Seasonal Changes in Gaelic Football Match-Play Running Performance
Bibliographic record
Abstract
Mangan, S, Ryan, M, Shovlin, A, McGahan, J, Malone, S, O'Neill, C, Burns, C, and Collins, K. Seasonal changes in Gaelic football match-play running performance. J Strength Cond Res 33(6): 1686-1692, 2019-Time of season influences performance in many team sports; however, the anomaly has not yet been examined with regards to elite Gaelic football. Global positioning systems (4 Hz; VX Sport, Lower Hutt, New Zealand) were used to monitor 5 elite Gaelic football teams over a period of 5 years (2012-2016). In total, 95 matches equated to 780 full player data sets. Running performance was characterized by total distance (m) and high-speed distance (≥17 km·h; m). High-speed distance was further categorized into 4 match quarters. Time of season was determined by month of the year. Time of season had a significant effect on total distance (p ≤ 0.001 partial η = 0.148) and high-speed distance (p ≤ 0.001 partial η = 0.105). August and September were significantly different from every other month for total distance (p ≤ 0.001) and high-speed distance (p ≤ 0.002). Month of season and match quarter had a significant interaction with high-speed distance (p ≤ 0.001 partial η = 0.106). High-speed distances run in the fourth quarter in August (478 ± 237 m) and in September (500 ± 219 m) were higher than any other quarter in any other month. This is the first study to show that time of season influences running performance in Gaelic football. The findings have major implications for training practices in Gaelic football.
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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 | 0.000 | 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".