Low Thigh Muscle Coactivation and High Ball Velocity in an Elite Windmill Softball Pitcher
Bibliographic record
Abstract
Aim: To examine whether strength and thigh muscle activation patterns were associated with throwing velocity in a collegiate fast-pitch softball pitcher with national-level experience. Methods: Five female pitchers from a college team in Ontario, Canada, participated for comparison. The team’s top pitcher, recently selected for national team training, was classified as elite; the remaining four were categorized as high-performance. Upper- and lower-body strength was estimated using one-repetition maximum tests. Surface electromyography (EMG) of the rectus femoris (RF) and biceps femoris (BF) was recorded during windmill pitches, while ball velocity was measured with radar. The maximum peak-to-peak RMS EMG signal, as well as timing of activation and inactivation, were analyzed across pitchers. Results: The elite pitcher demonstrated the highest average throwing velocity (59 mph vs. 54 mph) and greater overall strength. She also displayed a distinct three-phase activation pattern of thigh musculature with minimal coactivation, while the high-performance pitchers showed less distinct patterns and greater overlap of RF and BF activity. Conclusion: This case highlights that both superior strength and a distinct thigh activation profile may contribute to higher throwing velocity in elite softball pitchers. Further research integrating EMG with biomechanical video analysis may clarify how neuromuscular coordination supports performance.
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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".