Chess-engine evidence
Cash games can be analyzed across different search depths and difficult positions, including whether a player repeatedly finds unusually strong moves when the position demands precision.
SkillR FairPlay evaluates multiple forms of game and account evidence. The goal is to identify engine assistance and other unfair behavior while reducing false accusations against legitimate players.
Cash games can be analyzed across different search depths and difficult positions, including whether a player repeatedly finds unusually strong moves when the position demands precision.
FairPlay can consider move timing, position difficulty, consistency, selective strength, and other behavioral patterns rather than treating every strong move as suspicious.
A single game is only one piece of evidence. Account-level history can help distinguish isolated exceptional play from recurring behavior that warrants deeper review.
Player reports and automated signals can route suspicious activity for review. Serious account or withdrawal actions should consider the total evidence rather than one isolated model prediction.
SkillR can explain the categories of evidence used to protect competition without publishing exact thresholds, weights, or evasion rules. Detection methods can also change as FairPlay is tested and improved.
Detection systems have limitations. SkillR can update models, rules, review procedures, and account controls as new evidence and abuse patterns emerge.