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A Note on Team-Specific Home Advantage in the NBA

Author

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  • Jones Marshall B

    (Penn State University)

Abstract

Recently it was reported that in the NBA as a whole, two thirds of the home advantage which teams enjoy when playing at home is accumulated in the first quarter. Home advantage can also be determined for individual teams, and there is good reason for doing so. For example, the relation of home advantage to team statistics such as assists, rebounds, and turnovers can be studied team-specifically but not in the league as a whole. Before any such project is undertaken, however, a major technical problem must be addressed. Formally, team-specific home advantage is a difference score between positively correlated variables (games won at home minus games won away), and difference scores are notoriously unreliable. This unreliability, moreover, is not just an empirical generalization. There is a formal basis for it in the theory of mental tests. This study reports that over a four-year period in the NBA the estimated reliability of team-specific home advantage was 0.284, even though the estimated reliabilities of games won at home and games won away were 0.772 and 0.833 respectively. The implications for research on home advantage are discussed.

Suggested Citation

  • Jones Marshall B, 2008. "A Note on Team-Specific Home Advantage in the NBA," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 4(3), pages 1-15, July.
    • Handle: RePEc:bpj:jqsprt:v:4:y:2008:i:3:n:5
    DOI: 10.2202/1559-0410.1128
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    References listed on IDEAS

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    1. Jones Marshall B, 2007. "Home Advantage in the NBA as a Game-Long Process," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 3(4), pages 1-16, October.
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    Cited by:

    1. Christian Deutscher, 2011. "Productivity and New Audiences: Empirical Evidence From Professional Basketball," Journal of Sports Economics, , vol. 12(3), pages 391-403, June.
    2. Doyle Joanne M. & Leard Benjamin, 2012. "Variations in Home Advantage: Evidence from the National Hockey League," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 8(2), pages 1-27, June.
    3. Manner Hans, 2016. "Modeling and forecasting the outcomes of NBA basketball games," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 12(1), pages 31-41, March.
    4. Zimmer Timothy & Kuethe Todd H, 2009. "Testing for Bias and Manipulation in the National Basketball Association Playoffs," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 5(3), pages 1-13, July.

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