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【AI Side Project Vol.02】Develop web app of "Daily Underestimated MLB Player Ranking" ⚾

·3 mins
AI Practice Journal Using AI App Side_Project

Welcome to visit the site to find out MORE ! Daily Underestomated MLBPlayers Ranking ⚾

Nov 2024: I had the vision and the domain expertise, but I faced a technical ceiling. At that time, I could use AI to generate “polished UI mockups,” but building a live, data-integrated application as a solo creator required a level of manual engineering that was out of reach.

Feb 2026: The landscape has fundamentally changed. By evolving my workflow to manage autonomous AI agents, I have transitioned from “visualizing ideas” to shipping a production-ready, full-stack web application.

⚾️ The Project: MLB Underestimated Player Analyzer #

As an avid baseball fan for over a decade (Go Dodgers!). I’ve spent years diving deep into advanced analytics to fuel my Fantasy Baseball obsession and satisfy my inner “Armchair GM.” To take my analysis to the next level, I am looking to develop a streamlined, interactive platform that allows for efficient, one-stop access to real-time data.

The core philosophy behind this project is rooted in the fact that in real-world baseball, a player’s box score results are heavily influenced by external factors—such as defensive positioning, ballpark dimensions, and environmental variance. Consequently, surface-level statistics like wOBA often fail to accurately reflect a player’s underlying skill set or true performance level in real-time. To understand how a player is actually performing, a deeper analysis of “Value vs. Outcome” is required.

  • wOBA (Weighted On-Base Average): This represents a player’s actual offensive contribution on the field. It is the “realized” result—the Outcome.
  • xwOBA (Expected wOBA): Using Statcast data, this calculates what a player’s output should have been based on the physical characteristics of their hits (Exit Velocity and Launch Angle). This is the Value, a pure measure of contact quality independent of defensive shifts or luck.
  • The Differential ($wOBA - xwOBA$): When realized results significantly lag behind contact quality, a player is Underestimated. Statistical history shows that these gaps are usually temporary; eventually, a player’s on-field results will regress to the mean, aligning with their underlying performance profile.

My dashboard identifies these statistical anomalies in real-time, highlighting hitters who are poised for a significant rebound.

Concept outcome

Concept outcome developed in 2024 Nov

Web app

Web app developed in 2026 Feb

🛠 The Orchestration: Architect + AI Agent #

The most significant shift isn’t just the speed of AI—it’s the Managerial Workflow. I didn’t just “ask” for an app; I orchestrated it through Documentation-Driven Development:

  1. The Blueprint: I authored comprehensive technical documentation defining the Design System, User Interaction Logic, and Backend Data Architecture.
  2. The Management: I acted as the Project Manager and Lead Architect, guiding AI agents to execute the Full-Stack build (React/Vite Frontend + Python Data Pipeline).
  3. The Automation: Together, we implemented an automated workflow that handles daily data ingestion and processing, ensuring the dashboard reflects the most current MLB metrics without any manual intervention.

📈 Case Study: Performance Convergence #

Using Tyler O’Neill as an example: his early-season batting average appeared low, but the analyzer flagged an elite 16.5% Barrel rate and a massive negative differential. The data suggested his “underperformance” was a result of statistical variance rather than a decline in skill. As predicted, his outcomes eventually converged with his elite contact quality.

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The Takeaway #

In 2026, the competitive advantage for developers is no longer just “writing code”—it’s structuring logic and managing agentic execution. One person with a clear vision and rigorous documentation can now deliver the output of an entire engineering team.

#AI #GenerativeAI #AIAgents #DataAnalytics #FullStack #MLB #SportsTech #ProductManagement #DataVisualization #FutureOfWork

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