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Record W2213754417 · doi:10.17192/es2024.0367

Individual Performance after Success and Failure - A Natural Experiment

2024· preprint· en· W2213754417 on OpenAlexaboutno aff
Christoph Bühren, Stefan Krabel

Bibliographic record

VenueEconstor (Econstor) · 2024
Typepreprint
Languageen
FieldSocial Sciences
TopicExperimental Behavioral Economics Studies
Canadian institutionsnot available
Fundersnot available
KeywordsOvertimeBasketballQuarter (Canadian coin)Natural experimentPsychologyOverconfidence effectAssociation (psychology)Affect (linguistics)Social psychologyApplied psychologyEconomicsStatisticsMathematicsLabour economicsGeography

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.308
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0010.000
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.019
GPT teacher head0.297
Teacher spread0.277 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2024
Admission routes1
Has abstractyes

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