## A beginner's guide to analyzing teams using stats

The next in our continuing series where Kirsten asks me all (or at least one or two of) the questions you could ever want answered about using stats. This time we're talking about analyzing teams.

KS: So, even after all this education, I’m not quite clear on which stats apply to individuals, and which to teams. Is expected goals the number one valuation used for both players and teams?

MG: There are a bunch of stats that might be applied to players and teams. Pretty much everything you can count a player doing you can then total up and look at as a team number. So numbers, of course, are more useful than others. And for starting a conversation about team analysis, xG is definitely the way to go. As a metric xG is actually probably more useful at the team level than the player level.

KS: In order for me not to panic about not knowing this teams v individuals numbers, Mike sent me a piece entitled (in hindsight, ironically), “Who's gonna fix Wolves?” It’s actually a great place to start thinking about team numbers, given that when the FA hit the pause button on the season, Wolves were in sixth—but when the article was written, they were second from bottom and had yet to record a win. Mike asks if it’s time for Wolves to panic, but unequivocally states that it’s not. Turns out he was right, so it’s appropriate to ask what numbers he used to predict the future . . . and possibly to ask what other factors influenced what he saw in the numbers. Obviously the first number he looks at is xG, but for a defensive team like Wolves, it might be more appropriate to ask about what they’ve conceded. What’s the stat for that, Mike?

MG: The stat for that is xG conceded, it’s just like xG except on the other side of the ball. Look at all the shots a team has conceded, total up the expected amount of goals those shots might lead to, and then look at the actual amount of goals they’ve given up. The great thing about xG as a metric at the team level is that it operates just like goals does. You look at a team’s xG and the xG they’ve conceded and then the difference between the two, so just like you’d look at a team’s goal difference you can also look at their xG difference. And the basic rule is that we should all expect teams to have their goals and xG converse to the same values. Or, to say it slightly more nerdily, xG and xG conceded predict future goals scored and conceded better than current goals scored and goals conceded does. In Wolves' case the side had conceded a bunch more goals, 11, than the xG of the shots they’d conceded, 6.49, predicted, so it was easy to predict that their defense would improve going forward.

KS: And you were right! However, when you go on to explain the bad news, you state that Wolves’ xG isn’t nearly what it was last season—enough to land them just outside the top of the table. Instead, they’re in the European places. Does this indicate that looking at just xG and xG conceded isn’t enough?

MG: Well, I think there are three components to that answer. First is that position in the table is always going to be contingent on not only how well one team plays, but how well everybody else plays. So, part of what was going on in this once and maybe future season is Arsenal struggling, Spurs collapsing, and just in general a season where the league’s big six are being underwhelming. So, even if xG is fully capturing the contours of Wolves performance, the side’s performance in relation to everybody else can certainly change.

Second, we need to separate out two different ideas surrounding xG. There’s the idea of how a team’s actual goals stack up against their expected goals. In that arena we can say with confidence what we expect to happen (the goals they score and concede will eventually come in line with xG), but the story of how that is likely to happen is where science meets art. The question of what exactly is causing the divergence is an interesting one and highly relevant for players and managers and fans, even if we can say that whatever it is is likely temporary.

And finally, xG levels can themselves change. While xG is a pretty good proxy for how good a team is, teams get better and worse all the time. So we might observe a team’s xG improve on either side of the ball from season to season, or even within a season, and then we’d want to look for reasons why that was happening. Usually xG is pretty predictive of itself, which is to say that usually teams don’t improve a ton or get dramatically worse over the course of a season, but there are always exceptions.

So, to sum it up. Yeah.looking at xG itself isn’t enough. You need to look at a team’s metrics in relation to the rest of the league, look at how a team’s actual results are in relation to that metric and why they might differ, and then look at the movement of the metric itself and what might be causing it to change.

KS: Now, despite being barricaded inside my apartment, I don’t have time to go back and look for an article in which a team’s expected xG differed wildly from the actual number of goals scored. Here, Wolves were pretty much on track: They’d scored 6 with an xG of 5.61. However, they had conceded 11 goals from 6.49 xG conceded—and this isn’t viewed as a problem. So given that defense is simply attack in reverse (shocking!) I can look at those defense numbers and wonder, what on earth causes this difference between what is predicted and what happens in reality?

MG: Right, early this season the biggest thing that was wrong with Wolves is that the team’s goals conceded was much higher than their xG conceded. And while we’d expect that the numbers would come back in line, fixing the problem, that doesn’t tell us much about how that’s going to happen. The biggest element involved in these kinds of divergence is generally just variance in finishing. There’s really not much you can do if your opponents keep launching into the top corner against you, except rely on the numbers to reassure you it won’t keep happening.

But, there are other things that can contribute that are at least worth looking into. We can isolate keeper performance, for example. StatsBomb has a separate model, a post-shot xG model, that looks at the performance of keepers given the shots they’ve faced (this is different from normal xG models because it takes factors like the trajectory of the ball into account, which you’ll just have to trust me is the best way to go about things because working through those differences is a whole article unto itself). Using those tools we can determine if some of the divergence is down to keeper performance. Or we can separate out set pieces from open play. If the problem is that a team is conceding a lot more on set pieces than expected, it would be worthwhile to examine if there is in fact something going wrong in that phase of the game.

All of which is to say that the fact that a team is diverging from their xG or xG conceded is the start of the story, the fact that they’ll eventually come back to expectations is the end, but there are chapters and chapters to investigate in the middle about why that divergence occurs and what the likely path back is.

KS: Ok, one last question because it’s been bugging me: why are penalty goals removed from the equation?

MG: Penalties are just not particularly predictive of anything. Just because one team got a bunch of penalties doesn’t mean they’re any more likely to get them in the future. So, including them doesn’t help us get a true picture of how good or bad a team is. It’s mostly just a function of the kind of variance that likely won’t continue. And, since the entire point of xG is to strip out the noise and look at what’s likely to continue, away go the penalties.

## The StatsBomb Superstar Combined Five-a-Side and Basketball Team

The risk with this draft is that you go all one-sided to NBA or football/soccer and can't compete effectively in the other sport. The tiebreaker is soccer-based but even so, I wanted to put out a balanced lineup that could give us a chance in either sport. I also resisted the desire to try and pull Steve Nash out of retirement, but it did cross my mind.
1) Joel "The Process" Embiid
Joel Embiid can literally do anything he wants in the athletic arena. And not only that, he will obsessively work until he becomes insanely good at whatever skills he needs to learn. Since this tournament is short term, we might even keep him healthy for the whole season. Type 'Joel Embiid soccer' into YouTube in case you have any doubts.

2) Giannis "The Greek Freak" Antetokounmpo I think a lot of people will have Giannis as their number 1, and it just makes sense. He's definitely in the top 5 NBA players in any particular year and he's at least had some exposure to juggling the ball. However, having seen enough clips of him interacting with a soccer ball, I'm fairly comfortable that Embiid is the better number one overall.

3) Steph Curry
In terms of a single skill set, Steph is the most influential player basketball has ever seen. His gravitational pull warps the court in a way no one in soccer does except maybe Messi. He's probably not going to be super effective as a two-way, two-sport player, but he will affect what happens on the basketball side of the competition like no other.
4) Luka Doncic
There was some discussion in our Slack channel that Doncic's skill set would not necessarily be electric in this competition. I disagree, and he would make for one of the more effective two-way guys from the NBA side of things.
5) Kylian Mbappe
This is my number one pick from the world of football. He's one of the two best players in the world the last two seasons, has amazing ball control, and electric acceleration. He's also a bit bigger than Messi, which makes him potentially less of a liability on the defensive side in hoops, and he loves basketball.
6) Neymar Jr.
I know this is weird that I am picking Neymar above Messi, but bear with me for a second. When he ever-so-briefly cared - when Messi was out for half a season at Barcelona - Neymar was the next Messi. His production was out of this world and he looked like the second coming of Superman, except with a bit of Brazilian flair. He also likes basketball, and if the other drafters in this hypothetical tournament let me put these two together, we have a chance to dominate the soccer portion of the competition without sacrificing too much.
7) Lionel Messi
He's a pure defensive liability on the NBA side of things, but whatever. However, I'm only making this pick if I don't get Mbappe/Neymar.

8) Russell Westbrook
Our head of technical scouting Nikos Overheul pitched this and I think it's a good one. Russ is insanely athletic, and I feel like he could be a major contributor on both ends of the floor in both sports.
9) Marc Gasol
I wanted to pick Pau, but he's 39. Marc is 35, nearly as good as his brother, and perfectly capable of contributing in soccer and hoops. OTOH his aerial dominance might be more pronounced if we had 11 v 11 with the ball in the air more often.
10) Paul Pogba
Has the size and athletic ability to compete well in both sports. Actually likes basketball. Has some unexplained ongoing health issues in recent seasons, but is currently Rai-habbing well and those are expected to clear up at his next club.

Ali Elfakharany Despite the fact that the tiebreak is football-related, my train of thought was to mainly pick basketball players for a couple of reasons:

• Football is more popular globally so it’s easier to find base level football skills
• Basketball is more unique physically and so it’s harder to find basketball physique!
• Big basketball players might make good goalkeepers for the shootout
• Absolute domination matters for mental purposes
• Most StatsBomb folks don’t know basketball as well as I do

1) Giannis.
The Greek Freak grew up in Athens and played both soccer and basketball.  The combination of innate knowledge of both sports, speed, plays "small" like a point guard and thus could star as a striker in a 5 a side football.
2) Luka Doncic.
For almost all the same reasons as Giannis.  In a 5 a side module I would employ a diamond shape with a keeper.  Giannis at the top of it, Doncic one of the two "flankers" along with number 3
3) Embid.
Embid claims he has mad soccer skills and I need a beast who can anchor my defense in soccer too,

4)
Christian Pulisic. Played hoops and would really up the services to Giannis as well as trail the attack.  Skilled enough to track back, plus growing up in America he had plenty of indoor/5 a side football, which other countries can lack.
5) David DeGea. I'm not a fan of the guy in general, but a quick search on google and he can throw down dunks and would play in my frontline of Giannis, Doncic and DeGea. https://www.youtube.com/watch?v=naUfWxM9JDk In 5 a side I need a keeper expert. De Gea makes many bad decisions on a big net, but giving him a small net? I'll take him 5 a side in hoops, I start a back court of Doncic and Pulisic with Embiid, DeGea and Giannis in the frontcourt. With the matchup problems Luka and Giannis cause, I can anchor my strategy around having them score and also rim protect with Embid and play inside. In soccer, De Gea is my key to preventing any matchup woes, I rely on the size and strength of Embiid in the back, the creativity of Doncic and Pulisic in the middle and the insane matchups Giannis would cause The Next Five 6) Pogba. All reports show me that a healthy Pogba had some hoop game too. I also need one defensive minded player at the back of my next five a side. 7) Lukaku. Strength in the post could help box out some of the bigger guys he'd be sure to face in basketball. In my "next 5" he'd be the lone striker 8) Griezmann. YouTube shows him canning threes, and if he can supply Lukaku with a few balls, that's great too. Plus he and Pogba can whine together. 9) Lebron James. I'm putting him in goal for soccer. I realize he has played almost no soccer to rave about, BUT he is the smartest player I have ever watched, a matchup nightmare and has quick reflexes. In the basketball 5 on 5, I need a superstar to take over whilst the three footballers struggle 10) Pau Gasol. Given the list started with three footballers, I need another beast for basketball. I also need a bit of size. Edging Goran Dragic (my #11) I choose a man who was schooled in the Macia of basketball, FC Barcelona's very own Pau Gasol.

Header image courtesy of the Press Association

## Classic Game Rewind: Manchester City 7-2 Stoke City, October 2017

There won't be much Stoke discussion in this one, but I don’t think their fans will be particularly upset about that. Kevin De Bruyne or David Silva picks up the ball in a central area just outside the box. He slides it out wide to Raheem Sterling or Leroy Sané, who then plays in a low cross for someone to score a tap in on the cutback. Has any single goal been scored more times in the history of the Premier League? They added more variance to their game in the years since, but 2017–18 saw Pep Guardiola’s Manchester City take this approach to extremes, working everything around creating these sequences.  The 7–2 victory over Stoke was, somehow for this side, not their most impressive result. But it was certainly notable as it saw several changes to the side, especially in light of Benjamin Mendy’s injury, and really set the template for the rest of the season. Let’s break it down a little:

#### Fullback/Winger combinations

This is the thing that really stood out to me at the time as the difference between City and other top sides, and it wasn’t even the plan. In the early weeks of 2017–18, City favoured a back three system with Benjamin Mendy as a left wing-back, or as a left back in a four at times. He’s much more of a conventional attacking fullback, similar to the way Guardiola used Dani Alves at Barcelona but on the opposite flank. That all blew up when he suffered a cruel injury, and a rethink was needed. The 3-5-2ish system was dead and buried in favour of the more conventional 4-3-3ish shape (look, it’s Pep, you can’t just throw out some numbers and describe a formation here). Kyle Walker remained at right back, while Fabian Delph came in on the left. Delph was a fullback very much in the mould of Guardiola’s Bayern side, where Philipp Lahm and David Alaba would invert and become central midfielders at times. Delph doesn't come close to their ability, but he does have interesting skills. He was one of the most two-footed players in the league that season, making 63% of his passes with his left foot (the vast majority lean very much toward a preferred foot). The graphic below from the Stoke game shows just how comfortable he was switching between passing with his left foot (left) and his right foot (right). You can barely tell that he’s left-footed. Though he and Sterling sometimes switched places, Delph generally had Sané in front of him. This combination felt so perfect it's almost impossible to believe they just stumbled onto it. As James Yorke wrote for StatsBomb previously, Sané is just about the only true left-footed left winger at the top level in England. This graphic of where left footers pass the ball is from the following season, but it tells the same story of how rare Sané’s skillset is. With Delph underlapping and Sané taking a very wide role, City had the entire left flank covered. It allowed Sané to consistently play in those low crosses from wide, knowing Delph would fill the more narrow space behind him. On the other side, Walker and Sterling played a little more straightforward. Walker generally took on a more reserved role, often becoming a third centre back at times (which famously became his main position for England). In this game, though, he was allowed to effectively swap with Sterling at times, becoming the more advanced attacker making runs into the box. Both players are mobile right footers, so they have much more of a skillset overlap than Delph and Sané. This could have become too predictable, but their ability to switch against Stoke made things very flexible. The system ensured that City always had someone at the byline on each flank waiting, without pushing the fullbacks too far forward all the time.

#### Free Eights

Ok, so we have the wingers in those wide areas ready to deliver the cutbacks into the box. But how do we feed them the ball? That, of course, revolves around Kevin De Bruyne and David Silva. De Bruyne would broadly play on the right of the midfield three and Silva on the left, though they could frequently switch. These two were about the freest eights you could find, given licence to push right up and join the attack, threading passes in advanced areas, secure in the knowledge that Fernandinho and an underlapping Fabian Delph had them covered. While there's been so much discussion of De Bruyne and Silva as a pair, their differences aren’t often appreciated. Here’s Silva's (left) and De Bruyne's (right) passes against Stoke: Silva is the more involved, successfully completing 76 passes to De Bruyne’s 45. He makes a lot more short passes in the final third, linking up on the left as he does. De Bruyne, on the other hand, is more direct, pinging more long balls straight to the attackers from deep and taking more risks. No Guardiola side ever had a midfielder so willing to risk losing the ball like De Bruyne, and he’s exactly what makes City different to his previous teams. The Catalan's sides have always been the best around at covering space, with this City team certainly no exception. As Sterling and Sané covered the widest areas, with De Bruyne and Silva in the half-spaces and Jesus (here, but frequently Sergio Agüero) as the striker, it was impossible for opposing sides to get out of their own third even when they won the ball.

#### Subsequent Years

In pure numbers terms, City haven’t seen much of a drop off since their “best” season. Their play is stylistically different, though, and feels a little less unique. Bernardo Silva, then generally a substitute, put up such good form that he had to start more often. At times he's in central midfield, but Guardiola primarily favours him cutting in from the right. Riyad Mahrez, the biggest attacking addition in the years since, also favours cutting in from the right onto his weapon of a left foot. That, combined with the ever-improving Sterling’s preference for playing on the left, has seen Guardiola move to a more common inverted winger format. Delph’s slight decline and departure also hastened this move. Sané hasn’t become a lesser player, but Guardiola’s increased disinterest in him may be partly driven by the lack of an ideal partner like Delph. And then of course a catastrophic injury took the winger out of the picture altogether this season. The patterns are different now. City often play with a true double pivot and an attacking quartet rather than a front five. It’s changed. It’s not necessarily bad, but there’s something about the 2017–18 incarnation that feels a little elusive.

## A Beginner's Guide To Analyzing Players Using Stats

Last week Kirsten Schlewtiz and I talked about the basics of football analytics. We had so much fun that we decided to do it again this week. Specifically, we’re going to talk through what it’s like to use stats to analyze a player. What it adds, what it misses, and how best to understand what’s going on if you’ve never seen a number that didn’t give you flashbacks to failing a test at ten years old. So, take it away Kirsten.

KS: Mike, personally I had to pass AP stats to graduate high school and I’m still not sure how I did it … likely something to do with those giant TI-85 calculators and a notecard hidden inside the case. Anyway, we want a player to talk about. There’s the top two in the world, but that’d be boring; besides, Lionel Messi has been covered exhaustively on this site. We could analyze the Borussia Dortmund teens, but being a Gladbach fan, that’s too big a reach.

So let’s go with Timo Werner. He’s fast, he’s capable, and he’s helping light up the league with RB Leipzig. But those who have seen him with the Germany national team only likely have more disparaging words to say about the attacker. So what’s the first statistical value we can use to establish his dominance?

MG: Werner’s a great place to start. For a bunch of reasons. Our go to stat for strikers is expected goals per 90 minutes played. We do that for a few reasons. First, we want to look at expected goals and not actual goals because finishing is very noisy but getting good shots is a pretty consistent skill. That’s not to say that individual players aren’t good finishers, the best strikers in the world as a group do tend to average a little higher finishing than a model would predict, but that’s a relatively small part of the picture. The majority of what separates the best from the rest is their ability to find and take lots of good shots. Then we look at it per 90 minutes played because we want to be able to compare players based on what they do when they are on the pitch. You’d rather have a player that plays most of the game and scores a lot of goals than one who plays absolutely all the minutes but only scores a little bit more. So, on to Werner. He averages 0.59 xG per 90. That’s really good. Of players who have played over 900 minutes, only Robert Lewandowski is better, though he’s at 0.79 which is kind of a “break the game” obscenely high level. Does that all make some semblance of sense?

KS: You say we should first look at expected goals, but despite being with StatsBomb for nearly six months, and recognizing the importance of that stat, I still don’t know how you arrive at a player’s xG. I assume you don’t just have a bunch of drones watching that player and inputting their thoughts about when they should score a goal, as opposed to when they actually do. So how is it calculated?

MG: So the way the stat works is it takes tons of factors into consideration and then measures how likely any individual shot is to become a goal given those factors. Start with the location on the pitch, and see how many of the thousands of shots we have go in from there, then keep adding in factors like what part of the body the shot was taken with, what kind of pass led to the shot, was it a set play or not, where were the defenders at the time the shot was taken (a feature unique to StatsBomb data). Then do some fancy math to make it as accurate as possible and you have an xG model. The thing about these models is that they aren’t all that useful when looking at an individual shot because individual shots can vary a lot. But, when the shots start to add up you get a pretty accurate picture of how often a player or team will score.

KS: Focus, Mike — we’re leaving teams for next week. So turning back to individuals, how many shots would you need to come up with a decent estimation? I’m going to assume there are certain players you just wouldn’t have a reliable xG for, such as goalkeepers, but I’m guessing there’s a cutoff point. And Timo’s clearly made that point, as evidenced by you saying his xG is really good . . . but given you don’t mention an average xG, then we need to both take your word that there is a number of shots that should be taken, and that Timo’s number is high enough for him to be measured and come out looking quite grand.

MG: Good question! There’s two ways to look at that. If you’re asking how many shots does it take to ensure that xG is faithfully representing how many goals a player should have scored from the shots he’s taken, the answer is very few. That’s because an xG model is built on all the shots that everybody across the sport has taken. Though to give you a specific answer I’d have to point to things like confidence intervals, and such, and I don’t want to give you flashbacks to high school. The second way to interpret the question is trickier, because if you’re asking how long before we know that Timo Werner’s xG per 90 so far this season of 0.59 is what his xG will continue to be in the future, well we don’t have a good answer for that. Individual stats are contextual and lots of things can change. For example, young players get better. Werner is 24 right now and entering what we’d anticipate to be his peak performance years, but there’s a considerable amount of variability in how any individual player will develop. He’s great now but does that mean he’s done getting better or there’s more to come? And even if his skill level is done improving lots of other things can change. He could change teams, he could get injured, the division of his minutes between the wing and striker could shift dramatically. He could decide that he really likes ice cream and doesn’t like to exercise. The universe is vast and the future is unknowable. So, why use xG at all then? Because even though we can’t say with certainty what the future holds, we can say that xG is better at explaining how good Werner is now, and how good he will be in the future than actual goals scored is. It’s true that his xG might move around a lot in the years to come, but it’s simply better at predicting how good he’ll be than his actual goals scored number. To that end, he’s scored 0.88 goals per 90 this season in the Bundesliga, so we should expect that his xG is a more accurate representation of what he’ll be going forward than the goals.

KS: Actually, what I meant was how do we know that 0.59 is very good? Apparently, I need to work on writing clarity.

MG: Well that one’s easy! You know it’s good because it’s the second highest in the Bundesliga. Also, conveniently it lines up with the “goal every other game” common wisdom about strikers that has floated around forever.

KS: You mentioned his division between winger and striker. I feel like he hasn’t performed well for the German men’s national team because he’s been alone up top. I know StatsBomb has limited national team stats, but I’m sure that even without those, there’s a way to use stats to either affirm or deny my assertion that Werner just doesn’t function as well in that role. Am I right?

MG: It’s actually more difficult to parse that out than you’d think. It’s definitely true that he struggled at the World Cup in 2018 where he only averaged 0.22 xG per 90 minutes while playing two-thirds of his time as striker. Is it because of something he’s doing as a forward? Is it because of how Germany elected to play as a team? Is it because three games is a tiny sample? The answer to all those questions is maybe. But one thing it’s worth noting is that Werner is himself much more than a pure goalscorer. And even during a disastrous World Cup the other stuff he does stands out, He actually assisted almost as much xG, 0.20 per 90, as he attempted himself. And those other statistics are something that make him stand out during club play as well. In addition to being a great goal scorer he’s a great creator, a feature of his game that persisted at a national level even during periods where his goal scoring seems to have abandoned him.

KS: Ugh, Mike, you took my next question; it was meant to be about more than Werner’s goalscoring abilities, and assessing him as an all-around attacker. So let’s talk about speed. To my semi-trained eye, Timo appears quite quick on the pitch, allowing him to link up with his teammates up front. You’ve already mentioned he’s able to provide assists (although feel free to tell us the statistical term used for that, along with his numbers for RB Leipzig thus far this season), but is there any way to measure just how fast a player is?

MG: So, it’s deceptively difficult to measure the actual speed a player is running at, although teams can do things like make players don wearable technology to help with that kind of thing. But, for our purposes we’re looking at the things that enable performance on the pitch. So, for example, we can look at a shot chart from Werner and look for all the shots that come as a result of him receiving a throughball (the little triangles).

Or we can look at where and how he’s receiving certain kinds of passes. Below are all the passes he received in the attacking half which originated in his team’s own half. And what we can see is that he consistently peels off to the side — usually the left — and receives balls, the pattern of a player running the channels as a speedster rather than one who is a more central physical target man.

These are all somewhat indirect ways of using the data to get at the underlying reality but they paint the picture of a player whose speed is a central part of his game.

KS: Ok, I think one final question should cover both my impressions of Timo, and the use of numbers to really parse out an attacker’s abilities. Being a big Bundesliga fan, he’s been on my radar for awhile, and it feels to me that while Leipzig as a team have improved considerably in the last few years, Werner’s passing in particular has also improved. Are there any stats that back up what I think I’ve been seeing? MG: Well, RB are an interesting case as a team, since they arrived in the Bundesliga under such unusual circumstances and qualified for the Champions League their first season there. But, I’m not supposed to be talking about teams. Werner himself has certainly improved. Maybe the easiest way to show it is to just do a visual representation of his collection of stats last season vs his collection of stats this season?

The radar shows how he’s doing basically the same things this season that he did last season,.but a lot more of them. How much of that is him developing as a player, and how much of that is his team improving? That’s something that is as much art as science to diagnose. So, the answer is that he’s playing better this year than last and his team is as well, but those two facts are difficult to disentangle. That seems like a good place to leave things for now, although there’s always a lot more to cover. We haven’t even touched on xG per shot or set pieces, or any of the other ways we profile how a striker gets their xG. But that’ll just have to wait for another week.

## Serie A mailbag, episode 1

What better occasion for a Serie A mailbag than in the middle of a forced break of the five major European leagues (and of most sports)? We won't see much, if any, football played for quite some time; there is no plan that covers a scenario like COVID-19, and so we don't even know when it will return. This is the first time in Italy that football has stopped due to a health crisis. In 1973, despite the cholera epidemic, football continued its season. The only tournament in history that never finished was back in 1915, when the championship was suspended due to the war. In 1919 the title was awarded to Genoa, but the documents of the time were lost, so why this occurred cannot be officially established. So without football to watch, we'll take this opportunity to answer your questions. You can continue to send me questions on Twitter or to my email.

#### Have you ever written an article about players playing poorly or underachieving? (from Zach)

In fact, two Serie A players started the season with very high expectations that they were unable to live up to, and those expectations may have been based on biased performance evaluations. Simone Verdi arrived in Turin as the most expensive purchase in Torino history. The attacker came from Napoli on a €3 million loan with a €20 million obligation to buy, plus another €2 million possible in bonuses. Torino and Sampdoria fought over Verdi, which probably pushed the price up, yet the strange thing was he'd had a rather poor season. He played just 8.6 90s in his only year in Naples, and missed12 games due to injury. That makes it difficult to justify a price of around 10 million higher than what Napoli paid Bologna. Despite limited playing time under Carlo Ancelotti he scored three goals, including one against Torino, but probably the club's decision was likely not based on that goal but rather on his 2017–18 season with Bologna. There, Verdi seemed fantastic, scoring 10 goals and 10 assists. But two of those goals were from penalties and three from direct free kicks, while seven of his assists came from crosses and four from free kicks or corners. This creates an overall scoring contribution of 0.63 goals+assists per 90, yet his open play contribution was just 0.35 — still good for a winger, but not as impressive. The other misunderstanding lies in his role on the pitch. At Bologna, Verdi proved himself to be a winger equally skilled with both feet and one who could play on both sides of the pitch. Ancelotti also played him on either flank, but under Walter Mazzari he is used as one of the attacking midfielders in a 3-4-2-1, a role not particularly suited to a player accustomed to starting out wide and dribble inside, or to delivering crosses from out wide. In his only match under new coach Moreno Longo, he played at right wing in a 3-4-3 and scored his team's only goal. When the league resumes, that's where he'll need to start if Torino want to put their investment to good use. My other choice may seem surprising, but in my opinion, he's offering much less than he could and far less than the club paid. This summer, Cagliari spent a record-setting €16.9 million to bring in midfielder Nahitan Nández from Boca Juniors. Nández's performance may be biased by his team's overperformance in the first part of the season, yet in neither the 13-game undefeated streak that lasted until December, nor in the current streak of 11 games without a win — which marked Cagliari's inescapable regression toward the mean and led to the sacking of coach Rolando Maran — has he impressed me, and the numbers back me up. Sure, some of his plays are flashy and his tackles imperious, but he lacks consistency. Even in Cagliari's 4-3-2-1, which utilizes direct play and transitions, he misses too many passes — the worst among midfielders — and his risk/reward balance is quite unfavorable, as his contribution to xG build-up and the number of his deep progressions is marginal when compared to his teammates. He's a decent tackler, but he rarely intercepts the ball and his rough tackles+interceptions numbers (2.6 per 90) are almost half the amount (4.8 per 90) of the other number 8, Marko Rog (who in all fairness is dribbled past more easily). Nández is probably a better player than he has shown at Cagliari, but nothing in the numbers supports the potential €40 million supposedly offered for him in January. If the rumors were true, the club would've been wise to accept.

#### Dejan Kulusevski at Juventus

https://twitter.com/FootballSthlm/status/1237513472579006464 This is not an easy question to answer because no one's really quite worked out what Maurizio Sarri's Juventus is. The coach himself delivered several alarming statements throughout the season, pointing out that the Bianconeri players have different qualities to those he coached at Napoli, implying he could not achieve the same quality of play and explaining the effort he's making to convey to his squad the concept of moving the ball faster On the other hand, we are beginning to understand what kind of player Dejan Kulusevski is. As I previously wrote, his mix of size, speed, size, creativity and athleticism is hard to find, especially in such a young player. He currently plays in a team unique in the league because of the speed at which they play. Parma has the highest value of pace toward goal in Serie A, with an average speed of buildup of 3.10 metres per second of 3.10. So he's used to playing the ball fast as Sarri would like his players to, even though his current coach Roberto D’Aversa's tactics are much different. At Juventus, Dejan Kulusevski could play wide on the right to complete a three-man attack with Cristiano Ronaldo on the left and one of Gonzalo Higuaín or Paulo Dybala in the middle. He doesn’t take a lot of shots as he's more of a pass-first player, which could help him bring space into an attack that includes a center-forward and a shot-monster like Ronaldo on the opposite side. Dejan Kulusevski is perhaps more effective when he moves inside to look for the decisive pass than when he dribbles down the line. He currently averages 0.24 xG assisted per 90, a figure higher than any Juventus player. Therefore, Sarri could play him as the offensive midfielder in a 4-3-1-2, where he often fielded Federico Bernardeschi at the beginning of the season. Kulusevski is also very clever when entering the penalty area (as shown by his number of touches in the box), a fundamental characteristic for Juventus, who this season averages fewer than two players in the penalty area when shooting, which is also due to how rarely their number 8s not named Aaron Ramsey tend to push forward. Moving from Parma to Juventus will be a huge step, but Kulusevski's talent is unquestionable. While Sarri will need to gradually integrate him into the side, he could help solve some of Juventus’ offensive issues.

#### Could Genoa stay up?

https://twitter.com/Tacche9/status/1231932913580728320 In terms of results, Genoa have been one of the worst teams this season. Last year the Rossoblu finished with the same number of points as Empoli, who were relegated, but managed to survive on the final day. This season they are again in a similar situation. Despite being one of the 12 teams to have played 26 games, they are fourth from bottom, with the same points of Lecce who are in the relegation zone. Their situation is even worse than last year as they are 5th from the bottom in non-penalty goal difference (-0.41 xG on average) while last season they managed -0.20 xG per game, a respectable 12th in the league. However, Genoa's situation has improved after bringing in Davide Nicola, their third coach, each of which oversaw 9 games. The former coach of Crotone is literally saving the season, with a 9-game average of 1.55 points, compared to that of 0.66 under first-time professional coach Thiago Motta and 0.62 of Aurelio Andreazzoli, who started the season on the bench of the team that the season before had condemned him to relegation with Empoli. Andreazzoli, and especially Thiago Motta, attempted a proactive approach, which perhaps needed more time to bring the hoped-for results. But Genoa couldn’t buy time and so bet on the more pragmatic Nicola, who had already saved Crotone from relegation in 2017–18. Nicola has accelerated the lethargic pace of Thiago Motta's team, who was dead last for pace to goal at 1.70 m/s, to 2.96 m/s, also higher than Andreazzoli's 2.17 m/s. He also cut the number of passes needed to consolidate possession that had made Genoa vulnerable to opposition pressing. Since his arrival, Genoa have conceded 3.33 high-press shots on average, while his predecessors were both above 4. In the defensive phase, there's not a particularly low defensive distance, but in return, PPDA has climbed to 10.34, a sign that the team is pressing with less intensity. Andreazzoli maintained an average of 8.23, and Thiago Motta, 8.77. While the change of style appears more productive, the underlying numbers are not exactly positive. Nicola's Genoa is still above 15 shots conceded per game but have dropped to 10 shots made on average. Their xG per shot has improved to 0.12 (it was 0.08 under both Andreazzoli and Thiago Motta) while xG per shot conceded also decreased (0.10). Overall, there is no significant change in the non-penalty xG difference trend. But with Nicola, Genoa have finally started to reap what they have sown, as evident from the convergence between non-penalty xG difference and non-penalty goal difference. If they continue like this, they could once again avoid relegation, when and if the season starts again.

## La Liga Mailbag

This week, I take your questions on La Liga for a mailbag special that includes Mikel Merino’s play style, Lionel Messi’s pressing under Quique Setién, league comparisons and more.

#### Do you think Mikel Merino has a similar style of play to Axel Witsel? And what went wrong during his time at Borussia Dortmund? (Daryl Gouilard)

I can kind of see where you’re coming from here, but if we look at their respective outputs this season they don’t really have that much in common.

They do their defensive work in similar areas, and the difference between their numbers there could easily be accounted for by the fact that Real Sociedad are more proactive out of possession than Dortmund. Elsewhere, though, it's difficult to find many parallels.

Witsel is a lot safer in possession. He has a much higher passing completion percentage largely because he passes the ball over shorter distances than Merino and aims it forward less than half as often. Other players carry the weight of ball progression at Dortmund. Merino, meanwhile, is much more vertical in his passing; at La Real, only Martin Ødegaard moves the ball into the final third more often.

That difference is evident in situations when they are pressed by opposition players. Witsel maintains a high completion percentage by largely making low-risk choices, as shown by the map of his passes under pressure. Red = successful; yellow = unsuccessful.

In the same circumstances, Merino passes longer and forward more often. His completion rate drops down to 70% — one of the lowest for pressured passes amongst midfielders in La Liga.

They also differ in where and when they attempt to directly take on opponents. Witsel completes a solid 76.2% of his dribbles, and rarely takes risks inside his own half.

Merino more often seeks to dribble past opponents in deep areas, but he's not always successful. Across the entire pitch, he only completes 58.1% of his dribbles — again one of the lower rates among midfielders in La Liga.

On the ball, they fit two distinct profiles: Witsel is a secure receiver and recycler of possession; Merino takes greater risks in search of verticality. But they both provide what is required of them within the systems their teams play.

What went wrong for Merino at Dortmund? I’m not a Bundesliga expert, and our data for the league doesn’t stretch back that far. It looks like they simply had an overbooking of midfielders that season. In the starting XI, Julian Weigl was mainly joined by Gonzalo Castro, while Nuri Şahin and Sebastian Rode were also ahead of Merino in the pecking order. Dortmund were happy to move him on for a decent profit just over a year after signing him.

#### Is Lionel Messi really doing more defensive work (pressing) under Quique Setién? (@Adeolu_KI)

We're working with a relatively small sample size, so it’s a bit too early to draw any concrete conclusions, but under Setién, Barcelona are defending a little deeper and smidgin less aggressively than they did under Ernesto Valverde.

Little suggests Messi is doing more defensive work under the new coach. His volume of interceptions and tackles is marginally down, his defensive actions less often lead to opposition turnovers and his pressures and pressure regains are lower. He commits the same number of fouls as before.

What he is doing more of, and what may be catching the eye, is pressing directly after Barcelona lose possession. Both his counterpressures and the number of times his counterpressures lead to turnovers are up, the latter significantly so.

#### On which footballing parameters does La Liga score above the Premier League? (@sapre)

Let’s broaden this out to a comparison of the big five European leagues. I’ve written before about how La Liga compares to the others in terms of shots and expected goals (xG). On a per-match basis, the league sees the least shots and the lowest xG totals.

But what other differences stand out?

Teams in La Liga seem to find it more difficult to create shots in transitional phases of play. A lower proportion of shots come from counterattacks and high press situations than in any other league.

Average shot quality across the five leagues varies little, but the proportion of shots taken from outside the area differs. The Premier League has the lowest share (26.89%); Serie A the highest (31.46%). Rolando Mandragora seems to be on a one-man mission to drive up the Italian percentage. No player in the five leagues has taken more shots without scoring.

Dribble completion percentages are fairly consistent across the leagues, but La Liga sees fewer dribbles and consequently less successful dribbles than any other.

Where La Liga does stand out is in how high and aggressively its teams defend. Only Serie A is close in terms of defensive distance (the average distance from their own goal that a team attempts its defensive actions) and PPDA (passes per defensive action). Aggressive high presses tend to result in lower shot counts, which probably goes a long way towards explaining why La Liga sees fewer shots than the rest.

Finally, the ball spends a full three minutes less time in play in La Liga than it does in any of the other leagues, and four less than in Ligue 1.

#### How good is Mikel Oyarzabal, and how has he been affected by Isak and Ødegaard? (@RobRogers94)

Oyarzabal is good.

The soon-to-be 23-year-old is a competent attacking midfielder who provides solid top-line and underlying output in terms of combined goals and assists. He has 10 non-penalty goals and assists this season, while his tally of 0.36 xG and xG assisted per 90 puts him in the 67th percentile in his position across the last few seasons in the big five leagues.

That is decent. As noted above, matches in La Liga feature fewer shots and xG than those in any of the other big five leagues. If that trend continues, we might have to start recalibrating our idea of what sort of attacking output can be expected from players there. If he was to move on to a more dominant club — Manchester City have been linked in the past — his numbers would likely edge up.

One concern for potential suitors is that his combined xG and xG assisted total has remained steadily in the 0.33 to 0.36 range over each of the last three seasons. This suggests a lack of developmental progress, at least in that area of his game. La Real are a genuinely good team this year, very much in the hunt for a top-four finish, but his individual numbers remain pretty much the same.

As for the second part of your question, his game doesn’t seem to have changed all that much since Ødegaard came in at the start of the season. If anything, Portu has had a more direct effect. His arrival resulted in Oyarzabal starting almost exclusively on the left flank. From a slightly deeper starting position and without the ability to move infield onto his stronger left foot, his average shot quality has decreased. He takes slightly more shots but from worse positions.

In terms of Alexander Isak, I’ve previously highlighted the differences between his style of play and that of La Real’s other striking option, Willian José. But playing with Isak again doesn’t seem to have altered Oyarzabal’s output in any significant way.

## The beginner's guide to reading, writing and pitching about football analytics

Do you find yourself with time on your hands these days? Suddenly staying in on a Saturday night for the good of humanity? And just to top it all off, you have to seclude yourself with no sports to watch. Separately, have you noticed an explosion of numbers in football? A sudden rash of xGs springing up all over the place? Suddenly everybody seems to be spouting off about stats and you’ve got only the vaguest notion of what they’re on about?

Well, you’re in luck. StatsBomb copy-editor and general woman about town Kirsten Schlewitz is just like you! While she’s an expert at correcting the incredibly sloppy copy you’ve all come to know and love from me, she also came in as a relative novice at this whole stat thing. So, I roped her into asking me every question she could think of that she might have otherwise been afraid to ask. So, let’s get started.

K: First of all, let’s get the elephant out of the room. What is xG?

M: xG is short for expected goals. It’s a statistic that attempts to measure how likely any given shot is to become a goal. It’s really good at predicting the future. That is, xG is better at telling you which teams will score and concede goals going forward than any other statistic we have. That’s the most basic barebones definition I can think of.

K: If you already have xG to predict who will score and who won’t, why are so many other numbers needed? I see a great deal of figures and maps when I edit pieces, and sometimes I don’t understand what their purpose is. For example, when comparing two players, you can’t rely on xG for a team. So what numbers would be used there?

M: The answer to your first question is that knowing who is more likely to score goals going forward isn’t a particularly interesting thing to know (unless all you care about is betting, which, fair enough). The interesting questions are the hows and the whys. A single number like xG doesn’t help you very much with that. I like to think of xG as being a statistic that makes sure the conversation starts in the right place, as opposed to one that tells us anything remotely close to what we need to know.

So after the conversation gets started that’s why we need all the other stuff, to examine how teams play, what individual players are doing, basically what’s going on on the pitch that leads to the xG number at the end. And when those numbers come to particular players things can get very complicated very quickly. That’s because while xG works fine for players (specifically it can tell us when a player is on a hot or cold streak that’s unlikely to continue), shots are only a relatively small part of what’s happening. And, quite frankly, the further we get away from the actual shot, the less definitive our numbers become about what’s good and what’s bad, and the more we rely on them to try and accurately describe the game, as opposed to predict outcomes.

K: So you’re saying there are more or less two sets of numbers that a StatsBomb article could use: ones to predict which team will play better going forward, and ones that tell us what happened in a previous game (games?) in a way that dives deeper than simple match reports. If someone wants to write an article about, say, how they think the Champions League would have panned out this year, would they only use the prediction numbers, or would they also examine numbers that show what happened in previous matches?

Or am I way off base here and all the StatsBomb stats are used in conjunction with one another, rather than existing as two separate sets that focus on past and future?

M: So this is exactly right conceptually. The problem is that the numbers often overlap in ways which make the divide not particularly clear cut. For example, xG is an excellent stat for predicting the future, but it’s also a pretty ok one for explaining what happened. We know more about a match if we say that Arsenal had 1.5 xG than if we said Arsenal had 15 shots. Using the xG from a single game is kind of a quick and dirty way to describe what happened, albeit one with plenty of faults.

The best use of numbers though will always combine prediction and explanation. If I wanted to look at upcoming, now cancelled, Champions League matches, I would use general xG numbers as a starting place and say, “Here’s what I think will happen based on these numbers” and then use everything else to say, “And here’s why.” Now that also doesn’t mean xG is perfect. Doing good work in stats means trying to understand the limitations of the numbers as well so that we can understand when they might be missing something. So, in theory, it might be possible to analyze all the whys and hows and decide beforehand that even though a team like Liverpool might seem much better based on xG, they would struggle against Atléti (that’s not a conclusion I would have come to, but it’s not like completely beyond the pale to suggest).

K: We keep talking about “the rest of these numbers.” For someone who’s completely intimidated by stats, to the point they’re afraid to even click on a StatsBomb link, much less pitch you an idea, what other types of numbers would you anticipate they’d need to understand?

M: From a writing perspective, understanding the numbers is somewhat less important than understanding the game. If a writer is making accurate assertions about the game then those claims are going to be reflected in the numbers and in the editing process we can work together so that your friendly neighborhood StatsBomb editor (me) can help give you the appropriate statistical support you need.

So, if a writer wanted to write about how a team relied on a midfielder for a lot of their buildup play, they wouldn’t need to know the ins and outs of StatsBombs numbers. But I’d be able to call upon stats of ours like “deep progressions” to look at how frequently they move the ball up the field, or at passing percentages when they’re pressured and not pressured to explain how they’re cool in the face of a defense, or information and graphics on pass length, etc. etc. etc.

Now, if the numbers don’t match a writer’s argument that makes for an interesting challenge. The question of why a writer perceives the game a certain way while the numbers don’t capture it is generally a really exciting place to do analysis. Figuring out why there’s a disconnect between what the numbers capture and what the eye might see is usually an interesting endeavor for everybody involved.

K: I’m here editing and writing articles, and I fully admit I don’t comprehend exactly what half of these numbers mean. But if I wanted to submit an article that showed I do understand a few of the statistics, which do you think would be most important to understand?

M: You do need to understand the basic mechanics of xG and why it works so well. It’s important to understand that a player having more goals than xG expects he “should” is likely to start scoring less. Beyond that I’m looking less for knowledge of a specific stat than for a way of thinking about questions. Questions like, “Do you have a statistic that measures XYZ” are good, questions like, “How do you go about measuring ABC” are even better.

K: From xG and its variations (non-penalty xG, open play xG etc), it’s relatively easy to assess the offensive strength, or lack thereof, of a side, even if you’re new to stats — and I can attest to this, believing I had no ability to comprehend sports statistics before I took this job. But what still tends to confuse me is the defensive measurements . . . I see the maps and figures, but even those don’t help me quite get it.

M: Yeah. Defense is hard. We can look at xG conceded, or shots conceded, or any number of other things, but those are still fundamentally measurements about what the other team’s attack is doing. And that makes sense, because on some level all defense is is preventing the other side from attacking. But it’s also unsatisfying because defenders are obviously doing SOMETHING and it would be nice to describe what those things are.

The traditional measures are things like tackles, interceptions and blocks, and while those are useful numbers, they have some major problems. The biggest is that you can’t commit those defensive actions while you have the ball, so players on bad teams tend to have more defensive actions than players on good ones that keep the ball all the time. One thing we do is adjust all of those numbers for possession, to try and give a better picture of what’s going on.

On top of that we track pressures. That is, we track every time a defender is close to an attacker with the ball and impacting him in some way. This gives us a lot of information — adding pressures into the mix demonstrates where on the field a team is making defensive actions.That gives us the ability to look at a heatmap of a team’s activity and really get a picture of where on the pitch they like to defend (the redder the square the further above average the number of defensive actions are in the zone, the bluer the square, the further below). Manchester City defend basically in their opponents penalty area, for example.

All of that’s a long winded way of saying that it’s really really hard to evaluate defenses!

K: So we know how offense is evaluated, and we know how defense is judged — somewhat, anyway. With these two necessary halves of the game described, I have one final question: What would you like to see a writer be able to demonstrate with the numbers, keeping in mind that the StatsBomb blog is there to both educate readers and show potential purchasers what they can do with the data?

M: The major thing I want to see isn’t a specific proficiency with data, but rather a framework for thinking about issues. Think about a question you want to answer, and how can you use data to answer that question. That’s what we’re all trying to do, whether it’s determining if a potential signing will be worth it, or why a player is having a career year, or if a keeper's yips will pass, everybody is fundamentally doing the same thing. Whether it’s analysts with teams, or fans in the stands, or writers for StatsBomb, they’re looking at the game, developing a question and then trying to answer it.

## The Pathway to the Championship Playoffs

It’s kicking off at the top. It’s kicking off at the bottom. It’s kicking off in the upper-middle. Well, it would be if anything ever kicked off again. Eventually the games will return (we hope) and if the remainder of the season gets played it'll be a heck of a run in. Typical of a Championship season, with just nine games to go there’s still plenty to be sorted. The relegation battle is dragging a new team into it on a weekly basis, whilst the two-horse race for the title continues to flip between West Brom and Leeds. Fulham, Brentford and Nottingham Forest are near certainties to make the playoffs, but who joins them? *inhales deeply* All of Preston, Bristol City, Millwall, Cardiff, Blackburn, Swansea, Derby and Queens Park Rangers are separated by six points between the final play-off spot in 6th and 13th in the table. That most of them have endured poor recent form only makes the situation more curious. Let’s look into their respective chances.

#### 6th: Preston North End

I wrote a bit on Preston’s season just a month ago describing how their form tailed off after a hot start that saw them in 2nd in early November. As noted, their drop in form since then, which leaves them clinging to a playoff spot, is largely due to a significant reduction in the number of penalties they manage to win and injuries to key players that disrupted their rhythm. The story hasn’t changed much since then. Their form over the last third of a season has swung pendulously, offering hope of a recovery then quickly snatching it away. Data-wise, there really isn’t much to it. Over the course of the season, Preston rank 6th in expected goal difference, which doesn't include the penalties that’ve helped them along the way (Preston’s 10 penalty goals are the most in the Championship by a fair distance). It's clear that across the season they’ve done a lot right and are far from in a false position. The major concern at this late stage is the output of their forwards. Attacking midfielder Daniel Johnson is the top scorer with 11, but 6 of those were penalties. Winger Tom Barkhuizen has done his bit with 9 goals, but Sean Maguire (4 goals), Jayden Stockley (2) and David Nugent (1) have all failed to pull their weight. As a collective, they’ve underperformed their xG to the tune of nearly eight goals. Like each of these sides, it’s hard to believe Preston will actually go on to win the playoffs. But in terms of getting there, as long as their core players stay fit and play together for once, and the forwards start finishing a bit closer to expectation, there’s a good chance Preston will consolidate their position in the top six.

#### 7th: Bristol City

Assuming that you’re reading this because you have an interest in both the Championship and football analytics, then chances are you’ve probably already heard murmurs of the mystery surrounding the Robins’ playoff push. If not, let me inform you. For virtually the entire season, Bristol City’s goal difference trends at a higher rate than their xG difference; in other words, their results are better than their performances. This is evident even in the raw shot numbers. Of course, the quality of the shooting opportunities matters — hence the invention of expected goals — but Bristol City’s shot differential (shots taken — shots conceded) across the season is -183. Their opponents average five more shots per game. Of more concern to their playoff chances is that whilst results have finally seemingly caught up with performances, with just one win in their last seven, their actual performance levels are declining. A goal difference of -2 doesn’t exactly lend itself to thoughts of a promotion charge, but given the memory of Huddersfield achieving promotion with a -2 goal difference just three years ago remains fresh, it can't be ruled out.

#### 8th: Millwall

Since taking over in October, Gary Rowett has had the Lions roaring up the table (sorry), improving the on-pitch process at The Den to the extent that Millwall are now rightful contenders for the top six. Rowett’s turned them into something of a defensive beast, the 5-2-3 formation making them very hard to play through.  Since Rowett took charge, in just 6 out of 25 Millwall matches has a team generated more than 1.0 xG against them; looking at the league as a whole Millwall have the 3rd best defence in the league by xG conceded. When you consider that against Leeds and West Brom, the league's best two teams, Milwall conceded a collective 6.88 xG, they’ve really yet to put in an unexpectedly disappointing defensive performance this season Whilst Rowett remains philosophical about the team’s playoff chances, probably rightly given he only took over five months ago, there’s no denying the opportunity is there for the Lions to chase promotion. Add in that each of their nine remaining fixtures are against teams below them in the table and one might argue that it’s even in their own hands.

#### 9th: Cardiff City

You might be forgiven for forgetting that Cardiff finished 18th in the Premier League last season, such has been their rapid acclimatisation to being a steady-but-not-much-more Championship side. As it quickly became clear that Neil Warnock wouldn't lead them to an instant return to the top tier, Neil Harris has been trying to get the Bluebirds singing again since November. Harris’s impact has been gradual rather than instant. Cardiff’s points-per-game has increased from 1.31 to 1.57 since his appointment but, like most of their playoff rivals, their form since the turn of the year hardly suggests an imminent run to glory. Where they do have the edge over their rivals and even over the rest of the league is in the set play department. Cardiff lead the Championship in both set piece xG and set piece goals, scoring 19 of their 52 goals this way. A number of players are able to pose a threat from dead balls; centre backs Sean Morrison and Aden Flint are notoriously deadly at this level whilst Curtis Nelson is an able deputy. The danger they pose in these situations could make them an uncomfortable opponent to come up against in the playoff format should they get over the line.

#### 10th: Blackburn Rovers

While a section of the fanbase would have you believe their season is over already, in fact they find themselves in the middle of a race to the playoffs that they have every chance of winning should they get their act together in the remaining nine games. That Blackburn are in touch with the top six is a commendable achievement for Tony Mowbray, given this is their second season post-promotion and they’ve had to do it without their jewel in the crown, Bradley Dack, who blew his ACL in December. That they haven’t missed Dack to a greater extent is largely down to England youth international Adam Armstrong, still just 23, having the most productive Championship season of his career so far. Crucially, Blackburn’s goal difference of +7 is the best of the playoff-chasing sides, something that could play into their favour at the season's end.

#### 11th: Swansea City

Making a case for Swansea to win the race is a little difficult when they’re in this position mostly because of a hot start six months ago. Take their opening six games, in which they picked up 16 points, out of the equation, and their form is that of a lower-mid table side. They’ve picked up 37 points from 31 games since then. That’s not to denigrate the work Steve Cooper’s doing. The Swans are clearly a side in transition and the need to trim the wage bill whilst bringing in cheaper replacements since their relegation from the Premier League is well-documented. One of the Championship’s brightest lights plays his football at the Liberty Stadium this season, ironically so given he was one whose wages the Swans tried to shift in the summer window. André Ayew hasn’t come cheap, but the experienced winger looks to have offered sufficient return on the wages invested. Perhaps surprisingly — given they don’t have a reputation for it — Swansea lead the Championship in high press shots (shots generated within 5 seconds of a possession turnover in the opposition half). The Swans rank between 7th and 8th for pressures and counterpressures in the opposing half, so it’s a positive sign that the team is clearly adept at converting these turnovers into goalscoring opportunities.

#### 12th: Derby County

There’s something a bit strange about writing about Derby in a promotion context. This is a side that’ve spent most of the season muddling through, trying to close their ears to multiple sources of off-pitch distraction, and have only recently found their feet under Philip Cocu and started putting a spell of good form together. It may well be out of their hands anyway should the EFL decide their crimes off the pitch are worthy of a points deduction, but a playoff berth seems as premature as it does unlikely. Their position in the league table is powered almost entirely by their good record in home matches against bottom-half teams, picking up 27 points from 11 matches as opposed to 24 points in the other 26 matches, so Derby will have to start beating those above them in order to make the top six, something they’ve struggled to do all season. The odds are stacked against the Rams given five of their remaining nine fixtures are against current top six teams. But at least Wayne Rooney’s had a positive impact.

#### 13th: Queens Park Rangers

Well done for making it this far. Thirteenth in the table but just six points of sixth, QPR round us off. A real bastion of inconsistency this season, it looks like the Hoops might be coming into one of their purple patches at the right time of season, arguably the form side of all of the contenders. In simplistic terms, QPR started the season good, then were bad, but are now good again. How long that’ll last is anyone’s guess, but they at least seem to have shaken off their mid-season malaise. We all wondered whether midfield magician Ebere Eze would manage to sustain his outstanding early season form and luckily for QPR he has, continuing to be as influential as ever in recent matches. He's now aided by the emergence of electric winger Bright Osayi-Samuel, who not only provides able support to Eze in attacking output but also opening up more space for him, ensuring opposition defenders now have two major problems to worry about in QPR’s attack. What goes against QPR is that it may just be too late to make up the gap. Six points and an eight-goal swing is a lot for just nine games. Should one of the league’s best players in Eze and able sidekick Osayi-Samuel continue their hot form it'd be foolish to rule them out of it.