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Incorporating uncertainty for enhanced leaderboard scoring and ranking in data competitions
  • Quality

Incorporating uncertainty for enhanced leaderboard scoring and ranking in data competitions

Publication:
Quality Engineering
Date:
April 2021
Issue:
Volume 33 Issue 2
Pages:
pp. 189-207
Author(s):
Lu, Lu, Anderson-Cook, Christine M.
The copyright of this article is not held by ASQ.

Abstract

Data competitions have become a popular and cost-effective approach for crowdsourcing versatile solutions from diverse expertise. Current practice relies on the simple leaderboard scoring based on a given set of competition data for ranking competitors and distributing the prize. However, a disadvantage of this practice in many competitions is that a slight difference in the scores due to the natural variability of the observed data could result in a much larger difference in the prize amounts. In this article, we propose a new strategy to quantify the uncertainty in the rankings and scores from using different data sets that share common characteristics with the provided competition data. By using a bootstrap approach to generate many comparable data sets, the new method has four advantages over current practice. During the competition, it provides a mechanism for competitors to get feedback about the uncertainty in their relative ranking. After the competition, it allows the host to gain a deeper understanding of the algorithm performance and their robustness across representative data sets. It also offers a transparent mechanism for prize distribution to reward the competitors more fairly with superior and robust performance. Finally, it has the additional advantage of being able to explore what results might have looked like if competition goals evolved from their original choices. The implementation of the strategy is illustrated with a real data competition hosted by Topcoder on urban radiation search.

*Supplemental material accessed online through Taylor & Francis.

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