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”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….
Continue reading “Retrospective for Bundesliga season 2018/19”
A data journey – market values (part 3)
In the 3rd part of this series, I take a look at the – from my point of view – most important part about my market value data journey: Does the team market value holds some predictive power? If so, I could use it as another feature for my predictive models.
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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Betting history: Bundesliga match days 1 – 10 / 2018
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.
Continue reading “Betting history: Bundesliga match days 1 – 10 / 2018”
Team strength MLP (part 3)
Part one defined the basic architecture of the Team Strength MLP (multi layer perceptron). The training process and its monitoring via Tensorboard was explained in part two. Now it is time to take a look at the prediction of football matches. Primarily this consists of following steps:
- Load the prediction data set
- Re-build neural network architecture and load pre-trained weights
- Execute prediction
The Bundesliga season 2017/18 will be the test case for this example. The season 2008 – 2016 were used to train the mode.
Team strength MLP (part 2)
The first part of this series covered the definition of the network architecture for my Team Strength MLP. This neural network must now be trained. To explain and visualize the training process, Tensorflow offers the web frontend TensorBoard. This post will explain, how you use TensorBoard and what are some basic indicators for a well-trained model.
Team strength MLP (part 1)
It is time to build and test my first predictive model with Tensorflow! As I am currently totally unexperienced in creating and optimizing neural networks, I will start with a very simple one, which just uses the predictive variables of the Poisson model. By doing this, I will be able to compare the resulting network with the Poisson model. I am excited to see, whether Tensorflow is able to outperform this statistical model with such a low number of predictive variables. In this series I will provide some basic information, how you are able to build a simple multilayer perceptron (MLP) with Tensorflow, supervise the training process with Tensorboard and use the trained neural network to predict the outcomes of the matches.
BeatTheBookie goes Tensorflow
After gaining much experience over a complete season, it is time to set myself some new goals. Until now I just used or tested predictive models, which were invented or described by other people. Now I want to try something new. I would like to create my first own predictive model, which should of course provide a better performance as the current Poisson model. This is where Tensorflow comes into play.
Retrospective for Bundesliga season 2017/18
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.
Continue reading “Retrospective for Bundesliga season 2017/18”
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