Inspiration

We first thought about how sports analytics has become a trending theme in terms of technology as well as sports in general. Name an NBA game you last watched where there wasn't some form of headline statistics mentioned throughout the game? Truth is, the idea of looking at the data and its eventual progression allows us to track player performance and better understand how the season had progressed!

What it does

We have tools made that come into a pip package that allows anyone in the world to use it! These tools are machine learning and statistical based that allow people to better analyze the data within the NBA. We have algorithms in place that pick the various records pertaining to any given player or any season out there and performs various analyses on it!

How we built it

We built it using python mainly within VSCode, however we used many libraries such as mathplotlib, numpy and beautiful soup to name a few!

Challenges we ran into

The biggest challenge we ran into was the packaging of the file into a pip file for various people to install. However, besides that, working out the nitty gritty bits of the analyses and the complex data structures employed were very challenging as well.

Accomplishments that we're proud of

We were able to make fairly accurate prediction based modelling for any given statistic for a player in the NBA. We were also able to skillfully load and correct any mistakes within the data to allow it to be comprehensible by our tools and functions. We've made it possible for various people to use our tools anywhere around the world and conduct any analyses related to the NBA player per game stats.

What we learned

Learning how the whole piecing together of data from web scraping to Machine Learning regressions and prediction modelling was part of the whole experience! Despite the challenges faced, this was a very fun project and allowed us to enjoy various new topics within Computing that my group members and I have never thought about or engaged in before!

What's next for basketball_analysis

Having updates for inclusion of team stats as well prediction modelling for team based statistics which will lead us to help predicting which games could be won by who which is very useful and applicable within the betting sector of sports and gaming.

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