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