## 2 Literature Review
### Research and Findings
They use exploratory data analysis and feature engineering to carefully curate a feature set necessary for result prediction. They demonstrate the high accuracy of their ML-based predictive system, with the model using gradient boosting achieving a performance score of 0.2156 on the ranked probability score (RPS) metric. Over two English Premier League seasons (2014-2015 and 2015-2016), this performance evaluation covers game weeks 6 to 38. Despite the encouraging results, their model does not outperform those of betting companies Bet365 and Pinnacle Sports, which had an RPS value of 0.2012 for the same period. Their model, while promising, did not surpass the accuracy of the bookmakers' predictions, as shown by the fact that the lower the RPS value, the higher the predictive accuracy. Assessing the impact and reasons for each type of injury has been of keen interest in the football research world and is of great importance for this study. Predicting injuries using statistical data is closely observed by many clubs around the world; any model that can accurately predict player injuries can be groundbreaking. However, not all kinds of injuries are predictable. For example, if a player sustains a leg fracture in a match due to a vicious tackle from an opponent, or if he/she gets a concussion or a fractured skull by accidentally hitting their head on the goalpost, these are one-off incidents. They cannot be predicted. However, some injuries, like medial collateral ligament (MCL), hamstring injuries, etc., are sometimes workload injuries, occurring due to the continuous strain on the respective muscle. Such injuries can be predicted as they have a direct relationship with the match statistics of a player, such as minutes played, matches played, and physical attributes such as age, strength, height, etc. A systematic video analysis conducted by researchers [11] aimed to identify the factors contributing to acute hamstring injuries in professional male football (soccer) players. This analysis included video footage from the top two divisions of German male football from 2014 to 2019. The study focused on moderate to severe hamstring injuries caused by non-contact and indirect contact situations during matches, resulting in a seven-day time loss. The inciting events were classified, and two primary injury patterns emerged: sprint-related injuries (48%), occurring during linear acceleration or high-speed running, and stretch-related injuries (52%), associated with various closed and open chain movements. Despite the variety of inciting events, rapid movements with high eccentric demands on the posterior thigh appeared to be a common factor in hamstring injuries. These findings shed light on the mechanisms of hamstring injuries in professional male football players and highlight the importance of customized, multi-component risk reduction programs. Though the deep investigation into workload, injury, and performance has laid a foundation for this study, the main crux lies in assessing the correlation between these aspects to derive insights and construct models. This can be impactful for several reasons:  
* **Injury Prevention**: Understanding the relationship between workload and injury is crucial for developing effective injury prevention strategies. By identifying workload thresholds and patterns associated with higher injury risks, clubs and medical staff can implement targeted measures to reduce the likelihood of injuries. * **Performance Optimization**: The correlation between workload and performance is a critical aspect of player management. Balancing the right level of workload can positively impact player performance, ensuring optimal physical and technical abilities on the pitch. * **Player Management**: A deep understanding of the workload-injury-performance relationship allows for better player management. There are many more advantages and use cases for football teams.