Jimmy Boyd in 
When it comes to learning how to bet sports the only real metric that matters is how much you win. It doesn’t matter what sports handicapping method that you use to accomplish your goal.
I do think you are better off building a sports betting model to give you an accurate idea of how a game will play out, but how accurate does that model need to be in order to win? This guide will help you figure that out.
What Does Directionally Correct Modeling Mean?
I’ve seen on X (formerly Twitter) discussions on how it doesn’t really matter how accurate your model is as long as it is directionally correct. What this means is that it doesn’t matter if you model a game with a line of -13, but the books have the odds at -3, and the team you have favored wins by seven.
Sure, the book was “more accurate” in their line of three, but you still won your bet which is the goal. The key here is that you were directionally correct in knowing that the team should be favored by more than they are.
The truth is that your model isn’t going to incorporate all of the factors that possibly go into deciding who is going to win a game. It’s just not.
You are never going to achieve 100% accuracy with any model. There are those other factors but there is also the pure randomness of sports. You can’t know everything that will happen no matter how complex your model is.
How Accurate Does Your Model Need to Be
When I’m judging the accuracy of my model I’m looking at the win percentage or the total profit on betting the model recommendations. I’ve talked about the break even percentage needed to win before and how it can vary if you are betting spreads in -110 sports and against money lines.
Your model needs to spit out bets that let you win backing that side more than the break-even percentage. But it doesn’t need to be 100% accurate.
I do think a quality model will win at a higher rate when the perceived edge is higher. I look for that whenever I’m building or incorporating new factors into existing models.
If I’m looking at two different games, both at -110 juice, and my model gives and edge of six points on one game and ten points on another. In a large enough sample size with games of similar edges the larger edge better hit at a higher percentage.
Building in a Margin of Error for Your Model
One thing novice bettors struggle with is the margin of error in their models. It’s not going to incorporate every single factor that goes into a game.
That means you need a little margin of error before your advantage is big enough to place a bet. For -110 sports I’m normally looking at an edge greater than 2.5% before putting my money down.
This cushion allows me to avoid making bets on games where I don’t have the complete picture and the market is accurately more accurate than I am.
However, I didn’t come at this number randomly. What I do is back test my model against a large sample size of data, and see where it comfortably gets to profitability.
This is different for each sport and each model. Some sports I have more data on (think baseball with their 162 game seasons) and some I have less.
The more data, the more comfortable I am trusting my model. The more factors I have in the model, the more confidence I have that I have the complete picture.
Conclusion
It’s easy to get too caught up in fine tuning your model until it’s as accurate as possible, but it’s more important to be directionally correct. You want your model to tell you which teams have an edge so you can make profitable bets.
You do want it accurate enough that it improves with a higher edge, but also realize that a margin of safety can help give you a buffer.