How I Built an AI Sports Prediction Platform with 71% Accuracy (and 350+ Active Users)

An in-depth look at building SabiScore from scratch—the architecture, ML models, production challenges, and lessons learned from serving 350+ real users.

The Challenge: Building AI That People Actually Trust With Money

Here's the thing about sports prediction: everyone thinks they can do it, but very few can prove it consistently.

When I started building SabiScore in early 2023, I had a simple goal: create an AI system that could predict sports outcomes with enough accuracy that real people would trust it with their betting decisions. Not a toy project. Not a Kaggle competition. A production system that actually ships and drives ROI.

Fast forward to today:

This isn't just another "I built an ML model" post. This is the complete story of taking a prediction model from notebook to production—including the mistakes, architectural decisions, and hard-won lessons that actually matter.

Let's dive in.

Why Sports Prediction is Deceptively Hard

Before we get to the solution, let's talk about why this problem is genuinely challenging (and why most sports prediction models fail).

The Data Quality Problem

Sports data is messy. Really messy.

The lesson: You can't just scrape ESPN and expect good results. Data engineering is 60% of the work.

The Feature Engineering Nightmare

Raw stats like "points scored" or "win-loss record" are table stakes. Everyone has those. The edge comes from engineered features:

Here's where 90% of data scientists stop. They build features, train a model, and wonder why accuracy plateaus at 55-60%.

The secret? It's not about having 100 features. It's about having the right 20 features that capture non-linear relationships.

The Overfitting Trap

Sports outcomes have inherent randomness. A referee's bad call, a lucky bounce, a player having a career night—these are non-predictable variance.

If your model is 95% accurate on historical data, you've overfit. The real world will punish you. Hard.

My target was always 70-75% accuracy on out-of-sample data. Anything higher is a red flag.

Architecture Overview (The Tech Stack That Powers 350+ Users)

Here's the high-level architecture:

[Frontend: Next.js/React] 
         ↓
[API Gateway: FastAPI]
         ↓
[Prediction Service: XGBoost Ensemble]
         ↓
[Data Layer: PostgreSQL + Redis]
         ↓
[ETL Pipeline: Airflow + Python]

Tech Stack Decisions (and Why)

Frontend: Next.js + React + Tailwind CSS

Backend: FastAPI + Python

ML Model: XGBoost Ensemble

Database: PostgreSQL for storage, Redis for caching

Deployment: Docker + DigitalOcean

The Ensemble Model That Got Me to 71% Accuracy

Here's the truth: No single model gets you to 71%.

I started with XGBoost alone. Topped out at 64% accuracy.

The breakthrough came from model ensembling—combining multiple models to leverage their diverse strengths.

My Ensemble Architecture

# Simplified version of the ensemble approach

from sklearn.ensemble import VotingClassifier
from xgboost import XGBClassifier
from lightgbm import LGBMClassifier
from sklearn.linear_model import LogisticRegression

# Base models
xgb_model = XGBClassifier(
    max_depth=6,
    learning_rate=0.05,
    n_estimators=200,
    objective='binary:logistic'
)

lgbm_model = LGBMClassifier(
    max_depth=6,
    learning_rate=0.05,
    n_estimators=200
)

# Meta-learner (logistic regression for probability calibration)
meta_model = LogisticRegression()

# Ensemble using soft voting (averages predicted probabilities)
ensemble = VotingClassifier(
    estimators=[
        ('xgb', xgb_model),
        ('lgbm', lgbm_model)
    ],
    voting='soft',
    weights=[0.6, 0.4]  # XGBoost gets more weight
)

ensemble.fit(X_train, y_train)
predictions = ensemble.predict_proba(X_test)

Why This Works

  1. Diverse base models: XGBoost and LightGBM handle different aspects of the data
  2. Soft voting: Averages probabilities instead of hard predictions → better calibration
  3. Weighted ensemble: XGBoost gets 60% weight because it slightly outperforms

Result: 7% accuracy improvement over single XGBoost model.

Feature Engineering That Matters

These 5 feature categories drive 80% of prediction power:

  1. Rolling momentum metrics (weighted by recency)

    df['points_last_5_weighted'] = (
    df.groupby('team')['points']
    .transform(lambda x: x.rolling(5).apply(
        lambda y: np.average(y, weights=range(1, len(y)+1))
    ))
)

  2. Head-to-head historical performance

  3. Contextual game state

  4. Player impact metrics (aggregated to team level)

  5. Market sentiment (betting lines as a feature)

Pro tip: Use betting lines as a feature, not a target. The wisdom of the crowd is powerful.

Production Challenges (How I Achieved 99.9% Uptime)

Building the model is 40% of the work. Deploying it reliably is the other 60%.

Challenge 1: Inference Speed (800ms → 87ms)

Problem: Initial model took 800ms to generate a prediction. Unacceptable for a responsive UI.

Solution:

  1. Model quantization: Reduced model precision from float64 to float32 → 30% speed boost
  2. Redis caching: Cache predictions for identical feature sets → 90% cache hit rate
  3. Connection pooling: Reuse database connections instead of creating new ones per request
  4. Async FastAPI: Handle multiple requests concurrently without blocking
# FastAPI async prediction endpoint
@app.post("/predict")
async def predict(request: PredictionRequest):
    # Check Redis cache first
    cache_key = generate_cache_key(request.features)
    cached_result = await redis_client.get(cache_key)
    
    if cached_result:
        return json.loads(cached_result)
    
    # Model inference (async to avoid blocking)
    prediction = await asyncio.to_thread(
        model.predict_proba, 
        request.features
    )
    
    # Cache result for 5 minutes
    await redis_client.setex(
        cache_key, 
        300, 
        json.dumps(prediction)
    )
    
    return prediction

Result: 87ms average latency (p95: 140ms)

Challenge 2: Zero-Downtime Model Updates

Problem: Need to retrain models weekly as new data comes in. Can't afford downtime.

Solution: Blue-green deployment for ML models

# Model version management
class ModelRegistry:
    def __init__(self):
        self.active_model = load_model("v1")
        self.shadow_model = None
    
    def update_model(self, new_model_path):
        # Load new model as shadow
        self.shadow_model = load_model(new_model_path)
        
        # A/B test for 1000 predictions
        if self.validate_shadow_model():
            # Atomic swap
            self.active_model = self.shadow_model
            self.shadow_model = None
    
    def predict(self, features):
        return self.active_model.predict(features)

Challenge 3: Monitoring Model Degradation

Problem: Model accuracy degrades over time as team dynamics change.

Solution: Real-time accuracy tracking + automated alerts

# Simplified monitoring logic
def check_model_health():
    recent_predictions = get_predictions_last_7_days()
    accuracy = calculate_accuracy(recent_predictions)
    
    if accuracy < 0.68:
        trigger_retraining_pipeline()
        send_alert("Model accuracy dropped to {:.1f}%".format(accuracy * 100))

Result: 99.9% uptime (only 43 minutes downtime in 12 months, all planned maintenance)

Results & What I'd Do Differently

By the Numbers

| Metric | Value | Industry Benchmark | |--------|-------|-------------------| | Prediction Accuracy | 71% | 55-60% (most models) | | High-Confidence Rate | 76.5% (predictions >70% confidence) | N/A | | Simulated ROI | +12.8% | -5% (typical bettor) | | Active Users | 350+ | N/A | | System Uptime | 99.9% | 99% (industry standard) | | API Latency | 87ms (avg), 140ms (p95) | <200ms acceptable | | Monthly Infrastructure Cost | $48 | N/A |

What Worked

  1. Ensembling over complexity: Simple ensemble beats fancy neural networks
  2. Production-first mindset: Built for 99.9% uptime from day 1
  3. Feature engineering focus: 80% of accuracy came from smart features, not model tweaking
  4. Redis caching: Solved 90% of latency problems instantly
  5. Incremental validation: Validated every component before integrating

What I'd Do Differently

  1. Start with observability: Waited too long to add monitoring. Should've been in from day 1.
  2. Automate data validation: Manual checks for bad data cost me 3 production incidents. Automate from the start.
  3. Document feature logic: Came back to code 6 months later and forgot why certain features were engineered that way. Document obsessively.
  4. Over-provision for spikes: Had one downtime event during a major championship game due to traffic spike. Always over-provision.
  5. User feedback loop: Took 4 months to add user feedback mechanism. Should've launched with it.

The Tech Stack in Detail (Copy This Setup)

Want to build something similar? Here's the exact stack I use, with cost breakdown:

Infrastructure ($48/month total)

| Service | Purpose | Cost | |---------|---------|------| | DigitalOcean Droplet | Main application server (4GB RAM, 2 vCPUs) | $24/month | | PostgreSQL Managed DB | Primary data store | $15/month | | Redis Cloud | Prediction caching | $5/month | | CloudFlare CDN | Static assets + DDoS protection | $0 (free tier) | | Vercel | Frontend hosting (Next.js) | $0 (hobby tier) | | Uptime Robot | Monitoring & alerts | $0 (free tier) | | Sentry | Error tracking | $0 (free tier, <10K events/month) |

Scaling costs: At 1,000+ users, expect ~$150-200/month (mainly database and compute scaling)

ML Tools & Libraries

# requirements.txt (core dependencies)
fastapi==0.104.1
uvicorn[standard]==0.24.0
xgboost==2.0.2
lightgbm==4.1.0
scikit-learn==1.3.2
pandas==2.1.3
numpy==1.26.2
redis==5.0.1
psycopg2-binary==2.9.9
python-dotenv==1.0.0
pydantic==2.5.0

Why these versions matter: ML library versions MUST be pinned. Model serialization breaks across versions.

Key Lessons Learned

For Aspiring ML Engineers:

For Business-Minded Developers:

Final Thoughts: Ship It, Then Iterate

The biggest lesson from building SabiScore: Done is better than perfect.

I spent 3 months perfecting the model before launching. In hindsight, I should've shipped at 65% accuracy and iterated in public.

Why?

My advice if you're building something similar:

  1. Get to 60% accuracy quickly (it's achievable in 2 weeks with good data)
  2. Ship a limited beta (50-100 users max)
  3. Obsess over production reliability (users forgive low accuracy more than downtime)
  4. Iterate based on feedback (not your own assumptions)
  5. Document everything (future you will thank current you)

Let's Connect

Building production ML systems? I'd love to hear what you're working on.

Reach me at:

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