Assessing Personality Traits of Team Athletes in Virtual Reality
Bibliographic record
Abstract
Assessment of personality traits is highly relevant in team sports in order to analyze the performance of an athlete under pressure when in competitive situations, for team-strategic decisions, to optimize command transmission, and ultimately to understand top-level performers. It further facilitates the development and application of personalized exercises, coaching to improve performance in competition, and can be considered a valuable criterion for talent scouting and development. The current state of the art method to assess personality traits in sports relies on validated questionnaires. However, these often provide non-sport-specific, subjective self-reported information and lack the ability to measure how these characteristics are reflected in context-based performance.We developed a virtual reality (VR) tool for the assessment of personality traits in team sports, in our case for soccer. An evaluation of this tool within a study with 24 subjects yielded a benchmark of its immersion through user experience and provided an objective description of athletes’ personalities based on performance indicators extracted from activity-tracking. Within the tool, we implemented two realistic virtual soccer environments to assess the motivational orientation of soccer players (i.e. action- and state-orientation) which we discerned from the gold standard questionnaire.Results show that user experience and presence of the implemented virtual environments scored significantly higher compared to benchmark measurements. Additionally, a significant difference between the two groups of action and state-oriented athletes could be observed. Measures of failure rate, pass accuracy, number of perceived opponents, and achieved bonus goals are parameters that differ significantly among the two athlete groups. These findings show that VR technology is applicable for the assessment of athletes’ motivational orientation and thus demonstrate the feasibility of virtual environments as functional game scenario-based assessment tools for athletes.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".