In recent years, the battery research community has deployed data-driven methods to predict battery lifetime. The dataset published by Severson et al. in 2019 , among several others, is widely used for benchmarking model performance. In this dataset, charging conditions are varied to broaden the distribution of cycle life, while discharging conditions are kept constant across the cells. We demonstrate that a prediction model using only cycling conditions and no aging data can achieve decent (26.4% MAPE) predictions (compared to a dummy regressor, 41.2% MAPE) because cycling conditions strongly influence battery lifetime. More importantly, we show that models with features encoding the cycling conditions are more accurate than models that do not rely on cycling conditions. However, these models do not maintain the same level of performance when predicting intrinsic cell-to-cell variability among subsets of cells cycled identically.