Introduction

The purpose of this analysis is to show how efficient rushers (QBs and running backs) were in the in the NFL during the 2022 season.This analysis only considers rushers that had at least 50 attempts.

Efficiency here is defined as how many yards more did the rusher produce than they were expected relative to the rest of league’s average rush yards based on the same set of conditions. The conditions being how many yards are needed for a first down. Yards expected to produce is calculated as the average of the number of yards produced versus the number of yards needed to gain a first down.

The analytics uses regression modeling based on plays that were intended to be running plays (i.e. not plays that are broken down pass plays that turn into run plays).

This first graph shows how many yards were rushed (the y axis) relative to how many yards that were needed to go to get a first down (the x axis). You will note that there is a heavy emphasis on data points at the 10 yards to go point. That is because that there are a lot of runs done in the first down area.

All rushes

This next graph shows the same data, but with a regression line. The only point of the regression line is to show the general relationship between how many yards were rushed versus how many were needed to get a first down.

The blue line shows you that as the number of yards needed, the rusher will will typically rush more. This shouldn’t be surprising as defenses will typically soften as the more yards needed.

All rushes with regression

Aevrage rush yards given a number of needed yards

The next graph shows you the average numbers of yards gained for a given number of yards needed. This more clearly shows the positive relationship that was discussed in the previous section.

Finally, a calculation using the graph and the data in the graph above was used to calculate a metric which is named RYOE (rush yards over epxected). The data / graph above give us what we would expect a given rusher to run given a certain number of yards needed. RYOE is calculated as the number of yards rushed versus the average number of yards expected. Total RYOE is the sum of of the number of yards over expected in the season. RYOE_PER is the average number yards above expected per play.

The table below ranks players by run yards over expected per play using the data discussed above. Based on this we can see that Lamar Jackson was the most efficient runner in the league per play. Saqoun Barkley though produced more yardage above expected across the season, but that is because Philadelphia called more intended rush plays for him across the season (number of plays is noted in the table as “n”). On average, his per play rushes were less efficient than both Henry and Jackson. So the most productive runner in the league was Lamar Jackson.

Top 10 leading rushers

The following graph shows that there was little influence on rush yards based on if a team was winning or losing and by how many points.

#{r, echo = FALSE, message = FALSE, warning = FALSE, fig.width = 20, fig.height = 20} #pbp_r_run %>% # ggplot(aes(x = Team_Name, y=yards_gained)) + # geom_boxplot() + # facet_wrap(~week, ncol = 2) #

The graphs below show you the average rushing yardage per down by team. There is outlier behavior in the fourth (Houston only attempted 5 4th down rushes, which you can see in the table below) down area because there are few fourth down rush attempts. But, what we can tell is that Baltimore was one of the best rushing teams in the season regardless of down.

Below is the table mentioned above, which organizes rushing by average rushing yards by down. It can be filtered based on team, down, or average rush yards. It’s base filtering is based on average rush yards regardless of down. It can be sorted by any column.