A Random Forest approach to identify metrics that best predict match outcome and player ranking in the esport Rocket League

Abstract Notational analysis is a popular tool for understanding what constitutes optimal performance in traditional sports. However, this approach has been seldom used in esports. The popular esport “Rocket League” is an ideal candidate for notational analysis due to the availability of an online r...

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Autores principales:	Tim D. Smithies, Mark J. Campbell, Niall Ramsbottom, Adam J. Toth
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/ffddef81c8494bf29df3342eb3b5a2d2
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spelling	oai:doaj.org-article:ffddef81c8494bf29df3342eb3b5a2d22021-12-02T18:51:29ZA Random Forest approach to identify metrics that best predict match outcome and player ranking in the esport Rocket League10.1038/s41598-021-98879-92045-2322https://doaj.org/article/ffddef81c8494bf29df3342eb3b5a2d22021-09-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-98879-9https://doaj.org/toc/2045-2322Abstract Notational analysis is a popular tool for understanding what constitutes optimal performance in traditional sports. However, this approach has been seldom used in esports. The popular esport “Rocket League” is an ideal candidate for notational analysis due to the availability of an online repository containing data from millions of matches. The purpose of this study was to use Random Forest models to identify in-match metrics that predicted match outcome (performance indicators or “PIs”) and/or in-game player rank (rank indicators or “RIs”). We evaluated match data from 21,588 Rocket League matches involving players from four different ranks. Upon identifying goal difference (GD) as a suitable outcome measure for Rocket League match performance, Random Forest models were used alongside accompanying variable importance methods to identify metrics that were PIs or RIs. We found shots taken, shots conceded, saves made, and time spent goalside of the ball to be the most important PIs, and time spent at supersonic speed, time spent on the ground, shots conceded and time spent goalside of the ball to be the most important RIs. This work is the first to use Random Forest learning algorithms to highlight the most critical PIs and RIs in a prominent esport.Tim D. SmithiesMark J. CampbellNiall RamsbottomAdam J. TothNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Tim D. Smithies Mark J. Campbell Niall Ramsbottom Adam J. Toth A Random Forest approach to identify metrics that best predict match outcome and player ranking in the esport Rocket League
description	Abstract Notational analysis is a popular tool for understanding what constitutes optimal performance in traditional sports. However, this approach has been seldom used in esports. The popular esport “Rocket League” is an ideal candidate for notational analysis due to the availability of an online repository containing data from millions of matches. The purpose of this study was to use Random Forest models to identify in-match metrics that predicted match outcome (performance indicators or “PIs”) and/or in-game player rank (rank indicators or “RIs”). We evaluated match data from 21,588 Rocket League matches involving players from four different ranks. Upon identifying goal difference (GD) as a suitable outcome measure for Rocket League match performance, Random Forest models were used alongside accompanying variable importance methods to identify metrics that were PIs or RIs. We found shots taken, shots conceded, saves made, and time spent goalside of the ball to be the most important PIs, and time spent at supersonic speed, time spent on the ground, shots conceded and time spent goalside of the ball to be the most important RIs. This work is the first to use Random Forest learning algorithms to highlight the most critical PIs and RIs in a prominent esport.
format	article
author	Tim D. Smithies Mark J. Campbell Niall Ramsbottom Adam J. Toth
author_facet	Tim D. Smithies Mark J. Campbell Niall Ramsbottom Adam J. Toth
author_sort	Tim D. Smithies
title	A Random Forest approach to identify metrics that best predict match outcome and player ranking in the esport Rocket League
title_short	A Random Forest approach to identify metrics that best predict match outcome and player ranking in the esport Rocket League
title_full	A Random Forest approach to identify metrics that best predict match outcome and player ranking in the esport Rocket League
title_fullStr	A Random Forest approach to identify metrics that best predict match outcome and player ranking in the esport Rocket League
title_full_unstemmed	A Random Forest approach to identify metrics that best predict match outcome and player ranking in the esport Rocket League
title_sort	random forest approach to identify metrics that best predict match outcome and player ranking in the esport rocket league
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/ffddef81c8494bf29df3342eb3b5a2d2
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A Random Forest approach to identify metrics that best predict match outcome and player ranking in the esport Rocket League

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