MLSA 2017
Machine Learning and Data Mining for Sports Analytics

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