Saturday 15 July 2017

Lloris, the Best with Room to Improve?

Expected goals, saves or assists are now a common currency with which to evaluate players and teams, with an over achievement often being sufficient to label a player as above average/and or lucky, depending on the required narrative.

By presenting simple expected goals verses actual goals scored, much of the often copious amount of information that has been tortured to arrive at two simple numbers is hidden from the view of the audience.

Really useful additional data is sometimes omitted, even simple shot volume and the distribution in shot quality over the sample.

The latter is particularly salient in attempting to estimate the shot stopping abilities of goal keepers.

Unlike shot takers, it is legitimate to include post shot information when modelling a side's last line of defence.

Extra details, such as shot strength, placement and other significant features, like deflections and swerve on the ball, can hugely impact on the likelihood that a shot will end up in the net.

A strongly hit, swerving shot, that is heading for the top corner of the net is going to have a relatively high chance of scoring compared to a weakly struck effort from distance.

Therefore, the range probabilistic success rates for a keeper based shot model is going to be wider than for a mere shooter's expected goals model. not least because the former only contains shots that are on target.

We've seen that the distribution of the likely success of chances can have an effect on the range of actual goals that might be scored, even when the cumulative expected goals of those chances is the same.

To demonstrate, a keeper may face two shots, one eminently savable, with a probability of success of say 0.01 and one virtually unstoppable, with a p of 0.99. Compare this scenario to a keeper who also faces two shots, each with a 0.5 probability of success.

Both have a cumulative expectation of conceding one goal, but if you run the sims or do the maths, there's a 50% chance the latter concedes exactly 1 goal and a near 98% chance for the former.

The overall expectation is balanced by the former having a very small chance of allowing exactly 2 goals, compared to 25% for the keeper facing two coin toss attempts.

Much of this information about the shot volume and distribution of shot difficulty faced by a keeper can be retained by simulating numerous iterations of the shots faced to see how the hypothetical average keeper upon whom these models are initially built and seeing where on that distribution of possible outcomes a particular keepers actual performance lies.

Hugo Lloris has faced 366 non penalty shots and headers on goal over the last 3 Premier League seasons.

Those attempts range from ones that would result in a score once in 1,400 attempts to near certainties with probabilities of 0.99.

An average keeper might expect to conceded goals centred about 120 actual scores based on the quality and quantity of chances faced by Lloris.

Spurs' keeper allowed just 96 non penalty, non own goals and no simulation based on the average stopping ability of Premier league keepers did this well.. The best the average benchmark achieved begins to peter out around 100 goals.

Therefore, an assessment of the shot stopping qualities of a keeper might better be expressed  as the percentage of average keeper simulations that result in as many or fewer goals being scored than the keeper's actual record.

This method incorporates both the volume and quality of attempts faced.

The table above shows the percentage of average keeper simulations of all attempts faced by Premier League keepers since 2014 that equalled or bettered the actual performance of that particular keeper.

For example, there's only a 2.5% chance, assuming a reasonably accurate model, that an average keeper replicates or betters Cech's 2014-17 record and they would expected to equal or better Bravo's
in perpetuity.

Lloris' numbers are extremely unlikely to be replicated by chance by an average keeper and it seems reasonable to surmise that some of his over achievement is because of above average shot stopping talent.

Lloris over performs the average model across the board. Saving more easy attempts compared to the model's estimates and repeating this through to the most difficult ones.

Vertical distance from goal is a significant variable of any shot model and  Lloris' performs to average keeper benchmark save rates, but with the ball moved around 20% closer to the goal.

Intriguingly, this exceptional over performance is partly counter balanced by an apparent less than stellar return when faced with shots across his body.

Modelling Lloris when an opponent attempts to hit the far post produces a variable that his a larger effect on the likelihood of a goal then is the case in the average keeper model.

Raw figures alone hint at an area for improvement in Lloris' already stellar shot stopping.

The conversion rate for players who got an attempt on target, while going across Lloris' body converted 35% of the time, compared to the league average of 32%. He goes from the top of the tree overall to around average in these types of shots.

An average keeper gets more than a look in in this subset and the average model equals or beats Lloris' far post, on target actual outcome around 22% of the time. That's still ok, but perhaps suggests that even the very best have room to improve.

Below I've stitched together a handful of Lloris' attempts to keep out far post, cross shots to give some visual context.

For more recent good work, check out Will and Sam's twitter feed and Paul's blog & podcasts.

Data from Infogol.InfoGol


  1. So Courtois is an extremely average keeper with a top class defence in front of him.

  2. Shot stopping is just one facet, but he does seem to be fairly average , with a low shot volume. He doesn't make will's top ten.