MLB UAT Automation Platform

Automation · Pricing Validation · Risk Controls · Internal Tooling

Executive summary

I designed and delivered a full UAT automation suite for MLB pricing, replacing manual workflows with a scalable validation engine. The platform performs complete market checks within minutes, reducing regression errors by 80% and cutting test cycles by 50%. It now serves as the blueprint for automation across NBA, NFL, and future risk systems.

Strategic context

MLB pricing generates thousands of markets and selections per game. Historically, each release relied on:

Large Excel comparisons
Manual SQL validation
Inconsistent test coverage
No automated regression suite

The business needed a scalable, reliable, and repeatable validation framework to support rapid model updates.

The problem

Manual testing failed at scale. It was slow, inconsistent, and prone to human error. The lack of automation meant:

Missed behaviour differences between operators
Edge-case pricing issues surfaced late
Complexity of ladders and envelopes couldn't be validated end-to-end
Regression relied on memory rather than structured checks

Constraints

No guaranteed engineering resources — had to be self-built
Had to run from static Athena snapshots
Validation logic needed to mirror real model behaviour
Tool needed to support operator-specific differences (BETMGM, Entain)

My role

I owned the initiative end-to-end:

Mapped the MLB pricing lifecycle and its edge cases
Designed the automated rules engine
Built the initial Python validation tool
Product-managed transition to a multi-user React + SignalR web app
Defined coverage, KPIs, metrics, and regression standards
Worked daily with traders, modelling, and engineering teams

What I delivered

Automated rules engine covering alignment, ladders, envelopes, correlation, and settlement
Support for multiple operator behaviours
Exception reporting via CSV + Excel with automated highlighting
Regression test library for model release validation
Web-based multi-user platform for internal teams
Framework now adopted across sports

Impact

50% faster model release cycles
80% fewer missed regression errors
Full-market coverage, not sample-based
Standardised validation across MLB, NBA, and NFL
Lower operational risk with consistent test evidence

Relevance to fintech & banking

The automation design aligns with financial risk controls:

Pricing envelopes ≈ risk limits
Settlement logic ≈ trade lifecycle verification
Operator differences ≈ multi-region compliance rules
Validation engine ≈ pre- and post-trade controls

System overview

This diagram shows how pricing data flows through the automated validation engine, rules layer, and reporting pipeline.