# Constructing a Gaming Model for Professional Tennis Players Using the C5.0 Algorithm
## 4 Results  
This study used the C5.0 decision tree algorithm and notational analysis to construct a gaming model utilizing three variables (position, technique, and location) in the tennis matches between the players [PERSON] and [PERSON]. The goal was to identify the combination with the highest scoring-rate. The three parts of the model analysis were as follows:  
This study analyzed 10 games between 2 professional male tennis players, [PERSON] and [PERSON], played between 2007 and 2019, defining the 13 categories of tennis techniques (Table 1) and the players' volleys and locations of strokes and serves (Figures 1-3) [39]. The authors invited 3 tennis players with more than 10 years' experience to annotate and record the players using the methods outlined in Part 1 while watching a video with 100 shots to check the classification accuracy of the results of the C5.0 algorithm. Therefore, we calculated the process using the Equations (6) and (7):  
\[\text{Average MA}=(0.98+0.94+0.95)/3=0.957 \tag{6}\]  
\[\text{R}=(3\times 0.957)/[1+(3-1)\times 0.957]=0.977 \tag{7}\]  
This 0.977 (97.7%) accuracy is higher than that of any other method, that is, the 92.81% accuracy of KNN by [PERSON] et al. [34], the 89.97% of CNN, the 92% of [PERSON] and [PERSON] [36], and the 90% of [PERSON] et al. [40]. This shows that the agreement between the 3 coders met the standard, and that the 10 games can be fully coded. In Table 2, the \"Experts\" column reports the results of the assessment that the 3 experts performed on 10 videos of [PERSON] and [PERSON]'s matches played between 2007 and 2019. The experts jointly decided where the players served, where they received the ball, and how they used tennis techniques. These results can provide the basis for the accuracy comparison of various algorithms. The 2 players shot 13,230 times. To analyze the 10 videos of the games, the annotation and classification calculation processes were as follows:  
1. All records were set as a node. 2. According to Part 3, Steps 1 and 2, the videos of the games were compared. Then, based on video analysis, appropriate, automatic dividing points were applied for the variables in each division method, such as tennis techniques, players' position, returner's impact location, landing location, and relative player locations. 3. The analysis continued until every strike satisfied the node classification. Table 2 reports the results of the C5.0 algorithm on the statistics of the techniques used by [PERSON] and [PERSON] following the analysis of the 10 videos, preceded by the assessment of the experts' statistics using [PERSON]'s method [39]. The three techniques that were most commonly used by the two players were forehand, backhand, and first serve. Therefore, this database can be used for making comparisons between various artificial-intelligence algorithms in the future. Based on the experts' analyses, [PERSON] had 6789 strokes, and [PERSON] had 6441, for a combined total of 13,230 for both players. The algorithm proposed by [PERSON] [39] identifies the classification results as follows: [PERSON] had 6207 hits with an average accuracy of 91.56%, [PERSON] had 6040 hits with an average accuracy of 89.22%, and the two players had a total of 12,247 ball hits. For the C5.0 algorithm, Table 2 shows the following classification results: [PERSON] had 6547 strokes with an average accuracy of 94.61%, [PERSON] had 6256 strokes with an average accuracy of 95.75%, with the two players hitting a total of 12,803 balls. Overall, [PERSON] struck more balls, with a higher percentage of forehand wins and first-serve points than [PERSON]. [PERSON] had a higher percentage of backhand wins than [PERSON]. Table 2 also reports the classification accuracy of [PERSON]'s and [PERSON]'s forehand and backhand techniques as achieved by each of the 3 methods. With regard to the forehand, the C5.0 algorithm had a classification accuracy of 95.03% for [PERSON], 5.84% higher than that of [PERSON]'s method, which was 89.19%.