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Wednesday, 22 November 2017

An xG Timeline for Sevilla 3 Liverpool 3.

Expected goals is the most visible public manifestation of a data driven approach to analyzing a variety of footballing scenarios.

As with any metric (or subjective assessment, so beloved of Soccer Saturday) it is certainly flawed, but useful. It can be applied at a player or team level and can be used as the building block to both explain past performance or track and predict future levels of attainment.

Expected goals is at its most helpful when aggregated over a longer period of time to identify the quality of a side's process and may more accurately predict the course of future outcomes. rather than relying on the more statistically noisy conclusion that arise from simply taking scorelines at face value.

However, it is understandable that xG is also frequently used to give a more nuanced view of a single game, despite the intrusion of heaps of randomness and the frequent tactical revisions that occur because of the state of the game.

Simple addition of the xG values for each goal attempt readily provides a process driven comparison against a final score, but this too has obvious, if easily mitigated flaws.

Two high quality chances, within seconds of each other can hardly be seen as independent events, although a simple summation of xG values will fail to make the distinction.

There were two prime examples from Liverpool's entertaining 3-3 draw in Sevilla, last night.


Both Firmino goals followed on within seconds of another relatively high quality chance, the first falling to Wijnaldum, the second to Mane.

Liverpool may have been overwhelming their hosts in the first half hour, they were alert enough to have Firmino on hand to pick up the pieces from two high quality failed chances, but a simple summation of these highly related chances must overstate Liverpool's dominance to a degree.

The easy way around this problem is to simulated highly dependent scoring events as such, to prevent two goals occurring from two chances separated by one or two seconds.

It's also become commonplace to expand on the information provided by the cumulative xG "scoreline" by simulating all attempts in a game, with due allowance for connected events, to quote how frequently each team wins an iteration of this shooting contest and how often the game ends stalemated.



Here's the xG shot map and cumulative totals from last night's match from the InfoGolApp.

There's a lot of useful information in the graphic. Liverpool outscored Sevilla in xG, they had over half a dozen high quality chances, some connected, compared to a single penalty and other, lower quality efforts for the hosts.

Once each attempt is simulated and the possible outcomes summed, Liverpool win just under 60% of these shooting contests, Sevilla 18%, with the remainder drawn.

Simulation is an alternative way of presenting xG outputs rather than as totals that accounts for connected events, the variance inherent in lots of lower quality attempts compared to fewer, better chances and also  describes most likely match outcomes in a probabilistic way that some may be more comfortable with.

Liverpool "winning" 2.95-1.82 xG may be a more intuitive piece of information for some (although as we've seen it may be flawed by failing to adequately describe distributions and multiple, common events), compared to Liverpool "winning" nearly 6 out of ten such contests.

None of this is ground breaking, I've been blogging about this type of application for xG figures for years, But there's no real reason why we need to wait until the final whistle to run such simulations of the attempts created in a game.

xG timelines have been used to show the accumulation of xG by each team as the game progresses, but suffer particularly from a failure to highlight connected chances.

In a simulation based alternative, I've run 10,000 attempt simulations of all attempts that had been taken up to a particular stage in last night's game.

I've then plotted the likelihood that either Liverpool or Sevilla would be leading or the game would be level up based on the outcome of those attempt simulations.


Liverpool's first dual attempt event came in the first minute. Wijnaldum's misplaced near post header, immediately followed by Firmino's far post shot.

Simulated as a single event, there's around a 45% chance Liverpool lead, 55% chance the game is still level and (not having had an attempt yet) a 0% chance Sevilla are ahead.

If you re-run the now four attempt simulation following Nolito's & Ben Yedder's efforts after 19 minutes, a draw is marginally the most likely current state of the game, followed by a lead for either team.

A flurry of high quality chances then make the Reds a near 90% to reach half time with a lead, enabling the halftime question as to whether Liverpool are deservedly leading to be answered with a near emphatic, yes.

Sevilla's spirited, if generally low quality second half comeback does eat into Liverpool's likelihood of leading throughout the second half, but it was still a match that the visitors should have returned from with an average of around two UCL points.






Sunday, 22 October 2017

Excitement Quotas in the Premier League.

Excitement at a sporting event is a subjective measurement.

It doesn't quite equate to brilliance, as a 7-2 thrashing has to be appreciated for the excellence of the performance of one of the teams, but as the score differential climbs, morbid fascination takes over, at least for the uncommitted.

Nor does it tally with technical expertise. A delicately crafted passing movement doesn't quite set the pulse racing like a half scuffed close range shot that deflects off the keepers knee and loops agonisingly over the bar with the game on the line.

You can attempt to quantify excitement using a couple of benchmark requirements.

The game should contain a fair number of dramatic moments that potentially might have changed the course of the outcome or actually do lead to a significant alteration to the score.

It's easy to measure the change in win probability associated with an actual goal.

A goal that breaks a tied game in the final minutes will advance the chances of the scoring team by a significant amount, whilst the seventh goal in a 7-2 win merely rubs salt into the goal difference of the defeated side.

Spurned chances at significant junctures are only slightly more difficult to quantify.

You can take a probabilistic view and attach the likelihood that a chance was taken based on the chance's expected goals figure to the effect that an actual goal would have had on the winning chances of each side.

Summing the actual and probabilistic changes in win probability for each goal attempt in each match played in the 2016/17 Premier League season gives the five most "in the balance", chance laden matches from that season.

                               Top Five Games for Excitement 2016/17 Premier League


No surprise to see the Swansea/Palace game as the season's most exciting encounter, with Palace staging a late comeback, before an even later Swansea response claimed all three points in a nine goal thriller.


Overall I've ranked each of the 380 matches from 2016/17 in order of excitement as measured by the actual and potential outcomes of the chances created by each team in the game

Bournemouth's games had the biggest share of late, game swinging goals, along with the most unconverted endeavour when the match was still in the balance.

While Tottenham, despite playing in the season's second most exciting game, a very late 3-2 win over West Ham, more typically romped away with games, leaving the thrill seekers looking for a match with more competitive balance to tune into.

Middlesbrough fans not only saw their side relegated, but they did so in rather bland encounters, as well.

Saturday, 14 October 2017

Player Projections. It's All About The Distribution Part 15

A couple of football analytics' little obsessions are correlations and extrapolations.

Many player metrics have been deemed flawed because they fail to correlate from one season to the next, but there are probably good reasons why the diminished sample sizes available for individuals lead to poor season on season correlation.

Simple random variation, players suffer injury, a change in team mates or role within a club, atypically small sample sizes often lead to see sawing rate measurements and inevitably players age and so can be on a very different career trajectory to others within the sample.

The problems associated with neglecting the age profile of a group of players when attempting to identify trends for use in future projections is easily demonstrated by looking at the playing time (as a proxy for ability) enjoyed by players who were predominated aged 20 and 30 when members of a Premier League squad and how that time altered in their 21st and 31st years.

The 30 year oldies played Premier League minutes equivalent to 15 full matches, falling to 12 matches in their 31st year. So they were still valued enough to play fairly regularly, but perhaps due to the onset of decline in their abilities they featured, on average, less than they had done.

The reverse, as you may expected was true for the younger players. They won the equivalent of seven full games in their 20th year and nine the following season.

It seems clear that if you want to project a player's abilities from one season to the next and playing time provides a decent talent proxy, you should expect improvement from the youngster and decline from the older pro.

However, as with many such problems, we might be guilty of attempting to impose a linear relationship onto a population that is much better defined by a distribution of possible outcomes.


The table above shows the range of minutes played by 21 and 31 year olds who had played 450 minutes or fewer in the previous season as 20 or 30 year old players.

As before, we may describe the change in playing time as an average. In this subset, the older players play very slightly more than they did as 30 year olds, the equivalent of two games, improving to 2.2.

The younger players jump from 1.8 games to 3.6.

However, just as cumulative xG figures can hide very different distributions, particularly of big chances which subtly alter our expectation for different teams, the distribution of playing minutes that comprise the average change of playing time can be both heavily skewed and vary between the two groups.

Over three quarters of 30 year old didn't get on the field at all during the next Premier League season, likewise 2/3 of the younger ones..

21% of young players played a similar amount of time to the previous season, between one and 450 minutes, compared to just 14% of the older ones. And 17% of youngsters exceeded the total from the previous season, as did just 10% of the veterans.

So if you use the baseline rate of increased playing time as a flat rate across all players that fall into these two categories in the future, you might be slightly disappointed, because overwhelmingly the experience of such players is one where they fail to play even a minute in the following season.

Knowing that there is an upside, on average for these two groups of players, based on historical precedent is a start, but knowing that 3 out of 4 the oldies and 2 out of 3 youngsters who you are considering didn't merit one minutes worth of play in an historical sample is also a fairly important, if not overriding input. 

Wednesday, 11 October 2017

World Cup Qualification So Far.

To save my Twitter feed from viz overload, here's a couple of plots from the completed World Cup qualifiers.




FIFA ratings usually get a good kicking, but if you know their limitations they do a decent job and have done in predicting the qualifying teams so far for 2018.

Some higher rated teams will miss out, it's only 10 games in some cases, after all.

But if you want a benchmark FIFA rating at the time qualifying began in 2015, the definite qualifiers had a median rating of 891.

Those still waiting on a playoff were rated 676 and those rooting for other countries were 464.

Check your country and see if they ended up roughly in the position they deserved based on 2015 FIFA rankings.

FIFA don't seem to want you to find historical ratings, but to the best of my knowledge these were the ratings each side had in October 2015, apart from the three I couldn't find & made up.

Sunday, 8 October 2017

Premier League Age Profiles Through the Ages

I found some data I collected but never got round to analysing for the joint OptaProForum presentation with Simon Gleave a few years ago.

It simply consists of minutes played by each age group in the four highest tiers of English domestic football.

There are a variety of methods to describe the ageing curve in football, where players initially show improvement, peak and then decline with age. I prefer the delta approach, which charts the change of a variety of performance related indicators or their proxies.

We may condense the age profile of a team or league down into three main groups. Young players, under 24 who are still improving,

Peak age performers from around 24 to 29 and ageing players of 30 or more, who may still be good enough to command some playing time, but are diminishing compared to their own peak levels.



Using the amount of playing time allowed to each of the three groups as a performance proxy, the peak age group of Premier League players have been increasing their share at the expense of both the younger and older groups since 2004/05. Peak share has risen from 48% of the available playing time at the start of the period to 60% by 2014/15.

The wealth of the Premier League and the limited alternative destinations for the best, prime aged talent would appear to be a reasonable cause for this increase. Perhaps only Spain's Barcelona and Real Madrid (Suarez and Bale) account for the few realistic destinations for peak age, Premier League talent.

By contrast, League Two, the fourth tier of English football, appears to have a very different age profile.



Here, youth and peak aged players share playing time, with 30 & over players lagging well below these levels, implying a different market further down the pyramid.

Players are not being recruited from the extreme right hand tail of the talent pool, so more options of similar ability are available and there is also an extensive pool of buyers in the two or three divisions immediately above League Two, ready to take on the cream of the peak age performers.

Finally here's the plots for the best Premier League teams compared to the remainder of the clubs.

.

Peak shares are similar for both groups, but the top teams have played a larger share of (talented) younger players, while the remainder of the Premier League have swayed slightly more towards experience (perhaps ageing players from the top teams dropping in grade, but remaining in the Premier League).



Crouch at Stoke, for example.

Liverpool's individual profile appears to illustrate how their age profile has remained similar to the average for top Premier League teams across the 11 seasons.



Over 30's make up the lowest proportion of playing time, followed by younger players and topped of by peak age talent.

30+ contribution falls away, to be replaced by ageing peak age talent, which in turn is refreshed by maturing younger players. Replacement buys can then be made in the 22-24 range to continue the cycle.



By contrast, Everton has chosen to largely swap around the over 30 group and the under 24 group, leading to seasons where older players dominate.

Wednesday, 4 October 2017

Quick & Dirty Strength of Schedule.

I've recently posted some xG, strength of schedule adjusted figures for the Premier League and justin_mcguirk has asked for a method.

The sos values have been intended to be purely descriptive, rather than attempting to more accurately portray underlying team quality.

But intuitively you can look at WBA's start where they've not faced one genuine title contender, in Bournemouth, Burnley, Stoke, Brighton, WHU, Arsenal and Watford and compare it to Everton's lucky seven of Stoke, Man City, Chelsea, Spurs, ManUtd, Bournemouth and Burnley and immediately think that Everton's start has been more difficult than that of the Baggies.

Strength of schedule can be calculated using a steal from the NFL, particularly so called least squares Massey ratings..

In the case of the Premier League, each teams schedule is laid out, followed by a performance parameter, such as goal or expected goal difference. The seven inputs (the teams they've played out of a possible twenty for each team) are then calculated, such that the errors arising when trying to solve each of the twenty simultaneous equations are reduced to a minimum.

The maths is doable using matrices, although 20x20 matrices can sometimes resist inversion and I'm sure many packages will undertake the heavy lifting as well.

For those who would like a simpler and probably equally informative approach you can average the goal or expected goal difference of the seven teams a side has played.

These seven teams will have played 49 matches, admittedly seven will be against the side whose strength of schedule you are attempting to estimate, but their 49 games will have been against a broadly league representation.


Here's the sos table using this method after six games. It is broadly similar to the one I posted on Twitter after 7 and using a least squares approach.

Everton still had the toughest start & WBA the easiest. Chelsea moved up towards a more taxing unbalanced schedule by hosting Man City as did Palace visiting Manchester United.

Also more information about each team and their opponents has become available after seven games.

Finally, here's the individual calculations for WBA & Everton. Stoke's xG for after 6 games was 5.2 and they'd allowed 9.2 xG.


Data from @InfogolApp



Tuesday, 3 October 2017

Crystal Palace.....The Only Way Is Up.

A quick post to try to put Crystal Palace's current predicament into some kind of historical context.

In terms of points, they've (obviously) had the worst start through seven matches in the lifetime of the 20 team Premier League.

Zero points, zero goals and not one iota of friendly randomness to break their duck in either category, despite bad, but not completely hopeless xG figures.

Particularly in chances created.

Points won are just one factor in determining how bad a side has started their campaign. The aim of the majority of teams in the Premier League is to simply stay in it for next season and your proximity to your nearest rivals is therefore just as important as merely your own points total.

One this basis, there's arguably a few teams ahead of Palace in claiming the worst initial seven game record.

Southampton in 1998/99, Portsmouth in 2009/10, their administration year and Sunderland in 2013/14 could be considered to have been worse off than Palace are now. Each may have won more points than Palace has, but Palace are marginally closer to both their immediate rivals and even mid table than were this trio.

Also, poor starts aren't an automatic ticket to the Championship.

50% of the 20 worst placed teams, compared to their 19 rivals after seven matches managed to stay up, although conversely the better the start, the more likely survival becomes.

27 teams have been comfortably placed equidistant from the leaders and the 20th placed side after seven matches and four ultimately fell through the trapdoor. But after that it became plain sailing and survival has been universal.

If we use Palace's proximity to their rivals as a measure of their start and compare the fate and the ranking of all teams in the 20 team Premier League era after seven games, there is more than a glimmer of hope.

Based on historical precedent and that alone, Palace have around a 28% chance of escaping relegation.

Of course a side is not relegated just on a single statistic. Injuries, the January window and their underlying stats all contribute to the reckoning in May.

Palace have had around the fourth toughest start in terms of opposition faced. It gets much less arduous after the play Chelsea in game eight, but they haven't enjoyed good luck with injuries to key attackers.

Their 10 game rolling xGD and actual GD since 2014 has been trending downwards over time, but the precipitous disconnect between process and outcome in recent matches is unlikely to persist.

They are far from the worst team in the current Premier League when measured over a more prolonged time frame. And although they have given inferior sides a start, it is a start that has been run down in the past.

Supporters will be correct to be pessimistic, Palace are probably more likely to be relegated than not, but the bookies price of 1.53, with an implied probability 65% still leaves their survival chances somewhere around the mid to low 30%'s.

A similar level of success enjoyed by their single cause predecessors mentioned earlier in this post.

Saturday, 23 September 2017

30 Year old Messi is Likely in Decline.

'Tis the season for small sample sized hyperbole to be liberally launched on a expectant audience and the latest recipient of the "If he continues at this rate" award for unrealistic dreamland is none other than Lionel Messi.

While Ronaldo has been kicking his heels and the occasional Real Betis player, Messi has single-handedly (with the help of 10 teammates) launched Barcelona seven points clear of their perennial rivals from Madrid.

Messi turned 30 in the close season, he's playing in his 14th La Liga season and is undoubtedly one of the two best players of the last decade.

But he is still human and bound by the natural athletic decline that eventually sets in for every footballer.

Players improve with maturity and experience, peak, usually in their late twenties and then begin an inexorable decline, albeit from differing peaks.

Messi's post birthday, six game return in the UCL and La Liga, but discounting a two legged Spanish Super Cup defeat at the hands of Ronaldo's Madrid, has been spectacular, even by his standards.

It has spawned at least one article, liberally salted with stats to enhance credibility, eagerly anticipating the untold riches to come.

Unfortunately, five or six games is so small that you will inevitably get extremes of performance, either very good or very bad.

Particularly, if you selectively top and tail the games to eliminate a comprehensive defeat, devoid of any Messi goals from open play at the hands of your nearest rivals, but conclude with a three open play scoring performance from the Argentine.

Small samples are noisy, unbalanced and rarely definitively indicative of what will happen in the longer term or even just a single season.

Barcelona has played Alaves, Eibar, Getafe, Espanyol and Betis, only the latter is currently higher than 13th.

As a data point it is all but useless to project Messi's 2017/18 season.

Individual careers are statistically noisy. Injury, shifted positional play and team mate churn are just some of the factors that can make for an atypical seasonal return, even before we try to decide which metric is sufficiently robust to reflect individual performance.

If we use goals and assists to judge Messi up to his 30th birthday, his delta, the change in non penalty goals and assists per 90 from one season to the previous season trends negative when Messi was 27, guesstimating this was when he peaked.

If we include 2017/18's small sample sized explosion as a fully developed rate for this upcoming season, the trendline still becomes negative this year.

If we regress this current hot rate towards Messi's most recent deltas, as we should, Messi's peak stretches to his 28th birthday.

But by his own standards he has likely peaked.

Open play goals and expected goals for the last three and the first 5 games of 2017/18 tell a similar gentle decline, even allowing for Messi's recent spurt of scoring.


Actual, non penalty, open play goals/90 are trending downwards, as are Messi's xG per 90 on a 10 game rolling average.

The actual trendline is also probably more shallower because of the narrative driven choice of his three open play goal spree against Eibar providing the doorstop.

That Messi consistently over performs the average player xG isn't surprising, but the peaks, like the one he's currently enjoying is often driven by a glut of relegation threatened sides turning up in Barcelona's lumpy quality of schedule.

Enjoy the blips, but don't draw conclusions based on so little evidence.

Data from Infogolapp.

Sunday, 10 September 2017

Messi and Ronaldo. Expected Goals Makers, Takers or a Bit of Both.

With the increased availability of granular data, there has been a similar influx of advanced metrics, both for players and sides across a wider range of domestic leagues.

And while performance based numbers, often to a couple of decimal places, are the raw material for much of the analytically based content, their attractiveness and clarity of meaning rarely extend beyond the spreadsheet.

It therefore falls to visualisations to convey some of the rich seams of information available in such manipulated data sets in a clear and easily digestible format, such as Ted Knutson's  hugely popular radars.

Expected goals remain the flavour of the month, although BBC pundits are still immune, imploring players to "do better" with opportunities that are scored fewer than one time in 10.

A team or individual's attacking contribution can be neatly summarised by their expected goals and assists, standardised at least to a per 90 figure, with respect given to those who have achieved their numbers over a larger sample size compared to noisy small sample interlopers, ripe for regression.


Here's the xG/90 and xA/90 for the 70 largest cumulative, goal involvement achievers from La Liga's 2016/17 season.

Data is from @InfogolApp and has been restricted to open play chances and assists.

Messi and Ronaldo are among a clutch of players who have broken away from the main body of the plot, although they are also quite a distance remove from each other.

Messi was involved in around 0.85 xg+xA per 90 and Ronaldo around 0.65.

However, the former, while slightly under-performing against the latter in getting on the end of xG scoring chances, more than compensated by creating over double the amount of expected assists per 90.

So a simple scatter plot can begin to reveal fundamental differences between even the most high profile of players.

More information can be extracted by simply running a straight line between a particular player's point on a scatter graph and the origin.

Moving down such a line, you'll encounter players who in the season under scrutiny, achieved ratios for xg and xA that closely resemble those of the line owning player.

The magnitude of their cumulative performance is less than those players that are further away from the origin, but their shot/assist characteristics will be consistent with any near neighbours.

Messi was a more sharing team mate in open play in 2016/17, whereas Ronaldo headed the line of takers, rather than makers.

Friday, 8 September 2017

Shot Blocking and the State of the Game.

It has long been appreciated that the dynamics of a game subtly alters as time elapses, scorelines alter or remain the same and pre match expectations are met, exceeded or under shot.

This shifting environment has traditionally been investigated using the simple measure of the current score.

This has been unfortunately labelled as games state, when simply "score differential" would have both succinctly described the underlying benchmark being applied, without hinting at a more nuanced approach than just subtracting one score from another.

As I blogged here, the problem is most acute when lumping the not uncommon, stalemated matches together.


Consider a game between a strong favourite and an outsider that finishes goalless.

Whereas the latter more than matches their pregame expectation, the former falls disappointingly short of theirs.

The average expectation at any point in a game can be represented in a number of ways, but perhaps the most intuitive is an estimation of the average number of points a team will pick up based on the relative strengths of themselves and their opponent, at the current scoreline and with the time that remains.

The plot above shows the relative movement of the expected points for a strong favourite playing weaker opposition to a 0-0 conclusion.

The favourite would expect to average around 2.5 points per match up at kick off, decaying exponentially to one actual point at full time.

So at any point in the match we can measure the favourite's current expectation compared to their pregame benchmark and use this to describe their own level of satisfaction with the state of the game.

Game state would be preferable, but that's already taken.

The same is true for the outsider. Their state of the game gradually increases compared to their much reduced pregame expectation.

Although the game is scoreless throughout for each side, things are getting progressively worse for the favourite and better for their opponents.

We can use these shifting state of the game environments to see if they have an effect on in game actions.

Intuitively you would expect the team doing less well compared to their expectations to gradually commit more resources to attack, in turn forcing their opponents onto the defensive.

This may increase shot volume for the former, but it is also likely that these attempts, particularly from open play will fall victim to more defensive actions, such as blocks.

The reverse would seem likely to be true for the weaker team. Although their shot count may fall, with less defensive duties being carried out by their opponents, their sparser shot count may evade more defensive interventions, again such as blocks.


Here's what the modelled fate of a shot from regular play from just outside the penalty area in a fairly central position looks like between two unequal teams as the match progresses.

Data is from a Premier League season via @infogolApp

In building the model, the decay in initial expectation has been used to describe the state of the game for the attacking team when each individual shot was attempted, rather than simply using score differential.

Initially the weaker team is less likely to have their shot blocked, although it is probably more accurate to say that the favoured side is more likely to suffer this fate.

As the game progresses, the better team sees a slight increase in the likelihood that a shot from just outside the box is blocked, perhaps suggesting that their opponents are initially heavily committed to a defensive structure.

The weaker side has a lower initial likelihood that such a shot is blocked, again implying a more normal amount of defensive pressure early in the game. But as the match progresses this likelihood that their shots are blocks falls even more.

This nuanced model appears to be illustrating the classic potential for a prolonged rearguard action from an underdog, followed by a late smash and grab opening goal, mitigated by the relative shot counts from each team.


Tuesday, 5 September 2017

Premier League Defensive Profiles.

Heat maps and the like have been around for ages as a way of visualising the sphere of a particular players influence.

However, it's always nice to have some numerical input to work with, so I've used the Opta event data that powers InfoGol's xG and in running app to develop metrics that describe how teams and individuals contribute over a season.

Defensive metrics have lagged well behind goals and assists, so I looked at that neglected side of the ball.

Unlike goal attempts, counting defensive stats tends to be a fairly futile exercise. No one willingly wants to keep making last ditch tackles and racking up ever higher defensive events is more often the sign of a team in trouble.

There's also the disparity in possession time which gives the possession poor team more chances to accrue defensive events.

Therefore, pitch position, rather than bulk events seems an obvious alternative.

Allowing a side lots of touches deep in your territory is intuitively a bad idea and the higher up the field a side is willing or able to engage their opponent would appear preferable.

Measurements have been calculated from the Opta X, Y point of an event to the centre of a team's own goal line.

Thus a tackle or clearance made on the half way line will be further from this point of reference if it is made near the touchline compared to if it completed on the centre spot.

This allows for defensive event profiles for both a team and also their opponents.


A quick eye test appears to show that the more successful Premier League teams do their defending further away from their own goal than the lesser sides are either willing or able to do.

That the idea that doing defensive stuff higher up the pitch is the product of a good team is further developed by plotting where a side defends on average and where they allow their opponents to defend, again on average.


The relegated teams from 2016/17 mostly suffered the doubly whammy of choosing or having to defend an average of around 34 yards from the centre of their own goal line compared to nearly 40 yards for some of the top 6 and they also allowed their opponents the luxury of making defensive actions around 38 yards from their own goal line.

Notably Pulis again muscles into an area apparently reserved for relegation fodder with his defensive voodoo.

At a player level it's a trivial problem to find the average pitch position where he makes a defensive action and then find how closely or far flung each individual action is from this average point.

These numbers can then be used as the average position for a player's defensive contribution, measured from the centre of his own goal and also how widely this area extends to.

N'Golo Kante's an obvious candidate to see if this simple exercise again passes the eye test.

In 2016/17 the average pitch position for Kante's defensive actions was 45 yards from his own goal.

The average distance between this average position and all the defensive actions he made was 23 yards

The latter was greater than the average for all defensive midfielders as a group.

We could perhaps say that Kante was relatively advanced in his defensive actions (he was seven yards further up field that his former team mate Nemanja Matic) and his field of influence was also more expansive compared again to Matic and his peers.

Charlie Adam, by contrast appears more constrained by the role required from him. In 2016/17 he tackled deeper than both Kante and Matic and strayed less far afield.

He more resembled a disciplined central defender in his defensive foraging and in doing so remained roughly where his energy bar lands on the pitch around the 70th minute.



Wednesday, 23 August 2017

Chance Quality From 1999.

Back in the late 90's when Gazza's career was on the wane and what might become football analytics was mainly done in public on gambling newsgroups, shot numbers where the new big thing.

"Goal expectation", calculated from a weighted and smoothed average from a side's actual number of goals from their last x number of matches, was often the raw material to use to work out the chances of Premier League high flyers, Leeds beating mid table Tottenham.

Shot numbers (which included headers) then became the new ingredient to throw into the mix and a team's shooting efficiency quickly became a go to stat.

Multi stage precursors to goal expectation models where further developed when shot data became available which was broken down into blocks, misses and on target attempts.

To score, a side had to avoid having their shots blocked, then get them on target and finally beat David James.

This new data allowed you to attach team specific probabilities to each stage of progression towards a goal and arrive at a probabilistic estimate of a team's conversion rate per attempt.

Unlike today's xG number, the figure told you nothing specific about a single shot, nor was it particularly useful in helping to describe the outcome of a single game, even with double digit attempts.

Aggregated over a larger series of matches by necessity, this nuanced conversion rate, that included information about a side's ability to avoid blocks, get their efforts on target and thereafter into the goal, allowed you to deduce something about a side's preferred attacking and defensive style.

Also if that preference persisted over seasons, this team specific conversion rate could be used alongside each team's raw shot count in the recent past to create novel, up to date and hopefully predictive set of defensive and attacking performance ratings.

Paper and pencil only lasts slightly longer than today's hard drive, so unfortunately I don't have any "goal expectation" figures for Liverpool circa 2002.

However, with the additional, detailed data from 2017, I decided to re-run these turn of the century, slightly flawed goal expectation models to see if these old school, team specific conversion rates offer anything in today's more data rich climate.

To distinguish them from today's xG I've re named the output as "chance quality".


Chance quality is an averaged likelihood that a side would negotiate the three stages needed to score.

Arsenal had the highest average chance quality per attempt in 2015/16.

The Gunners were amongst the most likely to avoid having their attempts blocked, those that weren't blocked were most likely to be on target and those that were on target were most likely to result in a goal.

Leicester, in their title winning season also created high quality chances per attempt, but Tottenham appeared to opt for quantity verses quality. They were mid table for avoiding blocks and finding their target, but their on target attempts were, on average among the least likely to result in a goal.

Only Palace of the surviving sides were less likely to score with an on target attempt than Spurs.

 

Here's the same chance quality per attempt, but for attempts allowed, rather than created by the non relegated teams from the 2015/16 season.

The final two columns compare the estimated goal totals for each team using their shot count in that season and their conversion, chance quality from the previous year, to their actual values.

The thinking back in 2000 was that conversion rate from a previous season remained fairly consistent into the next season and so multiplying a side's chance quality by the number of shots they subsequently took or allowed would give a less statistically noisy estimate of their true scoring abilities.

Here's the correlation between the estimated and actual totals using chance quality from 2015/16 and shot numbers from 2016/17 to predict actual goals from 2016/17.


 


There does appear to be a correlation between average chance quality in a previous year, attempts made the next season and actual goals scored or allowed.

The correlation is stronger on the defensive side of the ball, perhaps suggesting less tinkering with the back 3, 4 or 5.

With full match video extremely rare in 2000, it might have been tempting to assume chance quality had remained relatively similar for most sides and any discrepancy between actual and predicted was largely a product of randomness.

Fortunately, greater access to granular data, availability of extensive match highlights and Pulisball, as a primitive benchmark for tactical extremes, has made it easier to recognise that tactical approaches and chance quality often varies, particularly if there is managerial change.

In this post I compared the distribution of xG for Stoke under Pulis' iron grip (fewer, but high chance quality attempts) and his successor Mark Hughes (higher attempt volumes, but lower quality attempts).

Subsequently, under Hughes, Stoke have tended to morph towards the Hughes ideal and away from Pulis' more occasional six yard box offensive free for all.

So a change of manager could lead a a genuine increase or decrease in average chance quality, which in turn might well alter a side's number of attempts. And any use of an updated version of chance quality should come with this important caveat.

For anyone who wants to party like it's 1999, here's the average chance quality per attempt from the 2016/17 season using this pre-Twitter methodology allied to present day location and shot type information.



Use them as a decent multiplier along with shot counts to produce a proxy for the more detailed cumulative xG now available during the upcoming season or as a new data point to assist in describing a side's tactical evolution across seasons.

In 2016/17, Crystal Palace improved their chance quality compared to 2015/16 with half a season of Allardyce and Arsenal maintained their reputation for trying to walk the ball into the net.

All data is from infogolApp, where 2017 expected goals are used to predict and rate the performance of teams in a variety of leagues and competitions.

Monday, 14 August 2017

Liverpool's Split Personality

Everyone likes a good mystery and Constantinos Chappas provided the raw material for a great one when he posted this breakdown of Liverpool's points per game performance in 2016/17 against the six teams from Everton and above and against the remaining 13 sides.


It's a great piece of work from Constantinos and Liverpool's split personality when playing very well against title contenders and Everton compared to when they do less well against lower class teams has generated much speculation.

These have generally fallen into two mutually exclusive groups, either narrative based tactical flaws of Klopp and Liverpool or odds based simulations that attempt to explain away the split as mere randomness.

It is unlikely that either approach will wholly account for Liverpool's apparent failure to dispatch mid and lower table teams with the authority they appeared to preserve for the league's stronger sides.

Football is awash with randomness as well as tactical nuances, so it seems much more likely that a combination of factors will have contributed to the 2016/17 season.

It's a simple task to simulate multiple seasons, often using bookmaker's odds as a proxy for team strength to arrive at the chances that a side, not necessarily Liverpool might exhibit a split personality.

However, it's a stretch to then conclude that either chance was the overriding factor or it can be excluded as a cause merely because this likelihood falls above or below an arbitrary level of certainty.

There is so much data swirling around football at the moment, particularly ExpG, that it seems helpful to use these number to shed some light on Constantinos' intriguing observation.

Rather than a pregame bookmaker's estimate a a side's chance, we have access to ExpG figures for all of Liverpool's 2016/17 matches.

ExpG have arisen from the tactical and talent based interaction that took place on the field and spread over 90+ minutes of all 38 games they perhaps provide a larger sample of events with which to explain a series of game outcomes, rather than simply using 38 individual sets of match odds, however skillfully assembled.

One aspect of a low scoring sport, such as football, where ExpG struggles is how teams adopt different approaches to achieve the aim of winning the most available number of points.

A side may take a fairly comfortable lead early in a contest and then chose to commit more to defence against a weaker or numerically deficient opponent.

An extreme case was Burnley's win over Chelsea, where early actual goals allowed the visitors to concede large amounts of ExpG and just few enough actual ones to handsomely lose the ExpG contest, but win the match.

ExpG figures are inevitably tainted by actual real events, such as goals and red cards, but it is still at its most useful when used in conjunction with simulations to attempt to describe the range and likelihood of particular events occurring.

Scoring first (and 2nd and 3rd, along with Chelsea going down to 10 men) was a big assistance to Burnley and Andrew Beasley has written about the importance of the first goal here, for Pinnacle.

If we look at the size of the ExpG figures for all goal attempts in a game and the order in which they arrived, there may be enough data that is not distorted by actual events to estimate which side was most likely to open the scoring, allowing them then to be able to more readily dictate how the game evolves.


In games against the 13 lowest finishing teams, Liverpool took the initial lead 16 times, compared to a most likely figure of 15.

With the interaction of attempts allowed and taken, Liverpool ended up 1-0 to the good or bad or goalless throughout about as often as their process deserved.


They fared much better against the top teams.

In those 12 games Liverpool took the 1-0 lead nine times compared to a most likely expectation of just six based on the ExpG in their games.

It was around a 7% chance that an average team repeats this if Liverpool carve out and allow the chances for them.

It's understandable to look to the heights that may be achieved, rather than the lowly foothills left behind.

But based on Liverpool's 2016/17 process from an ExpG and first goal perspective, perhaps their relatively disappointing record against lower grade sides is not the outlier, but rather their exceptional top 6 results are.

Scoring fewer first goals than they actually did in these top of the table clashes would likely decrease their ppg in these games, while inevitably increasing those of their six challengers.

This would shift the top six group gradually to the right in the initial plot and Liverpool slightly more substantially to the left until they perhaps formed a more homogenous group with no outlier.

It's traditional to wind up with "nothing to see, randomness wins again", particularly when one set of data is taken from a small, extreme inducing sample of just 12 inter connected matches per team.

But we now have the data, a place to look and video to see if there is some on pitch, if possibly transient cause to the effect of Liverpool finding the net first in big games or if the usual suspect in Constantinos'  mystery does indeed turn out to be the major guilty party.

All data from @InfoGolApp

Tuesday, 8 August 2017

"It's All about The Distribution Part 2"

First the disclaimer, this isn't a "smart after the event" explanation for Leicester's title season.

It is a list of the occasional, nasty or pleasant surprises that can occur and the limitations of trying to second guess these when using a linear, ratings based model.

Building models based around numbers and averages do work extremely well for the majority of teams in the majority of seasons.

But as the financial world found to the cost of others, neglecting distributions, especially ones that appear normal, but hide fatter than usual tails can leave you unprepared for the once in a lifetime event.

The previous post looked at a hypothetical five team scenario, where the lowest rated, but under exposed side had a much better chance of winning a contest than implied by the respective ratings, simply because the distribution of potential ratings were markedly different for this side.

Again, full disclosure, this model wasn't from football, it was a five runner race run at Uttoxeter and Team 5 was actually a very lightly raced horse against exposed rivals.

I assumed that the idea that distributions of potential performance sometimes matters also carries over into football and the obvious example of an unconsidered team taking a league by storm was Leicester's 2015/16 title winning season.

I went back to 2014/15 and produced some very simple expected goals ratings for all 20 sides going into the 2015/16 season.

I also looked at how diverse and spread out the performance ratings from 2014/15 were for each side.

Three teams whose performances had fluctuated most and might be considered as having a bit more meat in their distribution tails and might be less likely to adhere to their "average" expectations were champions, Chelsea, West Ham and Leicester.

I then set up a distribution for each team based around their average rating and the standard deviation from their individual game by game performances in 2014/15.

I then drew from these tailored distributions as a basis to simulate each game in the 2015/16 season, Leicester's winning season.

And this is how the Foxes and their fellow in and out teams fared in simulations that take from a distribution, rather than a rating.

.

Leicester project as a top half team, who were as likely to finish in the top two as they were to be relegated and West Ham put themselves about all over the place, but predominately in the top half, which is where they ended up.

Chelsea have a minute chance of ending up tenth, so kudos to Mourinho for breaking this particular model.

There are some really interesting figures emerging today, both for teams and players and usually it's fine to run with the average.

But these averages live in distributions and when these distributions throw up something inevitable, if unexpected, as the bankers found out, someone has to pay.

"It's All About The Distribution".

You've got five teams.

One is consistently the best team, their recruitment is spot on with a steady stream of younger replacements ready and able to take over when their starts peak and wane.

Then we've got two slightly inferior challengers, again the model of consistency, with few surprises, either good or bad.

The lowest two rated teams complete the group of five.

The marginally superior of these also turns in performances that only waver slightly from their baseline average.

For the final team, however we have very limited information about their abilities, partly due to a constantly changing line up and new acquisitions.

The current team has been assembled from a variety of unfashionable leagues and results and we only have a handful of results by which to judge them.

So we group together the initial results of similarly, newly assembled teams to create a larger sample size to describe what we might get from such a team.

Instead of a distribution that resembles the four, more established teams, we get one that is much more inconsistent. Some such teams did well, others very badly.

The distribution of performances for the first four sides is typical of teams from this mini league, whereas the distribution we have chosen to represent the potential upside and downside of this unexposed side is not.

Team 5's distribution has a flatter peak and fatter tails, both good and bad.

The average "ratings" of the five teams are shown below.



Team 5 has the lowest average rating, but by far the largest standard deviation based on the individual ratings of the particular cohort of sides we have chosen to represent them.

As Team 5 is the lowest rated, they're obviously going to finish bottom of the table, a lot, but just to confirm things we could run a simulation based on the distribution of performances for all five teams.

First we need to produce a distribution that mimics the range of performances for the 5 teams and we'll draw a random number from that distribution to decide the outcome of a series of contests.

The highest performance number drawn takes the spoils.

Run 10,000 simulated contests and Team 5 does come last more frequently than any other side, roughly half the tournaments finish with Team 5 in last position.

However, because their profiled performances are inconsistent and populated by a few very good performances, they actually come first more frequently than might be expected from their average performance rating.

In 10,000 simulations, Team 5 comes first 22% of the time, bettered only by Team 1, whose random draw of ratings based on their more conventional distribution of potential performances grants them victory 36% of the time.

Not really what you'd expect simply from eyeballing the raw ratings.

Team 5, based on the accumulated record of teams that have similar limited data, are likely to be sometimes very bad, but occasionally they can produce excellent results.

Such as Leicester when they were transitioning into a title winning team?

As someone once said at an OptaProForum.......

"It's all about the distribution"

......and simple averages can sometimes miss sub populations that could be almost anything.

Straight line assumptions, extrapolated from mere averages will always omit the inevitable uncertainty that surrounds such teams or players, where data is scarce and distribution tails might be fatter than normal.

Friday, 4 August 2017

What Might Leicester Get from Kelechi Iheanacho?

Hidden behind Neymar's unveiling in Paris was Kelechi Iheanacho's departure from Manchester City to last season's Champions League quarter finalists, Leicester City.

There's probably no need to measure the height of Iheanacho's transfer fee in piles of tenners, but it does amount to a substantial investment in young talent for the East Midlands side and an opportunity  for Kelechi to gain larger amounts of playing time, especially from kick off.

His stats are impressive for a young player.

Any playing time at such a raw age, particularly at a regular title contender is impressive and during his 1275 minutes he's scored 12 from 50 shots (24% conversion rate, without the need for a calculator) and provided 4 assists.

Many appearances have been from the subs bench and it is well known that scoring generally accelerates as the game progresses, so he'll have had a slight boost from that.

He's not really been thrown in solely against the Premier League minnows.

The weighted expected goals conceded by the teams he has faced is only slightly above the league average and he's scored against teams such as Stoke, Spurs, Stoke, Manchester United, Bournemouth, Stoke, Swansea and Southampton.

Nothing too much to worry about him being a flat track bully, although he does quite like Stoke.

In simpler, pre expected goals times, you would take his 24% conversion rate and regresses it fairly heavily towards the league average rate to get a more realistic future expectation.

Devoid of any shot location context, Iheanacho's conversion rate since 2015/16 is second only to Llorente at Swansea, another 50 odd attempt player and just ahead of renowned goalscorer, Gary Cahill.

Small samples often lead to unrepresentative extremes and if any media outlet is still quoting raw conversion rates in this enlightened era, they'll probably be disappointed in the long run.

Higher volume shooters over the two seasons Iheanacho's been around in the Premier League are peaking at around 18% conversion rates and as a group, players with 40 or more attempts are converting around 1 in ten.

Regressing his 24% rate by around 50% wouldn't have been out of order and back in the day you would probably pitch him it at around a 17% conversion rate, which is still elite and wait for more data.

Nowadays, lots of Heisenberg expG models are attempting to extract the truth from lots of noisy data produced by players whose fitness peaks and troughs, along with their team mates and opponents.

Most will put Iheanacho's cumulative expected goals from his 50 attempts at around 9 expG compared to his actual total of 12 goals.

Act is > ExpG, case solved, he's an above average finishing capture.

But this doesn't account for natural randomness in a process or outrageous good fortune (such as
the ball hitting you on the back and looping into the net against Swansea in December 2015).


Here's the range of simulated successful outcomes for an average finisher, assuming he could have got onto the end of Iheanacho's 50 attempts.

There's roughly a 14% chance an average Premier League finisher scores as many or more goals than the 12 that Leicester's new signing managed at Manchester City and his highlighted 24% strike rate slightly pales under the scrutiny of shot type and location.

It's also wise to see if your Heisenberg model at least roughly matches the actual distribution of output from the many guinea pigs who are run through it.... and Inheancho is initially a pretty poor fit.

The chance that his actual distribution of goals from his attempts is consistent with the model used in the simulations, is only around 1 in 1000.

In these cases it is well worth looking at each attempt, the outcome and the attached expG value.

The problem with Iheanacho fitting the model is that two of his goals come from very low probability chances (the aforementioned back deflected goal at Swansea) and the remaining ten come from virtually the ten most likely goal scoring opportunities he received.

He's scored one long range shot against Southampton, one with his back against the Swans and then nails almost every high quality chance with an expG above 0.4 that he's presented with.

Mitigate for the fluke and the model fit becomes more forgiving.

Delving into the attempts, looking at the outcomes and seeing where the (imperfect) model breaks down can tell us a lot more about Leicester's £25 million purchase than merely saying "he over-performs his ExpG".

He may thrive on quality chances, he certainly has done in his short time in the Premier League.

Over the previous two campaigns, Manchester City created the second highest proportion of the high quality chances that Iheanacho excels at converting.

Around 7% of Manchester City's created attempts have an ExpG in excess of 0.4 in my model.

Leicester are third in this list over the last two seasons, also with around 7% of their chances being high quality ones, suggesting he's a decent fit for the Foxes.

However, numerically, Manchester City are much more prolific both overall and in this creative area. Their play makers carve out five such highest quality chances every four games, compared to just three for Leicester.

Iheanacho may be able to bridge that gap between the two Cities by his positional nous and undoubted pace, but he'll also be competing with Leicester's main beneficiary of these high quality chances, a quarter of which fell to Jamie Vardy.

In short, just a few caveats to one of the upcoming season's major purchase by a team outside the top six.

Friday, 21 July 2017

Shots, Blocks And Game State

In this post I described a way to quantify game state by reference to how well or badly a side was doing in relation to their pregame expectations.

So rather than simply using the current scoreline to define game state, it gave a much more nuanced description of the state of the game, particularly in those frequent phases of a match when the sides were level.

It also incorporates time remaining into the calculation. 

A team level after 10 minutes might be in a very different situation compared to the same score differential, but with ten minutes remaining. How they and their opponents played out the subsequent time may be very different in the two scenarios.

At a simplistic level, those teams in a happy place may be more content to prioritise actions that maintain the status quo, such as defend more, while those who'd wish to alter the state of the game might put more resources into attack than had previously been the case.

It seems logical that a more defensive approach should result that team accumulating more products of a packed defence, such as blocked shots, while any chances they do create may be meet with increasingly fewer defenders.

I took at look at the correlation between blocks and clear cut or so called big chances and the prevailing state of the game and there was a significant relationship between them.

A side in a poor state of the game had more chance of their goal attempts being blocked and his increased as their game state deteriorated.

Similarly, a side in a positive state of the game was more likely to create a chance that was deemed a big chance.

This appears to fit which the hypothesis of content teams packing their defence more, and increasing the likelihood that they block an attempt and if they do scoot off upfield, they're more likely to be met with a depleted defence.

However, correlation doesn't prove causation etc etc. 

In the case of a side being more likely to create big chances, there may be a confounding factor that is causing both the good state of the game and the big chances. (Think raincoats, wet pavements and weather).

That factor is possibly team quality.

The top six account for 30% of the Premier League, but took 48% of the wins, 43% of the goals scored and 45% of the league points won.

They're a league within a league, more likely to be in a very good game state and they also accounted for 43% of the league's big chances.

Team quality may be the causative agent for a good game state and for creating big chances, which correlates the two without either being causative agents of the other.

So I stripped out all games involving the big six to get a more closely matched initial contest, but the correlation persisted.

Teams in a good place against sides of similar core abilities were more likely to create very good chances and more likely to find defensive bodies to block the anticipated  onslaught from their opponents.

As a tentative conclusion, intuitive events that you might expect to be more likely to occur as strategies subtly alter do appear to be identifiable in the data.

Data from InfoGolApp

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