Scouting Automated Ratings Analyzing Habits (SARAH): A Statistical Methodology for Scouting and Player Development
Bibliographic record
Abstract
The project serves a two-fold purpose: to reduce the time that scouts and coaches spend trying to identify what players have foundational on-ice habits, and to streamline the process of evaluating the developmental progress of a players' habits. Essentially what we did was first look at the various national women's hockey teams and identify the set of "habits" a player regularly executes (i.e., edgework, catching the puck in the hip pocket, pass placement, etc). Combining the dataset of players' habits with a set of players' microstats (entries via pass/stickhandling, exits via stickhandling/pass, accurate/inaccurate passes, etc.), we developed a random forest classification model to accurately predict if a player possesses a certain habit based on their set of microstats. We also used random forest regression on our data to see how habits impacted each specific microstat. Combining this with an estimate of how frequently players used each habit, we created a Player Development Matrix for a player's habits based entirely on their microstats. To help coaches, scouts, and anyone else access & use these tools, we've also created an interactive visualization for these models using our training dataset of national women's hockey teams in the last Worlds and Olympics.
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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 | 0.013 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".