This morning I noticed my full league win/loss predictions were still on my desktop. I thought, why don't I just release a division a day for a while?
For those of you that didn't read my prior post on modeling NFL team results prior to the season, this is a followup to yesterday's post.
THE AFC EAST
As any football fan knows, the New England Patriots have dominated this conference for the better part of a decade, but they may have to play with fully inflated balls next year. Also my model has a hard time accounting for Tom Brady's potential four game suspension.
The Patriots came out of the model as a borderline 10/11 win team. Due to the outstanding questions regarding the impact of deflategate, I didn't round up. But they are clearly still the best team in the division.
Of note, is the New York Jets, who are projected to win three more games this year than last year, which is a pretty big statistical jump. The main reason for this is that the underlying statistics of the Jets show a team that has potential to play somewhat better football.
Finally, a methodological note on these predictions: These predictions are a general estimate of how well or poorly a team will do next year, especially compared to their division. Are they perfect estimates? No, for a variety of reasons I cited yesterday. But some additional explanation:
The estimates tend to be variance reducing, meaning that they smooth out volatility. An example of this is that in 2014 the best record in the NFL was 12-4, a record which five teams achieved. However in my rankings only one or two teams will get this rating. This is intentional, as predictions like this tend to predict well for overall trends (e.g. the Denver Broncos will win more games than other AFC West teams), but miss teams that have exceptionally good or bad years.
Overall, the goal is to give an early prediction of how teams stack-up and are likely to perform. Will there be teams that greatly outperform or under-perform this forecast? Absolutely. But this at least serves as a starting point.