Identifying patterns in squash contests using dynamical analysis and human perception.
Bibliographic record
Abstract
This report examines the space-time patterns of squash players as they move around the squash court in the context of a dynamical system. The phase relations that describe the squash dyad (i.e., where one player is in relation to the other player) demonstrated a strong tendency towards an anti-phase (180°) relation, as expected. When the data from a number of squash rallies (N = 47) were combined a second stable phase relation of 135° emerged, thus indicating the existence of a previously undetected lead-lag phase relation within the squash dyad. The lead phase relation belonged to the server of the rally in each instance. Further inspections of individual squash rallies demonstrated other properties consistent with a dynamical system description, namely the existence of phase fluctuations (i.e., increased variability in the phase relations), phase transitions (i.e., a switch between stable phase relations), and phase slippages as a result of a missing, or extra, phase cycle for one of the two players. Together, these results indicate that the space-time interactions of squash players might usefully be described in the context of dynamical principles of self-organizing (complex) systems. These findings furthermore suggest that the dynamical properties of the squash dyad may contain important information for identifying the squash patterns that we think we see using visual inspection. To examine this supposition we used the point-light method to represent the movements of the two squash players within a rally as contrasted against a distracter set of varying complexities. Interestingly, humans retained the ability to identify the squash dyad beyond chance even when the distracter set contained squash-like properties. Whether a dynamical analysis of these data is likewise discriminatory in its ability to detect squash behaviours from squash-like behaviours remains to be determined in future research.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".