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Case Studies/CS2 Team Balancer with FACEIT API Integration
CubeBali, Indonesia

CS2 Team Balancer with FACEIT API Integration

Automated CS2 team balancer that pulls live FACEIT stats and creates fair teams using Bayesian scoring and snake draft algorithm.

Gaming & Esports
Industry
Custom Solutions
Solution Type
4+
Technologies

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Measurable Results

Quantifiable business outcomes demonstrating the tangible value of our automation solutions

Instant balanced team generation from player nicknames — zero manual stat lookup or spreadsheet work required

Strategic Impact

Eliminating manual stat lookup and spreadsheet balancing saves 10-15 minutes per match setup, which for regular community events (2-3 times per week) translates to hours of saved organizational effort monthly.

Business Value

Instant team generation removes the organizational friction that discourages community match organizers, increasing event frequency and player engagement.

Challenge & Solution

Real business challenges and our targeted automation solutions

Business Bottleneck
Solution Points

Business Bottleneck

Manual team balancing based on subjective skill estimates resulted in consistently lopsided matches No tool combined real-time Elo data with recent performance metrics for accurate player assessment

Impact Areas
Operations

Solution Points

1

Built React 19 + Vite web application with multi-step wizard flow: landing → team count selection → player nickname input → loading → results

2

Integrated FACEIT Data API v4 for real-time player data: profile lookup, Elo rating, skill level, lifetime stats, and last 30 match history

3

Developed Bayesian-weighted scoring algorithm combining Elo (50%) with adjusted K/D ratio (50%), using Bayesian prior (weight=10, prior K/D=1.0) to normalize players with few recent matches

4

Implemented snake draft distribution algorithm that sorts players by composite score and alternates pick direction each round for optimal balance

5

Built serverless API endpoint on Vercel with batch processing (5 players per batch) and rate limiting (100ms delay) to respect FACEIT API constraints

6

Created bilingual interface (English/Russian) with React Context-based i18n system and language toggle

7

Developed results display with color-coded K/D ratios, skill level indicators, team average stats, and balance spread percentage

8

Implemented responsive design optimized for both desktop and mobile use during LAN events

9

Added support for 2-10 teams with dynamic player count validation (5 players per team)

I'm genuinely speechless — this thing is insane. The tool works exactly as needed and completely eliminates the headache we had organizing local and online tournaments. I'm honestly blown away.

Slava

Founder at Cube

Technical Deep Dive

Explore the technical architecture and implementation details

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