  Cross country running races are different to track and road races in that the
courses are not typically accurately measured and the condition of the course
can have a strong effect on the finish times of the participants. In this paper
we investigate these effects by modelling the finish times of all participants
in 28 cross country running races over 5 seasons in the North East of England.
We model the natural logarithm of the finish times using linear mixed effects
models for both the senior men's and senior women's races. We investigate the
effects of weather and underfoot conditions using windspeed and rainfall as
covariates, fit distance as a covariate, and investigate the effect of time via
the season of the race, in particular investigating any evidence of a pre- to
post-Covid effect. We use random athlete effects to model the participant to
participant variability and identify the most difficult courses using random
course effects. The statistical inference is Bayesian. We assess model adequacy
by comparing samples from the posterior predictive distribution of finish times
to the observed distribution of finish times in each race. We find strong
differences between the difficulty of the courses, effects of rainfall in the
month of the race and the previous month to increase finish times and an effect
of increasing distance increasing finish times. We find no evidence that
windspeed affects finish times.
