Overview
We built a scouting and head-to-head analysis tool built for competitive VALORANT teams. The problem we wanted to solve is that preparing for an opponent usually means watching hours of VODs, digging through stats, and piecing together a game plan manually. That takes a lot of time, and it’s hard to stay consistent. So our goal was to automate that process and turn match data into something a coach or player can use right away.
When you select an opponent, our app automatically generates a scouting report based on their recent matches. It focuses on three main things: how to approach the map veto, who the team's biggest threats are, and what kind of playstyle they usually have. This gives you a quick overview of who you’re about to face before even loading into the server.
One of the main features is the map pool analysis. For each map, we show the win and loss percentages for both teams and compare them side by side. From this, the system recommends which map you should pick, which one you should ban, and which one would make the most sense as a decider. Instead of guessing during veto, you get a data-backed plan for how to approach it.
On top of that, we provide a breakdown of each player on the opposing team. You can see their individual performance and trends, which help you understand whether this is a team that relies heavily on one carry player or one where everyone contributes evenly.
Overall, this project aims to make scouting faster and more accessible. Instead of spending hours manually analyzing an opponent, you can generate a clear scouting report in seconds that highlights which maps to play, who the biggest threats are, and how to approach the match strategically. It’s important to note that this tool doesn’t replace traditional VOD review or coaching judgment. Instead, it streamlines the scouting process by giving coaches a focused starting point, helping them know exactly what patterns, players, and situations to look for when reviewing gameplay. Our goal is to provide a simple, data-driven way for teams to prepare more effectively and efficiently for competition.
What's next
In future versions, we plan to extend this analysis even further. We want to add: • a system that shows common defensive and attacking setups on each map, • the types of plays teams like to run and at what points in the round, • pistol round strategies and economy-based tendencies, such as full buy, low buy, and eco patterns. We also want to visualize: • how aggressive teams are at different stages of the round, • which players tend to lurk, • where players usually start at the beginning of rounds, • and how they rotate between sites.
This would allow teams to prepare not just at a macro level, but also at a round-by-round tactical level.
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