## 2 Literature Review
### Research and Findings
Football performance evaluation heavily relies on player positions and their dominant areas on the field. By using clustering techniques similar to Key Performance Indicators (KPIs), [PERSON] and [PERSON] (2018) establish composite indices for various performance areas (e.g., technical, mental, and physical) categorized by player roles. This strategy aims to give coaches and scouts an impartial player performance evaluation and objective tools for decision-making. The analysis helps coaches compare players with similar characteristics and positions by enabling a thorough understanding of player performance in particular roles and pitch areas (2018). Although the rules of football are quite straightforward, there are many factors to consider when assessing the quality of a player: how he/she plays on the ball, off the ball, the reactions, stamina, etc. However, when it comes to comparisons, attackers or forward players typically receive the most attention. This is because what matters the most in football are goals. Scoring more goals than the opponent is basically how a team wins a football match. Hence, players who contribute towards the team's total goals are vital to the team's success. Therefore, analyzing the performance of forwards to determine their goal-scoring ability and predicting their goals in a match/tournament/season can be a pivotal tool for teams when deciding whom to play and which opponent player to be cautious of. In a recent study, a thorough football analytics approach explores goal prediction and player performance assessment [8]. By using historical football data and sophisticated analytics, this research makes use of a variety of attributes to build a Goal Prediction Model (GPM). The GPM allows for the evaluation of player and team performance while also providing reliable goal predictions. The study also examines and records the unique skills and statistical subgroups that set exceptional goal scorers apart from others. It is important to remember that the model's outcomes can be affected by variables like feature selection, data size, and parameters. The large training dataset contains information from 9074 games over five seasons in the top five leagues in Europe. In the end, this study has the potential to transform football analytics. Another recent study [9] highlights a notable surge in the creation and application of predictive models in football analytics, which have greatly influenced various aspects of club operations. The Expected Goals (xG) metric, which quantifies the likelihood of a shot resulting in a goal, has gained prominence. However, traditional xG models often overlook essential factors like player and team ability and psychological effects, leading to limited trust in the model within the football community. This study addresses these concerns by leveraging ML techniques to enhance xG modelling. It introduces previously untested features and assesses their impact on predictive performance. Results indicate that the developed xG models perform competitively with optimal values from previous research. Moreover, xG is shown to be a superior predictor of a team's future success compared to traditional statistics, outperforming industry-standard metrics. The difficulty of integrating predictive models in football is that it is a highly unpredictable game. More statistics cannot determine the outcome of a match. A historically stronger team, statistically speaking, cannot be 100% certain of winning the match, as football does not work this way, even if the winning percentage is higher. On the day of the match, anything could happen, and there are a lot of factors to consider when predicting a football match. One notable example of odds being dealt a heavy blow is when Leicester City won the English Premier League in the 2015/2016 season, despite finishing in the bottom three the previous season, which stat-wise does not make sense. In their research [10], the authors explore ML-based predictive analysis and modeling of football match outcomes in the English Premier League.