Expected Pass Turnovers (xPT) - a model to analyse turnovers from passing events in football

Article

Peters, A., Parmar, N., Davies, M., Reeves, M., Sormaz, M. and James, N. 2024. Expected Pass Turnovers (xPT) - a model to analyse turnovers from passing events in football. Journal of Sports Sciences. https://doi.org/10.1080/02640414.2024.2379697
TypeArticle
TitleExpected Pass Turnovers (xPT) - a model to analyse turnovers from passing events in football
AuthorsPeters, A., Parmar, N., Davies, M., Reeves, M., Sormaz, M. and James, N.
Abstract

The aim of this study was to create a novel metric, Expected Pass Turnovers (xPT), that could evaluate possession retention from player passing events in football. Event and positional data were analysed from all 380 matches in the 2020-21 English Premier League season, which encompassed 256,433 passes in the final dataset. A logistic mixed effects model was implemented to attribute the probability of each pass getting turned over. The use of positional data enabled the identification of a) opposition players present in radii surrounding the ball carrier and b) availability of teammates with respect to the ball carrier. The addition of these positional features improved the accuracy (+ 6.1 AUC Score) of the model. xPT serves as a practitioner Key Performance Indicator, as analysts can identify players that lose possession more often or not than expected, given the situational context of each pass, from game to game. Future work may include modelling the turnover probability of dribble and carry actions, as this would lead to a more comprehensive understanding of turnover events in football.

KeywordsExpected pass turnovers; logistic regression; football metric
Sustainable Development Goals3 Good health and well-being
Middlesex University ThemeHealth & Wellbeing
Research GroupPerformance Analysis at the London Sport Institute
PublisherTaylor & Francis (Routledge)
JournalJournal of Sports Sciences
ISSN0264-0414
Electronic1466-447X
Publication dates
Online22 Jul 2024
Publication process dates
Submitted24 Apr 2023
Accepted05 Jul 2024
Deposited23 Jul 2024
Output statusPublished
Publisher's version
License
File Access Level
Open
Copyright Statement

© 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent.

Digital Object Identifier (DOI)https://doi.org/10.1080/02640414.2024.2379697
Web of Science identifierMEDLINE:39036961
WOS:001273906100001
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