Space–time coordination dynamics in basketball: Part 2. The interaction between the two teams
Bibliographic record
Abstract
In this article, we examine the space-time coordination dynamics of two basketball teams during competition. We identified six game sequences at random, from which the movement data of each player were obtained for analysis of team behaviours in both the longitudinal (basket-to-basket) and lateral (side-to-side) directions. The central position of a team was measured using its spatial (geometric) centre and dispersion using a stretch index, obtained from the mean distance of team members from the spatial centre. Relative-phase analysis of the spatial centres demonstrated in-phase stabilities in both the longitudinal and lateral directions, with more stability in the longitudinal than lateral direction. As anticipated, this finding is consistent with the results of an analysis of individual playing dyads (see companion article, this issue), as well as the more general principle of complex systems conforming to similar descriptions at different levels of analysis. Phase relations for the stretch index demonstrated in-phase attraction in the longitudinal direction and no attraction to any values in the lateral direction. Finally, the difference between the two stretch indexes at any instant showed phase transitions between two stable patterns when the difference was represented in binary form. This result is attributed to the reciprocity between teams in their amounts of expansion and contraction when possession of the ball is won and lost.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".