Data-driven team ranking and match performance analysis in Chinese Football Super League
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo |
Idioma: | por |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10174/28251 https://doi.org/Li, Y., Ma, R., Gonçalves, B., Gong, B., Cui, Y., & Shen, Y. (2020). Data-driven team ranking and match performance analysis in Chinese Football Super League. Chaos, Solitons & Fractals, 141, 110330. doi:https://doi.org/10.1016/j.chaos.2020.110330 https://doi.org/10.1016/j.chaos.2020.110330 |
Resumo: | Recent years have seen an increasing body of research into the evaluation of the team-level technical- tactical performance in association football using match events data. However, most studies used mono-dimensional approach and modeled the influence of each performance aspects on match result in iso- lation, which limited the interpretability of the results. The study was aimed to apply a state-of-the-art algorithm to the ranking of team performance and exploitation of key performance features in relation to match outcome based on massive match dataset. Data of all 1200 matches from 2014 to 2018 Chinese Football Super League (CSL) were used. From the original 164 match events, we extracted 22 features that were related to attacking, passing, and defending performance and most. A Linear Support Vector Classi- fier (LSVC) model was subsequently built with these 22 input features and trained in order to rank the teams by their performance and analyze the features that influence most match outcome (win/not win), with the dataset being divided into a ratio of 4:1 to train and validate the model. The results have shown that the data-driven LSVC model displayed a prediction accuracy of 0.83 and the ranking of teams’ match performance and prediction of teams’ league standings were highly correlated with their actual rank- ing. Saves, pass success and shot on target in penalty area were demonstrated as top positive features for winning whereas shots on target during open play, pass and bad shot% were three negative features most influential for the match result. The team ranks of all teams were highly correlated with their real final league rankings. In general, CSL winning teams build their success based on defensive ability and shooting accuracy, and high-ranked teams could always maintain better performance than their coun- terparts. The team-rank framework could provide a consolidated and complex approach to evaluate the match performance quality of the teams, refining decisions-making during match preparation and player transfer at different periods of the season. |
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Data-driven team ranking and match performance analysis in Chinese Football Super LeagueSoccerMatch analysisRankingPredictive modelingBig dataRecent years have seen an increasing body of research into the evaluation of the team-level technical- tactical performance in association football using match events data. However, most studies used mono-dimensional approach and modeled the influence of each performance aspects on match result in iso- lation, which limited the interpretability of the results. The study was aimed to apply a state-of-the-art algorithm to the ranking of team performance and exploitation of key performance features in relation to match outcome based on massive match dataset. Data of all 1200 matches from 2014 to 2018 Chinese Football Super League (CSL) were used. From the original 164 match events, we extracted 22 features that were related to attacking, passing, and defending performance and most. A Linear Support Vector Classi- fier (LSVC) model was subsequently built with these 22 input features and trained in order to rank the teams by their performance and analyze the features that influence most match outcome (win/not win), with the dataset being divided into a ratio of 4:1 to train and validate the model. The results have shown that the data-driven LSVC model displayed a prediction accuracy of 0.83 and the ranking of teams’ match performance and prediction of teams’ league standings were highly correlated with their actual rank- ing. Saves, pass success and shot on target in penalty area were demonstrated as top positive features for winning whereas shots on target during open play, pass and bad shot% were three negative features most influential for the match result. The team ranks of all teams were highly correlated with their real final league rankings. In general, CSL winning teams build their success based on defensive ability and shooting accuracy, and high-ranked teams could always maintain better performance than their coun- terparts. The team-rank framework could provide a consolidated and complex approach to evaluate the match performance quality of the teams, refining decisions-making during match preparation and player transfer at different periods of the season.Chaos, Solitons & Fractals2020-11-02T15:36:42Z2020-11-022020-10-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://hdl.handle.net/10174/28251https://doi.org/Li, Y., Ma, R., Gonçalves, B., Gong, B., Cui, Y., & Shen, Y. (2020). Data-driven team ranking and match performance analysis in Chinese Football Super League. Chaos, Solitons & Fractals, 141, 110330. doi:https://doi.org/10.1016/j.chaos.2020.110330http://hdl.handle.net/10174/28251https://doi.org/10.1016/j.chaos.2020.110330porhttps://www.sciencedirect.com/science/article/pii/S0960077920307256ndndbgoncalves@uevora.ptndndnd391Li, YuesenMaa, RunqingGonçalves, BrunoGong, BingnanCui, YixiongShen, Yanfeiinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-01-03T19:24:22Zoai:dspace.uevora.pt:10174/28251Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T01:18:08.762736Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Data-driven team ranking and match performance analysis in Chinese Football Super League |
title |
Data-driven team ranking and match performance analysis in Chinese Football Super League |
spellingShingle |
Data-driven team ranking and match performance analysis in Chinese Football Super League Li, Yuesen Soccer Match analysis Ranking Predictive modeling Big data |
title_short |
Data-driven team ranking and match performance analysis in Chinese Football Super League |
title_full |
Data-driven team ranking and match performance analysis in Chinese Football Super League |
title_fullStr |
Data-driven team ranking and match performance analysis in Chinese Football Super League |
title_full_unstemmed |
Data-driven team ranking and match performance analysis in Chinese Football Super League |
title_sort |
Data-driven team ranking and match performance analysis in Chinese Football Super League |
author |
Li, Yuesen |
author_facet |
Li, Yuesen Maa, Runqing Gonçalves, Bruno Gong, Bingnan Cui, Yixiong Shen, Yanfei |
author_role |
author |
author2 |
Maa, Runqing Gonçalves, Bruno Gong, Bingnan Cui, Yixiong Shen, Yanfei |
author2_role |
author author author author author |
dc.contributor.author.fl_str_mv |
Li, Yuesen Maa, Runqing Gonçalves, Bruno Gong, Bingnan Cui, Yixiong Shen, Yanfei |
dc.subject.por.fl_str_mv |
Soccer Match analysis Ranking Predictive modeling Big data |
topic |
Soccer Match analysis Ranking Predictive modeling Big data |
description |
Recent years have seen an increasing body of research into the evaluation of the team-level technical- tactical performance in association football using match events data. However, most studies used mono-dimensional approach and modeled the influence of each performance aspects on match result in iso- lation, which limited the interpretability of the results. The study was aimed to apply a state-of-the-art algorithm to the ranking of team performance and exploitation of key performance features in relation to match outcome based on massive match dataset. Data of all 1200 matches from 2014 to 2018 Chinese Football Super League (CSL) were used. From the original 164 match events, we extracted 22 features that were related to attacking, passing, and defending performance and most. A Linear Support Vector Classi- fier (LSVC) model was subsequently built with these 22 input features and trained in order to rank the teams by their performance and analyze the features that influence most match outcome (win/not win), with the dataset being divided into a ratio of 4:1 to train and validate the model. The results have shown that the data-driven LSVC model displayed a prediction accuracy of 0.83 and the ranking of teams’ match performance and prediction of teams’ league standings were highly correlated with their actual rank- ing. Saves, pass success and shot on target in penalty area were demonstrated as top positive features for winning whereas shots on target during open play, pass and bad shot% were three negative features most influential for the match result. The team ranks of all teams were highly correlated with their real final league rankings. In general, CSL winning teams build their success based on defensive ability and shooting accuracy, and high-ranked teams could always maintain better performance than their coun- terparts. The team-rank framework could provide a consolidated and complex approach to evaluate the match performance quality of the teams, refining decisions-making during match preparation and player transfer at different periods of the season. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-11-02T15:36:42Z 2020-11-02 2020-10-01T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10174/28251 https://doi.org/Li, Y., Ma, R., Gonçalves, B., Gong, B., Cui, Y., & Shen, Y. (2020). Data-driven team ranking and match performance analysis in Chinese Football Super League. Chaos, Solitons & Fractals, 141, 110330. doi:https://doi.org/10.1016/j.chaos.2020.110330 http://hdl.handle.net/10174/28251 https://doi.org/10.1016/j.chaos.2020.110330 |
url |
http://hdl.handle.net/10174/28251 https://doi.org/Li, Y., Ma, R., Gonçalves, B., Gong, B., Cui, Y., & Shen, Y. (2020). Data-driven team ranking and match performance analysis in Chinese Football Super League. Chaos, Solitons & Fractals, 141, 110330. doi:https://doi.org/10.1016/j.chaos.2020.110330 https://doi.org/10.1016/j.chaos.2020.110330 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.relation.none.fl_str_mv |
https://www.sciencedirect.com/science/article/pii/S0960077920307256 nd nd bgoncalves@uevora.pt nd nd nd 391 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.publisher.none.fl_str_mv |
Chaos, Solitons & Fractals |
publisher.none.fl_str_mv |
Chaos, Solitons & Fractals |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
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RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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1799136663471915008 |