Poisson vs Reality

The Poisson distribution is widely used to predict the result of a football matches. Multiple articles can be found in the internet and I also already provided a comparison of different Vanilla Poisson models. But the Poisson distribution as some limitations. The Poisson distribution assumes the number of goals a team scores are independent. But everybody watching football knows, that a team being one goals behind is way more motivated to score a goal in comparison to being already 4 goals behind. So let’s have a look how a simple Poisson distribution compares to the actual scored goals.

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Why is it so hard to beat the Bookie?

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.

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Scoring functions vs. betting profit – Measuring the performance of a football betting model

“What’s the best model?” – That’s a very important questions, when creating, training and testing new predictive models for football. Various machine learning algorithms and packages offer by default a set of scoring functions like accuracy, log-loss, brier score or ROC-AUC, which measure the accuracy of a probabilistic prediction. But I already recognized in older posts, that the best model based on a scoring function, was not always the best model, when it’s about using the prediction results for betting. So let’s have a look and compare the rank of some scoring functions in comparison to the betting profit of some models.

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The predictive power of xG

“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.

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xG data journey – What are ExpectedGoals?

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?”

Retrospective for Bundesliga season 2018/19

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….

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Four things I have learned after using a neural network for 6 months

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.

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HowTo: Losing streaks & stake size

I regular listen the Business of Betting Podcast, when driving to work. There I stumbled over a episode, where a experiment with a 60/40 coin was discussed. People were provided a modified coin, which shows head with a probability of 60%. Knowing this fact they should choose a stake, which they think maximizes their profit, and flip the coin 100 times. The result of this experiment was really surprising: Despite a positive edge many people gone busted. But why did this happen?

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