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• W3147122783 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." @default.
• W3147122783 created "2021-04-13" @default.
• W3147122783 creator A5021124874 @default.
• W3147122783 creator A5050870183 @default.
• W3147122783 date "2021-03-29" @default.
• W3147122783 modified "2023-10-16" @default.
• W3147122783 title "A Goal Scoring Probability Model for Shots Based on Synchronized Positional and Event Data in Football (Soccer)" @default.
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• W3147122783 doi "https://doi.org/10.3389/fspor.2021.624475" @default.
• W3147122783 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/8056301" @default.
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• W3147122783 hasPublicationYear "2021" @default.
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