1. ESTIMATING THE EFFECT OF HOME COURT ADVANTAGE IN THE NBA Jason Kotecki

2. Introduction During the 2012-13 NBA season, the Houston Rockets compiled a record of 45-37 The Rockets had a road record of just 16-25, but they were 29-12 at home The Utah Jazz won 30 games at home last year, but they only won 13 on the road How can this big difference in records be explained? Home Court Advantage

3. Home court advantage is “the consistent finding that home teams win over 50% of the games played under a balanced schedule.” Each NBA team plays 41 games at home and on the road They also play each team at least twice, once at home, once on the road

4. Research Hypothesis But, how much of a factor does home court advantage have in producing wins? Logit regression with a dependent variable of wins I hypothesize that home court advantage exists, and that it can be explained mostly by fan attendance, familiarity with the court, and referee bias

5. Literature Review Well-Established in the literature Carron et al. (2005) Design a conceptual framework for analyzing home court advantage Game location factors, critical and psychological behavioral states, and performance outcomes

6. Game Location Factors Crowd factors, learning/familiarity factors, travel factors, and rule factors Schwartz and Barsky (1977) Compared home advantages between baseball, football, hockey and college basketball Home advantage is greatest in indoor sports and primarily due to fan support rather than any other factor

7. Game Location Factors The literature dealing with crowd factors and attendance is extensive Forrest et al. (2005); Greer (1983); Nevill (1999); Nevill et al. (1996); Smith (2005) Salminen (1993) Fan audiences cheering for the home team is not related to greater home team success Ashman et al. (2010) and Nutting (2010) Game Frequency

8. Critical/Psychological Behavioral States These deal with how coaches, competitors, and officials affect the outcome of the game Referee Bias Carron et al. (2005); Page and Page (2010); Moskowitz and Wertheim (2011)

9. Performance Outcomes Statistically based variables Performance based analysis Harville and Smith (1994); Cao et al. (2011)

10. Theory Stefan Kesenne’s “Economic Theory of Professional Sports” Win maximizing Shirking Katie Stankiewicz (2009) Players are less likely to shirk in front of their home fans Referee Bias Theory Psychological theory that people want to be liked and to be confirmed in their judgments

11. Data Basketball-reference.com Statistics for every NBA team for every year NBA.com Attendance for every game All teams and almost all games* for three season (2008-11) - 3,642 game entries

12. Variables List VariableDescriptionExpected Effect Ln_AttendanceNatural log of attendancePositive Home_FG%Field goal % of the home teamPositive Home_FT%Free throw % of the home teamPositive Foul_RatioRatio of fouls called on the visiting team over fouls called on the home team Positive Away_Win %_of_Visiting_Team Control variable of the away team’s win % on the road Negative Days_RestNumber of days of rest the home team has before each competition Positive

13. Descriptive Statistics VariableMeanStd. DeviationMinMax Attendance17,305. 2 2,8408,86623,129 Home FG %.467.057.279.675 Home FT %.765.096.3641 Foul Ratio1.08.280.3793 Away Win % of Visiting Team.397.167.073.707 Days of Rest1.25.980011 Win.605.48901

14. Results VariableModel AModel B CoefficientStd. ErrorCoefficientStd. Error Ln_Attendance0.816***0.2241.02***0.212 Home_FG%22.1***0.90319.5***0.830 Home_FT%2.88***0.427 Foul_Ratio2.66***0.170 Away_Win% _of_Visiting_Team -1.92***0.248-2.04***0.234 Days_Rest-0.0220.041-0.00470.039 Sample Size3,462 Pseudo R^20.2468 ***Significant at the 1% level **Significant at the 5% level *Significant at the 10% level

15. Model A Probability Table

16. Model B Probability Table

17. Model A Probability Table

18. Conclusions Home court advantage is discovered through attendance, referee bias, and performance variables Future Research More games and more years to increase sample size Individual teams could be analyzed Travel Factors (e.g. time zones crossed, length of road trips) Different sports (e.g. baseball)