4a.1 Theory of Success Rate

Searching the web you can find many methodologies for soccer betting predictions. It has no sense to follow a prediction if you don’t know the success rate of it.

The definition of Success Rate (SR) is:

 

SR%= (number of succeeded bets) / (number of predicted bets)

 

Let’s make an example:

Platform “System Hunter” can filtered the database of calculated probabilities of bets, that betpractice studio published in the past.

Users can filtered the spacing of probability to search the Success Rate.

League : Premier League

Month : October 2015

Bet : Home wins

Spacing of Probability to search : Between 0 to 100%

Number of predicted bets : 37 bets. System Hunter searched 37 bets of Home wins with probability between 0 to 100%.

Number of succeeded bets : 13 of 37 bets succeeded (Home team won).

SR : 13 / 37 = 0.3514 or 35.14%

 

The table below show the above example.

 

If we change the probability spacing…

League : Premier League

Month : October 2015

Bet : Home wins

Spacing of Probability to search : Between 60 to 90%

Number of predicted bets : 5 bets. System Hunter searched 5 bets of Home wins with probability between 60 to 90%.

Number of succeeded bets : 4 of 5 bets succeeded (Home team won).

 SR : 4 / 5 = 0.80 or 80.00%


 

The analytical results of each game showing the betpractice “real” probabilities and the final score of the game.

As you can see all calculated probabilities were between 60 to 90% and only at the game Chelsea vs Southampton Home team loss.

 

 

The parameter of SR is very important because you have a real picture if your methodology for oddscompiling gives remarkable results.

In the example above Betpractice Studio gave 5 bets with a probability for “Home wins” between 60 to 90% and the 80% of them succeed.

That means the methodology for calculated “Home wins” probabilities seems to works perfectly.