At the end of every NFL regular season, football analysts and fans everywhere try their best to predict who will end up in the . Analyst predictions often have merit, coming from experts who study the sport in great depths or have insider knowledge about nuances and subtleties that may tip a game in favor of a given team. However, they are primarily subjective or qualitative, and they may be colored by inherent biases leading to inaccurate predictions.Predictions using mathematical models based on statistical measures are becoming more commonplace. These models can be based on a variety of factors, such as player performance, wins and lo ses and strength of schedule. I developed a mathematical model based on regular-season team statistics to predict which teams would represent their respective conferences in 50. Using data from 2004 to 2011, the model correctly predicted the last two matchups (the and in XLVIII and the and in XLIX), suggesting that it has some value.By applying the same model to the 2015 regular season, I developed the following predictions:That's right -- based on this model, you can expect the and to square off in 50.The model incorporates numerous factors, including three (; , or SRS; and , or OSRS) used by Pro Football Reference to measure teams' offensive efficiency and quality. The expected points statistic reflects the fact that all yards are not created equal, illustrating which teams are able to make the most of their offensive opportunities. That is, a 12-yard gain on third-and-20 adds more to a team's yardage total than a 3-yard gain on third-and-1, but the latter play is more valuable. This measurement is meant to account for that difference. The simple rating system statistics are a measure of a team's caliber relative to the league average, based on margin of victory and strength of schedule. The model also rewards wins and penalizes teams that punt or turn the ball over frequently. In other words, teams that are efficient on offense, play well against tough opponents, and take care of the football will rate higher than those that don't.Perhaps somewhat surprisingly, the (third seed) and (sixth seed) are favored to represent the AFC over the higher-seeded (No. 1) and (No. 2). The NFC predictions are more in line with seeding, as Kendall Graveman Jersey the top two seeds -- the (No. 2) and (No. 1) -- are ranked highest.The model suggests New England was a much stronger team relative to the competition heading into the 2014 playoffs than it is going into the 2015 postseason -- regardle s of and Bill Belichick's reputation for playoff dominance. It also suggests the are better poised to make the than in previous years -- regardle s of their reputation for playoff futility.Of course, the modeling approach is not perfect. First, it does not account for changes in key personnel, such as the fact that the will start backup at quarterback, or that the , or that the could welcome back to the lineup. Consider that the model predicted the would thrive in the 2014 postseason, a prediction that was thwarted in part because Pittsburgh's replacement running backs (Ben Tate and at the time) could not fill the shoes of injured starter in the wild-card lo s to the . That said, McCarron's pa ser rating (97.1) bodes well for the this season, while the effect of Williams' absence is tougher to predict, as we don't know what to expect from projected replacements and .Second, since the model was created using data from previous years, the PAT rule change (longer extra-point kicks) might add an unexpected wrinkle that was not quantified.The model also does not put more weight on recent performance, meaning the ' 6-2 finish, the ' 10-game win streak, the ' unexpected lo s to the in Week 16 or the ' 4-4 second half are not nece sarily reflected. However, between 2012 and 2014, just one of 10 teams that won their last four regular-season games made the . This is not a definitive conclusion, given the small sample size, but that data suggests late-season streaks and momentum might not be as important as some think.Interestingly, punt return stats ended up having some predictive value. This aspect of the game often gets overshadowed, but it suggests a dynamic return man like rookie could provide a significant boost.It's important to remember something about football: a few key plays can drastically change the outcome of a game. Consider, for example, the 2014 NFC title game, in which the completed a late comeback thanks to a number of seemingly improbable plays, including a botched onside kick recovery. This model can predict who's more likely to win, but it can't, obviously, account for this unpredictable element of football. A play such as a red zone pick-six could, potentially, cause a 14-point swing. This makes modeling football challenging -- but it also makes it fun.So while this model is far from perfect, it does offer an objective prediction based on team performance over the entire season. This was sufficient to predict the last two matchups. If the same holds for this year, then we can plan on watching Cincinnati and Arizona battle on football's biggest stage a few weeks from now.For more than a decade, Nasir Bhanpuri, PhD, has been applying analytics and modeling techniques to addre s challenges in a wide range of fields, including sports, healthcare, fitne s, education, neuroscience, robotics, wearables and music. He is currently a member of the Clinical Analytics team at NorthShore University HealthSystem, a Chicago-area hospital network. Walt Weiss Jersey