Today I’m going to tackle a topic that is somewhat controversial. Myths, misconceptions and disinformation are everywhere, there is passionate debate from all directions, and there is a lot of complex stuff going on. Let’s talk about the different ways that a centralized* sportsbook can be run; specifically, how they set their odds and howContinue reading “Sharp Books, Soft Books: Inside the Sportsbook Ecosystem”
Professor Harry Crane is one of the people I respect the most within our little community of sports betting enthusiasts. He has an ability to blend the technical rigor of an academic with the common sense of a real-world practitioner, and he has been nothing but honest and trustworthy in my dealings with him. SoContinue reading “An Announcement”
Diversity is one of the most important keys to good scientific inquiry. I’m not talking about diversity of race, gender, sexual orientation, etc. – those are fine but they’re superficial. I’m talking about intellectual diversity – different ways of thinking about things and different ways of doing things. Gambling Twitter is a great source ofContinue reading “How Winners Win: A Framework for Breaking Down Expected Value”
Around the house, I am far from a handyman. I can change a light bulb, but anything more difficult than that and I’m gladly paying someone else to do it. Put me in cyberspace though and I become a regular Al Borland. There are plenty of plug & play software tools for building regression-type modelsContinue reading “DIY Linear (and Nonlinear!) Regression using Maximum Likelihood”
Well, we’re 14 months into this pandemic and while a return to full-attendance live events is on the horizon in some parts of the world, most of North America is still somewhere between limited attendance and no fans, and here in Toronto I can’t even go to the damn golf course let alone a crowdedContinue reading “Next Generation Ticket Pricing”
In Part 4, we took our simple ballast model and re-derived it as a Bayesian model complete with priors, posteriors, hyperparameters, loglikelihoods…the whole shebang. We ended with an interpretation of the prior alpha parameter as a measure of both the signal to noise ratio in the emerging data and the quality of our prior. OfContinue reading “Building a Bayesian Model: Part 5”
We ended Part 3 with the Shyamalan-eque twist that the simple ballast model we’ve been building has been a Bayesian model all along. Time to take a step back and look at the same model through a Bayesian lens. I mentioned way back in Part 1 that we’d be gradually building this thing up, andContinue reading “Building a Bayesian Model: Part 4”
In Part 2, we introduced the concept of a “ballast model” and applied it to our NBA defensive shooting percentage data. Just one problem though…we selected our ballast values judgmentally (read: made them up randomly). Now, we’re going to approach the selection with a bit more rigor. There were 1230 games played in the 2018-19Continue reading “Building a Bayesian Model: Part 3”
In Part 1, we built a very simple model for NBA shooting percentage defense that didn’t work very well. The underlying hypothesis was that all team-to-team variance in opponent shooting percentages is completely random. While overall we can say that hypothesis is busted, our back-test did suggest that it might be partially true, especially earlyContinue reading “Building a Bayesian Model: Part 2”
All of the data and models described in this series of articles can be downloaded here: In this series of articles, I’m going to show you how to build a Bayesian model to solve a “small data” problem. We’re going to start very simple, so simple that you’ll be doing Bayesian analysis without even realizingContinue reading “Building a Bayesian Model: Part 1”
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