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.Continue reading “Poisson vs Reality”
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 getting all this expected goals data, it’s of course most obvious to take a look at the insights such data can produce and in which way xG can be interpreted. I have decided to take a look at the current development of Borussia Moenchengladbach in the Bundesliga . Even if RB Leipzig took over now the first place, the development of Gladbach in comparison to the last seasons is impressive. And now I just want to know: Does xG data reveals the secret of Marco Rose?Continue reading “xG data journey – the raise of M. Gladbach”
Before I started analyzing data for sports betting I have worked as a Business Intelligence (BI) consultant in different industries. During this time I learned how Business Analytics helps you to improve your business performance by analyzing data. This also helped me to understand, what’s needed to improve the performance of a sports team or the betting performance of a punter with the help of data.Continue reading “From Business Analytics to Sports Analytics”
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?”
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.
The 2018/19 season in the Premier League started entirely normal. After a record-breaking season Manchester City was of course favoured to win again the title with Liverpool just having an option as the runner-up. But things changed in December and, according to both coaches, we can expect the match between “the best team in the world” and “the best team in the world”.
So which match could be more usefull to take a look at the MACD analysis for both teams and get a better feeling how this type of analysis can be used to identify the performance trend of a team.
The first 10 matchdays of the current season in the Bundesliga revealed some clear disadvantages of my Poisson model. The predictor variables attack and defence strength respond too slowly to performance changes of single teams. This was clearly shown by the loss produced by the poorly performing FC Cologne. A normal SMA (simple moving average) does not use a weight. So latest results, which represent the current form, have not a higher priority over older results. As I looked for solution for this problem I stumbled over the EMA (exponential moving average). This post will explain the use of the EMA and how you can implement it inside the Exasol, so that it is usable as an analytical function for the predictor variables. On top I will show you, how you can analyse the team performance with help of MACD (Moving Average Convergence/Divergence oscillator ).
No matter what predictive model you want to build, you have to go through several steps. You find many different approaches to describe such a development process for statistical models or predictive models in the internet. I have chosen a relative simple one, which is based on papers for a SAS training.