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47M neural network parameters 2 sec for a full match breakdown 200+ factors per outcome xG form, lineups, schedule, weather 20 years of top-league history in model training LIVE
AI SPORTS ANALYTICS

The future of sports predictive analytics.

An institutional-grade neural network model for deep analysis of sports statistics. The AI evaluates team form, lineups and schedule density, building an objective probability map.

Model parameters47M
Match breakdown2 sec
Factors per outcome200+
First purchase−50%

Free picks for today

Five picks for today — free, no sign-up. Results update during the day.

Picks with odds 2.00+ — paid access

Every pick of the day with odds from 2.00 — most confident first, with predicted score and results.

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How the free picks performed

Paid picks statistics — in your account after signing in. Sign in

Big Data and serious math under the hood

ORAKL is not an "expert opinion" — it's a computational pipeline: from raw data to a calibrated probability for every outcome.

01 / DATA

Big Data core

A database of tens of thousands of matches is updated in real time. For every match, the system collects over 200 features: expected goals (xG/xGA), pressing style, tempo, market odds movements, lineups, injuries, schedule density, referee assignments, and even the weather at the stadium.

200+ features xG / xGA live updates
02 / MODEL

Our own computing engine

Built from scratch for football: the model weighs team strength, line-ups and match context and turns it all into the probability of every outcome. No pundit opinions, no manual tweaks — the same calculation for every match.

in-house technology line-ups and context no manual tweaks
03 / EDGE

Finding the edge over the line

The model compares its own probabilities against the bookmaker's line and calculates the expected value (EV) of every outcome. Only predictions where the market systematically underestimates the probability make it to the feed — everything else is filtered out by a confidence threshold and historical quality control.

EV selection backtesting risk gates

Not an LLM. A proprietary computation engine

We deliberately do not use large language models to compute predictions. That's not marketing — it's an engineering decision.

Ограничения LLM

What a language model is not

  • The context window is physically limited: 20 years of matches times 200+ features fit into no existing LLM — the model sees only a fragment of the picture
  • An LLM predicts the next word, not the probability of an outcome: its “confidence” is not calibrated on historical data
  • Hallucinations: the model can confidently operate on statistics that don't exist
  • No reproducibility: the same match yields different answers on different runs
Движок ORAKL

What we built instead

  • A specialized stack: gradient boosting, a 47M-parameter neural network, and a bivariate Poisson score model
  • Context without a window: the entire data history is available to matrix computations directly, not squeezed into token “memory”
  • Calibrated probabilities: a stated 65% comes true about 65% of the time — verified on out-of-sample data
  • Deterministic and backtested: every prediction is reproducible and validated on 20 years of top-league history

What sets the engine apart from professional analysts

The predictions market is overheated with promises. We take a different position — and we have grounds for it.

01 / TRUST

Transparency instead of fabricated stats

The vast majority of private tipsters live off appearances: deleted losing posts, unverified screenshots, claimed “90% hit rates”. Plenty of them earn not from predictions but from bookmaker referral links — where your loss is their income. We never delete history, run no referral programs, and don't partner with bookmakers.

02 / SCALE

Human factor eliminated

Even an honest, skilled analyst is limited by biology: attention, sleep, emotions after a losing streak. A full breakdown of a single match takes a human hours — analyzing every match every day is physically impossible, and important details inevitably slip through. The engine breaks down every match in full in 2 seconds, with no fatigue and no tilt.

03 / PROOF

Stats you can verify

Your account holds detailed stats for every published prediction: date, odds, result. Nothing is deleted or edited retroactively. Any period can be checked independently — exactly how a model should be evaluated.

04 / HONESTY

No guarantees — and that's honest

We don't promise winnings — and you shouldn't trust those who do. ORAKL works like meteorology: the model computes probabilities rather than predicting the future. “70% chance of precipitation” doesn't guarantee rain — but it helps you make decisions more accurate than the market's, over the long run.

Plans

Credits
1 credit
credits
990 ₽
first purchase — ₽525 (−50%)
  • Open any standard prediction
  • Credits never expire
  • Results and stats update automatically
Buy or subscribe in the Telegram bot
5 credits
credits
3 990 ₽
₽850 per credit
first purchase — ₽2 115 (−50%)
  • Open any 5 standard predictions
  • Credits never expire
  • Results and stats update automatically
Buy or subscribe in the Telegram bot
15 credits
credits
9 990 ₽
₽710 per credit
first purchase — ₽5 295 (−50%)
  • Open any 15 standard predictions
  • Credits never expire
  • Results and stats update automatically
Buy or subscribe in the Telegram bot
Subscription

How it works

From choosing a plan to your first prediction — under two minutes.

01

Choose a plan

A single prediction, a package, or a 30-day subscription. Newcomers get 50% off their first prediction.

02

Pay on the site

Card payment via the secure YooKassa gateway. Access activates automatically — right after payment.

03

Get predictions

Predictions with breakdowns and model confidence — in your personal account. Results settle automatically, stats and ROI — right there.

For new users

Put the model to the test

First prediction — 50% off: 1 050 ₽ 525 ₽
A full match breakdown by the neural network — the rest is up to you.

Start analysis

Technical questions

What artificial intelligence architecture does the ORAKL platform use?
The ORAKL ecosystem is built on proprietary deep machine learning (Deep Learning) models and custom recurrent architectures. The neural network processes structured and unstructured Big Data arrays with sub-100ms latency. The model was trained on a historical sample of 500,000+ football matches, allowing it to find hidden non-linear correlations unavailable to classical statistical methods.
How does the ORAKL algorithm compute xG models and probabilities of football match outcomes?
The ORAKL AI platform does not merely sum basic metrics — it builds a dynamic match simulation on 200+ controlled metrics. The program accounts for the quality of created and conceded chances (dynamic xG/xGA), biometric schedule density of teams, squad logistics (roster depth, suspensions, key player injuries), and micro-structural changes inside clubs. Human factors and cognitive biases are fully excluded from predictive modeling.
How is ORAKL analytics distributed to institutional clients?
To ensure maximum fault tolerance and speed of predictive data delivery, ORAKL uses a hybrid infrastructure. Simulation results are delivered through automated gateways: direct Enterprise API integration for funds and B2B partners, and a secure, real-time Telegram bot interface plus a personal account on the website for professional and retail analysts.