Racing prediction schemes have been with mankind a long time. From following crowd wisdom and betting on favorites tomathematicalmethods like the Dr. Z System, we introduce a different class of prediction system, the S&C Racing system that derives from machine learning. We demonstrate the S&C Racing system using Support Vector Regression (SVR) to predict finishes and analyzed it on fifteen months of harness racing data from Northfield Park, Ohio. We found that within the domain of harness racing, our system outperforms crowds and Dr. Z Bettors in returns per dollar wagered on seven of the most frequently used wagers: Win $1.08 return, Place $2.30, Show $2.55, Exacta $19.24, Quiniela $18.93, Trifecta $3.56 and Trifecta Box $21.05. Furthermore, we also analyzed a range of race histories and found that a four race historymaximized system accuracy and payout. The implications of this work suggest that an informational inequality exists within the harness racing market that was exploited by S&C Racing.While interesting, the implications of machine learning in this domain show promise