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Outcome prediction has always been an important part of practicing law. Clients rely heavily on their attorneys to provide accurate assessments of the potential legal consequences they face when making important decisions (such as whether to accept a plea bargain, or risk a conviction on a much more serious offense at trial). And yet, notwithstanding its enormous importance to the practice of law (and notwithstanding the handsome legal fees it commands), outcome prediction in the law remains a very imprecise endeavor. The reason for this inaccuracy is that the three principal tools lawyers have traditionally relied on to facilitate outcome predictions—legal analysis, lawyerly experience, and the use of certain types of empirical information (e.g., jury verdict reporters)—are all subject to significant problems and limitations. This article examines in detail the reasons for these problems and limitations, concluding that they are essentially intractable. Thus, there is little hope that the traditional tools of outcome prediction on their own can ever enable consistently accurate assessments of potential legal outcomes. Fortunately, however, recent advances in data science offer some grounds for optimism. Already, these advances are beginning to alter the way law firms operate, and there are good reasons to believe that data science (or more specifically, predictive analytics) will soon enable more accurate outcome predictions as well. Of course, predictive analytics is not a panacea: significant challenges remain if it is going to enable accurate outcome predictions on its own. And so it is doubtful that predictive analytics will supplant the traditional tools of outcome prediction in the foreseeable future. Rather, predictive analytics is likely to complement the traditional tools in order to power more accurate outcome predictions. However, even that modest change is likely to have a significant effect on the way lawyers practice law, and it should also come as very welcome news to their clients.