# Sport Performance Analysis with a Focus on Racket Sports: A Review
## 3 Results
### SR1: An Overview of Sport Performance Analysis in General
#### 3.1.5 Performance Prediction  
Performance prediction is a topic that has been extensively discussed in a number of articles and books. In this paper, the authors describe the various approaches that were used, starting with standard statistical methods and progressing to sport-specific software at the end. The following questions have been addressed by researchers in an attempt to provide answers:  
* Which performance indicator best predicts success?
* Can game statistics be used to forecast success?
* How can a successful player be identified?
* How much of an impact does travel/match location have on player/team performance?  
[PERSON] et al. [121] used Hawk-Eye ball and player tracking data to identify unique player styles and predict within-point events. The study used spatial and temporal information to better characterize each player's tactics and tendencies, in addition to coarse match statistics (i.e., serves, winners, number of shots, and volleys). To model player behavior, a probabilistic graphical model is used. Because player behavior is affected by the opponent, model adaptation was used to improve our prediction. As a new measure for table tennis players, [PERSON] et al. [122] proposed the mutual point-winning probabilities (MPW) as server and receiver. The MPWs quantify a player's chances of winning a point against a given opponent and thus complement the traditional match statistics history between two players nicely. These new quantities are based on a statistical model of the type Bradley-Terry, which takes into account the significance of individual points. A random forest algorithm with parameters such as acceleration, rally duration, and time between rallies was used in a study by [PERSON] et al. [123] for prediction of conative stages with a prediction accuracy possibility for badminton. To demonstrate the potential impact of home vs away matches, [PERSON] et al. [124] used general and generalised mixed linear models for rugby points difference and match won or lost prediction. Article published by [PERSON] et al. [125] proposed prediction method combines the equation for the range of a projectile in free flight with the measured relationships between the athlete's take-off speed, take-off height, and take-off angle. The results of this method agreed well with the athletes' competition take-off angle.  
[PERSON] et al. [126] took an interesting approach to optimizing sport skill level predictors for female judo athletes using Probabilistic Neural Network (PNN). In a research conducted by [PERSON] et al. [94], network analysis and machine learning are used to estimate the contribution of the network of matches in predicting an athlete's success. [PERSON] et al. [127] research also discusses career path prediction. The introduced prediction method is based on thirty years of longitudinal data that included 82 top ten professional players between 2007 and 2017. The practical implications of the above-mentioned articles' findings, specifically informing career planning, predicting professional success, monitoring, and assessing emerging tennis players, are discussed.  
**Summary:** Prediction performance is an area of study that has been debated in many publications and books due to the high level of interest in the subject that has been expressed not only by players and coaches but also by team leaders, bookmakers, and team supporters. Despite the high level of interest in this subject, there is no reliable solution for result prediction in the literature. Since large databases are available, a machine learning approach that is potentially fuzzy has a great opportunity to be used, as we have stated earlier. While it is true that many predictions had as their goal the prediction of the athlete's career path, there is room for improvement, such as the inclusion of more sophisticated computing methods.