Investigating The Effects Of 3-Dimensional Motion Object Tracking (3D-MOT) Training On In-game College Soccer Performance
Bibliographic record
Abstract
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 3D-MOT training and in competition decision making metrics over the duration of a soccer season. The purpose of this study was to investigate the relationship between the effects of 4-weeks of 3D-MOT training on in-game soccer performance measures. METHODS: 25 NCAA Division I soccer players were split into two groups, after exclusionary criteria (played >10 minutes per game) only 13 were utilized for analysis NT (n = 6) and CON (n = 7). Participants completed a baseline assessment of visual tracking speed (VTS) and subsequently 10 training sessions composed of 20 trials each were performed by the Neurotracker (NT) group over a 4-week period on a 3D-MOT software Neurotracker (NT; CogniSens Athletic, Inc., Montreal, Quebec, Canada). The soccer performance metrics were obtained from WyScout where 2 game averages were examined to represent pre-NT and post-NT (Wyscout, Chiavari, Italy). An analysis of covariance (ANCOVA) was utilized to compare NT and CON mean differences among variables. RESULTS: There was a no significant difference between the CON group (53.96 ± 9.55%) and NT group (58.38 ± 11.98%) successful actions post-NT (p = 0.453, η = 0.057). There was a no significant difference between CON group (64.79 ± 9.97%) and NT group (71.71 ± 12.35%) passing accuracy post-NT (p = 0.304, η = .105). There was a no significant difference between the CON group (76.1 ± 3.1%) and NT group (77.0 ± 3.3%) Short+medium passes post-NT (p = 0.847, η = 0.004). CONCLUSIONS: This is the first study to examine the effects of Neurotracker on collegiate in-game soccer performance metrics related to in-game decision-making. While none of the metrics of interest were found to have a significant difference, passing accuracy was found to a have a moderate effect (η = .105) with the NT group increasing passing accuracy by 8.5% (pre: 63.2% vs post: 71.7%) compared to the CON group increasing by 3.5% (pre: 61.3% vs post: 64.8). Further research is needed utilizing multiple sites to increase sample size and examine a potential transfer effect through training with 3D-MOT given the practical significance of improved passing accuracy.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".