Individual Performance after Success and Failure - A Natural Experiment
Bibliographic record
Abstract
The main goal of our study is to analyze how success and failure in crucial situations affect subsequent individual performance. Our study is based on evidence from a natural experiment of NBA (National Basketball Association) players: Based on play-by-play statistics of NBA games in 10 seasons (1818 observations of 345 sportsmen), we identify players who are responsible for the overtime by taking the last shot of the game. Players who miss the shot when the game is tied perform better in overtime than in the last quarter (within-subject comparison) but not significantly different to their game and season averages. Players who score the equalizer in the last shot of the regular game perform substantially worse in overtime compared to their 4th quarter performance as well as compared to their game and season averages. Yet the average performances in overtime of both groups do not differ significantly (between-subject comparison). We conclude that success in crucial situations leads to lower subsequent individual performance. Psychological explanations for this phenomenon, e.g. the role of overconfidence, are discussed. We argue that our findings can be transferred to behavior after success or failure in business settings since we have distinct identifications of performance and responsibility: the observed overtimes are clear and immediate outcomes of the last shots of our analyzed players; without their success or failure, the game would have been over after regular time.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".