(TickEngage) GT Sports Innovation Hackathon

Inspiration

The love of data science combined with the large database of information in baseball led to the creation of this hack

What it does

Through various machine learning methods we were able to determine the trends that led to an increased attendance at Atlanta Braves Baseball Games

How we built it

The model was built, by collecting and interpreting data obtained from the 2019 season of the Atlanta Braves, their promotions and their opponents. From this information, various machine learning methods were used to extract knowledge from the data.

Challenges we ran into

The 2019 season is a small data set of 81 home games. The opposing star pitchers do not pitch enough games against the Atlanta Braves for us to understand their effect on attendance. This is important for one of the factors attributed to higher attendance was the opposing winning percentage.

Accomplishments that we're proud of

Building this model that enables us to practice the knowledge we have learned in our graduate and undergraduate programs.

What we learned

Just do it!

What's next for GT Sports Inovation Hackathon

We need more data to train a more robust model.

Built With

Submitted to

2nd Annual Sports Innovation Challenge by Georgia Tech Athletics

Created by

I proposed the initial idea and implemented three different machine learning models. In addition to developing the models, I conducted the sensitivity analysis to figure out correlations and contributions of the design variables. Last, I created the presentation slide for the first judge round.

Junghyun (Andy) Kim
https://junghyunandykim.wixsite.com/home
I worked on Data Analysis part

Rui Jia
Love baseball, I thought of various variables that could bring people to games or keep them away. Also, thought of various ways teams tried to draw larger crowds for games.

Russell Hill
Data mining and Analytics

Aditya Singhal
Georgia Tech Student at College of Computing
Tan Le