How to Host a Data Competition: Statistical Advice for Design and Analysis of a Data Competition

TL;DR

This paper offers strategic guidance on designing, analyzing, and interpreting data competitions to improve learning outcomes, prevent overfitting, and gain deeper insights into algorithm performance beyond leaderboard rankings.

Contribution

It introduces a comprehensive framework for competition design and post-competition analysis using exploratory data analysis and generalized linear models, enhancing understanding of algorithm strengths and weaknesses.

Findings

Effective competition design reduces overfitting risks.

Post-competition analysis reveals detailed algorithm performance insights.

Richer summaries improve interpretation beyond leaderboard rankings.

Abstract

Data competitions rely on real-time leaderboards to rank competitor entries and stimulate algorithm improvement. While such competitions have become quite popular and prevalent, particularly in supervised learning formats, their implementations by the host are highly variable. Without careful planning, a supervised learning competition is vulnerable to overfitting, where the winning solutions are so closely tuned to the particular set of provided data that they cannot generalize to the underlying problem of interest to the host. This paper outlines some important considerations for strategically designing relevant and informative data sets to maximize the learning outcome from hosting a competition based on our experience. It also describes a post-competition analysis that enables robust and efficient assessment of the strengths and weaknesses of solutions from different competitors, as well as greater understanding of the regions of the input space that are well-solved. The post-competition analysis, which complements the leaderboard, uses exploratory data analysis and generalized linear models (GLMs). The GLMs not only expand the range of results we can explore, they also provide more detailed analysis of individual sub-questions including similarities and differences between algorithms across different types of scenarios, universally easy or hard regions of the input space, and different learning objectives. When coupled with a strategically planned data generation approach, the methods provide richer and more informative summaries to enhance the interpretation of results beyond just the rankings on the leaderboard. The methods are illustrated with a recently completed competition to evaluate algorithms capable of detecting, identifying, and locating radioactive materials in an urban environment.