In this study, an automated method for predicting the ball’s location during a soccer match has been developed using optical tracking data. The rolespecific analysis using the individual player attributes has been conducted on a dataset of 300 matches from the Turkish Football Federation Super League 2017-2018 season (≈34,000,000 data points).
The data is provided by an optical tracking system developed by start-up company Sentio Sports Analytics.
The project contains data analysis, features construction, model development and testing files written using python.
This repository is part of our 2022 paper titled: "Prediction of the Ball Location on the 2D Plane in Football Using Optical Tracking Data"
Orange and blue point -> home and away team players, respectively
Green dot -> actual ball location
Red dot -> predicted ball location
This library (all the notebooks) is distributed under Apache License 2.0 . Please see Apache License 2.0 terms to learn about how to use this library.
-
Clone the repository, and navigate to the downloaded folder.
git clone https://github.com/anaramirli/predict-soccer-ball-location.git cd predict-soccer-ball-location -
Create (and activate) a new environment with Python 3.6 and the numpy package.
- Linux or Mac:
conda create --name my_env python=3.6 source activate my_env- Windows:
conda create --name my_env python=3.6 activate my_env -
Check requiremenets.
requirements.py