Baseball players swung very light and very heavy bats through our instrument and the speed of the bat was recorded. These data were used to make mathematical models for each person. Then these models were coupled with equations of physics for bat-ball collisions to compute the Ideal Bat Weight for each individual. However, these calculations required the use of a sophisticated instrument that is not conveniently available to most people. So, we tried to find items in our database that correlated with Ideal Bat Weight. However, because many cells in the database were empty, we could not use traditional statistical techniques or even neural networks. Therefore, three new methods were used to estimate the missing data: (i) a neural network was trained using subjects that had no empty cells, then that neural network was used to predict the missing data, (ii) the data patching facility of a commercial software package was used, and (iii) the empty cells were filled with random numbers. Then, using these fully populated databases, several simple models were derived for recommending bat weights.