{"id":290,"date":"2019-08-02T16:06:38","date_gmt":"2019-08-02T15:06:38","guid":{"rendered":"https:\/\/research.reading.ac.uk\/football-research\/?p=290"},"modified":"2019-08-27T17:29:21","modified_gmt":"2019-08-27T16:29:21","slug":"can-we-predict-when-to-sack-a-football-manager","status":"publish","type":"post","link":"https:\/\/research.reading.ac.uk\/football-research\/can-we-predict-when-to-sack-a-football-manager\/","title":{"rendered":"Can we predict when to sack a football manager?"},"content":{"rendered":"
2 August 2019<\/h5>\n

And why Steve Bruce isn’t so bad after all…<\/h2>\n

With a week to go until the first games of the new football season get underway, already the media are using betting odds to speculate on who will be first manager to be sacked.\u00a0 Frank Lampard at Chelsea and Ole Gunnar Solskjaer at Manchester United are the bookies\u2019 favourites. The fans don\u2019t rate the chances of Steve Bruce at Newcastle either, with some threatening to boycott the club before he even started.<\/p>\n

The\u00a0FT<\/span><\/a>\u00a0argued last year that economists try to use their models to predict football events so they can prove they are actually useful – a bit like the Ancient Greek philosopher Thales of Miletus, described by Aristotle, who created the world\u2019s first derivative contracts in olive oil presses in order to prove that philosophers were not\u00a0worthless.<\/p>\n

With this in mind, we also developed a model to see if we could predict when a football manager was no longer being effective, and in common parlance, should be sacked. But does sacking a manager even\u00a0work?<\/p>\n

Over the last 10 years we have not found one example of a sacking leading to a club winning the league within the same season.\u00a0 Although some teams have won cup competitions after replacing a manager \u2013 most prominently, Roberto di Matteo winning the Champions League for Chelsea. Changing manager at the top of the table can improve the team\u2019s final position but not really significantly.<\/p>\n

The real work of sacking is all at the mid-table survival or relegation zone, where even multiple sackings in a single season can be shown to have led to a club surviving for another season in the Premier League. To summarise, from 2009-2018, there have been around 97 managerial changes, and only 17 of these sacking teams were relegated, from a possible 30 relegation\u00a0places.<\/p>\n

So what about our model? A straightforward way of looking for good performance is to use points achieved against total wage spend per club. But this doesn\u2019t fully take into account the differing resources that clubs have access to. So there is a lot of justification for trying to build a model that can look at manager performance game by game and using the actual resources of each club individually, and other factors that might affect performance but be beyond the manager\u2019s control such as injuries or suspensions among key players. This allows us to adjust for expected against achieved\u00a0results.<\/p>\n

We can then try to measure the actual manager\u2019s performance against a simulation based on what would be expected from the model given their available resources, to identify the statistically most likely outcomes. This means we can rank managers based on the difference between their actual levels of performance, measured by the average number of points per game compared with the model-predicted level of performance. To do this we therefore collect data game by game on injuries, suspensions, total wages, net transfer spend, and extra games played by the teams. But how far does each of these elements contribute to explaining the points per game gained by each\u00a0manager?<\/p>\n