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SEW Soccer Analytics

The SEW Sports Economics research group is happy to present its project, called Soccer Analytics. In this project we developed our own and independent forecast for the German Bundesliga. The forecast is produced using empirical methods of the so-called machine learning. 

(last update 23.05.2017)

On this page we present answers to the following questions: What is the range of final rankings of a team? Who are the favourites and underdogs in each specific game of the upcoming round? How the final table of the season will most likely look like? Which teams are the positive and negative surprises of the season so far?

We provide short explantions how the predictions are calculated and provide more details under further explanations. Additionally, we present results for other interesting analyses. We provide further analyses and assess forecast quality.

News: Updates announced now on Twitter.

Summary:
Our pre-season prediction for the season 2016/17 outperformed 5 out of 6 competitors. More information here.

Rank in the final table
The table below shows how likely every team finishes on a certain rank. To calculate these probabilities, we simulate 50,000 different seasons by a computer program based on our prediction model. If you want to know how, please read our further explanations. The interactive graph shows how often each team finished in each position at the end of the simulated seasons. In further analyses, we illustrate these numbers in an interactive graph and show probabilities to achieve different seasonal goals.


Probabilities smaller than 5% are not displayed
ER: Expected ranks
EP: Expected points (graphical illustration in further analyses)
↑↓: Difference in EP of the final table between current prediction and prediction before season (details in further analyses)

 

Next round
Here fans can check the probabilities according to which their team wins, looses or draws in the next round. It is often erroneously assumed that we predict the team with higher winning probability will win. This interpretation is misleading. You should think about the presented numbers in the following way: Imagine the same teams in the same situation play against each other 100 times. Then the reported probabilities show how many of these imaginary 100 games are expected to have the respective outcome.

Over- and underperformers
The graph below shows for each team whether its achievements so far are above (green) or below (red) the expectations. The bars show the difference between actually achieved points and points that a team was predicted to achieve so far according to the model in the beginning of the season.

Model update:
The forecasting model considers additional information starting with matchday 10. The updated model should improve the performance of our forecasts and led to some changes in the predicted final table.

For questions and suggestions please contact us via soccer.analytics@unisg.ch.