MLSA 2016
Machine Learning and Data Mining for Sports Analytics

Table of Contents

• Preface
  
  

Session 1: Prediction Challenge

• Generalised Linear Model for Football Matches Prediction
  Antoine Adam
• Feature Extraction and Aggregation for Predicting the EURO 2016
  Maryam Tavakol , Hamid Zafartavanaelmi , Ulf Brefeld
• EURO 2016 Predictions Using Team Rating Systems
  Jan Lasek
• The Player Kernel: Learning Team Strengths Based on Implicit Player Contributions
  Lucas Maystre , Victor Kristof , Antonio J. González Ferrer , Matthias Grossglauser

Session 2

• Applications of Machine Learning in Dota 2: Literature Review and Practical Knowledge Sharing
  Aleksandr Semenov , Peter Romov , Kirill Neklyudov , Daniil Yashkov , Daniil Kireev
• Predicting the Outcome of ODI Cricket Matches: A Team Composition Based Approach
  Madan Gopal Jhanwar , Vikram Pudi
• Explaining Soccer Match Outcomes with Goal Scoring Opportunities Predictive Analytics
  Harm Eggels , Ruud van Elk , Mykola Pechenizkiy
• Qualitative Spatial Reasoning for Soccer Pass Prediction
  Vincent Vercruyssen , Luc De Raedt , Jesse Davis
• Decision Making in American Football: Evidence from 7 Years of NFL Data
  Konstantinos Pelechrinis

Session 3

• Detecting Strategic Moves in HearthStone Matches
  Boris Doux , Clement Gautrais , Benjamin Negrevergne
• Wages of Wins: Could an Amateur Make Money from Match Outcome Predictions?
  Albrecht Zimmermann

Session 4

• Does Training Affect Match Performance? A Study Using Data Mining And Tracking Devices
  Javier Fernández , Daniel Medina , Antonio Gómez , Marta Arias , Ricard Gavaldà
• Heart Rate Modelling as a Potential Physical Fitness Assessment for Runners and Cyclists
  Dimitri de Smet , Marc Francaux , Julien M. Hendrickx , Michel Verleysen
• Data Mining in Stabilometry: Application to Patient Balance Study for Sports Talent Mapping
  Juan. A. Lara , David Lizcano , David de la Peña , José M. Barreiro