Introduction
Football managers form a large part of football culture in Europe and beyond with hirings and sacking making up a decent chunk of the column inches written on football. Similarly, they are paid handsomely, with wages of the top managers comparing well with wages of the top players. Today I am looking at data from about 40 manager sackings in the 2024/25 European season across 5 different leagues. I want to see what the data says about how this manager upheaval influenced the performance of these teams.
I am going to focus on two different metrics, one is team performance which considers shots, passes and goals and the other is derived by team’s betting odds at kick off.
The 5 leagues in this study are from the English Premier League, English Championship, English League 1, Serie A and La Liga. On the following graphs there are 40 lines representing teams that changed manager last season. There are some teams not included because the manager change was either too early or too late in the season to make a proper comparison of the team’s performance before and after.
As you will see every line meets at (0,0) in the centre because the team rating is relative to the team rating at the time of the manager change and 0 refers to zero matches since the manager was fired. (The graphs are high resolution so you should be able to zoom in, if required.)
Results
Figure 1
The ratings here include as many adjustments as possible, such as fixtures, red cards, rebounds e.t.c. This thick red line (the MEAN of all 40 lines) shows many teams suffered a decline in performances before seeing their manager sacked (not a surprise). After another 5 games team performance starts to pick up again.
Figure 2 – (The image is high resolution, please zoom in)
The thick red line is the mean of all teams in the sample (40 teams).
This graph derives team ability from betting odds at kick off. While we do see a slide in team ability in the run up to a manager sacking, this slide continues for 6 more games after a manager change. It’s worth noting the values on the y-axis – this method of rating teams is less subject to fluctuation than the previous method that solely measured on pitch performance.
Conclusion
I had a prior scepticism over the importance of most managers that has not been alleviated in this study. While the first plot does imply a manager change has a positive influence this can’t be separated from natural variation.
To prove this, I calculated team performance in groups of 10 games to get the following plot:
Figure 3
The x-axis is comparing performance between games 1-10 and 11-20 of a season for the same 5 leagues I have used for the previous games. This means data points/team to the right of the y-axis have experienced an increase in performance for the 2nd 10 games of the season compared to the 1st 10 games. The y-axis then compares the team performance for games 21-30 compared to games 11-20. The correlation and the line of best fit shows that a lot of data points to the right of the y-axis are also below the x-axis – i.e. all the teams that ‘improved’ in the 2nd set of 10 games regressed by 50% in the next 10 games.
In fact, this is really intuitive! Consider a team having performance of some number, x, for the first 10 games, and then a performance of 0.5x for the second ten games. What performance do we expect for the 3rd set of 10 games? Assuming recent form is negligible we have to answer the average of x and 0.5x, so we get 0.75x. This is exactly what figure 3 demonstrates – performance change in the 2nd set of 10 games regresses in the 3rd set of 10 games. The fact the slope is slightly less than 0.5 likely relates to recent form playing a small role.
Now let’s go back and reconsider figure 1. We can not conclude there is any evidence that manager changes are affecting team performances. Team performance for the 10 games after a manager change almost exactly represents the team performance for the 20 games before the manager change (the data point at x=10 is the average of the data point at x=0 and x=-10). The red line simply displays a natural regression.
The second graph also fails to show any benefit of firing a manager, in fact, it’s the opposite. Betting odds at kick off are very efficient – I think they have summed up nearly all of the human knowledge of the strength of the 2 teams lining up on the day. They demonstrate quite a dramatic contradiction of the perceived ‘new manager bounce’. While this trope holds some weight in terms of results (although likely nothing to do with the manager change itself) the opinion of the smart money in the world often sees a team weaker than before. In this specific sample the rating recovers after 14 games. It’s possible new managers experimenting with the lineup or caretaker managers with less experience result in the team experiencing a further drop in performance.
In my opinion it is quite hard to judge a manager in terms of results as it’s hard to say what level a team should be performing at. So many times, has a manager been credited with a fair chunk of his team’s positive fortunes moved to a different team only to disappoint. Similarly sacked managers have seen their reputation recover elsewhere.
If had to choose a manger I would study his/her opinions and perspective on the game more closely than I would study his/her results.
Thanks for reading I hope you found this interesting today!
Thanks to the following resources that helped me create my post today
Transfermarkt pages on managerial changes, e.g. https://www.transfermarkt.co.uk/ligue-1/trainerwechsel/wettbewerb/FR1/plus/?saison_id=2024
Whoscored data for advanced team data www.whoscored.com
And Joseph Buchdahl’s extensive betting market data at www.footballdata.co.uk
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