Inter-distance differences in aiming error and visual perception influence shooting performance in basketball
Bibliographic record
Abstract
This study is the first to use inter-distance differences in shooting direction error to investigate the influence of visual perception on basketball shooting performance. Thirty-two experienced basketball athletes (NCAA Division I-III: n = 15, Canadian U Sports Association: n = 13, National Basketball Association: n = 4) attempted blocks of 25 jump shots from a near (free throw) and far distance (three-point attempt). Differences in the root-mean-square deviation of lateral direction error as distance increased, ΔLDE, were used to measure an individual’s change in lateral accuracy as the target changed within their visual field. The mean ΔLDE was -0.18 degrees (p < .001, 95% CI: -0.25 – -0.11) indicating that an individual’s lateral direction accuracy worsened as shooting distance decreased and external visual cues transitioned away from their central vision. Shooting performance had a strong positive correlation with ΔLDE (r = 0.57, p = .001) indicating that better shooters have a higher ability to adapt to the changes in visual perception with distance and experienced smaller reductions in lateral accuracy as shooting distance decreased. These findings show that visual perception has a significant role in basketball shooting performance and that ΔLDE is a valuable measure for assessing how an athlete’s proficiency in visual perception contributes to their performance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".