One of the challenges of learning-to-rank for information retrieval is that ranking metrics are not smooth and as such cannot be optimized directly with gradient descent optimization methods. This gap has given rise to a large body of research that reformulates the problem to fit into existing machine learning frameworks or defines a surrogate, ranking-appropriate loss function. One such loss ListNet's which measures the cross entropy between a distribution over documents obtained from scores and another from ground-truth labels. This loss was designed to capture permutation probabilities and as such is considered to be only loosely related to ranking metrics. In this work, however, we show that the above statement is not entirely accurate. In fact, we establish an analytical connection between softmax cross entropy and two popular ranking metrics in a learning-to-rank setup with binary relevance labels. In particular, we show that ListNet's loss bounds Mean Reciprocal Rank as well as Normalized Discounted Cumulative Gain. Our analysis sheds light on the behavior of that loss function and explains its superior performance on binary labeled data over data with graded relevance.