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Research on pattern recognition and sports performance of table tennis game strategy based on big data mining technology

  
21 mar 2025

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Figure 1.

MHSA Module
MHSA Module

Figure 2.

Based on YOLOv8’s table tennis trajectory prediction results
Based on YOLOv8’s table tennis trajectory prediction results

Figure 3.

The game of table tennis in different situations in the game
The game of table tennis in different situations in the game

Figure 4.

FZD line combination form utilization card
FZD line combination form utilization card

Analysis of the specific route characteristics of the variation line

Number Variant line combination form frequency score Lose point Usage rate(%) Scoring rate(%) Contribution rate(%)
1 Reverse skew - anyway straight 150 105 45 16.59% 70.00% 11.62%
2 Middle reverse oblique - reverse straight 23 16 7 2.54% 69.57% 1.77%
3 Anti-center oblique – center Positive and oblique 58 45 13 6.42% 77.59% 4.98%
4 Medium straight - medium positive oblique 23 18 5 2.54% 78.26% 1.99%
5 Anti-integrity – right Positive and oblique 134 90 44 14.82% 67.16% 9.96%
6 | medium oblique – Positive and oblique 49 40 9 5.42% 81.63% 4.42%
7 Anticlinal. - Anticlinal 36 30 6 3.98% 83.33% 3.32%
8 Straight forward and backward mesoblinia 7 6 1 0.77% 85.71% 0.66%
9 · Anti-center oblique - center straight 15 13 2 1.66% 86.67% 1.44%
10 Median oblique - median straight 9 7 2 1.00% 77.78% 0.77%
11 Positive oblique - median oblique 25 22 3 2.77% 88.00% 2.43%
12 Counterup rightness - median oblique 17 17 0 1.88% 100.00% 1.88%
13 Positive oblique - positive straight 65 53 12 7.19% 81.54% 5.86%
14 Medium oblique - straight forward and backward 25 20 5 2.77% 80.00% 2.21%
15 Straight forward and backward 112 80 32 12.39% 71.43% 8.85%
16 Middle antiskew - antiskew 83 50 33 9.18% 60.24% 5.53%
17 Median straight - median anticlinal 38 25 13 4.20% 65.79% 2.77%
18 Median oblique - median anticline 35 24 11 3.87% 68.57% 2.65%

The application of the variation and the non-linear strategy

/ Frequency Score Lose point Scoring rate Usage rate Contribution rate
Overall data 6154 3077 3077 50.00% 100.00% 50.00%
Variation line 2463 1536 927 62.36% 40.02% 24.96%
Invariant line 3691 1625 2066 44.03% 59.98% 26.41%
T 21.756 14.564 1.485
P 0.000 0.000 0.135

Various forms of variation

/ Frequency Score Lose point Scoring rate Usage rate Contribution rate
Variation line (total) 2463 1536 927 62.36% 100.00% 50.00%
Meet... Intermediate level 615 452 163 73.50% 24.97% 18.35%
Meet... Become forehand 926 592 334 63.93% 37.60%** 24.04%**
Meet... Variable backhand bit 922 452 470 49.02% 37.43% 18.35%
F 0.785 21.264 11.265
P 0.422 0.000 0.000

Comparison of table tennis positioning and tracking method

Method Accuracy rate Single frame detection time /ms Model size /Mibyte
Color detection 55.56% 8.15 0.2
Contour detection 64.22% 9.23 0.2
YOLOv4 97.26% 27.01 244.66
YOLOv5s 96.18% 15.21 13.75
Faster regional convolution neural network 75.12% 198.25 113.48
YOLOv8 98.39% 15.23 13.24
Method Central position pixel error Detection speed /(frame •s−1) Model size /Mibyte
YOLOv4 14 pixels 43 244.66
YOLOv5s 36 pixels 75 13.75
Faster regional convolution neural network - 3 113.48
YOLOv8 11 pixels 64 13.24