An interesting idea put forward in certain parts of golf punditry is that wider fairways actually
benefit short and accurate golfers, while
narrow fairways (and long rough) benefit long and inaccurate golfers. The intuition given for this is typically
twofold: first, accurate golfers can't realize their advantage if everyone is hitting from the rough, and
second, it's a bigger disadvantage to lose distance to the field in the rough than the fairway.
This is a counterintuitive claim
that hasn't received the scrunity it deserves.
Quick aside — As our loyal readers know,
DG blog posts have a tendency to be technical.
While this is great for those who want to understand the details of an analysis,
it makes some articles less approachable for readers with limited statistics experience.
Therefore, in an attempt to appeal to a wider audience, we've put a nontechnical summary
of this article at the
bottom of the page.
The starting point will be to think through how we should expect accurate golfers'
advantage in terms of
fairways hit to vary as a function
of the width of a fairway. That is, should accurate golfers hit more fairways relative
to inaccurate golfers when the fairway is hard to hit, or when it's easy to hit?
If we were to plot the advantage of accurate golfers as a function of fairway width, we
would expect an upside down U shape: the advantage will be zero at the two extremes (where everyone
is either missing or hitting the fairway), and
will peak somewhere in the middle. Where exactly this advantage peaks will depend on the specific
differences in driving disperson between accurate and inaccurate golfers. For example,
if the difference is that accurate golfers hit fewer foul balls, then we would expect
the peak advantage to occur with relatively wide fairways; if the difference is that accurate golfers hit more
"very straight" drives, then their biggest advantage should be on relatively narrow fairways.
The plot below uses PGA Tour data from 20192021 for nonpar 3 holes to estimate the
driving accuracy difference between accurate and inaccurate golfers
as a function of the
overall driving accuracy percentage of the hole
(we'll use this instead of fairway width,
as the conditions of the course — e.g. firmness — can change the
"effective" fairway width). Holes are grouped into bins at 5% intervals (e.g. the value at 55% uses
holes with a DA % between 52.5% and 57.5%), and accurate golfers are defined to be those at least 1 standard deviation
above average in our
measure of driving accuracy skill,
while inaccurate golfers are at least 1 standard
deviation below average in that measure.
The "average" line indicates that across all PGA Tour holes, accurate drivers have a DA % that is
13.6 percentage points higher than inaccurate drivers (73.2% to 59.6% — note that we are counting balls
in the intermediate rough as a fairway hit).
The data point at 55% tells us that on holes where
the field's driving accuracy was between 52.5% and 57.5%,
accurate drivers were slightly more than 15 percentage points more likely to hit the fairway than inaccurate drivers.
Only 1.5% of holes had an overall driving accuracy percentage below 40%, so they are all placed in the 40% bin.
For holes with driving accuracy percentages between 50% and 80%, which characterizes
80% of holes on the PGA Tour, the driving accuracy advantage for accurate players
is roughly constant at 1315%. For holes with a DA % above 0.85 (6% of all holes) this advantage declines
sharply; it's likely that a similar decline would be observed
for holes below 0.40 DA %, but there simply aren't many PGA Tour holes in that range.
Even if their fairwayshit advantage does not decline substantially as fairway widths shrink,
short and accurate golfers could be disadvantaged by hardtohit fairways if the 10 or 15 yards they give
up to longer hitters is more penalizing from the rough than the fairway.
That is, if hitting approach shots from 15 yards further back in the fairway is less of a penalty than
hitting approaches from 15 yards back in the rough,
then holes with a low overall driving accuracy percentage would tend to disadvantage short players.
But, as the graph below (reproduced from
this
blog post) shows, this isn't true:
For distances in the meat of the approach distribution (75 yards to 225 yards) the expected number of strokes
to hole out declines at a similar rate for shots from the fairway or the rough. For example, moving a ball from 175 yards from the pin
to 150 yards from the pin is worth about 0.1 strokes whether that move occurs in the fairway or the rough.
From distances beyond 225 yards, an additional few yards makes less of a difference from the rough
than the fairway (presumably because it's very unlikely to hit the green at those distances from the rough).
The discussion so far could be characterized as an indirect argument for why
shorter and accurate players likely don't benefit from wide fairways
(and longer, inaccurate players likely don't benefit from narrow fairways).
Next, we'll actually look at scoring data for some direct evidence.
The table below summarizes the strokesgained performance of four (overlapping) groups
of golfers as a function of the overall driving accuracy percentage on a hole.
I've used the quartiles of the DA % distribution to form the bins, so each bin contains
the same number of holes (e.g. ~25% of holes on the PGA Tour have a driving accuracy percentage
below 58%).

< 58% 
>= 58% and < 66% 
>= 66% and < 75% 
>= 75% 
long 
0.036 
0.028 
0.028 
0.019 
short
 0.046 
0.043 
0.030 
0.030 
accurate
 0.008 
0.014 
0.01 
0.027 
inaccurate 
0.038 
0.042 
0.038 
0.047 
Golfers who are at least 1 standard deviation above the
field average in the relevant skill — at the time of
the event — are included in this table (e.g. "long" golfers are >= 1 SD above average in driving distance).
As an example, the first cell tells us that long golfers on average gain 0.036 strokes on holes that have
a DA % below 58%.
Surprisingly (to me), this table shows that longer and less accurate golfers perform
better on narrow holes (the pattern is weak for the inaccurate group), while shorter
and more accurate golfers do the opposite. Alas, the support for this counterintuitive
pattern will be shortlived. As it turns out, holes that have fairways
that are easier to hit also tend to have other, important, characteristics:
driving accuracy % 
par 
yardage 
missedfairway penalty 
rough penalty 
< 58% 
4.29 
477 
0.31 
0.24 
>= 58% & < 66% 
4.23 
468 
0.35 
0.26 
>= 66% & < 75% 
4.22 
459 
0.37 
0.26 
>= 75% 
4.13 
437 
0.42 
0.30 
Now the strokesgained averages from the first table take on a different meaning:
holes with low driving accuracy percentages tend to be longer and have smaller
penalties for missing the fairway. The penalty for missing a fairway is calculated
by comparing the hole scores of drives hit into the fairway versus nonfairway locations,
while the rough penalty is calculated by comparing the number of strokes to hole out
from the rough versus the fairway. (There are important details behind these estimates,
which you can read about
here.)
One course that fits this mould is Riviera: its par 4s and 5s are well above average in length,
it has
the hardest fairways
to hit on tour, and it consistently has the
smallest penalty
for missing the fairway. And, according to our
course fit tool,
inaccurate golfers play substantially better than their baseline skill at Riviera.
To put this all together, we'll now look at some regressions to get a
full picture of which hole characteristics favour certain player types.
Shown below are the coefficients from a regression of holelevel strokesgained
on a set of hole characteristics for each of our four groups of golfers:

yardage 
missedfairway penalty 
rough penalty 
driving accuracy % 
sd of driving distance 
long 
0.019* 
0.012* 
0.000 
0.003 
0.009* 
short 
0.014* 
0.009* 
0.002 
0.001 
0.004 
accurate 
0.016* 
0.020* 
0.004 
0.004 
0.006 
inaccurate 
0.006 
0.017* 
0.003 
0.004 
0.000 
Let's focus on the first row to gain some understanding of the numbers in this table.
This row contains a set of coefficients from a regression performed only using data for "long" golfers;
the goal is to estimate the impact of each hole characteristic (yardage, missedfairway penalty, etc)
on the strokesgained performance of long golfers. Importantly, regression allows us to "control" for
the effect of the other variables;
that is, loosely speaking, the effect of a hole's yardage on long golfers' strokesgained performance (+0.019) is
estimated by comparing the performance of long golfers on holes of different length but with
similar missedfairway penalties, rough penalties, etc.
To make the estimates directly comparable to each other, I scale each variable by its standard
deviation (SD). This scaling allows each coefficient to be interpreted as the effect on long golfers' SG
from a 1 SD increase in the relevant variable.
What this means in practice is the following:
the coefficient for yardage
tells us how much the strokesgained
of long golfers is expected to increase when the yardage of a hole increases by 64 yards (because the SD for hole yardages on
the PGA Tour is 64 yards). Similarly,
the missedfairway penalty coefficient tell us the expected SG increase from a 0.22 stroke
increase in a hole's missedfairway penalty.
This scaling makes the magnitudes of the coefficients comparable, as they roughly
represent the difference in expected performance between the median hole in a given characteristic and the 85th percentile hole.
The other three rows display the coefficients from similar regressions
for the remaining three groups of golfers.
The one variable that hasn't been introduced yet is "sd of driving distance", which is a measure how spread out tee shots
were (in terms of distance) on a given hole. The course that had the highest average SD in driving distance for its nonpar 3 holes
was Club de Golf Chapultepec, which makes a lot of sense for anyone who watched those WGCMexico events. The asterisks on the table
indicate coefficient estimates that were "statistically significant", which, in basic terms,
tells you that the estimate cannot be easily explained by noise.
The main takeaways from the table are that the length of the hole and the penalty for missing the fairway
are the most important predictors of over or underperformance for a specific group of golfers.
Long golfers perform better on long holes, and accurate golfers perform better on holes with
large missedfairway penalties. The field's driving accuracy percentage on a hole, which was our proxy for fairway width,
does not have much predictive power for the performance of any group of golfers. If anything, long/inaccurate players
perform better with easiertohit fairways while short/accurate golfers perform worse on them.
The one other variable that did show
a sizeable effect was "SD in driving distance" in the long golfer regression.
This also seems intuitive. One final remark on interpretation:
an effect size of 0.02 strokes would translate to 0.28 strokes per round (if there were 14 nonpar 3 holes) — which
is a meaningful magnitude when seen in the context of our
skill rankings.
Interestingly, the severity of the rough on a hole does not have much predictive power for any group's performance.
However, recall that we are controlling for the overall missedfairway penalty of the hole,
which means the rough penalty coefficient is estimated by comparing holes with similar missedfairway penalties
but different rough penalties. This is actually quite tricky to interpret, and it makes me question whether its useful
to include both of these variables in the regression. If the rough penalty variable is removed,
the missedfairway penalty coefficients are basically
unchanged which is reassuring; if we instead remove the missedfairway penalty variable, the coefficients on rough penalty are equal
to about 5070% of the
missedfairway penalty estimates shown in the table. There are several possible interpretations of this result, but one interesting
interpretation is that when the primary penalty for missing the fairway is ending up in difficult rough,
this does not help short, accurate golfers (or hurt long,
inaccurate golfers) as much as when the primary penalty for the missed fairway is a nonrough location or a hazard. This provides
a nice segue into some concluding thoughts on the limitations of this simple analysis.
The regressions performed above provide predictions for how each group of golfers is expected to perform on each hole.
If we aggregate up these predictions by course and compare them to actual performance, we can see where the model's
predictions were most off. For long players, the two largest overperformances relative to model predictions occurred at
the
2021 U.S. Open at Winged Foot,
and the
2019 PGA Championship at Bethpage Black.
Given the characteristics of
the (nonpar 3) holes at Winged Foot, we expected long golfers to gain 0.038 strokes per hole; in fact, they gained
0.099 strokes per hole! A similar story played out at Bethpage: the model predicted +0.02 strokes
per hole but long golfers actually gained 0.11. These two tournaments are often pointed to as examples of why
narrow fairways and long rough don't favour short, accurate golfers.
However, it's not clear whether there are generalizable lessons to be learned from them.
One possibility is that the above regressions are too simplistic in that they won't
pick up
interaction effects:
perhaps it is the case that length combined with long rough and difficulttohit
fairways creates a situation that is especially
advantageous for long golfers. Olympia Fields, played in 2020 for the BMW Championship, and Bay Hill are two
courses that mostly fit this theory: both are long courses with big rough penalties and hardtohit fairways that
favour long golfers, however they don't disadvantage accurate golfers.
The South Course at Torrey Pines presents a problem however, as
it has the required course profile yet
doesn't favour long golfers any more than what would be predicted on the basis of its length.
Firestone Country Club, whose setup fits the bill perfectly, did not favour long players, or disadvantage
accurate players, anymore than expected (using the regression models above)
from 20152018 at the WGCBridgestone. Given these varying results with similar course setups, it's hard to claim
that the combination of length, rough, and narrow fairways always act in concert to favour longer players.
Two other courses worth pondering are Muirfield Village and East Lake.
Muirfield is above average in length and has
the worst rough
on the PGA Tour, but it tends to
favour accuracy over distance.
(Muirfield does, however, have an aboveaverage
driving accuracy percentage.) East Lake's par 4s and 5s are slightly shorter than Bethpage Black, but has
a similar rough penalty and driving accuracy percentage; despite these similarities, East Lake is
far from a bomber's paradise.
There are surely ways to explain these findings, but, to my eyes, there is not a simple
unifying theory.
If the goal is to make course setups more favourable to short and accurate players, increasing (or decreasing) fairway width should not
be a top priority. We have provided an indirect argument to support this — the difference in DA % between accurate and inaccurate players
is similar regardless of how hard the fairway is to hit — and also direct evidence from holelevel
strokesgained regressions. The analysis here shows that the blueprint for lessening the advantage of bombers is simple: shorten the holes
and make the penalty more severe for a wayward drive.