Welcome
Nineteenth issue. Some leaves are falling from a tree approximately a half-swing 60-degree from my house. Too early for that.
A little less football this week, but included is one research paper on creating an in-game win probability model. An NHL player forecasting system is also introduced, along with different approaches to taking pitches in baseball, how to make US Open tennis projections, and an interview around business intelligence and sports businesses.
Cheers!
This Week's Lineup
How To Create NHL Player Forecasting System
Hockey isn't too far off, and player projections, specifically for Connor McDavid, are all over the place. This read walks us through an approach to better project points for the upcoming year based on time on ice, incorporating regression and games played.
Football In-Game Win Probability Model
A research paper that goes deep into how the authors built the model, including which variables contribute to winning with their Bayesian model, and why other sports models cannot be ported over for football.
Using the Value of Taking Pitches to Describe Different Hitter Approaches
Just because a hitter might be good at taking pitches doesn't guarantee his success when swinging at pitches. A few different examples of good hitters (Tatis) and poor hitters (Bellinger) highlight this point.
US Open Tennis Fantasy Predictions
Data-driven approach to run outcomes of the US Open men’s and women’s draws, predict match statistics for each hypothetical match, and tabulate the corresponding fantasy points that are up for grabs with each match. This is because the US Open introduced a new way to engage with the event this year.
Interview: Impact of Business Intelligence and Measurment
Q&A around how the acquisition of Block 6 Analytics will impact the sports business landscape.
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Unexpected Points Added is curated and maintained by Patrick Hayes. If you have questions or suggestions for the newsletter, just reply to this email. I answer every single one.