Performance analysis of show jumping rounds at a national pony competition
Bibliographic record
Abstract
Abstract Performance analysis is utilised by coaches and athletes to identify areas to work on in training and to identify strengths in athlete performance in various sports. However, performance analysis is not commonly used within equestrian sports. The purpose of this study was to evaluate minors and their ponies competing in show jumping at a national pony competition to see if course variables affected performance. All jumping rounds were watched online. Type of faults (e.g. rails, refusals, time faults, fall of horse and or rider), type of fence (e.g. vertical, oxer), approach angle, section of the course where fault(s) occurred and round time were recorded. Spearman’s Correlation assessed if round time was correlated to total faults and a series of Kruskal-Wallis analyses determined if significant differences in faults occurred between sections of the course, where these existed, post hoc tests established where differences occurred between rounds. There was no significant difference in total faults across the 4 rounds of competition and no meaningful correlation between round time and total faults (r = 0.34; ). There were no differences between fence type and faults although more faults occurred at verticals (51.7%, n = 46 faults at verticals versus 48.3%, n = 43 at oxers; ). Faults were more likely to occur during the final quarter of the course (32.6%, n = 29) when compared to the first quarter (23.6%, n = 21; ). These results showed that faults were more likely to occur in the final quarter of a round. The information gained from this performance analysis could be beneficial to inform training or riding strategies, especially when preparing for a competition.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".