Softball Pitching Propulsion and Performance Differences According to Body Fat Percentage
Bibliographic record
Abstract
Abstract Softball pitchers with a high body-fat percentage (bf%) can often be successful, despite the heightened risk of injury associated with high bf%. Given the importance of propulsion during pitching, those with high bf% may have an advantage performance-wise. Therefore, the purpose of this study was to examine the differences in ground reaction force (GRF) development between two groups of pitchers: those with a high-fat percentage (≥32 bf%) and a healthy-fat percentage (<32 bf%). Thirty-two female high-school softball pitchers (1.70±.06 m, 76.09±17.50 kg, 15±1 yrs) completed dual-energy x-ray absorptiometry (DEXA) scans. GRF data were collected during pitch propulsion via a force plate, pitch speed was captured using a radar gun, BMI was calculated from pitcher height and mass, and fat free mass index (FFMI) and fat mass index (FMI) were calculated using DEXA data and pitcher height. Multivariate analysis of variance revealed pitcher group GRFs differed significantly (F3,30=3.45, p=.030). Univariate follow-up analyses showed healthy bf% pitchers presented greater weight-normalized peak medial GRF (F1,30=7.17, p=.012). BMI and FFMI were positively associated with pitch speed while bf% and FMI were negatively associated with pitch speed. While pitchers can be successful and carry excess bf%, results indicate potential performance disadvantages associated with having an increased bf%.
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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) | 0.004 | 0.001 |
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".