Moneyball Coach AI: Building a Real-Time Esports Analytics Platform with Next.js and GRID API 🎮📊

The "Moneyball" Moment for Cloud9

In the movie Moneyball, Billy Beane changed baseball forever by ignoring gut feelings and trusting the data. He looked for the hidden stats that actually led to wins.

For this hackathon, we asked ourselves: Can we build a "Billy Beane" for Cloud9?

Esports is digital-native, yet so much coaching is still done via spreadsheets and VOD review. We wanted to change that. Enter Moneyball Coach AI, a comprehensive analytics platform that processes live match data to deliver real-time insights, player performance tracking, and strategic win-probability models.

Here is the story of how we built a 96% TypeScript application to turn raw data into victory.

🏗️ The Tech Stack

We needed a stack that could handle high-frequency data updates without stuttering. Speed was our top priority.

• Framework: Next.js (App Router)
• Language: TypeScript (96.8% of the repo!)
• Data Source: GRID Esports API
• State Management: Zustand + TanStack Query
• Visualization: Recharts
• Styling: Tailwind CSS (Cloud9 Theme)

🔌 Integrating the GRID API

The heartbeat of our application is the GRID API. It provides an incredible depth of data, but that depth comes with complexity. A single match can generate thousands of events—gold changes, positional updates, item purchases, and ability usage.

Our biggest challenge was Type Safety. The JSON objects from GRID are deeply nested. We didn't want to use any; we wanted strict typing to prevent runtime crashes.

We created a robust schema to normalize this data:

// Example Interface for Normalized Player Data
interface PlayerMetric {
  summonerName: string;
  championId: number;
  currentGold: number;
  visionScore: number;
  laneProximity: number; // Calculated metric
  winProbabilityContribution: number; // AI Model output
}

By strictly defining these interfaces, our IDE (VS Code/WebStorm) could autocomplete properties and warn us if we tried to access a null value—a lifesaver during the final hours of the hackathon!

🚀 Handling the "Firehose" of Data

One of the coolest technical hurdles we cleared was the "Firehose Effect."

When a team fight breaks out in-game, the API sends a burst of events. If we tried to update the React DOM for every single event, the dashboard would freeze.

We solved this using a Buffering Strategy and Optimistic UI.

1. Ingestion: We catch the websocket stream.
2. Batching: We group events into 500ms "ticks."
3. React Query: We use useQuery to fetch the stable state, while a custom hook applies the "tick" updates instantly for a smooth visual experience.

This created a "Glass-to-Glass" latency that feels instantaneous to the user.

🧠 The "Moneyball" Insight

The core feature we are most proud of is the Efficiency Engine.

Instead of just showing "Kills" (which can be a vanity metric), our algorithm calculates Gold Efficiency.

If a player has 10,000 gold but hasn't spent it, they are statistically weaker than an opponent with 8,000 gold all spent on items.

We visualized this on our dashboard. If a Cloud9 player is holding too much gold before a crucial dragon fight, the dashboard flashes a warning. This is the kind of actionable data that helps coaches make real-time calls.

🎨 Designing for the Pros (The Cloud9 Aesthetic)

We didn't just want it to work; we wanted it to look like it belonged in the Cloud9 HQ.

We used Tailwind CSS to strictly enforce the team's identity.

• Primary: bg-slate-900 (Dark Mode default)
• Accent: text-cyan-400 (Electric Blue)
• Visuals: We used backdrop-blur effects on our data cards to give them a futuristic, "heads-up display" feel.

🛠️ Tooling Spotlight & Bonus Challenges

⚡ Scaling a 96% TypeScript Esports Platform with WebStorm

📖 The Story
"Building Moneyball Coach AI required handling massive, deeply nested JSON objects from the GRID API in real-time. We chose JetBrains WebStorm as our primary IDE, and it was instrumental in maintaining our 96.8% TypeScript codebase."

✅ Completeness & Viability
The most impactful feature was WebStorm's deep static analysis and refactoring capabilities. When defining the complex interfaces for the GRID API live streams, we frequently had to refactor types across our Next.js backend and React frontend. WebStorm’s 'Rename Symbol' and 'Find Usages' worked flawlessly across the full stack, catching type mismatches that VS Code plugins often missed.

🚀 Potential Impact
Furthermore, the integrated AI Assistant helped us generate boilerplate code for our Recharts visualizations, saving us hours of manual coding. The debugger's ability to handle high-frequency state changes in our useLiveStream hooks allowed us to isolate race conditions in the data pipeline much faster than standard browser tools. WebStorm didn't just write code; it acted as a stability guardrail for our high-velocity development.

🛡️ Optimizing Real-Time Data Streams with Junie

📖 The Story
"For Moneyball Coach AI, we built a high-performance dashboard that ingests thousands of events per second via the GRID API. We utilized Junie to audit our code quality and ensure our complex logic remained performant."

🔍 Completeness & Viability
Junie provided critical insights into our React rendering patterns. Since our dashboard updates in real-time, preventing unnecessary re-renders is crucial. Junie analyzed our custom hooks and flagged dependencies in useEffect and useMemo that were causing performance leaks—issues we hadn't caught during manual review. It also helped identify potential null-reference errors in our deep-nested API response handling.

⚡ Potential Impact
The feedback provided by Junie was directly actionable. By implementing its suggestions, we noticed a smoother frame rate on our live graphs. For a hackathon project where speed is everything, having an automated code reviewer that understands modern TypeScript patterns allowed us to ship a production-grade 'Moneyball' experience without getting bogged down in technical debt.

🏁 What We Learned

Building Moneyball Coach AI taught us that Context is King.

Raw data is useless without context. Telling a coach "Blaber has 5 kills" is okay. Telling a coach "Blaber's pathing is 15% more efficient than the enemy jungler, leading to a 60% higher win probability" is game-changing.

We pushed the limits of what Next.js can do with real-time data, and we can't wait to keep refining our models.

GG WP! (Good Game, Well Played)

Check out the code on GitHub: MiChaelinzo/moneyball-coach-ai-p

Test the Web-app on GitHub: View Live Demo