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The correlation is very weak and the scatter is very large. Also notice all those data plots below the x-axis. Those are upsets, which account for roughly 25 percent of all games in a given season, every season. So, instead of just the average margin of victory, Figure 3 below shows the standard deviation of the point differential vs.
The standard deviation is points, which if you think about it, is huge. So, another way to think about this is that roughly two-thirds of all games will have a margin of victory that is plus-or-minus two touchdowns from the spread.
The crazy thing is, that implies that a full one-third of games will have a margin of victory over two touchdowns from the spread. AND, for about five percent of games, you can expect the spread to be off by more than four touchdowns, in either direction! Considering there are games a week, this result is likely to be observed two to three times per week. If we take all this data together, we can also plot the odds that the favored team will win any given contest, based on the spread:.
The trend line in Figure 5 is not just a best fit line. It is derived from the data shown above where for each spread I assume actual result point differential is normally distributed with a standard deviation of about 14 The mean of each distribution is equal to the Vegas line, and the win probability is the fraction of the normal distribution greater than zero.
Despite having over 12, data points, there is still some scatter, but if I use a seven-point box car smoothing function, it looks like this:. You can see how well the trend line fits the data. This correlation is one of the cornerstones of my college football and basketball analysis. Several very interesting questions result from this data. Does Vegas adjust the lines based on the known betting habits of certain fan bases?
They almost certainly do, but I have never been able to detect any clear bias in the data. Also, it would certainly be very easy to do that for just a handful of games, and that data would just get swamped by all the other data. So, I just ignore this possibility. Does this mean: If those teams were to play times, one team would roughly win 25 times and the other would win 75 times? OR, does it simply mean that in any given game with a point spread, the favorite will win 75 percent of the time by an average margin of 10 points.
I think that the second statement is clearly the correct one. The first statement is a fascinating concept in itself. I am also sure that there are Michigan fans out there that believe that both the and Wolverine squads would have beaten their contemporary Spartan opponents percent of the time as well. Those statements are all certainly false.
But, what I think the Vegas line does in effect if not in intent is to estimate this likelihood, based on all the information available at the time. By the end of the season, I think it is pretty likely that they get close to this reality.
Finally, I have one new piece of data to share, which is the likelihood of a given big upset per year. But, sometimes this is hard to understand how likely or unlikely this even actually is, especially since most of the contests in a given year have a spread that is between one and two touchdowns.
If you factor in the number of games typical in a given year with a given spread, you can create a kind of cumulative distribution function of the number of expected upsets observed in a given year, as a function of the spread. Those data are shown below in Figure 7. An upset when the spread gets above 14 is a once-a-week type of occurrence not shown.
It's a sign of how much it has grown. High handle-producing teams such as Notre Dame are always featured prominently, but the Nugget has also added some less obvious matchups to its catalog more recently. The Broncos opened as point favorites, in case you were curious or interested in taking sides. Power ratings are a lot like the power rankings you dive through weekly. The only difference is these ratings serve a distinct purpose beyond angering fan bases and dousing comment sections in flames.
These ratings allow oddsmakers to consistently handicap teams and games across the entire season, and they are especially integral to this process. There are various ways and methods to create these more on that in a bit , but they are critical for oddsmakers in the line-creation process. Once they are in place, from No. You take the larger number higher-ranked team , subtract the smaller number the lower-ranked team , add in home-field advantage and you get your spread.
Once this is complete, the numbers are compared to create—you guessed it—more numbers. These adjustments from the raw numbers can be huge. Because the season is still off in the horizon, the Golden Nugget has the difficult task of handicapping teams with enormous questions. How will Everett Golson look in his return to Notre Dame? What kind of impact will Jacob Coker have at Alabama?
How will Clemson replace so many key weapons on offense? These are just a handful of the countless items that can flux a line one direction or another. In this instance, that process begins and ends with outside consultant Bruce Marshall. Marshall has worked in the industry for some time at places such as The Gold Sheet and elsewhere. More importantly, this is someone Kessler trusts and works remarkably well with.
Before the lines officially hit the floor, sportsbook director Tony Miller will take one last look and have final say. At that point, the Golden Nugget will open its doors—as it did on June 13—and take real-life wagers on college football games, some of which are more than five months away. These principles carry over to the week-to-week grind of regular season oddsmaking. ET each and every Sunday during the season, the Wynn sportsbook releases its college football point spreads for the week ahead.
For those in tune with this process—the individuals pacing below the sportsbook board or those refreshing a webpage from afar—it is a week ritual. The closer you get to the actual game, the bigger the bet a sportsbook may take. Prior to that, a sportsbook might be reluctant to take it simply because the betting market has not matured. Before you get to the adjusting phase, however, you must have something to adjust. For Todd Fuhrman, his adjusting while at Caesars started well in advance.
To do this, Fuhrman would go to his power ratings, the most consistent and misunderstood part of this process. These ratings serve as the key figure of the oddsmaking blueprint, a way to compute and order teams against one another using a variety of factors.
Paul Bessire, architect of the popular website The Prediction Machine , is a numbers junkie.
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Thursday Friday Saturday Legend of Predictions. Mathematical Football Predictions. Our mathematical football predictions are made on the basis of complex computer calculations with a specially modified algorithm.
There is no one like this anywhere in the world. How can we make a profit from them. The most important thing before making a bet is to be informed. These predictions save you time in analyzing matches, tracking stats, or other similar factors, and are available for free.
The game is taking place in Miami, so snow or heavy wind is unlikely to impact the high-powered offense of the Kansas City Chiefs and give an advantage to San Francisco's strong running game and defensive line. Plus, the Matchups page shows betting history on ATS results. In the sports betting world ATS stands for Against the Spread , which measures the profitability of a team for bettors in terms of the point-spread.
The Total result Over-Under shows the combined points scored in the game while the Cover result shows how many points the team exceeded the spread by. Along with Cover, you could see the word Push and that means that neither team won against the spread, rather they tied. Plus, you can get an informative Recap too. If you are in a state where sports betting is legal, please check out our online sportsbook directory to find the best and most secure places to make football bets.
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