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Soccer Analytics - Forecast Quality

On this page we check the quality of previous forecasts. To this end, we compare our forecasts to forecasts from other external sources.

The main objective of the model is to forecast the final table. It is not expected that the final table can be predicted exactly. Every season has its own surprises and disappointments. To check how good our forecast was last season, we compare our forecast with the tips from many other forecasts collected on the BStat homepage. All of these predict the ranks of the final table. We calculate the rank correlation coefficient with the real final table and the respective forecasts. The closer a forecast was to the truth, the higher this value. Our new forecast method was beaten by three other forecasts in the 2017/18 season. Guessing the exact position is often a matter of luck, especially in a narrow league like the Bundesliga. Therefore, we additionally look at the forecast quality for expected points using the root mean squared error. This quality measure is smaller the closer the forecast is to the actual points. In this category, our new forecast outperforms all others.
In the table "Coach dismissals 2017/18" all released coaches of the Bundesliga season 2017/18 are listed with the standardized deviation of the points to our forecast at the time of their dismissal. All coaches were dismissed with negative deviations. We regard this as evidence that the managers of the Bundesliga teams apply similar standards as our model.

  Rank correlation
Root mean
squared error
Season 2017/18
SEW new
0.64 8.9*
SEW old
0.47 10.8 0.60 -
Goalimpact 0.71 9.0
Fupro 0.63 11.5
0.43 16.8
Kick-Forum 0.61 9.4
Spiegel Online
0.75* -
FiveThirtyEight 0.63 9.9
 Euro Club Index
0.61 9.0 0.63 12.1
General-Anzeiger 0.74  - 0.58 16.4
Club Elo
0.62 -
 * indicates best prediction performance.
Coach Dismissals 2017/18
After match day Team  Coach Deviation / matches in office
 4 VfL Wolfsburg  Andries Jonker  -0.27 
 6 Bayern Munich  Carlo Ancelotti  -0.08 
 10 Werder Bremen  Alexander Nouri  -0.56 
 14 FC Koln  Peter Stoeger  -0.84 
 15 Borussia Dortmund  Peter Bosz  -0.25 
 19 Hamburg  Markus Gisdol  -0.32 
 20 Stuttgart  Hannes Wolf  -0.07 
 23 VfL Wolfsburg  Martin Schmidt
 26 Hamburg  Bernd Hollerbach  -0.38

In the 2016/17 season, our forecast was only beaten by Spiegel Online in predicting the rankings.

Season 2016/17
Quality measure SEW Soccer Analytics F.A.Z. Spiegel Online
Rank correlation coefficient
 0.47 0.44 0.29 0.50* 0.42
RMSE 10.14* 10.87 - - 10.83
 * indicates best prediction performance.

In season 2015/16 our predictions were at least as good as all considered competitors.

Season 2015/16
Quality measure SEW Soccer Analytics F.A.Z. Spiegel Online
Rank correlation coefficient
 0.64* 0.64* 0.57 0.60
RMSE 8.57* 8.76 - -
 * indicates best prediction performance.