Not quite the same old Arsenal

The narrative surrounding Arsenal has been strong this week, with their fall to fourth place in the table coming on Groundhog Day no less. This came despite a strong second half showing against Southampton, with Fraser Forster denying them. Arsenal’s season has been characterised by several excellent performances in terms of expected goals but the scoreline hasn’t always reflected their statistical dominance. Colin Trainor illustrated their travails in front of goal in this tweet.

I wrote in this post on how Arsenal’s patient approach eschews more speculative shots in search of high quality chances and that this was seemingly more pronounced this season. Arsenal are highly rated by expected goal models this season but traditional shot metrics are nowhere near as convinced.

Analytical folk will point to the high quality of Arsenal’s shots this season to explain the difference, where quality is denoted by the average probability that a shot will be scored. For example, a team with an average shot quality of 0.10 would ‘expect’ to score around 10% of their shots taken.

In the chart below, I’ve looked at the full distribution of Arsenal’s shots in open-play this season in terms of ‘shot quality’ and compared them with their previous incarnations and peers from the 2012/13 season through to the present. Looking at shot quality in this manner illustrates that the majority of shots are of relatively low quality (less than 10% chance of being scored) and that the distribution is heavily-skewed.

ShotQualFor_Arsenal

Proportion of total shots in open-play according to the probability of them being scored (expected goals per shot). Grey lines are non-Arsenal teams from the English Premier League from 2012/13 to the present. Blue lines are previous Arsenal teams, while red is Arsenal from this season. Data via Opta.

In terms of Arsenal, what stands out here is that their current incarnation are taking a smaller proportion of ‘low-quality’ shots (those with an expected goal estimate from 0-0.1) than any previous team by a fairly wide margin. At present, 59% of Arsenal’s shots reside in this bracket, with the next lowest sitting at 64%. Their absolute number of shots in this bracket has also fallen compared to previous seasons.

Moving along the scale, Arsenal reside along the upper edge in terms of these higher quality shots and actually have the largest proportion in the 0.2-0.3 and 0.3-0.4 ranges. As you would expect, they’ve traded higher quality shots for lower quality efforts according to the data.

Arsenal typically post above average shot quality figures but the shift this season appears to be significant. The question is why?

Mesut Özil?

One big change this season is the sustained presence (and excellence) of Mesut Özil; so far this season he has made 22 appearances (playing in 88% of available minutes) compared to 22 appearances last season (54%) and 26 matches in his debut season (63%). According to numbers from the Football in the Clouds website, his contribution to Arsenal’s shots while he is on the pitch is at 40% compared to 30% in 2014/15. Daniel Altman also illustrated Özil’s growing influence in his post in December.

Özil is the star that Arsenal’s band of attacking talent orbits, so it is possible that he is driving this focus on quality via his creative skills. His attacking contribution in terms of shots and shot-assists is among the highest in the league but is heavily-skewed towards assisting others, which is unusual among high-volume contributors.

Looking at the two previous seasons though, there doesn’t appear to be any great shift in Arsenal’s shot quality during the periods when Özil was out of the team through injury. His greater influence and regular presence in the side this season has probably shifted the dial but quantifying how much would require further analysis.

Analytics?

Another potential driver could be that Wenger and his coaching staff have attempted to adjust Arsenal’s tactics/style with a greater focus on quality.

Below is a table of Arsenal’s ‘volume’ shooters over the past few seasons, where I’ve listed their number of shots from outside of the box per 90 minutes and the proportion of their shots from outside the box. Note that these are for all shots, so set-pieces are included but it shouldn’t skew the story too much.

Arsenal_OoB_Shots_TableThe general trend is that Arsenal’s players have been taking fewer shots from outside of the box this season compared to previous and that there has been a decline proportionally for most players also. Some of that may be driven by changing roles/positions in the team but there appears to be a clear shift in their shot profiles. Giroud for example has taken just 3 shots from outside the box this season, which is in stark contrast to his previous profile.

Given the data I’ve already outlined, the above isn’t unexpected but then we’re back to the question of why?

Wenger has mentioned expected goals on a few occasions now and has reportedly been working more closely with the analytics team that Arsenal acquired in 2012. Given his history and reputation, we can be relatively sure that Wenger would appreciate the merits of shot quality; could the closer working relationship and trust developed with the analytics team have led to him placing an even greater emphasis on seeking better shooting opportunities?

The above is just a theory but the shift in emphasis does appear to be significant and is an interesting feature to ponder.

Adjusted expectations?

Whatever has driven this shift in Arsenal’s shot profile, the change is quite pronounced. From an opposition strategy perspective, this presents an interesting question: if you’re aware of this shift in emphasis, whether through video analysis or data, do you alter your defensive strategy accordingly?

While Arsenal’s under-performance in terms of goals versus expected goals currently looks like a case of variance biting hard, could this be prolonged if their opponents adjust? It doesn’t look like their opponents have altered tactics thus far based on examining the data but having shifted the goalposts in terms of shot quality, could this be their undoing?

Shooting the breeze

Who will win the Premier League title this season? While Leicester City and Tottenham Hotspur have their merits, the bookmakers and public analytics models point to a two-horse race between Manchester City and Arsenal.

From an analytics perspective, this is where things get interesting, as depending on your metric of choice, the picture painted of each team is quite different.

As discussed on the recent StatsBomb podcast, Manchester City are heavily favoured by ‘traditional’ shot metrics, as well as by combined team ratings composed of multiple shooting statistics (a method pioneered by James Grayson). Of particular concern for Arsenal are their poor shot-on-target numbers.

However, if we look at expected goals based on all shots taken and conceded, then Arsenal lead the way: Michael Caley has them with an expected goal difference per game of 0.98, while City lie second on 0.83. My own figures in open-play have Arsenal ahead but by a narrower margin (0.69 vs 0.65); Arsenal have a significant edge in terms of ‘big chances’, which I don’t include in my model, whereas Michael does include them. Turning to my non-shots based expected goal model, Arsenal’s edge is extended (0.66 vs 0.53). Finally, Paul Riley’s expected goal model favours City over Arsenal (0.88 vs 0.69), although Spurs are actually rated higher than both. Paul’s model considers shots on target only, which largely explains the contrast with other expected goal models.

Overall, City are rated quite strongly across the board, while Arsenal’s level is more mixed. The above isn’t an exhaustive list of models and metrics but the differences between how they rate the two main title contenders is apparent. All of these metrics have demonstrated utility at making in-season predictions but clearly assumptions about the relative strength of these two teams differs between them.

The question is why? If we look at the two extremes in terms of these methods, you would have total shots difference (or ratio, TSR) at one end and non-shots expected goals at the other i.e. one values all shots equally, while the other doesn’t ‘care’ whether a shot is taken or not.

There likely exists a range of happy mediums in terms of emphasising the taking of shots versus maximising the likelihood of scoring from a given attack. Such a trade-off likely depends on individual players in a team, tactical setup and a whole other host of factors including the current score line and incentives during a match.

However, a team could be accused of shooting too readily, which might mean spurning a better scoring opportunity in favour of a shot from long-range. Perhaps data can pick out those ‘trigger-happy’ teams versus those who adopt a more patient approach.

My non-shots based expected goal model evaluates the likelihood of a goal being scored from an individual chain of possession. If I switch goals for shots in the maths, then I can calculate the probability that a possession will end with a shot. We’ll refer to this as ‘expected shots’.

I’ve done this for the 2012/13 to 2014/15 Premier League seasons. Below is the data for the actual versus expected number of shots per game that each team attempted.

xShots_historic_AVB

Actual shots per game compared with expected shots per game. Black line is the 1:1 line. Data via Opta.

We can see that the model does a reasonable job of capturing shot expectation (r-squared is at 0.77, while the mean absolute error is 0.91 shots per game). There is some bias in the relationship though, with lower shot volume teams being estimated more accurately, while higher shot volume sides typically shoot less than expected (the slope of the linear regression line is 0.79).

If we take the model at face value and assume that it is telling a reasonable approximation of the truth, then one interpretation would be that teams with higher expected shot volumes are more patient in their approach. Historically these have been teams that tend to dominate territory and possession such as Manchester City, Arsenal and Chelsea; are these teams maintaining possession in the final third in order to take a higher value shot? It could also be due to defenses denying these teams shooting opportunities but looking at the figures for expected and actual shots conceded, the data doesn’t support that notion.

What is also clear from the graph is that it appears to match our expectations in terms of a team being ‘trigger-happy’ – by far the largest outlier in terms of actual shots minus expected shots is Tottenham Hotspurs’ full season under André Villas-Boas, a team that was well known for taking a lot of shots from long-range. We also see a decline as we move into the 2013/14 season when AVB was fired after 16 matches (42% of the full season) and then the 2014/15 season under Pochettino. Observations such as these that pass the ‘sniff-test’ can give us a little more confidence in the metric/method.

If we move back to the season at hand, then we see some interesting trends emerge. Below I’ve added the data points for this current season and highlighted Arsenal, Manchester City, Liverpool and Tottenham (the solid black outlines are for this season). Throughout the dataset, we see that Arsenal have been consistently below expectations in terms of the number of shots they attempt and that this is particularly true this season. City have also fallen below expectations but to a smaller extent than Arsenal and are almost in line with expectations this year. Liverpool and Tottenham have taken a similar number of shots but with quite different levels of expectation.

xShots_Historic_plus_Current

Actual shots per game compared with expected shots per game. Black line is the 1:1 line. Markers with solid black outline are for the current season. Data via Opta.

None of the above indicates that there is a better way of attempting to score but I think it does illustrate that team style and tactics are important factors in how we build and assess metrics. Arsenal’s ‘pass it in the net’ approach has been known (and often derided) ever since they last won the league and it is quite possible that models that are more focused on quality in possession will over-rate their chances in the same way that focusing on just shots would over-rate AVB’s Spurs. Manchester City have run the best attack in the league over the past few seasons by combining the intricate passing skills of their attackers with the odd thunder-bastard from Yaya Touré.

The question remains though: who will win the Premier League title this season? Will Manchester City prevail due to their mixed-approach or will Arsenal prove that patience really is a virtue? The boring answer is that time will tell. The obvious answer is Leicester City.

Unexpected goals

A sumptuous passing move ends with the centre forward controlling an exquisite through-ball inside the penalty area before slotting the ball past the goalkeeper.

Rewind.

A sumptuous passing move ends with the centre forward controlling an exquisite through-ball inside the penalty area before the goalkeeper pulls off an incredible save.

Rewind.

A sumptuous passing move ends with the centre forward controlling an exquisite through-ball inside the penalty area before falling on his arse.

giphy

Source: Giphy

Rewind.

Events in football matches can take many turns that will affect the overall outcome, whether it be a single event, a match or season. In the above examples, the centre forward has received the ball in a super-position but what happens next varies drastically.

Were we to assess the striker or his team, traditional analysis would focus on the first example as goals are the currency of football. The second example would appeal to those familiar with football analytics, which has illustrated that the scoring of goals is a noisy endeavour that can be potentially misleading; focusing on shots and/or the likelihood of a shot being scored is the foundation of many a model to assess players and teams. The third example will often be met with a shrug and a plethora of gifs on social media.

This third example is what I want to examine here by building a model that accounts for these missed opportunities to take a shot.

Expected goals

Expected goals are a hugely popular concept within football analytics and are becoming increasingly visible outside of the air-conditioned basements frequented by analysts. The fundamental basis of expected goals is assigning a value to the chances that a team create or concede.

Traditionally, such models have focused on shots, building upon earlier work relating to shots and shots on target. Many models have sprung up over the past few years with Michael Caley and Paul Riley models being probably the most prominent, particularly in terms of publishing their methods and results.

More recently, Daniel Altman presented a model that went ‘beyond shots‘, which aimed to value not just shots but also attacking play that moved the ball into dangerous areas. Various analysts, including myself, have looked at the value of passing in a similar vein e.g. Dustin Ward and Sam Gregory have looked at dangerous passing here and here respectively.

Valuing possession

The model that I have built is essentially a conversion of my dangerous possession model. Each sequence of possession that a team has is classified according to how likely a goal is to be scored.

This is based on a logistic regression that includes various factors that I will outline below. The key thing is that this is based on all possessions, not just those ending with shots. The model is essentially calculating the likelihood of a shot occurring in a given position on the field and then estimating the probability of a potential shot being scored. Consequently, we can put a value on good attacking (or poor defending) that doesn’t result in a shot being taken.

I’ve focused on open-play possessions here and the data is from the English Premier League from 2012/13-2014/15..

Below is a summary of the major location-related drivers of the model.

xG_factors

Probability of a goal being scored based on the end point of possession (top panel) and the location of the final pass or cross during the possession (bottom panel).

By far the biggest factor is where the possession ends; attacks that end closer to goal are valued more highly, which is an intuitive and not at all ground-breaking finding.

The second panel illustrates the value of the final pass or cross in an attacking move. The closer to goal this occurs, the more likely a goal is to be scored. Again this is intuitive and has been illustrated previously by Michael Caley.

Where the possession starts is also factored into the model as I found that this can increase the likelihood of a goal being scored. If a team builds their attack from higher up the pitch, then they have a better chance of scoring. I think this is partly a consequence of simply being closer to goal, so the distance to move the ball into a dangerous position is shortened. The other probable big driver here is that the likelihood of a defence being out of position is increased e.g. a turnover of possession through a high press.

The other factors not related to location include through-ball passes, which boost the chances of a goal being scored (such passes will typically eliminate defenders during an attacking move and present attackers with more time and space for their next move). Similarly, dribbles boost the likelihood of a goal being scored, although not to the same extent as a through-ball. Attacking moves that feature a cross are less likely to result in a goal. These factors are reasonably well established in the public analytics literature, so it isn’t a surprise to see them crop up here.

How does it do?

Below are some plots and a summary table comparing actual goals to expected goals for each team in the dataset. The correlation is stronger for goals for than against, although the bias is larger also as the ‘best’ teams tend to score more than expected and the ‘worst’ teams score fewer than expected. Looking at goal difference, the relationship is very strong over a season.

I also performed several out-of-sample tests to test the regressions by spitting the data-set into two sets (2012/13-2013/14 and 2014/15 only) and ran cross-validation tests on them. The model performed well out-of-sample, with the summary statistics being broadly similar when compared to the in-sample tests.

Non_shots_Plot

Comparison between actual goals and expected goals. Red dots are individual teams in each season. Dashed black line is 1:1 line and solid black line is the line of best fit.

Stats_Table

Comparison between actual goals and expected goals. MAE refers to Mean Absolute Error, while slope and intercept are calculated from a linear regression between the actual and expected totals.

I also ran the regression on possessions ending in shots and the results were broadly quite similar, although I would say that the shot-based expected goal model performed slightly better overall. Overall, the non-shots based expected goals model is very good at explaining past performance and is comparable to more traditional expected goal models.

On the predictive side, I ran a similar test to what Michael Caley did here as a quick check of how well the model did. I looked at each clubs matches in chronological order and calculated how well the expected goal models predicted actual goals in their next 19 matches (half a season in the Premier League) using an increasing number of prior matches to base the prediction on. For example, for a 10 match sample, I started at matches 1-10 and calculated statistics for matches 11-30, followed by matches 2-11 for matches 12-31 and so on.

Note that the ‘wiggles’ in the data are due to the number of teams changing as we move from one seasons worth of games to another i.e. some teams have only 38 games worth of matches, while others have 114. I also ran the same analysis for the next 38 matches and found similar features to those outlined below. I also did out-of-sample validation tests and found similar results, so I’m just showing the full in-sample tests below.

Capability of non-shot based and shot-based expected goals to predict future goals over the next 19 matches using differing numbers of previous matches as the input. Actual goals are also shown for reference. R-squared is shown on the left, while the mean absolute error is shown on the right.

I’m not massively keen on using r-squared as a diagnostic for predictions, so I also calculated the mean absolute errors for the predictions. The non-shots expected goals model performs very well here and compares very favourably with the shots-based version (the errors and correlations are typically marginally better). After around 20-30 matches, expected goals and actual goals converge in terms of their predictive capability – based on some other diagnostic tests I’ve run, this is around the point where expected goals tends to ‘match’ quite well with actual goals i.e. actual goals regresses to our mean expectation, so this convergence here is not too surprising.

The upshot is that the expected goal models perform very well and are a better predictor of future goals than goals themselves, particularly over small samples. Furthermore, they pick up information about future performance very quickly as the predictive capability tends to flat-line after less than 10 matches. I plan to expand the model to include set-play possessions and perform point projections, where I will do some more extensive investigation of the predictive performance of the model but I would say this is an encouraging start.

Bonus round

Below are the current expected goal difference rankings for the current Premier League season. The numbers are based on the regression I performed on the 2012/13-2014/15 dataset. I’ll start posting more figures as the season continues on my Twitter feed.

Open-play expected goal difference totals after 19 games of the 2015/16 Premier League season.

Open-play expected goal difference totals after 19 games of the 2015/16 Premier League season.

On single match expected goal totals

It’s been a heady week in analytics-land with expected goals hitting the big time. On Friday, they appeared in the Times courtesy of Rory Smith, Sunday saw them crop up on bastion of proper football men, Sunday Supplement, before again featuring via the Times’ Game Podcast. Jonathan Wilson then highlighted them in the Guardian on Tuesday before dumping them in a river and sorting out an alibi.

The analytics community promptly engaged in much navel-gazing and tedious argument to celebrate.

Expected goals

The majority of work on the utility of expected goals as a metric has focused on the medium-to-long term; see work by Michael Caley detailing his model here for example (see his Twitter timeline for examples of his single match expected goal maps). Work on expected goals over single matches has been sparser, aside from those highlighting the importance of accounting for the differing outcomes when there are significant differences in the quality of chances in a given match; see these excellent articles by Danny Page and Mark Taylor.

As far as expected goals over a single match are concerned, I think there are two overarching questions:

  1. Do expected goal totals reflect performances in a given match?
  2. Do the values reflect the number of goals a team should have scored/conceded?

There are no doubt further questions that we could add to the list but I think these relate most to how these numbers are often used. Indeed, Wilson’s piece in particular covered these aspects including the following statement:

According to the Dutch website 11tegen11, Chelsea should have won 2.22-0.77 on expected goals.

There are lots of reason why ‘should’ is problematic in that article but ignoring the probabilistic nature and uncertainties surrounding these expected goal estimates, let’s look at how well expected goals matches up over various numbers of shots.

You’ve gotta pick yourself up by the bootstraps

Below are various figures exploring how well expected goals matches up with actual goals. These are based on an expected goal model that I’ve been working on, the details of which aren’t too relevant here (I’ve tested this on various models with different levels of complexity and the results are pretty consistent). The figures plot the differences between the total number of goals and expected goals when looking at certain numbers of shots. These residuals are calculated via bootstrap resampling, which works by randomly extracting groups of shots from the data-set and calculating actual and expected goal totals and then seeing how large the difference is.

The top plot is for 500 shot samples, which equates to the number of shots that a decent shots team might take over a Premier League season. The residuals show a very narrow distribution, which closely resembles a Gaussian or normal distribution, with the centre of the peak being very close to zero i.e. goal and expected goal values are on average very similar over these shot sample sizes. There is a slight tendency for expected goals to under-predict goals here, although the difference is quite minor over these samples (2.6 goals over 500 shots). The take home from this plot is that we would anticipate expected and actual goals for an average team being approximately equivalent over such a sample (with some level of randomness and bias in the mix).

The middle plot is for samples of 50 shots, which would equate to around 3-6 matches at the team level. The distribution is quite similar to the one for 500 shots but the width is quite a lot wider; we would therefore expect random variation to play a larger role over this sample than the 500 shot sample, which would manifest itself in teams or players over or under-performing their expected goal numbers. The other factor at play will be aspects not accounted for by the model, which may be more important over smaller samples but even out more over larger ones.

One of these things is not like the others

The bottom plot is for samples of 13 shots, which equates to the approximate average number of shots by a team in an individual match. This is where expected goals starts having major issues; the distributions are very wide and it also has multiple local maximums. What that means is that over a single match, expected goal totals can be out by a very large amount (routinely exceeding more than one goal) and that the total estimates are pretty poor over these small samples.

Such large residuals aren’t entirely unexpected but the multiple peaks make reporting a ‘best’ estimate extremely troublesome.

I tested these results using some other publicly available expected goal estimates (kudos to American Soccer Analysis and Paul Riley for publishing their numbers) and found very similar results. I also did a similar exercise using whole match totals rather than individual shots and found similar.

I also checked that this wasn’t a result of differing scorelines when each shot was taken (game state as the analytics community calls it) by only looking at shots when teams were level – the results were the same, so I don’t think you can put this down to differences in game state. I suspect this is just a consequence of elements of football that aren’t accounted for by the model, which are numerous; such things appear to even out over larger samples (over 20 shots, the distributions look more like the 50 and 500 shot samples). As a result, teams/matches where the number of shots is larger will have more reliable estimates (so take figures involving Manchester United with a chip-shop load of salt).

Essentially, expected goal estimates are quite messy over single matches and I would be very wary of saying that a team should have scored or conceded a certain number of goals.

Busted?

So, is that it for expected goals over a single match? While I think there are a lot of issues based on the results above, it can still illuminate upon the balance of play in a given match. If you’ve made it this far then I’m assuming you agree that metrics and observations that go beyond the final scoreline are potentially useful.

In the figure below, I’ve averaged actual goal difference from individual matches into expected goal ‘buckets’. I excluded data beyond +/- two expected goals as the sample size was quite small, although the general trends continues. Averaging like this hides a lot of details (as partially illustrated above) but I think it broadly demonstrates how the two match up.

Actual goals compared to expected goals for single matches when binned into 0.5 xG buckets.

Actual goals compared to expected goals for single matches when binned into 0.5 xG buckets.

The figure also illustrates that ‘winning’ the expected goals (xG difference greater than 1) doesn’t always mean winning the actual goal battle, particularly for the away team. James Yorke found something similar when looking at shot numbers. Home teams ‘scoring’ with a 1-1.5 xG advantage outscore their opponents around 66% of the time based on my numbers but this drops to 53% for away teams; away teams have to earn more credit than home teams in order to translate their performance into points.

What these figures do suggest though is that expected goals are a useful indicator of quality over a single match i.e. they do reflect the balance of play in a match as measured by the volume and quality of chances. Due to the often random nature of football and the many flaws of these models, we wouldn’t expect a perfect match between actual and expected goals but these results suggest that incorporating these numbers with other observations from a match is potentially a useful endeavour.

Summary

Don’t say:

Team x should have scored y goals today.

Do say:

Team x’s expected goal numbers would typically have resulted in the following…here are some observations of why that may or may not be the case today.

Recruitment by numbers: the tale of Adam and Bobby

One of the charges against analytics is that it hasn’t really demonstrated its utility, particularly in relation to recruitment. This is an argument I have some sympathy with. Having followed football analytics for over three years, I’m well-versed in the metrics that could aid decision making in football but I can appreciate that the body of work isn’t readily accessible without investing a lot of time.

Furthermore, clubs are understandably reticent about sharing the methods and processes that they follow, so successes and failures attributable to analytics are difficult to unpick from the outside.

Rather than add to the pile of analytics in football think-pieces that have sprung up recently, I thought I would try and work through how analysing and interpreting data might work in practice from the point of view of recruitment. Show, rather than tell.

While I haven’t directly worked with football clubs, I have spoken with several people who do use numbers to aid recruitment decisions within them, so I have some idea of how the process works. Data analysis is a huge part of my job as a research scientist, so I have a pretty good understanding of the utility and limits of data (my office doesn’t have air-conditioning though and I rarely use spreadsheets).

As a broad rule of thumb, public analytics (and possibly work done in private also) is generally ‘better’ at assessing attacking players, with central defenders and goalkeepers being a particular blind-spot currently. With that in mind, I’m going to focus on two attacking midfielders that Liverpool signed over the past two summers, Adam Lallana and Roberto Firmino.

The following is how I might employ some analytical tools to aid recruitment.

Initial analysis

To start with I’m going to take a broad look at their skill sets and playing style using the tools that I developed for my OptaPro Forum presentation, which can be watched here. The method uses a variety of metrics to identify different player types, which can give a quick overview of playing style and skill set. The midfielder groups isolated by the analysis are shown below.

Midfielders

Midfield sub-groups identified using the playing style tool. Each coloured circle corresponds to an individual player. Data via Opta.

I think this is a useful starting point for data analysis as it can give a quick snap shot of a player and can also be used for filtering transfer requirements. The utility of such a tool is likely dependent on how well scouted a particular league is by an individual club.

A manager, sporting director or scout could feed into the use of such a tool by providing their requirements for a new signing, which an analyst could then use to provide a short-list of different players. I know that this is one way numbers are used within clubs as the number of leagues and matches that they take an interest in outstrips the number of ‘traditional’ scouts that they employ.

As far as our examples are concerned, Lallana profiles as an attacking midfielder (no great shock) and Firmino belongs in the ‘direct’ attackers class as a result of his dribbling and shooting style (again no great shock). Broadly speaking, both players would be seen as attacking midfielders but the analysis is picking up their differing styles which are evident from watching them play.

Comparing statistical profiles

Going one step further, fairer comparisons between players can be made based upon their identified style e.g. marking down a creative midfielders for taking a low number of shots compared to a direct attacker would be unfair, given their respective roles and playing style.

Below I’ve compared their statistical output during the 2013/14 season, which is the season before Lallana signed for Liverpool and I’m going to make the possibly incorrect assumption that Firmino was someone that Liverpool were interested in that summer also. Some of the numbers (shots, chances created, throughballs, dribbles, tackles and interceptions) were included in the initial player style analysis above, while others (pass completion percentage and assists) are included as some additional context and information.

The aim here is to give an idea of the strengths, weaknesses and playing style of each player based on ranking a player against their peers. Whether a player ranks low or high on a particular metric is a ‘good’ thing or not is dependent on the statistic e.g. taking shots from outside the box isn’t necessarily a bad thing to do but you might not want to be top of the list (Andros Townsend in case you hadn’t guessed). Many will also depend on the tactical system of their team and their role within it.

The plots below are to varying degrees inspired by Ted Knutson, Steve Fenn and Florence Nightingale (Steve wrote about his ‘gauge’ graph here). There are more details on these figures at the bottom of the post*.

Lallana.

Data via Opta.

Lallana profiles as a player who is good/average at several things, with chances created seemingly being his stand-out skill here (note this is from open-play only). Firmino on the other hand is strong and even elite at several of these measures. Importantly, these are metrics that have been identified as important for attacking midfielders and they can also be linked to winning football matches.

Firmino.

Data via Opta.

Based on these initial findings, Firmino looks like an excellent addition, while Lallana is quite underwhelming. Clearly this analysis doesn’t capture many things that are better suited to video and live scouting e.g. their defensive work off the ball, how they strike a ball, their first touch etc.

At this stage of the analysis, we’ve got a reasonable idea of their playing style and how they compare to their peers. However, we’re currently lacking further context for some of these measures, so it would be prudent to examine them further using some other techniques.

Diving deeper

So far, I’ve only considered one analytical method to evaluate these players. An important thing to remember is that all methods will have their flaws and biases, so it would be wise to consider some alternatives.

For example, I’m not massively keen on ‘chances created’ as a statistic, as I can imagine multiple ways that it could be misleading. Maybe it would be a good idea then to look at some numbers that provide more context and depth to ‘creativity’, especially as this should be a primary skill of an attacking midfielder for Liverpool.

Over the past year or so, I’ve been looking at various ways of measuring the contribution and quality of player involvement in attacking situations. The most basic of these looks at the ability of a player to find his team mates in ‘dangerous’ areas, which broadly equates to the central region of the penalty area and just outside it.

Without wishing to go into too much detail, Lallana is pretty average for an attacking midfielder on these metrics, while Firmino was one of the top players in the Bundesliga.

I’m wary of writing Lallana off here as these measures focus on ‘direct’ contributions and maybe his game is about facilitating his team mates. Perhaps he is the player who makes the pass before the assist. I can look at this also using data by looking at the attacks he is involved in. Lallana doesn’t rise up the standings here either, again the quality and level of his contribution is basically average. Unfortunately, I’ve not worked up these figures for the Bundesliga, so I can’t comment on how Firmino shapes up here (I suspect he would rate highly here also).

Recommendation

Based on the methods outlined above, I would have been strongly in favour of signing Firmino as he mixes high quality creative skills with a goal threat. Obviously it is early days for Firmino at Liverpool (a grand total of 239 minutes in the league so far), so assessing whether the signing has been successful or not would be premature.

Lallana’s statistical profile is rather average, so factoring in his age and price tag, it would have seemed a stretch to consider him a worthwhile signing based on his 2013/14 season. Intriguingly, when comparing Lallana’s metrics from Southampton and those at Liverpool, there is relatively little difference between them; Liverpool seemingly got the player they purchased when examining his statistical output based on these measures.

These are my honest recommendations regarding these players based on these analytical methods that I’ve developed. Ideally I would have published something along these lines in the summer of 2014 but you’ll just have to take my word that I wasn’t keen on Lallana based on a prototype version of the comparison tool that I outlined above and nothing that I have worked on since has changed that view. Similarly, Firmino stood out as an exciting player who Liverpool could reasonably obtain.

There are many ways I would like to improve and validate these techniques and they might bear little relation to the tools used by clubs. Methods can always be developed, improved and even scraped!

Hopefully the above has given some insight into how analytics could be a part of the recruitment process.

Coda

If analytics is to play an increasing role in football, then it will need to build up sufficient cachet to justify its implementation. That is a perfectly normal sequence for new methods as they have to ‘prove’ themselves before seeing more widespread use. Analytics shouldn’t be framed as a magic bullet that will dramatically improve recruitment but if it is used well, then it could potentially help to minimise mistakes.

Nothing that I’ve outlined above is designed to supplant or reduce the role of traditional scouting methods. The idea is just to provide an additional and complementary perspective to aid decision making. I suspect that more often than not, analytical methods will come to similar conclusions regarding the relative merits of a player, which is fine as that can provide greater confidence in your decision making. If methods disagree, then they can be examined accordingly as a part of the process.

Evaluating players is not easy, whatever the method, so being able to weigh several assessments that all have their own strengths, flaws, biases and weaknesses seems prudent to me. The goal of analytics isn’t to create some perfect and objective representation of football; it is just another piece of the puzzle.

truth … is much too complicated to allow anything but approximations – John von Neumann


*I’ve done this by calculating percentile figures to give an indication of how a player compares with their peers. Values closer to 100 indicate that a player ranks highly in a particular statistic, while values closer to zero indicate they attempt or complete few of these actions compared to their peers. In these examples, Lallana and Firmino are compared with other players in the attacking midfielder, direct attacker and through-ball merchant groups. The white curved lines are spaced every ten percentiles to give a visual indication of how the player compares, with the solid shading in each segment corresponding to their percentile rank.

OptaPro Analytics Forum 2016 accepting abstract proposals

OptaPro are inviting proposals to present at their Analytics Forum, which according to their announcement:

aims to connect football clubs with analytical communities and experts working outside of the professional game

This will be the third year that the forum has taken place and an impressive number of clubs and other football organisations are represented at the forum, along with plenty of laptop gurus with no relevant playing experience.

I was lucky/skillful enough to have my proposal accepted last year, so I thought it might be useful if I posted my abstract as an example. I’m told that the judges liked it as it was tailored to the audience i.e. club analysts.

When I wrote it, my aim was to define a clear and (hopefully) relevant question and give some idea of how feasible it was and how it could be used. I posted the slides and video of my presentation here if you want to check it out.

If you’re thinking of submitting, then I would highly recommend it. The forum is a great way to meet others working in football analytics and as a member of the online analytics community, it was great to properly meet people I had ‘known’ via Twitter. Presenting was a valuable experience also and led to interesting discussions with people during and after the event.

The closing date for submissions is midnight Sunday 18th October. My abstract is below and good luck with your submissions.

Finding square pegs for square holes: identifying player types for scouting

Proposed area of study: player evaluation

Proposed method: Principal component analysis and cluster analysis of on-ball player data

One consideration when scouting potential player signings is how well they will fit into their new team environment. A common criticism of a perceived failed player transfer is that the player was a “square peg for a round hole”. This study will aim to identify certain player types based on their statistical output to aid finding the “right fit” when scouting players.

I propose using Principal Component Analysis (PCA) to distinguish players based on their underlying performance data (specifically Opta’s on-ball data). PCA is an ideal method for exploring datasets with multiple variables in order to discern patterns in the underlying data. This study builds on my previous analysis that used a similar method to study playing styles at the team level1. I will further extend this by applying cluster analysis to the data to group the players into certain types based on their attributes.

I have already explored the feasibility of this method using publically available Opta data from WhoScored.com and the results are promising. In order to extend the analysis for the forum, I would look to apply the method to more granular data, with a focus on player actions in open-play; the current dataset I have used groups all on-field actions together, which is not ideal. Furthermore, inclusion of location data would provide additional context for the analysis and aid differentiation of players and styles.

The persistence of player traits and classification will be assessed. Providing the dataset is large enough, it should be possible to test this persistence for players staying at the same team and for those who transfer to a new one. This will be a crucial aspect of the analysis and its utility.

The output from the analysis can serve as an additional tool when identifying potential transfer signings by categorising players according to their team role and providing statistical baselines for their performance compared to their peers. For example, the method separates different styles of central midfielders, such as deep-lying playmakers and defensive midfield “destroyers”. Players can then be compared against their peers in that style category based on the important traits of those player types.

By applying these techniques, this study will aim is to provide a more robust “apples-to-apples” comparison technique and find the appropriate square peg for the square hole in question.

1Relevant blog posts available here:

http://2plus2equals11.wordpress.com/2012/11/14/assessing-team-playing-styles/ http://2plus2equals11.wordpress.com/2013/02/19/is-playing-style-important/

Networking for success

In my previous post, I described my possession danger rating model, which classifies attacks according to their proximity to goal and their relative occurrence compared to other areas of the pitch. Each possession sequence in open-play is assigned a value depending on where it ends. The figure below outlines the model, with possession sequences ending closer to goal given more credit than those that break down further away.

Map of the pass weighting model based on data from the English Premier League. Data via Opta.

Map of the pass weighting model based on data from the English Premier League. Data via Opta.

Instead of just looking at this metric at the team level, there are numerous ways of breaking it down to the player level.

For each possession, a player could be involved in numerous ways e.g. winning the ball back via a tackle, a successful pass or cross, a dribble past an opponent or a shot at goal. Players that are involved in more dangerous possessions may be more valuable, particularly when we compare them to their peers. When viewing teams, we may identify weak links who reduce the effectiveness of an attack. Conversely, we can pick out the stars in a team or indeed the league.

Networking

One popular method of analysing the influence of players on a team is network analysis. This is something I’ve used in the past to examine how a team plays and who the crucial members of a team are. It looks at who a player passes the ball to and who they receive passes from, with players with many links to their teammates usually rated more highly. For example, a midfield playmaker who provides the link between a defence and attack will often score more highly than a centre back who mainly receives passes from their goalkeeper and then plays a simple pass to their central defensive partner.

In order to assess the influence of players on attacking possessions, I’ve combined the possession danger rating model with network analysis. This adjusts the network analysis to give more credit to players involved in more dangerous attacks, while also allowing us to identify the most influential members of a team.

Below is an example network for Liverpool last season during a 10 match period where they mainly played in a 3-4-3 formation. The most used eleven players during this period are shown according to their average position, with links between each player coloured according to how dangerous the possessions these links contributed to were.

Possession network for Liverpool for the ten matches from Swansea City (home) to Burnley (home) during the 2014/15 season. Lines are coloured according to the relative danger rating per each possession between each player. Player markers are sized by their adjusted closeness centrality score.

Possession network for Liverpool for the ten matches from Swansea City (home) to Burnley (home) during the 2014/15 season. Lines are coloured according to the relative danger rating per each possession between each player. Player markers are sized by their adjusted closeness centrality score (see below). Data via Opta.

Philippe Coutinho (10) was often a crucial cog in the network as he linked up with many of his team mates and the possessions he was involved with were often dangerous. His links with Sakho (17) and Moreno (18) appears to have been a fruitful avenue for attacks – this is an area we could examine in more detail via both data and video analysis if we were scouting Liverpool’s play. Over the whole season, Coutinho was easily the most crucial link in the team, which will come as no surprise to anyone who watched Liverpool last season.

Making the play

We can go further than players on a single team and compare across the entire league last season. To do this, I’ve calculated each players ‘closeness centrality‘ score or player influence score but scaled it according to how dangerous the possessions they were involved in were over the season. The rating is predominantly determined by how many possessions they are involved in, how well they link with team mates and the danger rating of the possessions they contribute to.

Yaya Touré leads the league by some distance due to him essentially being the crucial cog in the best attack in the league last season. Many of the players on the list aren’t too surprising, with a collection of Arsenal and Manchester City players high on the list plus the likes of Coutinho and Hazard also featuring.

The ability to effectively dictate play and provide a link for your team mates is likely desirable but the level of involvement a player has may be strongly governed by team tactics and their position on the field. One way around this is to control for the number of possessions a player is involved in to separate this out from the rating; Devin Pleuler made a similar adjustment in this Central Winger post.

Below are the top twenty players from last season according to this adjusted rating, which I’m going to refer to as an ‘influence rating’.

Top twenty players (minimum 1800 minutes) per the adjusted influence rating for the 2014/15 Premier League season. The number of completed passes each player made per 90 minutes is shown on the left. Data via Opta.

When accounting for their level of involvement, Mesut Özil rises to the top, narrowly ahead of Santi Cazorla and Yaya Touré. While players such as these don’t lead the league in terms of the most dangerous passes in open-play, they appear to be crucial conduits for their respective attacks. That might entail choosing the best options to facilitate attacks, making space for their team mates or playing a crucial line-breaking pass to open up a defence or all of the above and more.

There are some surprising names on the list, not least the Burnley duo of Danny Ings and George Boyd! Their level of involvement was very low (the lowest of those in the chart above) but when they were involved, Burnley created quite dangerous attacks and they linked well with the rest of the team. Burnley had a reasonably decent attack last season based on their underlying numbers but they massively under-performed when it came to actual goals scored. The question here is would this level of influence be maintained in a different setup and with greater involvement?

Ross Barkley is perhaps another surprising inclusion given his reputation outside of those who depict him as the latest saviour of English football. Looking at his passing chart and links, this possibly points to the model not accounting for crossing often being a less effective method of attack; his passing chart in the final third is biased towards passes to wide areas, which often then results in a cross into the box. Something for version 2.0 to explore. He was Everton’s attacking hub player, which perhaps helps to explain their lack of penetration in attack last season.

Conclusion

The above is just one example of breaking down my dangerous possession metric to the player level. As with all metrics, it could certainly be improved e.g. additional measures of quality of possession could be included and I’m aware that there are likely issues with team effects inflating or deflating certain players. Rating across all players isn’t completely fair, as there is an obvious bias towards attack-minded players, so I will look to break it down across player positions and roles.

Stay tuned for future developments.

Valuing Possession

Regular visitors will know that I’ve been working on some metrics in relation to possession and territory based on the difficulty of completing passes into various areas of the pitch. To recap, passes into dangerous areas are harder to complete, which isn’t particularly revelatory but by building some metrics around this we can assess how well teams move the ball into dangerous areas as well as how well they prevent their opponents from doing so. These metrics can also be broken down to the player level to see which players complete the most ‘dangerous’ passes.

Below is the current iteration of the pass danger rating model based on data from the 2014/15 Premier League season; working the ball into positions closer to the goal is rewarded with a larger rating, while passes made within a teams own half carry very little weight.

Map of the pass weighting model based on data from the English Premier League. Data via Opta.

Map of the pass weighting model based on data from the English Premier League. Data via Opta.

One particular issue with the Territorial-Possession Dominance (TPD) metric that I devised was that as well as having a crap name, the relationship with points and goal difference could have been better. The metric tended to over-rate teams who make a lot of passes in reasonably dangerous areas around the edge of the box but infrequently complete passes into the central zone of the penalty area. On the other side of the coin, it tended to under-rate more direct teams who don’t attack with sustained possession.

In order to account for this, I’ve calculated the danger rating by looking at attacks on a ‘possession’ basis i.e. by tracking individual chains of possession in open-play and looking at where they end. The end of the chain could be due to a number of reasons including shots, unsuccessful passes or a tackle by an opponent. Each possession is then assigned a danger rating based on the model in the figure above. Possessions which end deep into opponent territory will score more highly, while those that break down close to a team’s own goal are given little weight.

Conceptually, the model is similar to Daniel Altman’s non-shot based model (I think), although he views things through expected goals, whereas I started out looking at passing. You can find some of the details regarding the model here, plus a video of his presentation at the Opta Pro Analytics Forum is available here, which is well worth watching.

Danger Zone

The ratings for last season’s Premier League teams are shown below, with positive values meaning a team had more dangerous possessions than their opponents over the course of the season and vice versa for the negative values. Overall, the correlation between the metric and goal difference and points is pretty good (r-squared values of 0.76 and 0.77 respectively). This is considering open-play only, so it ignores set pieces and penalties, plus I omitted possessions made up of just one shot. The correlation with open-play goal difference is a little larger, so it appears to be an encouraging indicator of team strength.

Open-Play Possession Danger Rating for the 2014/15 English Premier League season. Data via Opta.

Open-Play Possession Danger Rating for the 2014/15 English Premier League season. Zero corresponds to a rating of 50%. Data via Opta.

The rating only takes into account the location of where the possession ends so there is plenty of scope for improvement e.g. throughball passes could carry more weight, counter-attacks could receive an increased danger rating, while moves featuring a cross might be down-weighted. Regardless of such improvements, these initial results are encouraging and are at a similar descriptive level to traditional shot ratios and expected goal models.

Arsenal are narrowly ahead of Manchester City here, as they make up a clear top-two which is strongly driven by their attacking play. Intriguingly, Manchester City’s rating was much greater (+7%) for possessions ending with a shot, while Arsenal’s was almost unchanged (-1%). Similarly to City, Chelsea’s rating for possessions ending with a shot was also greater (+4%)  than their rating for all possessions. I don’t know yet if this is a repeatable trait but it suggests Chelsea and City were more efficient at generating quality shots and limiting their opponents.

Manchester United sit narrowly ahead of Liverpool and Southampton and round out the top four, which was mainly driven by their league-leading defensive performance; few teams were able to get the ball into dangerous positions near their goal. Manchester United’s ability to keep their opponents at arms length has been a consistent trend from the territory-based numbers I’ve looked at.

Analytics anti-heroes Sunderland and a West Brom team managed by Tony Pulis for a large chunk of last season reside at the bottom of the table. Sunderland comfortably allowed the most dangerous possessions in the league last season.

Possessed

So, we’re left with yet another team strength metric to add to the analytics pile. The question is what does this add to our knowledge and how might we use it?

Analytics has generally based metrics around shots, which is sometimes not reflective of how we often experience the game from a chance creation point of view. The concept of creating a non-shot based chance isn’t a new one – the well worn cliché about a striker ‘fluffing a chance’ tells us that much but what analytics is striving to do is quantify these opportunities and hopefully do something useful with them. Basing the metric on all open-play possessions rather than just focusing on shots potentially opens up some interesting avenues for future research in terms of examining how teams attack and defend. Furthermore, using all possessions rather than those just ending with a shot increases our sample size and opens up the potential for new ways of assessing player contributions.

Looking at player contributions to these possessions will be the subject of my next post.

Liverpool Looking Up? EPL 2015/16 Preview

Originally published on StatsBomb.

After the sordid love affair that culminated in a strong title challenge in 2013/14, Liverpool barely cast a furtive glance at the Champions League places in 2014/15. Their underlying numbers over the whole season provided scant consolation either, with performance levels in line with a decent team lacking the quality usually associated with a top-four contender. Improvements in results and underlying performance will therefore be required to meet the club’s stated aim of Champions League football.

Progress before a fall

Before looking forward to the coming season, let’s start with a look back at Liverpool’s performance over recent seasons. Below is a graphic showing Liverpool’s underlying numbers over the past five seasons, courtesy of Paul Riley’s Expected Goal numbers.

Expected goal rank over the past 5 seasons of the English Premier League. Liverpool seasons highlighted in red.

Expected goal rank over the past 5 seasons of the English Premier League. Liverpool seasons highlighted in red.

From 2010/11 to 2012/13, there was steady progress with an impressive jump in 2013/14 to the third highest rating over the past five years. Paul’s model only evaluates shots on target, so Liverpool’s 2013/14 rating is potentially biased a little high given their unusual/unsustainable proportion of shots on target that year. However, the quality was clear, particularly in attack. Not to be outdone, 2014/15 saw another impressive jump but unfortunately the trajectory was in the opposite direction. Other metrics such as total shots ratio and shots on target ratio tell a similar story, although 2013/14 isn’t quite as impressive.

The less charitable among you may ascribe Liverpool’s trajectory with the presence and performance of one Luis Suárez; when joining in January 2010, Suárez was an erratic yet gifted performer who went on to become a genuine superstar before departing in the summer of 2014. Suárez’s attacking wizardry in 13/14 was remarkable and he served as a vital multiplier in the sides’ pinball style of play. Clearly he was a major loss but there were already reasons to suspect that some regression was due with or without him: Andrew Beasley wrote about the major and likely unsustainable role of set piece goals, while James Grayson and Colin Trainor highlighted the unusually favourable proportions of shots on target and blocked shots respectively during their title challenge. I wrote about how Liverpool’s penchant for early goals had led to an incredible amount of time spent winning over the season (a handy circumstance for a team so adept at counter-attacking), which may well have helped to explain some of their unusual numbers and that it was unlikely to be repeated.

These mitigating and potentially unsustainable factors notwithstanding, the dramatic fall in underlying performance, points (22 in all) and goals scored (an incredible 49 goal decline) is where Liverpool find themselves ahead of the coming season. Such a decline sees Brendan Rodgers go into this season under pressure to justify FSG’s backing of him over the summer, particularly with a fairly nightmarish run of away fixtures to start the season and the spectre of Jürgen Klopp on the horizon.

So, where do Liverpool need to improve this season?

Case for the defence

With the concession of six goals away at Stoke fresh in the memory, the narrative surrounding Liverpool’s defence is strong i.e. the defence is pretty horrible. Numbers paint a somewhat different story with Liverpool’s shots conceded (10.9 per game) standing as the joint-fifth lowest in the league last year according to statistics compiled by the Objective-Football website (rising to fourth lowest in open play). Shots on target were less good (3.8 per game and a rank of joint-seventh) although the margins are fairly small here. By Michael Caley’s and Paul Riley’s expected goal numbers, Liverpool ranked fourth and sixth respectively in expected goals against. Looking at how effective teams were at preventing their opponents from getting the ball into dangerous areas in open-play, my own numbers ranked Liverpool fifth best in the league.

It should be noted that analytics often has something of a blind spot when it comes to analysing defensive performances; metrics which typically work very well on the offensive side often work less well on the defensive side. Liverpool also tend to be a fairly dominant team and their opponents typically favour a deep defence and counter strategy against them, which will limit the number of chances they create.

One area where their numbers (courtesy of Objective-Football again) were noticeably poor was at set-pieces where they conceded on 11.6% of the shots by their opponents, which was 3rd worst in the league, compared to a league average conversion of 8.7%. Set-piece conversion rates are notoriously unsustainable year-on-year though, so some regression towards more normal conversion rates could potentially bring down Liverpool’s goal per game average compared to last season.

While Liverpool’s headline numbers were reasonable, their tendency to shoot themselves in the foot and concede some daft goals was impressive in its ineptitude at times. Culprits typically included combinations of Rodgers’ tactics, Dejan Lovren’s ‘whack a mole’ approach to defending and the embers of Steven Gerrard’s Liverpool career. The defensive structure of the team should be improved now that Gerrard no longer needs to be accommodated at the heart of midfield, while Glen Johnson’s prolonged audition for an extra role in the Walking Dead will continue at Stoke. Nathaniel Clyne should be a significant upgrade at full back, with youngsters Ilori and Gomez presently with the squad and aiming to compete for a first team role.

Broadly speaking though, Liverpool’s defensive numbers were reasonable but with room for improvement. Their numbers looked ok for a Champions League hopeful rather than a title challenger. A more mobile midfield should enhance the protection afforded to the central defence, however it should line up. Whether the individual errors were a bug and not a feature of this Liverpool team will likely determine how the narrative around the defence continues this year.

Under-powered attack

Liverpool’s decline in underlying performance in 2014/15 was driven by a significant drop-off in their attacking numbers. The loss of Suárez was compounded by Daniel Sturridge playing just 750 minutes in the league all season; Sturridge isn’t at the same level as Suárez (few are) but he does represent a truly elite forward and the alternatives at the club weren’t able to replace him.

The loss of Suárez and Sturridge meant that Coutinho and Sterling were now the principal conduits for Liverpool’s attack. Both performed admirably and were among the most dangerous attackers in the division. The figure below details Liverpool’s players according to the number of dangerous passes per 90 minutes played, which is related to my pass-danger rating score. In terms of volume, Coutinho and Sterling were way ahead of their teammates and both ranked in the top 15 in the league (minimum of 900 minutes played). James Milner actually ranked seventh by this metric, so he could well provide an additional source of creativity and link well with Liverpool’s forward players.

Dangerous passes per 90 minutes played metric for Liverpool players in 2014/15. Right hand side shows total number of completed passes per 90 minutes.

Dangerous passes per 90 minutes played metric for Liverpool players in 2014/15. Right hand side shows total number of completed passes per 90 minutes.

As good as Coutinho and Sterling were from a creative perspective, they did lag behind the truly elite players in the league by these metrics. As with many of Liverpool’s better players, you’re often left with the caveat of stating how good they are for their age. That’s not a criticism of the players themselves, merely a recognition of their overall standing relative to their peers.

What didn’t help was the lack of attacking contribution from Liverpool’s peak-age attacking players; Lallana’s contribution was decidedly average, Sturridge is obviously capable of making a stellar contribution but injuries curtailed him, while Balotelli certainly provided a high shot volume powered by a predilection for shooting from range but a potential dose of bad luck meant his goal-scoring record was well below expectation.

While there were clearly good elements to Liverpool’s attack, they were often left shooting from long range. According to numbers published by Michael Caley, Liverpool took more shots from outside the box than any other team last year and had the fourth highest proportion of shots from outside the box (48%). Unsurprisingly, they had the third lowest proportion of shots from the central region inside the penalty area (34%), which is the so-called ‘danger zone’ where shots are converted at much greater rates than wide in the box and outside the area. With their shot volumes being pretty good last season (third highest total shots and fourth highest shots on target), shifting the needle towards better quality chances would certainly improve Liverpool’s prospects. The question is where will that quality come from?

Bobby & Ben

With Sturridge not due back until the autumn coupled with his prior injury record, Liverpool moved to sign Christian Benteke as a frontline striker with youngsters Ings and Origi brought in to fill out the forward ranks. Roberto Firmino was added before Sterling’s departure but the expectation is that he will line-up in a similar role as the dynamic attacking midfielder/forward.

Firmino brings some impressive statistical pedigree with him: elite dribbler, dangerous passer, a tidy shot profile for a non-striker and stand-out tackling numbers for his position. If he can replicate his Bundesliga form then he should be a more than adequate replacement for Sterling, while also having the scope to develop over coming seasons.

Benteke brings a good but not great goal-scoring record, with his record in open-play being particularly average. Although there have been question marks regarding his stylistic fit within the team, Liverpool have seemingly been pursuing a physical forward to presumably act as a ‘reference point’ in their tactical system over the past few years; Diego Costa was a target in 2013, while Wilfred Bony was linked in 2014. Benteke brings that to the table alongside a more diverse range of skills than he is given credit for having been seemingly cast as an immobile lump of a centre forward by some.

Whether he has the necessary quality to improve this Liverpool team is the more pertinent question. From open-play, Benteke averages 2.2 shots per 90 minutes and 0.34 goals per 90 minutes over the past three seasons, which is essentially the average rate for a forward in the top European leagues. For comparison, Daniel Sturridge averages 4.0 shots per 90 minutes and 0.65 goals per 90 minutes over the same period. Granted, Sturridge has played for far greater attacking units than Aston Villa over that period but based on some analysis of strikers moving clubs that I’ve done, there is little evidence that shot and goal rates rise when moving to a higher quality team. Benteke does provide a major threat from set-pieces, which has been a productive source of goals for him but I would prefer to view these as an added extra on top of genuine quality in open-play, rather than a fig leaf.

Benteke will need to increase his contribution significantly if he is to cover for Sturridge over the coming season, otherwise Liverpool may find themselves in the good but not great attacking category again.

Conclusion

So where does all of the above leave Liverpool going into the season? Most of the underlying numbers for last season suggested that Chelsea, Manchester City and Arsenal were well ahead of the pack and I don’t see much prospect of one of them dropping out of the top four. Manchester United, Liverpool and Southampton made up the trailing group, with these three plus perhaps Tottenham in a battle to be the ‘best of the rest’ or ‘least crap’ and claim the coveted fourth place trophy.

When framed this way, Liverpool’s prospects look more viable, although fourth place looks like the ceiling at present unless the club procure some adamantium to alleviate Sturridge’s injury woes. While Liverpool currently operate outside the financial Goldilocks zone usually associated with a title challenge, they should have the quality to mount a concerted challenge for that Champions League spot in what could be a tight race. They did put together some impressive numbers during the 3-4-3 phase of last season that was in-line with those expected of a Champions League contender; replicating and sustaining that level of quality should be the aim for the team this coming season.

Prediction: 4-6th, most likely 5th.

P.S. Can Liverpool to be more fun this year? If you can’t be great, at least be fun.

Uncertain expectations

In this previous post, I describe a relatively simple version of an expected goals model that I’ve been developing recently. In this post, I want to examine the limitations and uncertainties relating to how well the model predicts goals.

Just to recap, I built the model using data from the Premier League from 2013/14 and 2014/15. For the analysis below, I’m just going to focus on non-penalty shots with the foot, so it includes both open-play and set piece shot situations. Mixing these will introduce some bias but we have to start somewhere. The data amounts to over 16,000 shots.

What follows is a long and technical post. You have been warned.

Putting the boot in

One thing to be aware of is how the model might differ if we used a different set of shots for input; ideally the answer we get shouldn’t change if we only used a subset of the data or if we resample the data. If the answer doesn’t change appreciably, then we can have more confidence that the results are robust.

Below, I’ve used a statistical technique known as ‘bootstrapping‘ to assess how robust the regression is for expected goals. Bootstrapping belongs to a class of statistical methods known as resampling. The method works by randomly extracting shots from the dataset and rerunning the regression many times (1000 times in the plot below). Using this, I can estimate a confidence interval for my expected goal model, which should provide a reasonable estimate of goal expectation for a given shot.

For example, the base model suggests that a shot from the penalty spot has an xG value of 0.19. The bootstrapping suggests that the 90% confidence interval gives an xG range from 0.17 to 0.22. What this means is that on 90% of occasions that Premier League footballers take a shot from the penalty spot, we would expect them to score somewhere between 17-22% of the time.

The plot below shows the goal expectation for a shot taken in the centre of the pitch at varying distances from the goal. Generally speaking, the confidence interval range is around ±1-2%. I also ran the regressions on subsets of the data and found that after around 5000 shots, the central estimate stabilised and the addition of further shots in the regression just narrows the confidence intervals. After about 10,000 shots, the results don’t change too much.

Test.

Expected goal curve for shots in the centre of the pitch at varying distances from the goal. Shots with the foot only. The red line is the median expectation, while the blue shaded region denotes the 90% confidence interval.

I can use the above information to construct a confidence interval for the expected goal totals for each team, which is what I have done below. Each point represents a team in each season and I’ve compared their expected goals vs their actual goals. The error bars show the range for the 90% confidence intervals.

Most teams line up with the one-to-one line within their respective confidence intervals when comparing with goals for and against. As I noted in the previous post, the overall tendency is for actual goals to exceed expected goals at the team level.

Expected goals vs actual goals for teams in the 2013/14 and 2014/15 Premier League. Dotted line is the 1:1 line, the solid line is the line of best fit and the error bars denote the 90% confidence intervals based on the xG curve above.

Expected goals vs actual goals for teams in the 2013/14 and 2014/15 Premier League. Dotted line is the 1:1 line, the solid line is the line of best fit and the error bars denote the 90% confidence intervals based on the xG curve above.

As an example of what the confidence intervals represent, in the 2013/14 season, Manchester City’s expected goal total was 59.8, with a confidence interval ranging from 52.2 to 67.7 expected goals. In reality, they scored 81 non-penalty goals with their feet, which falls outside of their confidence interval here. On the plot below, Manchester City are the red marker on the far right of the expected goals for vs actual goals for plot.

Embracing uncertainty

Another method of testing the model is to look at the model residuals, which are calculated by subtracting the outcome of a shot (either zero or one) from its expected goal value. If you were an omnipotent being who knew every aspect relating to the taking of a shot, you could theoretically predict the outcome of a shot (goal or no goal) perfectly (plus some allowance for random variation). The residuals of such a model would always be zero as the outcome minus the expectation of a goal would equal zero in all cases. In the real world though, we can’t know everything so this isn’t the case. However, we might expect that over a sufficiently large sample, the residual will be close to zero.

In the figure below, I’ve again bootstrapped the data and looked at the model residuals as the number of shots increases. I’ve done this 10,000 times for each number of shots i.e. I extract a random sample from the data and then calculate the residual for that number of shots. The red line is the median residual (goals minus expected goals), while the blue shaded region corresponds to the standard error range (calculated as the 90% confidence interval). The residual is normalised to a per shot basis, so the overall uncertainty value is equal to this value multiplied by the number of shots taken.

BootStrap_xGdiff_col

Goals-Expected Goals versus number of shots calculated via bootstrapping. Inset focusses on the first 100 shots. The red line is the median, while the blue shaded region denotes the 90% confidence interval (standard error).

The inset shows how this evolves up to 100 shots and we see that over about 10 shots, the residual approaches zero but the standard errors are very large at this point. Consequently, our best estimate of expected goals is likely highly uncertain over such a small sample. For example, if we expected to score two goals from 20 shots, the standard error range would span 0.35 to 4.2 goals. To add a further complication, the residuals aren’t normally distributed at that point, which makes interpretations even more challenging.

Clearly there is both a significant amount of variation over such small samples, which could be a consequence of both random variation and factors not included in the model. This is an important point when assessing xG estimates for single matches; while the central estimate will likely have a very small residual, the uncertainty range is huge.

As the sample size increases, the uncertainty decreases. After 100 shots, which would equate to a high shot volume for a forward, the uncertainty in goal expectation would amount to approximately ±4 goals. After 400 shots, which is close to the average number of shots a team would take over a single season, the uncertainty would equate to approximately ±9 goals. For a 10% conversion rate, our expected goal value after 100 shots would be 10±4, while after 400 shots, our estimate would be 40±9 (note the percentage uncertainty decreases as the number of shots increases).

BootStrap_xGdiff_col_wTeams

Same as above but with individual teams overlaid.

Above is the same plot but with the residuals shown for each team over the past two seasons (or one season if they only played for a single season). The majority of teams fall within the uncertainty envelope but there are some notable deviations. At the bottom of the plot are Burnley and Norwich, who significantly under-performed their expected goal estimate (they were also both relegated). On the flip side, Manchester City have seemingly consistently outperformed the expected goal estimate. Part of this is a result of the simplicity of the model; if I include additional factors such as how the chance is created, the residuals are smaller.

How well does an xG model predict goals?

Broadly speaking, the central estimates of expected goals appear to be reasonably good; the residuals tend to zero quickly and even though there is some bias, the correlations and errors are encouraging. When the uncertainties in the model are propagated through to the team level, the confidence intervals are on average around ±15% for expected goals for and against.

When we examine the model errors in more detail, they tend to be larger (around ±25% at the team level over a single season). The upshot of all this is that there appears to be a large degree of uncertainty in expected goal values when considering sample sizes relevant at the team and player level. While the simplicity of the model used here may mean that the uncertainty values shown represent a worst-case scenario, it is still something that should be considered when analysts make statements and projections. Having said this, based on some initial tests, adding extra complexity doesn’t appear to reduce the residuals to any great degree.

Uncertainty estimates and confidence intervals aren’t sexy and having spent the last 1500ish words writing about them, I’m well aware they aren’t that accessible either. However, I do think they are useful and important in the real world.

Quantifying these uncertainties can help to provide more honest assessments and recommendations. For example, I would say it is more useful to say that my projections estimate that player X will score 0.6-1.4 goals per 90 minutes next season along with some central value, rather than going with a single value of 1 goal per 90 minutes. Furthermore, it is better to state such caveats in advance – if you just provided the central estimate and the player posted say 0.65 goals per 90 and you then bring up your model’s uncertainty range, you will just sound like you’re making excuses.

This also has implications regarding over and under performance by players and teams relative to expected goals. I frequently see statements about regression to the mean without considering model errors. As George Box wisely noted:

Statisticians, like artists, have the bad habit of falling in love with their models.

This isn’t to say that expected goal models aren’t useful, just that if you want to wade into the world of probability and modelling, you should also illustrate the limitations and uncertainties associated with the analysis.

Perhaps those using expected goal models are well aware of these issues but I don’t see much discussion of it in public. Analytics is increasingly finding a wider public audience, along with being used within clubs. That will often mean that those consuming the results will not be aware of these uncertainties unless you explain them. Speaking as a researcher who is interested in the communication of science, I can give many examples of where not discussing uncertainty upfront can backfire in the long run.

Isn’t uncertainty fun!

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Thanks to several people who were kind enough to read an initial draft of this article and the proceeding method piece.