The relationship between efficacy and performance in hockey goaltender dyads
Bibliographic record
Abstract
Past research has shown a positive relationship between efficacy and performance (Feltz & Lirgg, 1998; Hodges & Carron, 1992; Lichacz & Partington, 1996) although there is limited evidence of this relationship in small teams. Feltz and Lirgg (1998) found a positive relationship between efficacy and sport performance in hockey players, however they excluded goaltenders due to their unique position. The present study replicated Feltz and Lirgg (1998) however goaltenders were included and other team members were excluded. Data was collected from 12 goaltenders from three Ontario hockey leagues. Efficacy was measured through an online questionnaire and official game statistics provided the performance measures. Data was collected for 70 games providing a total of 112 questionnaire responses. Results of this study revealed non-significant relationships between self-efficacy and save percentage [R2 = .012, F(1, 58) = .617, p < .05], self-efficacy and minutes played [R2 = .03, F(1, 58) = 1.79, p < .05], collective efficacy and save percentage [R2 = .012, F(1, 58) = .700, p > .05] and collective efficacy and minutes played [R2 = .000, F(1, 58) = .017, p > .05]. Findings suggest there may be other factors unique to the goaltender position involved with predicting goaltender performance other than efficacy. Results of the present study are not consistent with Feltz and Lirgg’s (1998) findings, however other published research has found a non-significant relationship between efficacy and sport performance (Sitzmann & Yeo, 2013).
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".