NFL totals betting is rather self-exploratory. A prop bet is a special kind of bet that has nothing to do with the outcome or final score of a game. Some of them are player-based — how many yards or touchdowns a specific player scores. Some of them are based in live betting, i. In order for the bettor to win the wager, all outcomes must unfold accordingly.
This might involve a handful of other bets such as a totals bet and a moneyline bet. According to a prominent Vegas oddsmaker, one of the most integral statistics for betting the NFL is… duh duh duh dahhh… pass yards per attempt. Teams that are successful and efficient in their passing game tend to carry the edge over their less successful opponents. Taking this little known or acknowledged stat into account in your handicapping will no doubt help you find success in your waging.
The team who averages more turnovers per game is likely to give up more scoring opportunities and thus the whole game. So it goes without saying that our model analyzes far more than just turnovers and passing yards per attempt. Nonetheless, betting in any case involves a level of intuition. Therefore, even if you subscribe to the Simulator, it would behoove you to do your own research.
Check out important stats. Take your time with each pick. Choose wisely based on time-tested methods and patterns. For instance, what is the most pivotal quality of any team in the NFL? Teams with good quarterbacks flounder all the time in the NFL.
If an O-Line is good, the quarterback and running back will look good too. And you better bet your bottom dollar that an NFL team with a solid O-Line is far more likely to go the distance than a team with big holes and weaknesses in their front five. Think the Dallas Cowboys. Sure, they failed to win a Super Bowl with a rookie quarterback and running back. But the majority of their success last season was owed directly to the strength of their offensive line.
That means knowing what your bankroll is for a given season and being disciplined with your money. PFF offers a host of different statistics and some of the best minds in football. Their grading system is the gold standard in the industry and is extremely thorough. They break down every play of every NFL game to give players the best insight into each contest and skill level of each player. Their ranking system is excellent concerning evaluation offensive and defense matchups and offers plenty of value in handicapping player and team propositions.
Game Pass allows bettors the ability to view full games in their entirety, on-demand, as soon as they are finished being played. Film study is an excellent tool for handicappers. The best available feature at Game Pass has to be their condensed games. These cut out the commercials and stoppages between plays, allowing bettors to watch entire NFL games in about 30 minutes. If game film study helps you win just one or two bets a year, the service has paid for itself. It is worth noting that much of this information and data is utilized by the oddsmakers when they create their odds.
These tools and statistics are particularly valuable for smaller NFL markets, such as player and team props. They are also can be excellent for futures wagers and season-long win totals. They have a long history in the industry but have truly come on in the last couple of years to become a leader in sports betting. Timely payouts are something worth noting. Sportsbetting is the top betting site in the industry and easily the most professional.
They offer competitive odds in all sports and tons of NFL markets, from propositions to live betting. Please wait
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The average bettor might have a chance at real success. A sports bettor has to select His work with professional sports organizations includes optimizing scout travel, in-depth player analysis and lineup configurations. Correctly predict the winning team Among his greatest innovations was the discovery of neural networks as a powerful tool for sports betting. While the model was initially developed around NBA betting, it has since been applied to other sports — chief among them, the NFL.
From this model, we derive our picks for each game. And the best part is, our system is a living, breathing predictive model — it possesses machine learning capacities that allow to detect trends and potentials that we mere humans could only dream of finding. For more information, check out this handy dandy video on how it works. Essentially there are six different ways to bet on the NFL.
Bookmakers set a spread with a favorite and an underdog. Pretty straightforward stuff. Moneyline betting is an equally common form of sports betting as spread bets. The difference is that with moneylines, bookmakers will set lines representing the favorite and the underdog. NFL totals betting is rather self-exploratory. A prop bet is a special kind of bet that has nothing to do with the outcome or final score of a game. Some of them are player-based — how many yards or touchdowns a specific player scores.
Some of them are based in live betting, i. In order for the bettor to win the wager, all outcomes must unfold accordingly. This might involve a handful of other bets such as a totals bet and a moneyline bet. According to a prominent Vegas oddsmaker, one of the most integral statistics for betting the NFL is… duh duh duh dahhh… pass yards per attempt. Teams that are successful and efficient in their passing game tend to carry the edge over their less successful opponents.
Taking this little known or acknowledged stat into account in your handicapping will no doubt help you find success in your waging. The team who averages more turnovers per game is likely to give up more scoring opportunities and thus the whole game. My first step in getting a feel for what is going on is to compute the observed winning percentage for a given line. For each game in my data set, I record the line and whether or not the favorite won.
Then, knowing how many times a given spread showed up, I can divide number of wins by a favorite for a given spread by the number of times that spread was given for a game to determine winning percentage for a given spread. Here is what I found:. At first glance, this looks pretty good. We expect that as the spread gets larger implies the favorite is expected to win by 15 points , the winning percentage generally gets larger.
However, two things are evident. If we simply look at this chart and take it as Gospel, then we would say that being a 17 point favorite means you have a lower chance at winning then if you were a 15 point favorite. This is nonsensical. The reason 17 point favorites have won less often than 15 point favorites is because of the fact that being a 17 point favorite is relatively rare so the small sample size leads to relatively large errors. The second thing we observe is that this is only half the graph.
The true graph also has win rates for underdogs. I have included the full graph below. Budding data scientist should be able to recognize this approximate shape: the logistic function. Logistic regression is the process of fitting a logistic curve to available data.
Note for the technically minded: We need to be quite careful when performing logistic regression here. Logistic regression must have a binary response variable with a continuous predictive variable. Therefore, the data set we are performing the regression has labelled training data where the input is the spread and the output is 1 if that team won, 0 if that team lost.
Now that we understanding logistic regression, we can continue our sports analytics example for historical betting analysis in NFL games. The table below has our results which converts from Vegas line information to winning probability in the NFL. This was the goal in our historical betting analysis and tells us how to determine how likely a team is to win based on the stated line information.
One thing to ask is whether our choice of a logistic model was correct. For example, what if we used a shifted and scaled version of arctangent to fit to our data set? This function has largely the same shape as the logistic function, perhaps it fits our data better. One would need to think quite carefully about which function to use because the particular choice will have an effect on our results. A residual is the difference between the observed and the actual value.
For example, if we predicted 3. A good, general rule of thumb is that if the residuals are small in magnitude the model and the observed data are close to each other and the residuals have no pattern, the our model is fairly strong. If there is not pattern, then there is no more information to be gained by using a more complex model. Here is a graph of both the modelled winning percentages as well as the empirical observed winning percentages on the same plot.
Again, these residuals are fairly indicative of a good model. I will point our two bits of structure or patterns one might observe in the above and try to explain why they do not worry me when it comes to the accuracy of my model. First, the residuals are symmetric about the origin. Rotate the picture by degrees around an axis sticking straight out of your screen and it is the exact same.
However, because of the nature of our data, we already remarked that we have this nice symmetry. Therefore, any model we use will have this type of symmetry. However, I claim that this artifact is actually attributable to systemic error in the data set rather than attributable to the model. Recall, as we get further from the center of our model, the sample size decreases dramatically.
Therefore, teams with very large or very small spreads always lost or won respectively. Moreover, any of the other sigmoidal models I suggested will have this same artifact.