With a betting system you are using a series of rules or criteria to replace subjective decisions to guide your betting. Using trends from past results of games or events, you attempt to find the most logical ways to wager similar future games. Essentially, you are looking for situations where, consistently over time, after x has happened, y has tended to happen. Then, when you next see x happen you can bet on y. To find these systems, if you chose to develop them yourself instead of getting them from others, you would use some form of data mining. There are countless types of betting systems. You could be looking for occurrences in statistics. You could be studying schedules for situations that force teams to be better or worse than expected. You could be looking for surprising line movement, or a particular type of betting action that triggers your interest. Whatever the system, once you have your criteria in place you can quickly sort through games, identifying the ones that have any interest and ignoring the rest.

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In theory systems seem perfect. You put the work in on the front end, then you apply the system faithfully and count the money that comes out the other end. Simple. Unfortunately, it isn’t that easy. For starters, sportsbooks are not dumb, so if they are regularly vulnerable to a certain situation they are going to find a way to stop it. More significantly, though, finding true systems that provide a long-term edge for bettors is much tougher than square bettors think it is, and it’s easy to make bad mistakes in your research.

The biggest problem comes down to your sample size. We can use an example of a coin flip to understand. We know that a coin flip is a 50/50 proposition. Over a million flips, then, we’d expect to see heads pretty close to 500,000 times. Over 10 flips, though, it wouldn’t at all be rare to see seven heads or seven tails come up. If you were to assume that seven out of 10 was a reasonable expectation then you could create a system that would quickly make you go broke. It seems obvious in this sample, but when things are a bit more complicated, and people get lazy and greedy after seeing some positive results in testing, all sorts of problems can be created.

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