Some days ago I extended my ZIP Poisson model by some additional leagues. These are: Championship, Seria B, La Liga 2, Eredivise, Liga Portugal. It’s always helpful to be able to select more possible bets. Playing more bets reduces the variance of your hit rate and provides a more stable average profit. So let’s have a look, how the ZIP Poisson model performs including the new leagues.Continue reading “ZIP model performance incl. minor leagues”
Image you see following picture for two different profit lines. Both betting simulations are based on the same stacking method: Each identified value bet is set with 1 unit flat stack. Which of both simulation would you prefer? I think the answer is easy.
Of course everybody would prefer the green proft line. But both profit lines are based on the same predictive model. All predictions and bet selections are based on the EMA10 Vanilla Poisson xG model, which I already used for multiple blogs.
The difference between both lines: The yellow line represents the betting profit, when betting against the provided odds of a bookie. The green line represent the betting profit, when betting against a bookie without the bookie margin. This bookmaker margin eat up the whole advantage of the model.
“Goals are the only statistic, which decide a match” – sentences like this appeared not only once, while reading discussion about the latest xG statistics of single matches on Twitter. Even if the statistic xG is more and more used by sport journalists and during broadcasts, the meaning and importance of the statistic is not yet widely understood. This might be caused by the usage of xG for single matches or single shots. The final result of a match and the corresponding xG values might differ a lot. But over the long-term xG is a statistic, which tells us way more about a football team than goals and shots alone. To prove this, this post will take a look at the predictive power of xG in comparison to goals. The more information a statistic contains the more it should help us to predict the result of future matches.Continue reading “The predictive power of xG”
After I realized my available data is definitely not enough to beat the bookie, I decided to start a new data journey and take a look at some more advanced statistics. And what could be better suited as Expected Goals (xG). This statistic is used more and more to explain this specific luck / bad luck factor, you feel, when watching a football match. In the first part of this journey I will explain, what are xG and what they tell you about a football match. Continue reading “xG data journey – What are ExpectedGoals?”
Before the new season will start I should take a look at the last season. Everybody following my pick history already knows: the last season again was very disappointing! But I again have to point out, that I of course did not expect to find the “holy grail” after just two seasons of model testing. So how bad do the numbers really look, and what are the most important “lesson learned” are….
When you follow my twitter account, you may have noticed, since several month I started also writing blogs and articles for other platforms. Even so these are most of the time not about sports betting, I thought it would be a good idea, to share them also via my blog and also share some thoughts about the topics as the main message is often the same: Get the most out of your data!
This time, after over 20 matchdays in the German Bundesliga, I don’t want to take a look at the predicted results. I used my Team Strength MLP now for about 6 months. During this time I analysed the predictions and tried to learn some more stuff about deep learning. So let’s summarize some lessons I have already learned and what could be improved for my model for the next season.
With beginning of the new season in the Bundesliga I started to use my new predictive model “Team Strength MLP“. This model is no longer a statistical model like the Poisson model from last season. It is trained neural network. After the first 10 matchdays it is time to check the current result of my betting history.
The Bundesliga season 2017/18 has taken a dramatic end. The last never-relegated founding member Hamburger SV is on its way to the 2nd division. I am really happy about this, as there will be two thrilling derbies next season and St. Pauli will be able to defend there derby title.
But the end of this season does also mean something else: I am now using the Poisson model for one year in a productive way by populating picks, betting with some mini stacks and analysing the results. So it is time to do a retrospective and sum up all the experiences and all weaknesses discovered.
Some days ago I read an interesting article about how bookies arrange their margin to the possible outcomes. All bookies keep this of course secret as this offers them a specific range, where they can shorten or lengthen the odds depended on the amounts of placed bets. But I need this information, because I simulate my prediction models for back and lay markets, with just the odds of the back markets. This post will explain, how you should calculate the bookie margin, and how you should not do it. I handled this topic a little bit naively during the development of my Poisson model, which causes some problems.