Machine Learning and Data Mining for Sports Analytics [electronic resource] : 7th International Workshop, MLSA 2020, Co-located with ECML/PKDD 2020, Ghent, Belgium, September 14–18, 2020, Proceedings / edited by Ulf Brefeld, Jesse Davis, Jan Van Haaren, Albrecht Zimmermann.

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.

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.