Personal data product

NCAA 2026 Bracket Model

A data product built from public rankings, schedules, historical tournament data, MLX models, simulations, and readable bracket outputs.

Personal Labs Shipped 2026
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Role

Builder

Tags

Data ProductML WorkflowInteractive Showcase

Problem

I wanted a practical office-pool decision tool that went beyond gut picks by combining public basketball data, modeling, simulation, and a readable product interface.

Users

Bracket-pool participants and portfolio reviewers evaluating data sourcing, modeling, and product storytelling skill.

Ownership

  • Owned the full workflow from data sourcing and cleanup through modeling, simulation, evaluation, and UI packaging.
  • Built the static interactive showcase around the model outputs so the results were usable, not just notebook artifacts.

Hard Parts

  • Collected and normalized data from NCAA NET, AP Top 25, Sports Reference, Bracket Matrix, official bracket, schedules, logs, boxscores, and historical tournament rows.
  • Compared MLX logistic baselines, priors, a tree benchmark, and a production ensemble.
  • Resolved the full field and turned probabilistic model outputs into readable bracket decisions.

Leadership

  • Demonstrates the ability to make technical model work legible through product UI, narrative, and decision-support framing.
  • Shows end-to-end ownership across data, modeling, evaluation, calibration, and frontend storytelling.

Shipped

  • Office-pool decision tool and playful portfolio UI.
  • 10,000 Monte Carlo simulations.
  • Bracket paths, title odds, upset boards, model notes, and team search.

Impact

  • Proves data sourcing, cleanup, feature engineering, modeling, evaluation, calibration, UI design, and storytelling in one visible artifact.

Signals

10,000 Monte Carlo simulations.68 of 68 field teams resolved.Production ensemble reached 68.5% validation accuracy.

Highlights

  • Collected and normalized NCAA NET, AP, Sports Reference, Bracket Matrix, official bracket, schedule, and boxscore snapshots.
  • Built historical training datasets, current matchup features, recent-form fields, model artifacts, bracket variants, and confidence reports.
  • Trained and compared MLX logistic models, matchup features, a tree benchmark, and an ensemble path for calibrated picks.
  • Packaged the result as a static interactive showcase with bracket paths, title odds, upset boards, model notes, and team search.

Tools

PythonPandasBeautifulSoupMLXModel evaluationMonte Carlo simulationStatic HTMLJavaScriptData visualization