Heat Maps and the Prettiest Lie in Modern Football

In June 2026, in a bar in Liverpool, I watched France play Argentina. On the big screen the whole room screamed Kylian Mbappé's name every time he accelerated, and he deserved every scream. But my eyes were fixed on someone else: the shortest man on the pitch, the most touches in France's midfield, and — in my own notes across the match — a player who did not lose the ball once under pressure. That man was N'Golo Kanté. Three days later I wrote a piece titled 'Kanté was the Mbappé of this match' and posted it on a small fan page. It was mocked first, shared later, and then an admin of a tactics group invited me to write regularly.

Eight years on, I still read that match the same way. And the longer I work, the more I believe something uncomfortable: the data toolkit the whole football industry worships is getting worse at describing what actually happens on the pitch. Not because it is mathematically wrong. Because it answers, brilliantly, questions nobody really needs.

I grew up with the generation that watched data enter football like a new religion. When I was in high school in Liverpool, 'analysis' meant rewinding tapes and taking notes by hand. By the time I reached university everything had changed. Clubs hired whole data-science teams; every match was logged as thousands of events; and anyone could open a website and see a player's heat map after a single click.

That was a real revolution, and I do not dismiss it. Thanks to data, small clubs like Brentford and Brighton found a way to compete with giants by buying the right players the naked eye overlooked. Thanks to data, injuries are caught earlier and young talents are managed better. But alongside that, a generation of fans — and a sizeable slice of the commentariat — learned to watch football through exactly one lens: the lens of ready-made numbers.

The trouble is this: heat maps, touch charts, and even the most complex models exist to answer 'where was this player' and 'where did the ball go.' Very few of them answer the harder question: 'why.' And when a match we watch is framed by the easy questions, we slowly forget we are reading a map instead of watching the whole territory.

Start with the most popular tool of all: the heat map. On any player's profile, it is the first image people look at. But a heat map is nothing more than a map of touches. It does not distinguish a touch in midfield while your team controls the game from a rushed touch while your team is being pinned back. It does not distinguish a line-breaking pass from a safe sideways one. A player who drops deep, receives, and passes back twenty times will produce a prettier heat map than a midfielder who touches the ball seven times but unlocks the defence on all seven. The heat map has become a new kind of fortune-telling: it gives us the comfort of feeling we understand, while in truth it merely redraws what happened, never explaining why it happened.

Based on my experience watching matches, I saw this most clearly when I revisited the path of Trent Alexander-Arnold. In 2026, when he had only a handful of Liverpool appearances and was heavily criticised for his defending, I wrote a long piece on my personal blog. Across five Premier League matches I tracked, he created twelve chances — more than anyone else in the squad among defenders. But the interesting part was not the number. It was that when I placed those actions beside his heat map, the picture became contradictory: read only the heat map and you would think this was a right-back charging up and down without order. Watch the match and you realise he was playing a role closer to a deep-lying playmaker — a right-sided tempo-setter that no heat map can describe. People called Trent an enemy of defending. I saw a man holding a map most people had not learned to read.

Now look at the stat pundits cite most: possession. It is the most deceptive number in modern football. A team with sixty per cent of the ball — most of it sideways passes between centre-backs — gets described as 'controlling the game.' Meanwhile a team with forty per cent that drives the ball into the space behind the defence every time it has it is the one truly controlling the match. Possession measures who is holding the ball, not who is dictating the game. Those are two different things, and confusing them has produced countless hollow compliments and unfair criticisms.

Here I have to mention xG — expected goals — because it is the darling of the new analytics generation. xG is genuinely useful: it tells you the quality of a shot based on position, angle, and other variables. But xG does not measure the process that created the chance. A team can finish a match with a high xG thanks to a single random moment, while spending the whole game pinned back with no attacking idea at all. xG is output data; and we are using output data to judge the quality of an input process we never watched. That is why I resist analysis made only of numbers. Once it becomes a ritual of the commentariat, it stops being analysis — it becomes a retelling of the result in a more scientific voice.

I learned this involuntarily in 2026. When the Premier League was suspended indefinitely and Liverpool — my city's club — sat twenty-five points clear of Man City but could not be crowned, I fell into emptiness. Then, out of curiosity, I turned to old matches. Empty stadiums, the noise switched off, and suddenly what the crowd's atmosphere had hidden began to surface: players standing in the wrong positions, pressing traps nobody spotted, passes missed because there was no roar from the stands. The stadium empty, the noise dead, and something I thought was dead began to grow — the ability to watch a match at its deepest layer. That is where I started writing about football economics, because I realised data cannot explain why small clubs collapse faster than big ones, or why an injury strikes exactly when a team needs one man most.

And here is what I want to make clear, because it sits at the centre of everything: when your tool fails to capture the phenomenon you want to measure, you get an empty result — and the human habit is to fill that gap with guesswork. That is how football's daily verdicts are born. A player with pretty numbers is raised into a star; a player without supporting statistics is framed as a burden, even when he is doing exactly the job his coach gave him. The ball does not roll by calculation. It rolls by the fear of being left behind, by the instinct of a player trying to survive inside a system no outsider can see. When I sit at the screen, I try to read the lines the ball writes — not the numbers printed after the match is over.

But if I stopped here, I would turn myself into a man who opposes science just to look different. The truth is data has saved many careers and many clubs. Brentford reached the Premier League by recruiting with a model no big league would use. Brighton sold undervalued players for many times what they paid. Small clubs in overlooked countries found a lifeline through analytics. If I denied all of that, I would just be performing on the stage of cynicism.

What I doubt is not data, but how we read it. People call Trent an enemy of defending; I see a man holding the map backwards — but I also have to admit that sometimes that backwards map led him genuinely into a dead end. There were games where Trent defended so badly it was hard to excuse. And there were moments when I was so drunk on hunting the 'quiet hero' that I quietly diminished stars who truly deserved it. The most hated man is only the one who dares stand before the mirror everyone else avoids — but not everyone who is hated is the victim of a misunderstanding. That is what I remind myself every time I write.

So my argument is not 'throw the data away.' My argument is this: data must serve observation, not replace it. If you begin and end with a spreadsheet, you are not analysing football — you are doing football's accounts.

Eight years after that France–Argentina night in a Liverpool bar, I still believe the same thing: the real match lives where no heat map shines. If the big season ahead is overwhelming you with hundreds of metrics, here is a different way to start: turn the match on, close every data tab, and for the first twenty minutes ask who is trembling, who is hiding, who is driving. Then open the heat map and see whether it agrees with your eyes. The gap between those two answers — that is where football actually lives. Will you trust your eyes, or the chart?