Comparison of Balance, Reaction Time, Attention and Bmi Values in Individual and Team Sports
Bibliographic record
Abstract
In this study, we aimed to investigate static and dynamic balance, reaction time, attention and BMI values at the athletes being in the individual and team sport. 78 individual athletes (karate 17, judo 15, Gymnastics 14, table tennis 12, wrestling 20) and 58 team athletes (handball 23, basketball 13, volleyball 10, football 12) with formal licensed and average ages 13.08±1.36 years were participated as voluntarily. For demographic characteristics, participants have filled in questionnaires and Bass Stick static balance, Y dynamic balance, Nelson hand reaction time and D2 attention test were applied. First, we have applied the Kolmogorov-Smirnov test for normality of the data, the normal distribution is observed and then independent t test was used. In order to see the relationship between data, Pearson’s correlation test was applied. The average sporting age of participants was 4.32 ± 1.87 years, height 1.59 ± 0.11m, body weight 50.18 ± 12.74 kg and per week training time 10.33 ± 4.41 hours. In addition, their BMI values were19.49 ± 3.10 kg/m2 which is in the normal range, i.e., no difference between individual and team athletes was observed. It has been observed that for individual athletes, total number of items is 525.42 ± 108.29, total number of errors is 454.04 ± 105.86, concentration performance is 182.14 ± 62.53, reaction rime is 0.29 ± 0.06 sec, static balance is 7.10 ± 4.49 sec, dynamic balance is 208.37 ± 32.20 cm. On the other hand, for team athletes, these values were 500.59 ± 79.70, 382.31 ± 93.60, 168.35 ± 56.51, 0.26 ± 0.05 sec, 5.68 ± 2.67 sec and 187.15 ± 29.99 cm, respectively. We have found that while age increased the static and dynamic balance increased, but training time increased solely the dynamic balance increased. In addition, when taking into account the static and dynamic balance, it has been observed that the individual athletes were better than the team athletes but for the reaction time was contrary, i.e., team athletes were better than the individual ones.
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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 | 0.001 | 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".