About CF

The team

Tomáš Daníček

Writer, Data Interpreter, Webmaster

The one who talks to you all the time and you are fed up with him. “My credentials” are, in fact, his.

Jakub Křižanovský

Pizza Chart, Comparison Table and Depth Chart Craftmaster

A graphic designer of 5 years, dabbling in marketing, photo and film production alongside his studies.

Jakub Černý

Master Forecaster

Running an actual model, not just a fancy spreadsheet, the beloved Český Mistr is his unique brainchild.

Dominik Kršík

CF Logo Creator and Team Card Craftmaster

While active in the music industry, you can find his original footprints across the popular Fantasy account.

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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 and coaches do follow me — mostly the young ones who speak decent English — but I think it’s barely over a dozen in total. I don’t personally know any agents or other influential figures and while some foreign club scouts or analysts text me for a recommendation or reference every now and then, it’s not a regular occurrence 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 outsource these.

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 is. What I can offer then, I suppose, are the resources (Wyscout), 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 semi-regular use.

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. In 2023/24, I zeroed in on involvement in goals/chances created/allowed, to speak further to one player’s positive value in build-up or, conversely, negative value in defending his own zone. In 2024/25, I’ve included more contribution types for variety and focused on over-reliance on certain chance/goal creators, mostly just playing around the edges of a database I was now happy with.

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 greater need for manual corrections so that my conscience is clear in serving you all the pizzas and other charts.

What’s behind each quirky metric referred to?

While they don’t ultimately matter, fully understanding them does.

Read on
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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’m trying 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; the insightful podcasts iSkaut/ZOOM mostly stand tall as exceptions.

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; so no goals, assists, clean sheets on our charts.

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, Kuba & Kuba are always there to confront me with a second opinion on analytics, 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 analysts would write fans off for their inherent bias; I rather see positives.

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The caveats

Sample size

It should be universally recognized by now that sample size matters and you should never compare a 30-start mainstay with a super-sub worth of a mere 6.5 starts once you put all mins together. Yet apparently, this point needs to be driven home over and over fucking again. 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 absorbed).

Context/Environment

It also matters who you play for. 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 presents the data the way it does — splitting one dataset into 3-4 areas of play/skill to capture 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 easily harnessed.

Player specialists

So, more to the previous point: when 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% regulars on his position), it doesn’t necessarily mean he’s useless. It might just mean he’s not a two-way winger or a constructive holding midfielder, but that doesn’t necessarily prevent them from offering dangerous/defensively sound options in one way or another — ultimately be useful.

And finally…

One universal caution: don’t believe anything you see straight away and never take any number at face value. That’s not how 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.