# Applications of Machine Learning to Optimize Tennis Performance: A Systematic Review
## 3 Results and Discussion
### Studies' Characteristics
#### 3.3.1 Psychological and Affective States  
Regarding the psychological and affective states, two studies [43; 55] employed different methodologies to investigate players' psychological state during competition. [PERSON] et al. [55] employed a sensor-based approach, leveraging data from inertial measurement units (IMUs) worn by a small group of elite coaches (n = 2) and professional players (n = 4). In contrast, [PERSON] et al. [43] used video analysis, examining five players in their study. Both studies implemented distinct algorithms and metrics to evaluate the efficacy of their machine learning models. [PERSON] et al. [55] utilized a long short-term memory recurrent neural network model, achieving a prediction accuracy of 85%. [PERSON] et al. [43] employed a neural network, achieving a prediction accuracy range of 63.9% to 68.9%. Additional information regarding the included studies' characteristics (No. of participants or data, attributes/features, aim of prediction, algorithm, % of the sample used to train the algorithm, and prediction accuracy metrics) for the psychological and affective states are presented in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Author (Year)** & **Item 1** & **Item 2** & **Item 3** & **Item 4** & **Item 5** & **Item 6** & **Item 7** & **Item 8** & **Item 9** & **Item 10** & **Score** & **Quality** \\ \hline [PERSON] et al. (2024) [37] & \(1\) & \(1\) & \(0\) & \(0\) & \(1\) & \(1\) & \(1\) & \(1\) & \(1\) & \(1\) & \(8/10\) & High \\ [PERSON] and [PERSON] (2022) [38] & \(1\) & \(1\) & \(1\) & \(1\) & \(1\) & \(1\) & \(1\) & \(1\) & \(1\) & \(10/10\) & High \\ [PERSON] et al. (2014) [53] & \(1\) & \(1\) & \(0\) & \(0\) & \(1\) & \(1\) & \(1\) & \(1\) & \(1\) & \(0\) & \(7/10\) & Low \\ [PERSON] (2022) [39] & \(1\) & \(1\) & \(0\) & \(0\) & \(1\) & \(1\) & \(1\) & \(1\) & \(1\) & \(1\) & \(8/10\) & High \\ [PERSON] et al. (2014) [54] & \(1\) & \(1\) & \(0\) & \(1\) & \(0\) & \(1\) & \(1\) & \(1\) & \(1\) & \(0\) & \(7/10\) & Low \\ Giles et al. (2020) [40] & \(1\) & \(1\) & \(1\) & \(1\) & \(1\) & \(1\) & \(1\) & \(1\) & \(1\) & \(10/10\) & High \\ [PERSON] et al. (2023) [41] & \(1\) & \(1\) & \(1\) & \(1\) & \(1\) & \(1\) & \(1\) & \(1\) & \(1\) & \(1/10\) & High \\ [PERSON] and [PERSON] (2017) [42] & \(1\) & \(0\) & \(1\) & \(0\) & \(1\) & \(1\) & \(1\) & \(1\) & \(1\) & \(8/10\) & High \\ [PERSON] et al. (2022) [55] & \(1\) & \(1\) & \(0\) & \(0\) & \(0\) & \(1\) & \(1\) & \(1\) & \(1\) & \(0\) & \(6/10\) & Low \\ [PERSON] et al. (2024) [43] & \(1\) & \(1\) & \(1\) & \(1\) & \(1\) & \(1\) & \(1\) & \(1\) & \(1\) & \(1/10\) & High \\ Kudder and Fujko (2022) [56] & \(1\) & \(1\) & \(0\) & \(1\) & \(0\) & \(1\) & \(1\) & \(1\) & \(1\) & \(0\) & \(7/10\) & Low \\ [PERSON] et al. (2017) [44] & \(1\) & \(1\) & \(0\) & \(1\) & \(1\) & \(1\) & \(1\) & \(1\) & \(1\) & \(1\) & \(9/[PERSON] et al. [55] investigated the feasibility of AI for predicting a player's state of optimal performance, termed \"the zone\", using wearable sensor data and coach labels. [PERSON] et al. [43] focused on recognizing broader affective states (e.g., frustration and joy) through video analysis of real-world tennis matches. Both studies demonstrated the potential of ML in this field. [PERSON] et al. [55] achieved high accuracy (above 85%) in predicting \"the zone\" utilizing a personalized modeling approach. Their approach relied on machine learning algorithms, likely leveraging techniques such as support vector machines (SVMs) or random forests, to identify patterns in the wearable sensor data associated with coach-labeled instances of \"the zone\" [55]. This approach offers an opportunity for real-time feedback on mental state, potentially allowing for targeted interventions to maintain focus and optimize performance during competition. Moreover, [PERSON] et al. [43] highlighted the advantage of training models on real-world data, achieving an accuracy of 68.9% for affective state recognition from video footage.