McHale, IG and Relton, SD orcid.org/0000-0003-0634-4587 (2018) Identifying key players in soccer teams using network analysis and pass difficulty. European Journal of Operational Research, 268 (1). pp. 339-347. ISSN 0377-2217
Abstract
We use a unique dataset to identify the key members of a football team. The methodology uses a statistical model to determine the difficulty of a pass from one player to another, and combines this information with results from network analysis, to identify which players are pivotal to each team in the English Premier League during the 2012–13 season. We demonstrate the methodology by looking closely at one game, whilst also summarising player performance for each team over the entire season. The analysis is hoped to be of use to managers and coaches in identifying the best team lineup, and in the analysis of opposition teams to identify their key players.
Metadata
Item Type: | Article |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | Copyright (c) 2018 Elsevier B. V. All rights reserved. This is an author produced version of a paper published in European Journal of Operational Research. Uploaded in accordance with the publisher's self-archiving policyCrown Copyright © 2019 Published by Elsevier B.V. This is an author produced version of a paper published in Journal of Hydrology. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Sport; Big data; Football; Moneyball; Random effects |
Dates: |
|
Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Medicine and Health (Leeds) > School of Medicine (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 15 Aug 2018 08:12 |
Last Modified: | 31 Jan 2020 01:38 |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.ejor.2018.01.018 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:134600 |