Turn one match video into a real tactical report.

01

What MatchMind AI actually is

Professional clubs analyze matches using optical tracking rigs — arrays of fixed cameras around the stadium that log every player's position twenty-plus times a second. That infrastructure produces the pass maps, pressing intensity scores, and expected-threat numbers that shape modern coaching decisions. It also costs more than most school teams, university sides, and amateur clubs will ever have access to.

MatchMind AI is built for everyone on the other side of that gap. You already have a video — filmed from the sideline, from the stand, on a phone propped against a fence. That's the only input it needs. Upload it, and a multimodal AI model watches the entire match as video, not a slideshow of sampled frames, the way a human scout would. From that single pass it writes a tactical report: formations, pressing triggers, attacking patterns, defensive shape, individual player performances where they could actually be followed, and a prioritized list of things to work on in training.

It's a web app. There's nothing to install. You sign in, upload a video, and a few minutes later you have a report you can read on your phone, export, or come back to later.

Who it's actually for

• Amateur and school coaches who film matches already but have no way to turn that footage into anything beyond a highlight reel.
• University and academy sides who want a second opinion on tactics between the analyst's report and the next session.
• Individual analysts and scouts building a library of opposition reports without paying for a professional data feed.
• Anyone curious what an AI actually sees when it watches ninety minutes of football closely.

02

The rule that shapes everything else

Here's the uncomfortable truth about asking an AI model to analyze a match: it's very good at sounding confident about things it didn't actually see. Ask most systems for "pass accuracy" on a player the camera only caught twice, and you'll get a number back — clean, specific, plausible, and invented.

MatchMind AI is built around refusing to do that. If the model can genuinely determine a statistic from the footage, it reports it. If it can't — a player was too far from camera, the match had a hard cut between angles, a stat simply wasn't visible in the available shots — that field is left empty, not filled in with a reasonable-looking guess.

Practically, this means two things worth knowing before your first upload. First, better footage gets you a better report — the model can only report what it can see, so a single continuous camera angle at a reasonable distance will always outperform a shaky, far-away, or heavily-cut recording. Second, an empty field is information, not a bug. If a chart or a stat is missing from your report, it means the model genuinely couldn't determine it from your specific video — not that something broke.

03

What's actually in a report

Every completed analysis opens on a match hero — final score, formations, and possession split — followed by six tabs. Nothing here is templated filler; every tab only shows sections the model actually populated for that specific video.

04

Getting started

Open the app and you'll land on a sign-in screen. If you don't have an account yet, use Sign up to create one — you'll need an email address and a password. There's also a pre-filled demo account on the login screen (coach@matchmind.ai) if you just want to look around before committing to your own account. Google sign-in appears on the login screen but isn't enabled on every deployment — if it says so, use email sign-up instead.

Once you're signed in, you'll land on the Dashboard. If you're brand new, it'll be mostly empty — that's expected. It fills in as you analyze real matches, not before.

05

Your first analysis, step by step

1. Open Upload Match

   From the top navigation, click Upload Match. You'll see a drop zone and a Match Details form.

2. Add your video

   Drag your file onto the drop zone, or click it to browse. Here's exactly what's accepted:

   Formats

   MP4 MOV AVI MKV

   Max size

   500MB

   Best case

   One continuous camera angle, steady, wide enough to see most of the pitch

3. Fill in the match details

   Home Team, Away Team, Competition, and Match Date. All are free text — type exactly what you want to see on the report.

4. Add the final score, if you know it

   This field is marked optional but recommended — and it's worth the ten seconds. Giving the real score constrains the model's player-level goal attribution to match reality. Leave it blank and the report will honestly derive a score from whatever goal-scoring moments it could individually attribute — which is sometimes slightly different from the actual result if a goal happened off-camera or too far away to confirm who scored.

5. Click Analyze Match Video

   The upload starts immediately — larger files simply take longer to transfer before analysis begins. Once it's uploaded, you'll see a live progress screen (see the next section for exactly what happens here).

6. Read the report

   When it's done, you're dropped straight onto the finished report, starting on the Overview tab. It's also saved to Match Reports so you can come back to it any time.

06

While it's processing

A typical analysis takes around two to three minutes end to end, most of it spent on the video upload and the AI actually watching the footage. The progress screen tells you exactly what stage it's in, not just a spinner:

• Stage 1 ☁️ Uploading video to the AI model...
• Stage 2 👁️ AI model watching the match (tactics + players)...
• Stage 3 📊 Formations detected — visible once identified
• Stage 4 📝 Generating tactical report...

07

Getting a genuinely better report

Because every number in a report has to be actually observed, footage quality has a real, direct effect on how complete your report comes back — more so than with tools that fill gaps with estimates. A few things that consistently help:

Camera and framing

One continuous shot beats a heavily-edited broadcast with cuts between wide and close angles. A hard cut resets what the model can visually follow, which is the single biggest reason a player-tracking count comes back lower than you'd expect. Wide enough to see most of the pitch beats a tight zoom that only shows the ball.

Give it the score

As covered above — it's the single easiest way to sharpen player-level goal attribution.

Kit contrast

Two teams in visually similar colors (both in dark kits, or bibs over similar training tops) make individual and team identification genuinely harder for any visual system, human or AI. Bright, clearly different kits help across the whole report, not just the Players tab.

Full matches over highlight reels

A full match, even at lower quality, generally gives a fuller tactical picture than a highlight reel — pressing patterns and defensive shape show up in the passages between the exciting moments, not just in the goals.

08

Your dashboard

The Dashboard is your home screen — a live rollup of everything you've analyzed, not a static summary. Four numbers sit at the top: Total Reports, Analyses Done, Processing (anything still running), and Avg. Confidence across your completed analyses. Below that, a Confidence Trend chart plots real AI confidence per analysis over time, your latest report gets a featured card with its single top coaching tip, and a Recent Matches list gives you one-click access back into anything you've run.

Everything here is computed from your own real analyses — there's no seeded or placeholder data mixed in once you've started uploading your own matches.

09

Beyond a single match

Three more sections in the navigation work across everything you've analyzed rather than one report at a time:

Teams

Aggregated tactical tendencies for any team you've analyzed more than once — formations used, recurring strengths and weaknesses, built up automatically as your library of reports grows.

Players

Every individually-tracked player across every report, in one browsable grid, so you can follow someone across multiple matches rather than digging through reports one at a time.

Scouting

A dedicated opposition-scouting view pulling the Opposition tab from every relevant report into one place — built for prepping against a specific upcoming opponent.

10

What to actually expect

In the same spirit as the honesty rule that governs the reports themselves, here's what's genuinely true about running MatchMind AI today — not glossed over:

11

Research beyond the live app

Separately from the product described above, a second, offline detection system has also been built and tested: a pretrained computer-vision model, fine-tuned specifically on football broadcast footage, with a frame-to-frame tracking layer added on top of it. Run entirely on a local machine, it produces genuine bounding-box detections and persistent tracking IDs for players and the ball from real match video.

This is real, working research — but it is not part of what analyzes your video when you use MatchMind AI today. It exists as a separate prototype exploring what a fallback path that doesn't depend on any external AI model could look like. It's kept deliberately apart from the live app, because running it alongside the deployed service would risk the kind of resource crash the live app is specifically engineered to avoid.

12

Frequently asked questions

13

Quick start

The whole guide, compressed:

• Sign up (or try the demo account on the login screen)
• Have one continuous match video ready — MP4, MOV, AVI, or MKV, under 500MB
• Go to Upload Match, add the video and team names
• Add the final score if you know it — worth the ten seconds
• Click Analyze, and give it two to three minutes
• Read Overview first, then dig into Tactics, Players, and Coaching
• Trust the confidence notes — an honest gap beats an invented number