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

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书目名称Machine Learning and Data Mining for Sports Analytics影响因子(影响力)




书目名称Machine Learning and Data Mining for Sports Analytics影响因子(影响力)学科排名




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书目名称Machine Learning and Data Mining for Sports Analytics网络公开度学科排名




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书目名称Machine Learning and Data Mining for Sports Analytics被引频次学科排名




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书目名称Machine Learning and Data Mining for Sports Analytics年度引用学科排名




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MELON

Saline

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.

ABASH

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

隼鹰

SOBER

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

纪念

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

Arrhythmia

inspiration

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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