Why First Baskets?

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Slam Dunk Bets started predicting first baskets in 2020, mostly because sports betting was just becoming legal in Illinois, and one of the sports books was running consistent specials on the market.

We discovered that most of the available books were offering first basket props, and that the odds usually varied from book to book.

The play-by-play data for NBA games was freely available, and could be parsed effectively using data science tools. This enabled us to develop models using machine learning tools to predict the likelihood of players to score first in a game.

Once we refined our models, we learned that first baskets were relatively predictable. “Relatively” is doing a lot of work in that sentence, of course, but by nerdy machine learning model evaluation metrics, we felt pretty good.

In addition, we learned that popular players were typically overpriced compared to what our models expected, which we think makes sense: popular players typically get more action from bettors, so books can get away with setting their opening prices high.

The inverse was also true - less popular players were often very favorably priced compared to what our models expected, i.e. they were often positive expected value (+EV). And that’s what we wanted to find!

So to answer the question we raised rhetorically, we started out focusing on first basket markets because: the data were available to build models; the models we built were useful at predicting the thing we wanted to predict; our model predictions enabled us to find +EV bets to make.