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Machine Learning and Data Mining for Sports Analytics

7th International Workshop, MLSA 2020, Co-located with ECML/PKDD 2020, Ghent, Belgium, September 14–18, 2020, Proceedings

  • Conference proceedings
  • © 2020

Overview

Editors:
  1. Ulf Brefeld

    1. Leuphana University of Lüneburg, Lüneburg, Germany

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  2. Jesse Davis

    1. Katholieke Universiteit Leuven, Leuven, Belgium

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    You can also search for this editor in PubMed   Google Scholar

  3. Jan Van Haaren

    1. SciSports, Amersfoort, The Netherlands

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  4. Albrecht Zimmermann

    1. Université de Caen Normandie, Caen, France

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Part of the book series: Communications in Computer and Information Science (CCIS, volume 1324)

Included in the following conference series:

Conference proceedings info: MLSA 2020.

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About this book

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.


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Table of contents (11 papers)

  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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Other volumes

  1. Machine Learning and Data Mining for Sports Analytics

Editors and Affiliations

  • 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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