The Contribution of Expert Coaches’ Experiential Knowledge in Understanding Punching Performance
Bibliographic record
Abstract
Traditionally, the field of sports science has been interested in conducting research that is predominately quantitative in nature. Although this approach has provided significant findings, this has led to expert coaches’ experiential knowledge being neglected in favour of empirical knowledge. By investigating punching in boxing, we are interested in developing an understanding of whether elite coaches, through their experiential knowledge, intuitively identify key characteristics of effective punching as identified in controlled experimental research. For this purpose, five interviews were conducted with professional and amateur boxing coaches. From this qualitative approach it was evident that coaches’ knowledge was consistent with that of the empirical research on effective punching performance with four principal components emerging from the interview data. These included: 1) whole body movement, 2) footwork, 3) hip and shoulder rotation, and 4) hand and arm position. The data illuminated how coaches’ knowledge can be used to strengthen empirical findings in sports performance, in this case punching in boxing. Additionally, characteristics of performance that were discussed by coaches that were not identified in the empirical literature highlight directions for further research regarding effective punching technique, an area that requires further investigation before conclusive structures of good practice can be applied.
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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 | 0.010 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".