Goal programming has proven a valuable mathematical programming form in a number of venues.

There has been a similar rapid growth in interest in data mining, where a variety of different data types

are encountered. This paper applies goal programming formulations to compare relative performance

of L|, L2, and L^ norms as well as ordinal and ratio data types in a dynamic predictive environment.

The models are applied to compare relative accuracy and stability in forecasting a professional athletic

environment. Results confirm that ratio data provide more accurate forecasts than ordinal data.

Responsiveness to error can be good and bad in prediction. Too much response to outlying events

makes the predictor "nervous" and unreliable, L, metric models are much easier and faster to solve,

but involve higher levels of ambiguity than nonlinear models, L] metric models also were more

responsive to changes, but correspondingly tend to be more affected by unexpected outcomes.