Part 1 of 4: The Gecko Edge Performance Series

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We took every recommendation Gecko Edge produced over a one and half month window (Jan – Feb 2026) — pre-match and in-play, across 66 competitions — and ran the numbers. No cherry-picking. No filters. Every single bet, win or lose.

8,439 +EV recommendations. Flat 1-point stakes. Here’s what happened.

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The Headline Numbers

Across the full sample:

• +398.26 points profit on flat 1pt stakes
• 4,185 winning bets from 8,439 total recommendations
• 66 competitions analysed across six continents

Breaking that down by dataset:

Pre-match (all user recommendations): 2,974 bets, +190.19 points, +6.40% ROI, 54.1% strike rate

Pre-match (unique recommendations): 1,747 bets, +104.71 points, +5.99% ROI, 53.7% strike rate

In-play: 5,465 bets, +208.07 points, +3.81% ROI, 47.9% strike rate

The “all” pre-match figure includes duplicate recommendations — the same analysis surfaced to multiple users running the same prompt on the same fixture. The “unique” figure strips those duplicates out and gives the cleanest performance picture. We’ll use the unique dataset for the deeper analysis throughout this series.

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What Do These Numbers Actually Mean?

Let’s put +398 points into context.

On flat 1-point stakes, that’s the equivalent of turning a 1,000-unit bankroll into 1,398 units in less than two months. At £10 per point, that’s £3,983 profit. At £50 per point, it’s £19,913.

But the raw profit isn’t the real story. What matters more:

Every dataset is independently profitable. Pre-match works. In-play works. They’re not propping each other up — they’re both generating a genuine edge.

The strike rates are healthy. 53.7% pre-match at average odds of 2.13 is sustainable. You’re winning more than half your bets at prices that pay. InPlay runs at 47.9% but at higher average odds, so the maths still works.

The sample isn’t small. 8,439 bets across 66 competitions over less than two months gives genuine statistical weight. This isn’t 50 bets on a lucky streak — it’s thousands of independent events across different leagues, different bet types, and different market conditions.

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Where the Profit Comes From

Here’s where it gets interesting. The +398 points profit is spread broadly across markets, leagues and prompts. However, there a number of clear trends where the profit concentrates in specific areas.

Some of the early signals from the data:

The model’s best pre-match bet type returned +50% ROI from 172 bets. When the model flags this market, it’s right nearly three-quarters of the time.

One 15-minute in-play window generated 90% of all in-play profit. Timing your entries isn’t just useful — it’s the single biggest variable in whether in-play betting will work for you.

Gecko Edge’s confidence rating creates a clean dividing line. Above a certain threshold, +9.98% ROI and a 17.5 percentage point improvement is achievable.

This stands to reason. Gecko Edge delivers probabilities, not tips. The more accurate the data, the clearer the trend and the more confident that the Gecko Edge AI is, the better the returns.

Mid-tier leagues outperform the Premier League by a significant margin. The model finds more miss-pricing where market efficiency is lower — which makes complete sense when you think about where bookmakers focus their sharpest pricing.

We’ll unpack all of this across the next three parts of this series.

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This Isn’t Guaranteed Success

This isn’t a track record. We’re not running a tipping service that claims “follow us and you’ll make X.”

What this is: a transparent, data-led look at how the tool performs when you point it at real fixtures and let it run. The purpose is to show you what works, what requires more research and decision makring, and — most importantly in Parts 2, 3, and 4 — an example framework to help you get the most from the insights.

Because the real insight from this analysis isn’t “Gecko Edge makes money.”

It’s that the profit concentrates in specific bet types, specific timing windows, and specific confidence levels.

Nobody has the time to place 8k bets a month. But if you know where to focus; what to look for; the most optimal opportunities, then you multiply your edge.

That’s what this series is about.

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What’s Coming Next

Part 2: Where To Find The Best +EV AI Bets Which bet types deliver, which ones the model gets wrong, and the odds sweet spot where value concentrates. Plus why one bet type is so bad it should carry a warning label.

Part 3: The 15-Minute Profit Window The in-play deep dive. When to enter, which prompts to use, and the single most profitable in-play angle in the entire dataset.

Part 4: The Gecko Edge Playbook – How To Profit With AI Betting The decision framework. Combining every insight from Parts 1-3 into a clear, actionable set of rules for getting the most from Gecko Edge — whether you’re pre-match, in-play, or both.

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About the Data

• Sample period: January – February 2026
• Staking: Flat 1-point stakes throughout (no variable staking)
• No survivorship bias: Every recommendation included, win or lose
• Result types: Win, Loss, Half-Win, Half-Loss, and Void (scratched)
• Competitions: 66 leagues and tournaments across Europe, South America, Middle East, Asia, and Oceania