Exploring experiences of tilt and ragequitting in competitive and recreational videogamers
Bibliographic record
Abstract
Gamers refer to tilt as a cyclical phenomenon where performance failures cause negative emotions leading to even poorer performance. When tilt experiences go unresolved, they can lead to ragequitting – an abrupt, and sometimes destructive, early removal of oneself from competition fueled by frustration. Few empirical studies have investigated gamers’ perceptions of these averse affective experiences and their consequences. The purpose of this study was to explore gamers’ experiences of tilt and ragequitting using the process model of emotion regulation as a sensitizing concept. Participants (N = 30; 77% men, 17% women, 3% non-binary, 3% agender, Mage = 25.5) were recruited using purposeful and snowball sampling across three broad categories of videogame engagement: recreational gamers, ranked gamers, and professional/collegiate esports athletes. Semi-structured interview transcripts were analyzed using qualitative description from a critical realist perspective. Participants tended to describe tilt as having a compounding negative “snowball” effect on performance wherein performance failures prompt negative emotions which cloud judgements leading to poorer decisions and more performance failures. Ragequitting was described as the extreme result of an unmitigated bout of tilt, but also as a disdainful act that gamers regard very poorly. Participants described how external factors like pre-game moods, general dispositions, and social identities can influence the onset and trajectory of tilt and ragequitting. The discussion section provides insight into how practitioners can prevent performance failures and negative emotions from compounding on each other when things go awry in competition.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".