GAMEPLAN AI

Inspiration

• Modern esports teams generate massive amounts of match data, yet coaches and analysts still spend countless hours manually reviewing VODs and spreadsheets. We were inspired by a simple question:
• What if esports teams had an AI assistant that thinks like a strategic coach, not just a statistics engine?
• With access to high-quality esports data through GRID and powerful development tools from JetBrains, we set out to build a system that converts complex data into clear, coach-ready insights.

What it does

• GAMEPLAN AI acts as a next-generation assistant coach for esports teams by:
• Analyzing historical and match-level esports data
• Generating opponent scouting reports
• Recommending draft and composition strategies
• Estimating win probability based on picks, trends, and performance metrics
• Visualizing insights through an intuitive analytics dashboard
• Instead of just showing numbers, GAMEPLAN AI explains why certain strategies work and where teams should adapt.

How we built it

We ingested structured esports data provided by GRID Designed a backend pipeline to clean, aggregate, and normalize match statistics Built analytical models to detect patterns in: Team compositions Map control Player and team tendencies Created a web-based dashboard that presents insights visually for coaches and analysts Integrated AI-assisted development using JetBrains IDEs to accelerate iteration and maintain code quality The system was designed with real coaching workflows in mind—not just data science experiments.

Challenges we ran into

Data complexity: Esports data is high-dimensional and highly contextual Turning data into strategy: Raw stats don’t automatically translate into coaching decisions UX balance: Presenting deep analytics without overwhelming non-technical users Time constraints: Building something meaningful within a hackathon timeframe required sharp prioritization

Accomplishments that we’re proud of

Built an end-to-end system from raw esports data to actionable insights Delivered a coach-centric analytics experience instead of a generic dashboard Successfully aligned AI outputs with real competitive decision-making Created a project that could realistically be adopted by professional teams

What we learned

High-quality data is powerful only when paired with strong interpretation Esports analytics must bridge the gap between numbers and strategy Clear visualization and explanation matter as much as model accuracy AI is most impactful when it augments human decision-making—not replaces it

What’s next for GAMEPLAN AI

Live match adaptation and real-time strategy alerts Natural-language AI chat for coaches and analysts Expanded support for more esports titles Player-level micro-performance analysis Team collaboration features and report exports Our long-term vision is to make GAMEPLAN AI a standard tool in professional esports coaching rooms.

Built With

• grid-esportsdata
• javascript
• jetbrains
• python
• react
• tailwindcss