Real-time Application of Optimal Experimental Design to Study Factors Related to Doubles Pickleball Match Outcome
Bibliographic record
Abstract
In kinesiology and exercise science, researchers want to identify factors associated with players’ performance. In racquet sports, matches are played in a tournament format, and researchers often find observational data for their studies rather than collecting experimental data. Even when an experiment is conducted, which is very rare in literature, a randomized design has been considered to produce unbiased results. Given a small number of participants and limited time, in theory, an optimal experimental design can produce more statistical information about parameters of interest than a completely randomized design. This article includes simulations and a real application of an optimal experimental design to racquet sports research. In our research plan, there were some logistical considerations (e.g., no replicated matches, computation time), and simulations demonstrated that the c- and D-optimal designs result in higher statistical power for single- and multiple-parameter hypothesis testing, respectively, than the completely randomized design. For our research, the D-optimal design was applied to a doubles pickleball tournament with 16 subjects and one half of a day. Participants’ fitness and skill levels were measured in the morning, the doubles tournament was designed in real time on site using a pre-written algorithm, and the tournament design was executed on the same day. To make this computation accessible, an interactive applet is provided in the Appendix of this article with instructions.
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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 | 0.057 | 0.137 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".