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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2019-01-01
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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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AT austinrharris nbateamhomeadvantageidentifyingkeyfactorsusinganartificialneuralnetwork AT pauljroebber nbateamhomeadvantageidentifyingkeyfactorsusinganartificialneuralnetwork |
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