A Preliminary Investigation Regarding the Effect of Tennis Grunting: Does White Noise During a Tennis Shot Have a Negative Impact on Shot Perception?
Bibliographic record
Abstract
BACKGROUND: There is a growing chorus of critics who complain that many of the top-ranked professional tennis players who grunt when they hit the ball gain an unfair advantage because the sound of the grunt interferes with their opponent's game. However, there is no scientific evidence to support this claim. METHODOLOGY/PRINCIPAL FINDINGS: We explored this potential detrimental effect of grunting by presenting videos of a tennis player hitting a ball to either side of a tennis court; the shot either did, or did not, contain a brief sound that occurred at the same time as contact. The participants' task was to respond as quickly as possible, indicating whether the ball was being hit to the left- or right-side of the court. The results were unequivocal: The presence of an extraneous sound interfered with a participants' performance, making their responses both slower and less accurate. CONCLUSIONS/SIGNIFICANCE: Our data suggest that a grunting player has a competitive edge on the professional tennis tour. The mechanism that underlies this effect is a topic for future investigation. Viable alternatives are discussed. For example, the possibility that the interfering auditory stimulus masks the sound of the ball being struck by the racket or it distracts an opponent's attention away from the sound of the ball.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".