Bibliographic record
Abstract
Abstract: The predictive performance of various team metrics is compared in the context of 105 best-of-seven national hockey league (NHL) playoff series that took place between 2008 and 2014 inclusively. This analysis provides renewed support for traditional box score statistics such as goal differential, especially in the form of Pythagorean expectations. A parsimonious relevance vector machine (RVM) learning approach is compared with the more common support vector machine (SVM) algorithm. Despite the potential of the RVM approach, the SVM algorithm proved to be superior in the context of hockey playoffs. The probabilistic SVM results are used to derive playoff performance expectations for NHL teams and identify playoff under-achievers and over-achievers. The results suggest that the Arizona Coyotes and the Carolina Hurricanes can both be considered Round 2 over-achievers while the Nashville Predators would be Round 2 under-achievers, even after accounting for several observable team performance metrics and playoff predictors. The Vancouver Canucks came the closest to qualify as Stanley Cup Finals under-achievers after they lost against the Boston Bruins in 2011. Overall, the results tend to support the idea that the NHL fields extremely competitive playoff teams, that chance or other intangible factors play a significant role in NHL playoff outcomes and that playoff upsets will continue to occur regularly.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".