When it comes to analyzing NFL teams, traditional statistics such as a team’s record or a team’s number of points scored and allows per game may not reveal the full extent of a team’s strength. While these statistics are above-average indicators of how a team is likely to perform in the future, one must dig deeper and take into account the more relevant statistics in order to predict a team’s success against another or its prospects for the future. Often, when a team has had a relatively easier schedule or has had a ton of luck on its side in close games, the team’s record may be skewed and may not be an accurate reflection of its true potential. As a result, here are the statistics that matter most when it comes to the NFL.
A team’s point differential is known to be a better measure of its potential to tack on wins in the future rather than a team’s actual number of wins. The Pythagorean expectation revolves around the point differential and was invented by Bill James, who applied it to a baseball setting. The concept was later used by Daryl Morey to apply to football as well. Essentially, the Pythagorean formula provides a “true” winning percentage that can be multiplied by 16 to produce an expected win total. If a team’s actual number of wins exceeds its “Pythag” win total, that team will usually regress the following year. On the other hand, if a team’s actual number of wins falls under its Pythag win total, that team will usually progress the following year.
Record In Close Games
Over the long run, such as over the course of five seasons, a team’s record in close games should statistically result in a 50/50 split. However, in a single season, a team’s record in close games will usually not be a 50/50 split. Sometimes, a team may be very fortunate and lucky in close games, winning 70% of these games. On the other hand, a team may have had a bunch of unlucky bounces and bad calls, resulting in a winning percentage in close games of 30%. While teams may end up with extremely favourable or unfavourable winning percentages in close games over the course of one season, this trend is unlikely to be maintained in the following years. If two teams produced winning percentages of 70% and 30% respectively in close games in one year, both of their records are expected to regress to the mean, with the mean being 50%.
Defense-Adjusted Value Over Average
DVOA was created by Aaron Schatz and is a statistic that measures a team’s success of any given play, through points and yards gained or lost, versus what would have been expected after accounting for the distance, downs, game situation, and quality of the opponent. With the statistic measured in percentages, a team with a DVOA of 10% of higher is much better compared to the league average on a play-by-play basis. The DVOA statistic is extremely useful due to the fact that the statistic can be broken down into numerous different ways. If one wishes to determine how a team fares on offense with three downs or when it is in the red zone, DVOA is able to provide meaningful insights. From a defensive standpoint, the DVOA can also be used – a DVOA of –10% or higher is an indication that a team’s defensive ability is higher than the league average.