Sports Economic Operation Index Prediction Model Based on Deep Learning and Ensemble Learning

Area of Science:

• * Sports Economics
• * Data Science
• * Machine Learning

Background:

• * Traditional methods for sports economic analysis often lack predictive accuracy.
• * Evaluating sports event profitability requires sophisticated modeling techniques.
• * The need for advanced algorithms to analyze complex sports economic data is growing.

Purpose of the Study:

• * To develop a robust prediction model for sports economic operation indicators.
• * To integrate and enhance deep learning and ensemble learning algorithms for sports economics.
• * To evaluate the reliability and effectiveness of the proposed model through experimental research.

Main Methods:

• * Combination of deep learning and ensemble learning algorithms.
• * Analysis of the LightGBM ensemble learning model and its hyperparameters.
• * Application of break-even analysis for sports event operations.
• * Construction of a comprehensive evaluation structure for sports items.

Main Results:

• * The integrated model demonstrates reliability in predicting sports economic indicators.
• * The study successfully identifies the critical profit-and-loss points for sports events.
• * Experimental validation confirms the model's effectiveness in evaluating sports events.

Conclusions:

• * The proposed model offers a reliable approach to predicting sports economic indicators.
• * Integrating deep learning and ensemble learning enhances the analysis of sports economic operations.
• * The findings provide a valuable tool for assessing the financial viability of sports events.