Mechanics of the Jump Shot: The “Dip” Increases the Accuracy of Elite Basketball Shooters
Bibliographic record
Abstract
The present study assessed the mechanics of the basketball jump shot to determine whether or not the "dip" increased shot accuracy. There remained a debate between coaches who believed "dipping" was too slow and coaches who believed "dipping" increased accuracy. A mixed design was used for the present study with elite high-school and university players all performing shots with and without the "dip" at four distances: the last hash mark before the free throw line (3.125 m), the length of an imaginary hash mark beyond the free throw line (4.925 m), the top of the free throw circle (6.025 m), and the three-point line (6.750 m). These distances best emulated where the majority of shots were attempted in a game. Thirty-six athletes completed the study, with accuracy and shot quality being measured using Hardy-Parfitt's six-point scale. The results of the present study indicated that the "dip" led to approximately a 7-9% increase in accuracy of the jump shot for both high school shooters, and university shooters, suggesting that coaches should begin to teach the "dip" in a player's shooting motion to improve scoring results.
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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 | 0.004 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".