## 2 Subjects and Statistical Analysis
### Data Standardized and Sampling  
The age of the sample group for this research is from elementary through high school students and adults, sex is both. Therefore, the use of raw data means age and sex could be variables. For example, an excellent elementary athlete could have better physical ability than a non-excellent adult athlete or an excellent female athlete could have better physical ability than a non-excellent male athlete. To solve \begin{table}
\begin{tabular}{c c c} \hline \hline
**Type** & **Classification Criteria** & **Detailed Sports Type** \\ \hline \multirow{7}{*}{A} & Physical factors that decide & \\  & performance: short-term muscular endurance & \\  & Short-term muscular power and & (throwing), cycle (200–1000 m), short-track (500–1500 m), \\  & anaerobic endurance training sports & diving, gymnastics, judo, wrestling, siserum, golf, fencing, \\  & Anaerobic 40–100\%, aerobic & kendo, weightlifting, bodybuilding, sports climbing, \\  & 0–60\% sports & snowboarding (cross), alpine skiing, freestyle skiing (cross) \\  & Super Short time (less than 10 s), & \\  & shot-term performance (10–180 s) sports & \\ \hline \multirow{7}{*}{B} & Physical factors that decide & \\  & performance: mid-term muscular & \\  & endurance & \\  & Mid-term muscular endurance and & Track \& field (1000–10,000 m), cycling (4000 m), short-track (3000 m), speed skating, boxing, takekwondo, synchronized \\  & aerobic training sports & swimming, canoe, water polo, kayak, rowing, kickboxing, \\  & Anaerobic 10–40\%, aerobic & wuushu, aerobics, race walking, inline skating, swimming, \\  & 60–90\% sports & fin swimming \\  & Short time (3–20 min), mid time & \\  & performance (21–60 min) sports & \\ \hline \multirow{7}{*}{C} & Physical factors that decide & \\  & performance: long-term & \\  & cardiorespiratory endurance & \\  & Long-term muscular endurance and & \\  & long-term cardiorespiratory endurance & \\  & training sports & \\  & anaerobic 0–10\%, aerobic 90–100\% & \\  & sports & \\  & Long-time (1–4 h) performance sports & \\ \hline \multirow{7}{*}{D} & Physical factors that decide & \\  & performance: agility, coordination & \\  & capabilities, flexibility and balance & \\  & AGility, coordination capabilities & \\  & training sports & \\  & ATP-PCr 60–90\%, glycolysis & soccer, sepaktakraw, softball, archery, squash, tennis, billiards \\  & 0–20\% sports & \\ \hline \hline \end{tabular}  
* A: short-term muscular power and short-term muscular endurance, B: mid-term muscular endurance. C: long-term cardiorespiratory endurance, D: agility, coordination capability, balance. Reference: Research task report on local sports science 2017 from the development of physical training in each sports event [6].  
\end{table} 
Table 2: Classification criteria of similar sports type grouped according to the sports physiological viewpoint. this problem, I considered sex, age, sport events, and calculated the personalized T-score which is standardized to each group. Calculation formula of T-score is as follows. \[T=\left(\frac{X-M_{i}}{S_{i}}\right)\times 10+50 \tag{1}\]  
In Equation (1), \(X\) means individuals' original score, and \(M_{i}\) means the average of sex, age, and sport type group. \(S_{i}\) means the standard deviation between sex, age and sport type group. After calculating the T-score of individuals, if it scores over than 90 and less than 10, I regarded them as churn values and deleted them. For the physical score, used average of each measurement items to calculate. In Table 4, as suggested earlier, the number of sports events group excellent athletes and non-excellent ones differs. Among a total number of 16,645 athletes, excellent athletes are 1873, which represents 11.3%. When a logistic regression model is applied, if there is a big difference in the sample sizes of a dependent variables, so the fidelity of the model could be low. Concretely, the non-excellent athletes sample size is larger, so there is a possibility of the model that might focus on classification prediction of the non-excellent athletes.