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| Format: | Preprint |
| Veröffentlicht: |
2024
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| Online-Zugang: | https://arxiv.org/abs/2406.00814 |
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| _version_ | 1866910469161222144 |
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| author | Shelopugin, Andrei |
| author_facet | Shelopugin, Andrei |
| contents | Estimation of football players' skills is one of the key tasks in sports analytics. This paper introduces multiple extensions to a widely used model, expected possession value (EPV), to address some key challenges such as selection problem. First, we assign greater weights to events occurring immediately prior to the shot rather than those preceding them (decay effect). Second, our model incorporates possession risk more accurately by considering the decay effect and effective playing time. Third, we integrate the assessment of individual player ability to win aerial and ground duels. Using the extended EPV model, we predict this metric for various football players for the upcoming season, particularly taking into account the strength of their opponents. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_00814 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Expected Possession Value of Control and Duel Actions for Soccer Player's Skills Estimation Shelopugin, Andrei Machine Learning Artificial Intelligence Estimation of football players' skills is one of the key tasks in sports analytics. This paper introduces multiple extensions to a widely used model, expected possession value (EPV), to address some key challenges such as selection problem. First, we assign greater weights to events occurring immediately prior to the shot rather than those preceding them (decay effect). Second, our model incorporates possession risk more accurately by considering the decay effect and effective playing time. Third, we integrate the assessment of individual player ability to win aerial and ground duels. Using the extended EPV model, we predict this metric for various football players for the upcoming season, particularly taking into account the strength of their opponents. |
| title | Expected Possession Value of Control and Duel Actions for Soccer Player's Skills Estimation |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2406.00814 |