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Continuous football player tracking from discrete broadcast data.

Matthew J Penn1, Christl A Donnelly1,2, Samir Bhatt3,4

  • 1Department of Statistics, University of Oxford, Oxford, UK.

Royal Society Open Science
|October 27, 2025
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Summary

This study introduces a new method to generate continuous player tracking data from low-quality broadcast footage, making advanced analytics accessible for more football teams. This approach bridges the gap for clubs lacking high-quality video for computer vision applications.

Keywords:
footballforecastingtime seriestracking

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Area of Science:

  • Sports Analytics
  • Computer Vision
  • Football Technology

Background:

  • Professional football teams often lack access to high-quality player tracking data due to limitations in video feed quality for computer vision.
  • Existing event data is widely available but does not provide the continuous tracking insights needed for advanced analysis.

Purpose of the Study:

  • To develop and validate a method for estimating continuous full-pitch player tracking data from discrete data derived from broadcast footage.
  • To make advanced player tracking analytics accessible to a wider range of football clubs, including semi-professional levels.

Main Methods:

  • A novel method was developed to process discrete data from broadcast footage into continuous player tracking data.
  • The method was tested using open-source tracking data.
  • A version of the method was applied to a dataset of over 200 games with discrete data.

Main Results:

  • The study successfully demonstrated a method to estimate continuous player tracking data from lower-quality broadcast feeds.
  • The proposed approach offers a cost-effective solution comparable to event data collection.
  • Validation was performed using existing datasets, showing the method's applicability.

Conclusions:

  • The developed method can bridge the gap in player tracking data accessibility for football teams with limited video quality.
  • This innovation allows for more comprehensive performance analysis at a lower cost.
  • The approach has the potential to democratize advanced sports analytics in football.