In recent years, prediction tournaments have been organized more frequently. Organizers of these tournaments aim to identify statistical models that perform best in predicting future events. In most cases, the winner of a prediction tournament receives a reward.

In a prediction tournament, each contestant is asked a number of questions about the probability that an event will occur before a specific date. Simulations indicate that contestants who perfectly predict these probabilities almost never win the tournament. This effect suggests that an accurate forecaster could increase her chance of winning by introducing some noise into her predictions. The aim of this report is to identify strategies that contestants can use to increase their chance of winning.

In this report, five strategies are introduced: hard-thresholding, soft-thresholding, polynomial strategy, exponential strategy, and random exponential strategy. Each strategy depends on a single parameter. For each strategy, simulations are performed under different settings to determine which strategy results in the most victories. To determine the best parameter for each strategy, polynomial regression is applied to the simulation data.

The simulations suggest that the exponential strategy has the largest positive impact on the number of wins for accurate contestants when all opponents use no additional strategies. If half of the opponents use an exponential or random exponential strategy, then the most accurate contestants are recommended to use a random exponential strategy. Using a strategy appears to have only a negative impact on a contestant’s chance of winning if all opponents use an exponential or random exponential strategy.

The best parameter for an exponential strategy appears to be smaller for less accurate forecasters. The least accurate contestants competing in a prediction tournament are recommended to use no strategy in all previously described situations. ... Read more