Empirical Validation of the Independent Chip Model

TL;DR

This paper empirically evaluates the independent chip model (ICM) in large-scale poker tournaments, confirming its relative accuracy and revealing biases against large-stacked players, thus providing valuable insights for future algorithm development.

Contribution

Introduces a large dataset of poker tournaments and empirically validates the ICM's performance, highlighting its biases and improving understanding of its real-world applicability.

Findings

ICM performs more accurately than a baseline model.

ICM underestimates large-stacked players' performance.

ICM overestimates short-stacked players' performance.

Abstract

The independent chip model (ICM) forms a cornerstone of all modern poker tournament strategy. However, despite its prominence, the ICM's performance in the real world has not been sufficiently scrutinized, especially at a large scale. In this paper, we introduce our new dataset of poker tournaments, consisting of results of over ten thousand events. Then, using this dataset, we perform two experiments as part of a large-scale empirical validation of the ICM. First, we verify that the ICM performs more accurately than a baseline we propose. Second, we obtain empirical evidence of the ICM underestimating the performances of players with larger stacks while overestimating those who are short-stacked. Our contributions may be useful to future researchers developing new algorithms for estimating a player's value in poker tournaments.