Last week I looked at the basics of offensive identity in 2018: plays from scrimmage; how many fantasy points (FP) those plays generated; how many of those plays were “wasted” from a fantasy perspective; and the relationships between wins and plays and FP. This week, I’ll dig into the running games of the 2018 offenses.
I’ll start with the run-pass mix:
The average team ran on 41% of their plays from scrimmage (sacks count as passing plays) with a standard deviation (SD) of plus or minus 4.6%.1 Teams in green had at least one SD more “share” of rushing plays than average; those in red were at least one SD below average. In other words, the teams with green bars were “running teams” and those with red bars were “passing” teams.
My first reaction is, wait, the Saints were a running team and the Steelers were a passing team? What alternate universe am I in? Seattle and Baltimore, to a lesser extent Tennessee, Buffalo, and Chicago, I knew were running teams. But the Saints were a surprise. And Pittsburgh was a shock.
Here’s a look at how wins are tied to rushing play percentage:
My first point is winning and having a run-heavy offense are not strongly connected. The R-squared of just 0.1528 tells us that, as well as the placement of the team “dots” and the equation of the blue-dotted regression line (the coefficient of 0.0063 on the “x” factor says that every win added just 0.6% rushing “share”). Still, teams that win more generally have a large share of rushing plays, even if it’s a fairly weak relationship.
My second point is what an outlier Pittsburgh’s offense was last year, even ignoring the franchise’s history of running the ball. Only Green Bay threw on a greater percentage of its plays, and that was just by a tenth of a percentage point – and with three fewer wins.
Both of those teams were historical exceptions, too. Here’s the same Wins vs. % rushing Plays but for the entire period since the league expanded to 32 teams:
Only three teams had a smaller share of rushing plays than last year’s Packers, and just four compared to the 2018 Steelers. The Steelers last year had the offense with the most passing share compared to wins (i.e. farthest distance below the blue-dotted regression line) of any team since 2002. I highlighted the 2004 and 2005 Steelers’ offenses for the contrast.
A few other historical notes:
- The 2009 Jets of the ground-and-pound Rex Ryan era were the most run-heavy team compared to what we’d expect based on their wins (i.e. farthest above the regression line).
- Over the entire period, teams have averaged runs on 43% of their plays compared to just 41% last year, an objective measure of the shift in how the game is played that confirms what you already knew subjectively.
- Because of the shift in the game, the NO 2018 dot is not historically run-heavy; contrast that team, though, with the pass-happy 2010 and 2013 editions of the Saints.
Aside from the historical curiosity, an important point for 2019 is that the Steelers are unlikely to be that pass-happy again: regression to the mean is going to pull their share of running plays up; it’s important not to overweight the passing plays in this offense when predicting 2019 fantasy performance. And look at the run-pass mix in TEN vs. GB in 2018, think of where Matt LaFleur was, and think about what that means for the Packers this year.
The next chart combines the Plays from Scrimmage data I showed you last time with the rushing plays share from above:
The chart is laid out like the other charts in terms of average, standard deviation, and color-codes. Note that Baltimore, although it ran on “just” 48% of its plays vs. Seattle’s 53%, still topped the Seahawks in Total Rushes because the Ravens had so many more plays from scrimmage (1135 to SEA’s 1012). Tennessee falls out of the “green” teams because the Titans had the 3rd lowest play total – this is why it’s important to blend pace of play or total plays with run-pass mix in forecasting play totals and, therefore, fantasy potential. Meanwhile, NE and HOU crept above the +1 SD line (neither was that far below it in % Rushing Plays).
On the low-end, ARI’s horrible number of plays from scrimmage meant the Cards had a very low number of Total Rushes despite a close-to-average 39% Rushing Play share. The Bengals also followed this pattern, although they were less extreme.
But Total Rushes can be a little tricky when we’re evaluating offenses and the opportunities available to RBs. Because Total Rushes includes QB carries, WR runs, and assorted hand-offs to other players, I like to isolate just the RB carries, which are more important for RB fantasy production than Total Rushes:
When I strip out Lamar Jackson’s 147 rushes, the BAL RB carries don’t look that extraordinary. Seattle’s 451 is very high, especially in recent years. NO and NE are now clearly among the leaders in RB carries (despite the Pats giving 55 carries to their WRs, 20 more than the 2nd place Rams).
Houston and Buffalo RBs also lost a lot of touches to other positions, mostly Deshaun Watson (99 carries) and Josh Allen (89).
Some other teams which dropped quite a bit when other runs are stripped out were CAR (Cam Newton, 101 carries) and KC (Mahomes had 60 carries plus Chiefs’ WRs combined for 28, 3rd-most in the league).
The Giants, although they had a well-below-average number of runs overall, were much closer to average in RB rushes because only 29 of their carries went to other positions: just 9 to their wideouts and Eli ran less than any full-time QB with just 13 carries. The same was true of Cardinal RBs, with just 33 non-RB runs – that number is likely to go up considerably with their new QB.
Let’s compare the significance of total rushes and RB rushes for Team RB fantasy scoring (I think you know the answer):
I won’t spend a lot of time on this chart. Note the regression equation and R-squared values, I’ll come back to them after showing the chart with just RB rushes:
Here’s the regression equations for the two charts:
1st Chart: Team RB FP = 0.3052 * Total Rushing Plays + 265.76
2nd Chart: Team RB FP = 0.6646 * RB Rushing Plays + 164.92
Every rush a team gives its RBs adds about 0.7 FP to their total compared to about 0.3 for each total rush by the team. While there is something to be said for running QBs helping RB productivity, it’s still not as good as your RB ACTUALLY CARRYING THE BALL. As long as the team is using its backs a lot, the QB taking some carries may help. But when the QB starts running instead of the RB, that is not good for the RBs.
Not surprisingly, the R-squared of the first regression is just 0.0554: total team rushes explain only 5% of team RB FP (down a lot from 19% in 2017 and even the 11% in 2016). The R-squared for the RB rushes regression is larger: 0.1298 or RB carries explain 13% of total RB FP, that’s still a small share. Of course, that number would be higher in non-PPR formats.
Besides the obvious receiving value of RBs, how a team divides its carries is far more important than just total volume. The R-squared between an individual RB’s carries per game and his FP/G is 0.762, although that is inflated by a lot of low-carry RBs. For the Top 50 RBs in rushes/game, about 50% of individual RB FP/G can be explained by rushes per game. An RB on a team that runs a lot is likely to get more carries, but his share of carries on that team is far more important.
The next chart looks at the identity of 2018 teams in terms of the Rush percentage of its RB1 (Rush % RB1). RB1 is defined as the RB on the team who got the largest number of carries. So I’m talking about NFL teams’ RB1s, not fantasy RB1s.
The Rush % RB1 is the percentage of the team’s total RB rushes claimed by its RB1. Note this is not necessarily a good predictor of points game-to-game because it doesn’t account for injuries (for example, LAR with/without Todd Gurley), and we have to recognize that the identity of a team like JAX or KC in 2018 is affected by not just injuries but other factors. Leonard Fournette led all Jaguars RBs in carries despite only playing 8 games in Jacksonville. Kareem Hunt led the Chiefs in carries in just 11 games but his share (61%, 11th) was depressed by getting kicked off the team. So some additional knowledge has to be applied to these numbers:
Ezekiel Elliott and Saquon Barkley had dominant shares (86% and 80% respectively). But you might be surprised that David Johnson was right there with Barkley at 80%. The abysmal Cardinals offense, particularly its low play total and horrific offensive line, meant that although Johnson was 3rd in RB1 rushing share and 3rd in total carries, his 3.6 yards per carry greatly hurt his FP (fun fact: Johnson had one more TD than Elliott).
Last year, 7 RBs topped a 74% share of team RB carries. That was up from 5 in 2017. Not since 8 backs did it in 2010 have that many RBs carried such a heavy load (admittedly, 74% is an arbitrary cutoff).
The 34% share by Eagles RB1 Josh Adams was very low, and affected by the various injuries to the team’s RBs, but well above the 23% for the Seattle RB1 in 2017.
How does share of RB carries translate to FP?
Most of the top-scoring RB1s also had a high share of their team’s carries. And the highest scoring backs were generally “more efficient.” That’s a little misleading if you think of running back efficiency as yards per carry. In this context, it’s short-hand for more FP than would be expected based on the share of carries the back received. It’s perhaps a better measure of involvement in the passing game: the “green” RB1s had over 60% of their team’s RB targets as well as an above average share of its rushes.
David Johnson’s (ARI) inefficiency mostly relates to his poor yards per carry (3.6). Joe Mixon’s (CIN) was partly due to a middling 55 targets but also due to a poor 6.9 yards per reception (the average RB netted 8.0 yards per catch). But they still turned their share/volume into above-average total FP.
But three RB1s with above shares of the RB carries had below average FP totals, even though they played 16 games. Adrian Peterson (WAS), Jordan Howard (CHI) and Peyton Barber (TB) all turned workhorse shares of carries (68%- 79%) into below average FP. One common factor was low use in the passing game (26-29 targets). While their offenses used a dominant back in the running game, they all had a committee approach to the position in that they used a clear receiving back who got 50+% of the RB targets (Chris Thompson, Tarik Cohen and Jacquizz Rodgers). Barber’s 9.5 FP/G was in the Bottom 50 for a team’s top scoring RB since 2002 – and no other RB in that group had more the 69% of his team’s RB carries.
It’s fair to wonder what Tampa is doing at this position. Barber was below average in most efficiency measures: 3.7 yards per carry (RB average last year was 4.4); 4.6 yards per reception (8.0); 69% catch rate (76%). Rodgers was a decent receiving back (8.0 and 84%) but poor running the ball (3.2 yards per carry). Ronald Jones was a high pick but showed nothing. The team did not sign a veteran free agent back or draft an RB. Undrafted rookie FA Bruce Anderson may have real sleeper appeal – or Bruce Arians sees something in the backs he already had that was not apparent in their stats last year.
From the regression equation for this chart, an increase in 1% share of RB carries is worth about 4 FP (RB1 Total FP = 409.79 * Rush % RB1 – 34.053; remember that Rush % is a fraction). Small changes in rush percentage are unimportant, but adding 10% share adds 41 FP – now we’re talking real points. And with an R-squared of 0.4712, about half of RB1 fantasy points can be explained by their share of team carries. Considering that ignores the passing game and TDs, that’s a pretty good factor to hone in on, better than all the others discussed so far.
Summary:
- Teams that win more generally have a large share of rushing plays, although it’s a fairly weak relationship.
- The Steelers are unlikely to throw on 67% of their plays again; it’s important not to overestimate the passing plays in this offense when predicting 2019 fantasy performance.
- Matt LaFleur’s Titan offense ran on 48% of its plays vs. 33% for the 2018 Packers; look for GB to run more in 2019.
- It’s important to look at RB rushing totals, not TEAM rushing totals when projecting RB fantasy numbers; a running QB doesn’t help RB performance all that much.
- Even more important for RB fantasy production is how much a team concentrates its carries on its RB1; RB1 share of RB carries explains about half of their FP.
- Of course, usage in the passing game is very important too; even RB1s with high percentages of the team’s RB carries can have their fantasy value killed if the team frequently uses a receiving back.
Part III will address the passing game part of identity.
1 Rounding in the y-axis labels accounts for the seemingly uneven increments in the chart; they are all 4.6% apart, despite the labels that say 4 or 5 percentage points depending on the segment.
