NBA team home advantage: Identifying key factors using an artificial neural network.

What determines a team's home advantage, and why does it change with time? Is it something about the rowdiness of the hometown crowd? Is it something about the location of the team? Or is it something about the team itself, the quality of the team or the styles it may or may not play? To answer...

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Main Authors: Austin R Harris, Paul J Roebber
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2019-01-01
Series:PLoS ONE
Online Access:https://doi.org/10.1371/journal.pone.0220630
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spelling doaj-ffb571fe4313439d8675eb628eeef1992021-03-03T19:52:10ZengPublic Library of Science (PLoS)PLoS ONE1932-62032019-01-01147e022063010.1371/journal.pone.0220630NBA team home advantage: Identifying key factors using an artificial neural network.Austin R HarrisPaul J RoebberWhat determines a team's home advantage, and why does it change with time? Is it something about the rowdiness of the hometown crowd? Is it something about the location of the team? Or is it something about the team itself, the quality of the team or the styles it may or may not play? To answer these questions, season performance statistics were downloaded for all NBA teams across 32 seasons (83-84 to 17-18). Data were also obtained for other potential influences identified in the literature including: stadium attendance, altitude, and team market size. Using an artificial neural network, a team's home advantage was diagnosed using team performance statistics only. Attendance, altitude, and market size were unsuccessful at improving this diagnosis. The style of play is a key factor in the home advantage. Teams that make more two point and free-throw shots see larger advantages at home. Given the rise in three-point shooting in recent years, this finding partially explains the gradual decline in home advantage observed across the league over time.https://doi.org/10.1371/journal.pone.0220630
collection DOAJ
language English
format Article
sources DOAJ
author Austin R Harris
Paul J Roebber
spellingShingle Austin R Harris
Paul J Roebber
NBA team home advantage: Identifying key factors using an artificial neural network.
PLoS ONE
author_facet Austin R Harris
Paul J Roebber
author_sort Austin R Harris
title NBA team home advantage: Identifying key factors using an artificial neural network.
title_short NBA team home advantage: Identifying key factors using an artificial neural network.
title_full NBA team home advantage: Identifying key factors using an artificial neural network.
title_fullStr NBA team home advantage: Identifying key factors using an artificial neural network.
title_full_unstemmed NBA team home advantage: Identifying key factors using an artificial neural network.
title_sort nba team home advantage: identifying key factors using an artificial neural network.
publisher Public Library of Science (PLoS)
series PLoS ONE
issn 1932-6203
publishDate 2019-01-01
description What determines a team's home advantage, and why does it change with time? Is it something about the rowdiness of the hometown crowd? Is it something about the location of the team? Or is it something about the team itself, the quality of the team or the styles it may or may not play? To answer these questions, season performance statistics were downloaded for all NBA teams across 32 seasons (83-84 to 17-18). Data were also obtained for other potential influences identified in the literature including: stadium attendance, altitude, and team market size. Using an artificial neural network, a team's home advantage was diagnosed using team performance statistics only. Attendance, altitude, and market size were unsuccessful at improving this diagnosis. The style of play is a key factor in the home advantage. Teams that make more two point and free-throw shots see larger advantages at home. Given the rise in three-point shooting in recent years, this finding partially explains the gradual decline in home advantage observed across the league over time.
url https://doi.org/10.1371/journal.pone.0220630
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