Live in Production

PZM Predict

Sports / AI / B2B · 2025–2026

AI football prediction engine with 3 custom ML models, verified accuracy, and 50+ leagues covered daily.

Project Overview

About the Client

PZM Predict is a sports analytics platform targeting data-driven football enthusiasts and professional bettors. The client needed transparent, AI-powered prediction tools with publicly verifiable accuracy across dozens of leagues.

What We Delivered

An AI platform with 3 independently trained ML models, each targeting specific prediction markets. Features daily automated predictions across 50+ leagues, public accuracy tracking, and a REST API for B2B integration.

The Challenge

Sports prediction markets are dominated by gut feelings and outdated statistical models. Bettors and data enthusiasts lack transparent, AI-driven prediction tools with verified, public track records.

Predictions based on intuition rather than data
No public accuracy verification for prediction services
Limited league coverage from existing tools
No specialized models for different market types

Our Process

Phase 1

Data Collection & Analysis

3 weeks

Collected 21,000+ historical match records
Engineered 62-65 features per match
Analyzed prediction market baselines

Phase 2

Model Development

4 weeks

Trained 3 specialized ML models (Draw, BTTS, Over 2.5)
Compared Scikit-learn vs deep learning approaches
Validated accuracy against historical baselines

Phase 3

Platform Build

6 weeks

Built FastAPI REST API for predictions
Developed public accuracy dashboard
Implemented daily batch processing with Celery

Phase 4

Production & Monitoring

Ongoing

Daily automated predictions across 50+ leagues
Continuous model accuracy monitoring
Performance optimization and feature expansion

What We Built

An AI platform with 3 custom-trained ML models analyzing 50+ football leagues worldwide. Each model targets a specific market (Draw, BTTS, Over 2.5 Goals) with fully transparent, publicly verified accuracy metrics.

ODYSSEUS Draw Model

44.7% accuracy on draw predictions vs 26% baseline, providing a +19% edge.

LEONIDAS BTTS Model

74.1% accuracy on Both Teams To Score predictions with daily automated updates.

ACHILLES Over 2.5 Model

72.3% accuracy on Over 2.5 Goals predictions across 50+ global leagues.

Public Accuracy Tracking

Fully transparent accuracy dashboard where anyone can verify prediction history.

Why We Made These Choices

Why Scikit-learn Over Deep Learning

Our feature-engineered approach with 62-65 features per match outperformed neural networks on this dataset. Simpler models with better features beat complex models every time.

Why 3 Specialized Models

Each prediction market (Draw, BTTS, Over 2.5) has fundamentally different patterns. A single general model performed 15-20% worse than specialized ones.

Why Daily Batch Processing

Match data updates daily, not in real-time. Celery workers process all 50+ leagues overnight, and predictions are ready before match day.

The Stack

AI/ML

Scikit-learnPandasNumPy

Backend

FastAPICeleryPython

Database

PostgreSQLRedis

Infrastructure

DockerSentryCI/CD

Results

3

ML Models in Production

50+

Leagues Covered

+19%

Edge Over Baseline

21K+

Matches Trained On

✓3 ML models in production with verified accuracy

✓ODYSSEUS Draw model: 44.7% accuracy (vs 26% baseline = +19% edge)

✓LEONIDAS BTTS model: 74.1% accuracy

✓ACHILLES Over 2.5 model: 72.3% accuracy

✓50+ leagues covered daily

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