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Machine Learning and Data Mining for Sports Analytics

10th International Workshop, MLSA 2023, Turin, Italy, September 18, 2023, Revised Selected Papers

  • Conference proceedings
  • © 2024

Overview

Editors:
  1. Ulf Brefeld

    1. Leuphana Universität Lüneburg, Lüneburg, Germany

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  2. Jesse Davis

    1. KU Leuven, Leuven, Belgium

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  3. Jan Van Haaren

    1. KU Leuven, Leuven, Belgium

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  4. Albrecht Zimmermann

    1. University of Caen Normandy, Caen CEDEX 5, France

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Part of the book series: Communications in Computer and Information Science (CCIS, volume 2035)

Included in the following conference series:

Conference proceedings info: MLSA 2023.

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About this book

This book constitutes the refereed proceedings of the 10th International Workshop on Machine Learning and Data Mining for Sports Analytics, MLSA 2023, held in Turin, Italy, in September 2023.

The 16 full papers included in this book were carefully reviewed and selected from 31 submissions. They were organized in topical sections as follows: Football/Soccer, Basketball, Other team sports, Individual sports.

Keywords

Table of contents (16 papers)

  1. Front Matter

    Pages i-x
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  2. Keynote

    1. Front Matter

      Pages 1-1
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    2. Sports Data Analytics: An Art and a Science

      • Martin Rumo
      Pages 3-7

  3. Football/Soccer

    1. Front Matter

      Pages 9-9
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    2. ETSY: A Rule-Based Approach to Event and Tracking Data SYnchronization

      • Maaike Van Roy, Lorenzo Cascioli, Jesse Davis
      Pages 11-23

    3. Masked Autoencoder Pretraining for Event Classification in Elite Soccer

      • Yannick Rudolph, Ulf Brefeld
      Pages 24-35

    4. Quantification of Turnover Danger with xCounter

      • Henrik Biermann, Weiran Yang, Franz-Georg Wieland, Jens Timmer, Daniel Memmert
      Pages 36-51

    5. Pass Receiver and Outcome Prediction in Soccer Using Temporal Graph Networks

      • Pegah Rahimian, Hyunsung Kim, Marc Schmid, Laszlo Toka
      Pages 52-63

    6. Field Depth Matters: Comparing the Valuation of Passes in Football

      • Leo Martins de Sá-Freire, Pedro O. S. Vaz-de-Melo
      Pages 64-73

  4. Basketball

    1. Front Matter

      Pages 75-75
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    2. Momentum Matters: Investigating High-Pressure Situations in the NBA Through Scoring Probability

      • Balazs Mihalyi, Gergely Biczók, Laszlo Toka
      Pages 77-90

    3. Are Sports Awards About Sports? Using AI to Find the Answer

      • Anshumaan Shankar, Gowtham Veerabadran Rajasekaran, Jacob Hendricks, Jared Andrew Schlak, Parichit Sharma, Madhavan K. R. et al.
      Pages 91-102

    4. The Big Three: A Practical Framework for Designing Decision Support Systems in Sports and an Application for Basketball

      • Francisco Javier Sanguino Bautiste, Dustin Brunner, Jonathan Koch, Timothé Laborie, Liule Yang, Mennatallah El-Assady
      Pages 103-116

  5. Other Team Sports

    1. Front Matter

      Pages 117-117
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    2. What Data Should Be Collected for a Good Handball Expected Goal model?

      • Alexis Mortelier, François Rioult, John Komar
      Pages 119-130

    3. Identifying Player Roles in Ice Hockey

      • Rasmus Säfvenberg, Niklas Carlsson, Patrick Lambrix
      Pages 131-143

    4. Elite Rugby League Players’ Signature Movement Patterns and Position Prediction

      • Victor Elijah Adeyemo, Anna Palczewska, Ben Jones, Dan Weaving
      Pages 144-154

    5. Boat Speed Prediction in SailGP

      • Benedek Zentai, László Toka
      Pages 155-164

  6. Individual Sports

    1. Front Matter

      Pages 165-165
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    2. Exploring Table Tennis Analytics: Domination, Expected Score and Shot Diversity

      • Gabin Calmet, Aymeric Eradès, Romain Vuillemot
      Pages 167-178
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Other volumes

  1. Machine Learning and Data Mining for Sports Analytics

Editors and Affiliations

  • Leuphana Universität Lüneburg, Lüneburg, Germany

    Ulf Brefeld

  • KU Leuven, Leuven, Belgium

    Jesse Davis, Jan Van Haaren

  • University of Caen Normandy, Caen CEDEX 5, France

    Albrecht Zimmermann

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