The Science Behind Our Prediction Models

At SykikAI, we believe in transparency. We don't just tell you what to bet — we show you why. This post explains the technology and methodology behind our prediction system.

Data Collection

Our pipeline ingests data from multiple sources across sports, crypto, and forex markets:

• Sports: Match results, player statistics, team form, expected goals (xG), set piece data, referee records, weather conditions, and historical head-to-head records going back 10+ years
• Crypto: Real-time prices, volume, on-chain metrics, exchange flows, whale wallet tracking, and social sentiment
• Forex: Price feeds, economic calendar data, central bank statements, positioning data (COT reports), and cross-pair correlations

Data is cleaned, normalized, and feature-engineered before being fed into our models. This preprocessing step is often where the real competitive advantage lies — the same algorithm trained on well-engineered features will dramatically outperform one trained on raw data.

Model Architecture

We use an ensemble approach — multiple models vote on each prediction, and the final output is a weighted average. Our ensemble includes:

• Gradient-boosted decision trees for structured tabular data
• Neural networks for sequential pattern recognition
• Logistic regression as a stable baseline

The ensemble approach is more robust than any single model because different model types capture different patterns in the data. Gradient boosting excels at feature interactions, neural networks at temporal patterns, and logistic regression at maintaining calibration.

Probability Calibration

A model that says "60% probability" should be right 60% of the time. This property — called calibration — is critical for betting applications because it determines whether the probabilities can be directly compared against bookmaker odds to find value.

We use Platt scaling and isotonic regression to calibrate our model outputs, then validate calibration on held-out test data. Our calibration plots are reviewed weekly, and any drift triggers model retraining.

Human Review

Every prediction passes through human analyst review before publication. Analysts check for factors the model might miss: key injuries reported after data cutoff, unusual tactical changes, or external factors like extreme weather. They can adjust confidence levels or flag predictions for exclusion.

Confidence Ratings

Our four-tier confidence system reflects the strength of the edge:

• High Confidence: Strong probability edge (>5%) with model consensus and analyst agreement
• Medium-High: Solid edge with good model agreement
• Medium: Moderate edge, some model disagreement or limited data
• Value Play: Lower probability but excellent odds offering strong expected value

This transparency allows you to make informed decisions about which predictions to follow and how to size your bets accordingly.