The Applicability of the Risk Score Approach to Competitive Sport: Development of a Physical Success Score for the Canadian Football League Combine
Bibliographic record
Abstract
Abstract Matsuo, H, Funasaki, K, and Yamada, S. The applicability of the risk score approach to competitive sport: development of a physical success score for the Canadian football league combine. J Strength Cond Res 36(3): 695–701, 2022—In the scouting combine of the Canadian Football League (CFL), players are measured for 6 athletic abilities, including 2 measurements of body size. For coaches and their players who desire to play in the CFL, knowing the players metrics that are important for being drafted and their levels will help coaches plan and evaluate their training. Thus, the purpose of this study was to provide a simple scoring system using the predictors of CFL Combines identified by multivariate analysis and their regression coefficients, and reference values. In this study, authors created a scoring system to classify draft success and failure of CFL Combines based on the measurement results. This scoring system was named as the physical success score (PSS). To do so, a repeated grid-search cross-validation for variable selection algorithm was performed on the players (line: n = 211, big skill linebacker, running back and tight end: n = 177, skill [defensive back and wide receiver]: n = 231) who participated in the CFL Combines between 2011 and 2019. The final binary logistic regression models and the reference values were used to generate the PSS for each group. As a result, PSS of the line, big skill, and skill were developed. In each group, all possible total scores and the estimated draft success probability were shown. From the results of this study, it was concluded that the PSS approach can provide useful information for coaches in setting training goals.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".