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

Daniel Goller

No 2013, Economics Working Paper Series from University of St. Gallen, School of Economics and Political Science

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.

Keywords: Causal machine learning; heterogeneity; contest design; social pressure; built-in advantage; incentives; performance; darts (search for similar items in EconPapers)
JEL-codes: C14 D02 D20 Z20 (search for similar items in EconPapers)
Pages: 39 pages
Date: 2020-09
New Economics Papers: this item is included in nep-big, nep-cmp and nep-spo
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Related works:
Journal Article: Analysing a built-in advantage in asymmetric darts contests using causal machine learning (2023)
Working Paper: Analysing a built-in advantage in asymmetric darts contests using causal machine learning (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:usg:econwp:2020:13

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