The NFL’s 2015 season sort of got underway last night with the Hall of Fame Game. Real preseason play doesn’t start until this weekend, and the first kickoff of the regular season is still a month away.

No matter, though — I’m taking the Hall of Fame Game as my cue to launch this year’s football forecasting effort. As it has for the past two years (see here and here), the process starts with me asking you to help assess the strength of this year’s teams by voting in a pairwise wiki survey:

In the 2015 NFL season, which team will be better?

That survey produces scores on a scale of 0–100. Those scores will become the crucial inputs into simulations based on a simple statistical model estimated from the past two years’ worth of survey data and game results. Using an R function I wrote, I’ve determined that I should be able to improve the accuracy of my forecasts a bit this year by basing them on a mixed-effects model with random intercepts to account for variation in home-team advantages across the league. Having another season’s worth of predicted and actual outcomes should help, too; with two years on the books, my model-training sample has doubled.

An improvement in accuracy would be great, but I’m also excited about using R Studio’s Shiny to build a web page that will let you explore the forecasts at a few levels: by game, by team, and by week. Here’s a screenshot of the game-level tab from a working version using the 2014 data. It plots the distribution of the net scores (home – visitor) from the 1,000 simulations, and it reports win probabilities for both teams and a line (the median of the simulated scores).

The “By team” tab lets you pick a team to see a plot of the forecasts for all 16 of their games, along with their predicted wins (count of games with win probabilities over 0.5) and expected wins (sum of win probabilities for all games) for the year. The “By week” tab (shown below) lets you pick a week to see the forecasts for all the games happening in that slice of the season. Before, I plan to add annotation to the plot, reporting the lines those forecasts imply (e.g., Texans by 7).

Of course, the quality of the forecasts displayed in that app will depend heavily on participation in the wiki survey. Without a diverse and well-informed set of voters, it will be hard to do much better than guessing that each team will do as well this year as it did last year. So, please vote here; please share this post or the survey link with friends and family who know something about pro football; and please check back in a few weeks for the results.