Process Mining of Football Event Data: A Novel Approach for Tactical Insights Into the Game
The paper explores process mining and its usefulness for analyzing football event data. We work with professional event data provided by OPTA Sports from the European Championship in 2016. We analyze one game of a favorite team (England) against an underdog team (Iceland). The success of the underdo...
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2020
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ftdoajarticles:oai:doaj.org/article:138fc278054b4eeb85b7f9a6f2c6f16e 2023-05-15T16:50:21+02:00 Process Mining of Football Event Data: A Novel Approach for Tactical Insights Into the Game Pavlina Kröckel Freimut Bodendorf 2020-07-01T00:00:00Z https://doi.org/10.3389/frai.2020.00047 https://doaj.org/article/138fc278054b4eeb85b7f9a6f2c6f16e EN eng Frontiers Media S.A. https://www.frontiersin.org/article/10.3389/frai.2020.00047/full https://doaj.org/toc/2624-8212 2624-8212 doi:10.3389/frai.2020.00047 https://doaj.org/article/138fc278054b4eeb85b7f9a6f2c6f16e Frontiers in Artificial Intelligence, Vol 3 (2020) football soccer process mining sports analytics tactics Electronic computers. Computer science QA75.5-76.95 article 2020 ftdoajarticles https://doi.org/10.3389/frai.2020.00047 2022-12-31T11:44:38Z The paper explores process mining and its usefulness for analyzing football event data. We work with professional event data provided by OPTA Sports from the European Championship in 2016. We analyze one game of a favorite team (England) against an underdog team (Iceland). The success of the underdog teams in the Euro 2016 was remarkable, and it is what made the event special. For this reason, it is interesting to compare the performance of a favorite and an underdog team by applying process mining. The goal is to show the options that these types of algorithms and visual analytics offer for the interpretation of event data in football and discuss how the gained insights can support decision makers not only in pre- and post-match analysis but also during live games as well. We show process mining techniques which can be used to gain team or individual player insights by considering the types of actions, the sequence of actions, and the order of player involvement in each sequence. Finally, we also demonstrate the detection of typical or unusual behavior by trace and sequence clustering. Article in Journal/Newspaper Iceland Directory of Open Access Journals: DOAJ Articles Frontiers in Artificial Intelligence 3 |
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Directory of Open Access Journals: DOAJ Articles |
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ftdoajarticles |
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English |
topic |
football soccer process mining sports analytics tactics Electronic computers. Computer science QA75.5-76.95 |
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football soccer process mining sports analytics tactics Electronic computers. Computer science QA75.5-76.95 Pavlina Kröckel Freimut Bodendorf Process Mining of Football Event Data: A Novel Approach for Tactical Insights Into the Game |
topic_facet |
football soccer process mining sports analytics tactics Electronic computers. Computer science QA75.5-76.95 |
description |
The paper explores process mining and its usefulness for analyzing football event data. We work with professional event data provided by OPTA Sports from the European Championship in 2016. We analyze one game of a favorite team (England) against an underdog team (Iceland). The success of the underdog teams in the Euro 2016 was remarkable, and it is what made the event special. For this reason, it is interesting to compare the performance of a favorite and an underdog team by applying process mining. The goal is to show the options that these types of algorithms and visual analytics offer for the interpretation of event data in football and discuss how the gained insights can support decision makers not only in pre- and post-match analysis but also during live games as well. We show process mining techniques which can be used to gain team or individual player insights by considering the types of actions, the sequence of actions, and the order of player involvement in each sequence. Finally, we also demonstrate the detection of typical or unusual behavior by trace and sequence clustering. |
format |
Article in Journal/Newspaper |
author |
Pavlina Kröckel Freimut Bodendorf |
author_facet |
Pavlina Kröckel Freimut Bodendorf |
author_sort |
Pavlina Kröckel |
title |
Process Mining of Football Event Data: A Novel Approach for Tactical Insights Into the Game |
title_short |
Process Mining of Football Event Data: A Novel Approach for Tactical Insights Into the Game |
title_full |
Process Mining of Football Event Data: A Novel Approach for Tactical Insights Into the Game |
title_fullStr |
Process Mining of Football Event Data: A Novel Approach for Tactical Insights Into the Game |
title_full_unstemmed |
Process Mining of Football Event Data: A Novel Approach for Tactical Insights Into the Game |
title_sort |
process mining of football event data: a novel approach for tactical insights into the game |
publisher |
Frontiers Media S.A. |
publishDate |
2020 |
url |
https://doi.org/10.3389/frai.2020.00047 https://doaj.org/article/138fc278054b4eeb85b7f9a6f2c6f16e |
genre |
Iceland |
genre_facet |
Iceland |
op_source |
Frontiers in Artificial Intelligence, Vol 3 (2020) |
op_relation |
https://www.frontiersin.org/article/10.3389/frai.2020.00047/full https://doaj.org/toc/2624-8212 2624-8212 doi:10.3389/frai.2020.00047 https://doaj.org/article/138fc278054b4eeb85b7f9a6f2c6f16e |
op_doi |
https://doi.org/10.3389/frai.2020.00047 |
container_title |
Frontiers in Artificial Intelligence |
container_volume |
3 |
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1766040513333428224 |