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dc.contributor.author | Toda, Kosuke | en |
dc.contributor.author | Teranishi, Masakiyo | en |
dc.contributor.author | Kushiro, Keisuke | en |
dc.contributor.author | Fujii, Keisuke | en |
dc.contributor.alternative | 戸田, 康介 | ja |
dc.contributor.alternative | 久代, 恵介 | ja |
dc.date.accessioned | 2023-01-13T06:21:32Z | - |
dc.date.available | 2023-01-13T06:21:32Z | - |
dc.date.issued | 2022-01 | - |
dc.identifier.uri | http://hdl.handle.net/2433/278396 | - |
dc.description.abstract | With the development of measurement technology, data on the movements of actual games in various sports can be obtained and used for planning and evaluating the tactics and strategy. Defense in team sports is generally difficult to be evaluated because of the lack of statistical data. Conventional evaluation methods based on predictions of scores are considered unreliable because they predict rare events throughout the game. Besides, it is difficult to evaluate various plays leading up to a score. In this study, we propose a method to evaluate team defense from a comprehensive perspective related to team performance by predicting ball recovery and being attacked, which occur more frequently than goals, using player actions and positional data of all players and the ball. Using data from 45 soccer matches, we examined the relationship between the proposed index and team performance in actual matches and throughout a season. Results show that the proposed classifiers predicted the true events (mean F1 score > 0.483) better than the existing classifiers which were based on rare events or goals (mean F1 score < 0.201). Also, the proposed index had a moderate correlation with the long-term outcomes of the season (r = 0.397). These results suggest that the proposed index might be a more reliable indicator rather than winning or losing with the inclusion of accidental factors. | en |
dc.language.iso | eng | - |
dc.publisher | Public Library of Science (PLoS) | en |
dc.rights | © 2022 Toda et al. | en |
dc.rights | This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. | en |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | - |
dc.subject | Sports | en |
dc.subject | Forecasting | en |
dc.subject | Statistical data | en |
dc.subject | Decision tree learning | en |
dc.subject | Team behavior | en |
dc.subject | Games | en |
dc.subject | Machine learning | en |
dc.subject | Running | en |
dc.title | Evaluation of soccer team defense based on prediction models of ball recovery and being attacked: A pilot study | en |
dc.type | journal article | - |
dc.type.niitype | Journal Article | - |
dc.identifier.jtitle | PLOS ONE | en |
dc.identifier.volume | 17 | - |
dc.identifier.issue | 1 | - |
dc.relation.doi | 10.1371/journal.pone.0263051 | - |
dc.textversion | publisher | - |
dc.identifier.artnum | e0263051 | - |
dc.identifier.pmid | 35085344 | - |
dcterms.accessRights | open access | - |
datacite.awardNumber | 19H04941 | - |
datacite.awardNumber | 20H04075 | - |
datacite.awardNumber | 20H04087 | - |
datacite.awardNumber.uri | https://kaken.nii.ac.jp/grant/KAKENHI-PUBLICLY-19H04941/ | - |
datacite.awardNumber.uri | https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-20H04075/ | - |
datacite.awardNumber.uri | https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-20H04087/ | - |
dc.identifier.eissn | 1932-6203 | - |
jpcoar.funderName | 日本学術振興会 | ja |
jpcoar.funderName | 日本学術振興会 | ja |
jpcoar.funderName | 日本学術振興会 | ja |
jpcoar.awardTitle | 目的志向的な相互作用を含む集団移動系列・経路の解析手法の開発 | ja |
jpcoar.awardTitle | 集団バイオメカニクスのためのデータ駆動的モデルに基づく利用しやすい情報提供技術 | ja |
jpcoar.awardTitle | チームワークの良さを支える神経基盤の解明 | ja |
出現コレクション: | 学術雑誌掲載論文等 |
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