# Applications of Machine Learning to Optimize Tennis Performance: A Systematic Review
## 3 Results and Discussion
### Studies' Characteristics
#### 3.3.3 Match Outcome Prediction
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Author (Year)** & **Participants/Data** & **Attributes/Features** & **Aim of Prediction** \\ \hline [PERSON] et al. & Tennis Match & & To predict the result of tennis singles matches using eight UCI databases of grand slam \\ (2014) [54] & Statistics dataset & NA & tennis tournaments and evaluate the classification performance \\ [PERSON] et al. & 86 ATP and 82 WTA matches from the 2016 Australian Open & Contextual and physical factors & To predict the likelihood of a challenge and predict the success of a challenge \\ [PERSON] et al. & Match Charting Project & Serve, receive, receive against serve, shot type, net play, turn back, and swing around & To predict point winners in ATP singles matches and identify shot patterns influenced by court conditions and players \\ [PERSON] and [PERSON] & ATP World Tour datasets from 2000 to 2016 & Physical, psychological, court-related, and match-related variables & To achieve accurate prediction of match outcomes and identify key components contributing to those predictions \\ [PERSON] and [PERSON] & Players since 1968 until 2020 & Rankings data, match variables, and tournament variables & To implement supervised machine learning models on a tennis match dataset \\  & & Probability of winning, number of aces, game dominance, and double faults per year & To predict player performance, this study employs a dynamic technique that analyzes the structure of performance using both linear and non-linear time series models \\ \cline{2-3}  & **Author (Year)** & **Algorithm** & **Training Algorithm** & **Prediction Accuracy Metrics** \\ \hline [PERSON] et al. & Decision tree, learning vector (2014) [54] & 70\% & AC men’s = 92.07\% to 98.96\% \\ [PERSON] et al. & Univariate and multivariate (2017) [44] & logistic regression & NA & AC women’s = 79.3\% \\ [PERSON] et al. & Logistic regression & NA & AC men’s = 79.2\% \\ (2020) [58] & SVM with a radial basis & & AC = 66.5\% \\ [PERSON] and [PERSON] & function kernel, random (2021) [39] & 80\% & AC \(\geq\) 80\% \\ [PERSON] and [PERSON] & Linear regression and (2022) [56] & 80\% & AC = 99.8\% \\ [PERSON] et al. & Non-linear neural network (2024) [37] & NA & RMSE = 0.0461 to 145.9614 \\  & & NA & MAE = 0.0398 to 110.6277 \\  & & MAPE = 4.9312 to 75.8978 \\ \hline \hline \end{tabular} Note. RMSE: root mean squared error; MAE: mean absolute error; MAPE: mean absolute percentage error; AC: accuracy; SVM: support vector machine; WTA: Women’s Tennis Association; ATP: Association of Tennis Professionals; NA: not available.  
\end{table} 
Table 5: Study characteristics of the match outcome prediction studies. Moreover, [PERSON] et al. [37] explored a different application of machine learning, focusing on predicting player performance throughout a season. Their study utilized a non-linear neural network auto-regressive (NNAR) model, demonstrating its advantage over conventional models in terms of accuracy metrics, such as root mean squared error (RMSE) [37]. This suggests that incorporating time series analysis can lead to more precise predictions of a player's performance trajectory. Therefore, machine learning algorithms proved to be a valuable tool for analyzing tennis data and predicting outcomes. While accuracy can vary depending on the specific task and model complexity, supervised learning algorithms have shown promise in classifying match winners (upwards of 80% accuracy) and identifying factors that influence in-game events, such as challenges and serves. As research continues to refine existing models and explore new algorithms, such as deep learning and NNAR models, machine learning holds potential for enhancing our understanding of the complex dynamics at play on the court.