Predictive models are a key component of every effective sports analytics program because these models translate raw data into useful information. For example, there is very little value in the motion capture data described in Part 1 without skilled analysis that transforms that data from millions of raw records into actionable information a decision-maker can understand and trust. Data sources alone are clearly unusable, as no decision-maker would be able to draw conclusions from these mountains of raw data. Once such data is analyzed, however, the analysis results have the potential to become a valuable and unique tool to aid decision-makers in making better decisions. Teams often begin their use of predictive analytics because they are looking for a tool to reduce the seemingly high error rate in decision-making around their sport`s amateur draft. Given the financial investment and opportunity cost associated with high-round draft choices, teams that spend a draft pick on a player who does not make a significant contribution find they have made an expensive mistake. Conversely, drafting well can make a huge impact on a team`s fortunes, sometimes immediately and often for several seasons to come. Given these risks and opportunities, the draft is a place where teams have historically spent a lot of time and energy, helping open the door for predictive analytics. The draft also provides a fairly easy starting point for teams because much of the data used is publicly available, building the models does not require the time and attention from decision-makers, and the use of the results does not require buy-in anyone other than the top decision-maker.
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