Login / Signup

Constructing a Talent Identification Index System and Evaluation Model for Cross-Country Skiers.

Xizhang HuangGang WangChao ChenJiangshan LiuBjorn KristiansenAndreas HohmannKewei Zhao
Published in: Journal of sports sciences (2020)
A talent identification index system for male and female cross-country skiers in four age groups (11-12 years old, 13-14 years old, 15-16 years old, and 17-18 years old) was established. The system comprises five body shape indexes ( i =5): Leg-to-Body Ratio (LBR), body fat percentage, maturity status, spreaded brachia index, and upper extremity length. The physiological function indexes ( i =2) are VO2max and haemoglobin mass (Hb). The psychological indexes ( i =5) cover reaction time, perception speed, a quality-of-will scale, an attention test, and operational thinking. The physical fitness indexes ( i =11) comprise upper limb explosiveness, vertical jump, 3000-metre run, orthostatic forward flexion, closed-eyes single-leg stand, standing long jump, 20-metre sprint, pull-ups (males), flexed arm hang (females), hexagon jump, and a Functional Movement Screen (FMS) test. The athletic performance indexes ( i =3) comprise on-snow time trials for 1.2 km, 5 km, and 10 km. The talent identification evaluation model was created using automated evaluation software. The talent identification index system and evaluation standard table for cross-country skiers passed the P60 shortlist and P90 elite boundaries established using the percentile method. Thus, the results of this test profile verify that the evaluative model is objectively effective.
Keyphrases
  • high throughput
  • bioinformatics analysis
  • machine learning
  • physical activity
  • deep learning
  • working memory
  • high intensity
  • resistance training
  • clinical evaluation