No Cover Image

E-Thesis 306 views

A machine learning and data-driven assessment model for characterising and predicting a teams success in championship football . / LEWIS EVANS

Swansea University Author: LEWIS EVANS

  • E-Thesis – open access under embargo until: 30th June 2028

Abstract

Measuring different styles of play (SoPs) that football teams can adopt and how they relate to their performance is crucial in analysing optimal play styles for the league in which the teams play. The first objective was to analyse all the matches in the English Second Division, the Championship, 20...

Full description

Published: Swansea, UK, Wales 2023
Institution: Swansea University
Degree level: Master of Research
Degree name: MRes
Supervisor: Brown, R
URI: https://cronfa.swan.ac.uk/Record/cronfa65461
first_indexed 2024-01-18T15:30:02Z
last_indexed 2024-11-25T14:16:07Z
id cronfa65461
recordtype RisThesis
fullrecord <?xml version="1.0"?><rfc1807><datestamp>2024-04-12T11:04:20.3614828</datestamp><bib-version>v2</bib-version><id>65461</id><entry>2024-01-18</entry><title>A machine learning and data-driven assessment model for characterising and predicting a teams success in championship football .</title><swanseaauthors><author><sid>c64433f8e7a636e9fc70e2df78accc92</sid><firstname>LEWIS</firstname><surname>EVANS</surname><name>LEWIS EVANS</name><active>true</active><ethesisStudent>false</ethesisStudent></author></swanseaauthors><date>2024-01-18</date><abstract>Measuring different styles of play (SoPs) that football teams can adopt and how they relate to their performance is crucial in analysing optimal play styles for the league in which the teams play. The first objective was to analyse all the matches in the English Second Division, the Championship, 2021/2022 season, to identify SoPs based on seven separate groups of metrics. Front Foot Defending, Defending the Goal, Build Up, Attacking the Goal, Physical Totals, Physicals In Possession and Physicals Out of Possession. Using Principal Component Analysis and K-Means Clustering, each team was clustered for each category of metrics. The Dixon-Coles Time Decay model was replicated for the second objective using match results from the 2018/2019 to 2021/2022 Championship seasons. This was then improved using StatsBomb&#x2019;s Expected Goals model for the 2021/2022 Championship season, which created a rating for all teams within the league. Combining these allowed for the identification of the Championship being a demanding league with high possession and high pressing teams with good physical outputs performing the best. Most other teams showed a variety of SoPs with varying success. The applications of this study are (1) 48 performance indicators served to identify styles of play and can be used to assist with opposition analysis and in identifying a player's current style of play when analysed by the recruitment department; (2) the Dixon-Coles expected goals model has assisted in analysing a team&#x2019;s quality which will assist opposition analysis and analysis of the current level a player is playing at; and finally; (3) teams can analyse what makes up the optimal style of play and adjust their style of play to improve results by copying other teams of a similar level who have better performances.</abstract><type>E-Thesis</type><journal/><volume/><journalNumber/><paginationStart/><paginationEnd/><publisher/><placeOfPublication>Swansea, UK, Wales</placeOfPublication><isbnPrint/><isbnElectronic/><issnPrint/><issnElectronic/><keywords>Sports Science</keywords><publishedDay>30</publishedDay><publishedMonth>6</publishedMonth><publishedYear>2023</publishedYear><publishedDate>2023-06-30</publishedDate><doi/><url/><notes>Part of this thesis has been redacted to protect personal information</notes><college>COLLEGE NANME</college><CollegeCode>COLLEGE CODE</CollegeCode><institution>Swansea University</institution><supervisor>Brown, R</supervisor><degreelevel>Master of Research</degreelevel><degreename>MRes</degreename><degreesponsorsfunders>Swansea City Football Club</degreesponsorsfunders><apcterm/><funders>Swansea City Football Club</funders><projectreference/><lastEdited>2024-04-12T11:04:20.3614828</lastEdited><Created>2024-01-18T13:14:29.5765250</Created><path><level id="1">Faculty of Science and Engineering</level><level id="2">School of Engineering and Applied Sciences - Sport and Exercise Sciences</level></path><authors><author><firstname>LEWIS</firstname><surname>EVANS</surname><order>1</order></author></authors><documents><document><filename>Under embargo</filename><originalFilename>Under embargo</originalFilename><uploaded>2024-01-18T13:30:28.3835128</uploaded><type>Output</type><contentLength>2731389</contentLength><contentType>application/pdf</contentType><version>E-Thesis &#x2013; open access</version><cronfaStatus>true</cronfaStatus><embargoDate>2028-06-30T00:00:00.0000000</embargoDate><documentNotes>Distributed under the terms of a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0). Copyright: The author, Lewis Evans, 2024.</documentNotes><copyrightCorrect>true</copyrightCorrect><language>eng</language><licence>https://creativecommons.org/licenses/by-nc-sa/4.0/deed.en</licence></document></documents><OutputDurs/></rfc1807>
spelling 2024-04-12T11:04:20.3614828 v2 65461 2024-01-18 A machine learning and data-driven assessment model for characterising and predicting a teams success in championship football . c64433f8e7a636e9fc70e2df78accc92 LEWIS EVANS LEWIS EVANS true false 2024-01-18 Measuring different styles of play (SoPs) that football teams can adopt and how they relate to their performance is crucial in analysing optimal play styles for the league in which the teams play. The first objective was to analyse all the matches in the English Second Division, the Championship, 2021/2022 season, to identify SoPs based on seven separate groups of metrics. Front Foot Defending, Defending the Goal, Build Up, Attacking the Goal, Physical Totals, Physicals In Possession and Physicals Out of Possession. Using Principal Component Analysis and K-Means Clustering, each team was clustered for each category of metrics. The Dixon-Coles Time Decay model was replicated for the second objective using match results from the 2018/2019 to 2021/2022 Championship seasons. This was then improved using StatsBomb’s Expected Goals model for the 2021/2022 Championship season, which created a rating for all teams within the league. Combining these allowed for the identification of the Championship being a demanding league with high possession and high pressing teams with good physical outputs performing the best. Most other teams showed a variety of SoPs with varying success. The applications of this study are (1) 48 performance indicators served to identify styles of play and can be used to assist with opposition analysis and in identifying a player's current style of play when analysed by the recruitment department; (2) the Dixon-Coles expected goals model has assisted in analysing a team’s quality which will assist opposition analysis and analysis of the current level a player is playing at; and finally; (3) teams can analyse what makes up the optimal style of play and adjust their style of play to improve results by copying other teams of a similar level who have better performances. E-Thesis Swansea, UK, Wales Sports Science 30 6 2023 2023-06-30 Part of this thesis has been redacted to protect personal information COLLEGE NANME COLLEGE CODE Swansea University Brown, R Master of Research MRes Swansea City Football Club Swansea City Football Club 2024-04-12T11:04:20.3614828 2024-01-18T13:14:29.5765250 Faculty of Science and Engineering School of Engineering and Applied Sciences - Sport and Exercise Sciences LEWIS EVANS 1 Under embargo Under embargo 2024-01-18T13:30:28.3835128 Output 2731389 application/pdf E-Thesis – open access true 2028-06-30T00:00:00.0000000 Distributed under the terms of a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0). Copyright: The author, Lewis Evans, 2024. true eng https://creativecommons.org/licenses/by-nc-sa/4.0/deed.en
title A machine learning and data-driven assessment model for characterising and predicting a teams success in championship football .
spellingShingle A machine learning and data-driven assessment model for characterising and predicting a teams success in championship football .
LEWIS EVANS
title_short A machine learning and data-driven assessment model for characterising and predicting a teams success in championship football .
title_full A machine learning and data-driven assessment model for characterising and predicting a teams success in championship football .
title_fullStr A machine learning and data-driven assessment model for characterising and predicting a teams success in championship football .
title_full_unstemmed A machine learning and data-driven assessment model for characterising and predicting a teams success in championship football .
title_sort A machine learning and data-driven assessment model for characterising and predicting a teams success in championship football .
author_id_str_mv c64433f8e7a636e9fc70e2df78accc92
author_id_fullname_str_mv c64433f8e7a636e9fc70e2df78accc92_***_LEWIS EVANS
author LEWIS EVANS
author2 LEWIS EVANS
format E-Thesis
publishDate 2023
institution Swansea University
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Engineering and Applied Sciences - Sport and Exercise Sciences{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Engineering and Applied Sciences - Sport and Exercise Sciences
document_store_str 0
active_str 0
description Measuring different styles of play (SoPs) that football teams can adopt and how they relate to their performance is crucial in analysing optimal play styles for the league in which the teams play. The first objective was to analyse all the matches in the English Second Division, the Championship, 2021/2022 season, to identify SoPs based on seven separate groups of metrics. Front Foot Defending, Defending the Goal, Build Up, Attacking the Goal, Physical Totals, Physicals In Possession and Physicals Out of Possession. Using Principal Component Analysis and K-Means Clustering, each team was clustered for each category of metrics. The Dixon-Coles Time Decay model was replicated for the second objective using match results from the 2018/2019 to 2021/2022 Championship seasons. This was then improved using StatsBomb’s Expected Goals model for the 2021/2022 Championship season, which created a rating for all teams within the league. Combining these allowed for the identification of the Championship being a demanding league with high possession and high pressing teams with good physical outputs performing the best. Most other teams showed a variety of SoPs with varying success. The applications of this study are (1) 48 performance indicators served to identify styles of play and can be used to assist with opposition analysis and in identifying a player's current style of play when analysed by the recruitment department; (2) the Dixon-Coles expected goals model has assisted in analysing a team’s quality which will assist opposition analysis and analysis of the current level a player is playing at; and finally; (3) teams can analyse what makes up the optimal style of play and adjust their style of play to improve results by copying other teams of a similar level who have better performances.
published_date 2023-06-30T08:27:35Z
_version_ 1821393347938353152
score 11.04748

Similar Items