Interested in exploring the relationships between factors in sports and game attendance, we set out to build a model for baseball.
What it does
This model will be able to receive input from a home game for the Atlanta Braves, including opponent, team performance, weather, and current popularity, in order to accurately predict fan attendance at the game.
How we built it
We scanned Baseball Reference for data that may be relevant to attendance, and we gathered environmental and location data for the opposing teams to form the input variables. After testing the parameters, we trained and tested the statistical model using MATLAB.
Challenges we ran into
Difficulty finding strong correlative factors for fan attendance, as well as an overall lack of experience at the start of this project
Accomplishments that we're proud of
Successfully predicting attendance data and completing a model.
What we learned
The powerful abilities of statistical software and the procedure for developing regression models and gathering API data.
What's next for Brave Bot
We will try to implement even more relevant factors such as special promotions. The end goal is to able to describe how ticket prices may influence game attendance.