## 2 Review of the Existing Literature
### Current Research on Machine Learning for Match Prediction  
The utilization of ML in League of Legends spans a wide array of investigative pursuits, encompassing areas such as forecasting match outcomes, examining qualitative elements that contribute to success, and analyzing aspects of human interaction such as teamwork and competition [35], as well as the underlying causes of intrateam conflict [36]. This domain is split into research relying on offline primary data derived from experiments or interviews and studies leveraging online secondary data obtained through the APL. The latter type predominantly focuses on predicting match outcomes via API and usually employs two distinct types of feature sets: those analyzing pre-game data, which include all the information available prior to the start of a match, and those assessing 'in-game' (in-game) data, which pertain to the dynamics and events occurring during the course of a match. Pre-game data encompass information about player attributes before they commence a match, such as their selected champion, level of experience, and historical performance in matches. [PERSON] et al. [17] reported that a neural network model trained exclusively on data concerning players' experience with their selected champions reached a prediction accuracy of approximately 75%. Nonetheless, this analysis was confined to data procured from North American players, and regional distinctions have been identified as a significant factor affecting match results [37]. [PERSON] et al. [38] expanded upon this by incorporating data on the synergistic relationships between champions within a team, achieving comparable accuracy rates, albeit with a reduced dataset size. Furthermore, strategic decisions made during the pre-game phase, such as the banning of certain champions, have been shown to significantly impact match outcomes [39]. While pre-game factors provide valuable insights, they alone do not conclusively determine the outcome of a match. The dynamic decisions, actions, and reactions players exhibit throughout gameplay significantly influence the chances of victory, highlighting the critical role of in-game data. In-game data capture real-time events during a match, such as player elimination, destruction of objectives, and acquisition of resources. [PERSON] [24] utilized gradient-boosted trees, which achieved prediction accuracies of approximately 95% with in-game data. However, the in-game data employed were collected at the match's end, leading to potential overfitting. This is because, by nature, winning teams are likely to have higher in-game metrics such as acquired resources and opponent eliminations (or lower \"negative\" features such as number of times eliminated), making it less effective for predictions before the match concludes. The features towards the end of the match are a direct consequence of the outcome. [PERSON] et al. [40] supported this notion by demonstrating that data from early game events possess less predictive value than data from later games. The variation in predictive power can also be attributed to differences in the machine learning techniques used. This underscores the complexity of accurately forecasting match outcomes solely on the basis of early-game data and highlights the importance of considering the temporal aspect of in-game data for more effective predictions. [PERSON] et al. [41] leveraged ensemble methods to construct models that integrate pre-game, in-game, and combined feature sets, reaching an impressive accuracy rate of 99.75%. Nonetheless, it is important to note that their research focused exclusively on data from professional esports matches. This specificity suggests that the findings may not be generalizable to the broader player base. The distinct dynamics and strategic considerations at the professional level may differ significantly from those encountered by the average player, potentially limiting the utility of these models outside the context of professional gaming.