Leveraging Business Intelligence for Community-Driven Esports: A Power BI Framework for Valorant Tournament Analytics
Lauryn Watanabe
Abstract
This project presents the application of business intelligence tools to grassroots esports, specifically within the context of community-run Valorant tournaments. We developed an interactive analytics dashboard using Microsoft Power BI to visualize tournament and player performance data. The system is fed from a data validated Google Sheets data pipeline, designed with authentication and data verification checks to minimize manual input errors. Additionally, it can support automated data collection using an API to populate the data directly from existing stat tracking websites such as Tracker.gg.
Unlike existing stat trackers that focus on professional play or individual performance, our dashboard highlights tournament focused insights such as top performers, community rank distribution, and comparative player metrics tailored for organizers, participants and viewers. The dashboard is embedded within a companion website, creating a centralized platform for both tournament management and data engagement. This approach bridges the gap between professional level analytics and accessible tools for amateur gaming communities, offering a scalable model for enhancing the competitive gaming experience at the grassroots level.
Introduction
Esports has seen exponential growth in recent years, but much of the infrastructure supporting data analytics and performance tracking remains focused on professional level competition. Grassroots and community driven tournaments for the average gamer, while increasingly popular, often lack access to tools to find meaningful insights about player and team performance. This gap limits the ability of organizers to evaluate outcomes, players to track their progress and audiences to engage with the competitive “authentic” esports experience.
This paper introduces a business intelligence solution designed specifically for FLV, a recurring tournament series for the game Valorant, a competitive first person shooter developed by Riot Games. Each tournament draws between 20 to 40 teams, each composed of five to seven players. FLV, hosted adjacent to coaching focused content creator, is one of the largest and most organized tournaments designed for the average gamer. However, despite the size and consistency of these events organizers lacked a reliable way to archive and analyze match data across seasons.
To address this need, we developed an interactive analytics dashboard using Microsoft Power BI. Power BI is generally used by companies to manage, analyze and display data for their business. However, we will be using it to consolidate structured player and match data into a centralized, visual format, allowing stakeholders to explore performance metrics, community wide trends and historical records.
By embedding the dashboard directly into the FLV community website, this solution not only streamlines access to insights but also establishes a framework for long term stat tracking in a space where such tools are typically unavailable. This work represents an important step in bringing accessible, professional grade analytics to the amateur esports ecosystem.
Related Work
The integration of data analytics into esports has predominantly centered around professional level competitions, leaving a significant gap at the amateur and community levels. Platforms like Esports Charts provide comprehensive analytics on esports viewership and event popularity, serving sponsors, organizers and viewers with data on live events. However, these services focus on macro level trends and do not cater to the specific needs of community tournaments.
For tournament organization, platforms such as Battlefy offer user friendly interfaces and robust features, including customizable tournament formats and integrated matchmaking. While effective for managing tournament logistics, these platforms lack integrated analytics tools that provide insights into player performance and community engagement over time.
In the realm of individual player performance tracking, Tracker.gg stands out as the widely used platform offering comprehensive statistics for Valorant players. It provides detailed insights such as match history, agent performance, and weapon statistics, aiding players in analyzing and improving their gameplay. Tracker.gg achieves this by utilizing Riot games’ official APIs to gather data directly from players’ in game activities. However, its primary focus remains on individual performance metrics rather than the collective analytics required for community tournaments. Additionally, while Tracker.gg offers in game overlays and live match scouting features, these are designed to enhance personal gameplay experience on the randomness of the ranked ladder and do not cater to the needs of organizers or captains seeking to analyze team dynamics, match outcomes or seasonal trends. Consequently, while platforms like Tracker.gg provide valuable tools they do not address the specific requirements of community tournament analytics, highlighting the need for tailored solutions like the one developed for FLV.
Methodology
The analytics system for the FLV community tournament was developed through a multi-phase process involving data collection, data validation, dashboard development, and web integration. The goal was to create an accessible, scalable, user friendly tournament focused analytics platform tailored for amateur esports organizers and players.
Match and player data is recorded by tournament organizers in a centralized Google Sheets after each competitive week. Google Sheets was chosen due to it being available for free, easily sharable, and FLV already having some of their system in Google Drive. This data set includes core gameplay metrics such as kills, deaths, damage statistics, and match outcomes. The sheet also tracks contextual metadata such as the player’s current team, whether they are a substitute and unique player identification numbers to account for duplicate player names.
To ensure consistency and prevent entry errors the spreadsheet employs extensive data validation rules. Understanding FLV’s status as a community tournament, these are important to ensure only correct data is entered. These include dropdown lists for agents, maps, team names, and ranks as well as conditional formatting that flags unusual or invalid values such as negative scores or unapproved text entries. This approach not only minimizes errors but also reduces cleanup time before import into Power BI.
Once the data set is finalized in Google Sheets, it is exported into Excel where it is formatted again into the correct table structure for Power BI. This is because the structure in Google Sheets is meant for human eyes to use and understand, where the Excel sheet is for the relational database. The data is split into tables such as Players, Matches and Player Performance to enable DAX calculation and cross filtering.
Power BI’s data modeling features, including relationships, calculated columns, and measures were used to handle complex stat aggregations like season points tracking, role based comparisons and player rivals.
The dashboard is composed of three main reports and a centralized leaderboard:
Player Dashboard: Displays individual player statistics across all tournaments, including average stats, agent usage frequency, teams players on and historical performance trends.
Season Dashboard: Focuses on tournament specific data such as team point progression, map usage, agent pick/win rates, and match results per season. Historical season filtering allows users to switch between tournament archives.
Community Dashboard: Highlights broader community trends such as new player participation, overall rank distribution and agent diversity.
Leaderboard View: Unlike the others, this dashboard is meant for FLV leadership, not the general player base. This allows players to be ranked by various performance metrics to look for suspicious anomalies or reward exceptional play.
To ensure broad accessibility, the Power BI dashboard is embedded directly into the FLV tournament website, which is hosted on Wix. Using Power BI’s “Publish to Web” feature, the interactive reports are displayed within the site while preserving functionality. This eliminates the need for users to access external platforms, creating a seamless experience for players, organizers and fans.
The system is designed to scale as the tournament grows. Its relational data model supports additional teams, season and new metrics with minimal restructuring. Filtering optionals already account for seasonal archiving, and further automation is planned to streamline the data collection process.
Conclusion
This project presents a practical and scalable solution for integrating business intelligence into grassroots esports. By designing a tournament focused analytics dashboard using Power BI and embedding it within the FLV community website, we’ve created a platform that delivers insights to players, organizers and viewers alike. The system addresses a critical gap in the esports system - providing stat tracking and performance visualization tools that are typically reserved for professional level play.
Through structured data collection, validation and visualization, the dashboard allows users to explore player performance, seasonal trends, team progress and overall community dynamics. The design prioritizes accessibility, scalability, and future extensibility, offering a foundation that can evolve with the needs of the community.
*Some names changed for anonymity