Analyzing Passing Sequences for the Prediction of Goal-Scoring Opportunities

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Abstract

Over the last years, more and more sport related data are being collected, stored, and analyzed to give valuable insights. Football is no exception to this trend. An important way of identifying a team’s “style” of play is through analyzing passing sequences. However, passing sequences either concentrate on the specific players involved or the structure of passes and ignore where these sequences took place. In this paper, we focus on identifying frequent passing zone subsequences that lead to created or conceded goal scoring opportunities. We partition the pitch into a set of disjoint zones and apply sequential pattern mining. Our experimental study on the 2020/21 Danish Superliga season shows that our method is able to predict goal scoring opportunities better than random subsequences that occurred, in median, 99.5% of the cases.

OriginalsprogEngelsk
TitelMachine Learning and Data Mining for Sports Analytics - 9th International Workshop, MLSA 2022, Revised Selected Papers
RedaktørerUlf Brefeld, Jesse Davis, Jan Van Haaren, Albrecht Zimmermann
ForlagSpringer Science+Business Media
Publikationsdato25. feb. 2023
Sider27-40
ISBN (Trykt)978-3-031-27526-5
ISBN (Elektronisk)978-3-031-27527-2
DOI
StatusUdgivet - 25. feb. 2023
Begivenhed9th International Workshop on Machine Learning and Data Mining for Sports Analytics, MLSA 2022, co-located with the 21st Joint European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 202 - Grenoble, Frankrig
Varighed: 19. sep. 202219. sep. 2022

Konference

Konference9th International Workshop on Machine Learning and Data Mining for Sports Analytics, MLSA 2022, co-located with the 21st Joint European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 202
Land/OmrådeFrankrig
ByGrenoble
Periode19/09/202219/09/2022
NavnCommunications in Computer and Information Science
Vol/bind1783 CCIS
ISSN1865-0929

Bibliografisk note

Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

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