Effects of the Use of Sports Analytics and Team Attributes on Success in Regular Season of National Hockey League
Bibliographic record
Abstract
In this paper, we study the effects of the use of sports analytics and team attributes on teams' success in the regular season of the National Hockey League.A team's belief in analytics, the number of analytics staff, and the number of professional staff hired are examined for the use of sports analytics.Some of the team attributes considered here are the average age of players in a team, payrolls of different positions (goalies, defensemen, forwards), and numbers of the first-round draft picks in the previous three years.We shall examine the empirical data of 2014-2019 seasons.The team payroll is shown to be significantly positively correlated with a team's success in the regular season.It is interesting to see that teams scored 96 points or more are very likely advancing to playoffs, whereas teams scored 92 points or less are very unlikely advancing to playoffs.Four commonly used predictive modeling techniques (decision trees, random forests, logistic regressions, and neural networks) are applied to the data for classifying teams into playoffs or no playoffs.Random forests appear to be the best or as good as the other three techniques to yield the lowest validation misclassification error rate.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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".