In the absence of historical data for use as forecasting inputs, decision makers often ask a panel of judges to predict the outcome of interest, leveraging the wisdom of the crowd (Surowiecki 2005). Even if the crowd is large and skilled, shared information can bias the simple mean of judges' estimates. Addressing the issue of bias, Palley and Soll (2019) introduces a novel approach called pivoting. Pivoting can take several forms, most notably the powerful and reliable minimal pivot. We build on the intuition of the minimal pivot and propose a more aggressive bias correction known as the neutral pivot. The neutral pivot achieves the largest bias correction of its class that both avoids the need to directly estimate crowd composition or skill and maintains a smaller expected squared error than the simple mean for all considered settings. Empirical assessments on real datasets confirm the effectiveness of the neutral pivot compared to current methods.

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