Passing is the most frequent event in a football match. This project scratches the surface how we can calculate different parameters of a 'pass' event using tracking data. Traditional metrics to value passes in football mostly utilises the widely available on-the-ball event data.
Tracking data captures much more insights as it includes every player position at each instant. Even though there is very limited public open source tracking data, this project discusses the algorithms for calculating parameters of a pass. As more tracking data would be available, we can compute and analyse how various aspects and scenarios of a pass influence the pass's impact. The pass parameters can itself be features to a trained model.
We also implement the Pitch Control x EPV and the VAEP metric for valuing passes.
Acknowledgement
- Metrica Sports: For publishing tracking + event data to the community online
Dataset Link: Metrica Sports sample tracking and event data - Laurie Shaw and FriendsOfTracking: For lectures and code to understand and get started with tracking data
Repo: Laurie's code for Metrica tracking data
Youtube: Friends of Tracking
References/Further Reading
- Valuing passes in football using ball event data
- Pitch Control: Lecture, Presentation
- EPV: A Multiresolution Stochastic Process Model for Predicting Basketball Possession Outcomes, A framework for the fine-grained evaluation of the instantaneous expected value of soccer possessions
- VAEP: Actions Speak Louder than Goals: Valuing Player Actions in Soccer
- A Framework for Tactical Analysis and Individual Offensive Production Assessment in Soccer Using Markov Chains