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Football Predictions Theory and how to calculate the probability of scoring a goal

26/09/2019

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The following example got me really excited and motivated to study the theory of probability in sports betting.

Imagine you have a team that scores on average 2 goals per match in the long term. Now, the exciting question: What is the probability of the team to score exactly 2 goals in the next match?

Please, meditate on this for a minute. If the average goal score per match is 2, then the probability of scoring 2 goals must be kind of close to 100%. At least this is what common sense is whispering to many of us.

Without teasing I am going to reveal that the probability of scoring 2 goals in our case is actually only 27%. The low probability might be surprising to many of us.

Q: Why is it so important to predict the count of scored and received goals?

A: The count of scored and received goals is the key parameter of the end score. Basically we are not predicting the end result (win, draw or loss), but the end score in the sense of the number of scored and received goals.

How to calculate the probability of scoring a goal

The formula we use for calculating the probability of scored goals per match is called Poisson distribution and here is the definition:

Poisson distribution expresses the probability of a given number of events occurring in a fixed interval of time or space if these events occur with a known constant rate and independently of the time since the last event.

To translate the definition into a language we understand: Poisson distribution expresses the probability of scoring a given count of goals per match based on the past results.

In our final score probability calculator we prepared for you the simple tool to calculate the probability of scoring the goal(s) based on the past average events. Please make sure to understand the whole functionality of the calculator, before you use it for real betting.

Now you see the formula of Poisson distribution function and explanation. Too complicated? You can go to the next section and simply use our calculator!

Poisson distribution function used in sports betting

  • λ is the average number of events per interval - in our case average number of goals scored per match in the past
  • e is the number 2.71828 - which is a constant that we are not going to explore any further
  • x has the values 0, 1, 2, 3, ... - in our case it is the count of goals we are about to calculate the probability for

After you fill up the formula with data, you get the expected probability for scoring the goal. We are presenting here only the simplified insight to the Poisson distribution function. Please read more about Poisson distribution at Wikipedia.

How did we calculate our football predictions

We give you now a little insight to how we created our paid mathematical football predictions.

Q: Is the average count goals sufficient for the prediction?

A: Of course not! Imagine you have a team that played

  • 96 games in the last 2 years.
  • 46 of them were on the home field,
  • 50 were played away.
  • 72 were in the national league,
  • 8 in the Cup competitions,
  • 8 Champions League matches
  • 8 were friendly matches or pre-season games.

Now we are about to predict the count of goals for the next game and come up with some decent betting tips today. This game is going to be a national league game in a home stadium. We have to answer the following questions.

  • Is the home stadium a significant advantage? You can find the answer in the statistics of each team.
  • Are the results from about a year ago still valid to predict tomorrow's game?
  • Are the Champions League and friendly matches statistics a valid predictor for the national league? Most likely not.

To have a valid prediction model, you have to feed it with the right data.

  1. In our automated predictions like e.g. football predictions UK we take into consideration many details mentioned above.
  2. If you decide to use our odds calculator, make sure to take into the "average goals scored" only the relevant statistics.

*I have simplified the example to an ideal situation. Meaning: no injury, no extra strong weak team is the oponent, nor any extraordinary situation.