The Wall Street Journal rates analysts on the basis of past earnings forecast accuracy. These analyst ratings are important to practitioners who believe that past accuracy portends future accuracy. An alternative way to assess the likelihood of “more” or “less” accurate forecasts in the future is to model the analyst characteristics related to the accuracy of individual analysts' earnings forecasts. No evidence yet exists, however, as to whether an analyst characteristics model is better than a past accuracy model for distinguishing more accurate from less accurate earnings forecasters. I show that a simple model of past accuracy performs as well for this purpose as a more complex model based on analyst characteristics. The findings are robust to annual and quarterly forecasts and pertain to estimation and prediction tests. The evidence suggests that practitioners' focus on past accuracy is not misplaced: It is as important as five analyst characteristics combined.