Built by quants, for bettors

Mission

Stop relying on subjective opinions and instead, understand the true probabilities of sporting outcomes. Zaiov leverages a sophisticated machine learning system combining XGBoost and Bradley-Terry models, rigorously calibrated with isotonic regression, to identify pricing inefficiencies within Kalshi prediction markets. Our backtesting demonstrates a consistent 83% win rate on qualifying picks at the Phase F gate, a level of performance demonstrably superior to traditional “gut-feel” tipsters. This data-driven approach, built on robust statistical modeling, creates a durable edge by systematically extracting predictive signals from the market itself, rather than relying on human bias.