You already know the odds are stacked against you.

Not because you lack knowledge or haven't put the effort in.

But because the machine you're betting against was built specifically to beat you before the starting gun fires.

They run AI pricing systems that chew through millions of data points and re-draw the odds in real time as money moves through their markets.

Those same systems watch the flow of that money right down to individual account level, and the moment a customer starts winning at decent prices, a flag goes up.

That is what you're sitting down against every morning with your phone and a mug of tea.

And for years, I was in the same position you're probably in right now.

Losing more than I won, not through bad luck, but because I was bringing a pen and a hunch to a fight the other side had automated.

Then I stopped trying to out-guess that machine with a form guide and a gut feeling.

And I spent 18 months building something that could meet it on its own terms, in its own language, on the same data.

My name is Ryan Harris, and I'm going to show you exactly what that something produces.

In May 2026, the software I built banked £5,960.40.

That's not a back-test figure or a best-case projection.

It's what settled into my betting accounts across a single month.

Before I explain how any of it works, let me hand you over to a few of the people who saw it first.

When I finally opened the app up beyond my own accounts, I gave a small group 30 days with the morning selections and asked them to keep a record of what actually happened.

Here is what four of them told me at the end of it.

So what do those four people actually share?

Not one of them had been turning a steady profit from betting before they joined.

Each of them arrived with the same profile you'll probably recognise in yourself.

A real love of the horses, a string of promising runs that never quite held together, and a nagging sense that something at the core of their approach was missing.

That missing piece was never more knowledge or another afternoon of research.

It's a systematic, data-driven advantage over a market that reads information faster than any person sat at a kitchen table can hope to match.

And you can have access to exactly the same thing.

But first it's worth understanding why this has been so hard to pull off on your own.

Most punters assume the answer is finding the right system, backing the right tipster, or simply pouring more hours into the form.

None of those things address the real problem.

The real problem is structural, and it has been sitting underneath every bet you've ever struck.

They run pricing algorithms built and maintained by teams of data scientists, refreshed continuously as money moves through their markets, and engineered to pull a long-term margin out of millions of bets a year.

Every horse in every race gets a price designed to be profitable for the house, not a price that accurately reflects each runner's true probability of winning.

The gap between the published odds and a runner's actual chance of winning is where all the real money in betting quietly lives.

Finding that gap reliably, before the market closes it back up, is something no person working by hand can do at the speed and the scale it demands.

The data volume is too large for a human workflow.

The window before the price corrects itself is too short.

And the cold detachment it takes to back a horse purely on the numbers rather than on preference isn't something most of us are wired for.

None of this makes profitable betting impossible.

A properly built model, feeding on the same data the bookmakers use, hunting out mispricings at scale every morning before the first race goes off, changes the picture completely.

That's the difference between finishing the month down and finishing it with a substantial withdrawal from your account.

That difference-maker is Race Decoder.

Let me show you what that gap looks like when it plays out in front of you, because you have almost certainly seen it happen and put it down to luck.

Think about the last 12 months of your betting.

The drift told you the sharp money didn't agree with your assessment.

But the horse still won, which means the morning price was wrong, not the result.

That is a value gap, playing out in plain sight, and paying you by accident.

Now picture a model that hunts those gaps down every single morning, across every meeting, on every race on the card, and hands you the best two or three of them before 8am.

That's not a fantasy or a projection.

It's exactly what the 12-month results table further down this page lays out for you.

Here's something most punters never fully take on board about how the bookmakers actually operate.

They don't just price races from the same form data you can read for free every morning.

Their systems track market movement across every major platform as it happens, follow the flow of money down to the individual account, and pick out which customers keep coming out in front.

If you start winning at fair odds with any regularity, they don't sit back and wait to see how long it lasts.

A flag goes up, your account gets a look, and your maximum stake gets trimmed, sometimes inside a week of a good run getting going.

That's not coincidence or poor timing on your part.

Their pricing models are far from perfect, though, and that is the important part.

They're built to generate margin across an enormous volume of bets, not to price every runner at every meeting with pinpoint accuracy.

At smaller mid-week meetings where public interest is thin, prices lurch around with whatever money arrives first rather than settling from deep independent analysis.

That leaves gaps in the pricing, small ones, brief ones, gaps that seal back up the moment serious money starts to move.

But they are repeatable gaps that the model scans for and is built specifically to catch before they vanish.

That is the territory Race Decoder works in, and it is the whole reason it works at all.

I want to paint you a proper picture of what those 25 years of losing actually looked like, because it will sound familiar.

It wasn't one single catastrophic failure.

It was a long accumulation of smaller ones.

A tipster service I followed for four months that started brilliantly and then quietly came apart at the seams.

A staking system I tried that looked mathematically sound on paper and produced a steady bleed in practice.

A Saturday in the autumn of 2019 where I chased a losing morning across seven meetings and turned a bad day into a properly stupid one, sat in the car outside a garden centre while my wife did the shopping.

Each experience taught me something about what doesn't work.

But none of it taught me what would.

The racing writers whose columns I read every morning had their good spells, but they were as prone to their own blind spots as I was to mine.

The systems I bought either worked for a fortnight and then died, or never worked at all.

And the tipsters I trusted, every last one of them, shared a single fatal thing in common.

They were all working from the same public information as everyone else, with no data infrastructure sitting behind their selections, and no reliable way to find the horses the market had priced wrong.

What I didn't understand then, but understand completely now, is that the problem was never the horses.

My name is Ryan Harris, a 51-year-old family man from just outside Bristol.

I've had two obsessions running in parallel my entire adult life.

My family, and betting on the UK horses.

The second one started young, on a cold Saturday at Chepstow when I was about nine, holding my grandad's betting slip while he lifted me up to see over the rail.

He backed one at 12/1 that afternoon that came from nowhere off the home turn, and I have been chasing that feeling, on and off, for the better part of 40 years since.

The horses got me into bother more than once, never the sort that wrecked anything that mattered, but the slow compounding kind that builds over years of knowing you're putting in the work and still coming up short.

I studied form the way other people revise for exams, tracked trainers, marked jockeys who were finding their stride, paid close attention to the going, and kept a record of every bet I struck going back to 2007.

Those records told me the same uncomfortable story year after year.

Slowly, steadily, more money was leaving my accounts than was finding its way back in.

Not dramatically, not in a way that threatened anything important.

I tried tipster services, bought systems off the internet, followed famous names and completely obscure ones.

I had good patches, weeks where everything clicked and I felt like I'd cracked it.

Then the run would end and the losses would quietly undo most of what I'd built.

I kept thinking I was one more piece of information, one better system, one smarter angle away from finally getting on top of it.

That feeling kept me going for a long time.

It also kept me losing, right up until the day I stopped thinking like a punter and started thinking like an engineer.

By profession, I'd spent 18 years working as a software systems architect.

My job was designing the data-processing infrastructure that large organisations rely on to handle huge volumes of complex information in real time.

The kind of systems that pull in millions of records a day, spot the patterns hidden inside them automatically, and turn that into a reliable answer in the time it takes you to blink.

I understood how to take a messy, complicated data problem and engineer something reliable out of it.

Banks, logistics firms, a couple of names you'd know, all paid me to do essentially one thing: find the signal buried in the noise and build something dependable on top of it.

But I kept those skills completely separate from my betting life.

Work was work, and the horses were the hobby I couldn't quite bring to heel.

Until one Tuesday afternoon in late 2023, sitting at my desk staring at a race card I'd already spent two hours on, something clicked.

I'd backed a horse the week before with complete confidence at 3/1 in the morning.

By post time it had drifted out to 9/2.

The market had been telling me something I'd ignored completely.

Money drifting away from a horse before a race usually means the sharper side of the market doesn't share your confidence in it.

But the raw data I'd never properly examined suggested that horse was actually better value at 9/2 than it had been at 3/1 first thing that morning.

I'd spent 25 years watching racing like a form student.

I should have been looking at it as a data engineer.

The maddening part was that I'd been that data engineer the whole time, forty hours a week, for someone else's problems.

That thought planted itself and didn't shift.

I started pulling historical race data that same week.

Not newspaper tips, not form guides.

Raw data from several feeds covering thousands of races going back multiple seasons: every declared runner, every result, every going description, every price movement from the overnight show through to the off.

The first thing that stopped me was the state of the data itself.

Field names that didn't match across sources, trainer names spelled three different ways depending on which feed you pulled from, going descriptions that meant slightly different things at different tracks, course names that wouldn't line up cleanly between providers.

Before a single predictive model could run, that entire pile had to be cleaned, standardised and stitched together properly.

That step alone ate up weeks of evenings after the kids were in bed.

Cross-referencing records, patching holes, building the data pipeline that everything downstream would depend on.

I'd spent enough of my professional life watching predictive systems fail because they were built on a bad foundation.

Feed a model corrupted or unreliable data and it learns the wrong lessons, producing plausible-looking outputs that fall apart the moment real money is on the line.

That's not a shortcut I was willing to take here.

There's a phrase we used to throw around at work: rubbish in, rubbish out.

In my old job that meant a wrong number in a quarterly report, which was embarrassing but survivable.

Here it would mean my own money walking out of the door, dressed up as a confident selection, so I treated the cleaning stage like the foundations of a house rather than a chore to hurry through.

Once the foundation was solid, the real work began, and it looked nothing like handicapping.

I didn't sit down and write a set of rules that said "back the horse if this and this and this are true."

That's how most betting systems are built, and it's exactly why most of them fall apart the moment they meet a race that doesn't fit the mould they were carved from.

Instead I trained a machine learning model on the history, which is a different animal altogether.

In plain terms, I fed it thousands upon thousands of past races, each one broken down into hundreds of measurable details, and let it work out for itself which of those details actually moved the needle on the result.

Nobody told it that a certain kind of horse tends to go well in a certain kind of race.

It found those relationships on its own, by being shown what happened tens of thousands of times and being scored, over and over, on how close its estimate came to reality.

The output I cared about was never a tip.

It was a probability, a cold percentage chance that each runner would win, built from the data rather than from a feeling.

And a probability is a thing you can test, which a hunch never is.

There's a concept in this world called calibration, and it became the yardstick I lived by while I was building the thing.

When a good forecaster says there's a 70% chance of rain, it should actually rain on roughly 70 of every 100 days they say that, no more and no less.

When it said a horse had a 30% chance, I went back and checked that horses it rated at 30% won close to 30 times in every hundred across the history.

If they were winning far less often, the model was overconfident and useless to me, however clever it looked.

Getting that calibration right, across every band of probability, took me the best part of a winter.

The other trap I spent months dodging is the one that quietly kills nearly every betting system ever sold.

It's called overfitting, and it's the difference between a model that has learned and a model that has simply memorised.

If you let a model study the same history for long enough, it will eventually "explain" every result perfectly, right down to the fluke wins and the freak days.

It looks spectacular on the races it was trained on, and it's worthless on the race being run tomorrow, because it has memorised the past instead of learning the patterns that carry into the future.

So I never let the model see the races I was testing it on.

Then I did it again, and again, rolling the window forward through the years, so that every judgement was made on races that were, as far as the model was concerned, still in the future.

That is a slow and humbling way to build something, because it kills your favourite ideas one after another.

It's also the only way I know to end up with a model you can hand real money to without lying to yourself.

Once the model could be trusted, I started asking it which factors actually predicted outcomes.

Not which ones sounded important to racing fans.

Which ones, tested coldly across thousands of historical races, kept lining up with results when you isolated them from everything else.

Some findings were straightforward, some were mildly surprising, and a handful of them flattened assumptions I'd carried around for 20 years.

Factors the racing public treats as gospel turned out to carry almost no independent predictive weight when you isolated them properly from everything else.

A big-name jockey in the middle of a hot streak.

A stable that had sent out a cluster of winners at a particular track recently.

A horse that looked overdue a win after a run of near-misses.

Not because the data reliably backs them up.

And that matters for one specific reason.

When a market prices a horse based on factors the data says don't reliably predict outcomes, that price is wrong.

When a price is wrong and you already know the true probability, you're holding a real advantage.

The entire app is built around finding exactly that, over and over, before anyone else does.

Here's what sets this apart from anything a human tipster can offer you.

The sharpest tipster alive weighs up somewhere between 20 and 30 factors when forming a view on a race.

That's their ceiling, not because they're cutting corners, but because that's the upper limit of what the human brain can manage reliably before fatigue, impatience and personal bias start bleeding into the process.

The model assesses hundreds of weighted variables for every declared runner across every meeting on the day's full card.

Not just whether a horse has won at this course before, but on what going, over what trip, in what grade of race, with which rider, coming off what kind of recent run, carrying what weight, and how its price has shifted from the overnight show to the morning market.

It also weights those variables differently depending on the exact shape of the race in front of it.

The position a horse comes out of the stalls matters enormously in a big-field sprint handicap round a tight, turning track where one side of the course has long held an advantage.

In a staying race over two miles on a wide, galloping track, that same starting position barely registers.

Trainer form is a strong signal in maiden races and novice events where there's limited individual performance history to analyse.

In a competitive open handicap with 20-plus runners, it counts for far less, because that information is already sitting in the price.

Ground preference matters hugely for a horse with a real leaning toward one particular surface, and hardly at all for one that handles most conditions equally well.

How a price has travelled, from its opening show through to the morning market, gets its own weighting that shifts depending on the race type and the track.

The model learned these distinctions from thousands of historical races.

That's the critical difference between a rule-based filter and a trained machine learning model.

Fixed rules crack at the edges of the data they were built for, or the moment conditions shift in a way the person who wrote them never saw coming.

A trained model adapts, because it's absorbed enough context to understand which signals actually matter in which situations.

The app also gets better on its own, every day, without me touching the underlying logic.

Every morning I feed in the full results from the previous day.

Winning selections confirm the model's probability estimates were in the right range.

Losing selections get pulled apart to work out which variables were overweighted for that specific race context.

The model adjusts those weightings before the next morning's selections are produced.

None of these adjustments are dramatic on any given day, and that's rather the point.

It's a nudge here and a fractional re-weighting there, the sort of small correction that would be invisible over a week and is unmistakable over a year.

Applied every single day for 18 months, that compounding improvement is exactly why the 2026 numbers are running ahead of the ones that came before them.

The core logic behind the selections is this.

I stopped trying to find horses I thought would win.

I started finding horses whose true chance of winning sits meaningfully above what the bookmaker's published odds are quietly implying.

Let me show you the maths, because it's simpler than it sounds and it's the whole game in one paragraph.

A price of 3/1 is the bookmaker telling you a horse has roughly a 25% chance of winning, because it wins one time in every four that price is on offer.

A price of 9/2 implies closer to an 18% chance.

So if my model has done the work and rates that horse's true chance at 25%, and the market is offering me 9/2, I'm being paid a 25% chance at an 18% price.

That gap is not a feeling or a fancy, it's a mispricing, and backing it enough times is how you end a month in front instead of behind.

A horse with a 25% chance of winning should be priced around 3/1.

If the market has drifted it out to 9/2 or 5/1, that runner is mispriced, and there's real money on the table.

The horse doesn't have to win that specific race for the bet to be the right decision.

It just needs to win often enough, at prices that reflect the gap between the model's probability and the bookmaker's price, across a large enough sample to produce a steady profit.

That is the exact logic the bookmakers use against you, extracting a margin by being right about probabilities slightly more often than not, across a colossal number of bets.

The app simply runs that same logic in reverse, hunting for the runners where the bookmaker's own probability assumptions have slipped.

The selections don't win every time.

But they win often enough, and at prices big enough, to build a real profit month after month.

Day one profit: £290.

By the end of that first January, the model had flagged 21 selections and 13 of them had won.

A 62% strike rate and £4,210 in profit, more in a single month than I'd made from betting in any full year before I built it.

The momentum carried on through the spring and summer that followed.

There were quieter months, weeks where the selections didn't land at the same rate.

Short-term variance is unavoidable in any probabilistic system.

But the model soaked those patches up the way a sound process should, running to the same criteria regardless of recent results, feeding every losing selection back in as fresh training data, and coming back sharper for it.

Not a single month across the whole of that first year finished at a loss.

Then April 2026 delivered the best single month since the app went live.

23 winners from 33 selections, a 70% strike rate, and £7,080 in profit.

By that point the model had digested another full year of live results on top of everything that came before, and the jump in selection quality showed up plainly in the numbers.

May kept the run going at £5,960, and that is the money I told you about at the very top of this page.

Five months, one after another, every one of them in profit, with the two quieter ones still clearing £4,300 apiece.

By the middle of 2026, I'd stopped being surprised by the monthly figures.

Not because the numbers had become ordinary, but because the process behind them had become so reliable that a profitable month felt like a confirmation rather than a result.

By then the model had processed well over 350 live races.

Each one had fed back into the training data.

Each losing selection had adjusted a weighting somewhere in the model, marginally, but cumulatively in a way that was showing up clearly in the outputs.

That's the compounding effect of a model that keeps learning.

And it's still improving.

Here's the full 12-month picture.

Let me put those figures into practical terms for a moment.

At £50 flat stakes per selection, which is what the results table above is based on, the worst month in the last 12 (June 2025 at £3,620) still represents a 72-point return on the month's total investment.

The best month, April 2026 at £7,080, represents a 139-point return.

The average across the 12 months comes to just over £5,367 per month at those stakes.

You don't have to bet at £50 a selection.

The point is that the advantage the app produces is steady and measurable, and it never once relies on backing long shots or stacking up an accumulator that has to land perfectly.

These are straightforward win-only bets on UK horse races, placed one at a time, with flat stakes.

The kind of betting that doesn't require a complicated staking plan or a large bank to get started with.

The real pricing slips live in the overlap between less obvious data points that nobody's looking at together.

A person working by hand simply can't juggle enough variables at once to keep finding those combinations.

It doesn't wake up on a grey Thursday morning and decide it doesn't fancy the card.

The same process runs at the same standard every single morning without exception, regardless of what happened the day before.

The third problem is the total absence of a feedback loop.

A standard tipster sends a selection out, it loses, and they move straight on to the next one.

Nothing about that lost bet feeds back into how the next selection gets made.

The fourth problem is scale.

The moment a successful tipping service grows its subscriber base significantly, the collective weight of money following the same selections starts to move the market price before everyone can get their bets placed.

The very value that made the selection worth taking begins to rot from the inside.

That's a structural ceiling every successful tipster eventually runs into.

Keeping the group deliberately small is the only way to protect that value for everyone inside it.

People often ask me whether the bookmakers will eventually work out what the app is doing and close the gap.

It's a fair question.

The bookmakers' own AI pricing systems do improve over time, and they will keep improving.

There's also a structural reason this advantage is more durable than most people assume.

The bookmakers price thousands of races per day across multiple sports.

Their pricing resources are spread across an enormous range of markets.

I want precision, and precision is a much easier thing to win at when you only have one pool to fish in.

Right now, that precision is producing the results you can see laid out in the table above.

That steadiness across every kind of condition is what makes the 12-month record actually mean something, rather than being a run of results from a narrow window of good weather.

Every morning, before the first race of the day goes off, the model runs through the full declared card.

Every runner at every meeting gets assessed against hundreds of weighted variables.

The selections where the model's calculated win probability sits meaningfully above the available market price are flagged and packaged into an email.

That email lands in your inbox before 8am.

You open it, place the bets at your usual bookmaker, and get on with your day.

No form guides to plough through and no statistics to decode, because the model has done the heavy lifting hours before you're even awake.

It won't require you to have...

Technical ability of any kind (all you do is copy the selections and place the bets).

Significant free time (5 minutes in the morning is the full commitment).

Prior racing knowledge or experience (the model has assessed everything there is to assess).

For the first time since I started running Race Decoder privately with a small group, I'm opening 75 places to the public.

75 spots, and not one more than that.

But I'd strongly suggest not relying on it being there if you come back later.

No hidden charges.

The model runs every morning regardless of what's on the card.

Whether it's a busy Saturday with seven meetings and 50-plus races, or a quiet Tuesday with three meetings and a thin card, it processes everything available and only sends a selection where the value gap is wide enough to act on.

On days where nothing clears the threshold, no selection goes out and I'll say so clearly in the email.

Quality over volume is the principle it runs on, and it's the reason the strike rate in the table above has stayed above 50% in every single month.