The Running Performance Profile of Elite Gaelic Football Match-Play
Bibliographic record
Abstract
Malone, S, Solan, B, and Collins, K. TITLE: The running performance profile of elite Gaelic football match-play. J Strength Cond Res 31(1): 30-36, 2017-The current study examined (a) the match running performance of Gaelic football and (b) the decrement in match running performance with respect to position. Global positioning satellite system technologies (4-Hz; VX Sport) were used with 3 elite intercounty teams across 3 full seasons with 250 full game data sets collected. Game movements were classified according to game actions and distance covered across speed zone thresholds (total distance [TD], high-speed running distance [HSRD; ≥17 km·h], sprint distance [SD; ≥22 km·h]; accelerations [n]; peak speed [km·h]). The influence of running performance in each quarter on the subsequent quarter was analyzed across all positional roles. The mean (±SD) TD and HSRD covered during the game were 8,889 ± 1,448 m and 1,596 ± 594 m, respectively. Results show a temporal profile for TD with reductions in the second (-4.1%), third (-5.9%) and fourth (-3.8%) quarters, respectively. There was a significant reduction in HSRD in the second (-8.8%), third (-15.9%), and fourth (-19.8%) quarters when compared to the first quarter (p < 0.001). Positional differences were observed for distance-based measures with the middle 3 positions (half-back, midfield, and half-forward) completing the highest running performances. These positions also showed increased decrements in TD and HSRD and SD across quarters. The current data indicate a reduction in exercise intensity over the duration of elite Gaelic football match-play. It is unclear if this reduction is because of fatigue, pacing, contextual factors, or nutritional strategies employed by players.
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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 | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".