Abstract
We analyze the impact of psychological pressure on performance with over 5500 handball penalties thrown in either the decisive stage or the rest of the game during matches of the 2019/2020 season in the first three German handball leagues. Contrary to the choking under pressure phenomenon, most of the analyzed players perform best when it matters the most. The positive effect of pressure on performance is highest when the score is even or when the thrower’s team is trailing. We control for gender and psychological traits assessed with a survey. In our sample, female players score with a higher probability than male players. The positive impact of pressure is not significantly higher for female players.
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Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
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Research funding: None declared.
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Conflict of interest statement: The authors declare no conflicts of interest regarding this article.
Robustness check with crunch time restricted to 5 min (Figure A1 and Table A1).
Shot success by crunch time (restricted to 5 min), gender, league, and game-related variables.
Marginal effects of probit regressions with crunch time restricted to 5 min.
| Dependent variable | Model 1 | Model 2 | ||
|---|---|---|---|---|
| Shot success | ME | SD | ME | SD |
| Individual variables | ||||
| Gender | −0.0316* | (0.0127) | −0.0294* | (0.0132) |
| Age | 0.0014 | (0.0014) | 0.0013 | (0.0014) |
| Throwing hand | −0.0119 | (0.0126) | −0.0127 | (0.0126) |
| League = 2 | 0.0009 | (0.0177) | −0.0000 | (0.0176) |
| League = 3 | −0.0129 | (0.0147) | −0.0142 | (0.0150) |
| CSE | −0.0133 | (0.0154) | −0.0160 | (0.0160) |
| Risk-taking | −0.0022 | (0.0064) | −0.0034 | (0.0064) |
| Self-report choking | −0.0144 | (0.0140) | −0.0145 | (0.0139) |
| Competition-related variables | ||||
| Location | −0.0079 | (0.0123) | −0.0045 | (0.0124) |
| Crunch time (CT) | 0.0457 | (0.0286) | −0.3805 | (0.3469) |
| Score = 1 (lagging) | −0.0042 | (0.0158) | −0.0035 | (0.0160) |
| Score = 2 (leading) | −0.0048 | (0.0161) | 0.0015 | (0.0166) |
| Interaction terms | ||||
| League*CT | 0.0189 | (0.0386) | ||
| CSE*CT | 0.0803 | (0.0795) | ||
| Risk-taking*CT | 0.0393 | (0.0262) | ||
| Score = 1*CT | −0.0063 | (0.0842) | ||
| Score = 2*CT | −0.1504 | (0.0939) | ||
| Location*CT | −0.0784 | (0.0558) | ||
| Gender*CT | −0.0604 | (0.0633) | ||
| Number of throws | 5506 | 5506 | ||
| Number of players | 236 | 236 | ||
| Wald χ2 | 15.56 | 30.29 | ||
| Prob > χ2 | 0.2124 | 0.0482 | ||
| Log-likelihood | −2930.65 | −2925.65 | ||
| Pseudo R2 | 0.0026 | 0.0043 | ||
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Marginal effects in the ME columns; standard deviations in parentheses in the SD columns; standard deviations clustered at player level; reference category for Score: Score = 0 (even), reference category for League: League = 1; significance levels: **p < 0.01; *p < 0.05. Table A3 shows the (corrected) p-values.
P-values and corrected p-values according to Benjamini, Krieger, and Yekutieli (2006) of Table 5.
| Dependent variable | Model 1 | Model 2 | ||
|---|---|---|---|---|
| Shot success | p | corr. | p | corr. |
| Individual variables | ||||
| Gender | 0.010 | 0.184 | 0.049 | 0.644 |
| Age | 0.360 | 1.000 | 0.375 | 1.000 |
| Throwing hand | 0.328 | 1.000 | 0.339 | 1.000 |
| League = 2 | 0.992 | 1.000 | 0.982 | 1.000 |
| League = 3 | 0.369 | 1.000 | 0.364 | 1.000 |
| CSE | 0.409 | 1.000 | 0.360 | 1.000 |
| Risk-taking | 0.737 | 1.000 | 0.624 | 1.000 |
| Self-report choking | 0.318 | 1.000 | 0.313 | 1.000 |
| Competition-related variables | ||||
| Location | 0.530 | 1.000 | 0.857 | 1.000 |
| Crunch time (CT) | 0.010 | 0.184 | 0.985 | 1.000 |
| Score = 1 (lagging) | 0.798 | 1.000 | 0.843 | 1.000 |
| Score = 2 (leading) | 0.811 | 1.000 | 0.627 | 1.000 |
| Interaction terms | ||||
| League*CT | 0.929 | 1.000 | ||
| CSE*CT | 0.689 | 1.000 | ||
| Risk-taking*CT | 0.489 | 1.000 | ||
| Score = 1*CT | 0.941 | 1.000 | ||
| Score = 2*CT | 0.054 | 0.644 | ||
| Location*CT | 0.131 | 1.000 | ||
| Gender*CT | 0.116 | 1.000 | ||
P-values and corrected p-values according to Benjamini, Krieger, and Yekutieli (2006) of Table A1.
| Dependent variable | Model 1 | Model 2 | ||
|---|---|---|---|---|
| Shot success | p | corr. | p | corr. |
| Individual variables | ||||
| Gender | 0.013 | 0.676 | 0.026 | 0.676 |
| Age | 0.339 | 1.000 | 0.369 | 1.000 |
| Throwing hand | 0.344 | 1.000 | 0.312 | 1.000 |
| League = 2 | 0.957 | 1.000 | 0.998 | 1.000 |
| League = 3 | 0.380 | 1.000 | 0.343 | 1.000 |
| CSE | 0.385 | 1.000 | 0.316 | 1.000 |
| Risk-taking | 0.735 | 1.000 | 0.600 | 1.000 |
| Self-report choking | 0.306 | 1.000 | 0.297 | 1.000 |
| Competition-related variables | ||||
| Location | 0.522 | 1.000 | 0.714 | 1.000 |
| Crunch time (CT) | 0.111 | 1.000 | 0.273 | 1.000 |
| Score = 1 (lagging) | 0.792 | 1.000 | 0.825 | 1.000 |
| Score = 2 (leading) | 0.766 | 1.000 | 0.926 | 1.000 |
| Interaction terms | ||||
| League*CT | 0.625 | 1.000 | ||
| CSE*CT | 0.313 | 1.000 | ||
| Risk-taking*CT | 0.132 | 1.000 | ||
| Lagging*CT | 0.940 | 1.000 | ||
| Leading*CT | 0.109 | 1.000 | ||
| Location*CT | 0.160 | 1.000 | ||
| Gender*CT | 0.340 | 1.000 | ||
Survey
CSE scale (Judge et al. 2003)
Willingness to take risks – (Beierlein et al. 2015):
Self-report choking (Clarke, Sheffield, and Akehurst 2020):
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Supplementary Material
This article contains supplementary material (https://doi.org/10.1515/jqas-2022-0070).
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