Pairwise Comparisons with Flexible Time-Dynamics

Inspired by applications in sports where the skill of players or teams competing against each other varies over time, we propose a probabilistic model of pairwise-comparison outcomes that can capture a wide range of time dynamics. We achieve this by replacing the static parameters of a class of popular pairwise-comparison models by continuous-time Gaussian processes; the covariance function of these processes enables expressive dynamics. We develop an efficient inference algorithm that computes an approximate Bayesian posterior distribution. Despite the flexbility of our model, our inference algorithm requires only a few linear-time iterations over the data and can take advantage of modern multiprocessor computer architectures. We apply our model to several historical databases of sports outcomes and find that our approach a) outperforms competing approaches in terms of predictive performance, b) scales to millions of observations, and c) generates compelling visualizations that help in understanding and interpreting the data.


Published in:
Kdd'19: Proceedings of the 25th ACM Sigkdd International Conferencce on Knowledge Discovery and Data Mining, 1236-1246
Presented at:
25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD), Anchorage, AK, August 04-08, 2019
Year:
Jan 01 2019
Publisher:
New York, ASSOC COMPUTING MACHINERY
ISBN:
978-1-4503-6201-6
Keywords:
Laboratories:




 Record created 2019-10-05, last modified 2019-10-08


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