xG Tables

There's been a lot of interest on Twitter in deriving tables from expected goals generated in matches that have already been played out.

Average expected points/goals/ are a useful, but inevitably flawed way to express over or under performance in reality compared to a host of simulated alternative outcomes.

Averages of course are themselves flawed, because you can drown in 3 inches........blah,blah.

Here's one way I try to take useful information from a simulated based approach using "after the fact" xG figures from matches already played, that may not be as Twitter friendly, but does add some context that averages omit.

If you have the xG that each side generated in a match, you can simulate the likely outcomes and score lines from that match by your method of choice.

A side who out xG'ed the opponent is usually also going to be the most likely winner, in reality and in cyberspace.

But sometimes Diouf will run 60 yards, stick your only chance through Joe Hart's legs, nick three points and everyone's happy.

It just won't happen very often, but it does sometimes and then the xG poor team get three points and the others get none.

Simulate each game played, add up the goals and points and you now have two tables.

One from this dimension and one that "might" have happened in the absence of games state and free will.

It's easy and most readily understood to then compare the points Stoke got in reality to the points the multiple Premier League winners got in this alternative reality.

But it might be better if instead we compared the relative positions and points of each team in this simulation to the reality of the table.

I do that and repeat the process for every one of the 1,000's of simulations using each side's actual points haul in relation to each of their 19 rivals as the over/under performing benchmark.

This is what the 2017/18 season looked like in May based on counting the number of times a side's actual position and points in the table relative to all others was better than a xG simulation.

Top two overperformed, 3rd and 5th did what was expected, 4th and 6th under performed in reality.

Only 15% of the time did the xG simulation throw up a Manchester City season long performance that out did their actual 2017/18 season.

The model might have under valued City's ability to take chances, prevent goals, they might have been lucky, for instance scoring late winners and conceding late penalties to teams who can't take penalties.

So when you come to evaluate City's 2018/19 chances, you may take away that they were flattered by their position, but concluded that the likely challengers were so far behind that they are still by far the most likely winners.

Man United, De Gea, obviously.

Liverpool, 4th but perhaps deserved better. Too far behind City to be a genuine title threat, unless they sort out the keeper & defence.

Burnley, score first, pack the defence and play a hot keeper, bound to work again.

Huddersfield, 16th was a buoyant bonus they didn't merit.

Relegated trio, Swansea, Stoke, pretty much got what they deserved, WBA, without actually watching them much last season, looked really hard done by. If you're going for the most likely bounce straight back team, it was the Baggies.

All of this comment was made in our pre season podcast.

You can use this approach for goals scored/allowed to see where the problems/regression/hot/cold might be running riot, plus simulations and xG are just one tool of many.