Vol-1971
urn:nbn:de:0074-1971-3

Copyright © 2017 for the individual papers by the papers' authors. Copying permitted for private and academic purposes. This volume is published and copyrighted by its editors.

MLSA 2017
Machine Learning and Data Mining for Sports Analytics

Proceedings of the 4th Workshop on Machine Learning and Data Mining for Sports Analytics
co-located with 2017 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2017)

Skopje, Macedonia, September 18th, 2017.

Edited by

Jesse Davis, KU Leuven, Belgium
Mehdi Kaytoue, INSA Lyon, France
Albrecht Zimmermann, University of Caen, France

Table of Contents

Preface
Predicting the Potential of Professional Soccer Players1-10
Ruben Vroonen, Tom Decroos, Jan Van Haaren, Jesse Davis
STARSS: A Spatio-Temporal Action Rating System for Soccer11-20
Tom Decroos, Jan Van Haaren, Vladimir Dzyuba, Jesse Davis
Who Is Going to Get Hurt? Predicting Injuries in Professional Soccer21-30
Alessio Rossi, Luca Pappalardo, Paolo Cintia, Javier Fernandez, F. Marcello Iaia, Daniel Medina
Enabling Training Personalization by Predicting the Sessioon Rate of Perceived Exertion31-40
Gilles Vandewiele, Youri Geurkink, Maarten Lievens, Femke Ongenae, Filip De Turck, Jan Boone
Dynamic Winner Prediction in Twenty20 Cricket: Based on Relative Team Strengths41-50
Sasank Viswanadha, Kaustubh Sivalenka, Madan Gopal Jhawar, Vikram Pudi
Linking Event Mentions From Cricket Match Reports to Commentaries51-61
Manish Gupta
Honest Mirror: Quantitative Assessment of Player Performance in an ODI Cricket Match62-72
Madan Gopal Jhawar, Vikram Pudi
An Artificial Neural Network-based Prediction Model for Underdog Teams in NBA Matches73-82
Paolo Giuliodori

We offer a BibTeX file for citing papers of this workshop from LaTeX.

2017-09-26: submitted by Albrecht Zimmermann, metadata incl. bibliographic data published under Creative Commons CC0
2017-09-27: published on CEUR-WS.org |valid HTML5|