Rowing Against the Wind: An Analysis of the Impact of Variable Wind Conditions on Current and Prospective Rowing Selection Methods

Abstract

In rowing, it is vitally important to select the best possible crew of athletes in order to win. This is no trivial task. The current method of selection (seat racing) requires inefficient direct comparisons between athletes but is used because it is assumed to generate correct results. As such two questions must be asked. Firstly, Does the current method of selection result in the correct athletes being selected?" Secondly, Are there alternative forms of selection that may be more accurate or efficient?" This paper finds the current method of selection produces incorrect and biased results when wind conditions vary. This was demonstrated by analysing selection races and regular races to identify statistical trends in their outcomes, finding unfair advantages for some athletes. This paper also examines alternative methods of selection using machine learning, measured on synthetic race data and real selection data. Whilst promising, the consistency of these model was similarly vulnerable to variable winds. Most, if not all, current and potential selection method are barely better than a coin flip when variable winds are at their worst.