For once I did my homework. At least that’s
my assumption.
Lately I have been playing around with the
Poisson distribution model to predict the 1X2, Over/Under, BTTS and correct
score of the matches in the Jupiler Pro League.
I have been trying out 4 different Poisson
models.
-
Poisson distribution based on
the goals averages of the home and away teams since 1998 in the Belgian Jupiler
Pro League.
-
Poisson distribution based on
the goal averages of the home and away teams of the 2016/2017 season.
-
Poisson distribution based on
the goal averages on the H2H matches since 1998 of the home and away team.
-
Poisson distribution based on
the goal averages on the matches between the home and away team (so all H2H
matches) since 1998.
I do know that Poisson is not the best
prediction method based on statistics and that it would benefit from a Dixon Coles
fit. But still working out how the Dixon/Coles adjustment works.
Without this fix, these would have been the
result for the 2016/2017 season in Belgium.
For all 4 of the distribution I did
calculate the prediction based on the % and the most probable score.
The first thing that I noticed was that
there is a huge difference between the prediction of draws based on the % and
most probable score.
The most probable score did predict between
53 and 154 draws, the % did only predict max 5 draws during the season. Still
both Poissin model based on the H2H did deliver a ROI of above 28%.
I also do know that these models are based
on the average goals at the end of the season, which will not happen in real
life.
But we do have the make the assumption that
the model gets better near the end of the season because then the goal averages
are getting close to the ones at the end of the season.
After this I also did some investigation
based on the value of the bets.
Only placing a bet if the odds were right.
The problem I did had here that I did had
to use the odds based on the % in combination with the prediction of the most
probable score. Which is not exact. But we do have to start somewhere.
This gives me the following results.
As you can see, I can minimize my number of
bets using the value betting, while not really affecting my total profit.
Based on these schemes I did decide to try
this out for the Play-off of the Belgian Jupiler Pro League.
On each match there are 4 possible bets. 1X2,
correct score, Over/Under 2.5 and BTTS. The 1X2 and correct score are placed
with taking value betting into consideration. I still have no fix for the draws
problem based on the Poisson % distribution, and the profit and ROI are almost
identical.
The BTTS and O/U bets are only placed when
the odds are above the Poisson distribution.
After running my first test for the upcoming
matches I’m doubting if the Poisson model can be used during the Belgian
Jupiler Pro League playoffs. This due to the fact that the strength of the
teams are almost equal.
Still I will give it a try an din the mean
time making my model better.
I also include a Yankee bet on 4 correct
scores, a accumulator of 4 on 1X2, BTTS and O/U.
For the first round this results in 21 bets
with a total ante of 24,1 points. Some matches are not included due to a lack
of H2H games or goal averages in the 16/17 season. This is because 3 teams from
the second division are also included in the Play-off 2.
To be complete you can find a list of the
bets I’m going to place for the next round.
Good luck,
Dremeber
