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You are a HUGE football fan. Every week you pick winners in an NFL pick-em’ league. Somehow, all that fan experience doesn’t translate into consistently winning your league. Perhaps you need a more systematic approach that takes some of the emotion out of it.

Where to start? Betting spreads provide a consistent and robust mechanism for encapsulating the variables and predicting outcomes of NFL games. In a weekly confidence pool, spreads also perform very well as opposed to intuition-based guessing and “knowledge” from years of being a fan.

Can we do better? In this talk, we will discuss an approach to use machine learning algorithms to make improvements on the spread method of ranking winners on a weekly basis as an exercise in winning your friendly neighborhood confidence pool.
https://datadialogs.ischool.berkeley.edu/2016/schedule/using-machine-learning-predicting-nfl-games
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Amit Bhattacharyya
Senior Data Scientist
Teachers Pay Teachers

Amit is the Senior Data Scientist at Teachers Pay Teachers, an online marketplace for teachers to buy, sell and share original educational resources. At TpT, Amit works on developing both technical and modeling infrastructure to analyze customer behavior and ways to more effectively connect buyers and sellers.

Amit also teaches in the MIDS program at the UC Berkeley School of Information. He received a Ph.D. in physics from Indiana Universtiy. Previously, he did a two-year stint in advertising, and worked as a quantitative analyst at various banks and hedge funds for twelve years. In his spare time, he likes to plan skiing and backpacking trips, and dabble with machine learning algorithms for fantasy football.

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Comments

White says:

This is the most conscise talk about ml wish I saw this a year ago. I don't think machine learning can solve my problem but it's been a fun journey and have learned a lot

Dino Jackson says:

Where can we find the dataset that was used for this example?

Ben Sang says:

Seahawks!!!

revenez says:

Very informative and enjoyable, thank you.

ExcelDFS.com says:

I've steered clear of NFL because there is much less near term data when compared to NBA and MLB

Michael Friedman says:

Is there a similar video for R instead of Python?

Sam Vasquez says:

Cool, dialogue.
The NFL is hard to predict.
To win this game you have to take chances on underdog games.

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