# Applications of Machine Learning to Optimize Tennis Performance: A Systematic Review
## 3 Results and Discussion
### Studies' Characteristics
#### 3.3.4 Spatial and Tactical Analysis  
Eight studies [40; 41; 46; 49; 51; 52; 57; 59] used ML techniques to analyze spatial and tactical factors. The studies encompassed a wide range of participant numbers. [PERSON] [59] analyzed 120 matches to evaluate tactical performance, while [PERSON] et al. [57] focused on a smaller group of just 6 players to study the impacts of batting strength. The data used for analysis were equally varied. [PERSON] [59] delved into match data, analyzing details such as serve type, shot selection, and player positioning. In contrast, [PERSON] et al. [57] focused on kinematic variables, and [PERSON] and [PERSON] [46] examined player movement patterns during serves and returns. The variety of ML algorithms employed further underscores the versatility of this approach. Backpropagation neural networks [59] and convolutional neural networks [57] were used for tasks such as evaluating tactical performance and classifying groundstroke stances. Random forests [41; 46] were utilized for tasks such as movement pattern classification and point-winning prediction. Interestingly, clustering techniques were also employed by [PERSON] and [PERSON] [51] and [PERSON] et al. [41] for ball trajectory analysis and exploring individual nuance in movement, respectively. The varying accuracy metrics across the studies reflect the different goals and data used. Notably, [PERSON] et al. [49] achieved a high accuracy (94%) in uncovering the variables associated with a greater serve effectiveness, while [PERSON] and [PERSON]'s [46] study on visual search strategies during serves yielded a wider range of accuracy values (21% to 78%). Table 6 presents the study characteristics of No. of participants or data, attributes/ features, aim of prediction, algorithm, % of the sample used to train the algorithm (training algorithm), and prediction accuracy metrics of the studies that used ML algorithms for special and tactical analysis. Spatial and tactical analysis is fundamental in tennis, as it provides crucial insights into player strategies, decision-making, and court positioning dynamics during matches [64]. Additionally, machine learning offers innovative tools to enhance spatial and tactical analyses by leveraging complex data patterns derived from player movements, shot placements, and court coverage. Each study utilized ML models to forecast various in-game aspects, encompassing serve outcomes [51], change-of-direction movements [40], returning player and performance categories [46], and forehand/backhand stances [52]. This data-driven approach hinges on the collection of diverse data, including match footage [46; 57], player tracking data [40], and notation systems [52]. These data allow training and evaluation of the ML models, aiming to provide valuable insights that empower players and coaches to elevate performance. This translates into optimizing training drills [46; 52] and comprehendingopoponent trends [46]. Similarly, [PERSON] et al. [49] delved into the serve in professional doubles tennis. Their study utilized machine learning algorithms to pinpoint key factors that maximize serve effectiveness, such as serve angle and lateral bounce distance. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Author (Year)** & **Participants/Data** & **Attributes/Features** & **Aim of Prediction** \\ \hline [PERSON] and [PERSON] (2017) [51] & 151 male tennis players & Spatiotemporal (impact location, speed, projection angles, landing location, and relative player locations) and contextual (score). & To quantify ball trajectories and player locations in first serves, with a view to differentiating faces from serves that were returned into play \\ [PERSON] et al. (2020) [40] & 19 professional players & Total Euclidean distance travelled, the lateral distance travelled, degree of change, minimum and maximum speed, minimum and maximum acceleration, and the average depth of the player’s position.