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

146 Pages·2020·17.686 MB·English

by  Ulf Brefeld, Jesse Davis, Jan Van Haaren, Albrecht Zimmermann

Ulf Brefeld
Jesse Davis
Jan Van Haaren
Albrecht Zimmermann (Eds.)
Communications in Computer and Information Science 1324
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
Communications
in Computer and Information Science 1324
Editorial Board Members
Joaquim Filipe
Polytechnic Institute of Setúbal, Setúbal, Portugal
Ashish Ghosh
Indian Statistical Institute, Kolkata, India
Raquel Oliveira Prates
Federal University of Minas Gerais (UFMG), Belo Horizonte, Brazil
Lizhu Zhou
Tsinghua University, Beijing, China
More information about this series at http://www.springer.com/series/7899
Ulf Brefeld Jesse Davis Jan Van Haaren
(cid:129) (cid:129) (cid:129)
Albrecht Zimmermann (Eds.)
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
123
Editors
UlfBrefeld Jesse Davis
LeuphanaUniversity of Lüneburg Katholieke Universiteit Leuven
Lüneburg,Germany Leuven,Belgium
Jan Van Haaren Albrecht Zimmermann
SciSports UniversitédeCaen Normandie
Amersfoort, The Netherlands Caen,France
ISSN 1865-0929 ISSN 1865-0937 (electronic)
Communications inComputer andInformationScience
ISBN 978-3-030-64911-1 ISBN978-3-030-64912-8 (eBook)
https://doi.org/10.1007/978-3-030-64912-8
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Preface
TheMachineLearningandDataMiningforSportsAnalytics(MLSA)workshopaims
to bring people from outside of the Machine Learning and Data Mining community
into contact with researchers from that community who are working on Sports Ana-
lytics. The 7th edition of the workshop was co-located with the European Conference
on Machine Learning and Principles and Practice of Knowledge Discovery (ECML
PKDD 2020).
SportsAnalyticshasbeenasteadilygrowingandrapidlyevolvingareaoverthelast
decade, both in US professional sports leagues and in European football leagues. The
recentimplementationofstrictfinancialfair-playregulationsinEuropeanfootballwill
definitelyincreasetheimportanceofSportsAnalyticsinthecomingyears.Inaddition,
there is the popularity of sports betting. The developed techniques are being used for
decision support in all aspects of professional sports, including but not limited to:
– Match strategy, tactics, and analysis
– Player acquisition, player valuation, and team spending
– Training regimens and focus
– Injury prediction and prevention
– Performance management and prediction
– Match outcome and league table prediction
– Tournament design and scheduling
– Betting odds calculation
Theinterestinthetopichasgrownsomuchthatthereisnowanannualconference
onSportsAnalyticsattheMITSloanSchoolofManagement,whichhasbeenattended
by representatives from over 70 professional sports teams in eminent leagues such as
the Major League Baseball, National Basketball Association, National Football Lea-
gue,NationalHockeyLeague,MajorLeagueSoccer,EnglishPremierLeague,andthe
German Bundesliga. Furthermore, sports data providers such as Statsbomb and Opta
have started making performance data publicly available to stimulate researchers who
have the skills and vision to make a difference in the sports analytics community.
Moreover, the National Football league has been sponsoring a Big Data Bowl where
they release data and a concrete question to try to engage the analytics community.
There has been growing interest in the Machine Learning and Data Mining com-
munity about this topic, and the 2020 edition of MLSA built on the success of prior
editions at ECML PKDD 2013, ECML PKDD 2015 – ECML/PKDD 2019.
In 2020, the workshop received 22 submissions of which 13 were selected after a
single-blind reviewing process involving at least 3 Program Committee members per
paper.Ofthesepapers,3wereextendedabstractsandarethereforenotincludedinthe
post-proceedings.Thecontributionscoveredawiderangeofsports,includingtheever-
popular soccer but also climbing, cricket, cycling, ice hockey, and long-distance
vi Preface
running. Topics included tactical analysis, outcome predictions, data acquisition, per-
formance optimization, and player evaluation.
The workshop featured an invited presentation by David Sumpter from University
ofUppsala,Sweden,on“Usingcollectivemotionmodelstohelpplayersimprovetheir
game.”
Furtherinformationabouttheworkshopcanbefoundontheworkshop'swebsiteat
https://dtai.cs.kuleuven.be/events/MLSA20/.
October 2020 Ulf Brefeld
Jesse Davis
Jan Van Haaren
Albrecht Zimmermann
Organization
Workshop Co-chairs
Ulf Brefeld Leuphana University, Germany
Jesse Davis KU Leuven, Belgium
Jan Van Haaren SciSports, The Netherlands
Albrecht Zimmermann Université de Caen, France
Program Committee
Max Adema SciSports, The Netherlands
Gennady Andrienko Fraunhofer, Germany
Harish S. Bhat University of California, Merced, USA
Matthew van Bommel Sacramento Kings, USA
Arne De Brabandere KU Leuven, Belgium
Lotte Bransen SciSports, The Netherlands
Paolo Cintia University of Pisa, Italy
Tom Decroos KU Leuven, Belgium
Kurt Driessens Maastricht University, The Netherlands
Martin Eastwood www.pena.lt/y, UK
Javier Fernandez FC Barcelona, Spain
Clément Gautrais KU Leuven, Belgium
Mehdi Kaytoue Infologic, France
Patrick Lambrix Linköping University, Sweden
Jan Lasek Systems Research Institute, Polish Academy
of Sciences, Poland
Arie-Willem de Leeuw Universiteit Leiden, The Netherlands
Christophe Ley Ghent University, Belgium
Laurentius Meerhoff Leiden Institute of Advanced Computer Science,
The Netherlands
Wannes Meert KU Leuven, Belgium
Tim Op De Beéck KU Leuven, Belgium
Luca Pappalardo University of Pisa, Italy
Konstantinos Pelechrinis University of Pittsburgh, USA
François Rioult Université de Caen, France
Pieter Robberechts KU Leuven, Belgium
Oliver Schulte Simon Fraser University, France
Jan Van Haaren SciSports, The Netherlands
Guillermo Vinue University of Valencia, Spain
Contents
Football
Routine Inspection: A Playbook for Corner Kicks. . . . . . . . . . . . . . . . . . . . 3
Laurie Shaw and Sudarshan Gopaladesikan
How Data Availability Affects the Ability to Learn Good xG Models . . . . . . 17
Pieter Robberechts and Jesse Davis
Low-Cost Optical Tracking of Soccer Players. . . . . . . . . . . . . . . . . . . . . . . 28
Gabor Csanalosi, Gergely Dobreff, Alija Pasic, Marton Molnar,
and László Toka
An Autoencoder Based Approach to Simulate Sports Games . . . . . . . . . . . . 40
Ashwin Vaswani, Rijul Ganguly, Het Shah, Sharan Ranjit S,
Shrey Pandit, and Samruddhi Bothara
Physical Performance Optimization in Football. . . . . . . . . . . . . . . . . . . . . . 51
Gergely Dobreff, Péter Revisnyei, Gábor Schuth, György Szigeti,
László Toka, and Alija Pašić
Predicting Player Trajectories in Shot Situations in Soccer. . . . . . . . . . . . . . 62
Per Lindström, Ludwig Jacobsson, Niklas Carlsson,
and Patrick Lambrix
Other Team Sports
Stats Aren’t Everything: Learning Strengths and Weaknesses
of Cricket Players. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
Swarup Ranjan Behera and Vijaya V. Saradhi
Prediction of Tiers in the Ranking of Ice Hockey Players. . . . . . . . . . . . . . . 89
Timmy Lehmus Persson, Haris Kozlica, Niklas Carlsson,
and Patrick Lambrix
Individual Sports
A Machine Learning Approach for Road Cycling Race
Performance Prediction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103
Leonid Kholkine, Tom De Schepper, Tim Verdonck, and Steven Latré
Mining Marathon Training Data to Generate Useful User Profiles. . . . . . . . . 113
Jakim Berndsen, Barry Smyth, and Aonghus Lawlor
x Contents
Learning from Partially Labeled Sequences for Behavioral
Signal Annotation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126
Anna Aniszewska-Stępień, Romain Hérault, Guillaume Hacques,
Ludovic Seifert, and Gilles Gasso
Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141