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SEW Soccer Analytics

The SEW Sports Economics research group is happy to present its Soccer Analytics project. 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.

Updates of the results are announced after each matchday on Twitter 

On this page we present answers to the following questions: What is the range of final rankings of a team? Who are the favorites 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 explanations 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.

Rank in the final table
The table below shows how likely every team finishes on a certain rank. To calculate these probabilities, we simulate over 1'000 different seasons by a computer program based on our prediction model. If you want to know how, please read our further explanations. The chart shows how often each team finished in each position at the end of the simulated seasons. In further analyses, we illustrate these numbers and the evolution of the expected points in the remaining season in an interactive dashboard.

Probabilities smaller than 3% are not displayed.

 

Dynamics after the latest matchday
The following heatmap displays the changes in probabilities to achieve distinct areas in the ranking after the latest matchday. Grid elements colored in blue indicates that the probability has risen for a team to finish in the corresponding season-goal, while orange dyed elements stand for a decrease in probability.

Probabilities smaller than 3% are not displayed.

 

Next matchday
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 (blue) or below (orange) the expectations before the season. Teams located on the diagonal line have so far achieved exact the same number of points as predicted at the beginning of the season. The more a team deviates form the diagonal the larger is the difference of its actual points to the forecasted points before the season.

 

Performance of coaches
Additionally to team performance, we consider the performance of the coaches (in the current season or since taking office). To this end, we calculate the deviations from the forecast on a normalized per match day basis. While coaches are save with green bars, red bars indicate hot seats. The yellow bars suggest increased danger for the coaches. The section forecast quality shows that all coaches have shown a negative value at the time of their dismissal in season 2017/18.

Coaches with less than three games are not displayed.

 

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