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generate_season_stats.py
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from datetime import datetime
import pandas as pd
import numpy as np
# from tqdm import tqdm
#############################################################################
# League stats
#
#############################################################################
def generate_league_batting_stats():
print("\nGenerating NCAA league batting stats.")
main_df = pd.DataFrame()
part_df = pd.DataFrame()
current_year = int(datetime.now().year)
for i in range(2013, current_year + 1):
print(f"\tLoading in the {i} batting stats.")
part_df = pd.read_parquet(
f"game_stats/player/batting_game_stats/parquet/{i}_batting.parquet"
)
main_df = pd.concat([main_df, part_df], ignore_index=True)
del part_df
main_df.loc[main_df["season"] != None, "G"] = 1
finished_df = pd.DataFrame(
main_df.groupby(["season", "season_id", "division"], as_index=False)[
[
"G",
"AB",
"R",
"H",
"2B",
"3B",
"HR",
"RBI",
"SB",
"CS",
"BB",
"K",
"IBB",
"TB",
"GDP",
"HBP",
"SH",
"SF",
"DP",
"Picked",
"OPP DP",
]
].sum()
)
## Runs Created (Technical version)
finished_df["RC"] = (
(
finished_df["H"]
+ finished_df["BB"]
- finished_df["CS"]
+ finished_df["HBP"]
- finished_df["GDP"]
)
* (
finished_df["TB"]
+ (0.26 * (finished_df["BB"] - finished_df["IBB"] + finished_df["HBP"]))
)
+ (0.52 * (finished_df["SH"] + finished_df["SF"] + finished_df["SB"]))
) / (
finished_df["AB"]
+ finished_df["BB"]
+ finished_df["HBP"]
+ finished_df["SH"]
+ finished_df["SF"]
)
finished_df["RC"] = finished_df["RC"].round(3)
## Plate Appearances
finished_df["PA"] = (
finished_df["AB"] + finished_df["BB"] + finished_df["SF"] + finished_df["SH"]
)
## Batting Average
finished_df["BA"] = finished_df["H"] / finished_df["AB"]
finished_df["BA"] = finished_df["BA"].round(3)
## Secondary Average
finished_df["SecA"] = (
finished_df["BB"]
+ (finished_df["TB"] - finished_df["H"])
+ (finished_df["SB"] - finished_df["CS"])
) / finished_df["AB"]
finished_df["SecA"] = finished_df["SecA"].round(3)
## On Base Percentage (OBP)
finished_df["OBP"] = (
finished_df["H"] + finished_df["BB"] + finished_df["HBP"]
) / finished_df["PA"]
finished_df["OBP"] = finished_df["OBP"].round(3)
## Slugging Percentae
finished_df["SLG"] = (
finished_df["H"]
+ (finished_df["2B"] * 2)
+ (finished_df["3B"] * 3)
+ (finished_df["HR"] * 4)
) / finished_df["AB"]
finished_df["SLG"] = finished_df["SLG"].round(3)
## On-Base + Slugging Percentages
finished_df["OPS"] = finished_df["OBP"] + finished_df["SLG"]
finished_df["OPS"] = finished_df["OPS"].round(3)
## Isolated power
finished_df["ISO"] = (
finished_df["2B"] + (finished_df["3B"] * 2) + (finished_df["3B"])
) / finished_df["AB"]
finished_df["ISO"] = finished_df["ISO"].round(3)
## Batting Average on balls in play
finished_df["BAbip"] = (finished_df["H"] - finished_df["HR"]) / (
finished_df["AB"] - finished_df["K"] - finished_df["HR"] + finished_df["SF"]
)
finished_df["BAbip"] = finished_df["BAbip"].round(3)
## eXtrapolated Runs
finished_df["XR"] = (
(finished_df["H"] * 0.5)
+ (finished_df["2B"] * 0.72)
+ (finished_df["3B"] * 1.04)
+ (finished_df["HR"] * 1.44)
+ (0.34 * (finished_df["HBP"] + finished_df["TB"] + finished_df["IBB"]))
+ (0.25 * finished_df["IBB"])
+ (0.18 * finished_df["SB"])
+ (-0.32 * finished_df["CS"])
+ (-0.09 * (finished_df["AB"] - finished_df["H"] - finished_df["K"]))
+ (-0.098 * finished_df["K"])
+ (-0.37 * finished_df["GDP"])
+ (0.37 * finished_df["SF"])
+ (0.04 * finished_df["SH"])
)
finished_df["XR"] = finished_df["XR"].round(3)
## eXtrapolated Runs Reduced
finished_df["XRR"] = (
(0.5 * finished_df["H"])
+ (0.72 * finished_df["2B"])
+ (1.04 * finished_df["3B"])
+ (1.44 * finished_df["HR"])
+ (0.33 * (finished_df["HBP"] + finished_df["TB"]))
+ (0.18 * finished_df["SB"])
+ (-0.32 * finished_df["CS"])
+ (-0.098 * (finished_df["AB"] - finished_df["H"]))
)
finished_df["XRR"] = finished_df["XRR"].round(3)
## eXtrapolated Runs Basic
finished_df["XRB"] = (
(0.5 * finished_df["H"])
+ (0.72 * finished_df["2B"])
+ (1.04 * finished_df["3B"])
+ (1.44 * finished_df["HR"])
+ (0.34 * (finished_df["HBP"] + finished_df["TB"]))
+ (0.18 * finished_df["SB"])
- (-0.32 * finished_df["CS"])
+ (-0.096 * (finished_df["AB"] - finished_df["H"]))
)
finished_df["XRB"] = finished_df["XRB"].round(3)
## Power-Speed Number
finished_df["PSN"] = (2 * finished_df["HR"] * finished_df["SB"]) / (
finished_df["HR"] + finished_df["SB"]
)
finished_df["PSN"] = finished_df["PSN"].round(1)
## Runs scored percentage
finished_df["RS%"] = (finished_df["R"] - finished_df["HR"]) / (
finished_df["H"] + finished_df["HBP"] + finished_df["BB"] - finished_df["HR"]
)
finished_df["RS%"] = finished_df["RS%"].round(3)
## Home Run percentage
finished_df["HR%"] = finished_df["HR"] / finished_df["PA"]
finished_df["HR%"] = finished_df["HR%"].round(3)
## Strikeout percentage
finished_df["K%"] = finished_df["K"] / finished_df["PA"]
finished_df["K%"] = finished_df["K%"].round(3)
## Strikeout percentage
finished_df["BB%"] = finished_df["BB"] / finished_df["PA"]
finished_df["BB%"] = finished_df["BB%"].round(3)
## Walks to strikeouts ratio
finished_df["K-BB%"] = finished_df["K%"] - finished_df["BB%"]
finished_df["K-BB%"] = finished_df["K-BB%"].round(3)
finished_df["BB/K"] = finished_df["BB"] / finished_df["K"]
finished_df["BB/K"] = finished_df["BB/K"].round(3)
## Convert infinates into Null values
finished_df = finished_df.mask(np.isinf(finished_df))
# print(finished_df)
finished_df.to_csv(
f"season_stats/league/batting_season_stats/csv/league_batting.csv", index=False
)
finished_df.to_parquet(
f"season_stats/league/batting_season_stats/parquet/league_batting.parquet",
index=False,
)
def generate_league_pitching_stats():
print("\nGenerating NCAA league pitching stats.")
main_df = pd.DataFrame()
part_df = pd.DataFrame()
current_year = int(datetime.now().year)
for i in range(2013, current_year + 1):
print(f"\tLoading in the {i} pitching stats.")
part_df = pd.read_parquet(
f"game_stats/player/pitching_game_stats/parquet/{i}_pitching.parquet"
)
main_df = pd.concat([main_df, part_df], ignore_index=True)
del part_df
main_df["App"] = 1
main_df = main_df.astype({"IP": "string"})
main_df[["whole_innings", "part_innings"]] = main_df["IP"].str.split(
".", expand=True
)
main_df = main_df.astype({"whole_innings": "int", "part_innings": "int"})
main_df["IP"] = main_df["whole_innings"] + (main_df["part_innings"] / 3)
# print(main_df.columns)
# print(main_df.dtypes)
finished_df = pd.DataFrame(
main_df.groupby(["season", "season_id", "division"], as_index=False)[
[
"App",
"GS",
"W",
"L",
"SV",
"CG",
"SHO",
"IP",
"H",
"R",
"ER",
"2B-A",
"3B-A",
"HR-A",
"BB",
"IBB",
"SO",
"HB",
"Bk",
"WP",
"BF",
"P-OAB",
"Inh Run",
"Inh Run Score",
"SHA",
"SFA",
"GO",
"FO",
"KL",
"pickoffs",
]
].sum()
)
# finished_df['PI'] = finished_df['Pitches']
# finished_df = finished_df.drop(['Pitches'], axis=1)
finished_df = finished_df.rename(
columns={
"2B-A": "2B",
"3B-A": "3B",
"HR-A": "HR",
"Inh Run": "IR",
"Inh Run Score": "IRS",
"HB": "HBP",
}
)
## Win-loss percentage
finished_df["W-L%"] = finished_df["W"] / (finished_df["W"] + finished_df["L"])
## Earned Run Average (ERA)
finished_df["ERA"] = 9 * (finished_df["ER"] / finished_df["IP"])
## Fielding Indipendent Pitching (FIP) Constant
finished_df["FIP_const"] = finished_df["ERA"] - (
(
(13 * finished_df["HR"])
+ (3 * (finished_df["BB"] + finished_df["HBP"]))
- (finished_df["SO"])
)
/ finished_df["IP"]
)
## Walks and Hits per Inning Pitched (WHIP)
finished_df["WHIP"] = (finished_df["BB"] + finished_df["H"]) / finished_df["IP"]
## Hits per 9 innings
finished_df["H9"] = 9 * (finished_df["H"] / finished_df["IP"])
## Home Runs per 9 innings
finished_df["HR9"] = 9 * (finished_df["HR"] / finished_df["IP"])
## Walks per 9 innings
finished_df["BB9"] = 9 * (finished_df["BB"] / finished_df["IP"])
## Strikeouts per 9 innings
finished_df["SO9"] = 9 * (finished_df["SO"] / finished_df["IP"])
## Strikeouts/Walks ratio
finished_df["SO/BB"] = finished_df["SO"] / finished_df["BB"]
## Runs Allowed per 9 innings pitched (RA9)
finished_df["RA9"] = 9 * (finished_df["R"] / finished_df["IP"])
finished_df = finished_df.mask(np.isinf(finished_df))
# print(finished_df)
finished_df.to_csv(
f"season_stats/league/pitching_season_stats/csv/league_pitching.csv",
index=False,
)
finished_df.to_parquet(
f"season_stats/league/pitching_season_stats/parquet/league_pitching.parquet",
index=False,
)
#############################################################################
## Park Factors
##
#############################################################################
def generate_park_factors(season: int):
""" """
main_df = pd.read_parquet(
f"game_stats/player/batting_game_stats/parquet/{season}_batting.parquet"
)
main_df["G"] = 1
home_gm_df = main_df[main_df["field"] == "home"]
away_gm_df = main_df[main_df["field"] != "home"]
home_gm_df = pd.DataFrame(
home_gm_df.groupby(
["season", "season_id", "school_id", "division"], as_index=False
)[
["runs_scored", "runs_allowed", "G", "AB", "H", "2B", "3B", "HR", "BB", "K"]
].sum()
)
home_gm_df = home_gm_df.rename(
{
"runs_scored": "home_runs_scored",
"runs_allowed": "home_runs_allowed",
"G": "home_G",
"AB": "home_AB",
"R": "home_R",
"2B": "home_2B",
"3B": "home_3B",
"HR": "home_HR",
"BB": "home_BB",
"K": "home_K",
},
axis=1,
)
away_gm_df = pd.DataFrame(
away_gm_df.groupby(
["season", "season_id", "school_id", "division"], as_index=False
)[
["runs_scored", "runs_allowed", "G", "AB", "H", "2B", "3B", "HR", "BB", "K"]
].sum()
)
away_gm_df = away_gm_df.rename(
{
"runs_scored": "away_runs_scored",
"runs_allowed": "away_runs_allowed",
"G": "away_G",
"AB": "away_AB",
"R": "away_R",
"2B": "away_2B",
"3B": "away_3B",
"HR": "away_HR",
"BB": "away_BB",
"K": "away_K",
},
axis=1,
)
finished_df = pd.merge(
home_gm_df,
away_gm_df,
left_on=["season", "season_id", "school_id", "division"],
right_on=["season", "season_id", "school_id", "division"],
how="left",
)
finished_df["PF"] = 100 * (
(
(finished_df["home_runs_scored"] + finished_df["home_runs_allowed"])
/ finished_df["home_G"]
)
/ (
(finished_df["away_runs_scored"] + finished_df["away_runs_allowed"])
/ finished_df["away_G"]
)
)
print(finished_df)
finished_df.to_csv(
f"season_stats/league/park_factors/csv/{season}_park_factors.csv", index=False
)
finished_df.to_parquet(
f"season_stats/league/park_factors/parquet/{season}_park_factors.parquet",
index=False,
)
# return finished_df
#############################################################################
## Season Player Stats
##
#############################################################################
def generate_season_player_batting_stats(season: int):
""" """
def parser(division: int, main_df: pd.DataFrame()):
# main_df = pd.concat([main_df,part_df],ignore_index=True)
league_df = pd.read_parquet(
"season_stats/league/batting_season_stats/parquet/league_batting.parquet"
)
league_df = league_df[league_df["season"] == season]
league_df = league_df[league_df["division"] == division]
if league_df.empty == True:
return pd.DataFrame()
lg_ops = league_df["OPS"].iloc[0]
lg_slg = league_df["SLG"].iloc[0]
main_df.loc[main_df["season"] != None, "G"] = 1
main_df = pd.DataFrame(
main_df.groupby(
["season", "season_id", "school_id", "stats_player_seq", "division"],
as_index=False,
)[
[
"G",
"AB",
"R",
"H",
"2B",
"3B",
"HR",
"RBI",
"SB",
"CS",
"BB",
"K",
"IBB",
"TB",
"GDP",
"HBP",
"SH",
"SF",
"DP",
"Picked",
"OPP DP",
]
].sum()
)
## Runs Created (Technical version)
main_df["RC"] = (
(
main_df["H"]
+ main_df["BB"]
- main_df["CS"]
+ main_df["HBP"]
- main_df["GDP"]
)
* (
main_df["TB"]
+ (0.26 * (main_df["BB"] - main_df["IBB"] + main_df["HBP"]))
)
+ (0.52 * (main_df["SH"] + main_df["SF"] + main_df["SB"]))
) / (
main_df["AB"]
+ main_df["BB"]
+ main_df["HBP"]
+ main_df["SH"]
+ main_df["SF"]
)
## Plate Appearances
main_df["PA"] = main_df["AB"] + main_df["BB"] + main_df["SF"] + main_df["SH"]
## Batting Average
main_df["BA"] = main_df["H"] / main_df["AB"]
main_df["BA"] = main_df["BA"].round(3)
## Secondary Average
main_df["SecA"] = (
main_df["BB"]
+ (main_df["TB"] - main_df["H"])
+ (main_df["SB"] - main_df["CS"])
) / main_df["AB"]
main_df["SecA"] = main_df["SecA"].round(3)
## On Base Percentage (OBP)
main_df["OBP"] = (main_df["H"] + main_df["BB"] + main_df["HBP"]) / main_df["PA"]
main_df["OBP"] = main_df["OBP"].round(3)
## Slugging Percentae
main_df["SLG"] = (
main_df["H"]
+ (main_df["2B"] * 2)
+ (main_df["3B"] * 3)
+ (main_df["HR"] * 4)
) / main_df["AB"]
main_df["SLG"] = main_df["SLG"].round(3)
## On-Base + Slugging Percentages
main_df["OPS"] = main_df["OBP"] + main_df["SLG"]
main_df["OPS"] = main_df["OPS"].round(3)
## OPS+
main_df["OPS+"] = 100 * (
(main_df["OPS"] / lg_ops) + (main_df["SLG"] / lg_slg) - 1
)
main_df["OPS+"] = main_df["OPS+"].round(0)
## Isolated power
main_df["ISO"] = (
main_df["2B"] + (main_df["3B"] * 2) + (main_df["3B"])
) / main_df["AB"]
main_df["ISO"] = main_df["ISO"].round(3)
## Batting Average on balls in play
main_df["BAbip"] = (main_df["H"] - main_df["HR"]) / (
main_df["AB"] - main_df["K"] - main_df["HR"] + main_df["SF"]
)
main_df["BAbip"] = main_df["BAbip"].round(3)
## eXtrapolated Runs
main_df["XR"] = (
(main_df["H"] * 0.5)
+ (main_df["2B"] * 0.72)
+ (main_df["3B"] * 1.04)
+ (main_df["HR"] * 1.44)
+ (0.34 * (main_df["HBP"] + main_df["TB"] + main_df["IBB"]))
+ (0.25 * main_df["IBB"])
+ (0.18 * main_df["SB"])
+ (-0.32 * main_df["CS"])
+ (-0.09 * (main_df["AB"] - main_df["H"] - main_df["K"]))
+ (-0.098 * main_df["K"])
+ (-0.37 * main_df["GDP"])
+ (0.37 * main_df["SF"])
+ (0.04 * main_df["SH"])
)
main_df["XR"] = main_df["XR"].round(3)
## eXtrapolated Runs Reduced
main_df["XRR"] = (
(0.5 * main_df["H"])
+ (0.72 * main_df["2B"])
+ (1.04 * main_df["3B"])
+ (1.44 * main_df["HR"])
+ (0.33 * (main_df["HBP"] + main_df["TB"]))
+ (0.18 * main_df["SB"])
+ (-0.32 * main_df["CS"])
+ (-0.098 * (main_df["AB"] - main_df["H"]))
)
main_df["XRR"] = main_df["XRR"].round(3)
## eXtrapolated Runs Basic
main_df["XRB"] = (
(0.5 * main_df["H"])
+ (0.72 * main_df["2B"])
+ (1.04 * main_df["3B"])
+ (1.44 * main_df["HR"])
+ (0.34 * (main_df["HBP"] + main_df["TB"]))
+ (0.18 * main_df["SB"])
- (-0.32 * main_df["CS"])
+ (-0.096 * (main_df["AB"] - main_df["H"]))
)
main_df["XRB"] = main_df["XRB"].round(3)
## Power-Speed Number
main_df["PSN"] = (2 * main_df["HR"] * main_df["SB"]) / (
main_df["HR"] + main_df["SB"]
)
main_df["PSN"] = main_df["PSN"].round(1)
## Runs scored percentage
main_df["RS%"] = (main_df["R"] - main_df["HR"]) / (
main_df["H"] + main_df["HBP"] + main_df["BB"] - main_df["HR"]
)
main_df["RS%"] = main_df["RS%"].round(3)
## Home Run percentage
main_df["HR%"] = main_df["HR"] / main_df["PA"]
main_df["HR%"] = main_df["HR%"].round(3)
## Strikeout percentage
main_df["K%"] = main_df["K"] / main_df["PA"]
main_df["K%"] = main_df["K%"].round(3)
## Strikeout percentage
main_df["BB%"] = main_df["BB"] / main_df["PA"]
main_df["BB%"] = main_df["BB%"].round(3)
## Walks to strikeouts ratio
main_df["K-BB%"] = main_df["K%"] - main_df["BB%"]
main_df["K-BB%"] = main_df["K-BB%"].round(3)
main_df["BB/K"] = main_df["BB"] / main_df["K"]
main_df["BB/K"] = main_df["BB/K"].round(3)
## Convert infinates into Null values
main_df = main_df.mask(np.isinf(main_df))
# print(main_df)
return main_df
finished_df = pd.DataFrame()
part_df = pd.DataFrame()
main_df = pd.read_parquet(
f"game_stats/player/batting_game_stats/parquet/{season}_batting.parquet"
)
for i in range(1, 4):
part_df = main_df[main_df["division"] == i]
part_df = parser(i, part_df)
finished_df = pd.concat([finished_df, part_df], ignore_index=True)
del part_df, main_df
finished_df.to_csv(
f"season_stats/player/batting_season_stats/csv/{season}_batting.csv",
index=False,
)
finished_df.to_parquet(
f"season_stats/player/batting_season_stats/parquet/{season}_batting.parquet",
index=False,
)
def generate_season_player_pitching_stats(season: int):
""" """
def parser(division: int, main_df: pd.DataFrame()):
league_df = pd.read_parquet(
"season_stats/league/pitching_season_stats/parquet/league_pitching.parquet"
)
league_df = league_df[league_df["season"] == season]
league_df = league_df[league_df["division"] == division]
if league_df.empty == True:
return pd.DataFrame()
lg_era = league_df["ERA"].iloc[0]
fip_const = league_df["FIP_const"].iloc[0]
del league_df
main_df.loc[main_df["season_id"] != None, "App"] = 1
main_df = main_df.astype({"IP": "string"})
main_df[["whole_innings", "part_innings"]] = main_df["IP"].str.split(
".", expand=True
)
main_df = main_df.astype({"whole_innings": "int", "part_innings": "int"})
main_df["IP"] = main_df["whole_innings"] + (main_df["part_innings"] / 3)
main_df = pd.DataFrame(
main_df.groupby(
["season", "season_id", "school_id", "stats_player_seq", "division"],
as_index=False,
)[
[
"App",
"GS",
"W",
"L",
"SV",
"CG",
"SHO",
"IP",
"H",
"R",
"ER",
"2B-A",
"3B-A",
"HR-A",
"BB",
"IBB",
"SO",
"HB",
"Bk",
"WP",
"BF",
"P-OAB",
"Inh Run",
"Inh Run Score",
"SHA",
"SFA",
"GO",
"FO",
"KL",
"pickoffs",
]
].sum()
)
# main_df['PI'] = main_df['Pitches']
# main_df = main_df.drop(['Pitches'], axis=1)
main_df = main_df.rename(
columns={
"2B-A": "2B",
"3B-A": "3B",
"HR-A": "HR",
"Inh Run": "IR",
"Inh Run Score": "IRS",
"HB": "HBP",
}
)
park_factors_df = pd.read_parquet(
f"season_stats/league/park_factors/parquet/{season}_park_factors.parquet"
)
park_factors_df = park_factors_df.filter(items=["school_id", "PF"])
main_df = pd.merge(
main_df,
park_factors_df,
left_on="school_id",
right_on="school_id",
how="left",
)
## Win-loss percentage
main_df["W-L%"] = main_df["W"] / (main_df["W"] + main_df["L"])
## Earned Run Average (ERA)
main_df["ERA"] = 9 * (main_df["ER"] / main_df["IP"])
## Earned Run Average (ERA-)
main_df["ERA-"] = 100 * (
(
main_df["ERA"]
+ (main_df["ERA"] - (main_df["ERA"] * (main_df["PF"] / 100)))
)
/ lg_era
)
main_df["ERA-"] = main_df["ERA-"].round(0)
## Field Indipendent Pitching
main_df["FIP"] = (
(
(13 * main_df["HR"])
+ (3 * (main_df["BB"] + main_df["HBP"]))
- (2 * main_df["SO"])
)
/ main_df["IP"]
) + fip_const
## Walks and Hits per Inning Pitched (WHIP)
main_df["WHIP"] = (main_df["BB"] + main_df["H"]) / main_df["IP"]
## Hits per 9 innings
main_df["H9"] = 9 * (main_df["H"] / main_df["IP"])
## Home Runs per 9 innings
main_df["HR9"] = 9 * (main_df["HR"] / main_df["IP"])
## Walks per 9 innings
main_df["BB9"] = 9 * (main_df["BB"] / main_df["IP"])
## Strikeouts per 9 innings
main_df["SO9"] = 9 * (main_df["SO"] / main_df["IP"])
## Strikeouts/Walks ratio
main_df["SO/BB"] = main_df["SO"] / main_df["BB"]
## Runs Allowed per 9 innings pitched (RA9)
main_df["RA9"] = 9 * (main_df["R"] / main_df["IP"])
main_df = main_df.mask(np.isinf(main_df))
return main_df
finished_df = pd.DataFrame()
part_df = pd.DataFrame()
main_df = pd.read_parquet(
f"game_stats/player/pitching_game_stats/parquet/{season}_pitching.parquet"
)
for i in range(1, 4):
part_df = main_df[main_df["division"] == i]
part_df = parser(i, part_df)
finished_df = pd.concat([finished_df, part_df], ignore_index=True)
del part_df, main_df
# print(finished_df)
finished_df.to_csv(
f"season_stats/player/pitching_season_stats/csv/{season}_pitching.csv",
index=False,
)
finished_df.to_parquet(
f"season_stats/player/pitching_season_stats/parquet/{season}_pitching.parquet",
index=False,
)
def generate_season_player_fielding_stats(season: int):
""" """
main_df = pd.DataFrame()
part_df = pd.DataFrame()
# for i in range(1,5):
# #print(i)
part_df = pd.read_parquet(
f"game_stats/player/fielding_game_stats/parquet/{season}_fielding.parquet"
)
main_df = pd.concat([main_df, part_df], ignore_index=True)
finished_df = pd.DataFrame(
main_df.groupby(
["season", "season_id", "school_id", "stats_player_seq", "division"],
as_index=False,
)[["TC", "PO", "A", "E", "CI", "PB", "SBA", "CSB", "IDP", "TP"]].sum()
)
## Fielding Percentage
finished_df["FLD%"] = (finished_df["PO"] + finished_df["A"]) / (
finished_df["PO"] + finished_df["A"] + finished_df["E"]
)
## Caught Stealing Percentage
finished_df["CS%"] = finished_df["CSB"] / finished_df["SBA"]
# print(finished_df)
finished_df = finished_df.mask(np.isinf(finished_df))
finished_df.to_csv(
f"season_stats/player/fielding_season_stats/csv/{season}_fielding.csv",
index=False,
)
finished_df.to_parquet(
f"season_stats/player/fielding_season_stats/parquet/{season}_fielding.parquet",
index=False,
)
#############################################################################
## Team Season Stats
##
#############################################################################
def generate_season_team_batting_stats(season: int):
""" """
def parser(division: int, main_df: pd.DataFrame()):
# main_df = pd.concat([main_df,part_df],ignore_index=True)
league_df = pd.read_parquet(
"season_stats/league/batting_season_stats/parquet/league_batting.parquet"
)
league_df = league_df[league_df["season"] == season]
league_df = league_df[league_df["division"] == division]
if league_df.empty == True:
return pd.DataFrame()
lg_ops = league_df["OPS"].iloc[0]
lg_slg = league_df["SLG"].iloc[0]
main_df["G"] = 1
main_df = pd.DataFrame(
main_df.groupby(
["season", "season_id", "school_id", "division"], as_index=False
)[
[
"G",
"AB",
"R",
"H",
"2B",
"3B",
"HR",
"RBI",
"SB",
"CS",
"BB",
"K",
"IBB",
"TB",
"GDP",
"HBP",
"SH",
"SF",
"DP",
"Picked",
"OPP DP",
]
].sum()
)
## Runs Created (Technical version)
main_df["RC"] = (
(
main_df["H"]
+ main_df["BB"]
- main_df["CS"]
+ main_df["HBP"]
- main_df["GDP"]
)
* (
main_df["TB"]
+ (0.26 * (main_df["BB"] - main_df["IBB"] + main_df["HBP"]))
)
+ (0.52 * (main_df["SH"] + main_df["SF"] + main_df["SB"]))
) / (
main_df["AB"]
+ main_df["BB"]
+ main_df["HBP"]
+ main_df["SH"]
+ main_df["SF"]
)
## Plate Appearances
main_df["PA"] = main_df["AB"] + main_df["BB"] + main_df["SF"] + main_df["SH"]
## Batting Average
main_df["BA"] = main_df["H"] / main_df["AB"]
main_df["BA"] = main_df["BA"].round(3)
## Secondary Average
main_df["SecA"] = (
main_df["BB"]
+ (main_df["TB"] - main_df["H"])
+ (main_df["SB"] - main_df["CS"])
) / main_df["AB"]
main_df["SecA"] = main_df["SecA"].round(3)
## On Base Percentage (OBP)
main_df["OBP"] = (main_df["H"] + main_df["BB"] + main_df["HBP"]) / main_df["PA"]
main_df["OBP"] = main_df["OBP"].round(3)
## Slugging Percentae
main_df["SLG"] = (
main_df["H"]
+ (main_df["2B"] * 2)
+ (main_df["3B"] * 3)
+ (main_df["HR"] * 4)
) / main_df["AB"]
main_df["SLG"] = main_df["SLG"].round(3)
## On-Base + Slugging Percentages
main_df["OPS"] = main_df["OBP"] + main_df["SLG"]
main_df["OPS"] = main_df["OPS"].round(3)
## OPS+
main_df["OPS+"] = 100 * (
(main_df["OPS"] / lg_ops) + (main_df["SLG"] / lg_slg) - 1
)
main_df["OPS+"] = main_df["OPS+"].round(0)
## Isolated power
main_df["ISO"] = (
main_df["2B"] + (main_df["3B"] * 2) + (main_df["3B"])
) / main_df["AB"]
main_df["ISO"] = main_df["ISO"].round(3)
## Batting Average on balls in play
main_df["BAbip"] = (main_df["H"] - main_df["HR"]) / (
main_df["AB"] - main_df["K"] - main_df["HR"] + main_df["SF"]
)
main_df["BAbip"] = main_df["BAbip"].round(3)
## eXtrapolated Runs
main_df["XR"] = (
(main_df["H"] * 0.5)
+ (main_df["2B"] * 0.72)
+ (main_df["3B"] * 1.04)
+ (main_df["HR"] * 1.44)
+ (0.34 * (main_df["HBP"] + main_df["TB"] + main_df["IBB"]))
+ (0.25 * main_df["IBB"])
+ (0.18 * main_df["SB"])
+ (-0.32 * main_df["CS"])