Introducing @CzechFooty’s 2024/25 Chance Liga Team Preview Series
Here we go again. One more showtime. As the old FORTUNA:LIGA makes way for a brand new Chance Liga, and as we are all figuring out how to best pronounce the top flight’s name or how close exactly is the fresh lime colour to the iconic lime tree colour our national team once opted for, I am not quite ready yet to make way for a revamped pre-season coverage. The band is back, even if busier than ever, and all your favourite [citation needed] preview sections remain intact as well. We begin on 12 July.
The roadmap
It’s going to be crowded once again. This time it’s not because an early European start for some clubs like in 2022, or simply a lot less runway than usual like last year (though the 5-day pause between the conclusion of Euros and the re-instated Friday curtain-riser is a challenge in itself), but rather because of my travels from a month-long Czech vacation back to my current home in Zambia, which effectively robs me of a full 24-hour day of publishing/writing. That said, we provisionally start 2 days earlier than last year, with the full 2024 running order looking like this (subject to change):
Friday 12 July: FC Slovan Liberec
Friday 12 July: FK Jablonec
Sunday 14 July: SK Sigma Olomouc
Monday 15 July: SK Dynamo České Budějovice
Tuesday 16 July: FK Pardubice
Tuesday 16 July: FC Viktoria Plzeň
Wednesday 17 July: FK Mladá Boleslav
Wednesday 17 July: Bohemians Praha 1905
Thursday 18 July: FC Baník Ostrava
Thursday 18 July: AC Sparta Praha
Friday 19 July: SK Slavia Praha
Friday 19 July: FC Hradec Králové
Saturday 20 July: MFK Karviná
Saturday 20 July: 1. FC Slovácko
Sunday 21 July: FK Teplice
What’s new in stock
Like each year, I try to evolve at least a little bit. Last summer, there was not much innovation to report, with our team expanding externally by Slovak expert and Statsbomb consultant Marek Kabát whose data references were sprinkled all across the What went right/wrong sections of each preview. That stays, along with an MVP per the so-called On-Ball Value (OBV), an innovative metric capturing just about everything happening inbetween shots. Marek once again joins Adam who keeps on delivering the neat graphics and Jakub who provides the previews with a unique forecasting section. All 2023 preview sections stay intact. Metrics feeding into all my positional models have changed a bit (as explained here), while I decided to no longer split game sample between multiple roles due to little added value compare to much work (e.g. Lukáš Mareček, who spent over 1000 mins as both a centre back and defensive midfielder is only featured as part of the CDM model which was still his predominant role). One new aspect I started tracking this season, however, is a particularly vital one:
Involvement in goals/chances allowed/created: The thing about you making it your mission to see all 277 FORTUNA:LIGA games isn’t only that other people think you’re insane, but also that you always think you’re not making the most of it. In 2022/23, I did think I was getting a more comprehensive picture of how one player defends/attacks than ever, but that alone wasn’t enough to make the dozens of hours spent behind a TV/computer screen feel worthwhile. This time around, then, I source far more valuable information from each meaningful action. For every regular CB, I have data on how many chances/expected/actual goals the team allows with him playing a full 90 and him missing from the starting XI; I quickly get to see team record with him present or absent; and the distribution of his mistakes as well as chance/goal-preventing actions. For every attacking-minded regular (W, CAM, CF), I see the same and a bit on top, like how dependent the team is on him particularly in terms of chance/goal creation, how big a percentage of chances he finishes off, etc. An interesting aspect can also be the home/away split in terms of danger generation, as there are high-profile guys who appear more effective on the road. Here was the early March teaser; since then, it’s developed further.
What stays the same
My credentials: They do me no favours, all told. I stopped kicking the ball regularly when I was about 13. I stopped doing any sport regularly when I was 18, as I did my knee while playing floorball at a reasonably high level and never returned due to the timing (I was just starting uni in a different city). I’ve only ever dipped into coaching — and it barely counts, as it was at youth floorball level. I’ve written about football pretty much nonstop since 2011, both in Czech and English, yet I’d never consider myself a “journalist” or even well-connected within the football industry. Some footballers do follow me — mostly the young ones who speak decent English — but I don’t think it’s over a dozen. I don’t personally know any agents or top flight coaches and while some foreign club scouts or analysts write me for a recommendation/reference every now and then, it’s not a regular occurrance by any stretch of imagination. I can’t do shit in Python or any other programming language, and don’t start about the graphics. I consider myself to be more of an influencer — an ugly word these days — than expert.
My day job is, in fact, miles away from the football industry and I’m quite happy with that. I spend insane hours on analyzing this bloody sport as it is.
What I can offer then, I suppose, are the resources (old, affordable Wyscout subscription expiring just this past June — RIP), advanced Google spreadsheet skills (fancy functions and all that) to help me extract intriguing stuff from raw, largely unintereresting data, and some willingness to put the aforementioned to good (?) use on a weekly basis.
It’s still true, after all, that I watched all the games last season even while abroad (hence relying on Wyscout downloads in the absence of VPN-proof O2TV) and did even more detailed tracking of game events than the year before. To give you a rough idea, I have this sort of data on both offensive and defensive side of the game for every game of each team. For the first time in 2022/23, I specifically tracked chances generated off of forced turnovers and credited creators for chances left unfinished not through their own doing. At the risk of sounding arrogant, I can confidently say there’s not a more comprehensive (semi)public dataset on the Czech top flight out there, even if my tracking is inherently subjective (but then so is Wyscout tagging, for example, as their definition of xG literally includes “tagger’s assessment of the danger of the shot” as one of the variables) and thus not flawless. In fact, on the subject of Wyscout and their raw data: I bugged them so frequently they stopped reviewing my tagging requests towards the end of last (22/23) season. That should be a bit concerning for any user of their database, as any goal-line clearance will now continue to be tagged as a shot on target (hence a goalkeeper’s save!), not to mention the odd shot wrongly showing up despite getting flagged for offside etc. For me, it meant even a bigger need for manual corrections so that my conscience is clear.
My mission: I started covering Czech football in summer 2019 to provide an alternative view of it. I was — and still am — missing a chunk of nuance as part of its coverage; something I had ambitiously set about to fix. That’s not to say there are no journalists capable of bringing necessary detail to their reporting, it’s just the broken industry mostly not allowing them to do so; with the immensely insightful podcast iSkaut standing tall as an exception.
There’s always someone to play your views off of, but strangely enough, I find that even those with a voice can’t quite find it in their actual job of a journalist. Articles need to be short to have readers, so they require shortcuts like “this guy has numbers” (ie. goals and assists), “this guy is quick/slow” etc. which ultimately does more harm than good in my eyes.
Longform read on Czech football is notoriously hard to come by, even though the quarterly magazine-turned-website Football Club is providing that platform now, to myself as well as others. That’s for Czech readers only, though. Here I am to (over)compensate in English, too. I can’t personally bring nuance to everything there is to cover, but I’ve luckily been blessed with many capable helpers along the way. Vojtěch Mrklas is always there to teach me a thing or two about tactics when we chat or podcast together, Jakub Dobiáš is always there to confront me with a second opinion on analytics derived from his 11Hacks database, and I have an entire army of fans who get to see the players in person regularly and have their guaranteed spot as part of my team previews. Most data analysts would write fans off for their inherent bias; I rather see advantage in them.
The sample size warning: It should be universally recognized by now that sample size matters and you should never EVER compare a 30-start mainstay with a super-sub worth of a mere 6,5 starts once you put everything together. Yet apparently, this point needs to be driven home over and over fucking again, so here you go. Even the difference between 10 and 20 appearances is notable, and I’ll make sure to point out whenever a comparison I personally make is far from perfect. You’d be surprised how much one start against a lowly bottom-feeder can influence your 10-game dataset as opposed to a 30-game one (where it gets easily buried).
The context caveat: It also matters who you play for, of course. It’s clear that starting in goal for Slavia, for instance, is a very different job to just about any other goalkeeping gig in the Czech top flight. While Slavia coaches may value sweeping and distributing qualities above much else, a less dominant side will mostly lean on the traditional shot-stopping skills of their custodian. This is why our comparison template looks much different compare to the first edition — splitting one dataset into 3-4 dimensions of player role to showcase the oft-missing nuance I mention above. A player might have a low overall percentile (considering all 18-19 metrics), but still be an elite contributor in one area of his game that can be harnessed.
So when you’re reading the previews themselves, please don’t linger too much on the overall percentiles. They are, in a way, one of the shortcuts I was complaining about earlier. The individual layers are where true merit lies. Those who come on top should, in theory, have less holes in their game or be more effective in all phases of the game than anyone else on their position (which does actually pass my eye test more often than not, to be fair). But even when your favourite player doesn’t sit in the comfortable 90+ percentile (meaning he’s done “better” than 90% comparable players), it doesn’t necessarily mean he’s useless. It quite possibly only means he’s not a two-way winger or a very constructive holding midfielder, but that doesn’t take away anything from the fact they might still be bloody dangerous/defensively sound options in one way or another.
Finally, one universal caution: don’t believe anything you see straight away and never take any number at its face value. That’s not how any stats, data of any kind works, and my model — however meticulously construed (and trust me, I’ve gone back and forth on all metrics) — won’t be any different.
Not now, not ever.
If you count yourself among the fans of my, Adam’s or Jakub’s work, please do consider supporting us all together by donating a small amount of money at BuyMeACoffee page. It would be too kind from you and much appreciated by us!
