Quantifying Passing Contribution.

Passing completion models have seeped into the public arena over the last couple of months, mimicking the methodology used in expected goals models.

Historical data is used to estimate the likelihood that a goal is scored by an average finisher based primarily on the shot type and location in the case of expected goals models. And a similar approach is used for passing models.

Historical completion rates based on the origin and type of pass is combined with the assumed target to model a likelihood that a pass is completed and actual completion rates for players are then compared with the expected completion rate to discern over and under performing passers.

However, this approach omits a huge amount of context when applied to passes.

A goal attempt has one preferred outcome, namely a goal. But the unit of success that is often used in passing models is a completion of the pass and that in itself leaves a lot of information off the table.

How much a completed pass advances a side should also be an integral ingredient of any passing model. Completion alone shouldn't be the preferred unit of success, because it isn't directly comparable to scoring in an expected goals model.

A player can attempt extremely difficult passes that barely advances the team's non shot expected goals tally. For example, a 40 yard square ball across their own crowded penalty area is difficult to consistently complete and the balance of risk and reward for success or failure is greatly skewed towards recklessness.

Completing such passes above the league average would mark that player as an above average passer, but if we include the expected outcome of such reckless passes, we would soon highlight the flawed judgement.

The premier passer of his generation is of course Lionel Messi. It isn't surprising that he would complete more passes than an average player would expect to based on the difficulty of each attempted pass.

But we can add much more context if we include the risk/reward element of Messi's attempted passes.

A full blown assessment of every pass Messi attempted in the Champions League group stages becomes slightly messy for this initial post. Instead I'll just look at the positive expected outcomes of his progressive passes.

150 sampled progressive passes made by Messi during the Champions League group stage have both an expected completion probability and an attached improvement in non shot expected goals should the pass be completed. (NS xG is the likelihood that a goal results from that location on the field, it isn't the xG from a shot from that location).

If we simulate each attempt made by Mess 1,000's of times based on these average probabilities and the NS gain should the pass be completed, we get a range and likelihood of possible cumulative NS xG values.

The most likely outcome for an average player attempting Messi's passes is that they would add between 2.4 and 2.6 non shot expected goals to Barcelona's cause.

The reality for Messi was that he added 3.1 non shot expected goals.

There's around a 10% chance that an average player equals or betters Messi's actual tally in this small sample trial. But it is quantified evidence that Messi may well be a better than average passer of the football.