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Posts by Derek Grifka

Hi! Launched a Streamlit app for the Deserve-to-Win-O-Meter. You can now easily search any game from the 2025 season (soon 2026). Note I added spray angle to the model, which accounts for where balls are hit on the field, not just how hard.

dtw-str.streamlit.app

3 months ago 1 1 1 0
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Introducing a daily playoff probability model and a "rooting interest guide" (inspired by @PFF_Moo from Twitter).
 
Playoff %s from 10K sims w/remaining SOS & home/away advantages.

Rooting Interest analyzes who each team's fans should root for today, based on playoff implications

10 months ago 1 2 1 0
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I'm excited to announce I've accepted a role as Data Scientist for the Chicago Fire.

Thanks to everyone who has given me feedback and advice. I'm ecstatic to be able to learn from/work with some very bright individuals and contribute to the success of a storied MLS franchise!

1 year ago 0 0 0 0
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Obviously, this assumes each Super Bowl is independent and identically distributed, which is false, but good enough for this exercise. We essentially calculate the probability of NOT seeing a Super Bowl by each age:

1 year ago 0 0 0 0
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I'm a Houston Texans fan and have never seen my team win the Super Bowl, which made me think:
If you were born today, then how likely is it that you would be alive to see your favorite team win the Super Bowl?
You have a 50% chance of seeing your team win 1 by age 22.
#NFL #SBLIX

1 year ago 1 0 1 0
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Why this two-way player had one of the best seasons of all time….but it’s not who you think 🧵

1 year ago 1 0 0 0
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Skeptical of Notre Dame's "middle 8" advantage, I thought good teams dominate throughout.
But data shows ND scores 3-18 more points (89% confidence) in these minutes vs other periods.
Whether or not this matters, they do in fact score more in the middle 8.

www.wsj.com/sports/footb...

1 year ago 1 0 0 0

When McLellan took over, the Red Wings were .433. Since then? 8-4.

Using a Beta-Binomial model with a .500 prior, there's an 81% chance they're now truly an above-.500 team.

Best estimate of their true win rate: .591 (90% CI: .418-.755)

1 year ago 1 0 0 0
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Hockey is cool

1 year ago 0 0 0 0
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Overlap: How can pre-snap motion exploit it? Explore and run machine learning code with Kaggle Notebooks | Using data from No attached data sources

I developed a neural net for this year's NFL's Big Data Bowl. Have a lot of ppl to thank, but wanted to share bc I enjoyed working on it and hope you enjoy reading it
www.kaggle.com/code/derekgr...

github.com/dgrifka/nfl_...

1 year ago 1 1 0 0