Increased Athletic Performance in Lighter Basketball Shoes: Shoe or Psychology Effect?
Bibliographic record
Abstract
PURPOSE: To determine the effect of shoe mass on performance in basketball-specific movements and how this affects changes if an athlete is aware or not of the shoe's mass relative to other shoes. METHODS: In an experimental design, 22 male participants were assigned to 2 groups. In the "aware" group, differences in the mass of the shoes were disclosed, while participants in the other group were blinded to the mass of shoes. For both groups lateral shuffle-cut and vertical-jump performances were quantified in 3 different basketball-shoe conditions (light, 352 ± 18.4 g; medium, 510 ± 17 g; heavy, 637 ± 17.7 g). A mixed ANOVA compared mean shuffle-cut and vertical-jump performances across shoes and groups. For blinded participants, perceived shoe-weight ratings were collected and compared across shoe conditions using a Friedman 2-way ANOVA. RESULTS: In the aware group, performance in the light shoes was significantly increased by 2% (vertical jump 2%, P < .001; shuffle cut 2.1%, P < .001) compared with the heavy shoes. In the blind group, participants were unable to perceive the shoe-weight variation between conditions, and there were no significant differences in vertical-jump and shuffle-cut performance across shoes. CONCLUSIONS: Differences in performance of the aware participants were most likely due to psychological effects such as positive and negative expectancies toward the light and heavy shoes, respectively. These results underline the importance for coaches and shoe manufacturers to communicate the performance-enhancing benefits of products or other interventions to athletes to optimize their performance outcome.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".