About the Project
My motivation:
I've always loved Formula 1 and constantly geek out about tech that makes racing smarter. I wanted to build something useful, not just flashy, that could make actual decision-making easier for teams, engineers, and fans like me.
What we researched:
We dug deep into how F1 and Formula E teams plan their race strategies—using expensive simulators, running millions of simulations, and analyzing crazy amounts of data. But this is all locked away behind big budgets and expert-only tools. We explored everything from manual spreadsheet approaches to advanced prediction methods, and realized the best-balanced solution is Monte Carlo simulation: it's simple, powerful, and works great for scenario testing.
Why we landed on this solution:
Monte Carlo lets us run thousands of possible “what if” scenarios fast. Instead of guessing or making one prediction, you see the whole spread of possible futures—so your decisions are backed by real stats, not gut instinct. And it works for more than racing: you can apply it to drones, deliveries, whatever moves.
What we aim with this product:
We want to level up strategy for EVERYONE—from students and club teams to real racing outfits, giving them tools that used to be out of reach. Anyone should be able to hit “simulate,” see a thousand futures, and find the smartest path, fast.
[And of course, it’s one step closer to feeling like an F1 strategist in my bedroom.]
Built With
- aggregation-logic-deployment:-vercel/netlify-(for-instant
- browser-based-access)-collaboration:-github-(source-control
- css3-frameworks:-react-(frontend-ui)-graphics/visualization:-html-canvas-api-simulation-engine:-custom-javascript-classes-(agent/event-models
- express.js
- html5
- montecarloengine
- nextjs
- node.js
- physics)-statistics:-in-browser-monte-carlo-runner
- vercel
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