Sports Revenue Analysis

This project explores how attendance, team performance, and stadium factors affect revenue generation in college athletics. Using real NCAA financial data, I ran regression analyses to understand what drives both ticket and concession sales.

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📊 Overview

• Goal: Identify the strongest predictors of athletic revenue.
• Models:
  1. Real Ticket Sales = f(Attendance, Capacity, Home Games, COVID, Win %, Lagged Win %)
  2. Real Concession Sales = f(Attendance, Capacity, Home Games, COVID, Win %, Lagged Win %)
• Tools Used: Excel · STATA

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💡 Key Insights

• Attendance is the biggest driver of both ticket and concession revenue.
• Winning percentage (current and past) significantly boosts ticket sales.
• Too many home games or oversized stadiums can lower per-game sales.

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🗂️ Files

• final-dataset.xlsx → Clean dataset used for regressions
• regression-summary.docx → Regression output and interpretation

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🧠 Summary

Higher attendance and strong team performance are the clearest paths to higher revenue.
The results show how economic modeling can guide real-world sports management decisions.