Friday, 25 January 2019

Putting Together a Possession Based Non Shot Model

I've previously written about non shot based models as an alternative to purely shot based xG, as well as a way of incorporating the 90+% of onfield actions that are omitted in the former.

A valid criticism of shot based models is that a goal attempt needs to be registered before expected goals tallies can be increased.

However, it is intuitively realised that continued incursions deep into an opponent's half are dangerous, even if a shot isn't forth coming and a dangerous ball that is played across the face of goal also carries a non recorded level of threat.

Similarly, a penalty kick gives a disproportionately large xG figure, particularly when compared to numerous other passes into the box that don't result in a reckless lunge and a favourable ref.

An alternative approach might be to count attack based events, such as final third passes or progressive runs and relate these to a likelihood of scoring. But this seems rather arbitrary and lacking a framework.

Our approach is to select a consistent unit to describe the model that is analogous to a goal attempt and we've chosen a possession.

We then need an equivalent figure to the expected goal figure for an attempt made on goal. And just as a shot based xG model is driven by the probability of scoring with a shot/header given a variety of identifiable parameters, we have used the likelihood that a possession will result in a goal.

Shot or header location are the primary factors n a shot based xG model, but modellers have shied away from such things as finishing skill and or goal keeping prowess, as the proliferation of statistical noise often swamps any signal.

However, in the more event rich environment of passes and ball progressions we may be more confident in including such skill differentials into a non shot model, without straying too far into xG2 shot based territory.

Anyone who watched Burton's second leg game with Manchester City couldn't not be swayed by the obvious individual and technical ability on show from City compared to their hosts. And the implied level of goal threat was much higher when City gained possession compared to the Brewers in a similar pitch location.

Therefore, in constructing a non shot based model, as well as such familiar universals as location, we also incorporate factors which identify both above average proficiency in passing as well as in disrupting passes or carries.

Here's a table I posted at the end of last season, showing the level of over or under performance for Premier League teams in pass completion and pass disruption.

It's notable that Man City were the best at completing passing sequences and suppressing opponent's attempts.

We now have an assembly of ingredients to produce a non shot equivalent to the purely shot based model.

Above is a game by game summary of the non shot xG differential for Manchester City in 2017/18.

Unsurprisingly, a team committed to possession and passing excellence, with high quality players almost always creates a possession environment that gives them a superior non shot xG differential.

And here's a game by game tally for Liverpool in 2017/18

Together with a shot based approach, a non shot model can perhaps add nuance to the balance of power between two sides, based on the frequency, location of possessions and pre game skill differentials of the sides, as well as exploring, via a shot based xG model the, now familiar occasions where a goal attempt was generated.

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