# Applications of Machine Learning to Optimize Tennis Performance: A Systematic Review
## 3 Results and Discussion
### Studies' Characteristics
#### 3.3.5 Injuries and Training Effects  
Three studies [42; 47; 50] used ML techniques to analyze injuries and training effects. The studies involved a diverse range of participants. [PERSON] et al. [50] analyzed data from 19 players, while [PERSON] et al. [47] worked with a larger dataset of video recordings from 129 athletes.  
The data used for analysis also differed considerably. [PERSON] et al. [50] and [PERSON] et al. [47] both focused on biomechanical data. [PERSON] et al. [50] utilized data from inertial measurement units, while [PERSON] et al. [47] analyzed video recordings to assess body movements and identify potential injury risks. In contrast, [PERSON] and [PERSON] [42] incorporated a broader range of internal and external factors, including physical attributes, weather conditions, and training performance metrics, to predict how effective a tennis training program would be for a particular athlete.  
Table 7 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 injuries and training effects.  
The studies by [PERSON] et al. [47] and [PERSON] et al. [50] showcased the versatility of video analysis in ML applications in tennis. [PERSON] et al. [47] focused on identifying biomechanical patterns indicative of ACL injury risk. Their approach involved training a long short-term memory (LSTM) network on data extracted from video footage. They obtained a 75% to 81% accuracy in identifying athletes at risk, highlighting the potential of this method for early ACL injury detection. [PERSON] et al. [50] employed video analysis for a different purpose, to label data from wearable inertial measurement units (IMUs) worn by players during training [50]. Their system can automatically classify and quantify different strokes performed, aiding in training load quantification. They achieved a high accuracy of 97.5% using a combination of algorithms, such as support vector machines (SVMs) and random forest, demonstrating the effectiveness of this approach for training load measurements [50]. Furthermore, existing research underlines the importance of identifying knee valgus loading--a movement pattern where the knee collapses inwards--as a risk factor for ACL injuries. Previous research focused on the applications of ML to different sports regarding injury prevention. Studies by [PERSON] et al. [65] and [PERSON] et al. [66] on professional soccer players of both sexes reported a high prevalence of knee valgus loading in ACL injuries. Similarly, [PERSON] et al. [67] observed knee valgus in basketball players across various skill levels, although they also found a higher frequency in females compared to males. It is important to note that these studies focusedon analyzing injury mechanisms in athletes with existing ACL injuries, not necessarily diagnosing ACL ruptures from other conditions using video analysis alone.  
Additionally, [PERSON] and [PERSON] [42] used a distinct approach, utilizing a radial basis function neural network to predict the effectiveness of a tennis training program. They focused on a dataset of performance metrics, such as VO2 max and % body fat, demonstrating the versatility of ML beyond video analysis. The selection of algorithms in these studies reflects the evolving nature of the field, with each approach demonstrating success using different metrics.