Fan response to the analytics revolution in hockey: possession metrics and NHL attendance
Bibliographic record
Abstract
After the "Moneyball" revolution in baseball, where advanced statistics and analytics were used in player personnel and coaching decisions, other sports followed in its footsteps. Although decidedly more difficult than baseball, with its highly individualized matchups, the other sports began to find better ways to measure player performance and incorporate this information into draft, free agency, and on-field decisions. One of the last sports to officially enter the analytics revolution in sports was ice hockey. This could be due to a variety of reasons that stem from the speed and within-game changes being difficult to measure and model, to resistance in the ranks of front office personnel; but, whatever the underlying reasons, hockey appeared slow to adapt. This pattern officially changed in the summer of 2015. Independent public hockey researchers and those that developed analytical systems at lower levels of hockey were hired by National Hockey League (NHL) teams. The biggest initial name was Kyle Dubas, who was hired by the Maple Leafs in the hockey hotbed of Toronto, Canada. Others who had developed a following on their advanced hockey statistics websites - such as Sunny Mehta, Tyler Dellow, and Tim Barnes - were also hired in the same short period of time by the New Jersey Devils, the Edmonton Oilers, and the Washington Capitals. The following season, John Chayka of the Arizona Coyotes became the youngest general manager in NHL history, based upon his background and use of analytics in hockey.
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How this classification was reachedexpand
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".