Inspiration
As a friend group of sports fans who couldn't get together to play sports we started playing Fantasy NBA Basketball. As we played, our interest in the way the platform worked increased.
What it does
This game gives the user a selection of players to choose from to create a roster and would give an accurate simulation of how the team would fare against NBA rivals using real-time statistics.
How we built it
We used the NBA-API to fetch game and player data with Python and pandas, then we created a data set to read from. The user selects players using a pygame window, and using the data from the API we give them a simulated result of a game against other teams.
Challenges we ran into
It was my first time using an API, and working with large data frames. The NBA-API timed-out due to our inefficient use, so I had to optimize and find better ways to make fewer requests. As well, finding a way to pass the data from the API into something the game could read was a challenge.
Accomplishments that we're proud of
Overall, we're proud of the way we were able to work with big data sets and an API for the first time.
What we learned
We learned how to use the pandas library to interpret large data, and implemented an API.
What's next for NBA Simulator
We hope to continue to expand our knowledge and look for more applications of large data in sports as this is an incomplete project.
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