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Golfers After 40: How Age Erodes Performance

I’ve written at length about aging – general aging curve, putting aging curve, & aging curves for driving, approach shots, and the short game – because it’s a critically important topic when discussing the trajectory of golfers’s careers and projecting their performance going forward. What I’ve generally found is that golfers improve slightly from the early to late 20s, peak for most of their 30s, and then begin declining in the late 30s, with that decline accelerating in the mid 40s. A golfer who’s one of the best in the world in the mid 30s – think Adam Scott or Sergio currently – will decline to around PGA Tour average by the time they’re 50. This piece today will specifically focus on how golfers change between their late 30s and early 40s, basically the stage of his career that Tiger Woods is currently going through.

How Much is Performance Affected by Turning 40?

I gathered a huge sample of PGA Tour golfers for this study, including everyone with at least three years worth of results between ages 35-39 and three more between ages 40-44. I used the PGA Tour’s adjusted scoring average as my metric of choice; it’s only available going back to 1988 so my sample is golfers born between 1951 and 1972 (Tom Watson to Phil Mickelson essentially). That left me with 131 golfers. Then I averaged their performances in each season between 35-39 and 40-44 and compared.

The average for the 35-39 sample was 70.66 (approximately equal to the 50th best player in the world) and the average for the 40-44 sample was 71.03 (approximately equal to the average PGA Tour cardholder/100th best player in the world). That indicates a decline of around a third of a stroke. My method is different from the delta method I used in the above studies; this study discards any golfers without enough data in the 35-39 or 40-44 group. Almost everyone discarded didn’t have any qualifying performance between 40-44 – meaning they weren’t good enough and dropped off the PGA Tour in their forties. This likely indicates that the decline is greater than a third of a stroke. The above general aging curve predicts a decline of half a stroke.

What about Vijay (or Phil, Stricker, etc.)?

There are certainly exceptions to this general rule of aging. Vijay Singh is often brought up when people talk about golfers aging because he had his two best seasons (and two of the best non-Tiger seasons ever) at age 40 and 41. Unfortunately, few players age as well as Vijay. Only five of my 131 golfers performed better than Vijay (who was 0.6 strokes better after 40 than before) and only 22% of my sample improved at all. Most of this improvement came from guys who weren’t at the top of the sport before turning 40 (Steve Stricker, Fred Funk, Hal Sutton), but improved after 40. Only two golfers who were top 25 level before 40 improved after 40 (Vijay and Nick Price, who only improved slightly). Every other top 25 golfer (Goosen, Mickelson, Tom Kite, Davis Love III, Jim Furyk, Greg Norman, Tom Lehman, Nick Faldo, Ernie Els, etc) declined after 40.

Steve Stricker is another guy held up as an example of golfers play great into the 40s. He had his big renaissance after years on the fringes of the Tour at age 39 and has been a top five player in the world in the last decade. Only Vijay has been better in his 40s – at least since the 1980s (Nicklaus, Ray Floyd perhaps). However, he’s also an enormous outlier. The tableau visualization at the end of this post indicates such. Stricker’s not an example of anything except that sometimes something crazy happens. It’s vastly more likely that a golfer will follow the general trend than pull a Stricker.

It’s important now to talk about what indicates a decline. I’ve chosen to use aggregate performance to measure performance – meaning I count performance in all PGA Tour rounds equally. When I say Phil Mickelson or Ernie Els has declined since 40 I mean that their overall level of play has declined. I understand both have won majors since 40, but they’re contending less overall (much less in Ernie’s case). Turning 40 doesn’t signal the end of a golfer’s professional career, but it does indicate they’ll be playing worse, contending less, and winning less going forward.

What this means for Tiger Woods:

When Tiger returns in 2015 it will be his age 39 season. His age 35-38 sample includes 2011 (69.9) when he was injured/changing his swing, 2012 & 2013 (68.9,68.9) when he was on top of the world, and 2014 (71.1) when he was injured again. Simply aggregating those seasons equally yields an average of 69.7 which would be the 9th best age 35-39 in my sample. Simply applying the amount of decline I found above to Tiger would leave him as something like the 15th-20th best player in the world in his early 40s. All that ignores any more specific injury concerns and just applies my general model.


That shows what an uphill battle Tiger is facing to remain towards the top of the sport. Even if he comes back healthy from this back injury, age is still going to erode his abilities steadily over the next half decade.

Here’s a link to my Tableau viz of the golfers in my sample and their data


Aging Curves for Scrambling and Driving Distance

In the past months I’ve posted about aging on the PGA Tour several times, including a general aging curve and an aging curve for putting performance. The general shape of the aging curve for PGA Tour players is a slight improvement from the early 20s to early 30s, followed by a period of relative stability through the mid 30s, and then a steady decline from the late 30s until 50. I assume from limited post-50 PGA Tour data and the paucity of Champions Tour golfers over 60 that aging continues to steadily erode a golfer’s game after 50. For putting, the curve was similar, but much less pronounced. Aging due to putting accounts for little of the improvement experienced in a golfer’s 20s and less than 10% of the decline experienced from the late 30s onward. The source of age-related improvement and decline is clearly some other part of a golfer’s game – either off the tee, the short game, or the long iron approach shot game. I’ve constructed aging curves for the first two components.


Aging Curve for the Short Game

I’ve calculated my own adjusted scrambling statistic previously which uses the PGA Tour’s scrambling stat as its base, but attempts to remove the influence of putting and difficulty of the lie to figure out which golfers play the ball into the best positions when they miss the green. I’ve described the calculation in this post. The spread in talent between the best and worst golfers each year by this metric is roughly a stroke. That is, the best golfers at scrambling gain half a stroke/round on the field and the worst golfers lose half a stroke/round. For comparison, the best putters gain nearly a stroke/round on the field and the worst putters lose nearly a stroke/round while the best golfers tee to green gain two strokes and the worst lose two strokes. Scrambling shots make up only 10% of a golfer’s strokes per round (6-8 strokes).

Unfortunately, this method does not include strokes where a golfer went for the green in two on a par 5, but missed the green. I estimate these shots comprise only roughly 1.5 strokes/round for the average golfer, though as many as two strokes for longer hitters and as few as one for shorter hitters. These shots are no different than scrambling shots on par 3s or 4s (besides scrambling for birdie rather than par), but the data just isn’t there to include them. So around 15-20% of the sample of short game strokes is missing. I’m confident this will not materially affect the results of this study.

I gathered data between 2008-2013 for which each golfer played consecutive seasons with at least 30 PGA Tour rounds. This resulted in 693 pairs of seasons to compare. As with prior aging studies, I used the delta method popularized by Mitchell Lichtman for use in baseball research. This method simply aggregates all improvements and declines between golfers at each age to see whether an age cohort generally improved or declined. From that data, a curve can be constructed.

Scrambling Aging Curve

That is not even a curve, but instead a steadily increasing trendline. The data indicates that scrambling ability increases linearly with age. Golfers under the age of 24 (34 seasons since 2008) performed around 0.05 strokes worse than the field average at scrambling. From there, golfers improved by around 0.01 strokes/season – a small amount, but one that indicates that experience on Tour leads to improvement in short game play. This finding runs contrary to any other aging study I’ve conducted.

I suppose it is not that shocking, however. Short game play involves a lot of technical skill – stance, position of hands, correct judgement of swing speed, club head position, etc. (in addition to strategy) – while not involving much of the physical strength that declines with age. Any golfer who can play on Tour can generate the proper amount of swing speed to play <50 yard shots. Not so much for the ability to generate the swing speed to hit 300+ yard drives or reach the green with 3 wood from 275 yards.

Compare scrambling here with the other component curves I’ve introduced. Putting generally declines slightly, while tee to green play (meaning all non-putting/scrambling strokes) improves only until around 27, stagnates until the mid 30s, and then sharply declines. I’ll discuss why I believe tee to green play stagnates around 27 next.

component aging curves

Aging Curve for Driving Distance

My catch-all category “tee to green” from above includes a wide array of shots: drives on par 4s/5s, tee shots on par 3s, going for the green shots on par 5s, long approach shots on par 4s/layups on par 5s, and short wedges on par 4s/par 5s. Some of those shots require a golfer to exert maximum effort to hit the ball near his peak ability (most drives/going for green shots), while most others require at least a full swing. In short, most of the shots contained in the “tee to green” bucket are going to be heavily affected by how much physical strength of golfer can exert. There are other factors certainly (strategy, precision, etc.), but physical strength is a large part of it.

The problem with that is the type of physical ability that combines body coordination with physical power – think driving a golf ball or hitting for power in baseball – begins declining as early as the mid 20s. This study from 2012 observed that while baseball hitters have tended to peak around 27, their ability to hit for power (home runs, doubles, etc.) has peaked at 25. I’ve observed the same phenomenon in golfers’ ability to drive for distance.

To measure the impact of aging on driving distance I gathered the Trackman data the PGA Tour has from 2008-2013. Typically the PGA Tour sets up Trackman on one hole per tournament to gather information about the club head speed, ball speed, launch angle, carry distance, etc. for the drive. I prefer using this data to measure driving distance because it places all golfers on an even surface. The hole is selected for whether most golfers will hit driver and the carry distance measures only distance in the air (removing the effects of firm or soft fairways. I gathered data for all golfers with qualifying number of Trackman readings (>20/year) from 2008-2013 (the extent of the data collected by the Tour). I used the same delta method as above to measure the increase or decline in carry distance between consecutive seasons. That yielded 696 pairs of seasons.

Carry Distance

This indicates that a golfer’s peak driving distance performance comes from age 25 and earlier. Golfers out-drive the field by 6 yards before the age of 24, declining to roughly average by age 35, and then decline heavily from that point onward – losing almost 20 yards to the field by age 48. This is the exact pattern suggested by the baseball power hitting aging curve above.

But What About…?

Now, at this point you may be wondering how a golfer can lose so much driving distance over the course of their career and still remain competitive. However, this aging curve doesn’t prove that every golfer ages similarly and it especially doesn’t mean that we should observe elite golfers losing so much distance. Elite golfers are elite because they have overcome many of the age-related obstacles that derail other golfers. This curve merely shows what we should expect out of the typical PGA Tour golfer. Very few golfers survive to have the type of career Davis Love III, Jim Furyk, or Phil Mickelson have had. That is a direct consequence of aging; a large number of golfers simply do not age well (whether due to injury, lack of commitment to practice, or general physical decline) and find themselves off the PGA Tour by age 40. Below is a graph of three golfers – two elite, top-ten-of-the-last-25-years types and one above-average player. It gives you an idea of how even very good golfers decline in driving distance.


Greg Norman was one of the best golfers in the world in the 1980s-90s; he won 20 times on the PGA Tour and 14 times in Europe, largely by relying on his superior distance off the tee. 1983 was Norman’s first season on Tour with reliable driving distance data. That year at age 28 he out-drove the field by 19 yards – equal in performance to Bubba Watson and Dustin Johnson currently. Norman continued to out-drive the field by large margins, but his advantage fell to 14 yards (’87-’89, age 33), 13 yards (’92-’94, age 38), and finally 2 yards (’97-’99, age 43). Norman’s game couldn’t sustain the massive drop in driving distance and he declined from one of the best players on Tour in his late 30s to one who didn’t win a PGA/European Tour tournament after 1997 (age 42). He was only a part-time player from that point.

Vijay Singh was a late-bloomer on Tour, not becoming a full-time member until 1993 when he was 30. He out-drove the field by 14 yards that season. Even at age 40 he out-drove the field by 16 yards as he rivaled Tiger Woods for the #1 ranking. However, in 2013 and 2014 Singh has been exactly PGA Tour average at driving – a decline of ~15 yards over ten years; exactly what the aging curve predicts between 40 and 50.

Stuart Appleby was a very good PGA Tour player by 2006, winning eight times and recording top 25 stroke averages in several seasons. He out-drove the field by 10 yards on average between ages 25-35 (1996-2006). However from 2006 onward he declined sharply, averaging only average driving performance between 2006 and 2013 – winning only a single tournament and basically being a non-factor on leaderboards.

Applying Driving Distance to Performance:

Driving distance is correlated strongly with performance. Mark Broadie’s work has shown that for every yard closer to the pin your tee shot lands, you save around 0.004 strokes. That looks small, but it suggests that the absolute best drivers are gaining roughly a 0.1 strokes/drive based on their distance. Applying the 0.004 figure to the above aging curve means we should expect a golfer to decline by 0.12 standard deviations between 25-35, another 0.12 standard deviations between 35-40, and twice that amount between 40-50. In all, a decline in driving distance explains roughly half of the decline in tee to green play. The component aging curve graph from above is reproduced with tee to green game separated into driving and non-driving shots (“Approach”).

comp with driving

This shows that approach shots exist in a middle-ground between the largely power based driving strokes (which begin declining by 25) and the precision/technique based scrambling strokes (which never decline). This makes sense as iron/wedge shots with a full swing combine both power and precision elements.

An Aging Curve for Putting

In prior posts on the PGA Tour aging curve I’ve established that golfers tend to peak in their late 20s and sustain that peak for nearly a decade. They begin to decline, on average, in their late 30s and their skills degrade far below where they started in the early 20s. In short, golfers experience a small and steady increase in performance in their twenties before suffering a large and steady decrease in performance in their forties. However, all of those studies considered performance in the aggregate – driving, approach shots, the short game, and putting – which prevents deeper analysis of why golfers improve slightly before declining greatly. This post attempts to construct a typical aging curve for PGA Tour golfers’ putting games.

Initial Thoughts:

I anticipated that golfers would noticeably improve their putting games in their twenties; they would learn to read greens better and approach putts using a more optimal strategy gained through experience. I then anticipated that they would decline by age 40. This decline is suggested by the constant references to age-related putting yips. Because putting is not the main driver of performance differences between PGA Tour players, I expected that the age-related putting improvements and declines would be modest relative to overall gains (the general aging curve for overall performance shows a per year improvement of ~0.01 standard deviations from age 20 to 30 and a per year decline of ~0.04 standard deviations from age 38 to 50).


As with my prior aging curve work, I’m using the delta method which measures the change between Year 1 and Year 2. Mitchell Lichtman explains the concept in this article and a general Google search for delta method aging curve provides more information.

The major impediment to this study is the consideration of survivor bias. The only accurate measure of putting skill is the PGA Tour’s strokes gained putting (SGP) statistic. This stat is the same as I use in general for my overall performance analyses, except it’s denominated in strokes instead of standard deviations. However, the PGA Tour only gathers the data to calculate SGP in regular PGA Tour events (not majors, events outside the United States, events opposite other PGA Tour tournaments, or Tour events). This means that for golfers who played on the PGA Tour in Year 1, but not in Year 2, would not have an SGP measure to calculate the delta from. When I ended up forming my sample, roughly a quarter of seasons that qualified in Year 1 did not qualify in Year 2.

I included in my sample all golfers who recorded at least 30 measured rounds (rounds where the Shot Link system was available to calculate SGP) in both Year 1 and Year 2. The years used were 2008-09, 2009-10, 2010-11, 2011-12, and 2012-13. 1021 seasons met the criteria for Year 1, while 769 met the criteria for Year 1 and 2 and were included in the study used in the sample. These included seasons averaged a SGP of 0.02 (above-average) and averaged 69 measured rounds. 252 seasons did not meat the criteria and were discarded from the sample. These seasons averaged a SGP of -0.04 (below average) and averaged 54 rounds played. This suggests that on average those included in the sample were better putters and likely better golfers overall.


My results showed a very slight increase in putting skill in the twenties, followed by a steady decline beginning in the mid-thirties. A graph of the curve follows with a smoothed aging curve in blue. I smoothed the curve using a weighted average of the two years before and after the age in question.


What surprised me was the small size of the improvement and decline. Recall in terms of overall performance golfers improve by around 0.01 standard deviations each season between age 20 and 30. The overall improvement in putting performance up to a peak in the early thirties is equal to one season’s worth of overall performance improvement. Putting improvements are a very minor part of the age-related improvement of golfers in their twenties.

Similarly, the general age-related performance decline per season from the late 30s is roughly 0.04 standard deviations. The decline due to putting declines in total only 0.07 standard deviations. I can only conclude, again, that putting does not form a significant part of the age-related declines in golfers.

Further Discussion:

When I initially observed these results I guessed that survivor bias was distorting the results somehow. In my first foray into constructing an aging curve, I failed to properly account for survivor bias and my result was an aging curve that was largely flat until the mid-thirties before declining steeply. That graph looks a lot like the one linked above.

To test whether survivor bias was affecting my results I constructed another overall aging curve using only the golfers and seasons used for this study (in fact, I also only included the results from rounds played on ShotLink courses). The same sample of 769 seasons was used, using the z-score method to measure performance on all strokes. The graph this study produced is linked below, smoothed using the five year weighting method described above. In red is performance on all strokes, in blue is performance on only putting strokes, and in green is performance on non-putting strokes.


The overall performance looks almost identical to my aging curves that incorporated measures to eliminate the impact of survivor bias. Overall performance shows a small steady improvement the the early thirties followed by a steady decline from the mid-to-late thirties. More importantly, this graph shows the impact of putting on overall improvement and decline. In short, there is very little impact. Almost 100% of the improvement up to age 30 is due to non-putting strokes and over 80% of the decline experienced from age 39 to 50 is in non-putting strokes.

This suggests that putting performance changes very little during a golfer’s career. While overall performance declines by 1.50 strokes on average from peak to age 50, putting performance declines by less than 0.25 strokes on average over the same period.

This suggestion has interesting implications. Most importantly, do golfers who rely on their putting for success decline differently than golfers who rely more on their long game? I’ll try to answer that in a future post.

The Aging Curve for PGA Tour Golfers (Part III) – Using Bayesian Prior

Several weeks ago I posted a two studies on aging among PGA Tour golfers, the most recent of which compared sequential seasons, regressing both seasons to PGA Tour average based on the number of rounds a golfer had played in the seasons. DSMok1 suggested modifying the amount and degree of regression by including a better prior, which makes more sense than regressing every golfer to the same mean. Instead of simply adding 25.5 rounds of average play to each golfer’s season, I found a Bayesian prior based on play in the prior season and measured the change in performance from that prior in the following season.

Sample and Design:

I included every player with >20 PGA Tour rounds in a season for 2010, 2011, and 2012. This limited my sample to 703 seasons. I then gathered data for YR N-1, YR N, and YR N+1 (ie, 2009, 2010, and 2011 for golfers with >20 rounds in 2010) on all major Tours (PGA, European,, and Challenge).

Using the equation ((prior mean/prior variance)+(observed mean/observed variance))/((1/prior variance)+(1/observed variance)) I found my prior expectation on performance, inputting data from YR N-1 for prior mean and variance and from YR N for observed mean and variance. That equation adjusts the observed performance based on what we’ve observed in the prior season to generate a true-talent level (True YR N) for YR N+1. I used the same equation to find the true-talent level for YR N+1. I inputted the prior generated from YR N-1 and YR N as the prior mean and the data for YR N+1 as the observed mean. This produced True YR N+1. I then compared both True YR N and True YR N+1to find the change in true-talent for each age group.

I weighted the results using the harmonic mean rounds played in YR N and YR N+1. For example, there were 18 golfers for age 26, so I took the sum of each harmonic mean of rounds and divided each golfer’s change in true talent by their share of the total rounds. This produced my total change in true-talent due to age for each age-group.

If a golfer had no performance in YR N-1 I used +0.08 (slightly below PGA Tour average) as their YR N-1 prior. In most cases, these players qualified via Qualifying School and +0.08 is the observed true-talent for Q-School golfers for 2009-2013. Only 8 golfers had 0 rounds in YR N-1 however.


20    -0.05    2
21    -0.06    3
22    -0.01    6
23    -0.05    8
24    -0.07    9
25    -0.11    11
26    -0.13    18
27    -0.13    23
28    -0.14    29
29    -0.12    36
30    -0.13    34
31    -0.11    39
32    -0.12    36
33    -0.11    34
34    -0.13    34
35    -0.12    36
36    -0.11    37
37    -0.10    42
38    -0.08    26
39    -0.05    30
40    -0.01    21
41    0.03    35
42    0.07    28
43    0.12    19
44    0.13    17
45    0.15    13
46    0.21    17
47    0.25    19
48    0.31    13
49    0.36    12
50    0.35    9
51    0.45    4
52    0.47    2

bayesian aging


The curve generated is very similar to that of the prior study regressing to a mean of +0.00. The peak is slightly lower and the decline is deeper in the late 40s, but otherwise this study supports my prior conclusion of aging with a peak in the mid 30s and subsequent decline.

The Aging Curve for PGA Tour Golfers (Part II)

Yesterday I posted the results of my study on aging among PGA Tour members. You can read the methodology at the link, but basically it compared pairs of seasons by age to find how much a player should be expected to improve or decline based solely on age (I included a mechanism to regress performance in an attempt to find “true talent”).  At the end I said I’d like to try a different regression mechanism that I hoped would produce a more accurate representation of true talent.

I’ve found before that it’s correct to regress PGA Tour performance around 30% to the mean to find true talent. However, that’s most accurate for golfers who play something like a full season (ie, 50-100 rounds worldwide/season). For regular Tour members, regressing 30% is correct, but for golfers playing only partial seasons it’s likely not regressing enough. A performance over 20 rounds is more likely to be the product of luck than a performance over 60 rounds. That’s problematic for this study because it doesn’t regress more extreme good or bad performances enough to the mean. You’ll see the errors that result when I compare the two studies below.

In prior research comparing sets of rounds [1], I’ve found that adding 25.5 rounds of average (0.00) performance properly regresses a performance to the mean. This means for a player with around 60 rounds, the 30% figure quoted above is accurate. For those playing more, like Brendon de Jonge’s 118 rounds in 2012, regressing 30% is way too much. We know a lot more about de Jonge’s true talent in 118 rounds than we do about, say, Jason Day’s 60 round sample in 2012, enough to regress de Jonge only 18%. Similarly, Hideki Matsuyama’s 26 major tour rounds in 2013 tell us much less about his true talent, and by adding 25.5 rounds of average he gets regressed 50% to the mean.

Sample & Design:

The same sample and methodology as the above quoted study were used, except instead of regressing using the equation True Talent=(.6944*Observed)+0.01, I simply added 25.5 rounds of average performance to every observed performance: True Talent=((Observed Z*Observed N)/(Observed N + 25.5)).

I still did not weight my data.

age         delta      N
19           0.02        3
20           -0.02      2
21           -0.03      4
22           0.01        8
23           -0.03      8
24           -0.01      11
25           -0.06      16
26           -0.02      23
27           -0.01      30
28           -0.01      39
29           -0.03      46
30           0.04        45
31           0.00        49
32           -0.01      44
33           -0.02      43
34           0.04        46
35           0.01        46
36           -0.02      49
37           0.01        51
38           0.04        38
39           0.03        34
40           0.03        38
41           0.05        40
42           0.03        28
43           0.01        27
44           0.04        21
45           0.10        18
46           0.00        28
47           0.03        22
48           0.06        15
49           0.03        16
50           0.02        10
51           0.00        6
52           0.07        2

aging w25.5regression

The smoothed curve averages the improvement of year N-1, N, and N+1.

The results here were much different using a more accurate regression mechanism. There is an observed slow increase in true talent of around -0.02/season from 19 to 29. Between 30 and 37 the curve is more or less flat, declining almost imperceptibly. Beginning in the late 30s is the steady decline of around 0.04/season that was also observed (though to a greater extent) in the previous study.

With this more accurate methodology, I think the previous study can be discarded. There IS age related improvement in a golfer’s twenties. Golfers tend to peak between 29 and 34, with a sharp decline around 38 onwards. This study does not necessarily disprove my prior hypothesis that there is a decline based on lessened commitment to practice/preparation among the more transient PGA Tour members, but it certainly means there is a larger improvement in the 20s being observed among the more permanent members.

[1] This study ordered PGA Tour rounds for a large group of golfers over a full-season from oldest to newest. I then selected two samples – one comprised of the even number rounds and one of odd number rounds – and compared them to see how predictive one half was of the other. I expect to reproduce that study with a larger sample of seasons and golfers soon.

The Aging Curve for PGA Tour Golfers

This is a short study I conducted on the typical aging curve for PGA Tour golfers. I stress again, this is the typical aging curve for the average PGA Tour member. As I discuss below, it is not likely to reflect the aging curves of the most elite golfers.

Sample & Design:
All PGA Tour golfers who in Year 1 played in >20 PGA Tour [1] rounds and who in Year 2 played at least 1 round of golf worldwide. I studied 2009-2010, 2010-2011, 2011-2012, and 2012-2013. My sample included 916 pairs of seasons.

I then compared these golfers in all worldwide rounds in Year 1 and in Year 2. I regressed each Year 1 and Year 2 to PGA Tour Average (0.00) using the equation Y=(.6944*X)+0.01. I regressed because I want the best estimate of a golfer’s “true talent”. Golf performance is heavily influenced by luck; over a normal 85 round season, a golfer’s displayed performance represents approximately 70% skill and 30% luck.

The delta of Year 2 – Year 1 provided my comparison point. I did not weight my data.

I included only golfers who appeared in >20 PGA Tour rounds in Year 1 because it is rare for a golfer to accumulate >20 PGA Tour rounds and subsequently fail to record a single round worldwide because of the nature of the international golf tour structure. Golfers who fail to re-qualify for the PGA Tour almost always are able to play on the Tour the following season. If I had used all golfers with >20 rounds in Year 1, many golfers who performed poorly on the Tour would’ve fallen completely out of my sample because they would have played on minor tours for which I do not gather data. By measuring only PGA Tour players I ensure that no matter how lucky or unlucky, good or bad a player was in Year 1, it’s very likely they will be included in the data for Year 2.

AGE       delta      N
19           0.02        3
20           -0.02      2
21           -0.01      4
22           0.00        8
23           -0.02      8
24           0.02        11
25           -0.04      16
26           0.00        23
27           0.01        30
28           0.00        42
29           -0.01      47
30           0.04        45
31           0.01        49
32           0.00        45
33           -0.01      44
34           0.04        46
35           0.00        47
36           0.00        51
37           0.06        51
38           0.04        38
39           0.05        35
40           0.02        39
41           0.07        41
42           0.04        29
43           0.00        27
44           0.07        22
45           0.13        18
46           0.07        28
47           0.09        22
48           0.08        15
49           0.08        16
50           0.13        11
51           0.01        6
52           0.04        2


The aging curve for this sample is basically flat from the age 21 to age 34, with a significant year-by-year decline beginning in the late 30s. This indicates that the golfers in this sample did not generally improve or decline due to age until the mid-30s. The sample is small until age 26, but it’s possible to observe a slight improvement of -0.01/season. From age 26-36 the decline is less than 0.01/season. From 37-47 the decline accelerates to 0.06/season. After 47, the sample is relatively small, but shows continued significant decline.

Obviously this is surprising, as I anticipated finding a normal aging curve where an athlete reaches peak performance in the late 20s before declining beginning in the mid-30s. Instead, the sample hardly improved through the late 20s and even slightly declined by the mid-30s. After that, the sample followed the sharp decline in the late 30s and 40s which is anticipated from other athletics-focused aging studies.

My main hypothesis about why golfers show no age related improvement relates to the sample I chose to work with. This study measures the typical PGA Tour professional. Most of the public is familiar with golfers who have remained on Tour for many years, decades even, like Tiger Woods, Phil Mickelson, and Ernie Els. However, the PGA Tour is a very transitory competition. Around 225 golfers play more than 20 rounds in a season, but only 125 retain full playing privileges the following seasons; the rest attempt to qualify via Q-School or, failing that, play with reduced status or on the minor league Tour. Playing on the PGA Tour is very lucrative – purses are on average ten times larger than Tour purses, meaning players earn approximately ten times more money on the PGA Tour. The Tour qualifies only the best 25 golfers to the PGA Tour every season, meaning not even 10% of the golfers receive promotion to the PGA Tour.

Because of this financial disparity, only a third of golfers who competed regularly on the Tour in 2013 earned more than the US median household income for 2013 (~$51,000). Professional golf requires endless hours of practice, separation from family/friends, and constant travel between tournament venues that regularly cover at least three or four continents. It may be that the average PGA Tour golfer just cannot handle the constant grind of professional golf and his skills slowly deteriorate from very early in his career. Because it’s unlikely that the average PGA Tour pro will even maintain their membership from year, most professional golfers face years of yo-yoing up and down between the lucrative PGA Tour and the relative penury of the Tour. Viewed like that, it’s understandable why the typical player does not improve.

Understand that there are many forces at work to produce the small improvements or declines due to age. Golfers certainly become more experienced at reading greens, making club decisions, and choosing how to play shots as they play deeper into their careers. At the same time, the athletic decline observed in other sports affects a golfer’s ability to generate club head speed or repeat their swing. Many commentators talk about how older players get the “yips” and putt worse than they did when younger. At the same time, golf requires constant dedication to practice and preparation. A golfer that isn’t prepared to commit hours to practice each day is going to watch his skills erode. It is likely that the aging curve observed above is a combination of all these factors.

Again, I have to stress how I looked at typical PGA Tour professionals. There are likely many different aging curves based on ability. I would be stunned if the aging curve for elite golfers resembled this slow decline. Golfers who are elite can expect significant and sustained rewards for high levels of performance. Elite golfers are unlikely to lose their playing privileges on the PGA Tour, so they know that by maintaining their practice and preparation they can expect to earn more than a million dollars in prize money per season plus endorsements and appearance fees. That is what fuels golfers like Mickelson and Vijay Singh to take care of their bodies, to practice, to prepare for each tournament, and to withstand the weekly grind of playing in different tournaments.

Future Work:
I’d like to follow this study up with one that does weight the data by rounds played. I’m also less comfortable with my regression technique than I would like. Instead of regressing every observed value by a fixed ~30% to the mean, I’ll regress the observed by adding a certain number of rounds of average play. For example, past work I’ve done estimates that adding 25.5 rounds of 0.00 properly regresses the observed data.

[1] – I defined PGA Tour rounds as any PGA Tour (co-)sponsored tournament plus the World Golf Championships and Majors.