Machine Learning and Data Mining for Sports Analytics
9th International Workshop, MLSA 2022, Grenoble, France, September 19, 2022, Revised Selected Papers
Published on 25. February 2023
Book
Paperback/Softback
X, 127 pages
978-3-031-27526-5 (ISBN)
Description
This book constitutes the refereed proceedings of the 9th International Workshop on Machine Learning and Data Mining for Sports Analytics, MLSA 2022, held in Grenoble, France, during September 19, 2022.
The 10 full papers included in this book were carefully reviewed and selected from 18 submissions. They were organized in topical sections as follows: Football, Racket sports, Cycling.
The 10 full papers included in this book were carefully reviewed and selected from 18 submissions. They were organized in topical sections as follows: Football, Racket sports, Cycling.
More details
Series
Edition
1st ed. 2023
Language
English
Place of publication
Cham
Switzerland
Publishing group
Springer International Publishing
Target group
Professional and scholarly
Illustrations
9 s/w Abbildungen, 38 farbige Abbildungen
X, 127 p. 47 illus., 38 illus. in color.
Dimensions
Height: 235 mm
Width: 155 mm
Thickness: 8 mm
Weight
224 gr
ISBN-13
978-3-031-27526-5 (9783031275265)
DOI
10.1007/978-3-031-27527-2
Schweitzer Classification
Other editions
Additional editions
Ulf Brefeld | Jesse Davis | Jan Van Haaren
Machine Learning and Data Mining for Sports Analytics
9th International Workshop, MLSA 2022, Grenoble, France, September 19, 2022, Revised Selected Papers
E-Book
02/2023
Springer
€69.54
Available for download
Content
Football
.- Towards expected counter - Using comprehensible features to predict counterattacks.- Shot analysis in different levels of German football using Expected Goals.- Analyzing passing sequences for the prediction of goal-scoring opportunities.- Let's penetrate the defense: A machine learning model for prediction and valuation of penetrative passes.- Evaluation of creating scoring opportunities for teammates in soccer via trajectory prediction.- Cost-efficient and bias-robust sports player tracking by integrating GPS and video.-
Racket sports
.- Predicting tennis serve directions with machine learning.- Discovering and visualizing tactics in table tennis games based on subgroup discovery.-
Cycling
.- Athlete monitoring in professional road cycling using similarity search on time series data.