Pass Evaluation in Women's Olympic Ice Hockey
Bibliographic record
Abstract
Much of modern sports analytics is based on player and ball tracking data. Such data are mostly collected using wearable devices or an array of carefully located cameras and detectors. Many teams do not have such a luxury, especially in undervalued sports such a women's ice hockey; of those that do, the data are not typically publicly available. Recent developments in computer vision have allowed for the collection of tracking data directly from widely available broadcast video. Using event and tracking data collected directly from broadcast video during the elimination round games of the 2022 Winter Olympics, we create a framework for evaluating passing in women's ice hockey. We begin with physics-based motion models for both players and the puck, which we use to develop a model for probabilistic passing. Next, we model the rink control for each team and the scoring probability of the offensive team. These models are then combined into novel metrics for quantifying the various aspects of any single pass. By looking at the entire corpus of plays, we create several summary metrics describing players' risk-reward tendencies, and overall passing ability. All of our metrics can be presented graphically, allowing for easy adaptation by coaches, players, scouts, and other front-office personnel.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".