  Predicting the future performance of young runners is an important research
issue in experimental sports science and performance analysis. We analyse a
data set with annual seasonal best performances of male middle distance runners
for a period of 14 years and provide a modelling framework that accounts for
both the fact that each runner has typically run in three distance events (800,
1500 and 5000 meters) and the presence of periods of no running activities. We
propose a latent class matrix-variate state space model and we empirically
demonstrate that accounting for missing data patterns in runners' careers
improves the out of sample prediction of their performances over time. In
particular, we demonstrate that for this analysis, the missing data patterns
provide valuable information for the prediction of runner's performance.
