Poisson distribution is the probability of the number of events that occur in a given interval when the expected number of events is known and the events occur independently of one another.In simple terms, the above definition connotes that, when bettors know the average number of times an event will happen, they can use the Poisson distribution method to calculate how likely other numbers deviate from that average.Like most betting strategies, the Poisson strategy can be tailored for use in nearly any sport of your choosing. The only requirements for this betting method are the league scoring average for both away and home teams, as well as away and home scoring averages for the two teams involved in the game you intend to predict.Notably, you don’t have to understand all the nitty gritties of Simeon Denis Poisson’s publications to use the method, as the Microsoft Excel application offers a universal formula that easily calculates what you need. The excel formula is =POISSON(x, mean, cumulative). The breakdown of the formula is:
- X: The number we seek to solve; in this case the numbers are 4.0 and 5.0.
- Mean: Our expectation in the results.
- Cumulative: This simply asks if we are solving for a range of data or an exact outcome.
Example of Using Poisson Distribution in NFL HandicappingIn the example below, we will be using the Poisson method to in trying to predict the number of sacks in an NFL game. Like turnovers, sacks are recorded as a single unit when they happen. Although sacks have a small probability of happening on every play, they have a large number of potential occurrences, making them a commonality in NFL games. That said, let’s get to the example, using the sample data below:
- Pinnacle: o4.0 -110/ u4.0 -110
- TopBet: o4.0 -107/ u4.0 -107
- 5 Dimes: o5.0 +210 / u5.0 –230
- Bovada: o4.5 +180/ u4.5 -200
- X: The numbers here will be 4.0 or 5.0
- Mean: In this case, we will go with an average of 4.0 sacks per game. Note that this number is usually based on historical averages or data, but the 4.0 used here is for purposes of an example
- Cumulative: In this case, we will be entering ‘true’ since we are not looking for an exact outcome