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"Predicting Ball Location From Optical Tracking Data" - contains data analysis, model development and testing

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Predicting Ball Location From Optical Tracking Data

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

License

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.

Project Instructions

Getting Started

  1. 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
    
  2. 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
    
  3. Check requiremenets.

    requirements.py