Home Team (Dis)Advantage Patterns in the National Hockey League: Changes Through Increased Emphasis on Individual Performance With the 3-on-3 Overtime Rule
Bibliographic record
Abstract
Past research examining National Hockey League (professional ice hockey; NHL) data from the 4-on-4 overtime era (seasons between 2005-06 and 2013-14) revealed an inconsistent home team (dis)advantage pattern such that home teams that were superior to their visiting counterparts had slightly greater odds of winning during regulation play compared to overtime (demonstrating home crowd advantages for team performance during regulation); in contrast, home teams experienced lower odds of winning in the shootout period than in overtime regardless of team quality (thereby demonstrating risks for individual choking from home crowd pressures). In this study, we explored the NHL home (dis)advantage pattern during four more recent seasons (2015-16 through 2018-19) in which the league instituted 3-on-3 play during overtime (perhaps increasing individual pressure for athletes competing in the 3-on-3 overtime period). We used archival data from the regular season (N=5,002 games) to compare home teams' odds of winning in regulation (with 5-on-5 skaters per team) to overtime (with 3-on-3) and in the shootout, adjusting for the quality of home and visiting teams. We conducted fixed-effects and multi-level logistic regression modeling. Evenly matched home teams were 1.66 times more likely to win than inferior home teams when games concluded in regulation versus overtime. Superior home teams were 4.24 times more likely to win than inferior home teams when games concluded in regulation rather than overtime. Thus, it is apparently more difficult for superior and evenly matched home teams to win in overtime than during regulation, suggesting that such home teams may be susceptible to choking in overtime. In contrast to the earlier 4-on-4 overtime era, home teams did not have lower odds of winning in the shootout compared to overtime. These results may have implications for NHL coaches' and players' tactical decision-making.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".