Machine Learning and Data Mining for Sports Analytics (eBook, PDF)

7th International Workshop, MLSA 2020, Co-located with ECML/PKDD 2020, Ghent, Belgium, September 14-18, 2020, Proceedings
Redaktion: Brefeld, Ulf; Zimmermann, Albrecht; Haaren, Jan van; Davis, Jesse

• Format: PDF

Produktbeschreibung

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.

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Produktdetails

• Verlag: Springer Nature Switzerland
• Seitenzahl: 141
• Erscheinungstermin: 9. Dezember 2020
• Englisch
• ISBN-13: 9783030649128
• Artikelnr.: 62294158

Inhaltsangabe

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.

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