SoccerNet: A Gated Recurrent Unit-based model to predict soccer match winners
<p dir="ltr">Winning football matches is the major goal of all football clubs in the world. Football being the most popular game in the world, many studies have been conducted to analyze and predict match winners based on players’ physical and technical performance. In this study, we...
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2023
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| _version_ | 1864513512409137152 |
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| author | Jassim AlMulla (16726296) |
| author2 | Mohammad Tariqul Islam (7854059) Hamada R. H. Al-Absi (16726299) Tanvir Alam (638619) |
| author2_role | author author author |
| author_facet | Jassim AlMulla (16726296) Mohammad Tariqul Islam (7854059) Hamada R. H. Al-Absi (16726299) Tanvir Alam (638619) |
| author_role | author |
| dc.creator.none.fl_str_mv | Jassim AlMulla (16726296) Mohammad Tariqul Islam (7854059) Hamada R. H. Al-Absi (16726299) Tanvir Alam (638619) |
| dc.date.none.fl_str_mv | 2023-08-01T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0288933 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/SoccerNet_A_Gated_Recurrent_Unit-based_model_to_predict_soccer_match_winners/26021131 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Health sciences Sports science and exercise Information and computing sciences Data management and data science Machine learning Mathematical sciences Statistics Sports Machine learning Qatar Neural networks Decision tree learning Forecasting Neurons |
| dc.title.none.fl_str_mv | SoccerNet: A Gated Recurrent Unit-based model to predict soccer match winners |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Winning football matches is the major goal of all football clubs in the world. Football being the most popular game in the world, many studies have been conducted to analyze and predict match winners based on players’ physical and technical performance. In this study, we analyzed the matches from the professional football league of Qatar Stars League (QSL) covering the matches held in the last ten seasons. We incorporated the highest number of professional matches from the last ten seasons covering from 2011 up to 2022 and proposed SoccerNet, a Gated Recurrent Unit (GRU)-based deep learning-based model to predict match winners with over 80% accuracy. We considered match- and player-related information captured by STATS platform in a time slot of 15 minutes. Then we analyzed players’ performance at different positions on the field at different stages of the match. Our results indicated that in QSL, the defenders’ role in matches is more dominant than midfielders and forwarders. Moreover, our analysis suggests that the last 15–30 minutes of match segments of the matches from QSL have a more significant impact on the match result than other match segments. To the best of our knowledge, the proposed model is the first DL-based model in predicting match winners from any professional football leagues in the Middle East and North Africa (MENA) region. We believe the results will support the coaching staff and team management for QSL in designing game strategies and improve the overall quality of performance of the players.</p><h2>Other Information</h2><p dir="ltr">Published in: PLOS ONE<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1371/journal.pone.0288933" target="_blank">https://dx.doi.org/10.1371/journal.pone.0288933</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_4b6abba4741109357ec04aa501ef5f6d |
| identifier_str_mv | 10.1371/journal.pone.0288933 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/26021131 |
| publishDate | 2023 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | SoccerNet: A Gated Recurrent Unit-based model to predict soccer match winnersJassim AlMulla (16726296)Mohammad Tariqul Islam (7854059)Hamada R. H. Al-Absi (16726299)Tanvir Alam (638619)Health sciencesSports science and exerciseInformation and computing sciencesData management and data scienceMachine learningMathematical sciencesStatisticsSportsMachine learningQatarNeural networksDecision tree learningForecastingNeurons<p dir="ltr">Winning football matches is the major goal of all football clubs in the world. Football being the most popular game in the world, many studies have been conducted to analyze and predict match winners based on players’ physical and technical performance. In this study, we analyzed the matches from the professional football league of Qatar Stars League (QSL) covering the matches held in the last ten seasons. We incorporated the highest number of professional matches from the last ten seasons covering from 2011 up to 2022 and proposed SoccerNet, a Gated Recurrent Unit (GRU)-based deep learning-based model to predict match winners with over 80% accuracy. We considered match- and player-related information captured by STATS platform in a time slot of 15 minutes. Then we analyzed players’ performance at different positions on the field at different stages of the match. Our results indicated that in QSL, the defenders’ role in matches is more dominant than midfielders and forwarders. Moreover, our analysis suggests that the last 15–30 minutes of match segments of the matches from QSL have a more significant impact on the match result than other match segments. To the best of our knowledge, the proposed model is the first DL-based model in predicting match winners from any professional football leagues in the Middle East and North Africa (MENA) region. We believe the results will support the coaching staff and team management for QSL in designing game strategies and improve the overall quality of performance of the players.</p><h2>Other Information</h2><p dir="ltr">Published in: PLOS ONE<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1371/journal.pone.0288933" target="_blank">https://dx.doi.org/10.1371/journal.pone.0288933</a></p>2023-08-01T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1371/journal.pone.0288933https://figshare.com/articles/journal_contribution/SoccerNet_A_Gated_Recurrent_Unit-based_model_to_predict_soccer_match_winners/26021131CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/260211312023-08-01T00:00:00Z |
| spellingShingle | SoccerNet: A Gated Recurrent Unit-based model to predict soccer match winners Jassim AlMulla (16726296) Health sciences Sports science and exercise Information and computing sciences Data management and data science Machine learning Mathematical sciences Statistics Sports Machine learning Qatar Neural networks Decision tree learning Forecasting Neurons |
| status_str | publishedVersion |
| title | SoccerNet: A Gated Recurrent Unit-based model to predict soccer match winners |
| title_full | SoccerNet: A Gated Recurrent Unit-based model to predict soccer match winners |
| title_fullStr | SoccerNet: A Gated Recurrent Unit-based model to predict soccer match winners |
| title_full_unstemmed | SoccerNet: A Gated Recurrent Unit-based model to predict soccer match winners |
| title_short | SoccerNet: A Gated Recurrent Unit-based model to predict soccer match winners |
| title_sort | SoccerNet: A Gated Recurrent Unit-based model to predict soccer match winners |
| topic | Health sciences Sports science and exercise Information and computing sciences Data management and data science Machine learning Mathematical sciences Statistics Sports Machine learning Qatar Neural networks Decision tree learning Forecasting Neurons |