A PERCEPTUAL-COGNITIVE PROGRAM TO TRAIN SOCCER PLAYERS’ DECISION MAKING
Bibliographic record
Abstract
Elite sport is all about making the right decision, at the right time, under pressure and in any context (Vickers, 2007).Many tools are available to train decision-making skills.One used commonly in elite level sports is video feedback.However, with young players, this tool is rarely used due to time constraints and athlete-coach ratio.Nowadays, an alternative to video feedback is used in the form of a perceptual-cognitive exercise (Zentgraf, Heppe, & Fleddermann, 2017).This exercise has significant effects on decision-making skills when used during warm-ups, but cumulative effects have not been measured (Holding, Meir, & Shi, 2017).Also, perceptual-cognitive exercise has mainly been studied in experimental contexts, leaving few considerations for athletes and coaches' reality.Therefore, this study aims to help a coach integrate perceptual-cognitive exercises in his training program.The first objective is to describe the clinical supervision process offered to a youth coach.Sub-objectives involve (a) understanding the different phases of the clinical supervision and (b) measuring players' performance as indication of change.The second objective is to describe players and coach's perceptions about quality and effectiveness of such program.One female coach participated in the study, as did her 27 young female players (M=12,25 ± 0,28).They completed a six-week perceptual-cognitive program, twice a week.To jot down the supervision process, a log-book was used by the researcher during 16 weeks.Players' performance was measured with Stivi software for response time and decision accuracy during practice (n=13).Decision accuracy in game was measured with observation grids, three times: week one, six, and three weeks post intervention.As for actors' perceptions, focus groups were used with players' (n=2x8) and an individual semi-structured interview was used with the coach (n=1).Findings indicate that the clinical supervision process presents three phases.In the programming phase, results show that building sequences alone for perceptual-cognitive exercises is complex and time consuming, and thus unlikely to be attempted by the coach alone.In the interaction phase, the six week program was easily integrated to the training schedule.Also, the coach noted positive changes in her players, such as search for information at play and verbal support to partners holding the ball.Also, results show a slight decrease for response time and increase for decision accuracy.Finally, during the evaluation phase, performance measures enabled an objective analysis of advantages and limits of the proposed program.
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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) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".