Goal Scoring In Women’s Ice Hockey Reflecting On Scoring Recommendations – Pyeongchang Olympics & Finnish Playoffs 2018
Bibliographic record
Abstract
The purpose of this case study was to determine how do women in top level score goals re-flecting on scoring recommendations. The other purpose was to compare if there are differ-ences in goal scoring between women’s ice hockey in the international level (Olympics) and national level (Finnish Playoffs). Also men’s ice hockey and goals scored by Canada and USA were compared with the results. \n \nThe design, or strategy, of this study can be seen as a (comparative) case study. Previous research has been done on goal scoring as a whole, but not any specifically on scoring in women’s ice hockey. In the theoretical part the thesis takes a look on the women’s ice hockey in general, the Olympics and Women’s National League in Finland. The framework of the literature review and the chosen variables in the study comes mainly from the Finnish Ice Hockey Association recommendations in goal scoring. Other parts of the literature focus on instructive information on goal scoring and references from men’s ice hockey. \n \nThe study covers only the goal scoring against the goalie and does not tackle any reasons behind the goals (e.g. puck possession, number of scoring chances etc.). The biggest defect of this study is the number of analysed goals. Is the sample big enough to draw conclusions about scoring in women’s ice hockey in general? \n \nThe key conclusion was that many of the scoring recommendations in men's ice hockey being analysed in this study seem to support goal scoring also in women’s hockey. Also many of the results seem to be in line with the men’s results, even though there are some differences too. The small amount of goals made from the blueline in women’s ice hockey was striking as well as the big percentage of goals that included crossing the center line. When compared with men’s ice hockey the number of one-timers to score a goal is lower – On the other hand the number is clearly bigger in the Olympics than on the national level. The key outcome of this study was a list of scoring recommendations for coaching women’s ice hockey.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".