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Performance under pressure: estimating the returns to mental strength in professional basketball

2012; Taylor & Francis; Volume: 13; Issue: 2 Linguagem: Inglês

10.1080/16184742.2012.742122

1746-031X

Christian Deutscher, Bernd Frick, Joachim Prinz,

Experimental Behavioral Economics Studies

Abstract Human capital theory, one of the cornerstones of modern labor and personnel economics, posits that individual salaries are a function of a person's skills and abilities. Irrespective of its undisputed theoretical importance and practical relevance, the empirical evidence on the impact of personality traits and characteristics on salaries, however, remains limited and inconclusive because most of the existing literature is based on self-reported questionnaire responses. Therefore, we include in our estimations not only the 'traditional' measures of a person's human capital, but also analyze the impact of an individual's personality traits that have so far been mostly neglected in the context of income determination. We avoid the 'subjectivity bias' that has been criticized in the previous literature and use an unbalanced panel including approximately 200 professional basketball players from the National Basketball Association in the four seasons 2003/2004–2006/2007. With this kind of secondary data we estimate standard Mincer-type earnings functions as well as more advanced quantile regressions. Our findings document a statistically significant and economically considerable impact of 'mental strength' on player salaries. Keywords: mental strengthchokingsalariesbasketball Notes 1. For a detailed discussion of the advantages of sports data to test different labor market theories see Kahn (Citation2000). 2. The remaining two dimensions of the five factors inventory (autonomy and conscientiousness) proved to be statistically insignificant. 3. Significant impacts of experience, performance, and peer reputation on salary can also be found in studies of other North American sports, see Kahn (Citation1993) for baseball, Berri and Simmons (Citation2009) for American football, and Idson and Kahane (Citation2000) for hockey. Moreover, similar effects have been found in studies on European soccer (see e.g. Frick, Citation2011; Lehmann & Schulze, Citation2008; Lucifora & Simmons, Citation2003). 4. Where sports teams differ is that they apply more stringent selection procedures into occupations. For example, poor performance by a player results in being dropped from team squad and very quickly being discarded; there are high levels of mobility within the industry (between teams) and into and out of the industry, with shorter careers than in most occupations. 5. A significant amount of research has examined choking under pressure in laboratory settings rather than in actual game situations. A laboratory environment provides a controlled setting in which performance failures can be studied while the amount and the players' perceptions of pressure can be manipulated. Leith (Citation1988), for example, found that individuals shooting free throws who were made aware of the fact that 'some people have the tendency to choke at the free throw line' performed significantly worse than those who had not received that kind of information. Dohmen (Citation2008) finds that professional football players are more successful in penalty shot-outs when playing away games, i.e. when not playing in front of their fans (a finding that supports the related 'social pressure hypothesis'). 6. Anecdotal evidence abounds: With 90 seconds left to play in the national championship game in the 2008 NCAA men's division I basketball tournament, the Memphis Tigers had a six-point lead over the Kansas Jayhawks. Kansas fouled strategically to send Memphis to the free throw line, hoping to see them fail. Memphis, with a previous 59% completion rate, missed four out of five free throws, helping Kansas to reach overtime and finally win the game. Another example: In the first game of the 1995 finals of the NBA between the Orlando Magic and the Houston Rockets, Nick Anderson, a 70% career free throw shooter for the Orlando Magic, had four straight free-throw attempts with a few seconds left to expand the team's lead of three points. He missed all four shots enabling the Houston Rockets to tie the game with a last second shot and finally win the match in overtime. Later on, the Rockets won the series in four games. Nick Anderson's free throw percentage declined significantly after this incidence to a level of 40% in the two seasons after that particular game. 7. We admit, however, that the term 'crunch-time' can be defined in different ways. Due to data availability we use the definition provided by http://www.82games.com. 8. During our observation period the Detroit Pistons had the highest average attendance (with 20,335 in the 2005/2006 season) while the Atlanta Hawks had the smallest crowd on average (with an average of 15,026 spectators in the 2003/2004 campaign). This difference is far smaller than, e.g., the difference between the strong and the weak drawing teams in European football. 9. Data on player performance under pressure were obtained from the website http://www.82games.com. 10. We also take into account that it is harder for a good free throw shooter to improve further during crunch-time situations compared to a mediocre shooter by multiplying the respective player's improvement/decline during crunch-time by his non-crunch-time free-throw percentage. This does not change any of the results presented in this paper. 11. If, as one might argue, a player's performance suffers due to fatigue, we should find in our data a negative correlation between our measure of 'mental strength' and the number of minutes played per game. The resulting correlation coefficient is close to zero and not statistically significant. Thus, the relative performance of players from the free-throw line does not suffer from having played more minutes. Moreover, one might also expect to observe a significantly positive correlation between a player's mental strength and his minutes on the court during crunch-time. Again, our data does not support this hypothesis, because the correlation is once more statistically insignificant. 12. The NBA had 1189 regular season games during the 2003/2004 season and, due to the arrival of an expansion team (the Charlotte Bobcats), 1230 games during the following three seasons. 13. Superstars have been shown to increase the public interest in a franchise and raise its market value considerably (e.g. Hausman & Leonard, Citation1997). 14. Bryson, Frick, and Simmons (Citation2013) show that left-footed soccer players are paid significantly higher salaries. 15. The average salary in our complete ('starting') sample is slightly below 3 million dollars. Moreover, the players in our final sample spend more than 32 minutes per match on the court (compared to 12 minutes in the whole sample). 16. Moreover, to test for potential endogeneity we performed a Durbin–Wu–Hausman test (see Hausman, Citation1978). This test analyzes whether there is sufficient difference between the coefficients of the instrumental variables regression (IV) and those of the conventional OLS specification. The Prob >chi2 statistic of our regression model (χ2= 17.08, p >0.1) clearly demonstrates that we cannot reject the null hypothesis that the OLS specification is a consistent unbiased estimator, supporting the assumption that an IV approach is not necessary. 17. Presence of non-normality in the dependent variable is indicated by a large kurtosis value and in our case the D'Agostino, Balanger, and D'Agostino (Citation1990) test is performed by the sktest command in Stata 10.1. We can investigate the impacts of mental strength at any quantile of the salary distribution, not just the conditional mean. Moreover, the quantile regression approach is semiparametric in that it avoids assumptions about the parametric distribution of the regression error term, an especially suitable feature where the data are heteroskedastic as in our case. 18. Throughout, we derive percentage impacts of changes in dummy variable from coefficients as exp (β) – 1, where β is an estimated coefficient (Halvorsen & Palmquist, Citation1980).