Machine Learning and Data Mining for Sports Analytics

1. Front Matter

   Pages i-x

   PDF

2. Soccer

   1. Front Matter

      Pages 1-1

      PDF

   2. Measuring Football Players’ On-the-Ball Contributions from Passes During Games

      • Lotte Bransen, Jan Van Haaren

      Pages 3-15

   3. Forecasting the FIFA World Cup – Combining Result- and Goal-Based Team Ability Parameters

      • Pieter Robberechts, Jesse Davis

      Pages 16-30

   4. Distinguishing Between Roles of Football Players in Play-by-Play Match Event Data

      • Bart Aalbers, Jan Van Haaren

      Pages 31-41

   5. Player Valuation in European Football

      • Edward Nsolo, Patrick Lambrix, Niklas Carlsson

      Pages 42-54

   6. Ranking the Teams in European Football Leagues with Agony

      • Stefan Neumann, Julian Ritter, Kailash Budhathoki

      Pages 55-66

3. US Team Sports

   1. Front Matter

      Pages 67-67

      PDF

   2. Interpreting Deep Sports Analytics: Valuing Actions and Players in the NHL

      • Guiliang Liu, Wang Zhu, Oliver Schulte

      Pages 69-81

   3. Player Pairs Valuation in Ice Hockey

      • Dennis Ljung, Niklas Carlsson, Patrick Lambrix

      Pages 82-92

   4. Model Trees for Identifying Exceptional Players in the NHL and NBA Drafts

      • Yejia Liu, Oliver Schulte, Chao Li

      Pages 93-105

   5. Evaluating NFL Plays: Expected Points Adjusted for Schedule

      • Konstantinos Pelechrinis, Wayne Winston, Jeff Sagarin, Vic Cabot

      Pages 106-117

4. Individual Sports

   1. Front Matter

      Pages 119-119

      PDF

   2. Real-Time Power Performance Prediction in Tour de France

      • Yasuyuki Kataoka, Peter Gray

      Pages 121-130

   3. Automatic Classification of Strike Techniques Using Limb Trajectory Data

      • Kasper M. W. Soekarjo, Dominic Orth, Elke Warmerdam, John van der Kamp

      Pages 131-141

5. Challenge Papers

   1. Front Matter

      Pages 143-143

      PDF

   2. Predicting Pass Receiver in Football Using Distance Based Features

      • Yann Dauxais, Clément Gautrais

      Pages 145-151

   3. Football Pass Prediction Using Player Locations

      • Philippe Fournier-Viger, Tianbiao Liu, Jerry Chun-Wei Lin

      Pages 152-158

   4. Deep Learning from Spatial Relations for Soccer Pass Prediction

      • Ondřej Hubáček, Gustav Šourek, Filip Železný

      Pages 159-166

   5. Predicting the Receivers of Football Passes

      • Heng Li, Zhiying Zhang

      Pages 167-177

This book constitutes the refereed post-conference proceedings of the 5th International Workshop on Machine Learning and Data Mining for Sports Analytics, MLSA 2018, colocated with ECML/PKDD 2018, in Dublin, Ireland, in September 2018.

The 12 full papers presented together with 4 challenge papers were carefully reviewed and selected from 24 submissions. The papers present a variety of topics, covering the team sports American football, basketball, ice hockey, and soccer, as well as the individual sports cycling and martial arts. In addition, four challenge papers are included, reporting on how to predict pass receivers in soccer. 

• artificial intelligence
• classification accuracy
• classification algorithm
• data mining
• learning algorithms
• machine learning
• neural networks
• pattern recognition
• regression analysis
• reinforcement learning
• Support Vector Machines (SVM)
• tree structures

• Leuphana University, Lüneburg, Germany

  Ulf Brefeld

• Katholieke Universiteit Leuven, Heverlee, Belgium

  Jesse Davis

• SciSports, Enschede, The Netherlands

  Jan Van Haaren

• Université de Caen Normandie, Caen, France

  Albrecht Zimmermann

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