Abstract: Winner-take-all competitions in forecasting and machine-learning suffer from distorted incentives.Witkowski et al. identified this problem and proposed ELF, a truthful mechanism to select a winner.We show that, from a pool of $n$ forecasters, ELF requires $\Theta(n\log n)$ events or test data points to select a near-optimal forecaster with high probability.We then show that standard online learning algorithms select an $\epsilon$-optimal forecaster using only $O(\log(n) / \epsilon^2)$ events, by way of a strong approximate-truthfulness guarantee.This bound matches the best possible even in the nonstrategic setting.We then apply these mechanisms to obtain the first no-regret guarantee for non-myopic strategic experts.