# Constructing a Gaming Model for Professional Tennis Players Using the C5.0 Algorithm
## 3 Materials and Algorithm  
The tennis-game model-flow, from video collection to usable decision, is shown in Figure 1. According to Figure 1, the first part consists of applying pre-established selection rules in the integration of primitive video data to decide whether that video data belongs. The next part is to reorganize the video data by discarding unnecessary or redundant video information, establishing video contents and record-keeping formats, and ensuringthe integrity and consistency of the data so as to construct a video-data platform. The third part is to organize the video-data part, in which the information on where the tennis ball falls and where the player stands is grouped into related subjects, and data-mining-processing methods are used to determine video-data models and to further define the relationships within the various tennis-game-model datasets for reference in storage and query computation. The resulting three-part tennis-game model can become a decision-making support system for players' technique and tactics. Part 1: Establishing Rules  
**Definition 1. **_Defining the classifications of tennis techniques and players' performance._  
The common techniques used in tennis are divided into 13 categories: forehand, backhand, forehand volley, backhand volley, forehand half-volley, backhand half-volley, backhand half-volley, backhand volley, backhand forehand spin, backhand spin, first serve, second serve, smash, unsuccessful shots, and other. The algorithm code for each technique is listed in Table 1[38]. **Definition 2**. : _Defining the players' volley and the placements of strokes and serves._  
Modifying the court-numbering of serve-drop and receive-return-drop originally compiled by [PERSON] and [PERSON] [16], we divided the tennis courts into 48 positioning areas as shown in Figure 2. As shown, the codes for the player positions for striking and receiving range from 1 to 48. The returner's impact location can be classified into 6 areas, coded from A to F, as shown in Figure 3[39]. Figure 3: The returner’s impact location. Figure 2: Player’s position codes. \begin{table}
\begin{tabular}{c c} \hline \hline
**Tennis Techniques and Players’ Performance** & **Code** \\ \hline Forehand & a \\ Backhand & b \\ Forehand volley & c \\ Backhand volley & d \\ Forehand half-volley & e \\ Backhand half-volley & f \\ Forehand spin & g \\ Backhand spin & h \\ First serve & i \\ Second serve & j \\ Smash & k \\ Unsuccessful shots & l \\ Other & m \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Tennis techniques and players’ performance algorithm codes. **Definition 3**. : _Defining the source of the video and the period of the game._  
This study developed an expert, decision-making system for tennis games. First, with notational analysis, 48 areas were delimited on each side of the centerline of the court, and 10 matches between the 2 professional tennis players, [PERSON] and [PERSON], played between 2007 and 2019 were analyzed. Part 2: Reorganizing Tennis Video  
Based on the decision tree framework [36; 37], the image recognition system can identify the location from which the tennis player is serving, where the tennis ball lands, and where the opponent is standing when they return. However, it is not possible to identify the tennis techniques used by both players. Therefore, this study uses notational analysis to identify and record the standing positions of the tennis players, the drops of the tennis balls, and the tennis techniques used by each player at that given time. To test the reliability of the labelling analysis method, a game was randomly selected and watched so as to be annotated. Thereafter, the mutual agreement (MA) and reliability (R) method proposed by [PERSON] [40] for reliability testing was used, as shown in Equations (1) and (2). IF R (reliability) is >0.8, then the fully reorganized coding can be started. [PERSON] et al. [41] proposed that R > 0.9 represents a good level of agreement under the [PERSON] method.