Machine Learning and Data Mining for Sports Analytics〈1st ed. 2020〉 : 7th International Workshop, MLSA 2020, Co-located with ECML/PKDD 2020, Ghent, Belgium, September 14–18, 2020, Proceedings

著者名：Brefeld, Ulf (EDT)/Davis, Jesse (EDT)/Van Haaren, Jan (EDT)/Zimmermann, Albrecht (EDT)

Springer（2020/12/09発売）

• 言語：ENG
• ISBN：9783030649111
• eISBN：9783030649128

Description

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.

Table of Contents

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.