© 2019

Machine Learning and Data Mining for Sports Analytics

5th International Workshop, MLSA 2018, Co-located with ECML/PKDD 2018, Dublin, Ireland, September 10, 2018, Proceedings

  • Ulf Brefeld
  • Jesse Davis
  • Jan Van Haaren
  • Albrecht Zimmermann
Conference proceedings MLSA 2018

Part of the Lecture Notes in Computer Science book series (LNCS, volume 11330)

Also part of the Lecture Notes in Artificial Intelligence book sub series (LNAI, volume 11330)

Table of contents

  1. Front Matter
    Pages i-x
  2. Soccer

    1. Front Matter
      Pages 1-1
    2. Edward Nsolo, Patrick Lambrix, Niklas Carlsson
      Pages 42-54
    3. Stefan Neumann, Julian Ritter, Kailash Budhathoki
      Pages 55-66
  3. US Team Sports

    1. Front Matter
      Pages 67-67
    2. Guiliang Liu, Wang Zhu, Oliver Schulte
      Pages 69-81
    3. Dennis Ljung, Niklas Carlsson, Patrick Lambrix
      Pages 82-92
    4. Yejia Liu, Oliver Schulte, Chao Li
      Pages 93-105
    5. Konstantinos Pelechrinis, Wayne Winston, Jeff Sagarin, Vic Cabot
      Pages 106-117
  4. Individual Sports

    1. Front Matter
      Pages 119-119
    2. Yasuyuki Kataoka, Peter Gray
      Pages 121-130
    3. Kasper M. W. Soekarjo, Dominic Orth, Elke Warmerdam, John van der Kamp
      Pages 131-141
  5. Challenge Papers

    1. Front Matter
      Pages 143-143
    2. Yann Dauxais, Clément Gautrais
      Pages 145-151
    3. Philippe Fournier-Viger, Tianbiao Liu, Jerry Chun-Wei Lin
      Pages 152-158
    4. Ondřej Hubáček, Gustav Šourek, Filip Železný
      Pages 159-166
    5. Heng Li, Zhiying Zhang
      Pages 167-177

About these proceedings


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

Editors and affiliations

  1. 1.Leuphana UniversityLüneburgGermany
  2. 2.Katholieke Universiteit LeuvenHeverleeBelgium
  3. 3.SciSportsEnschedeThe Netherlands
  4. 4.Université de Caen NormandieCaenFrance

Bibliographic information

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