Analysing a built-in advantage in asymmetric darts contests using causal machine learning. (English) Zbl 1520.91016
Summary: 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% 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.
MSC:
| 91A10 | Noncooperative games |
| 91A20 | Multistage and repeated games |
| 68T05 | Learning and adaptive systems in artificial intelligence |
Keywords:
operational research in sports; causal machine learning; heterogeneity; contest design; built-in advantage; incentivesReferences:
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