Comparison Of Physiological Demands Of Basketball Practice Sessions To A Pre-season Game
Bibliographic record
Abstract
An athlete’s practice is ideally constructed to prepare them for game-type conditions. Training volume however, can be influenced by several factors including frequency, intensity, time, type, and volume of practice. Differences between basketball practices, scrimmages, and games are already illustrated (Montgomery et al., 2010 & Klusemann et al., 2013). The ability to improve practices to mimic game-type conditions could better prepare the athletes to perform in games. PURPOSE: To compare the physiological demands of practice to a pre-season game. METHODS: Ten Division II men’s basketball players participated in this study (20.7 ± 0.9 yrs, 94.0 ± 13.2 kg, 1.90 ± 0.09 m). All players wore Hexoskin activity monitors (Hexoskin, Montreal, CAN) which measure heart rate (HR) via ECG, g-force (above that of the earth’s gravity) via triaxial accelerometry, and time of day (including time spent wearing the monitor). Monitors were worn at practices for the week leading up to and the week following a pre-season game. A dependent, two-tailed t-test compared the average of twelve days of practice (P) to the pre-season game (G). Coefficient of determination was utilized to compare change in time while wearing the monitor to change in training volume (total g-force). RESULTS: Time spent in practice was significantly greater than the game (P = 144 ± 2; G = 126 ± 2 min; p ≤ 0.05). While average HR did not differ (P = 121 ± 5; G = 121 ± 17 bpm), maximal HR was higher in the game (P = 180 ± 6; G = 189 ± 7 bpm; p ≤ 0.05). Average g-force was higher in practice (P = 0.38 ± 0.05; G = 0.30 ± 0.11 m/s2; p ≤ 0.05), however maximal g-force did not differ between the two (P = 3.64 ± 0.42; G = 3.97 ± 0.71 m/s2, p = 0.10). Total g-force (average g-force of the session multiplied by the minutes of the session) differed between the two conditions (P = 55.2 ± 7.7; G = 37.4 ± 13.6 min·m/s2; p ≤ 0.05). A coefficient of determination elucidated an r2 = 0.175, indicating that only 17.5% of the change in total g-force was explained by the difference in time between P and G. CONCLUSION: Practices were longer, less intense based on maximal HR and a trending maximal g-force, with a higher average g-force and total g-force compared to the pre-season game. Volume of training for practices could be better tailored to more closely mimic game-type conditions, although the goals of practice should be considered.
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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 | 0.000 | 0.000 |
| 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) | 0.002 | 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".