Following up on Slammin's point, you also generally want to create a dummy or 'null' model to compare the accuracy of your predictions against. One option would be 81-81 (in this case, that's the 'null' model), but also the previous year's record, the previous year's record averaged with 81-81, etc. The point being, you want to validate the fact that the variables you are using in your prediction actually provide additional information, rather than just noise, especially given a situation like baseball team records, where your sample size is going to be very small.

Also, a very minor point, but correlation isn't generally the best method of evaluating in case like this. We don't really have enough data points for it to matter that much. In an ordered series where points are not independent (since teams play eachother), we might prefer to use Spearman (rank) correlation, which is a non-parametric measure of correlation, which is preferred in cases like this, where one or both of the distributions can be significantly non-normal (since the actual winning percentage should be normal, i.e., symmetrical around 81-81), while the predictions don't have to be. The reason for the difference is that, using Pearson's metric, the outliers get much more influence, even though the bulk of the data is much more strongly correlated.