Analysing a built-in advantage in asymmetric darts contests using causal machine learning

TL;DR

This paper uses causal machine learning to analyze how a technical advantage and asymmetries in a darts contest influence the built-in advantage and fairness, revealing that first-mover and home advantage significantly affect winning probabilities.

Contribution

It introduces a causal machine learning approach to quantify built-in advantages in asymmetric contests, highlighting how social and positional factors impact fairness.

Findings

First-mover has an 8.6 percentage point higher chance of winning.

Low-performance and inexperienced players benefit most from built-in advantages.

Asymmetries due to social pressure and location affect fairness despite equal skill levels.

Abstract

We analyse a sequential contest with two players in darts where one of the contestants enjoys a technical advantage. Using methods from the causal machine learning literature, we analyse the built-in advantage, which is the first-mover having potentially more but never less moves. Our empirical findings suggest that the first-mover has an 8.6 percentage points higher probability to win the match induced by the technical advantage. Contestants with low performance measures and little experience have the highest built-in advantage. With regard to the fairness principle that contestants with equal abilities should have equal winning probabilities, this contest is ex-ante fair in the case of equal built-in advantages for both competitors and a randomized starting right. Nevertheless, the contest design produces unequal probabilities of winning for equally skilled contestants because of asymmetries in the built-in advantage associated with social pressure for contestants competing at home and away.