In the second round, our average probabilities went 3-1, and all models individually were either 3-1 or 2-2. Model 1 was the only one to correctly pick the Packers over the Cowboys. For the playoffs as a whole, Models 2 and 3 are performing best, as they have correctly picked 7 of the 8 games, while Model 6 is performing worst, only picking 5 of 8 games correctly. The average probabilities are also 7-1, only missing the Packers/Cowboys game. Click here to see all of our predictions from the past weekend. Check back later this week for our Conference Championship game predictions!
Continue reading 2016 NFL Divisional Round Recap →

We use team statistics to create models that predict outcomes of NFL games. Our models generate either a win probability for the home team or a point spread, with each model generating a unique prediction. Our models consider many different metrics, such as turnovers, first downs, points scored, pace of play, offensive and defensive efficiency, as well as a few metrics that we have created ourselves. Beyond these statistics, some models consider factors such as “did a team make the playoffs last year?” and “how far is the away team traveling?” See this article for a more detailed breakdown of each model. The article below focuses on the four divisional round matchups for the coming weekend, but a breakdown of predictions for the entire 2016 NFL Playoffs can be found here, and our predictions from round 1 are here, where our average probabilities correctly predicted all four winners.
Continue reading 2016 NFL Divisional Round Predictions →

Using probabilities from our five NFL Playoff Models, we have run a number of simulations for the upcoming 2016 NFL Playoffs. This allows us to estimate how often each team makes the division round, conference championship, Super Bowl, and ultimately win the Super Bowl. In order to do this, we took each game and generated a random number between 0 and 1. If the random number is less than our predicted probability of the home team winning, then we advance the home team as the winner of that game (and vice versa if the random number is greater than our probability). For example, Model 1 has a probability of 0.85 that the Houston Texans will beat the Oakland Raiders. If the random number is 0.95, we would pick the Raiders to advance, and if the random number is 0.55, we would pick the Texans to advance. This methodology was applied to the entire NFL Playoff bracket, running 10,000 Playoff simulations for each of our five models (so, each model generates 10,000 brackets and 10,000 super bowl champions).
Continue reading 2016 NFL Playoff Simulation →

We use team statistics to create models that predict outcomes of NFL games. Our models generate win probabilities and point spreads, and we consider six different models, each of which generates a unique prediction for a given game. Each model considers metrics from a number of different areas, such as turnovers, first downs, points scored, pace of play, offensive and defensive efficiency, time of possession, drive statistics, point differential, as well as a few metrics that we have created ourselves. Beyond these statistics, the models also consider factors such as “did a team make the playoffs last year?” and “how far is the away team traveling?” The article below focuses on the four Wild Card matchups for this coming weekend, but a breakdown of predictions for the entire 2016 NFL Playoffs can be found here.
Continue reading 2016 NFL Wild Card Predictions →

We use team statistics from the regular season to predict win probabilities and point spreads for NFL playoff games. We have a number of different models that are used to predict each game, and we build off of the first round matchups to create predictions all the way through the super bowl. Below are our predictions for this year’s playoffs.
Continue reading 2016 NFL Playoff Predictions →