The analyses and types of breakout by teams DHL extraliga junior in season 2015-2016
Bibliographic record
Abstract
Aim of the thesis The main aim of this thesis is an analysis breakouts by extraliga junior team, selection the most effective variant and their subsequent comparison with the Canadian national team to 20 years. Constituent part is to create a complete overview of the theoretical knowledge which can be used in the analysis breakouts. The analysis can serve as an inspiration for coaches of youth cathegory during training and acquisition in this phase of the game. Methods In the thesis tracking method was used, which was used in the analysis breakouts. The analysis was performed by using the method of direct observation of the game, but also indirect observation of video recordings provided by videocoaches or assistant coaches of selected teams. Results During analyzing domestic literature, it was found that it is not paid enough attention to the breakouts and there is no methodology developed compared to a foreign literature. Of the reporting teams and their variants for the breakout and development of the attack, three of the most effective were selected. For comparison, there were also mentioned variants of the breakouts of the Canadian national team under 20 years, which was followed at the World Cup to 20 years. The best breakouts from DHL Extraliga junior teams has HC Plzeň 1929, HC Sparta Praha...
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".