Machine Learning and Data Mining for Sports Analytics

1. Front Matter

   Pages i-x

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2. Football

   1. Front Matter

      Pages 1-1

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   2. Routine Inspection: A Playbook for Corner Kicks

      • Laurie Shaw, Sudarshan Gopaladesikan

      Pages 3-16

   3. How Data Availability Affects the Ability to Learn Good xG Models

      • Pieter Robberechts, Jesse Davis

      Pages 17-27

   4. Low-Cost Optical Tracking of Soccer Players

      • Gabor Csanalosi, Gergely Dobreff, Alija Pasic, Marton Molnar, László Toka

      Pages 28-39

   5. An Autoencoder Based Approach to Simulate Sports Games

      • Ashwin Vaswani, Rijul Ganguly, Het Shah, Sharan Ranjit S, Shrey Pandit, Samruddhi Bothara

      Pages 40-50

   6. Physical Performance Optimization in Football

      • Gergely Dobreff, Péter Revisnyei, Gábor Schuth, György Szigeti, László Toka, Alija Pašić

      Pages 51-61

   7. Predicting Player Trajectories in Shot Situations in Soccer

      • Per Lindström, Ludwig Jacobsson, Niklas Carlsson, Patrick Lambrix

      Pages 62-75

3. Other Team Sports

   1. Front Matter

      Pages 77-77

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   2. Stats Aren’t Everything: Learning Strengths and Weaknesses of Cricket Players

      • Swarup Ranjan Behera, Vijaya V. Saradhi

      Pages 79-88

   3. Prediction of Tiers in the Ranking of Ice Hockey Players

      • Timmy Lehmus Persson, Haris Kozlica, Niklas Carlsson, Patrick Lambrix

      Pages 89-100

4. Individual Sports

   1. Front Matter

      Pages 101-101

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   2. A Machine Learning Approach for Road Cycling Race Performance Prediction

      • Leonid Kholkine, Tom De Schepper, Tim Verdonck, Steven Latré

      Pages 103-112

   3. Mining Marathon Training Data to Generate Useful User Profiles

      • Jakim Berndsen, Barry Smyth, Aonghus Lawlor

      Pages 113-125

   4. Learning from Partially Labeled Sequences for Behavioral Signal Annotation

      • Anna Aniszewska-Stępień, Romain Hérault, Guillaume Hacques, Ludovic Seifert, Gilles Gasso

      Pages 126-139

5. Back Matter

   Pages 141-141

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This book constitutes the refereed post-conference proceedings of the 7th International Workshop on Machine Learning and Data Mining for Sports Analytics, MLSA 2020, colocated with ECML/PKDD 2020, in Ghent, Belgium, in September 2020. Due to the COVID-19 pandemic the conference was held online. 

The 11 papers presented were carefully reviewed and selected from 22 submissions. The papers present a variety of topics within the area of sports analytics, including tactical analysis, outcome predictions, data acquisition, performance optimization, and player evaluation.

• artificial intelligence
• computer hardware
• computer systems
• computer vision
• correlation analysis
• data handling
• data integration
• databases
• Human-Computer Interaction (HCI)
• integrated data
• machine learning
• sensors
• user interfaces

• Leuphana University of Lüneburg, Lüneburg, Germany

  Ulf Brefeld

• Katholieke Universiteit Leuven, Leuven, Belgium

  Jesse Davis

• SciSports, Amersfoort, The Netherlands

  Jan Van Haaren

• Université de Caen Normandie, Caen, France

  Albrecht Zimmermann

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