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I enjoy basketball. It’s a fast-paced competitive game and I’ve enjoyed both playing and watching it for a long time. The NBA is famous for generating very clean data, which has long been used by enthusiasts (like myself) for data visualizations, modeling and game predictions.
Recently, I was contacted by DraftKings regarding an interview for a potential job. As part of my preparations for the same, I started using their platform and competing in mock competitions to get acquainted with the DraftKings (DK) contest process. It was during this time period that I really started getting into the idea of using data to model and predict a winning roster.
I built the algorithm iteratively, and from scratch- starting with a naive version 1, a more robust version 2 and currently I’m working on a winning version 3.
I built the algorithm iteratively, and from scratch
You can follow along my algorithm design journey in the rest of the article.
Quick Level Set: Scoring and Rules
DK’s rules and scoring for their NBA classic fantasy contests are fairly intuitive, even if you have no prior basketball knowledge. In a nutshell, the objective is to:
Create an 8-player lineup while staying under the $50,000 salary cap.
Players get different points for different actions (more details below) and the draft with the most number of points, at the end of all games in a night, wins. Sounds simple enough :)
The breakdown for different actions that result in positive (or negative) points can be seen below.
NBA Fantasy points breakdown- DraftKings. Photo by Author.
One last constraint which makes drafting slightly more complicated is player positions. According to DK: Lineups will consist of 8 players and must include players from at least 2 different NBA games.
#algorithms #nba #data-science #sports-analytics #basketball #data analysis
