Visual Information in Basketball Jump-Shots: Differences between Youth and Adult Athletes
Bibliographic record
Abstract
Basketball shooting is a complex skill that requires visual routines and trained players typically evidence a specific oculomotor pattern. This study aimed to examine visual patterns in male novice youth and professional adult players while performing a jump shot. The sample included 20 basketball players grouped as under-16 youth (n = 10) and professional adult (n = 10) players. Each participant completed 50 shots at two distances (long range: 6.80 m; middle range: 4.23 m). Eye tracking glasses were used to obtain quiet eye (QE), the number of fixations, total fixation duration, duration of first and last fixation. An independent t-test was used to assess differences between groups. Shooting accuracy given by % of efficacy indicated that under-16 players attained poorer scores at both distances: long (t = -4.75, p < 0.01) and middle (t = -2.80, p < 0.012) distance. The groups also differed in QE time (long: 600 ms vs. 551 ms; middle: 572 ms vs. 504 ms) and total duration of the fixations (long: 663 ms vs. 606 ms; middle: 663 ms vs. 564 ms) in both long and middle distance shots. Significant differences also occurred in the last fixation (long distance: t = -4.301, p < 0.01; middle distance: t = -3.656, p < 0.01) with professional adult players presenting the value of, on average, 454-458 ms, while youth shooters 363-372 ms. In summary, visual strategy differed between under-16 youth and professional adult basketball players. To support their long-term sport development, it is recommended that youth basketball players focus their attention with longer final fixation before releasing the ball to improve their shot.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 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".