This paper introduces a predictive sports analytics model that can be used to i) rank teams, ii) estimate win probability, and iii) calculate the expected winning margin (spread) of a game. The model is based on an exponential distribution function and is solved using a log-loss function and maximum likelihood estimates (MLE). The most appealing aspect of this quantitative approach is that the model is objective, transparent, and testable. Readers can verify all calculations and results as the model does not employ any black box method. We apply this model to the 2018-2019 NFL season to rank teams, estimate scores, and make predictions across all pairs of teams. The model predicted more than 70% of the NFL games correct, and has 𝑅2 = 26% when comparing estimated spreads to actual spreads. This model can also serve as an objective approach to assist committees determine which teams should be selected for college post season tournaments such as the NCAA basketball tournament and the College Football Playoffs.