Machine Learning and Data Mining for Sports Analytics c - Brefeld, Ulf.; Davis, Jesse; Van Haaren, Jan.; Zimmermann, Albrecht; SpringerLink (Online service) (Cham : Springer International Publishing : Imprint: Springer, 2020)
This book constitutes the refereed post-conference proceedings of the 7th International Workshop on Machine Learning and Data Mining for Sports Analytics, MLSA 2020, colocated with ECML/PKDD 2020, in Ghent, Belgium, in September 2020. Due to the COVID-19 pandemic the conference was held online. The 11 papers presented were carefully reviewed and selected from 22 submissions. The papers present a variety of topics within the area of sports analytics, including tactical analysis, outcome predictions, data acquisition, performance optimization, and player evaluation.
Routine Inspection: A playbook for corner kicks -- How data availability aects the ability to learngood xG models -- Low-cost optical tracking of soccer players -- An Autoencoder Based Approach to SimulateSports Games -- Physical performance optimization in football -- Predicting Player Trajectoriesin Shot Situations in Soccer -- Stats Aren't Everything: Learning Strengths andWeaknesses of Cricket Players -- Prediction of tiers in the rankingof ice hockey players -- A Machine Learning Approach for Road CyclingRace Performance Prediction -- Mining Marathon Training Data to GenerateUseful User Proles -- Learning from partially labeled sequences forbehavioral signal annotation.
Keywords: Artificial intelligence; Computer engineering; 9; Education -- Data processing; Social sciences -- Data processing; Artificial Intelligence; Computer Engineering and Networks; Computers and Education; Computer Application in Social and Behavioral Sciences; Computer Communication Networks