The NFL kickstarted with an absolutely thrilling week 1 slate, with almost every game decided by one score and two overtime games! Unfortunately with that came a lot of upsets, and a LOT of unexpected results, which led to poor model performance. The overtime games really changed the outcome of this week, as if both…

For those interested, this article will cover how I use my dataset to built a machine learning model and accurately predict games, as well as some insights into the models. Note that the the most tedious part of the process will not be covered in this article, which consists of scraping the right stats for…

As expected, was a rough outing in week 1 early slate. The colts were a real surprise letdown! Here are some live updates: Live morning bets: 49ers ML: 0.1 unit(+100) Bengals ML: 0.25 units(+150) Jags ML: 0.25 units (+175) Jags ML, bengals ML, 49ers ML: 0.1 units (+1100) Afternoon games: Chiefs ML: 0.25 units (-265)…

My previous article dug into spread-based models, and how there is a guaranteed long term profitability if any given model has above ~53% accuracy on correctly predicting spreads. The other common bet, “moneylines” (simply the winner of the game) require binary classification models to be used: the predicted outcome from the model can only fall…

Let’s break down some common myths in this article! 1. “The house always wins” so they must be really accurate A commonly believed myth is that because sportsbooks are making profit, they must have really accurate predictions. Well to sum it up, they don’t!. Sportsbooks actually make a large portion of their money on something…

As I mentioned in the intro post, I have been working on these models for quite a while and I am completely self taught. If you would like to look into some of the earlier versions of my models they are posted on my GitHub and Medium pages linked below. I have some beginner level…