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Titlebook: Machine Learning and Data Mining for Sports Analytics; 5th International Wo Ulf Brefeld,Jesse Davis,Albrecht Zimmermann Conference proceedi

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發(fā)表于 2025-3-21 17:24:34 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Machine Learning and Data Mining for Sports Analytics
副標題5th International Wo
編輯Ulf Brefeld,Jesse Davis,Albrecht Zimmermann
視頻videohttp://file.papertrans.cn/621/620452/620452.mp4
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Machine Learning and Data Mining for Sports Analytics; 5th International Wo Ulf Brefeld,Jesse Davis,Albrecht Zimmermann Conference proceedi
描述This book constitutes the refereed post-conference proceedings of the 5th International Workshop on Machine Learning and Data Mining for Sports Analytics, MLSA 2018, colocated with ECML/PKDD 2018, in Dublin, Ireland, in September 2018..The 12 full papers presented together with 4 challenge papers were carefully reviewed and selected from 24 submissions. The papers present a variety of topics, covering the team sports American football, basketball, ice hockey, and soccer, as well as the individual sports cycling and martial arts. In addition, four challenge?papers are included, reporting on how to predict pass receivers in soccer.?.
出版日期Conference proceedings 2019
關鍵詞artificial intelligence; classification accuracy; classification algorithm; data mining; learning algori
版次1
doihttps://doi.org/10.1007/978-3-030-17274-9
isbn_softcover978-3-030-17273-2
isbn_ebook978-3-030-17274-9Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer Nature Switzerland AG 2019
The information of publication is updating

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Distinguishing Between Roles of Football Players in Play-by-Play Match Event Datatal importance. To gain insights into the general level of their candidate reinforcements, many professional football clubs have access to extensive video footage and advanced statistics. However, the question whether a given player would fit the team’s playing style often still remains unanswered.
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Player Valuation in European Football In this paper, we compare and contrast which attributes and skills best predict the success of individual players in their positions in five European top football leagues. Further, we evaluate different machine learning algorithms regarding prediction performance. Our results highlight features dis
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Interpreting Deep Sports Analytics: Valuing Actions and Players in the NHLccess, given the current match state. However, the black-box opacity of neural networks prohibits understanding why and when some actions are more valuable than others. This paper applies interpretable Mimic Learning to distill knowledge from the opaque neural net model to a transparent regression t
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Player Pairs Valuation in Ice Hockeycount the context of the players’ actions and perform look-ahead. However, as ice hockey is a team sport, knowing about individual ratings is not enough and coaches want to identify players that play particularly well together. In this paper we therefore extend earlier work for evaluating the perfor
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Evaluating NFL Plays: Expected Points Adjusted for Schedulek gains 4 yards, even though it will not necessarily show up in the yardage statistics. While this problem has been addressed to some extent with the introduction of expected point models, there is still another inequality omission in the creation of yards and this is the opposing defense. Gaining 6
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