What is DomModel?
The model is a machine learning algorithm (called a random forest model) that uses advanced NFL statistics to come up with what the spread should be. The model also considers variance by penalizing teams that do not have consistent week to week performances and gives a boost to teams with home field advantage.
We provide the model’s output on the DomModel Results page for each week. You’ll be able to see the results for every game, even the games we don’t bet. It will provide you with a quick idea of what games to bet on.
The model is named after the one of the site’s cofounders who wrote the code and developed it. Yes, his name is Dom and yes, he literally named the model after himself. Dom is known for being a beacon of creativity and modesty.
Great, so I just check the model and print money?
Kinda, sorta. There’s more to it than that. We will be recommending what bets you make and posts those to the home page. There’s a few things to keep in mind when it comes to the model:
- The model is doing a comparison based on the Vegas consensus when the model is run. Obviously, lines move throughout the week. If the model is recommending a bet at -3.5 and the line moves to -2.5, well that’s kind of a big deal. It’s probably no longer a good bet
- We’re all aware that injuries happen in the NFL way too often. These are going to have an impact on the game and the model may not be accounting for it. That’s where the blog posts on the home page come in handy. The model might like a bet that we may not bet on based on news that comes out, or it may be iffy on a bet that we end up liking. Again, the home page will have our recommended bets, and the blog posts will explain the games in further detail. Plus, I’m given to understand that the co-founder who writes the blogs is really handsome.
- Matchups: The model won’t necessarily discount teams that are in poor matchups (i.e., maybe a favorite has a poor pass defense and is up against an elite passing offense). The blog posts dives into this as well. Plus, handsome.
So the TL;DR version is this: yes, the model by itself is profitable. But to make even more money you should be paying attention to line moves and reading the handsome man’s analysis page.
When does the page get updated?
The goal is to have everything come out on Wednesday. This includes the full model output, the analysis blogs, and the recommendations on the home page. We do both have full time jobs, so this is subject to delay. We can keep you posted if there will be a delay.
When should I make my bets?
As soon as we give our advice. Closing lines tend to be sharper than opening lines, and we’ve found over the years that the lines tend to move toward the model. If you wait till Friday or Saturday, the line we are recommending may no longer be available to you.
I noticed the model only gives spread advice. What about moneylines, teasers, and parlays?
There definitely might be cases where you want to bet a moneyline rather than the spread. You can convert spreads to moneyline bets here. If the moneyline is a better payout, feel free to take it. We may also specifically recommend moneyline bets if the Vegas consensus is that far off. Unfortunately, since we do not know what lines you have available to you, it’s hard to recommend one over the other. The best bet is to just use the link above and see if the spread or moneyline is better.
We may recommend a teaser if the model recommends two games that fall into the Wong teaser range. We will have no problem telling you where to put your wong.
As for parlays, kindly leave. The only time a parlay is superior to a straight bet is if the bets are correlated. The biggest example is betting a massive favorite and an over in the same game, but those bets typically are not allowed by most books. If you’re into being a fish and donk betting your money away, feel free to bet parlays.
Why should I trust the information on this site?
Hey look, you’re betting your hard-earned money, I get it. You don’t know me, and you don’t trust me. All you know for certain is I’m handsome.
The reality is this – almost all handicapping services cost money. We give you everything for free. Why? Well, partially because nobody knows who we are, so in all likelihood nobody would actually pay for this. But it’s also because we have a rock solid process in place that actually works. We don’t need to charge, because once you spend some time getting familiar with the information we provide, you’ll keep coming back. There’s been other sites that follow a similar model of not charging in hopes of getting as many eyeballs on the page as possible. One example is facebook, maybe you’ve heard of it.
Handicapping services have to charge money because they’re mostly scams. They don’t have a real way of profiting off bets, so they try to take your money instead. If they posted everything for free, you may use their recommendations for a week or two – but then you’d quickly realize they actually suck at what they’re doing and you’d stop coming. They advertise by posting screen shots on twitter of their winnings and try to tell you that they know the secret ingredient. They’re lying, so don’t give anybody your money, just come here for free.
This site is unique because we show you our entire process. We give you the raw data from the model, then you can see how we view games and come up with our recommendations. You’ll learn a ton by coming here regularly.
***I make absolutely zero promises that we won’t one day charge. We will sell out so fast your head will spin.
Who runs and works the site?
The site has two founders. Dom is responsible for the model, and I (Brady) am responsible for the analysis content. The model is absolutely brilliant, and I spend an obscene amount of time stalking the internet for NFL news. Together we are a force.
What’s the story behind how this started?
We started doing this because we like betting on football. Dom thought he could put something together that could accurately predict spreads. When the first iteration of the model came out in 2016, we were shocked at how close it was coming to the Vegas lines. When we found games that differentiated from Vegas, we bet them.
We realized that injuries and matchups could throw the model results off, so I started giving context to what the model was telling us. In the 2016 and 2017 NFL seasons this was all done via email to a few friends. Then we decided that if we were going to do this anyway, we might as well start a site and see if people were interested in the information that we were providing anyway.
Why doesn’t the model provide total recommendations?
It did for the 2017 season, but then we realized it wasn’t profitable.
When Dom was analyzing the totals for the model, he realized that Vegas has little idea what the total of the game is going to be. The problem is the model had no earthly clue either. The reality is when you bet totals, you’re likely just flipping coins with the bookie and paying a vig to do it. So yeah, don’t bet totals. If you play DFS GPP’s, you should check the totals just to see what ownership is going to be like. The DFS community loves using totals for their projections, and you can get an edge fading that. It’s an incredibly easy way to be contrarian.
I’m a fantasy player, is there anything of use here?
Fantasy advice could become part of the site, but for now it’s spread betting only.
That said, this site is still going to be useful for fantasy purposes. If we find a team should be a much larger favorite than they are, it might be useful to use that running back. If we find a team is going to be a bigger dog then Vegas predicts, their passing attack could see larger volume. This is particularly useful in DFS GPP’s since these players will be lower owned.
The blog posts on the homepage is also going to have very useful information for understanding game flow as well. Even if you’re not interested in betting, the site is worth checking out.
I noticed you left out my favorite teams backup gunner in the injury news. What gives?
Yeah, it says KEY INJURIES. If I leave someone out, I don’t think he’s relevant to the game. Sorry.*
*I’m not actually sorry