Autoscout

Inspiration

Preparing for esports matches often requires manually analysing large amounts of match data, which is time-consuming and inefficient. We wanted to simplify this process by creating a tool that quickly transforms raw match data into meaningful insights that coaches and players can act on immediately.

What it does

AutoScout Pro automatically generates an esports scouting report for a selected team. It analyses match data to display win rate, most-played agents, and map preferences, and provides AI-style strategic insights to help teams prepare smarter and faster.

How we built it

We built AutoScout Pro using HTML, CSS, and JavaScript. Match data is processed directly in the application, where custom logic analyses trends such as agent usage, map frequency, and match outcomes. We used Chart.js to visualise insights and rule-based logic to generate AI-style summaries.

Challenges we ran into

One of the main challenges was designing meaningful insights without relying on complex backend systems or heavy machine learning. Ensuring clean data handling, dynamic updates, and clear visualisations in a limited time frame was also challenging.

Accomplishments that we're proud of

Built a complete, working scouting tool within the hackathon

Created clear and interactive data visualisations

Designed AI-style insights that are easy to understand

Kept the project simple, fast, and easy to demo

What we learned

We learned how to transform raw data into actionable insights, design user-friendly analytics dashboards, and balance ambition with practicality during a hackathon. We also gained experience in presenting technical ideas clearly.

What's next for Autoscout

Next, we plan to integrate real esports data sources like GRID, expand support for more games and teams, improve AI-driven analysis, and add features such as exporting reports and deeper player-level insights.