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

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Machine Learning and Data Mining for Sports Analytics978-3-030-17274-9Series ISSN 0302-9743 Series E-ISSN 1611-3349

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Lecture Notes in Computer Sciencehttp://image.papertrans.cn/m/image/620452.jpg

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https://doi.org/10.1007/978-3-030-17274-9artificial intelligence; classification accuracy; classification algorithm; data mining; learning algori

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Predicting Pass Receiver in Football Using Distance Based FeaturesL/PKDD 2018. Our solution uses distance based features to predict the receiver of a pass. We show that our model is able to improve prediction results obtained on a similar dataset. One particularity of our approach is the use of failed passes to improve the predictions.

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Interpreting Deep Sports Analytics: Valuing Actions and Players in the NHLansparent tree structure facilitates understanding the general action values by feature influence and partial dependence plots, and player’s exceptional characteristics by identifying player-specific relevant state regions.

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Evaluating NFL Plays: Expected Points Adjusted for Scheduledifferent point values to the offensive and defensive units of the same play, which is the rational thing to do especially in a league with an uneven schedule such as the NFL. The average absolute difference between the raw and adjusted points is 0.07 points/play (p-value < 0.001), while the median

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