Gold Prediction in League of Legends Matches. Analysis of Economic Variables
Abstract
This research uses data from competitive League of Legends matches in Brazil to predict the total amount of gold generated. This prediction, along with an exploratory analysis, is used to assess the total gold value as a metric for player efficiency, leveraging data from the teams and their players participating in the championship. Through an exploratory analysis, examining correlations between variables and applying the Ordinary Least Squares (OLS) regression algorithm, the objective of this work is to perform a linear regression to verify the importance of variables such as kills, assists, minion kills, monster kills, and towers in obtaining gold during matches. This study identifies linear and non-linear relationships for certain selected variables, categorized by aggregated team data and individual player data (divided by their respective in-game roles) to predict gold earned.
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