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HOW TO MAKE MONEY BETTING NFL FOOTBALL
Spot elusive but highly profitable scenarios.
Each sport has its own unique advantages from a handicapping aspect. The NFL is certainly no exception. Perhaps as much as 60% of all money bet on sports is bet on the NFL. This is true in spite of the fact that only 250+ games are played per year. (The total number of games played in all sports that have posted lines is over 30,000!)
What does this mean for the NFL gambler? It means the line-maker must take public opinion about the NFL into account more than any other sport.
Public opinion can be predictable. For one thing, the public tends to lean to favorites and overs. That results in posted lines that are - generally speaking - 'tilted' against favorites and against overs. Ask most any successful sports bettor and he will tell you that he bets more underdogs than favorites and more unders than overs. Underdogs and unders can be regarded as being a 'positive universe' because over time underdogs always cover more than 50% of the time and unders always beat overs more than 50% of the time.
Like all sports, we research our NFL database to try to isolate what we call “positive universes”. When we identify a bias, we try to look within it to find logical areas that can be eliminated from the group, thereby intensifying the bias. If we can subtract niches that are 50/50 or less, the winning percentage of the remaining universe increases, and often increases dramatically.
Like each sport, and for varying reasons, the NFL betting season changes as the season progresses. To demonstrate the thought process, we’ll use an example of over/under statistics...
In recent years, NFL games that went over the line has been 48.59%. That is a small bias of 51.41% under. But if you break it down by month, you find an interesting fact:
The December aberration is caused by the public wanting to bet ‘unders’ in December because they think the weather will cause lower scores. The linemaker takes this into account.
Thus, by taking out December, the number of games in our sample that went under during September, October and November is 1450 vs. 1298 that went over...52.77 % went under.
You would also quantify games by the line itself. In all September games, if the over/under line was greater than 40, the results were 226 overs and 296 unders, or 56.7% under. If the line was less than 40, the result was 170 overs and 165 unders, or 49.2% under. It doesn’t take a rocket scientist to understand that logic. And if we apply our projected scores to the data, we can further enhance the outcome. If the projected score is less than the line and the line is greater than 40, the outcome is now 102 overs and 156 unders, or 60.5% under. If our line is over the line and the line is greater than 40, the outcome is 113 overs and 123 unders, or 52.1% under. So we take out the under bets when our line is not leaning to the under...And we learn that our line has some validity, but it still should be used within the ‘positive universe’.
We also, as in all sports, rate the field itself for game and total values. The median field has a ‘4’ value for over/unders. In the example above, the results were 102 overs and 156 unders, or 60.5% under. If we ask our data for the outcomes if the field had a rating of greater than 4, the result is 31 overs and 35 unders, or 53.03 % under. By pulling that niche out of our ‘command’, we now have an outcome of 71 overs and 121 unders, or 63.0% under!
So now the ‘Command’ statement for this category would read, “In September, with an over/under line of greater than 40, a projected total of less than the line, and a field rating of less than 5, what were the results?"
O\U LINE PROJ TOTAL FIELD+
So while the ‘command’ held no relevance for the side bet, it did for the total.
Always keep in mind that the criteria must be logical, but while each sport has its own advantages and quirks, the method of research is the same. Find a large ‘positive universe’ and then ask relevant questions to try to eliminate low value categories within that field.