Say I am running a regression to predict race time in an upcoming race. For each runner, I have a demographic information and one past race performance. For some subset of runners, I have another race performance.
My model would therefore look like:
Future Race ~ Demographic Info + Past Race1 + Past Race2
However, for many modelling packages in r, the regression simply drops incomplete data rows.
I could present a simpler model of simply:
Future Race ~ Demographic Info + Past Race1
This seems to drop useful information. Are there modelling approaches to dealing with this incomplete data beyond simple deletion or imputation (generating random values from the distribution)?