# Revolutionizing Soccer Injury Management: Predicting Muscle Injury Recovery Time Using ML
## 4 Discussion  
Injuries can significantly impact the performance of sports teams, causing them to lose games and even suffer financial losses. Head coaches, therefore, need to know how long it takes for a particular injury to heal to make informed decisions. Investing in injury prevention and rehabilitation programs can help reduce the number of days lost due to injury, improve a team's overall performance, and increase their chances of success. Several studies have demonstrated that lower injury burdens and higher match availability are associated with higher final league rankings, increased points per league match, and success in prestigious tournaments such as the UEFA Champions League or Europa League [7; 24; 25]. Estimating the duration of recovery after an initial clinical examination is a challenging task, as it depends on various factors such as injury history, age, body type, mental state and more [4]. Additionally, the recovery process may not always follow a predictable pattern, and individual players may recover at different rates [17; 26]. This makes accurately predicting RTP time a daunting task, even for experts in the field. Figure 6: A bar plot showing the mean MSE of expert’s estimations (blue bars), XGB model (orange bars) and XGB model with the inclusion of expert’s predictions as a feature (green bars). The black lines on each bar indicate the corresponding standard deviation of the MSE. Figure 7: Visualization of the estimations made by the expert (blue circles), and XGB with the inclusion of the expert’s predictions as a feature (orange asterisks). The black dotted line shows ideal predictions, while green and red backgrounds indicate cases where the recovery was faster or slower than expected. State-of-the-art approaches for estimating the recovery time for a particular injury often involve providing physicians with various recommendations and instructions. However, despite these guidelines, the ultimate decision is usually based on the clinician's experience with different injury types and their subjective judgment. This decision-making process may be influenced by factors within the soccer club, as well as the clinician's internal state. Therefore, having a tool that can assist clinicians in their decision-making process could result in more accurate and confident estimates of recovery time. To date, there has been only one study that attempted to use ML to assess RTP time after hamstring injuries using the MLG-R classification system and the data derived from MR images [14]. The study involved male professional football players from FC Barcelona, and three different ML approaches to assess the importance of each factor of the MLG-R classification system. The results demonstrated that the most important factors to determine the RTP were whether the injury was at the free tendon of the biceps femoris long head or whether it was a grade 3r injury. The study found the MLG-R classification system to be reliable, with excellent results in terms of reliability, prognosis capability and objectivity. However, the performance of the system has not been tested in conjunction with assessments of a medical team. Nonetheless, ML assistance has been already demonstrated in the area of medicine, such as the use of CAD systems to increase radiologists' diagnosis accuracy [15]. A similar approach can be also applied in the context of clinicians' estimations regarding RTP in soccer. This paper presents an approach that enhances the process of recovery estimation by incorporating an expert's estimations as a feature in the model, in addition to comparing its performance with that of an expert. By integrating human knowledge into the algorithm, we achieve improved performance. This highlights the potential benefits of combining ML methods and human expertise to tackle difficult tasks such as recovery estimation. One limitation of our study is the relatively small amount of muscle injuries in the dataset.