## 2 Literature Review
### Research and Findings  
While ML models for football analytics have previously focused on injury prediction and prevention, skill or market value evaluation, and team or player performance prediction, [PERSON] and [PERSON] [3] take a broader approach. They extend their analysis beyond injury prediction and performance assessment to emphasize the prediction of long-term team and player performance. Their study demonstrates that by using historical data and sophisticated statistical methods, it is possible to accurately predict final league standings for specific leagues. Additionally, their study examines a team's performance to determine whether it will advance from a previous season. Due to the game's constant fluidity, quantitative analysis in football faces significant challenges, making it difficult to establish a rigid framework. Despite the abundance of spatiotemporal data, practical methods for extracting useful insights are limited. [PERSON] et al. (2015) propose a novel approach by presenting a method for extracting football-specific concepts from interviews, formalizing them within a performance model, and implementing data structures and algorithms in STREAM TEAM, a framework designed for the detection of complex team events. This paper provides a thorough examination of their approach, as well as insights into its potential applications. For this study, a deep understanding of workload, injuries, and performance is vital to establish or discover correlations between these factors and to build a framework that would ultimately help teams optimize their performance. Workload refers to the physical or mental strain a player endures, which can have numerous implications. Each player is affected differently, depending on their physical attributes and the level of physical or mental strain they experience. As a result, it is critical to investigate how workload affects a player, whether positively or negatively. The workload is a significant contributor to injuries. [PERSON] and [PERSON]'s (2018) proposed model emphasizes the importance of workloads. Internal risk factors are classified as modifiable or non-modifiable in this model, with workloads influencing injuries in three ways: (1) through exposure to external risk factors and potential inciting events, (2) through inducing fatigue and negative physiological effects, and (3) by promoting fitness and positive physiological adaptations. Exposure is solely determined by total load, whereas positive and negative adaptations are governed by both total workloads and changes in load, such as the acute to chronic workload ratio. This model explains the relationships between load and injuries in detail, encompassing total workloads, acute to chronic workload ratios, and the training load-injury paradox. Performance analysis in football has gained giant strides over the past few years. With the aid of new technology and equipment, researchers around the globe have established new ways to accurately visualize and explain the performance and contribution of each player to the team. Various types of analysis typically used in football nowadays include:  
* **Statistical Metrics**: Analytics involving the collection of statistical data such as goals scored, goals assisted, passes completed, etc., which reflects a player's overall contribution to the team's performance. * **Position-Specific Analysis**: Analytics estimating the effectiveness of various players across different areas of the playing field, allowing them to assess their strong and weak areas, which can highlight potential improvements. * **Physical performance**: Data related to player physical attributes, such as sprint speed, distance covered, and high-intensity runs, help gauge a player's fitness level and work rate during matches. * **Video Analysis**: In addition to statistical data, video analysis is used to evaluate a player's decision-making, movement, positioning, and technical skills during matches.