Quiet eye predicts goaltender success in deflected ice hockey shots<sup>†</sup>
Bibliographic record
Abstract
In interceptive timing tasks, long quiet eye (QE) durations at the release point, along with early tracking on the object, allow performers to couple their actions to the kinematics of their opponent and regulate their movements based on emergent information from the object's trajectory. We used a mobile eye tracker to record the QE of eight university-level ice hockey goaltenders of an equivalent skill level as they responded to shots that deflected off a board placed to their left or right, resulting in a trajectory with low predictability. QE behaviour was assessed using logistic regression and magnitude-based inference. We found that when QE onset occurred later in the shot (950 ± 580 ms, mean ± SD) there was an increase in the proportion of goals allowed (41% vs. 22%) compared to when QE onset occurred earlier. A shorter QE duration (1260 ± 630 ms) predicted a large increase in the proportion of goals scored (38% vs. 14%). More saves occurred when QE duration (2074 ± 47 ms) was longer. An earlier QE offset (2004 ± 66 ms) also resulted in a large increase in the number of goals allowed (37% vs. 11%) compared to a later offset (2132 ± 41 ms). Since an early, sustained QE duration contributed to a higher percentage of saves, it is important that coaches develop practice activities that challenge the goaltender's ability to fixate the puck early, as well as sustain a long QE fixation on the puck until after it is released from the stick.
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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 | 0.000 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".