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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

Thursday, 13 July 2017

Gylfi, "On me head, son"

Expected assists looks at the process of chance creation from the viewpoint of the potential goal creator.

An assisted goal is a collaboration between the player making the vital final pass and his colleague who tries to beat the keeper, but over a season these sample sizes tend to be small.

Manchester City's Kevin De Bruyne topped the actual assist charts in 2016/17 with 18, but these numbers may have benefited from a statistically noisy bout of hot finishing or suffered from team mates who frequently sliced wildly into the crowd.

Therefore, it makes sense to use the probabilistic likelihood of success in the 85 additional instances when the Belgian carved out a chance that went begging.

Here's the top ten expected chance creators from the 2016/17 Premier League, along with their actual returns, courtesy of the recipients of these these key passes.



The list contains the kind of players you'd expect to see when trawling the Premier League for creative talent.

The expected assists are based on a model derived from the historical performance of every assisted goal attempt from previous Premier League seasons.

So De Bruyne's over performance may reflect the above average talent, not just of himself, but also his team mates or it could be that creating and finishing talent is tightly grouped in the top tier of English and Welsh football and randomness accounts for the majority of the disconnect between actual and ExpA over a single season.

Swansea's Gylfi Sigurdsson, a constant topic of transfer speculation, lies 3rd in both expected and actual assists, with 9 ExpA and 13 actual ones. This backs up the Icelander's importance to the Swans, where he was involved in nearly a quarter of Swansea's ExpG in 2016/17.

His relatively large over performance, compared to his ExpA cumulative total of just under 9 may suggest he is particularly adept at presenting chances to his team mates.

However, a simple random hot streak from both or either participant in the goal attempt should not be ruled our.

In 9% of simulations, an average assister/assisted combination would score 13 or more goals from the 77 opportunities crafted by Sigurdsson.


Neither is there anything untoward in the fit of the model to Sigurdsson's 77 assists. Lower quality chances are converted at a lower rate than those which had a higher expectation of producing a goal.

So far there's nothing to set off warning bells for any potential purchaser, Sigurdsson appears to be legitimately a top echelon goal creator, albeit one who may have run slightly hot in 2016/17.

But if we make some direct comparisons to say De Bruyne, differences begin to emerge.

De Bruyne's ExpA per key pass is 0.15 compared to 0.11 for Sigurdson, which suggests that De Bruyne is, on average creating higher quality opportunities.

The profile of the position of the recipients of Sigurdsson's key passes is also strikingly different from those of the Manchester City player.


De Bruyne is supplying chances for a much larger proportion of attacking minded players, such as out and out strikers, wingers and attacking midfielders.

Whereas, over 50% of Sigurdsson's key passes are picking out defenders, notably central defenders and that usually means headed chances, from set pieces.

This appears to be confirmed by the final column in the first graphics of this post. Only a third of Sigurdsson's assists arrived at the feet of a team mate, well below the figures for the remaining nine assisters in the table.

All of whom check in with at least 67% of their potential assists being finished off with the boot.

Gylfi's penchant for set play deliveries to a defenders head also features in Ted's article on the transfer speculation surrounding Sigurdsson in The Independent as part of Ted's grand tour of the British press.

Despite Sigurdsson's apparent niche assistance role, at least in 2016/17, his ExpA per potential assist does still hold up well.

He's below De Bruyne, as we've seen, but is above the remaining eight players in the top ten, bar Fabregas and an anonymous Stoke player, who we want to keep.

So although he does deliver aerial passes to generally less skilled finishers, his relatively impressive ExpA per key pass does suggest that he can put the ball into extremely dangerous areas and with accuracy to find a team mate.

Also his actual assists from headed chances of 8 compared to and expected total of just over 5 suggests he may be more skilled at such deliveries than is the average case, although such small samples inevitably prevent random chance being eliminated as the main causative agent in any over performance.

Overall, Gylfi Sigurdsson may be worth a great deal of money.....to a side that is set up to benefit most from his particular creative skill set.

But those teams may be few in number and principal among them are his current employers.

All data via Infogol

Wednesday, 12 July 2017

Stoke Score More August Goals Than Andrew Cole

Hugely amusing tweet* doing the rounds, yesterday.


All great fun in the world of football bants and also an excellent case study in how to use "stats" to purvey a misleading impression that's likely to get picked up, circulated and no doubt recycled in September when the Premier League's fixture computer love affair with the Potters pitches them to the foot of the table.

So let's do a bit of due diligence .

Cole played 44 games to reach his 25 goals, playing, as he did in the 42 game, Premier League era, when they sometimes managed to cram six games in during the opening month.

Stoke scored their 23 goals in 28 matches.

So even this simple addition of context floors the deliberately provocative tweet.

Cole scored 0.57 Premier League goals/game in August, which is eclipsed by Stoke's 0.82 August goals/game.

The comeback would probably be "one is a team of 11 to 14 players".

But 1 of those 14 is a goal keepers, and keepers, with the exception of Stoke City ones, generally don't score.

Four or five are defenders who don't score a lot, which limits the fair comparison to Stoke players, from the August months of the Premier League era, who played in a similarly advanced role to Cole's position at Newcastle, Manchester United, Blackburn, Fulham, Manchester City and Portsmouth.

Designated Stoke forwards scored 13 of their 23 goals, so their scoring rate falls below Cole's 0.57 goals per game to 0.46 goals per game.

Stoke played an average of two out and out strikers over their Premier League existence, so we'll half that rate to 0.23 goals per game.

This puts Cole well back in the lead, allowing the rip to be taken out of the Potters again....?

However, we haven't considered the goal environments.

Stoke played against a batch of sides in August who conceded an average of 1,35 goals per game, as did Cole a decade earlier.

No change, there.

Cole's teams scored an average of 1.80 goals per game, meaning he played for sides who had a lot of attacking intent.

His 0.57 goals per game was around 30% of the baseline figure for his team.

The homogenised Premier League Stoke striker scored 22% of the 1.06 goals per game Stoke have averaged in the Premier League.

Those strikers included Dave Kitson, Mama Sidibe (legend), Ricky Fuller (legend), James Beattie, Kenwyne Jones and Peter Crouch.

Bottom line, Andrew Cole scored a higher proportion of goals for his club than did this mismatch of ageing, journeymen footballers did in their defensively structured, mid table team........in one particular month.

Ha ha.

*NOT