Longitudinal development of match performance in elite field hockey players training within a high-performance environment
Bibliographic record
Abstract
This study examined whether the performance characteristics of male university field hockey players were associated with undergraduate degree year of study. Fifty-two male university field hockey players (age 20.8 ± 2.4 years, stature 1.79 ± 0.06 m, body mass 75.8 ± 8.3 kg) were monitored over 85 matches played across four national league seasons in the UK (2018–2022) using 15 Hz Global Positioning System units and heart rate monitors. Total distance, high-speed running distance (≥15.5 km.h−1), accelerations (≥2 m.s−1), decelerations (≤ −2 m.s−1), average heart rate and percentage of time spent at >85% of maximum heart rate were compared between 1st year, 2nd year and final year players across 1090 player-match files. Hierarchical linear modelling of performance characteristics (Match – level 1, Player – level 2), normalised to 70 min match time, found that the total and high-speed running distance covered by final year players was lower compared to 2nd year players (by 163 m and 127 m, respectively, both p < 0.05). With increased training experience in a high-performance programme, running performance required to perform optimally could be reduced in university players due to the high level of match performance achieved, development of game understanding and improved technical ability.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".