Data Visualization
A historical look at the expected value of BetMGM's SGP insurance promotion. The promotion is unfortunately no longer running, but other sportsbooks have begun offering similar promotions to bettors.
A brief overview of the basics of becoming a profitable sports bettor using the positive expected value strategy.
A new form of fantasy football gaining popularity. It's simple: pick one team to win each week and if they do, you advance. But there is a catch. You can only pick a team once all season!
A random forest model trained on historical NFL play by play data and player grades by Pro Football Focus (PFF). The model uses five pre-season input parameters to predict a team's win total for the upcoming season.
An xg-boost model created using pre-game sportsbook odds and historical data throughout the game to determine a team's live win probability.
A logistic regression model used to determine a player's expected hit on a batted ball in play based on the exit velocity, launch angle, and spray angle.
A game theory view of an unconventional strategy to ensuring your star QB will get the ball back.
A running tab of my favorite data visualizations I have created over the years.
An in-depth view of the success of implementing the Moneyball strategy made popular by the early 2000 Oakland A's.
For those of you interested in learning how to do your own sports data analysis, create your own machine learning models or data visualizations, or are interested in learning more about sports analytics, the following places are a great place to start.
"A coding course created by a sports fan, for sports fans." A great course to start with if you have no coding experience.
"Master Python by building 100 projects in 100 days. Learn data science, automation, build websites, games and apps!" Another great spot to start if you have no coding experience. I built this website with what I learned from this course!
If you have any experience in R, or have some coding experience to where you could learn it as you go along, this is a great place to start. This book takes you from beginner, through data wrangling, visualization, and ultimately model creation.
The Official YouTube Channel of the Michigan Football Analytics Society (MFANS). Helpful to have some Python/R knowledge but a great introduction to using public NFL data.