Revisiting PlayeRank
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866916455907328000 |
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| author | Schmidt, Louise Lillo, Cristian Bustos, Javier |
| author_facet | Schmidt, Louise Lillo, Cristian Bustos, Javier |
| contents | In this article we revise the football's performance score called PlayeRank, designed and evaluated by Pappalardo et al.\ in 2019. First, we analyze the weights extracted from the Linear Support Vector Machine (SVM) that solves the classification problem of "which set of events has a higher impact on the chances of winning a match". Here, we notice that the previously published results include the Goal-Scored event during the training phase, which produces inconsistencies. We fix these inconsistencies, and show new weights capable of solving the same problem.
Following the intuition that the best team should always win a match, we define the team's quality as the average number of players involved in the game. We show that, using the original PlayeRank, in 94.13\% of the matches either the superior team beats the inferior team or the teams end tied if the scores are similar.
Finally, we present a way to use PlayeRank in an online fashion using modified free analysis tools. Calculating this modified version of PlayeRank, we performed an online analysis of a real football match every five minutes of game. Here, we evaluate the usefulness of that information with experts and managers, and conclude that the obtained data indeed provides useful information that was not previously available to the manager during the match. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_20038 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Revisiting PlayeRank Schmidt, Louise Lillo, Cristian Bustos, Javier Machine Learning In this article we revise the football's performance score called PlayeRank, designed and evaluated by Pappalardo et al.\ in 2019. First, we analyze the weights extracted from the Linear Support Vector Machine (SVM) that solves the classification problem of "which set of events has a higher impact on the chances of winning a match". Here, we notice that the previously published results include the Goal-Scored event during the training phase, which produces inconsistencies. We fix these inconsistencies, and show new weights capable of solving the same problem. Following the intuition that the best team should always win a match, we define the team's quality as the average number of players involved in the game. We show that, using the original PlayeRank, in 94.13\% of the matches either the superior team beats the inferior team or the teams end tied if the scores are similar. Finally, we present a way to use PlayeRank in an online fashion using modified free analysis tools. Calculating this modified version of PlayeRank, we performed an online analysis of a real football match every five minutes of game. Here, we evaluate the usefulness of that information with experts and managers, and conclude that the obtained data indeed provides useful information that was not previously available to the manager during the match. |
| title | Revisiting PlayeRank |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2410.20038 |