Sports awards have become almost as popular as the sports themselves bringing not only recognition, but also increases in salary, more control over decisions usually in the hands of coaches and general managers, and other benefits. Awards are so popular that even the start of a season pundits and amateurs alike predict or argue for athletes. It is odd that something so apparently data-driven does not work in determining whether it is, indeed, data-driven. The simple question arises, ``Are sports awards about sports?" Using ML (over a hundred potential models) this work aims to answer this question for professional basketball: Most Valuable Player, Most Improved Player, Rookie of the Year, and Defensive Player of the Year. Pertinent data is gathered including voting percentages. Our results are very interesting. MVP can be predicted well from the data, while the other three are more difficult. The findings suggest that either the data is insufficient (although no more sports data can be found) or more likely non-tangible factors are playing as critical roles. This outcome is worth reflecting on for fans of all stripes: should sports award be about sports? The source code can be found in our https://github.com/Nebbocaj/NBA_Awards.