# Applications of Machine Learning to Optimize Tennis Performance: A Systematic Review
## 3 Results and Discussion
### Studies' Characteristics
#### 3.3.3 Match Outcome Prediction  
Six studies [37; 39; 44; 54; 56; 58] used ML techniques to predict match outcomes. Sample sizes and data varied across the studies. [PERSON] et al. [37] focused on just three players. In contrast, [PERSON] and [PERSON] [39] utilized a broader range of variables encompassing physical, psychological, and match-related factors. Predicting match outcomes is a studied topic, as seen in the studies of [PERSON] and [PERSON] [39], [PERSON] et al. [54], and [PERSON] and [PERSON] [56]. Additionally, [PERSON] et al. [58] predicted point winners in ATP singles matches and identify shot patterns influenced by court conditions and players. [PERSON] et al. [37] used a unique approach, focusing on predicting player performance over time. The variety of ML algorithms employed further underscores the versatility of this approach. Logistic regression features in the studies of [PERSON] et al. [54], [PERSON] et al. [44], and [PERSON] et al. [58] achieved high accuracy for predicting match outcomes in both men's and women's tennis (up to 98.96% in [PERSON] et al. 's study [54]). Table 5 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 match outcome prediction. Each data point in supervised learning contains an input (xi) and an output (yi) that specifically describe the data, known as labeled data [62]. [PERSON] et al. [54] exemplified this effectively. Their study found decision trees to be proficient at classifying winners of grand slam singles matches based on historical data, achieving upwards of 80% accuracy. This suggests decision trees excel at extracting subtle patterns within past performance that can inform future win-loss predictions. Similarity, [PERSON] and [PERSON] [37] reinforced the value of supervised learning by employing both linear regression and decision trees. The study achieved an accuracy of 99.8%. Machine learning extends beyond predicting winners, offering insights into specific in-game situations. [PERSON] et al. [44] explored this by analyzing data from challenged and unchallenged serves. Their machine learning models achieved an accuracy of 79.2% in identifying factors such as ball location and shot speed that influence the success of a challenge. This allows players with data-driven insights to optimize their challenge decisions. Although these studies present valuable insights, accuracy limitations exist. [PERSON] and [PERSON] et al. [44] highlighted the focus on relatively simple models, suggesting that more advanced techniques, such as deep learning, might yield even higher accuracy. Additionally, the accuracy of these models can vary depending on factors such as the quality and completeness of the training data, as well as the specific task at hand (predicting a winner versus a successful challenge) [44]. Therefore, future research should address these limitations by exploring algorithms such as deep learning to potentially improve prediction accuracy. Additionally, investigating the generalizability of models across different surfaces, playing styles, and player levels is crucial. Incorporating additional data sources, such as player psychology or weather conditions, could create more comprehensive models.