       "1013                   11.416667                    16.609167   \n",
       "1014                    8.916667                    11.049167   \n",
       "1015                   11.166667                    12.823333   \n",
       "1016                    7.166667                    10.777500   \n",
       "1018                    7.333333                    11.035833   \n",
       "1019                   12.500000                    15.971667   \n",
       "1020                   12.916667                    20.512500   \n",
       "1021                   15.416667                    16.649167   \n",
       "1022                   18.166667                    16.949167   \n",
       "1023                   22.000000                    19.416667   \n",
       "1024                   16.916667                    20.133333   \n",
       "1025                    8.333333                    13.333333   \n",
       "1026                   14.750000                    19.666667   \n",
       "1027                   17.500000                    19.558333   \n",
       "1028                    5.916667                    13.991667   \n",
       "\n",
       "         Highest Jump (inches)  \n",
       "GAME_ID                         \n",
       "1012                 20.406667  \n",
       "1013                 21.746667  \n",
       "1014                 20.067500  \n",
       "1015                 19.259167  \n",
       "1016                 16.217500  \n",
       "1018                 16.660000  \n",
       "1019                 21.277500  \n",
       "1020                 24.951667  \n",
       "1021                 22.422500  \n",
       "1022                 23.796667  \n",
       "1023                 33.150000  \n",
       "1024                 25.841667  \n",
       "1025                 25.000000  \n",
       "1026                 25.208333  \n",
       "1027                 24.625000  \n",
       "1028                 15.533333  "
      ]
     },
     "execution_count": 401,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "games.groupby(by= ['GAME_ID']).mean().dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 402,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3185.9375"
      ]
     },
     "execution_count": 402,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_games = games.set_index(['    Player Name', 'GAME_ID']).dropna()\n",
    "new_games.loc['Gabriela Stavnetchei']['Movements'].mean()"
   ]
  }
 ],
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