  The International Swimming Federation (FINA) uses a very simple points system
with the aim to rank swimmers across all Olympic events. The points acquired is
a function of the ratio of the recorded time and the current world record for
that event. With some world records considered "better" than others however,
bias is introduced between events, with some being much harder to attain points
where the world record is hard to beat. A model based on extreme value theory
will be introduced, where swim-times are modelled through their rate of
occurrence, and with the distribution of the best times following a generalised
Pareto distribution. Within this framework, the strength of a particular swim
is judged based on its position compared to the whole distribution of
swim-times, rather than just the world record. This model also accounts for the
date of the swim, as training methods improve over the years, as well as
changes in technology, such as full body suits. The parameters of the
generalised Pareto distribution, for each of the 34 individual Olympic events,
will be shown to vary with a covariate, leading to a novel single unified
description of swim quality over all events and time. This structure, which
allows information to be shared across all strokes, distances, and genders,
improves the predictive power as well as the model robustness compared to
equivalent independent models. A by-product of the model is that it is possible
to estimate other features of interest, such as the ultimate possible time, the
distribution of new world records for any event, and to correct swim times for
the effect of full body suits. The methods will be illustrated using a dataset
of the best 500 swim-times for each event in the period 2001-2018.
