My model compares this season's games with 15 previous seasons of situational production metrics that led to wins and losses (between 2003 and 2017). Tracking personnel, matchups, play-calling and results from past seasons establishes historical "profiles." The results from the games that have already been played this season are then collected and analyzed in the same way, with the model revealing similarities between the current iteration of each team and its past versions. Then each remaining game is simulated. The reason every game isn't a 50/50 coin flip is because each team has different strengths and weaknesses, and the way they match up against each other has different historical references for "what happened most often." Because there are many different ways the situational aspect of football can play out, it's necessary to run many simulations for each remaining game, to see each of the involved teams' profiles stack up over a range of reasonable situations.