FairPlay Replay Review
An on-demand match-integrity service for competitive FPS tournament organizers and community leagues — upload server telemetry, get a published Fair Play Report flagging suspicious players with confidence scores, no in-house ML team required.
Tournament organizers and competitive league admins running FPS events
- Drop-in telemetry ingestion from popular FPS engines (Unreal, Unity, Source derivatives)
- Aimbot-pattern classifier using the same temporal features validated in research — aim velocity, shots-per-distance, movement entropy
- Tamper-evident Fair Play Report PDF that organizers can publish to players and partners
- Per-event or per-month pricing with no annual contract
Cheating accusations are the top driver of player churn in competitive FPS, and indie/mid-tier studios plus community tournament organizers cannot afford to build or operate an ML detection team in-house.
Strong macro: anti-cheat market projected $13.5B (2024) to $23.86B (2032) at 8.67% CAGR, but on-demand report-style service for indie FPS leagues has thinner, less-proven demand than incumbents serve.Anti-Cheat Software Market Report: Size, Growth, Trends & Forecast ↗FACEIT.com ↗
FACEIT (kernel anti-cheat), EAC, BattlEye and Sportradar UFDS dominate the high end; mid-tier indie/community organizers are underserved by an upload-telemetry, confidence-scored report format — Gamer.TD simply routes to existing providers rather than competing.Anti-Cheat Integration | Tournament Suite - gamertd.com ↗Bet Monitoring & Detection - Sportradar ↗
Battlefy/Challonge make tournament tooling free, putting indie organizers in a low-WTP segment; per-match integrity reports are plausibly billable but unvalidated — no public pricing reference found for comparable telemetry-review services.Battlefy | Find and Organize Esports Tournaments ↗SafePlay Gaming (Web Security & DDoS Protection) Features, Benefits ↗
FPS cheating is a persistent adversarial arms race; the cited arXiv trend shows continued ML investment in server-side aimbot detection, indicating a durable long-term category.
Building trustworthy ML cheat detection from telemetry is genuinely hard and adversarial — even though the 'no in-house ML' pitch is attractive, the vendor itself must operate top-tier ML infrastructure to survive reverse-engineering, pushing feasibility below average.