Differences In GPS Metrics Between Practice, Regular Season, And Playoffs In University Football Players
Bibliographic record
Abstract
American football players require elevated and sustained musculoskeletal performance to support repeated accelerations, top running speed and large distances covered during competition. Coaches often design practice sessions to mimic the demands of competition to physically prepare athletes. However, there remains a paucity of inquiry into the differences in work completed during practice and games during the regular season and playoffs in university football players. Purpose: To examine differences in peak running speed, peak acceleration, and total distance traveled between practice, regular season competition, and playoffs in Canadian university football players. Methods: A total of 28 university male football players with an age of (mean ± SD) 20.9 ± 1.5 y volunteered to be participants over an 8-week period. Playing positions ranged from defensive backs, wide receivers, running backs, linebackers, fullbacks, and tight ends. Each participant wore a GPS monitor during practice and competition. GPS indices examined were top running speed (mph), peak acceleration (m·s2), and total distance covered (km). Differences in performance indices between practice, regular season games, and play-off games were analyzed using a one-way ANOVA, with a Bonferroni correction procedure for post-hoc comparisons. This study was approved by the research ethics review board at the University of Windsor. Results: Significant differences in top running speed (mph) were observed between practice and both regular season competition (prac 17.1 ± 0.90 ‘vs’ reg 18.5 ± 0.99, p < 0.001) and playoffs (prac 17.1 ± 0.90 ‘vs’ ploff 18.2 ± 1.13, p < 0.001). Significant differences in total distance travelled (km) between practice and both regular season competition (prac 4.77 ± 0.71 ‘vs’ reg 5.59 ± 1.41, p < 0.001) and playoffs (prac 4.77 ± 0.71 ‘vs’ ploff 5.91 ± 1.73, p < 0.001) were observed. Significant differences in peak acceleration (m·s2) between practice and both regular season competition (prac 5.04 ± 0.18 ‘vs’ reg 5.08 ± .37, p < 0.001) and playoffs (prac 5.04 ± 0.18 ‘vs’ ploff 5.07 ± 0.57, p < 0.001) were observed. Conclusion: Both regular season games and playoffs elicit significant increases in peak acceleration, peak running speed, and total distance travelled when compared to practice.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".