MLSA 2013
Machine Learning and Data Mining for Sports Analytics

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