Relationships Between Key Performance Indicators Across Four Swimming Strokes and by Distances in Competitive Swimmers
Bibliographic record
Abstract
Key performance indicators (KPIs) are skill-based metrics, used by coaches and athletes to adjust technique and develop race strategies. The purpose of the study was to investigate the effect of key performance indicators (KPIs), such as stroke rate (SR;#/s), stroke count (SC;#), stroke length (SL;m) and kick frequency (KF;#/s) on total swim time (s), across four swimming strokes (butterfly, backstroke, breaststroke, freestyle) of the same swim distance, and within a swimming stroke between swim distances (50m and 100 m). Varsity-caliber competitive swimmers (n=12 males; 19yrs1.4) were recruited. Anthropometric measures including height (m), seated height (m), weight (kg), wingspan (m), hand length (m) and leg length (m) were recorded. Shoulder and ankle range of motion (ROM) measurements and a Y-balance test (YBT) were conducted to profile upper and lower limb mobility. Athletes completed four swim sessions; each session consisted of a standardized warm up, 50m kick, 50m pull, 50m swim and 100m swim distances per swimming stroke. Swimming KPIs and total swim time (s) were collected by a portable Triton 2 device (TritonWear, ON, Canada). A GoPro Hero 8 device (GoPro, California, USA) collected underwater video to facilitate calculating KF (#/s). Descriptives were calculated for all variables across all four swimming strokes and two swim distances. Pearson product-moment correlations revealed significant relationships between select anthropometrics and ROM and performance times (p<0.005), suggesting that both anthropometric and ROM measures have the potential to influence swim performance times. A series of repeated-measure ANOVAs with Greenhouse-Geisser corrections revealed significant differences in select KPIs [SR (#/s), SC (#), SL (m) and KF (#/s)] across the four swimming strokes and between 50m and 100m swim distances within a swimming stroke (p<0.05), suggesting that both swimming strokes and swim distances may utilize different KPI related strategies. A multiple regression analyses was conducted to identify the contribution of pull time (s) versus kick time (s) to total swim time (s) within 50m swim distance and each swimming stroke. Percent contributions were calculated and revealed differences by each stroke. Data-driven metrics obtained during swim performances facilitated a better understanding of the relationships between KPIs, anthropometrics, ROM, and YBT measures. Furthermore, metrics provided support to adjust select KPIs relative to an athlete’s anthropometrics. These sport specific skill-based metrics can empower coaches and athletes to use KPIs to optimize athlete potential and target performance goals.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".