The dual-threat QB is hardly a new phenomenon.
Quarterbacks were, of course, originally more like running backs in the early
days of football. While my command of football history is far short of
sufficient for this or any other analysis, such names as Fran Tarkenton,
Randall Cunningham, Donovan McNabb, Michael Vick and Steve Young have
demonstrated the power of a QB with the ability to run the ball.
That a dual-threat QB could offer an improvement over a
traditional drop back passer should not be in serious dispute. Given two QBs of
equal passing ability, the one of the two with greater rushing ability will
almost certainly have better performance – in passing as well as in rushing.
The statuesque, lumbering traditional QB will have far fewer scrambles for
first down, more sacks (assuming equal ability and judgment to throw the ball
away in a hopeless situation) and more marginal throws forced in.
The dual-threat QB will have a huge impact even before
the snap. The defense will have to consider him a threat to run even on a play
that looks like a certain pass, pulling one defender away from the passing game
and opening up better opportunities for completion. Once the play begins, the
dual-threat QB will have the benefit of an additional option should the
receivers look covered. Rather than forcing a marginal throw, throwing the ball
away or taking a sack, this QB can make something happen on his own. Again
assuming equal judgment and passing ability, the dual-threat QB will trade some
of the worst throws of his almost-doppelganger for some scrambles that are far
less likely to be turnovers and far more likely to yield positive yardage.
Despite all this upside the dual-threat QB seems prone to
injury concerns that will keep him out of reach. Redskins fans in particular
may be having second thoughts about Robert Griffin III tallying 120 runs – tied
for the league lead among quarterbacks.
Shortened Careers
The biggest problem with a QB who can run seems to be
that he may end up running. It is well-known that running backs have one of the
shortest average careers of any position. The short careers are assumed to be due
to the pounding that they take.
The 119 RBs drafted between 1994 and 1999 who actually
made a roster for at least a season averaged 5.4 seasons in the league. The 51
QBs who meet the same criteria averaged more than a full season more at 6.6.
The difference is even starker when looking at starts. Those 51 QBs averaged
40.1 starts (and Peyton Manning still going) while the 119 RBs averaged almost
a full season less with 27.6 starts. QBs have 50% more starts than RBs and it
seems likely that injuries account for a significant portion of that difference
– if anything I would suspect that QBs are more likely to be removed for
sub-par performance because of the visibility of their mistakes but I can’t
credibly claim any statistical support here[1].
QB Running
At this point all I’ve shown is that QBs have longer
careers than RBs. As I mentioned above: this is well-known. Let’s take a look
within the QB population to see if we can detect some differences between
dual-threat QBs and their traditional counterparts.
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Figure 1: 1993-2011 Seasons, QBs having started at least
50% of their games
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Figure 2: 1993-2011 Seasons, QBs having started at least
50% of their games
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Quarterbacks with more rushing attempts tend to play more
games and have a higher quarterback rating that those with fewer. To get a
little more clarity on where that higher rating comes from, we can compare QB
rating to passing attempts per game and rushing attempts per game.
Although passing attempts and rushing attempts do not
explain a great deal of the passer rating distribution (R^2 = 0.13), they are
both significantly positively correlated. The 95% interval for marginal rushing
attempts is a 0.82 to 2.25 improvement in QB rating. The 95% interval for
marginal passing attempts is 0.70 to 1.03. It makes sense that both would be
positively related – better QBs get more attempts, though the attempts
themselves aren’t improving the rating – but it is important to note that even
controlling for passing attempts, rushing attempts have a strongly positive
relationship. This supports the hypothesis that the ability to rush improves a
quarterback’s passing.
So dual-threat QBs appear to have a higher QB rating, and
there is evidence that the rushing attempts are related to higher QB rating
independent of passing attempts. Despite this, the highest bucket of rushing
attempts, 4+ per game, is associated with a larger decline in games played from
year to year than we might expect.
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*Note – These graphs are still for quarterbacks with at
least 50% of their games as starts in Yr 0
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The 1-2 and 2-3 buckets trend very close to the average
QB – 2.09 fewer games from one year to the next. These two buckets also contain
308 and 264 player seasons, respectively, out of 842 total. Since we saw
earlier that QBs with more rushing attempts are likely to have higher passer
ratings, it is odd that they would see such a large negative change in games
played absent some other factor.
The major components driving the change in games played
are likely to be age, performance (as captured by QB rating) and injury.
Obviously it is injury that we are looking for here with rushing attempts per
game standing in as a proxy.
Looking at year to year regressions is a fairly nebulous
business. A lot can happen to a player between two seasons that would affect
playing time more than injury from running the ball (e.g., riding a motorcycle
like an idiot
or a similar, but distinct, idiot).
One Layer Deeper
Looking at week to week data, on the other hand, may just
be granular enough for us to separate some factors and see if we can separate
the impact of rushing attempts – or more broadly, being hit – from other
factors causing injuries.
For this data we can look no further than pro-football-reference.com
once again, retrieving the game lines for all players who attempted at least
one pass in a given game. PFR also has the weekly injury reports for each team.
The NFL standardizes injury reporting into the categories of Probable (25% chance
to miss game), Questionable (50%), Doubtful (75%) and Out/Injured
Reserve/Physically Unable to Perform (100%). While individual teams may vary in
application, what we are really looking for is any week where there is an
increase. If a QB moves from Doubtful to Out from weeks 6 to 7, that will show
up in the data as week 6’s events having increased his level of injury by 25%. On
the other hand, if a team puts a player at Probable every week it won’t affect
the data because there is no change.
After pulling this for the 2011-12[2]
seasons and adding in number of times a QB attempts a rush (subtracting rushing
TDs), is sacked or makes a tackle (typically after an interception), we are all
set for the big reveal: How much more likely are QBs to be injured for each
additional hit?
Not much at all, or maybe a negative amount. Wait, what?
Comparing increase in percent chance of missing a game
against rushing attempts, sacks taken and tackles made (all for current week)
yields an overall correlation of 0.0206 with an R^2 of 0.00043 – not a good
fit. The details are below:
| Coefficients | Standard Error | t Stat | P-value | Lower 95% | Upper 95% | |
| Intercept | 0.04579 | 0.00793 | 5.77793 | 0.00000 | 0.03024 | 0.06134 |
| Ratt-TD | -0.00093 | 0.00179 | -0.51813 | 0.60446 | -0.00443 | 0.00258 |
| Tkl | 0.00179 | 0.02522 | 0.07087 | 0.94351 | -0.04769 | 0.05126 |
| Sacks | 0.00139 | 0.00266 | 0.52097 | 0.60249 | -0.00383 | 0.00661 |
Not only is it not a good fit, but it looks like rushing
attempts actually make a QB (very) slightly less
likely to be injured in a given week.
If we instead compare these factors and add a tracker for
cumulative sacks or rushing attempts – still terrible[3].
The overall correlation improves slightly to 0.0471 with an R^2 of 0.00222.
| Coefficients | Standard Error | t Stat | P-value | Lower 95% | Upper 95% | |
| Intercept | 0.040576 | 0.009017 | 4.499833 | 0.000007 | 0.022884 | 0.058268 |
| Ratt-TD | -0.000045 | 0.002009 | -0.022573 | 0.981994 | -0.003986 | 0.003895 |
| Tkl | 0.002046 | 0.025219 | 0.081132 | 0.935351 | -0.047434 | 0.051527 |
| Sacks | 0.001118 | 0.002678 | 0.417384 | 0.676475 | -0.004137 | 0.006373 |
| Cratt | -0.000479 | 0.000408 | -1.174507 | 0.240432 | -0.001279 | 0.000321 |
| Csacks | 0.000913 | 0.000638 | 1.431200 | 0.152641 | -0.000339 | 0.002165 |
The sacks and rushing attempts are much less interesting when
we control for cumulative rushing attempts and cumulative sacks. Interestingly,
the cumulative rushing attempts show a negative correlation with injury while
the rushing attempts themselves flip from negative to positive, albeit at very
low significance. Both individual game and cumulative season sacks have a
positive relationship.
To put the size of the relationship in perspective a
player with 100 rushing attempts entering a game would have his chance of
injury reduced by 4.79% while a player with 40 sacks would see his chance of
injury increased by 3.65%. These totals are near the league leaders at the end
of the season so you can see how small the overall impact is.
Conclusions
Based on the entirety of the data, I have a hard time
concluding that running increases the chance of injury for a quarterback. Sacks,
on the other hand, are slightly stronger in their relationship to injury and
are positively correlated. I could probably back into a story about
quarterbacks who are sacked being unprepared while runners brace for a hit, but
the weakness of the relationship and the minimal amount of injuries explained
speak to other, unobservable factors at work such as a player’s predisposition
to injury or the high variance of types of hit ranging from Deion Sanders to
James Harrison.
These findings conform to the conclusion of this
analysis by Omar Bashir and Chis Oates that was featured on Slate. Their
analysis found running quarterbacks – under several different filters – appear slightly
less likely to miss games due to injury. I realize this is a long way to go to
confirm that another analysis appears sound, but the methods are slightly
different so I hope that this is a useful contribution to research on the
topic.
Autopsy of Biases
I’ll admit that I went into this one with a pretty strong
conviction that there would be a positive and significant relationship between
number of hits and injury. In doing so, I have fallen prey to the availability
heuristic in a big way. I can think of lots of examples of running quarterbacks
getting injured (Michael Vick! Robert Griffin III!) but very few of the
circumstances when traditional quarterbacks went down. I was so certain in my
intuition that I drafted a 500 word analogy between playing a dual-threat QB
and drafting a player with known injury history (sadly, this has since been
deleted)
As the NFL reacts to a Super Bowl in which a traditional
QB stood statuesque in the pocket and won while the dual-threat QB could not
come through, I wonder whether some analysis now is falling into the
confirmation bias. Talking heads saying “I told you so” about the ability of
running QBs to win are revealing their own going-in position. Dual-threat QBs
have a number of advantages over similarly skilled passers without the same
rushing ability and while this doesn’t mean the Broncos should have Peyton
Manning running the zone-read, it seems like something that should be
considered by the many teams looking for their future QB every winter.
[1]
Some advanced stats might be able to answer this question where both positions
get an all-in-one ranking that is ideally comparable across positions, but at a
minimum allows you to see whether those players ranked lowest relative to their
peers are more likely to be benched at either position. That’s not what this
post is looking at so I’ll leave it there for now.
[2]
Pro-Football-Reference.com has available week to week injury reports that, once
collected over several more seasons, should allow a better look.
[3] I
looked into a few regressions with additional variables but ran up against some
collinearity between variables. The cumulative sacks and cumulative games terms
have a correlation of 0.90 so we need to be wary including them in a model
together. Even though the model appears better with them (correlation of 0.084)
this is probably down to overfitting more than true correlation.
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