Warming Up With a Dynamic Moment of Inertia Bat Can Increase Bat Swing Speed in Competitive Baseball Players
Bibliographic record
Abstract
INTRODUCTION: While most baseball players' warm-up with a weighted bat/donut, there is evidence to suggest the swing speed decreases after the warm-up even though the bat feels lighter. Warming up with a dynamic moment of inertia bat may not decrease the swing speed and therefore improve the performance of baseball players. The hypothesis is that a dynamic moment of inertia bat will negate the effect of the kinesthetic illusion observed with a weighted bat. OBJECTIVE: To measure the difference in bat swing speed between warming up with the dynamic moment of inertia bat compared with a weighted bat. METHODS: Thirty-nine competitive baseball players participated in the study. All players were randomly assigned a warm-up tool that could be either a dynamic moment of inertia bat or a weighted bat. After the players' warm-up, they swung their normal bat, and the bat swing speed was measured using a high-speed camera. We used motion analysis software to calculate the swing speed which measured the linear displacement during the last 15 frames before ball contact. The process was then repeated so that each player had the chance to try both warm-up bats. RESULTS: The post warm-up swing speeds using the dynamic moment of inertia bat were significantly faster compared with a weighted bat warm-up. There was a 0.56 (0.78) m/s (1.26 [1.74] mph) increase in swing speed when using the dynamic moment of inertia bat (P = .0001), which is an average increase of 2.10% compared with a weighted bat warm-up. CONCLUSIONS: Our findings suggest that using a dynamic moment of inertia bat before an at-bat can increase swing speed compared with a weighted warm-up. Future studies are needed to determine if using a dynamic moment of inertia bat as part of rehabilitation can facilitate returning to competition after injury by focusing on swing speed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".