Preliminary Analysis of Pacing Strategies in Professional Basketball
Bibliographic record
Abstract
Introduction:Pacing is the distribution of muscular work rate during exercise (Foster et al., 2004). Although pacing has been studied extensively in individual endurance sports, little research has investigated the distribution of work rates within team sports. The aim of thispreliminary investigation was to analyse workload distribution of an individual player over the course of a basketball game. Methods:A professional male basketball player (small forward; age (years) – 24; height (cm) – 196; body mass (kg) – 85.2) from a British BasketballLeague team participated in this study. 1 digital video camera (Sony DRV900E) was used to film a home game which was replayed onSportsCode software for coding of activity patterns. Activities were coded based on five arbitrary locomotor categories in a horizontaldirection (Dogramaci et al., 2011). Categories 0,1,2,3 and 4 corresponded to non-participation (i.e. substituted), stationary, walking, jogging, and sprinting respectively. Frequency of actions within each category were calculated for each quarter (Q1,2,3,4) of the match and the Coefficient of Variation (CV) was used to indicate the degree of variability in actions performed. Results:The player was active during Q1, Q2, and Q3 but did not participate in Q4. The frequency of category 1, 2, and 3 actions was greatest in Q1, while the greatest frequency of category 4 activity (sprinting) occurred in Q2. Analysis of CV indicates that the degree of variability in total activity values decreased from 68.5%, to 46.5% and 45% through Q1, Q2 and Q3, respectively. Discussion:Pacing changes were observed throughout the gamewith less heterogeneous patterns occurring in the second and third quarters, largely due to the high frequency of periods when theplayer was engaged in lower intensity (category 1 and 2) activities in Q1. However, no ‘end-spurt’ phenomenon could be identified sincethe player was substituted and did not play during Q4. Currently, there is little known about the exact mechanisms underpinning workrate distribution with a range of physiological, psychological and tactical factors potentially contributing to the selection and maintenance of different pacing strategies (St Clair Gibson et al., 2006). Match dynamics and positive emotional state may have affected the player’s pacing strategy with no reduction in work-rate as the match progressed because the team was winning. Therefore, further research examining pacing strategies during basketball is warranted. References:Dogramaci SN, Watsford ML, Murphy JA (2011). J Strength and Cond Res, 25 (3), 852-859. Foster C, de Koning JJ, Hettinga F, Lampen J, Dodge C, Bobbert M, Porcari JP (2004). Int J Sports Med, 25 (3), 198-204. St Clair Gibson A, Lambert EV, Rauch LHG, Tucker R, Baden DA, Foster C, Noakes DT (2006). Sports Medicine, 36 (8), 705-722.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".