Relationship Between Bench Press Strength and Punch Performance in Male Professional Boxers
Bibliographic record
Abstract
López-Laval, I, Sitko, S, Muñiz-Pardos, B, Cirer-Sastre, R, and Calleja-González, J. Relationship between bench press strength and punch performance in male professional boxers. J Strength Cond Res 34(2): 308-312, 2020-This study investigated the relationship between punching performance and the velocity at which different loads were lifted during the bench press (BP) exercise in 12 professional boxers (age = 22.6 ± 4 years; height = 177.7 ± 5 cm; body mass 70.6 ± 6.43 kg; years of boxing experience = 6.5 ± 3.5 years; weight class = from light to super welterweight). To determine the maximal punching velocity (PVmax) during both rear arm (RA) and lead arm (LA) punching, an accelerometer (Crossbow; Willow Technologies, Sussex, United Kingdom) was placed inside the boxing glove while executing 3 jabs at a maximal velocity with each arm. Upper-body strength was assessed through the direct 1-repetition maximum (1RM) BP test, and the maximum velocity at different percentages of 1RM was obtained with a linear encoder. The main finding was that RA PVmax was correlated with the BP velocity at all submaximal intensities (p < 0.05). Nevertheless, LA PVmax did not correlate with BP velocity at any intensity. When the correlated BP submaximal intensities were introduced in linear regression models, the velocity at 80% 1RM was the only predictor of RA PVmax (r = 0.75; p < 0.01) in professional boxers. Additional body mass adjustment to the regression model significantly affected the predictive value (r = 0.65; p < 0.005). Results encourage coaches and trainers to use BP exercise with high loads (i.e., 80% of 1RM) because this could be a reliable predictor of performance during the specific boxing action. Future research is needed to determine exercises and intensities that could explain LA PVmax because significant associations were not found.
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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 | 0.002 | 0.000 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".