Optimizing the Release Conditions for a Free Throw in Wheelchair Basketball
Bibliographic record
Abstract
The primary purpose of this study was to determine the optimal release conditions and corresponding arm movement pattern for the free throw for players classified as 3 to 4.5 on the international player classification system in wheelchair basketball. A 2-D, three-segment simulation model was used to investigate this problem. The computational process involved a two-step optimization scheme in which an outer computational loop was used to optimize the magnitude and timing of the muscle torques that generate the arm's motion, and an inner computational loop was used to determine the optimal angle and speed of the ball at the moment of release. The inner optimization loop revealed that Brancazio's (1981) and Hay's (1993) approaches to determining the optimal release angle produced identical results. The lowered seated height of the wheelchair basketball player required that the ball be released at a steeper angle with greater vertical velocity, and hence the need for greater shoulder torque. For the wheelchair player, the peak shoulder flexion torque generated by the model was reduced by approximately 43% when the upper arm was initially positioned at an angle approximately 40° below the horizontal, as compared to being positioned at an angle of 10° above the horizontal. For the wheelchair player, the optimal release angle and speed for a ball released at a horizontal distance of 4.09 m from the center of the basket, and 1.30 m below the rim, was computed to be 53.8° and 7.4 m/s, respectively.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".