Analyzing the Economic Impact of Sports Event with Machine Learning Approach

Abstract

The economic impact of mega sporting events including both tangible and intangible legacy is a complex issue and challenging to predict due to the difficulty of accurately estimating. However, traditional methods often encounter problems handling large and complex datasets, noisy input sequences, and missing values. Consequently, this study introduces a novel approach using machine learning to optimize the selection of key indicators, thereby enhancing the predictive accuracy for the multifaceted impacts associated with hosting sporting events, such as the FIFA World Cup. Specifically, we utilize the Boosting Algorithms to analyze the most predictive economic and sportive indicators. As a result, we have developed an economic model for estimating the event's impact, offering a robust analytical tool for policymakers and financial analysts.