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Monthly Archives: March 2014

Putting Driven Performance Changes are Illusory

Last week I posted about how repeatable performance on different shot types was from season to season. Tee to green play is more repeatable than putting which is more repeatable than scrambling. That makes sense once you realize that golfers play 2-3 times more tee to green shots than meaningful putts in a round; there’s just more inherent randomness in a season’s worth of putts than in a season’s worth of tee to green shots. Golfers play even fewer scrambling shots resulting in even more randomness in a season’s worth of scrambling.

Last month I also examined how repeatable small samples (4-8 tournaments) of putting performances are, in the context of discussing why I expected Jimmy Walker’s performance to regress to the mean. That micro-study indicated that there was very little correlation between a golfer’s performance in a 4-8 tournament sample of putts and the following 4-8 tournament sample of putts. In the whole, performances in such short samples regress almost entirely to the mean.

Those two lines of inquiry led me to examine whether putting was more random than tee to green performance. I have always believed that improvements/declines that were driven by over-performance in putting were less real than those driven by tee to green over-performance, but I had never actually tested that hypothesis. The key question is whether changes in performance driven by putting are less persistent than those driven by tee to green play. That is when a golfer performs better over the first half of a season, and much of the improvement can be traced back to an improvement in his putting stats, will that golfer continue to perform better in the second half of the season? The evidence says changes in performance driven by putting are more illusory than changes in performance driven by tee to green play.

Design:

I gathered the tournament by tournament overall, tee to green, and putting performances of all PGA Tour golfers in rounds measured by the ShotLink system for 2011-Present. I divided those rounds into roughly half-season chunks (January-May 2011, May-November 2011, January-May 2012, May-November 2012, January-May 2013, May-September 2013, October 2013-Present). Each chunk included around 15-18 tournaments. I considered all golfers who recorded at least 20 rounds in consecutive half-season chunks.

To measure putting performance I used the PGA Tour’s Strokes Gained Putting stat and to measure tee to green performance I used my own overall ratings with putting performance subtracted out. This methodology is consistent with my measurement of tee to green performance in numerous recent work.

Half-Season Correlations by Shot Type:

First, I measured how repeatable putting and tee to green performance was between half-season samples, much like the full-season samples used in this study. I included all golfers with at least 20 rounds in consecutive half-season samples and compared each half-season to the half-season that directly followed, including 2nd halves to 1st halves of following calendar years. This yielded samples of ~800 golfers for both tee to green and putting. Graphs are below.

half tee to green

half putting

Tee to green performance was again more repeatable than putting performance. In the study linked above consecutive full-seasons of tee to green performance were correlated at a R=0.69 level. I found a correlation of R=0.62 between consecutive half-seasons, understandably less given the smaller number of rounds/shots played. The full-season correlation for putting was R=0.55. Half-season putting performances were similarly less correlated than full-seasons at R=0.40. Both these findings are consistent with the understanding that randomness between samples increases when fewer rounds/shots are compared. Most importantly, putting is less repeatable than tee to green play.

Persistence of Changes in Performance by Shot Type:

Next, I measured how persistent changes in performance are when considering putting and tee to green play. That is, when a golfer improves their putting over a half-season sample, how much of that performance is retained in the following half-season? If 100% of the performance is retained, changes in putting performance over a half-season entirely represent a change in true talent. If 0% of the performance is retained, changes in putting performance over a half-season entirely represent randomness. The same for tee to green play. My assumption was that a larger percent of performance would be retained for tee to green play than putting, meaning that half-season samples of putting are more affected by randomness than half-seasons of tee to green play.

To measure the effect, I first established prior expectations of performance for every golfer in my sample. I simply averaged performance in tee to green play and putting for the three years prior to the beginning of each half-season sample. For example, for the May-November 2011 sample, I averaged play between May 2008 and May 2011. This is not an ideal measure of performance, but it provides a consistent baseline for comparisons to be made.

I removed all golfers from the sample who had no prior performances. This reduced my sample to around 750 consecutive half-seasons.

The values I compared were the initial delta (Prior minus 1st Half-season) and the subsequent delta (Prior minus 2nd Half-season). Using this method I can find how persistent a change in performance is between to half-seasons. I did this considering putting and tee to green play. Graphs are below.

persist tee to green

persist putting

Changes in tee to green play were twice as persistent as changes in putting play, meaning golfers who improved their tee to green play retained twice as much of those improvements as golfers who improved a similar amount in putting. Golfers maintained around 60% of their tee to green improvements, but only 30% of their putting improvements. This indicates that putting performances regress more sharply to prior expectations than tee to green performances.

Are Putting Performances More Illusory?

Finally, I gathered the data from above to measure whether changes in performance driven by putting less real than changes in performance driven by tee to green play. I ran a linear regression using the initial delta for overall performance and the initial delta for putting performance as independent variables and the subsequent delta for overall performance as the dependent variable. In short, given a certain overall change in performance and a certain change in putting performance over the first half-season, how much of that overall change in performance is retained over the second half-season?

As the following table shows golfers retain much more of their improvement or decline when that improvement or decline occurred in tee to green shots than if it occurred in putting. The columns show improvements/declines in overall play (considering all shots) and the rows show improvements/declines solely in putting. The table shows that a golfer who improves overall by 0.50 strokes will retain only a quarter of their improvement if all of the improvement was due to putting (0.50), while they will retain over half of their improvement if none of the improvement was due to putting (0.00). The equation used to produce this chart is Subsequent Delta = (0.56 * Initial Overall Delta) – (0.28 * Initial Putting Delta).

delta comparisons

Discussion:

These findings should fundamentally alter how we discuss short-term changes in performance. I’ve already shown repeatedly that performances better than prior expectation will regress to the mean over larger samples. That idea is consistent across sports analytics. However, these findings indicate that the amount of regression depends on which part of a golfer’s game is improving or declining. Golfers who improve on the basis of putting are largely getting lucky and will regress more strongly to the mean than golfers who are improve on the basis of the tee to green game. Those who improve using the tee to green game are showing more robust improvements which should be expected to be more strongly retained.

The golfers who represent either side of this for the 2014 season are Jimmy Walker and Patrick Reed. I’ve discussed both in the past month, alluding to how Walker’s improvements were almost entirely driven by putting and how Reed’s were mostly driven by tee to green play. Based off these findings, Reed is more likely to retain his improvements over the rest of the season, all else being equal, than Walker.

 

All graphs/charts are denominated in strokes better or worse than PGA Tour average. Negative numbers indicate performances better than PGA Tour average.

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Repeatability of Golf Performance by Shot Type

My main interest in analyzing golf is using past data to most accurately predict future golf performance. Inherent in that are the questions of how to figure out how much randomness is affecting the data and how to remove the effects of randomness from the data. The easiest way to find how much randomness is involved with data is to find the correlation between subsequent measures of performance. For example, in this post I found the correlation between a golfers performance in various samples of years from 2009-12 and their performance in 2013. Based on that, I concluded that using such basic methods produced a correlation of around 0.70 – meaning that in a subsequent season we can expect a golfer to repeat about 70% of their prior performance above or below the mean. I’ve achieved correlations slightly higher than that using more sophisticated methods and more detailed data, but an R of 0.70 should be viewed as the typical baseline for judging the repeatability of golf performance.

In this post, I attempt to find the repeatability of performance on different shot types using the same methodology as above. I will use Strokes Gained Putting to measure putting performance relative to the field, my own Z-Score measure minus Strokes Gained Putting to measure tee to green (driving, approach shots, short game) performance relative to the field, and I’ll use my own Scrambling metric (methodology here) to measure performance on only scrambling shots (a scrambling shot is the first shot following a missed green). I have not stripped these scrambling shots out from the tee to green measure; tee to green measures all non-putting strokes.

I gathered data for all PGA Tour golfers for 2008-2013 who had a qualifying number of rounds played (50). I then paired consecutive seasons for each of my three performance measures and graphed the correlations below.

repeat teetogreen

repeat putting

repeat scram

For all three graphs negative values represent performances better than the field. Values are expressed in standard deviations above or below PGA Tour average.

The measure that was most strongly correlated from season to season was tee to green performance. That makes intuitive sense as tee to green performance includes the most shots/round of any of my measures (roughly 40/round). In fact, tee to green performance is almost as repeatable as overall performance (R = 0.69 compared to 0.69 to 0.72 in the study linked above). This indicates that a golfer who performs well above or below average in a season should be expected to sustain most of that over or under-performance in the following season.

Putting was less repeatable than the tee to green measure, but skill still shows strongly through the noise. An R of 0.54 indicates that a golfer’s putting performance should be regressed by nearly 50% toward the mean in the following season (provided you only know their performance in that one season). I would mainly explain the lower correlation on sample size; golfers normally hit 25-30 putts/round, but many of these putts are short gimmes that are converted upwards of 95% of the time. The number of meaningful putts struck in a round is more like 15. This indicates that it takes a golfer over 2.5 seasons of putting to reach a season’s worth of tee to green shots. This suggests that putting is less repeatable from season-to-season than tee to green strokes, which indicates we should be wary of golfers who build their success in a season largely off of very good putting.

Off the three measures tested, Scrambling was the least able to be repeated (R =0.30). This indicates that performance on these short shots around the green is very random. It’s not atypical for a golfer to perform as one of the best 10% on Tour one season and average the next. Again, this is likely a function of sample size. A golfer hits only 6-8 scrambling shots/round (every time they miss a green). It takes a golfer around five seasons of scrambling shots to reach one season’s worth of drives/approaches.

There are two important caveats with this approach. These correlations explicitly measure only one season compared to the following season. Measures like scrambling and putting likely are more strongly correlated when several seasons are used as the initial sample. I intend to test this in a future post. In addition, this study only considers golfers who played consecutive seasons with >50 rounds/season. This leaves out a certain sample of golfers who played so poorly in the first year that they were not allowed to play >50 rounds the following season. These stats may be slightly more repeatable if those golfers were included.

Valero Texas Open Preview

The Course:

The Tour moves to the TPC San Antonio Oaks Course this week, site of the Valero Texas Open. Quite simply, this course is a beast to navigate. Relative to par, only PGA National (Honda Classic) and Congressional (AT&T Classic) have played more difficult over the last three years. Unfortunately this year the field is fairly weak; it’s headlined by Mickelson (first appearance in two decades), Spieth, Zach Johnson, Furyk, Kuchar, and three-time winner Jimmy Walker, but the kind of secondary talent that has been present in recent weeks at the Honda and Arnold Palmer just hasn’t shown up.

Unlike Congressional CC, the Oaks Course doesn’t primarily rely on distance as its defense. It is slightly longer than a normal par 72 course, but that distance is concentrated in the par 3s and par 5s (4th and 5th longest on Tour), while the par 4s are the 7th shortest. The main difficulty is hitting the greens; in the last three years golfers only hit 56% of the greens – one of the fewest on Tour. The other notable feature is how difficult it is to successfully scramble. Golfers only made par or better on 47% of their missed greens in last year’s tournament, by far the lowest on Tour last year. If that poor performance is maintained into this year’s tournament golfers who hit more greens will be advantaged by over 0.1 strokes/round simply because hitting greens is more valuable than normal this week.

Course Effects:

Beyond the aforementioned scrambling effect, I wanted to test whether this course provided an advantage to either longer golfers or more accurate golfers. I gathered the Driving Distance and Driving Accuracy stats for everyone who played the course from 2011 to 2013 and regressed those independent variables on the dependent variable of performance in strokes vs. the field. I also stripping out putting performance by subtracting the strokes gained putting from the overall performance. My regression attempted to predict performance tee-to-green relative to the field using simply driving distance and driving accuracy.

Unsurprisingly, the model worked as an overall proxy predicting nearly 20% of the variance in performance vs. the field (R^2 0.18). Both distance and accuracy were highly significant at the <.001 level (N=310 golfers). The results indicated that the course has favored good drivers over the more accurate golfers over the past three seasons. Long/inaccurate drivers performed 0.1 strokes better than the field, while Short/accurate drivers performed 0.4 strokes worse than the field. There’s no guarantee that that will continue over this tournament, but it may indicate an advantage for the longer/inaccurate golfers (guys like Jimmy Walker or Ryan Palmer), rather than shorter/accurate golfers (Furyk, Zach Johnson).

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).

Design:

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 Web.com 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.

Results:

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.

SGPAgingCurve

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 areĀ 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.

SGPAgingCompared

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.

Review of Every Shot Counts – Mark Broadie

Mark Broadie’s Every Shot Counts: Using the Revolutionary Strokes Gained Approach to Improve Your Golf Performance and Strategy (2014) is the long-awaited full-length explanation of his strokes gained research. Broadie had published numerous academic papers discussing his strokes gained method and the PGA Tour has been showing the Strokes Gained Putting stat for a few years, so much of this material is merely rehashed from articles others have written or from his 2011 paper “Assessing Golfer Performance on the PGA Tour“. It’s well known that Broadie’s research has disproved putting as the most important part of the game and has elevated the long-game (driving and long approach shots) in its place, but where this book shines is in its lessons for applying this new knowledge to actually playing the game, whether you’re a pro, advising a pro, or an amateur.

Broadie spends the first six chapters basically explaining the strokes gained method. He covers why putting is overrated, why traditional putting statistics are worthless, and how the strokes gained method works. He then introduces strokes gained for the tee-to-green game. Broadie establishes that why the long-game is so important in separating elite pros from average pros, average pros from good amateurs, and good amateurs from 90 handicappers; in the process he shows why Tiger dominated golf so much in the last decade (he was good at everything, but the #GOAT at playing long approach shots). This part of the book is worthwhile for the more in-depth exploration of the strokes gained method, but if you’ve read his academic work feel free to skim it for the handful of insights.

Essentially, Broadie’s work is about how fractional strokes are so important in separating pro golfers. The best and worst golfers in a PGA Tour tournament are separated by 2.5 strokes/round. Most of that separation is manifested in things like hitting an extra green each round, driving the ball five yards further, leaving your shots from the sand a foot closer, and/or hitting a single approach shot within birdie range. His research argues for a strategy that considers all possible shots and outcomes of those shots, and selects the highest expected value play. In this recent interview, Broadie says that most golfers don’t play aggressively enough; they leave putts short of the hole, lay-up on par 5s, and hit woods/hybrids off the tee when they’d be better served hitting driver. Central to his work is the idea that being much closer to the hole is worth playing out of the rough/fairway bunker.

Broadie finally explores new ground the final three chapters, laying out how this new knowledge should be applied to all aspects of the game. In Chapter Seven, Broadie explores what the strokes gained analysis means for putting and how to figure out how aggressive to be on long putts. He explains that many PGA Tour golfers aren’t aggressive enough with their putting; they often purposefully don’t hit putts with enough force to get to the hole, ensuring that they miss the putt. There’s a lot of work in this chapter figuring out optimal aim points from different locations on the green; very interesting work for amateurs who are looking to improve their strategy on the green.

In Chapter Eight he explores how to optimize your long game to shave wasted strokes. Much of the chapter is spent on figuring out the proper way to target drives to ensure you miss the dangerous hazards (water, out of bounds), even if you are forced to play less from the fairway. This section would be very useful for amateurs who often find themselves wasting strokes off the tee by not being cognizant of where the dangerous areas of the course are. Broadie also spends this chapter detailing why lay-ups are typically a minus EV play – particularly notable this week after the way Patrick Reed laid-up so poorly down the stretch at Doral.

Chapter Nine is a detailed look at numerous different practice methods that use the ideas behind valuing each stroke and playing the highest EV game. I mainly skimmed these, but amateurs might find the lessons/games useful for improving their play.

I did pick up a few interesting lessons:

1. It’s well established that those who hit for more distance off the tee usually hit fewer than average fairways, but Broadie has actually found that longer players have a smaller degree of error in terms of how off-line they hit their shots. In fact, the only reason many long hitters hit fewer fairways is because when they do hit a shot with a larger than average degree of error, the increased distance cause it to fly/roll further off-line – into bunkers and the rough. Driving is basically a geometry problem where a smaller angle and larger hypotenuse can produce a larger miss.

2. Broadie introduces the concept of “median leave” in Chapter Five. The PGA Tour publishes stats showing the average proximity to the hole from approach shots, the rough, green-side bunkers, etc. However, Broadie argues we should use the median proximity instead because it’s not distorted by larger misses (like when you fly the green and leave it 50 yards from the hole). Median leave is simply the distance remaining to the pin after the shot divided by the distance to the pin before the shot. So a 150 yard approach to 18 feet would be a median leave of 4%. The best approach shot players have a median leave of 5.5% – equivalent to hitting it a median proximity of 29 feet from the average PGA approach shot (175 yards).

3. When discussing optimal driving strategy he explains the idea of “shot pattern”. Your shot pattern is all the possible results of each type of shot, considering the distance you can hit with a club, the degree of accuracy, and any spin/fade/draw/slice/etc. you can play. Golf is a game where each swing is essentially slightly random – a golfer might swing perfectly, contact the ball perfectly, judge the wind perfectly, and get the right amount of spin when he lands it on the green, but more likely his swing will be slightly off or he’ll mishit it slightly or the wind will push it offline a bit, or it will roll-out when it hits the green. The optimal golfer will know their 95% confidence interval for a 125 yard wedge shot, his average degree of miss when he hits driver, and all the possible results of an approach shot if the greens are firmer than expected. The optimal golfer will play their shots with all that understanding and avoid playing shots that are excessively conservative or needlessly risky.

All in all, it’s a worthwhile book if you’re interested in applying Broadie’s research to your golf game or at least interested in how a pro might apply it to how they work around a golf course. Broadie has plenty of evidence of some of the elite golf instructors already using this kind of stuff to help their clients excel. On the other hand if you’re just interested in the research itself, reading the literature I linked above is sufficient. His initial six chapters don’t provide a substantial amount of expansion on his earlier papers.

Thoughts on Patrick Reed (Without Using “Confident” or “Cocky”)

Patrick Reed won a tournament yesterday – his third win since August – and in the process delivered pre-round and post-round interviews where he said he thought he was a top five player in the world. There’s been a lot of bullshit spewed already about his comments so I’m going to try to avoid any of that. I am going to lay out some reasons for and against the idea of Patrick Reed being an elite golfer, with the knowledge that anyone who thinks they know for sure is full of it.

Reasons to Doubt

The main argument against Reed being elite is his aggregate play up to this point in his career. Going beyond his three wins in 51 starts, when you consider all of his rounds (not just the ones since August), Patrick Reed’s performance hasn’t been much different than an average PGA Tour cardholder. I have 180 rounds for him between the Web.com Tour and PGA Tour going back to before he turned pro in 2011. In those rounds he’s played to just barely above the level of an average cardholder (-0.17). 180 rounds isn’t the definitive picture of a golfer, but it tells us that in general he’s been essentially average over a fairly large sample of results over mostly the last three seasons.

As I just wrote in a piece last week, the first two months of the season, when considered alongside the last two years of data, provide little extra information about how a golfer will perform going forward. Reed played to a rating of -0.08 in all rounds prior to January 1st 2014 and he’s played to a rating of -0.81 (over 2 strokes better) in 24 rounds since then. In general, past performances have shown that we should place about 3.5 times as much weight on those prior rounds compared to the rounds from the beginning of the season. Using this line of thinking, Patrick Reed should be considered an above-average PGA Tour player, but no better than Kevin Chappell or Russell Knox or other young guys who no one pays an extra second of attention.

Now some might point to his age saying that plenty of young players break-out in their early to mid 20s. I re-ran the study from the piece linked above to factor in age. The methodology is outlined in the piece, but basically I used a regression analysis to predict performance from March to December of a season using the January/February performance as one variable and the previous two full seasons as another variable. I ran the analysis this time using all seasons from age 27 and younger, age 28 to 38, and age 39 and older. I used the age 27 cut-off because that is where my prior aging studies have shown general age-related improvement halts.

In fact, the age of the player does affect how strongly we should believe in early season improvements/declines, though the evidence still favors the prior two seasons. For the age 27 and under group, the weight was about 2.4 times stronger for the prior seasons than the early season form. The weight on the prior seasons was around 5 times stronger for the age 28 to 38 group and nearly 4 times stronger for the age 39 and older group. Consider that all seasons produced a weight of 3.5 times for the prior seasons and it’s clear there’s an effect for younger golfers. So that indicates that we should believe more in early season improvements for young players, but that we should still defer heavily to the prior performance data. Using this method to project Patrick Reed, I’d compare his abilities now to Billy Horschel or Harris English. Plenty of folks think they’re very good players, but no one (bookmakers included) considers them elite by any stretch.

I then set-up a regression which attempts to predict the delta of the remaining ten months of performance with age and the delta between the prior two seasons and the first two months as dependent variables. The top row of the graph below is the delta between the prior two seasons and the first two months (negative means improvement/positive means decline), while the first column is age. Each cell represents the expected delta between the prior two seasons and the remaining ten months of the season based on a golfer’s first two month delta and their age. You can see that younger golfers that outperform their prior two years are expected to retain more of their improvements over the rest of the season than peak aged or past-peak golfers.

ValueHotStartGraphByAge

Reasons to Believe

Now that I’ve laid out the reasons to doubt Reed, here are a few reasons to think that this may be more real than the general model predicts.

1. Reed was an outstanding amateur golfer, especially during his final two seasons in college. The gold-standard for measuring collegiate golf performance is Jeff Sagarin’s rankings. Sagarin uses a method that compares who you beat/lose to in the same tournament and how much you beat/lose to them. College golf doesn’t provide a huge sample of results – a golfer might complete 40 rounds during a season – but it works in general. During Reed’s two seasons at Augusta State, he played 20 tournaments and finished 4th and 9th in the nation in Sagarin’ rankings (and led Augusta State to two straight NCAA Championships). Less than ten others have finished with a better average rank in college than Reed (including Bill Haas, Ryan Moore, and Dustin Johnson). Elite college performance at least establishes that Reed isn’t coming out of nowhere; this guy was lighting tournaments up in college.

2. I don’t consider Monday qualifier results in my database. The data is provided by local PGA chapters using multiple spellings of names and it’s generally a hassle to collect. For most guys that wouldn’t be a huge issue, but Reed was 6/8 in Monday Qualifiers in 2012, earning his way into six tournaments when he had no Tour status. Monday qualifiers are held at a nearby courses with around 100 golfers participating (mainly PGA Tour members without the status to enter the tournament directly, Web.com golfers, or minor tour pros). Of those ~100, the best four scores over a single round qualify to enter the tournament. Because only the top four advance, these qualifiers require a golfer to play around the level of peak Tiger Woods for a round to qualify. In short, Reed playing that well in 6/8 qualifiers should inflate his overall rating by a small amount.

3. Most importantly, Reed isn’t getting terribly lucky putting so far this season. Putting drives a lot of luck on Tour – largely because it’s easier to sink an extra eight footer every round for two months than it is to randomly pick up an extra couple yards of driving distance on every hole. When I examined Jimmy Walker’s game a few weeks ago, all of Walker’s improvement in 2014 could be attributed to a strokes gained putting that was inflated nearly a full stroke above his career average. In Reed’s case his putting numbers are slightly higher than his career average, but nothing similar to Walker’s stats.

Entering 2014 he had gained 0.27 strokes on the field through his putting and was basically Tour average in driving/scrambling/approach shots/etc. in his career. So far this season, he’s gained 0.52 strokes from putting and 1.91 strokes from driving/scrambling/approach shots/etc. About 10-15% of his improvement can be traced to his putting and the rest to his driving, iron play, and short game. In short, he’s not relying on a lucky putter like Walker, instead he’s hitting his driver and irons more consistently – leading to more distance, more greens hit, and more birdie opportunities.

This is where I should sum up all the evidence and declare a winner. Is Patrick Reed going to keep winning tournaments, maybe a Major this season? Or is he going to regress to just being another guy grinding for his card? But I don’t really have any idea. I do hope we start getting more post-round interviews that are heavier on bravado than modesty.