#008
October 24, 2025:- Matchmaking Refinement, Anti-Bot Groundwork & Referral Backend Stub
Purpose
To strengthen core competitive infrastructure through purpose-built matchmaking and anti-fraud systems, laying groundwork for future reputation layers and user acquisition flows while maintaining rigorous data ethics.
Key Highlights
🔍 Foundational Anti-Bot Heuristics (Phase 1, In-House Engine)
Launched Tapzi's native behavior monitoring layer—combines IP throttling, entropy scoring, and interaction timing analysis.
Data visualized through internal ELK + Grafana stack, fully anonymized (no PII or user fingerprinting stored).
Engine built from scratch to ensure full control over detection policy and tuning—no reliance on third-party providers.
Tuning in collaboration with Trust & Safety reviewers for socially aware detection thresholds.
🎯 Adaptive Matchmaking Progression
Core matchmaking engine (under internal development) enhanced with ELO drift support, allowing temporary bracket flexibility for players with inconsistent streaks.
Added detailed rejection telemetry (abandonment, connection mismatches, invalid rating gap) to help refine pairing logic.
📱 Real-Device Testing Matrix Expanded
Internal QA process validated matchmaking and anti-abuse behavior across 9 diverse mobile environments, including budget Androids and stylus inputs, ensuring fairness across device classes.
🧩 Referral Logic (Backend Laid, UX TBD)
Signed invite code and referral timestamping backend system implemented.
UI entry points and attribution visualization planned for Q4 once backend is stabilized.
Why It Matters
Trust-First Architecture: Every layer is built internally for full control, transparency, and long-term independence.
Infrastructure-Driven Integrity: Fraud resistance, fair matchmaking, and audit trails are part of Tapzi’s core—not tacked on.
Scalable Foundations: System design anticipates millions of matches and diverse players, not short-term shortcuts.
Ethical Data Practices: We observe gameplay behavior—not identity—ensuring Tapzi is future-compliant by design.
Growth Readiness: Referral logic allows low-friction user growth with attribution—ready when UI drops.
Open Issues
Edge Case False Positives from unique user hardware patterns (stylus, low-refresh Android tablets).
Thread Cleanup Gaps under rare match timeout conditions still causing lingering matchmaking locks.
Referral Interaction Missing—only backend support; users can’t yet send or redeem invites.
Fixed
Granular Rejection Logs—rejection types and timestamps now indexed for analytics and debug.
Match Queue Visualization—wait time percentile buckets now live in telemetry dashboards.
Next Steps
Iterate detection heuristics with QA + Trust reviewers to tune accuracy.
Build and test frontend referral interface, including invite generation and sign-up flow.
Introduce ELO decay policy for inactive users to maintain leaderboard competitiveness.
Expand anti-abuse monitoring: real-time dashboard for bot flag volumes, disputes, and manual overrides.
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