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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.

Results:
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.

Discussion:
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.

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8 responses to “The Aging Curve for PGA Tour Golfers (Part II)

  1. Pingback: The Aging Curve for PGA Tour Golfers (Part III) – Using Bayesian Prior | Golf Analytics

  2. Pingback: An Aging Curve for Putting | Golf Analytics

  3. Pingback: Tiger Woods and Father Time | time2sports.com

  4. Brandon July 28, 2015 at 8:27 PM

    Is there significantly more younger players playing in pga tour events than there was 2 years ago? If so do you expect this curve to be pushed back to the left (peaking and declining at a younger age), or do you expect players to peak sooner and maintain the peak (or near peak) longer?

    • jalnichols July 28, 2015 at 9:09 PM

      I would be surprised if there were more younger players than 2 years ago. In fact, I imagine eliminating Q-School qualification has culled the youngest players who were earning their way in.

      I’d like to get around to updating this study now that I have more and better data. If I do, I’ll take a look at the age distribution on Tour/how it’s changed recently.

      • Brandon July 28, 2015 at 9:32 PM

        Thanks for the reply, and for all the great articles on this site.

        I suppose my thinking there are more younger players is just a skewed perception based on the success of Spieth, Thomas, Finau and the rest of the young guys performing well.

  5. Alan February 10, 2017 at 11:11 AM

    What would you estimate your standard deviation is here converted to an average round metric? 2 to 3 strokes?

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