Methodology

Considering how successful this system has been over the years, it is relatively simple compared to other predictive models. Now, I unfortunately cannot give away every little detail. I want this site to be as transparent as possible, but at the same time I value my model like Coca Cola's secret ingredient. I will, however, go over the basics so that you know what I take into account when making picks and can feel more comfortable using my service.

First off, the base of my model utilizes just two simple stats: Points per Game (PPG) and Points per Game Allowed (PPGa). With each game, the model accounts for how each team generally performs whether they are home or away. For example, the Cavaliers are playing the Bulls in Chicago. The model uses the Cavaliers' Road PPG and PPGa, and also the Bulls Home PPG and PPGa. After that, these numbers are compared to the league average for each stat. The formulas I have created with my model produce a predicted score for each team and is then compared to the current spread and total.

Due to it's simplicity, it obviously isn't perfect. Therefore, I give it a 4 point buffer for spreads and 10 points for totals. In other words, let's say the Bulls are projected to win 96-94 with the spread being Bulls -1.5 (The Bulls are favored to win by 1.5 points). Although the model predicts the Bulls will win by 2, we will not bet this spread because there is only a 0.5 point buffer between the projected win margin and the spread. Had the projected score been 99-92, there would be a 5.5 point buffer and thus we would have a play.

Second, the model then separates games if one or both teams are playing a back-to-back (I note this as B2B). More often than not, teams do not play at their usual level on the second night of playing two games in a row. Because of this, I made my system have two averages for each PPG/PPGa stat - one that tracks games after rest, and one that tracks when the teams play in a back-to-back. The model uses the same formulas detailed above, but with the B2B averages instead. This has proven to be very successful for predicting B2B and games after rest.

Below are my results over the past three seasons, not including the 2015-16 season. You can see my performance in detail since the 2012-13 season by visiting my Past Results page.

3 Year Results NBA Betting System

The last thing to understand is the large volume of bets I make each season. As you can see, I bet A LOT with my system. This is much higher than the norm, but you can see I am very consistent.

Do other professional sports bettors have a higher win rate? Some do, but they definitely do not bet at the rate that I do. The volume of bets I make help me grow my bankroll greatly. To put this into perspective, let's say your betting unit size is $100. With my picks, you have made $12,433 over the past three seasons.

Not bad for a recreational bettor. This high volume betting obviously causes some volatility now and again, but over the long run it has been incredibly successful.

So that's the main idea behind the model and its past results. Like I said, I don't want to give away the exact formulas, but I want you to know the basic concept if you are considering subscribing to my picks or want to build your own model. If you are interested in building your own, please visit the Resources section that has plenty of great tools and products that I use regularly.

If you have any comments or questions, feel free to leave a comment below or send me a message!

Best of luck,

Stephen