# Constructing a Gaming Model for Professional Tennis Players Using the C5.0 Algorithm
## 2 Literature Review
### Applications of Decision Trees in Motion Analysis  
In order to enhance the image of the city and boost the local economy, [PERSON] and [PERSON] [24] used a decision tree to construct a decision-making model of alternatives. The factors in the evaluation of the alternatives included, among others, the cost of bidding, the probability of being awarded, the cost of hosting, the probability of hosting successfully, the financial benefit expected by an excellent event, and the financial benefit expected by a mediocre event, thus spurring cities around the world to fight for the right to host sports events and providing the reference basis for decision-making by local sports committees. [PERSON] et al. [25] used the C4.5 decision tree algorithm to ensure fairer and more objective granting of scholarships in higher education, based on students' in-class academic performance, extra-curricular activities, and various competition results. [PERSON] et al. [26] used C4.5 decision tree analysis to study six key factors affecting the results of National Basketball Association (NBA) games: field-goal percentage, three-point field-goal percentage, free-throw attempts, turnovers, defensive rebounds, and steals. [PERSON] et al. [27] used 10 years of data published by the Indian Premier League (IPL), applying the C4.5 decision tree, Naive Bayes, random forest, and k-nearest neighbors (KNN) algorithm to deal with the problem of predicting cricket-match results based on the historical match data of the IPL. The results found that the prediction accuracy of KNN was better than that of the decision tree, Naive Bayes, and random forest. [PERSON] et al. [28] employed the C5.0 decision tree to analyze the players who entered the major leagues and minor leagues each year and to assess the relevant data on their salaries in their first six seasons to decide signing contracts. [PERSON] and [PERSON] [29] used decision trees to analyze the choice of scoring or not scoring in the late stages of curling competitions. Based on observations of world-class curling competitions, it appeared that North American curling players never chose to score at the end of the game, while European players did. The results showed that not scoring in the last stage of the game is the better choice. [PERSON] and [PERSON] [30] used a decision tree to classify US Major League Baseball (MLB) teams into \"no playoffs\" or \"playoffs\" groups. Through research and analysis, it was found that a 0.537 winning percentage in the regular season may well be the threshold for advancing to the postseason. [PERSON] et al. [31] used a decision tree classification model to analyze the MLB's free agents between 2013 and 2017. The results evidenced that, for players offered a new contract, \"age\", \"wins above replacement\", and \"the team on which a player last played\" were the most significant factors in determining whether a player signed a new contract or not. [PERSON] and [PERSON] [32] analyzed a baseball database using the chi-squared test to decide the significant factors in a pre-processing step of pruning insignificant factors before constructing a decision tree. The results showed that the p-value of the chi-squared test provided the advantages of a lower storage cost, a faster prediction time, and a higher degree of accuracy for data classification than did the decision tree including all the original factors. [PERSON] et al. [33] used body-sensor networks to collect table-tennis stroke-recognition motion data and used principal component analysis (PCA) to analyze sports skills. The results of the PCA showed a classification accuracy of 97.41%. [PERSON] et al. [34] proposed the placement of sensors on the wrists of six table-tennis players, with each playing 100 returns and serves, and then the researchers used a decision tree to identify the classification of hits and misses using the differences in motion between the two.