VISUAL TRACKING SPEED AND SOCCER PERFORMANCE METRICS
Bibliographic record
Abstract
Julia Phillips, Thomas Andre, Jeremy Loenneke. University of Mississippi, Oxford, MS. BACKGROUND: Visual training has previously been shown to correlate to sport specific level of training and sport specific performance measures in controlled conditions. However, it remains unclear if these relationships exist between visual tracking thresholds and in competition decision making metrics over the duration of a soccer season. The purpose of this study was to investigate the relationship between visual tracking speed (VTS) baseline scores and soccer-specific performance measures. METHODS:19 NCAA Division I soccer players were tested before the 2021 spring soccer season, after exclusionary criteria (played in 7 of 9 matches and >10 minutes per game) only 13 were utilized for analysis. VTS was measured from 1-core session (20 trials) on a 3-demensional multiple object tracking (3D-MOT) software Neurotracker (NT; CogniSens Athletic, Inc., Montreal, Quebec, Canada). The soccer performance metrics were obtained from WyScout (Wyscout, Chiavari, Italy). Spearman’s rank order correlation coefficient was utilized to examine potential correlations between criterion variables. RESULTS: There was weak nonsignificant correlation between VTS score and passing accuracy (r = -0.380, p = 0.20). However, there was a strong correlation found between consistency score and passing accuracy (r=0.650, p = 0.016). When examining players based on their role of attackers compared to defenders, there were strong correlations for attacking players consisting of a nonsignificant strong correlation with consistency and passing accuracy (r = 0.730, p = 0.063) was observed. For defenders, consistency and defensive win rate had a strong correlation (r = 0.731, p = 0.099). CONCLUSIONS: This is the first study to examine NeuroTracker (NT) VTS and soccer performance metrics related to in-game decision-making. While consistency was found to correlate with some of the decision-making metrics, VTS did not correlate with any team performance metrics. Future research should seek to include multiple teams for improved sample size while also exploring a potential transfer effect through training.
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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 | 0.001 |
| 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".