Machine Learning and Data Mining for Sports Analytics
7th International Workshop, MLSA 2020, Co-located with ECML/PKDD 2020, Ghent, Belgium, September 14-18, 2020, Proceedings
Published on 9. December 2020
X, 141 pages
978-3-030-64912-8 (ISBN)
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.
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.
More details
Series
Edition
1st ed. 2020
Language
English
Place of publication
Cham
Switzerland
Publishing group
Springer International Publishing
Target group
Professional and scholarly
Product notice
Reflowable
Illustrations
X, 141 p. 6 illus.
ISBN-13
978-3-030-64912-8 (9783030649128)
DOI
10.1007/978-3-030-64912-8
Schweitzer Classification
Other editions
Additional editions
Ulf Brefeld | Jesse Davis | Jan Van Haaren
Machine Learning and Data Mining for Sports Analytics
7th International Workshop, MLSA 2020, Co-located with ECML/PKDD 2020, Ghent, Belgium, September 14-18, 2020, Proceedings
Book
12/2020
Springer
€53.49
Shipment within 7-9 days
Content
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.
