Presented at the 2017 Joint Mathematics Meeting in Atlanta, GA. Draft selections in Major League Soccer -- and all North American sports leagues -- can be viewed as an asset that can be exercised or traded, so it is useful to understand a draft selection's value and its evolution over time. This slide deck presents valuation models that result from a Bayesian local regression of the expected career value of a draft pick. The models are valid for a specific draft year and are trained by the career values of draftees in previous years. The models are differentiated by the use of a time horizon to filter players in the training set and restricting career values to those earned at the drafting team. The resulting regression curves and the 95% credible regions surrounding them demonstrate a significant difference between the expected value of an early drafted player over his MLS career and the expected value while playing for the club that drafted him. These valuation curves can be used to determine relative value of draft slots, assess individual draft selections, and identify over- and under-performing team organizations in MLS player drafts.