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# Sports betting with Poisson distribution

Sports betting strategy from Thomas

Sports betting with the help of the Poisson probability distribution

The basis of my system is a probability distribution, with which one can determine the probability of the outcomes of a random experiment ( Poisson distribution). That sounds complicated at first, but I'll give you a simple example.

An average of 20 people enter a post office per hour. With the help of the Poisson distribution, one can now predict the probability with which none, one, 20 or 30 people will enter the post office. We have similar framework conditions in football. A game lasts 90 minutes. On average, the home team scores x goals and the away team scores y goals.

Using the Poisson method, one can now determine the probability of fewer than 3 goals (Under 2.5) and with the probability of the bookmaker to compare.

The whole thing looks like this: The results that let us win a Under 2.5 goals bet:

• 0-0: = ((x ^ 0 / FACT (0)) * EXP (-x)) * ((y ^ 0 / FACT (0) * EXP (-y)))
• 1 -1: = ((x ^ 1 / FACT (1)) * EXP (-x)) * ((y ^ 1 / FACT (1) * EXP (-y)))
• 1- 0: = ((x ^ 1 / FACT (1)) * EXP (-x)) * ((y ^ 0 / FACT (0) * EXP (-y)))
• 0-1 : = ((x ^ 0 / FACT (0)) * EXP (-x)) * ((y ^ 1 / FACT (1) * EXP (-y)))
• 2-0: = ((x ^ 2 / FACT (2)) * EXP (-x)) * ((y ^ 0 / FACT (0) * EXP (-y)))
• 0-2: = ((x ^ 0 / FACT (0)) * EXP (-x)) * ((y ^ 2 / FACT (2) * EXP (-y)))

Don't be frightened! I have prescribed the formulas for each result. If you want to try out my betting strategy, you can simply copy the formulas into Excel. x represents the average number of goals by the home team (per game) and y represents the average number of goals by the away team (per game).

Let's look at the Example: Bayern - Dortmund
We determine the number of goals for Bayern's last 6 home games: 11 goals
Diese Zahl teilen wir dann durch 6: 11/6=1,83
That would be our x

Then we determine the number of gates for the last 6 away matches of Dortmund: 12 goals
Divided by 6 does: 12/6 = 2
This is our y

We now put x and y in the formulas above on and get

• 0-0: 0.022 = 2.2%
• 1-1: 0.079 = 7.9%
• 1-0: 0, 04 = 4%
• 0-1: 0.043 = 4.3%
• 2-0: 0.036 = 3.6%
• 0-2: 0.043 = 4.3%

The probability that fewer than 3 goals will be scored in the game is: 2.2 + 7.9 + 4 + 4.3 + 3.6 + 4.3 = 26, 3%

The probability for more than 2.5 goals is therefore: 100% -26.3% = 72.7%

The bookmaker gives us one for over 2.5 At odds of 1.5 we can check whether the bet is "worth it":
1 / 1.5 = 0.67 = 67%
72.7%> 67%

So we have a value bet !!

Extension:

In order to ensure an even more precise value for the average number of hits by the home team, it makes sense to take into account how many goals the away team conceded per game. Or in order to achieve an even more precise value for the average number of goals scored by the away team, it should be taken into account how many goals the home team conceded per game.

Let's look again at the fictitious Example Bayern vs. Dortmund:

To get an exact value for the average number of goals of To determine Bayern per game, take the last 6 home games of Bayern and the last 6 away games of Dortmund.

Example Bavaria has scored 11 times in the last 6 home games. Dortmund have conceded 5 goals in the last 6 away games. Then the average value for Bayern goals per game is:
(11 + 5) / 12 = 1.33
This is our new, more precisely x.

To determine the value for Dortmund's away goals, we proceed accordingly: In the last 6 away games Dortmund have scored 12 times and Bayern have conceded 5 goals at home in the last 6 games:
(12 + 5) / 12 = 1.42
This is our new, more precisely, y.

Finally the excel formulas for under 3.5 and under 1.5:

formulas for under 1, 5:

• 0-0: = ((x ^ 0 / FACT (0)) * EXP (-x)) * ((y ^ 0 / FACT (0) * EXP (-y)) )
• 0-1: = ((x ^ 0 / FACT (0)) * EXP (-x)) * ((y ^ 1 / FACT (1) * EXP (-y)))
• 1-0: = ((x ^ 1 / FACT (1)) * EXP (-x)) * ((y ^ 0 / FACT (0) * EXP (-y))) | || 397

Formeln für Unter 3,5:

• 3-0: = ((x ^ 3 / FACT (3)) * EXP (-x)) * ((y ^ 0 / FACULTY (0) * EXP (-y)))
• 0-3: = ((x ^ 0 / FACULTY (0)) * EXP (-x)) * ((y ^ 3 / FACULTY (3) * EXP (-y)))
• 2-1; = ((x ^ 2 / FACT (2)) * EXP (-x)) * ((y ^ 1 / FACT (1) * EXP (-y)))
• 0-0: = ((x ^ 0 / FACT (0)) * EXP (-x)) * ((y ^ 0 / FACT (0) * EXP (-y)))
• 1-1: = ( (x ^ 1 / FACT (1)) * EXP (-x)) * ((y ^ 1 / FACT (1) * EXP (-y)))
• 1-0: = (( x ^ 1 / FACT (1)) * EXP (-x)) * ((y ^ 0 / FACT (0) * EXP (-y)))
• 0-1: = ((x ^ 0 / FACT (0)) * EXP (-x)) * ((y ^ 1 / FACT (1) * EXP (-y)))
• 2-0: = ((x ^ 2 / FACT (2)) * EXP (-x)) * ((y ^ 0 / FACT (0) * EXP (-y)))
• 0-2: = ((x ^ 0 / FACT (0)) * EXP (-x)) * ((y ^ 2 / FACT (2) * EXP (-y)))
• 1-2: = ((x ^ 1 / FACULTY (1)) * EXP (-x)) * ((y ^ 2 / FACULTY (2) * EXP (-y)))

The whole process takes some work, of course, but if you can copied out the formulas and processed them with Excel, the effort is manageable.

Best regards
Thomas

Note: NO strategy can guarantee a secure profit. So always bet responsibly!