FORTUNA:LIGA 2023/24 Team Preview Series: Behind the quirky metrics (3.0)

22 min readJun 30, 2023

Below are some quick explanatory notes on what’s the thinking behind the use of some of the advanced metrics you’ll be seeing in the previews; how could they be interpreted and what do they effectively mean. As opposed to last year, there has been far less tinkering done on my side, but I didn’t stick to my original plan of not changing anything at all this year, so it’s only fair to you — the reader — to comment on any new stuff.

Initially, for this third installment, I was meaning to just link to the previous one and quickly run down the list of purely new metrics. In the end, I’ve decided to dump everything at one place again, so it’s easily accessible for all. Without futher ado, then, let’s go position after position:

Goalkeepers

Only a bit of tinkering around the edges done for the one position that saw the most seismic shift in the make-up of the pizza chart as well as the overall approach last year. Back then, I made the welcome move away from shooting percentage altogether, and now I’m ditching some more meaningless, abstract percentages. Generally, there has been a wide crackdown on passing accuracy as a relevant metric across the analytics sphere (contesting relevance), which is mirrored by me dropping the long pass accuracy % and pass to final third accuracy % metrics for this edition.

Shot-stopping qualities

Prevented goals: A familiar concept by now. This metric incorporates what is commonly known as post-shot xG, usually shortened as xG² or xGC (expected goals conceded) — the younger of two xG methods that only considers shots on target (ie. shots that can actually turn into goals) and accounts for stuff like shot power and shot placement (ie. stuff the traditional xG method typically doesn’t control for). This post-shot xG then gets turned into the so-called “prevented goals” metric that follows the simple equation of expected goals conceded-minus-conceded goals, divided by the number of starts (to get a 90-minute average). Crucially, I’m not including penalties and don’t count own goals among goals conceded, giving the stat a fresh look that truly reflects one’s shot-stopping (whereas earlier it played in randomness’ favour more).
High-danger saves: This stat has remained largely intact, and once again does include penalty saves for the benefit of the custodian. The only change is in lowering the bar: this year, any shot that would — per xGC — result in a goal scored in 40%+ of cases (value of at least 0,4 xGC) is credited to the goalkeeper as a high-danger save. Previously, the bar was set at 0,5 xGC which proved to be unnecessarily high. Going by my growing experience from looking at every shot ever, I can confidently conclude a 0,4 xGC save is already a hard, reflex one. As ever with arbitrary cut-offs, you could easily argue there’s no difference between 0,39 and 0,41 shots (and you’d be right) but we need an objective framework to complement our fundamentally subjective view of football and we wouldn’t be gaining anything by allowing for tolerance (a 0,02 xGC difference would be negligible at any point of the scale).
High-danger situation save percentage: My very own semi-scientific approach to the age-old “he doesn’t save anything extra” complaint about a mediocre goalkeeper — simply taking all 0,4+ xGC shots faced to see the proportion of goals actually conceded. Anyone who scores high in this metric should have no trouble avoiding the common complaint.
Steals: Here I only look at truly memorable game-long performances — both in positive and negative sense of the word. A goalkeeper gets credited for a “steal” whenever he prevents at least 0,81 goals (the equivalent of one penalty saved — again, we need an arbitrary cut-off and this felt suitable), but also loses a point for any game he just about “threw away” by allowing at least 0,81 goals above expected. I don’t care if he actually stole/threw away the game in the end, mind, because a goalkeeper can’t tangibly influence what his teammates do upfront. Also, this is one of the very rare cases where I don’t convert a stat to per-90-minute basis, as it wouldn’t make much sense (or achieve anything).

Consistency and dependability

Prevented goals from outside the box: I believe this is self-explanatory. Again, I reach for xGC values but only take the sum made up of shots taken from outside the box and deduct the mid/long-range goals.
Loss leading to shot: This is an interesting metric in that it accounts for both misplaced passes but also lost aerial or ground duels. The follow-up shot needs to occur inside the next 20 seconds per Wyscout rules, otherwise it’s rendered a “harmless” loss, which is yet another case of an arbitrary cut-off. As I said two years ago, my bigger problem with this metric is: what if the loss is truly staggering and awful, yet the opposition is even worse and passes its way to a corner flag instead of shooting? Let’s assume it’s down to the goalkeeper’s psychic powers…
Percentage share of preventable goals: This is where my subjective view comes to play, but since it’s me and only me considering things all the time (with my lens that could be challenged but at least remain consistent), I’d argue the subjectivity doesn’t cause too much harm. As for what it means: simply how big a portion of goals conceded I deemed to be “saveable”. It effectively replaces the original “soft goals conceded” metric where I used the arbitrary cut-off of 0,3 xGC value, which has however proven to be way too high/inconsistent over time. Wyscout has their own (subjective) approach to tagging, so I just decided to rely on my own set of eyes in this case — it’s my model, my analysis after all.
Grave errors leading to goal: Here, I don’t mess around with some vague “preventable” and “saveable” adjectives. Nothing like “he could’ve done better on this one” suffices anymore — I only count the real fuck-ups of which a pair of goalkeepers accumulated a maximum of four this season (that was also the record last term). Then the number of starts matter, of course. And just for the record, these grave errors are also part of the sum of the preventable goals — it’s just that not all preventable goals are a result of grave errors. It doesn’t work both ways.

Sweeping and distribution

Success rate of actions far outside the box: Last year’s introduction designed to measure sweeping qualities of a goalkeeper is kept on board despite its flaws. By a “successful action” Wyscout means anything from a pass while building from the back to a clearance while intercepting a counter-attack, as long as they keep/regain possession for the said team. By “far outside the box”, I more or less mean the area of the pitch below where a GK usually doesn’t operate. I don’t consider anything happening inside the attacking half, since that usually suggests desperation mode towards the end of a losing game. Not the point.

Byline spread consistency: Welcome to the first 2023 addition! This is a nice one because I effectively consider how often a goalkeeper looks to spread the ball to the byline as part of the build-up (generally only looking at balls directed to middle/final third) and how consistent he is. That means he gets a plus point for an accurate spread, and a minus point for any such pass heading out of bounds (setting up an attacking throw-in for the opposition), divided by the number of appearances.
Inaccurate pass inside own half: This is sort of connected to the previous metric in that it accounts for any pass going to the sidelines, but also simply ending up at the opponent’s feet, as long as it finishes its journey inside the goalkeeper’s own half. It’s pretty straight-forward.
Attacks successfully initiated: A bit of a outside-the-box product. Here, I sort through every positional and counter attack tagged by Wyscout and see who kickstarted it. The “kickstart” bit can mean all sorts of things — a short pass sideways or a long kick to launch a counter — so it does not necessarily speak to one’s kicking technique/qualities, but I do believe it sort of speaks to team’s willingness to lean on his distribution. At the very least, it’s a more intriguing (and slightly less vague) random stat than the final third pass % it effectively replaces within my model.

Damage control

Aerial duels success rate: I’m only interested in success rate to not automatically punish goalkeepers who tend to be more cautious (but also consistent in their efforts). Simply put, when you do engage in an aerial duel, you’d better be 100% sure about it and, ultimately, 100% successful at it (hint: only 7/24 regulars indeed remained spotless).
Left line to claim or punch: A metric confusingly shortened by Wyscout as “exit” only accounts for instances where goalkeeper comes out to deal with a cross and ends up being successful claiming/punching it.
Clearance from the centre of the box: This can be a punch or a kick into the stands, simply any solution to a mild or major problem that arises in the most heated area of the box. The area, by the way, roughly corresponds with the no. 2 on this scheme — and will show up later, too.
Backstopped vs bypassed: This is a metric tracked fully manually by yours truly. “Backstopped” usually entails a goalkeeper coming way out of his way (both literally and figuratively) to mend a mistake or simply prevent grave danger. It could be a difficult punch, claim, a final pass intercepted, a rush out of line to stop a developing counter. Sometimes, it’s actually a case of friendly fire — save made against his own teammate, which Wyscout naturally doesn’t account for. “Bypassed”, meanwhile, pretty much consists of any such attempt that backfires. What’s rather significant: I only look at chances (that go begging) and not goals, but the chances don’t necessarily need to be finished off.

Centre backs

No revolution on this position either, but it’s still worth revisiting a couple of not-so-straightforward old metrics along with the few new ones, what do they mean and what’s the thinking behind their inclusion on pizza charts:

Success in defensive duels in/around the penalty area: First of all, this is any duel undergone vs opposition in possession excluding aerials who have their own category (and I only consider those undergone in the defender’s own penalty area for my model’s purposes). Second of all, I move away from the general percentage and zero in on the danger areas where there’s a decent chance the defender in question is in his natural habitat (not out of position, but all set) and also under most pressure.
Shots blocked in vulnerable area: I’ve slightly transformed the approach to blocked shots, as well. Where I previously took Wyscout’s number of blocks at face value, I now track specifically the “vital” kind of blocks by myself — typically slashing their total number in more than half. That’s because this sort of approach, crucially, excludes A) most blocks performed outside the box, carrying a significant danger of deflecting a harmless shot and turning it into a bigger chance; B) shots blocked unknowingly, by player simply standing in the way by accident. I want my blocks to actually mean something; ie. the player has got to be blocking in vulnerable area and be proactive to get credit from me.
Success in loose ball duels: This occurs when no team controls the ball to be considered “in possession” (typically the crucial “second balls”).
Controlled clearance: Apart from clearances from centre of the box which I got into earlier for goalkeepers already, I also track those clearances that are “controlled” (expressed as sort of a success rate in %), ie. those that result in the team in question retaining the possession post-clearance. This started as a bit of fun, really, because it’s often random — and in many situations conceding a corner kick/throw-in, hence not controlling possession, is the best-case scenario anyway — but I also believe it can tell you something about one’s awareness/peripheral vision. See the “targeted headers” phenomenon re. Igoh Ogbu brought up by iSkaut podcast (spoiler: he wouldn’t be F:Liga leader this year).
Forward pass played into half-space: Oh yes, your hipster friend’s term — of course it makes a return. You always find the half-space here, and exploiting it is a noted weapon, so hitting them regularly is welcome.
Chance/goal-creating actions: This is basically any creative contribution that doesn’t lead to the centre back getting credited with an assist — ie. either a secondary/tertiary/quarternary contribution to a goal, or a primary/secondary contribution to a wasted goal-scoring chance per my notes. It’s important to remember that not every chance has a primary, let alone secondary contribution, and that I don’t consider every single involvement in the lead-up a “chance-creating action”. It must truly be vital for the chance to present itself, not just a hand-over on the half-line. It’s a fairly rare occurrence for a CB, but of course I don’t utilize this set of manually tracked data for this role alone!
Tendency to switch off on defence: While centre backs (and other defensive positions) also feature backstopped-bypassed differential introduced higher up, this metric is going a bit deeper — looking specifically at those situations where a player gets bypassed by, erm, falling asleep. This can either be through neglected backtracking (more often a case of midfielders/fullbacks), poor positioning, failure to apply adequate pressure on a crosser/passer/shooter, or loose marking.
Meters gained via run with the ball: This is straight-forward enough (caution: normalized per 90 mins, not a sum of total meters gained, mind), I just wanted to highlight in this space that this metric — already introduced for some attacking roles last year — replaces the less reliable “completed progressive runs” metric as part of the CB model, too. (The same development has followed fullbacks and progressive passing, too).

Fullbacks

For this position, I utilize a few more layers of my expanded manual tracking, along with a new metric designed on the back of Wyscout tagging:

Accurate knocks down the line: This metric falls right in line with the rest of the “passing range and smarts” grouping, as it complements the horizontal element (in accurate stretch passes; see below) with a new vertical one. Previously, I’d consider smart passes instead, but seeing that as many as 12 fullbacks scored a big fat zero last year, it felt like a waste of time and space. These knocks down the line — while filtering those played inside the attacking half (with some really long exceptions played in behind the defensive line) that also “stretch” opposition — meanwhile do feel like a significant part of every fullback’s job.
One-on-one defensive vulnerability: A metric deployed for the defensive midfielder position, too, it’s supposed to highlight one’s acumen in defending face-to-face, counting the number of times he’s either dribbled past or outright outmuscled in a duel. Again, this is one layer of the general “bypassed” metric that’s featured as a whole, too.
Lay-off, give-and-go, dummy affinity: Think of this as some sort of a “cleverness index”. Sometimes, doing less means doing the most/best (via dummy or a neat little lay-off for a better positioned player), other times, you need to inject some push/drive into build-up via a refreshing give-and-go. Either way, you’re doing your team a favour by making a calculated, smart move — and that’s why I find this tracked metric especially intriguing. It’s a fairly rare occurrence, but some of the cream of the crop at FB, CAM or CF positions score high on it by no accident.

That’s it for the new stuff. Now onto a brief refresher from prior years:

Successful loose ball duels: I’ve already introduced this stat earlier, but here’s an important distinction: while CBs get compared in terms of success rate (%) because there’s generally more loose ball duels occurring in their space, with fullbacks I’m more interested in simply how many of those duels they win per 90 minutes — highlighting their engagement/proactivity level on top of what a mere success rate tells us.
Offensive duels in final third won: This kind of sounds like dribbles, but it’s a tad different. You typically get tagged for an offensive duel even when receiving and shielding the ball under pressure, and you win it even when the ball bounces away for a throw-in in your favour or you get fouled for a set piece. Either way, while some of those wins are only partial ones, they do gain your team some valuable attacking zone time.
Proportion of crosses blocked early: Even in the inverted wingback era, crossing remains a big part of one fullback’s game which is why most public models or databases include crossing accuracy. I myself had considered “accurate crosses to centre of the box” in 2021, but I quickly dropped it as a metric, and I’ll explain why: it’s too random. Is it necessarily the fullback’s fault when he delivers the ball into the danger area only for no one else to take a hint and make the run? It quite often is not. Is it, however, the fullback’s fault when his delivery gets blocked before it even enters the penalty area, potentially kick-starting a dangerous counter? It rather often is, yeah. So that’s what I’m looking for here, basically. I don’t care if your cross doesn’t quite connect as long as it can feasibly lead to a lucky bounce or even an own goal.
Accurate stretch pass: This type of a pass is not readily available on Wyscout so I cherry-pick it, defining it as any accurate pass that flies across the field towards the opposite flank and finds a teammate. Previously, I included vertical stretch passes, now I only account for the horizontal ones (see above why). Both types however count as equal — they stretch the opposition and buy your team time and area to hit them.
Accurate cutback in danger zone: Effectively forming one part of a half-space, the “cutback zone” is another noted danger area of the pitch where you’d ideally want your modern fullback to function and do damage. I don’t necessarily limit myself to cutbacks from inside the box only, but they do have to be directed to centre(ish) of the box to qualify.

Defensive midfielders

The truly new introductions are diminishing (because they typically double up at other position), so apart from some minor changes — like switching from a number of successful loose ball duels per 90 mins to loose ball duel success rate expressed as % — I only have one new arrival to cover:

Counter-attacks kickstarted: This is a fascinating one in my eyes, as it usually kills two birds with one stone — to successfully initiate a counter, the player in question typically needs to show great positional sense/anticipation and then flash some vision/prompt decision-making. It therefore transcends two categories, playing into both “positional sense & agility” and “cutting edge & vision” areas of performance.

Now, onto some of the metrics that could be considered household by now:

Possession-adjusted interceptions: Interceptions are generally well understood, but they mean next to nothing without adjusting for context. If your team always controls the flow of the game, ie. retains possession, how the hell are you supposed to intercept anything? And why should you be worse for it in the eyes of the model? Enter the “possession-adjusted” bit on an interception. What Wyscout people do here is to pretend the possession game was equal in every matchup; so if the ball is, on average, in play only 60 minutes per game (I know, depressing), the stat is normalized for 30 minutes of possession. Only that way, even the guys on dominating teams can think of landing in the 90-100 percentile — like Lukáš Kalvach did in 2020/21, for example.
Progressive passes allowed to go past: This one is a bit vague because it simply tells you how many progressive passes were completed in the said player’s zone (ie. central areas here). Thus, it’s always a collective effort, but you’d expect your holding midfielder to be responsible for cohesion and organization of your team down the middle, which is why this metric shows up on this board in particular (and only re-appears with wingers to illustrate the level of pressure applied on fullbacks).
Diagonal pass into final third: Very much a CDM variation of the fullback’s “stretch pass”. It doesn’t necessarily have to travel on a diagonal trajectory, but it should come reasonably close for it to count. This type of a pass neatly substitutes the broader progressive pass and complements the more vertical through pass to give us a fairly complex picture of one holding midfielder’s passing range and cutting edge.

**Kaan Kairinen**. source: twitter.com/ACSparta_CZ

Attacking midfielders

Last year, almost no stone was left unturned at this position as I introduced eleven new metrics. This year, I don’t need such a radical approach. In fact, there’s nothing new to introduce at this point; I just took out set pieces from one metric looking at chance/goal-creation (because they are included in expected assists and that’s enough of an edge for set piece takers with far more opportunities to rack up numbers) and swapped the wildly inconsistently tagged “smart passes” for simple through passes.

Anyway, to remember last year’s overhaul, let’s do a brief run down:

Success rate of actions inside the box: This is basically any successful execution — be it a pass, a shot (only those directed on target count as a success) or a duel of any kind. I’ve opted for success rate to not disadvantage those who — not just through their own doing, or lack there of — don’t get to influence the game from inside the box too often.
Chance created for himself: This can either be interpreted in the more straight-forward sense — you get into a great shooting position by beating a man and setting yourself free on your own — but also in the less self-sufficient — by going for a quick one-two, neat give-and-go or even winning an aerial duel before making yourself available for a pass again. Again, one layer of chance-creating actions tracked manually.
Offensive duels won at the top of the box (%): Originally, I was happy with successful offensive duels in final third normalized per 90 minutes. Last year, I wanted to limit the scope significantly — once again, I’d rather consider the success rate to not discriminate, and I’m also focusing exclusively on true danger area entry points; ie. give or take:

High-danger shots: Make no mistake, while we are interested in post-xG when it comes to goalkeepers and their high-danger situations, here we only look at pre-shot xG to see who gets into high-danger situations in the first place. Finishing will only become a matter of interest once we get to centre forwards; with attacking midfielders, the sole ability to find those sweet spots inside the penalty area warranting an xG value of at least 0,2 (min. 20% likelihood of scoring) tells us all we need. It’s usually a decent pre-condition (projection) of fine offensive output.
Deep completed pass / cross: Originally, I combined both types of deep completions into one metric, now I think it’s interesting to see and compare them separately to measure one’s versatility. For the sake of a reminder, here’s where any completion must occur to count as deep:

Meters gained per ball loss: This one takes a look at ball progressing from a different, but no less important angle than other metrics measuring ability to push play forward — if you do attempt to move the needle often but also lose the ball frequently, you’re not going to look too fantastic in this column. This is very much what separates the wheat from the chaff, so to speak, though it can favour the more cautious, too.
Long-distance shooting tendency: Oh boy. He did go there after all. The ultimate beating stick of the “xG folks” and he reaches for it by himself. Yes, the more you try your luck the worse you look here, but that’s not to say you should never do it. If there’s an open lane and/or your team struggles to break down the opponent’s low block, pulling the trigger is sometimes the right move. That’s also why I don’t mind a mid-range outside-the-box attempt; I only count those from roughly 30+ meters. (And guess what, the 2021/22 Nico Stanciu graded out as the second best CAM despite being the worst culprit in this sense — it’s just one of 18 metrics fed into the model, remember, so it barely tanks your value.)

Wingers

Next to nothing left to introduce as we arrive to another attacking position:

Incisive dribbling for a chance created: You guessed it, there’s a whole sub-section of chance-creating actions dedicated to dribbling, so instead of looking at a vague dribbling success rate (another % dropped), I’ll zero in on the “incisiveness” part, ie. purpose. In turn, I’m interested in success rate while undergoing offensive duels in final third (instead of duels won per 90) to account for decision-making, too.

As for the rest, maybe just to quickly remind you of two other metrics:

Successful penalty area entry from open play: This is what it says on the tin, really, just want to specifically stress the “open play” bit (this distinction goes for deep completions, too; only xA generally includes set pieces, as Wyscout doesn’t offer a way how to easily filter them out), and also the fact you can crack the penalty area in possession of the ball (as a carrier), so it doesn’t exclusively capture passing/crossing ability.
Accurate cross field passes (~50 meters): This may mostly suit a pondering type of a tucked-in winger (it doesn’t quite, though, from my experience), which is fine because there are other metrics that will put a straight-line runner in pole position instead. Anyway, this metric represents any successful attempt to switch it up, to make the opponent shift from one side to the other and gain advantage, tracked by myself.

Centre forwards

Just like with goalkeepers, it makes sense to provide lowdown on one category (grouping of metrics) at a time, as they all describe “a type”.

Attacking the box & poaching

For the proverbial fox in the box, there’s still the specially extracted xG of all non-penalty shots from inside the box (combined, converted to a per-90 basis) and I stick to the success rate of actions performed inside the box, introduced earlier, but as far as finishing goes, I’d rather elaborate:

Quality added to finish (xGS-xG): Most data analysts would tell you finishing is too random to spend too much time — or any time — on dissecting it. It’s tempting to agree, since the very reason why Plzeň have won a shocking 2022 title was all the same a big reason why Plzeň bombed a year earlier. Per this metric, Jean-David Beauguel made for the 8th best finisher last year (excluding penalties), a wild swing from being the 11th worst the year before when he missed the second most high-danger chances. It’s exactly for this unpredictability and virtual impossibility to detect any longterm trends that most data analysts readily give up on sizing up finishing ability altogether. I refuse. First of all, this particular metric still tells us a valuable thing or two even if we shouldn’t read too much into it. If your individual sum of xGS — expected goals scored; ie. post-shot xG — far exceeds the sum of xG — expected goals; ie. pre-shot xG — it can effectively mean (one of) two things: the striker in question A) has a fine shooting technique so his shots tend to have a decent chance of beating the keeper, B) doesn’t miss the target all too often, because a shot off target doesn’t get assigned any xGS value. There are absolutely trends to be found along this line in particular.
Second of all, I think we can collectively do better. One little part of my attempt to do so is constructing this very simple luck index of sorts (only present in comparison graphics, not pizza charts) where I take away non-penalty goals scored from the sum of xGS and woodwork-hitting attempts — with the highest luck index then suggesting the striker in question had some bad luck to rue along the way; be it for only a few inches missing every now and then, or the opposing goalkeeper standing on his head to deny him far too often. In 2022, this little aid helped us to explain why Milan Škoda or Daniel Vašulín got stuck far away from the double digits after all, or why Jablonec had to fight off a real relegation threat — with both Martin Doležal and Jan Silný combining for roughly 7 goals scored below expectations. It’s not all-encompassing, but it’s something… and it’s definitely better (more valuable) than just looking at pre-shot xG and actual goal discrepancy.
While on the subject of tangibly influencing the scoreline, I dedicate a whole section of comparison tables to “clutch performance” where I look at both players’ involvement in team goals scored with them around (these are not just goals and assists, but also vital secondary, tertiary and quarternary contributions should he be credited for any) as well as expected points added (EPA). A faithful reader is already familiar with CSfotbal’s innovative EPA index, but what the database provides is either the sum of EPA or an average of EPA per goal scored. What I am after, meanwhile, is EPA relative to playing time received, so I normalize the metric for 90 minutes. For newcomers: EPA does a great job in assigning value to any goal scored depending on the game state (was it a go-ahead goal, equalizer or just a consolation strike) as well as the timestamp (how much space for reaction did the other team have).
Finally, I decided to ditch the original “high-danger chances missed” metric which, undoubtedly, tilted the field too heavily against the strikers on strong teams who had better service, and hence more opportunities to miss sitters. Instead, I’m now looking at the percentage of high-danger shots put off target which does a much better job in measuring one’s unease in front of the goal. To be clear, any shot that gets blocked (on the way to goal) or saved by the goalkeeper doesn’t work against the striker as part of this metric — it was an OK job done as far as giving him at least some chance to celebrate. Meanwhile, missing the target in a high-danger situation (at least 0,2 xG) pretty much never reflects well on the striker in question, so no slack is to be cut there.

Target man qualities

This set of skills has endured a departure of another vague, percentage-based metric (final third passing accuracy) to instead make way for…

Battling contribution to chances/goals created: Penultimate layer of my manual tracking of different types of chance-creation left for me to introduce. Here, the player basically gets credited for any duel undergone (ground or aerial) and foul drawn in/directly leading to threat. Since these types of contributions typically don’t constitute assists, I include indirect goal contributions (like penalties won *Kuchta screams with joy*) on top of such involvement in wasted chances.

As for old additions that made the 2022 model substantially better:

Back pass accuracy (%) in danger areas: This 2022 arrival neatly accompanies the already introduced “lay-off, give-and-go, dummy affinity”, as it zeroes in on neat little lay-offs in areas right around/in the box where they can feasibly lead to a direct goal-scoring chance. Here, for a change, I account for accuracy to, additionally, size up one’s first touch and soft technique. Eye test doesn’t always concur (Tecl, Beauguel looked awful here last year), but the intrigue remains to be substantial.
Aerial duels won in/around the box (%): Undoubtedly another big part of a target man’s skillset, this metric was sorely missing from my 2021 model. I may throw in the occasional inside own box success rate to demonstrate one’s usefulness at the back, on defensive set pieces, too.

Proactivity, efficiency & discipline

The last grouping of metrics ranges from offsides and foul differential (discipline) through to attacks initiated, chances created via applied pressure and balls recovered via engaging in a ground duel (efficiency, proactivity). There’s little to explain, so I’d suggest we wrap it up here.