Cargando…
In-play forecasting in football using event and positional data
Two highly relevant aspects of football, namely forecasting of results and performance analysis by means of performance indicators, are combined in the present study by analysing the value of in-play information in terms of event and positional data in forecasting the further course of football matc...
Autores principales: | , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8683419/ https://www.ncbi.nlm.nih.gov/pubmed/34921155 http://dx.doi.org/10.1038/s41598-021-03157-3 |
_version_ | 1784617414358466560 |
---|---|
author | Klemp, Maximilian Wunderlich, Fabian Memmert, Daniel |
author_facet | Klemp, Maximilian Wunderlich, Fabian Memmert, Daniel |
author_sort | Klemp, Maximilian |
collection | PubMed |
description | Two highly relevant aspects of football, namely forecasting of results and performance analysis by means of performance indicators, are combined in the present study by analysing the value of in-play information in terms of event and positional data in forecasting the further course of football matches. Event and positional data from 50 matches, including more than 300 million datapoints were used to extract a total of 18 performance indicators. Moreover, goals from more than 30,000 additional matches have been analysed. Results suggest that surprisingly goals do not possess any relevant informative value on the further course of a match, if controlling for pre-game market expectation by means of betting odds. Performance indicators based on event and positional data have been shown to possess more informative value than goals, but still are not sufficient to reveal significant predictive value in-play. The present results are relevant to match analysts and bookmakers who should not overestimate the value of in-play information when explaining match performance or compiling in-play betting odds. Moreover, the framework presented in the present study has methodological implications for performance analysis in football, as it suggests that researchers should increasingly segment matches by scoreline and control carefully for general team strength. |
format | Online Article Text |
id | pubmed-8683419 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-86834192021-12-20 In-play forecasting in football using event and positional data Klemp, Maximilian Wunderlich, Fabian Memmert, Daniel Sci Rep Article Two highly relevant aspects of football, namely forecasting of results and performance analysis by means of performance indicators, are combined in the present study by analysing the value of in-play information in terms of event and positional data in forecasting the further course of football matches. Event and positional data from 50 matches, including more than 300 million datapoints were used to extract a total of 18 performance indicators. Moreover, goals from more than 30,000 additional matches have been analysed. Results suggest that surprisingly goals do not possess any relevant informative value on the further course of a match, if controlling for pre-game market expectation by means of betting odds. Performance indicators based on event and positional data have been shown to possess more informative value than goals, but still are not sufficient to reveal significant predictive value in-play. The present results are relevant to match analysts and bookmakers who should not overestimate the value of in-play information when explaining match performance or compiling in-play betting odds. Moreover, the framework presented in the present study has methodological implications for performance analysis in football, as it suggests that researchers should increasingly segment matches by scoreline and control carefully for general team strength. Nature Publishing Group UK 2021-12-17 /pmc/articles/PMC8683419/ /pubmed/34921155 http://dx.doi.org/10.1038/s41598-021-03157-3 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Klemp, Maximilian Wunderlich, Fabian Memmert, Daniel In-play forecasting in football using event and positional data |
title | In-play forecasting in football using event and positional data |
title_full | In-play forecasting in football using event and positional data |
title_fullStr | In-play forecasting in football using event and positional data |
title_full_unstemmed | In-play forecasting in football using event and positional data |
title_short | In-play forecasting in football using event and positional data |
title_sort | in-play forecasting in football using event and positional data |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8683419/ https://www.ncbi.nlm.nih.gov/pubmed/34921155 http://dx.doi.org/10.1038/s41598-021-03157-3 |
work_keys_str_mv | AT klempmaximilian inplayforecastinginfootballusingeventandpositionaldata AT wunderlichfabian inplayforecastinginfootballusingeventandpositionaldata AT memmertdaniel inplayforecastinginfootballusingeventandpositionaldata |