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 Mining11-20
Guillaume Bosc, Mehdi Kaytoue, Chedy Raïssi, Jean-François Boulicaut
Maps for Reasoning in Ultimate21-27
Jeremy Weiss, Sean Childers
Predicting the NFL using Twitter28-38
Shiladitya Sinha, Chris Dyer, Kevin Gimpel, Noah A. Smith
Use of Performance Metrics to Forecast Success in the National Hockey League39-48
Joshua Weissbock, Herna Viktor, Diana Inkpen
Finding Similar Movements in Positional Data Streams49-57
Jens Haase, Ulf Brefeld
Comparison of Machine Learning Methods for Predicting the Recovery Time of Professional Football Players After an Undiagnosed Injury58-68
Stylianos Kampakis
Predicting NCAAB Match Outcomes Using ML Techniques – Some Results and Lessons Learned69-78
Albrecht Zimmemrann, Sruthi Moorthy, Zifan Shi
Key Point Selection and Clustering of Swimmer Coordination Through Sparse Fisher-EM79-88
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
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