Beyond Competitive Gaming: How Casual Players Evaluate and Respond to Teammate Performance
Bibliographic record
Abstract
Teammate performance evaluation fundamentally shapes intervention design in video games. However, our current understanding stems primarily from competitive E-Sports contexts where individual performance directly impacts outcomes. This research addresses whether performance evaluation mechanisms and behavioural responses identified in competitive games generalize to casual cooperative games. We investigated how casual players evaluate teammate competence and respond behaviourally in a controlled between-subjects experiment (N=23). We manipulated confederate performance in Overcooked 2, combining observations, NASA TLX self-reports, and interviews. We present two key findings. (1) Observations revealed frustration behaviours completely absent in self-report data. Thus, these instruments assess fundamentally distinct constructs. (2) Participants consistently evaluated teammate performance through relative comparison rather than absolute metrics. This contradicts task-performance operationalizations dominant in competitive gaming research. Hence, performance evaluation frameworks from competitive contexts cannot be directly applied to casual cooperative games. We provide empirical evidence that performance evaluation in casual games requires a comparative operationalization.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".