Cheating in baseball is diminishing MLB's product. Our solution was to code a program that took in historic data to determine whether a pitcher was likely to be cheating.
The inspiration was to work toward bettering the validity and excitement of baseball since cheating often takes away the fun of the game.
Our project uses historical data of players' spin rates and takes in newer data to determine if a player was using an illegal substance to increase their spin efficiency.
We built our program using Python with Jupyter Notebooks and csv files containing data regarding spin rates from 2020 and 2021. 2020 saw an increase in cheating, so in 2021, guidelines to curb this were put into place. 2021 acted as our control group since this was the first year to see these guidelines, so it is more likely that the stats for this year is closer to reality.
Challenges we ran into were debugging when we combined all our code together because something were not named the same and edited correctly.
We are proud that we completed our project as a whole because none of us have ever done anything data science related.
We learned how to read in csv files and how to use Jupyter Notebooks to analyze that data.
We are not currently planning on moving forward with this project, but if we rediscover this in the future, we might try to make it more advance and sell it to MLB.
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