| 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 Players 1-10
  Ruben Vroonen, Tom Decroos, Jan Van Haaren, Jesse Davis
- STARSS: A Spatio-Temporal Action Rating System for Soccer 11-20
  Tom Decroos, Jan Van Haaren, Vladimir Dzyuba, Jesse Davis
- Who Is Going to Get Hurt? Predicting Injuries in Professional Soccer 21-30
  Alessio Rossi, Luca Pappalardo, Paolo Cintia, Javier Fernandez, F. Marcello Iaia, Daniel Medina
- Enabling Training Personalization by Predicting the Sessioon Rate of Perceived Exertion 31-40
  Gilles Vandewiele, Youri Geurkink, Maarten Lievens, Femke Ongenae, Filip De Turck, Jan Boone
- Dynamic Winner Prediction in Twenty20 Cricket: Based on Relative Team Strengths 41-50
  Sasank Viswanadha, Kaustubh Sivalenka, Madan Gopal Jhawar, Vikram Pudi
- Linking Event Mentions From Cricket Match Reports to Commentaries 51-61
  Manish Gupta
- Honest Mirror: Quantitative Assessment of Player Performance in an ODI Cricket Match 62-72
  Madan Gopal Jhawar, Vikram Pudi
- An Artificial Neural Network-based Prediction Model for Underdog Teams in NBA Matches 73-82
  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|