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Research on the optimization system of athlete selection and training effect based on big data

Yongkang Guan

Yongkang Guan

School of Physical Education and Health, Institute of Information Technology GUETGuilin, China
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Guan, Yongkang
 et 
Weijia Xue

Weijia Xue

School of Physical Education and Health, Institute of Information Technology GUETGuilin, China
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Xue, Weijia
  
21 mars 2025
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Publié en ligne: 21 mars 2025
Reçu: 11 oct. 2024
Accepté: 05 févr. 2025
DOI: https://doi.org/10.2478/amns-2025-0563
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© 2025 Yongkang Guan, published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1.

Significant analysis between indexes(male)
Significant analysis between indexes(male)

Figure 2.

Significant analysis between indexes(female)
Significant analysis between indexes(female)

Figure 3.

Pearson correlation coefficient analysis between indexes(male)
Pearson correlation coefficient analysis between indexes(male)

Figure 4.

Pearson correlation coefficient analysis between indexes(female)
Pearson correlation coefficient analysis between indexes(female)

Figure 5.

The number of clustering and the fitting effect(male)
The number of clustering and the fitting effect(male)

Figure 6.

The number of clustering and the fitting effect(female)
The number of clustering and the fitting effect(female)

Figure 7.

Comparison of algorithms
Comparison of algorithms

Figure 8.

Basic structure diagram of the system
Basic structure diagram of the system

The cluster of male athletes

Total score BMI CV 50m SLJ ST 1000m PU Count
1 77.03 22.02 3524.06 6.28 236.62 15.52 240.14 8.19 401
2 67.31 23.53 3061.73 5.93 231.14 14.05 245.08 8.01 114
3 73.63 20.32 3226.11 5.92 234.86 15.14 242.22 8.57 295
4 77.95 20.82 3983.44 6.23 238.03 16.10 249.25 7.95 320
5 79.38 24.69 4598.46 5.90 239.67 17.05 253.55 6.90 135

The cluster of female athletes

Total score BMI CV 50m SLJ ST 1000m SU Count
1 78.61 22.83 3993.82 7.46 167.79 21.41 231.64 33.47 91
2 74.48 21.24 3035.79 7.26 163.68 20.02 235.85 31.72 285
3 67.00 23.59 2037.57 7.1 161.40 19.26 238.08 30.30 65
4 76.67 21.99 3455.24 7.21 165.65 20.56 234.49 32.42 237
5 72.69 20.78 2613.45 7.18 162.41 19.34 232.45 31.21 222

The true recommendation of the test set

Stud. FB. BB. VB. TT. B. T. S. Top2 recommendation
1 0.50 0.27 0.09 0.20 0.64 0.09 0.19 Badminton, football
2 0.34 0.24 0.09 0.15 0.42 0.13 0.27 Badminton, football
3 0.37 0.84 0.10 0.26 0.34 0.10 1.64 Badminton, swimming,
4 0.10 0.94 0.19 0.18 1.68 0.14 1.64 Badminton, swimming
5 0.19 1.20 0.13 0.19 2.22 0.13 0.61 Badminton, basketball
6 0.38 0.85 0.10 0.26 0.33 0.10 1.61 Swimming, basketball
7 0.35 1.27 0.09 0.20 0.20 0.09 0.94 Basketball, swimming
8 0.50 0.27 0.09 0.20 0.64 0.09 0.19 Badminton, football

Athletes selection indexes

Primary index Secondary index
Physical form Weight, height, sitting height, length of legs, finger distance, Kole index, chest circumference, shoulder width, arm’s length, body fat, upper and lower limbs length, hips width, waistline, arch
Body function Lung activity, maximum oxygen uptake, hemoglobin, blood pressure, cardio index, blood testosterone level
Physical quality Lead-up, 50m run, 1000(800)m run, fixed jump, seated forward bend(sit-ups)
Special quality Reading chart, using compass, route selection, sense of direction, distance judgment, checkpoint capture, repositioning
Mental quality Cognitive psychology, emotional emotion, will quality, personality characteristics, temperament type, general intelligence, motion intelligence
Trainer Sports ability, sports sense, acceptance ability, combat psychological quality, will-quality
Langue:
Anglais
Périodicité:
1 fois par an
Sujets de la revue:
Sciences de la vie, Sciences de la vie, autres, Mathématiques, Mathématiques appliquées, Mathématiques générales, Physique, Physique, autres
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