Real-time NFL Draft Prediction Model Deployment by Drake DamonReal-time NFL Draft Prediction Model Deployment by Drake Damon

Real-time NFL Draft Prediction Model Deployment

Drake Damon

Completed work

ML Engineer

Databricks

PySpark

XGBoost

Analytics

Machine learning model deployment on Databricks with Python SQL UDFs for real-time NFL draft predictions.

Technology Stack

DatabricksPySparkSQL WarehouseUnity CatalogPythonXGBoostMLflowNode.jsExpressNext.js

Problem

NFL draft analysts need real-time player evaluation tools that can predict draft position and success probability across different positions. Traditional scouting relies on subjective evaluation, while data-driven approaches often lack the speed and accessibility needed for live draft analysis.

Architecture

Built an end-to-end ML pipeline on Databricks with real-time serving capabilities:

Raw Data → Feature Engineering → XGBoost Training → MLflow Registry → SQL UDFs → REST API → Next.js UI

Results & Impact

Model Performance

Cross-validation accuracy: 78% across all position predictions

Position-specific performance: QB (82%), RB (76%), WR (74%)

Sub-second latency: <200ms response times for prediction requests

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Completed work

Posted Nov 10, 2025

Deployed ML model on Databricks for real-time NFL draft predictions.

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