Inspiration
We were frustrated that you have to pay to use advanced statistics on espn.com. In order to combat this, we decided that we should generate or own advanced statistics and do analysis on it.
What it does
It is a website that has the 5 top offensive performers and their basic statistics as well as their calculated advanced statistics. Moreover, there is an analysis part of the website in which we go in depth about the players.
How we built it
We created a web scraper in Python to get the data for the calculations. We then used c++ to compute the statistics and then we created a website to display our work using CSS, HTML, JavaScript, and JQuery.
Challenges we ran into
The major challenge that we ran into was scraping the web for the information. The data was unclean and unstructured and required a significant amount of preparation in order to clean the data. There were also challenges creating the right formulas for the c++ program and various technical difficulties behind the front-end aspect of the project (i.e. formatting of the webpage, learning new technologies).
Accomplishments that we're proud of
The website will be up and running! And the web scraper worked beautifully for us.
What we learned
There was so much that we learned over these past 36 hours, but I think the most important thing we learned was about delegating tasks to different members and working cohesively as a team to produce the desired result.
What's next for Advanced Basketball Statistics
We want to add a machine learning aspect so that we can predict which player in the NBA is most similar to the top offensive performers. Hopefully we will get done in the next 12 hours. However, it will not be a guarantee for us.
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