| Vol-1969 urn:nbn:de:0074-1969-2 Copyright © 2013 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 2013
Machine Learning and Data Mining for Sports Analytics

### Proceedings of the 1st Workshop on Machine Learning and Data Mining for Sports Analytics
co-located with 2013 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2013)

### Prague, Czech Republic, September 27th, 2013.

Edited by

### Jesse Davis, KU Leuven, Belgium
Jan Van Haaren, KU Leuven, Belgium
Albrecht Zimmermann, University of Caen, France

## Table of Contents

- Preface
- Why Do Sports Officials Dropout?1-10
  Fabrice Dosseville, François Rioult, Sylvain Laborde
- Strategic Patterns Discovery in RTS-games for E-Sport with Sequential Pattern Mining 11-20
  Guillaume Bosc, Mehdi Kaytoue, Chedy Raïssi, Jean-François Boulicaut
- Maps for Reasoning in Ultimate 21-27
  Jeremy Weiss, Sean Childers
- Predicting the NFL using Twitter 28-38
  Shiladitya Sinha, Chris Dyer, Kevin Gimpel, Noah A. Smith
- Use of Performance Metrics to Forecast Success in the National Hockey League 39-48
  Joshua Weissbock, Herna Viktor, Diana Inkpen
- Finding Similar Movements in Positional Data Streams 49-57
  Jens Haase, Ulf Brefeld
- Comparison of Machine Learning Methods for Predicting the Recovery Time of Professional Football Players After an Undiagnosed Injury 58-68
  Stylianos Kampakis
- Predicting NCAAB Match Outcomes Using ML Techniques – Some Results and Lessons Learned 69-78
  Albrecht Zimmemrann, Sruthi Moorthy, Zifan Shi
- Key Point Selection and Clustering of Swimmer Coordination Through Sparse Fisher-EM 79-88
  John Komar, Romain Herault, Ludovic Seifert

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

2017-10-30: submitted by Albrecht Zimmermann, metadata incl. bibliographic data published under Creative Commons CC0
2017-10-30: published on CEUR-WS.org |valid HTML5|