Case Article—Moneyball for Murderball: Using Analytics to Construct Lineups in Wheelchair Rugby
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Motivated by the problem of lineup optimization in wheelchair rugby (WCR), this case study covers descriptive, predictive, and prescriptive analytics. The case is presented from the perspective of a new assistant coach of Canada’s national WCR team, who has been tasked by the head coach to use various analytics techniques to improve their lineups. Whereas the data and actors are fictitious, they are based on real data and discussions with the national team coach and sport scientists. To solve the case, students must conduct data analysis, regression modeling, and optimization modeling. These three steps are tightly linked, as the data analysis is needed to prepare the data for regression, and the regression outputs are used as parameters in the optimization. As such, students build proficiency in developing an end-to-end solution approach for a complex real-world problem. The primary learning objectives for the students are to understand the differences between descriptive, predictive, and prescriptive analytics, to build proficiency in implementing the models using appropriate software, and to identify how these techniques can be applied to solve problems in other sports or other application areas. Supplemental Material: The Teaching Note and data files are available at https://www.informs.org/Publications/Subscribe/Access-Restricted-Materials .
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it