Watching match footage is still the most authentic way to understand how a team plays and what a player can truly do. Nothing replaces sitting through 90 minutes to feel the rhythm, the spacing, the moments of brilliance and panic.

But video is expensive. Time-expensive. Attention-expensive. You can’t scout every team, every match, every season this way.

That’s where data earns its place — not to replace the eye test, but to make decisions efficient. And if someone tells you “data is useless, only the match matters,” what they really mean is they haven’t found the right data to reflect what the match is showing.

Recently, I tested this idea on Arsenal’s 2024–25 season.

Using only public match data from Sofascore — no Opta, no StatsBomb, just freely available stats — I broke down Arteta’s open-play structure into three distinct attacking patterns:

• Pattern A · Right-Side Overload (~55%, primary) — White inverts high, Ødegaard occupies the right halfspace, Saka isolates the left-back. The chain ends in a cross or cut-back to the centre forward.
• Pattern B · Left Isolation / Underlap (~25%) — Martinelli pinned wide, Calafiori underlaps, low cut-back to Merino.
• Pattern C · Vertical Transition (~20%) — Rice and Raya spring play vertically; Havertz drops to link, the wingers attack the channels.

For each pattern I drew three views: a role-link map (the shape of the pattern), a per-player focus grid (each role’s linkages in isolation), and a role-execution grading pitch (how well each role actually performed its task, with the chain’s bottleneck ringed in black).

Pattern A · Right-Side Overload (~55%)

The primary pattern. The heavy links live on the right: Saliba’s carry into White and Ødegaard, the Saka–Ødegaard–White triangle, then a cross or cut-back. The per-player grid shows how concentrated the structure is — Ødegaard and Saka’s panels are dense, Martinelli’s nearly empty.

Grading the execution role by role: Saliba A+, Rice A+, Saka A+, Ødegaard A. The build and progression phases are elite. Then the chain reaches the box.

The bottleneck — the dark ring — sits on the centre forward. Havertz grades C: big-chance conversion poor. Every upstream role does its job; the last one doesn’t.

Pattern B · Left Isolation / Underlap (~25%)

The mirror-image idea: Martinelli held wide for the 1v1, Calafiori underlapping into the channel, a low cut-back arriving for Merino. The link weights show the Martinelli–Calafiori axis carrying the whole pattern.

This pattern has two breakers, and neither is in the build-up.

Martinelli grades C (1v1 success and finishing down across 24–26), and Calafiori grades C (underlap dynamism limited). The back seven all grade A or better — the platform is fine; the left-side blade is blunt. Havertz, on the near-post pin, again grades C.

Pattern C · Vertical Transition (~20%)

The direct route: Raya or Rice plays the first vertical pass, Havertz drops to link, Saka and Martinelli run the channels, the 8s arrive late.

Raya B+, Rice A+ on the first pass, Saka A on depth runs. The hinge of the whole pattern is the dropping striker — and that is exactly where it breaks.

Havertz, the designated link, grades C: link play and spin inconsistent. The launch pad is elite, the runners are ready, the connection point fumbles.

The Finding

For each pattern, I mapped all 11 roles’ tasks and graded execution. The finding was striking: in two of three patterns, the chain broke at the same role — centre forward. The structure built elite chances, but the finisher didn’t convert. In the third, the same player broke the chain in a different function.

The data-driven conclusion was simple: Arsenal’s number-one recruitment priority had to be an elite, ruthless No. 9.

This summer, Arsenal signed Viktor Gyökeres.

And this season, they ended a 22-year wait to lift the Premier League title.

The data didn’t replace the football. It pointed exactly where the football was already telling us to look — just faster, cheaper, and more communicable.

If you find yourself saying “the data doesn’t capture what I’m seeing,” the answer isn’t to abandon the data. It’s to design better data.

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Data: Sofascore public match data · Charts: synthetic role-link weights, style adapted from The Athletic’s pass-network charts · Stack: Python + matplotlib