Inspirations

Mahatma Gandhi, George Burdell, World Peace, Lou Gehrig

What it does

As of now, we have a working full stack data visualization project. The application can be broken down into 3 parts. Scraping, Analysis, Visualization. Data about each opposing team and each player is scraped from the NCAA statistics play-by-play. It is then compiled and analyzed for relevant statistics. Finally, this data is beautifully displayed on a interactive table. With time we hope to make the application even more helpful.

How WE (Because there's NO I in T-E-A-M) built it

(See above) The application can be broken down into 3 parts. Scraping, Analysis, Visualization. Data about each opposing team and each player is scraped from the NCAA statistics play-by-play and team stats. It is then compiled and analyzed for relevant statistics using natural language processing. Finally, this data is beautifully displayed on a interactive table. With time we hope to make the application even more helpful.

Challenges I ran into

Almost every stadium seems to have its own scorekeeper with their own scorekeeping notation making it very difficult to automate analyzing play by play data.

Accomplishments that WE're proud of

As a team, we are proud of the application we created. We learned a lot about the data analytics process, from collecting data to visualizing the collected data. Although, most importantly, how we can apply this knowledge to our future careers.

What WE learned

We learned about how some of the troubles facing compiling data straight from the NCAA’s site — both because of how slow and unresponsive the site is and also because of the inconsistency in recording every game. We learned a lot about how to combine disparate technologies into a clean automated pipeline. We walk away with a newfound respect for the incredible off-field work that coaches perform on a near-daily basis.

What's next for Hardball

Hardball as a whole serves to help softball coaches get an edge on their competition. As of now, it quickly summarized player stats for opposing teams. If we were to continue this project, we would expand the statistical analysis done on the data and the extent of the visualization. We're even ready to create a visualization that lets us chart a heat-map for hits by player and by team; providing coaches new and exciting ways to interact with the game. In order to do this we just need one more inning!

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