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https://hdl.handle.net/1959.11/58436
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DC Field | Value | Language |
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dc.contributor.author | Adeyemo, Victor Elijah | en |
dc.contributor.author | Palczewska, Anna | en |
dc.contributor.author | Jones, Ben | en |
dc.contributor.author | Weaving, Dan | en |
dc.contributor.author | Whitehead, Sarah | en |
dc.date.accessioned | 2024-04-18T02:17:10Z | - |
dc.date.available | 2024-04-18T02:17:10Z | - |
dc.date.issued | 2024-01 | - |
dc.identifier.citation | Science and Medicine in Football, 8(1), p. 68-75 | en |
dc.identifier.issn | 2473-4446 | en |
dc.identifier.issn | 2473-3938 | en |
dc.identifier.uri | https://hdl.handle.net/1959.11/58436 | - |
dc.description.abstract | <p>Determining key performance indicators and classifying players accurately between competitive levels is one of the classification challenges in sports analytics. A recent study applied Random Forest algorithm to identify important variables to classify rugby league players into academy and senior levels and achieved 82.0% and 67.5% accuracy for backs and forwards. However, the classification accuracy could be improved due to limitations in the existing method. Therefore, this study aimed to introduce and implement feature selection technique to identify key performance indicators in rugby league positional groups and assess the performances of six classification algorithms. Fifteen and fourteen of 157 performance indicators for backs and forwards were identified respectively as key performance indicators by the correlation-based feature selection method, with seven common indicators between the positional groups. Classification results show that models developed using the key performance indicators had improved performance for both positional groups than models developed using all performance indica-tors. 5-Nearest Neighbour produced the best classification accuracy for backs and forwards (accuracy = 85% and 77%) which is higher than the previous method's accuracies. When analysing classification questions in sport science, researchers are encouraged to evaluate multiple classification algorithms and a feature selection method should be considered for identifying key variables.</p> | en |
dc.language | en | en |
dc.publisher | Taylor & Francis | en |
dc.relation.ispartof | Science and Medicine in Football | en |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 International | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.title | Optimising classification in sport: a replication study using physical and technical-tactical performance indicators to classify competitive levels in rugby league match-play | en |
dc.type | Journal Article | en |
dc.identifier.doi | 10.1080/24733938.2022.2146177 | en |
dcterms.accessRights | UNE Green | en |
dc.subject.keywords | feature selection | en |
dc.subject.keywords | rugby league | en |
dc.subject.keywords | machine learning | en |
dc.subject.keywords | Sport Sciences | en |
dc.subject.keywords | Performance analysis | en |
dc.subject.keywords | team sport | en |
local.contributor.firstname | Victor Elijah | en |
local.contributor.firstname | Anna | en |
local.contributor.firstname | Ben | en |
local.contributor.firstname | Dan | en |
local.contributor.firstname | Sarah | en |
local.profile.school | School of Science and Technology | en |
local.profile.email | bjones64@une.edu.au | en |
local.output.category | C1 | en |
local.record.place | au | en |
local.record.institution | University of New England | en |
local.publisher.place | United Kingdom | en |
local.format.startpage | 68 | en |
local.format.endpage | 75 | en |
local.peerreviewed | Yes | en |
local.identifier.volume | 8 | en |
local.identifier.issue | 1 | en |
local.title.subtitle | a replication study using physical and technical-tactical performance indicators to classify competitive levels in rugby league match-play | en |
local.access.fulltext | Yes | en |
local.contributor.lastname | Adeyemo | en |
local.contributor.lastname | Palczewska | en |
local.contributor.lastname | Jones | en |
local.contributor.lastname | Weaving | en |
local.contributor.lastname | Whitehead | en |
dc.identifier.staff | une-id:bjones64 | en |
local.profile.role | author | en |
local.profile.role | author | en |
local.profile.role | author | en |
local.profile.role | author | en |
local.profile.role | author | en |
local.identifier.unepublicationid | une:1959.11/58436 | en |
local.date.onlineversion | 2022-11-14 | - |
dc.identifier.academiclevel | Academic | en |
dc.identifier.academiclevel | Academic | en |
dc.identifier.academiclevel | Academic | en |
dc.identifier.academiclevel | Academic | en |
dc.identifier.academiclevel | Academic | en |
local.title.maintitle | Optimising classification in sport | en |
local.output.categorydescription | C1 Refereed Article in a Scholarly Journal | en |
local.search.author | Adeyemo, Victor Elijah | en |
local.search.author | Palczewska, Anna | en |
local.search.author | Jones, Ben | en |
local.search.author | Weaving, Dan | en |
local.search.author | Whitehead, Sarah | en |
local.open.fileurl | https://rune.une.edu.au/web/retrieve/cb308555-0af7-4058-a60c-924ffcaa93b5 | en |
local.uneassociation | Yes | en |
local.atsiresearch | No | en |
local.sensitive.cultural | No | en |
local.year.available | 2022 | en |
local.year.published | 2024 | en |
local.fileurl.open | https://rune.une.edu.au/web/retrieve/cb308555-0af7-4058-a60c-924ffcaa93b5 | en |
local.fileurl.openpublished | https://rune.une.edu.au/web/retrieve/cb308555-0af7-4058-a60c-924ffcaa93b5 | en |
local.subject.for2020 | 4207 Sports science and exercise | en |
local.profile.affiliationtype | External Affiliation | en |
local.profile.affiliationtype | External Affiliation | en |
local.profile.affiliationtype | UNE Affiliation | en |
local.profile.affiliationtype | External Affiliation | en |
local.profile.affiliationtype | External Affiliation | en |
Appears in Collections: | Journal Article School of Science and Technology |
Files in This Item:
File | Description | Size | Format | |
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openpublished/OptimisingJones2024JournalArticle.pdf | Published Version | 976.25 kB | Adobe PDF Download Adobe | View/Open |
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