Author:
Anzer Gabriel,Bauer Pascal
Abstract
Due to the low scoring nature of football (soccer), shots are often used as a proxy to evaluate team and player performances. However, not all shots are created equally and their quality differs significantly depending on the situation. The aim of this study is to objectively quantify the quality of any given shot by introducing a so-called expected goals (xG) model. This model is validated statistically and with professional match analysts. The best performing model uses an extreme gradient boosting algorithm and is based on hand-crafted features from synchronized positional and event data of 105, 627 shots in the German Bundesliga. With a ranked probability score (RPS) of 0.197, it is more accurate than any previously published expected goals model. This approach allows us to assess team and player performances far more accurately than is possible with traditional metrics by focusing on process rather than results.
Reference48 articles.
1. Visual analysis of pressure in football;Andrienko;Data Mining Knowl. Discov,2017
2. Interpretable machine learning for demand modeling with high-dimensional data using gradient boosting machines and shapley values;Antipov;J. Rev. Pricing Manage,2020
3. Algorithms for hyper-parameter optimization;Bergstra,2011
4. Quantifying shot quality in the NBA;Chang,2014
Cited by
32 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献