# Constructing a Gaming Model for Professional Tennis Players Using the C5.0 Algorithm
###### Abstract  
Professional tennis players have their own habits of tactics and play. However, players' shortcomings can be corrected by constantly practicing professional techniques and by tactical analysis. Therefore, this study aimed to develop a two-stage, expert decision-making system for tennis matches. The first stage consisted in dividing the court area and defining the technical classification of the net. Tennis coaches were invited to assess tennis players' skills on the competition court, dividing it into 48 areas on both sides of the court centerline and identifying the skills used by the players. In the second stage, a classification model was developed, and the score, hitting habits, and tennis skills of the players, [PERSON] and [PERSON], over 10 matches, played from 2007 to 2019, were analyzed and classified using notational analysis and the C5.0 decision tree algorithm. The results show that the two players' highest scored techniques were the forehand stroke in the backcourt and the backhand stroke in the half court. Thus, using this expert decision-making system, our data can provide other players with imaginary training objects from two of the top players in the world to be used during training and can allow the accumulation of experience for players through continuous simulation and training analysis.  
video analysis; tennis and precision performance; notational analysis; C5.0 algorithm  
C5.0 Algorithm 20222022022022022022022022022022022022022  
In the 21 st century, tennis has taken the form of \"dominant\" tennis. Nowadays, the aggressive baseline player dominates; the key to winning a game is to combine a smart style with strength and speed [8; 9]. For example, [PERSON] is an aggressive baseline. He shreds the opponent's defense and breaks the opponent's serve by relying on a strong bottom line to smash the ball and win. [PERSON] is a counterpuncher, a defensive player, who can move quickly on the court and has a very low return-error rate. She keeps her shots deep, has good lobs, and places balls effectively during the game. She then patiently waits for her opponent to make a mistake and delivers the key blow, thereby scoring. [PERSON] is a serve-and-volleyer player. His weapon is to serve the net; his serve speed is fast, and the landing position is very tricky; hence, it is not easy for the opponent to receive the ball. [PERSON] is a full-court player with 27 styles of play. He is a jack-of-all-trades who can hit every shot in the book--consistent groundies, sound volleys, beautiful drop-shots, and dependable lobs [1; 2]. Therefore, by analyzing several videos, we have examined the use of tactics, playing habits, strengths, and weaknesses of the professional male players [PERSON] and [PERSON]. Thereafter, a decision tree algorithm was used to determine the positions, techniques, and placements used by the two players in the matches. Three variables for the two players were used to construct an artificial-intelligence tennis-game model, which can be extended to any other tennis players to construct their own game models and, by that, adjust their autonomous training and game strategies.  
In this study, a C5.0 artificial intelligence decision tree algorithm classification for tennis matches is proposed, which provides a classification for both players' hitting techniques and ball-landing points, analyzing the techniques and tactics which thus offer them the potential for the timely adjustment of their playing styles so as to enhance their sports performance during their matches. In the future, we may provide live, online games and adjust the play and tactics in time, thus hoping to improve players' sports performance.