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Rank and Performance in Dynamic Tournaments: Evidence from the PGA Tour
Daniel C. Hickman
Assistant Professor of Economics
Department of Business
University of Idaho
Craig Kerr
Associate Professor of Economics
Department of Economics
California State Polytechnic University – Pomona
Neil Metz
Associate Professor of Economics
Department of Economics
University of Central Oklahoma
Abstract
Using data from the final round of golf tournaments, we analyze the effect of player rank on
performance. The identification of varying levels of pressure is possible due to the detailed
nature of the dataset, which allows us to determine the rank of each player just prior to teeing
off on each hole. We find that players in the lead tend to underperform, especially near the
very end of the tournament and when the lead is closely contested. We also create a measure
to rank individual golfers based on how their performance is affected by high-pressure
situations.
Keywords: tournaments, performance incentives, psychological pressure, choking
JEL Classification: D03, J33, L83, Z20
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Rank and Performance in Dynamic Tournaments: Evidence from the PGA Tour
1. Introduction
A tournament setting, in which the returns for an individual depend on his or her performance
relative to others, is designed to provide incentives that increase effort (Ehrenberg and
Bognanno 1990; Lazear 2000; Lazear and Rosen 1981). However, a growing body of evidence
suggests that such settings can also create psychological pressure that may lead certain
individuals to underperform. Ariely et al. (2009), for example, use an experimental framework
to show that very large rewards cause individual performance to decline. The world of sports
also provides many forums with which to test the impact of a tournament setting on
performance. Previous studies have found evidence that pressure affects performance in soccer
(Apesteguia and Palacios-Huerta 2010; Dohmen 2008), basketball (Cao et al. 2011; Deutscher et
al. 2013; Toma 2015), hockey (Depken et al. 2012; Kolev et al. 2015), tennis (Gonzalez-Diaz et
al. 2012) and golf (Clark 2002a; Clark 2002b; Clark 2007; Hickman and Metz 2015; Hill et al.
2010; Wells and Skowronski 2012; Wright et al. 1991).1
Researchers have utilized many approaches to examine the relationship between
pressure and performance. Within the study of golf, for example, Hickman and Metz (2015)
analyze how the amount of money riding on a particular putt impacts the likelihood that putt is
made. Kali et al. (2015) study tournament round scores and find that non-monetary incentives
(Ryder Cup qualifying points) impact performance in an adverse manner. Brown (2011) finds
that playing in a tournament with superstars can also cause performance to decline. Genakos
and Pagliero (2012) examine tournaments in weightlifting and develop a novel approach to
estimating the relationship between pressure and performance. They utilize a measure of
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interim ranking at each stage of competition to see how relative standing influences both
strategic risk-taking and performance. Genakos et al (2015) provide a follow-up study that
analyzes performance in diving tournaments. Each of these settings produce results indicating
that the more highly ranked a competitor is at the beginning of a stage, the worse that
competitor’s performance will be during that stage.
In this study, we make use of a rich set of data from golf tournaments. The amount of
data available, as well as the nature of the game, has drawn researchers to golf in order to
examine many aspects of behavioral economics.2 Here we examine how players perform on
each hole during the final round of a golf tournament based on their standing in the
tournament at the start of that hole. This provides an interesting comparison to the findings in
weightlifting and diving. In each of the latter sports, an individual makes strategic decisions
regarding the task he or she will perform in each stage of the tournament. Weightlifters select
the weight they will attempt to lift immediately before each round of the tournament. Divers
must select their dives prior to the entire competition. In golf, however, each player must
attempt the same task at each stage (hole) of the competition. This allows us to hold constant
the task being performed and more cleanly estimate the effect of rank on performance. As far
as risk is concerned, a golfer may decide to play a hole more or less aggressively, but one
cannot directly observe risk in golf as it involves knowing a player’s line of play and intent. Also,
a recent study by Baldson (2013) on risk taking in golf finds that there is very little to no risk
taken by players in the final holes of a tournament. Another important difference is that players
are at different stages of the competition simultaneously, so the dynamic ranking process is
more complex. This may impact performance differently than other sports where players take
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turns and all complete one stage before proceeding to the next. One additional contribution of
our examination of golf tournaments comes from the size of the field during the final round.
The large numbers of players in each tournament allows us to examine whether the use of
competition-specific player fixed effects utilized in previous studies is appropriate by
implementing falsification tests.
Our results indicate that there are adverse effects of pressure on performance for highly
ranked competitors. However, unlike previous studies in other sports, we find that the decline
in performance is primarily for the player that holds the overall lead in the tournament.
Performance is estimated to be strongest for the competitor that is ranked second, and is
pursuing the leader. The dynamic nature of the tournament may make it difficult for those not
ranked near the top of the leaderboard to determine their exact standing, thereby lessening
the impact of any associated psychological pressure. This is consistent with the idea in Genakos
and Pagliero (2012) that providing less interim ranking information may improve the
performance of competitors. Also, our results for the top spots in a tournament may be due to
the large stakes in golf which do not exist in weightlifting or diving, as their monetary rewards
for first place range from zero to thousands of dollars. For the golf tournaments included in this
study, the median first place prize is just over $1 million.3
There are several potential explanations for why players ranked first in the tournament
might underperform. The pressure of maintaining the lead is one possibility. Another is that
players may choose to play a hole differently when in the lead to avoid the risk of an extremely
poor shot. We explore these theories by examining the relationship between rank and
performance at different points in the competition, as well as based on how closely contested
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the lead is. We find underperformance due to psychological pressure to be the most likely
explanation. Finally, we create a measure to rank individual players based on how adversely
their performance is impacted by the pressure of leading the tournament.
2. Data and Descriptive Statistics
The data used in this study come from the PGA Tour ShotLink Data program.4 Each observation
represents a specific hole for each player participating in the tournament. Our sample includes
all tournaments that consist of four rounds of play for which ShotLink data is available during
the 2003 to 2015 seasons.5 We focus on the 18 holes played during the final round of each
tournament. The final round is when players are likely to be most concerned about their
relative standing, as well as when they are likely to face the most psychological pressure. Our
final overall sample includes 597,582 player-hole observations.
The key variable in this analysis is a player’s rank at the time he begins a given hole. In
order to construct this variable, we use data on the timing of each shot, as well as the rankings
at the beginning of the final round. Using this information, we create a minute-by-minute
leaderboard, where each player’s ranking is determined by his score relative to the rest of the
playing field. The leaderboards created for the analysis match the way in which leaderboards
are reported in real-time during the actual playing of the golf tournament. For example,
suppose Jordan Spieth has completed four holes in the final round, and is 10 strokes under par
for the tournament. At the same time, Charley Hoffman is also 10 under par after playing eight
holes. These two players would have the same interim ranking in our dataset as each starts his
next hole. The number of holes remaining does not directly factor into our determination of the
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dynamic ranking variable. The rankings are based on time, not each player’s standing after all
players have completed the same hole on the golf course. To be precise, the rankings are
calculated for each minute of the tournament. A player’s ranking at the start of a hole is
determined by their rank in the minute before he takes his tee shot for the hole. For example, if
Jordan Spieth strikes his tee shot on the 5th hole at 10:42 a.m., his rank to start the hole will be
determined by his relative standing as calculated in the leaderboard for 10:41 a.m. We prefer to
use the rank at the start of the hole, as the ranking at this point is most likely to impact
performance on the hole in question.6 Tournaments in our sample have between 56 and 89
players in the final round, with the average number per tournament at 74.4 players.7
The other key piece of information in our analysis is a measure of a player’s
performance on a given hole. The primary measure we use to characterize the outcome is a
player’s score, relative to par, for each hole. We refer to this variable as RTP. If a player
achieves a score equal to par for a hole, his RTP takes a value of 0. If a player achieves a birdie
(scores one stroke below par) on a hole, RTP will take a value of -1. Thus, negative values for
RTP represent a higher level of performance. While we will generally focus on RTP as our
measure of performance, we also utilize the likelihood of specific scoring outcomes (birdie, par,
and bogey) in the course of our analysis.
Table 1 displays summary information and provides a preliminary glimpse at the key
relationship in our study. The overall average RTP score for all players during the final round is
essentially 0. When we look at the relationship between rank at the start of a hole and
performance on that hole, some minor differences are evident. Most notably, those that begin
a hole more highly ranked (in the top 10) perform better than those that begin with a lower
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rank. This is not surprising, those with a higher rank have been playing better in the
tournament. The more interesting questions include, for example, whether or not being in the
top position causes a player to perform differently than he otherwise would have. The
summary statistics show that the average performance for players ranked first is slightly worse
than that of players ranked second through fifth, although this difference is not statistically
significant.
While the summary statistics do not indicate a strong relationship between a player’s
rank and his performance, there are two key pieces of information missing in this simple
analysis. The first is the difficulty of each specific hole. While the par value gives general
information about a hole, the average RTP score for the field can vary greatly between two
distinct holes with the same par value. The second factor is information about the player
himself. Players that tend to be highly ranked may have different characteristics than those that
tend to be lower ranked. The question we have in mind is whether a given individual’s
performance will change based on differences in his ranking.
As an illustrative example of this question, Figures 1 and 2 display relative performance
at different rankings for two players: Tiger Woods and Dustin Johnson, respectively. In each
case, we compare the performance when the player starts the hole ranked first overall to
performance on holes that the player begins with a ranking of second through tenth.8 For Tiger
Woods, we see there is not much difference in his performance when ranked first. In each case,
his RTP score shows strong performance, and the likelihood of each specific outcome is virtually
the same. If anything, Woods’s already strong performance becomes slightly better when in the
lead. As seen in Figure 2, there is a very different relationship between rank and performance
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for Dustin Johnson. While his performance is strong when ranked in the top 10, it falls off
considerably when he is in first position during the final round. Birdies become less likely, while
bogeys and even double bogeys are much more likely when Johnson begins a hole with the
overall lead. In the more detailed analysis to follow, we examine more closely which of these
relationships is more typical among players overall while controlling for player-specific effects.
3. Empirical Methodology
The main goal of our analysis is to determine whether a player’s performance on a hole differs
based on his relative standing at the beginning of the hole. In order to analyze this relationship,
we estimate various permutations of the following general specification9:
(1) ihthitiiht RankgRTP +++= − ),( 11,
As discussed in the previous section, RTP is the score of player i, relative to par on hole h, at
minute t. Note that h here refers to each unique hole in each unique tournament, with 8,064
unique holes in the sample overall. Each regression we estimate includes hole fixed effects. This
is done to control for the general difficulty of each hole. We also include some form of
individual fixed effects in each specification, to control for characteristics of each player. We
estimate regressions with various levels of individual fixed effects (player, player-par value,
player-tournament), and discuss the differences in the next section of the paper. Throughout
our analysis, we cluster standard errors at the hole level to account for the likelihood that
variation in scoring may differ for each unique hole.10
We estimate various functional forms of the relationship between rank and
performance. To begin with, we use rank as a continuous variable and estimate both linear and
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quadratic models. Next, we estimate various specifications in which we use dummy variables to
further explore the relationship. It is possible that there is not a linear relationship between
rank and performance across the spectrum of rankings, but that a relationship still exists.
Perhaps there are certain specific rankings, such as being in the lead, that tend to influence a
player’s performance one way or the other. Aside from looking at different forms of the
relationship between rank and performance, we also utilize various sample restrictions in
different specifications. In certain cases, we compare only players within a certain range of the
standings, such as the top 10. We also look at how the relationship might change as the end of
the tournament approaches by restricting the sample to certain holes within the final round.
The nature of our dependent variable means that we could consider using other models,
such as a multinomial logit, to generate results. However, the large number of observations,
and our belief that both hole and individual-level fixed effects are essential to proper
identification, means we need to rely on linear methods for computational reasons. In some of
the analysis that follows, we also utilize linear probability models and examine how the
likelihood of a specific outcome (such as a birdie) might differ based on the ranking of the
player at the start of the hole.
4. Results
To begin our look into the relationship between rank and performance, we explore a simple
linear model. The results displayed in the first column of Table 2 come from a specification that
includes hole and player fixed effects, and includes all players from the final round of the
tournament. While the estimated coefficient on rank is highly statistically significant, there is,
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practically speaking, no meaningful relationship between performance and rank. The
specification in Column 3 utilizes player-par value fixed effects. Stone and Arkes (2016) suggest
that, since players differ in their ability to play long and short holes, the introduction of these
fixed effects can improve the model. This yields results similar to those using player fixed
effects. We utilize player-tournament fixed effects to generate results displayed in Column 5 of
Table 2. These are specific to each individual competition, and are similar to the preferred
specifications used in Genakos and Pagliero (2012) and Genakos et al. (2015 ). In this case, we
look at how a player’s rank varying within a specific tournament affects his performance. For
this specification, the coefficient on rank is negative and can be considered both statistically
and practically significant. The results indicate that when a player starts a hole more highly-
ranked, that player’s performance is expected to decline. As an example, if a player starts a hole
ranked eleventh overall, he can be expected to shoot almost a quarter of a stroke better
(relative to par) than when he starts a hole in the same tournament while ranked first.
However, we have serious concern over the use of player-tournament fixed effects, an issue
which we expand upon in the analyses presented in Tables 3 and 4.
As mentioned in the previous section, it seems unlikely that there is a strictly linear
relationship between rank and performance. To begin exploring this further, we introduce
quadratic specifications in Columns 2, 4, and 6 of Table 2. We again utilize the different levels of
individual fixed effects as indicated in the table. Both the player and player-par value fixed
effect specifications yield results showing that high rankings may lead to underperformance
near the top of the leaderboard, but this effect diminishes as we move further down the
rankings. As in the linear models, the magnitude of the estimated impact is very small in these
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results. For the player-tournament fixed effects specification, the quadratic term is not
significant in practical or statistical terms.
Finally, there are endogeneity concerns in estimating the relationship between
performance and rank as pointed out by Genakos and Pagliero (2012). For example, if a player
performed well in the previous round of the tournament, then that player’s rank in the current
round will be better, given that the player is already performing well, they are likely to perform
well again. This bias may cause rank to appear to have an impact on performance when in fact,
it does not. To address this concern, we implement an instrumental variable model in Column 7
of Table 2 following the methodology outlined in Genakos and Pagliero (2012). We find a result
very similar to Genakos and Pagliero (2012), the IV estimate dampens the impact of rank on
performance.
One issue with the analysis performed in Table 2 may be that we are including the entire
field of players in our sample. It is unlikely, for example that any difference in performance that
comes from being ranked first as opposed to second is the same as that from being ranked 31st
as opposed to 32nd. It is also possible that there are certain key positions that tend to influence
performance more than others. For example, perhaps being ranked first causes an individual to
feel pressure and underperform, while being ranked second does not induce the same effect.
For certain rankings that are not near the top, it is likely the player is unaware of his exact
positioning, and so there may be little reason for interim ranking to impact performance. In
Table 3 we implement a dummy specification replicating what is done in Genakos and Pagliero
(2012) for weightlifting and Genakos et al. (2015) for diving, so that we have a point of
comparison to their methods and results In this analysis, we restrict the sample to players that
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begin a hole ranked in the top 10 for the tournament. The reference category for our set of
dummies is the player(s) ranked first overall. For all three fixed effect specifications, the results
indicate that players perform better when ranked second through tenth as compared to when
they are ranked first. For the player and player-par value specifications, the player ranked
second is estimated to have the strongest performance, with the difference dwindling until it
picks up at ranks 9 and 10.
For the player-tournament fixed effects model, the performance is estimated to
improve linearly as we move down the leaderboard. This pattern is similar to the results in both
Genakos and Pagliero (2012) and Genakos et al. (2015).11 The magnitudes of the coefficients in
Column 3 are quite large. A player is estimated to shoot around 0.7 strokes better per hole
when ranked tenth as opposed to when he is ranked first.
While the results including player-tournament effects are similar to those in past
studies, they seem to be almost unbelievably large in magnitude. Our concern is that restricting
the analysis to within a specific tournament for a given player creates a severe problem causing
a spurious relationship between performance and rank. Consider a hypothetical player’s
performance during the final round of a tournament. This player’s start-of-hole ranking during
this round varies between a maximum value of 10 and a minimum value of 1. If we analyze the
relationship between the player’s rank and performance in this tournament alone, it must be
the case that he performs relatively worse when he is highly ranked and relatively better when
he is lower ranked. When the player is at his lowest rank during the round (10), he must
perform well (relative to the field), else this would not be his lowest rank. In this way, utilizing
player-tournament fixed effects could cause the results to be negatively biased, meaning that
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as rank declines performance appears to improve, when in fact there is no relationship
between these variables. Thus, it seems likely that these competition-specific effects are driving
the results, rather than helping us identify the true relationship between rank and
performance.
To support this claim, Figure 3 shows the distribution of correlations between rank and
score relative to par at the player-tournament level. That is, for each of the 32,955 player-
tournament combinations observed, we examine how performance and rank are correlated for
each player across the 18 holes in the final round of a specific tournament. As previously
theorized, there is a prevalent negative relationship between rank and performance at this
level, with a mean correlation of -0.3. This result is surprising considering the fact that we have
given no consideration to the specific ranking examined. It is also surprising as one would
initially expect player performance to be stronger when the player in more highly ranked, as
such a ranking should indicate the player is performing well. Overall, the nature of these player-
tournament correlations support our concerns about the inclusions of competition-specific
fixed effects.
As a way to further test whether the results in Column 3 of Table 3 are reliable, we
conduct a falsification test using a different subsample of rankings. In the results displayed in
Table 4, we focus on the sample of players that began playing a hole ranked between 41st and
50th in the tournament. As before, we implement a set of dummy variables for each rank, and
omit the highest ranked player in the sample (rank 41). Within this group, we believe there is
little reason to suspect that differences in rank at the start of a hole should impact the
performance of the player on that hole. There is no added pressure that comes from being
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ranked 41st as opposed to 42nd. Furthermore, it is unlikely that a player in this range would even
know his specific ranking at the start of each hole. For a player near the top of the leaderboard,
it is much more likely he is aware of his relative standing in the field throughout the final round.
In the first two columns of Table 4, we find the expected results. Both the player and player-par
value fixed effect specifications show no significant differences in performance for a player
when his rank varies between 41 and 50. The results for the player-tournament fixed effects,
however, follow the same suspicious pattern as those in Table 3. Performance improves linearly
as a player’s ranking drops.12 Based on these findings, we believe the use of player-tournament
fixed effects are not appropriate in this context. While, player-tournament fixed effects should
be a good control for player ability in a more precise time frame, the bias from their use is very
troublesome. In subsequent analyses, we focus on specifications using player-par value fixed
effects.13
While the use of player-par fixed effects allows for the possibility that unobserved
heterogeneity in player ability from tournament to tournament impacts the results, our
particular golf setting and sample are less likely to experience this problem than other studies
and our preferred method is less likely to experience bias than that in Genakos and Pagliero
(2012) and Genakos et al. (2015). Unobserved heterogeneity is less likely an issue as our study
only uses data from the final (4th) round of a golf tournament. Tournaments typically start out
with approximately 150 golfers and a cut is made to the top 70 and ties after the first 2 rounds.
Thus, the players in our dataset are those that played well in the initial rounds. Even if a
player’s performance varies from tournament to tournament, we are only examining the
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situations in which players are already in a high-performance state. If a player performed poorly
in a tournament, he would have missed the cut and would not be included in our dataset.
Additionally, the final round of each golf tournament is a time when players are likely to
be aware of (and concerned about) their relative position on the leaderboard. As the end of the
tournament nears, a player’s ranking becomes more clearly associated with his monetary
return, so he may be more likely to feel the pressure of maintaining or overtaking a certain
position. If this is the case, perhaps the relationship between rank and performance will change
within the final round as the end of the tournament approaches. In our next set of results, we
look at the entire field of players, but restrict the sample to various holes within the final round
of each tournament. For these specifications, we restrict the sample to those tournaments in
which all players play the holes in the same sequence. In some tournaments (about 20% of
those in our sample), some playing groups will start on the tenth hole while others start on the
first hole. Since we want to examine how the ranking might matter at different stages of the
round for the whole sample, it is important that all players play in the same order for these
specifications.14 We utilize player-par value fixed effects and use a flexible functional form for
the ranking dummies. Ranks 1 through 5 are included individually, while 6 to 10 and 11 to 20
are grouped together. The reference category are those ranked 21 and worse.
The results in Table 5 indicate that the relationship between rank and performance is
observed only for those at the top of the leaderboard. When we look at the final round overall,
the performance is strongest for those that start a hole ranked second in the field. Others that
are close to the top and chasing the lead also perform better than those ranked lower. For the
final round overall, being ranked first caused performance to significantly decline. The results in
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Columns 4 and 5 show the performances of those ranked first and second are particularly
different as the end of the tournament nears. For the last 6 holes, and especially the last 3,
being ranked first causes a player’s RTP score to increase, meaning his performance declines.
The second-ranked player, during the same part of the tournament, is performing at a
significantly higher level. The estimated coefficient for rank 3 on the last 3 holes is also
relatively large in magnitude, but is not estimated to be significant at any standard level (p-
value = 0.18).
To reiterate the earlier discussion, the fact that ranking only affects performance for the
few highest rankings is not surprising. Because of the simultaneous nature of the tournament, it
may be difficult for those in other positions to determine their specific position at a given point
in time. The very top of the leaderboard is also where we expect players to be most influenced
by psychological pressure. While there may be a substantial monetary difference between
finishing fourth and fifth in the tournament, this may not have the same impact on a player’s
mentality as the difference between winning the tournament and finishing in second place.
Prize money and rewards are quite large for the top two spots. Median payouts for the
tournaments included in our sample are $1,035,000 to first and $621,000 to second. Beyond
prize money, a tournament win includes an exemption on the PGA Tour for the next two years,
which can make a significant impact on a player’s career and expected lifetime earnings. Wins
also often exempt players into the major tournaments, such as The Masters. Stakes of this
magnitude may have an impact on behavior and performance for the very top spots that differs
from those observed in the less lucrative sports of weightlifting (Genakos and Pagliero 2012)
and diving (Genakos et al. 2015). Perhaps, a player in first thinks about the impending rewards
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associated with a win, which diverts his focus from the task at hand. These results indicate
players perform significantly better when they are chasing the lead as opposed to trying to hold
a lead.
One possible interpretation of the results displayed in Table 5 is that players ranked first
are choking under the pressure of leading the tournament. As a result, their performance is
worse than those that are trying to catch them. Another interpretation, however, is that the
player ranked first can afford to play more conservatively because he has the lead. Perhaps the
player is choosing not to take risky shots (that have a possibility of an excellent score) because
he simply needs to hold off the competition. In this case, especially if the player holds a
significant lead over the field, it may be incorrect to infer that being ranked first causes
underperformance. Fortunately, the detailed nature of the data allows us to explore this issue
directly.15
In Table 6, we focus our analysis on the final 3 holes of each tournament, and separate
the sample based on the size of the lead held by the player ranked first. In Columns 1 and 2, we
compare the impact of rank on RTP for cases in which rank 1 holds a lead of at least 2 strokes to
cases when the lead is 1 or less. If the conservative play of the first-ranked player is driving the
result, we expect to see a positive coefficient on rank 1 in Column 1. This strategy is most likely
to be implemented when a player holds a sizable lead, and is unlikely to be caught unless he
makes a big mistake. Instead we see that the significant results for ranks 1 and 2 are being
driven by cases where the competition is very close.
Here, we speculate on psychological factors which may lead to these results. When
there is a large gap between first and second, the player ranked first may not feel as much
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pressure as compared to when he leads by a single stroke. The player in the lead may not feel
as if any single mistake is disastrous, he can bogey a hole and still have the lead. This cushion
may take the pressure off and allow the first-ranked player to perform comfortably. At the
same time, the large gap between first and second may mean that the performance of the
second-ranked player is not necessarily stronger. Performing very well and accomplishing a
birdie for the hole will not be enough to overtake the leader, so the player may perceive that it
is as if the mountain is too high to climb. However, when the second-ranked player is within
one shot of first, it is a very different story. The players in each position may now frame the
situation in dramatically different ways. The first-ranked player has nothing to gain in terms of
additional prizes, while second would gain substantially by moving into first. The first-ranked
player may feel additional psychological pressure with the one shot lead, as any mistake could
lead to a drop in position, and thus he underperforms. The second-ranked player may feel that
first is now attainable, with a birdie they could likely move into a tie for first.
To explore the issue further, we look at the likelihood of specific outcomes, by ranking,
in cases where the competition is close (lead of one shot or less). We estimate separate linear
probability models for the outcomes of birdie, par, and bogey for the final three holes of each
tournament. The results are displayed in Columns 3, 4, and 5 of Table 6. We find that being
ranked first makes it less likely that a player will achieve a birdie and more likely that he will
achieve a score of par or bogey. The probability of scoring a birdie is estimated to be almost 5
percentage points lower for a player when he begins the hole ranked first. While the results for
birdie and par could be consistent with either ‘choking’ or ‘playing it safe’, the increased
likelihood of a bogey indicates that the more likely story is that first-ranked players are choking
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in high pressure situations. For second-ranked players, one might suspect that they would take
on more risk in an effort to birdie holes and reach first. When behind, a player may attempt to
hit an approach shot directly at a dangerous flagstick location in an attempt to make a birdie,
but slight misses aimed at dangerous flags often lead to bogeys. Results show the relatively
strong performance for second-ranked players is primarily due to a decrease in the likelihood of
a bogey. This result indicates that second is not merely taking on more risk, but rather
improving their overall play, even though they do not have a statistically greater chance of
making a birdie.
The pervasive underperformance of players ranked first begs the question, are there
any factors that might impact the level of performance specifically for first? To explore this, we
use information on player age and career earnings. Specifically, we interact these player
characteristics with the dummy variable for being ranked first at the start of the hole. The
results of this analysis are presented in Table 7. Column 1 is our baseline result and indicates
that players ranked first are expected to perform worse, all else equal, by 0.0126 strokes on a
given hole. The second column presents the results when career earnings are factored in, and
shows that the magnitude of the “rank 1” effect declines as earnings increase. In this case,
career earnings can also be thought of as a measure of experience, with the results showing
less experienced players are more affected by the pressure of being in first. Column 3 shows a
similar pattern, as a player ages they tend to perform better when in first, relative to younger
players. This result for age is in line the findings of a study by Fried and Tauer (2011) examining
performance under pressure in golf as it relates to age. They find that players perform better
under pressure as they age, but only up until a certain point, approximately age 40.
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5. Individual Player Rankings
The results of our empirical analysis indicate that there is a relationship between a player’s rank
in a golf tournament and that player’s performance. In particular, performance declines on
average for holes in which the individual begins play ranked first overall. However, as seen in
Figures 1 and 2, it is not expected that each individual will have the same relationship between
relative standing and level of play. Given the frequency of tournaments and the number of
years for which ShotLink data has been available, there is a large amount of information for a
sizable group of players. In what follows, we estimate how certain individuals perform when
facing the high-pressure situation of being ranked first overall during the final round of a golf
tournament. These estimates provide some insight as to which golfers tend to be the most
‘clutch’ and which have a tendency to wilt under the pressure.
Our sample for this analysis focuses, as before, on play during the final round of each
golf tournament. The idea is to compare how a given player performs on holes which he begins
ranked first overall to his performance on holes he begins with any other ranking.16 As such, we
limit the sample of players to ensure we have sufficient observations to determine an overall
pattern. We choose to limit the sample to the 50 players that are observed playing the most
holes ranked first overall. For each of these players, we observe at least 60 holes that were
played while leading the tournament. Tiger Woods played the most holes while ranked first in
the final round of the observed tournaments (412).
Table 8 presents the estimates from individual regressions, with the coefficients on the
rank 1 dummy variable representing the difference between performance while in first and
21
performance at all other ranks for that player during the final round of tournaments throughout
their career. Players with a negative coefficient on the rank 1 variable could be considered
clutch players. That is, the performance of each of these players improves when they are
leading the field. Players with a positive value could be considered to be choking (to varying
degrees) under the pressure of holding the lead. The top 50 players are split almost evenly
between the two categories. Davis Love III is estimated to be the player whose performance
improves the most while in the lead, while Dustin Johnson’s performance decreases by the
largest margin.
There are a few caveats that go along with this analysis. First, this measure may
understate how clutch a certain player is if that player’s performance in the final round always
tends to be strong. For example, Tiger Woods won many tournaments over our sample period,
but does not have a substantially better performance when ranked first as compared to other
ranks. This result is likely due to the fact that he is the strongest player overall throughout the
final round, thus his relative performance is not shown to have increased as dramatically when
leading. Another issue is that we are comparing players at different stages of their careers.
Patrick Reed, for example, is just 25 years old in the most recent tournament in our sample,
while Tiger Woods is observed between the ages of 27 and 39. However, the individual rankings
do correspond to some of our expectations. For example, a search of news items relating to
Dustin Johnson yields articles such as: “Dustin Johnson's U.S. Open choke was one of the worst
in golf history” (Chase 2015) and “Dustin Johnson is good enough to keep choking away majors”
(Cannizzaro 2016). Fortunately for Dustin, he appears to have rid himself of these choking
22
demons. In 2016, the year after the end of our sample period, he won 3 tournaments (including
a major) and walked away as the PGA TOUR player of the year.
6. Conclusions
Sporting events provide a practical opportunity to examine many economic theories, including
how individuals respond to the incentives created by a tournament setting. In this study, we
examine golf tournaments as another arena to test the relationship between interim ranking
and performance found in weightlifting (Genakos and Pagliero 2012) and diving (Genakos et al.
2015). Like these studies, we find rank impacts performance, however we find the nature of the
relationship to be different in this case. When we restrict our analysis to those ranked in the
top 10 of the tournament, for example, we find that being ranked first leads to
underperformance, while being ranked second leads to the highest levels of performance.
Unlike the studies in other sports, we do not find that individual performance continues to
improve as players move further down the leaderboard.
There are several reasons why the results in this study differ from similar studies in
other sports. One is the modeling specification that we utilize. While player-competition level
fixed effects are appealing in the sense that they may control for unobserved heterogeneity,
they also appear to cause a severe endogeneity problem that leads to failing a simple
falsification test. It should be noted that our results do follow those in previous studies when
this problematic specification is utilized.
In addition to differences in modeling specifications, there are also elements of the
competitions themselves that might lead to different results. One is the magnitude of the
23
stakes of the competition, as the winner of a golf tournament often receives more than $1
million. Another big difference involves the decisions of players within the different
tournaments. In weightlifting, for example, players announce what weight they will attempt,
and not all players attempt the same amount. In diving, each individual must choose the
specific set of dives he or she will perform. Golf provides a unique setting because each player
is attempting a task with the same difficulty in each stage of the competition. In this setting, a
stage of the competition is defined as each specific hole on the course during the final round of
the tournament. The fact that we have many stages observed is another reason why our results
differ. In golf, players are at different stages of the competition simultaneously, and it may be
difficult for those not near the very top of the rankings to know their exact position at each
point in time.
The fact that our significant results are concentrated at the very top of the rankings also
leads to the question of what might be causing those that are leading to underperform.
Certainly, a part of this result comes from the psychological pressure of trying to hold off the
competition. This theory aligns with our results indicating that the underperformance occurs
most notably as the final round of the tournament comes to a close. Another part of this
finding, however, could be due to a strategic decision to not take risks and ‘play it safe’. This
strategy would seem to be most likely when a player has a larger lead. We find that the
underperformance of those in the lead actually occurs in cases where the competition is very
close, and thus our result cannot be merely explained by first ‘playing it safe’.
Due to the amount of data available we are also able to examine heterogeneity in the
impact of pressure on performance. Specifically, we find that the degree of underperformance
24
while ranked first is significantly lessened as a player ages and/or amasses greater career
earnings. Finally, we collect observations for a sample of prominent players and utilize this
information to create a ranking of individuals based on their relative performance when ranked
first in the tournament.
25
Notes
1. A more thorough review of the ‘choking’ phenomenon in sports can be found in Beilock and
Gray (2007) and Hill et al. (2013).
2. See Balsdon (2013), Fearing et al. (2011), Hickman and Metz (2015), Hickman and Metz
(2018), Pope and Schweitzer (2011), and Stone and Arkes (2016) as recent examples.
3. Given the correlation between player ranking and monetary prizes, one may consider using
monetary stakes as a way to quantify the pressure a player faces when completing a task. We
choose to focus on rank here for a few reasons. First, doing so allows us to directly compare
with the findings of studies by Genakos and Pagliero (2012) and Genakos et al. (2015).
Secondly, using rank allows for an examination of non-linear impacts of pressure at certain
thresholds. For example, while moving from 4th place to 5th place and moving from 5th place to
6th place would each result in a similar monetary loss, perhaps the possibility of finishing in the
“top 5” for the tournament would cause very different levels of psychological pressure for the
typical player. Finally, a study by Hickman and Metz (2015) examines the impact of monetary
stakes on player performance on the final hole of golf tournaments.
4. Sponsored by CDW. There are several other studies which have used ShotLink data. Brief
summaries of the papers can be found at
http://www.pgatour.com/stats/academicdata/institutions.html. One of the most notable
contributions is the Pope and Schweitzer (2011) study of loss aversion.
5. The dataset contains PGA Tour events. The data does not generally include the major
championships (Masters, US Open, The Open, and PGA Championship), with a few exceptions.
ShotLink data is not typically recorded for the majors and other non-PGA Tour events. We also
exclude events with 4th rounds lasting more than one day, abnormal scoring (modified
stableford), and more than the typical 4 rounds (e.g. Bob Hope Classic) which occur a few times
in a season.
6. Players have the opportunity to know their rank at the completion of a hole as most holes
have an electronic scoreboard at the green which updates in real time. While we have no way
of knowing conclusively what rank information a player does or does not have, Hickman and
Metz (2015) cite anecdotal evidence from players indicating that they are aware of their
standing in the tournament. Those players who might ignore a leaderboard only do it because
they are feeling the pressure and knowledge of an exact lead or deficit that may lead to
increased pressure and the possibility of underperformance. Most evidence would suggest
players know where they stand in a tournament, particularly if they are near the top of the
leaderboard. Additionally, the results that follow are in line with the thought that players on
the leaderboard know where they stand.
26
7. There are a few tournaments each year (Tournament of Champions and TOUR
Championship) that only include approximately 30 players during the final round. These
tournaments were excluded from the analysis to maintain consistency of tournament field size.
Inclusion of these tournaments does not change the results of our analysis in any meaningful
way.
8. We choose to compare rank 1 to ranks 2 to 10 for these figures so that we are comparing at
times when the player is generally performing well overall. The figures look similar if we
compare rank 1 to other sets (ranks 2 to 5, all ranks greater than 1, etc.).
9. This equation is similar to those specified in Genakos and Pagliero (2012) and Genakos et al.
(2015).
10. We follow Hickman and Metz (2018) in implementing clustering at the hole level. However,
a case could also be made that clustering at the athlete level, as in Genakos and Pagliero (2012)
may be appropriate. While we report the results with errors clustered at the hole level, we did
run the analysis in each table of results with the alternate clustering strategy. In a few cases the
p-value on key coefficients changed from being significant at 5% (**) to being significant at 1%
(***) or 10% (***). In no cases did the coefficients go from being significant at one of the above
thresholds to insignificant, or vice versa. The alternate tables of results are available from the
authors upon request.
11. In results not displayed, we estimated regressions that included the top 15 and top 20
ranked players. The patterns in the results were similar to those seen for the top 10.
12. In results not displayed, but available upon request, we ran the same test for rankings 31 to
40 and 51 to 60. In each case, the player and player-par specifications indicate no significance,
while the player-tournament effects yield significant results that follow the same linear pattern.
13. We also estimated specifications involving player-year effects and player-year-par value
effects. These specifications are not reported, as they also failed the falsification tests
presented in Table 4.
14. This is not an issue when we look at the top 10 sample, for instance, because in cases where
some players start on the tenth hole, it will be the lower-ranked portion of the field that plays
in a different order. Those ranked in the top 10 will almost always play in the same order in the
final round.
15. One way to examine whether strategy is driving our results is to test whether the impact of
being ranked first changes on par 5 holes as compared to holes with par values of 3 and 4.
27
There is more opportunity for player strategy to differ in par 5 holes, so if the impact of being
ranked first differs in a significant way, it may indicate that it is not psychological pressure
driving our findings. We implement a model in which a dummy for holes that are par 5 is
interacted with each of the ranking dummies. We find there is no significant difference on the
impact of being ranking first on par 5 holes (p-value on interaction term is 0.59). The full results
of this specification are available from the authors upon request.
16. For the analysis that follows we compare play when ranked first to play on all other holes in
order to have as many observations as possible. We also performed the analysis using other
comparison sets, such as holes played while ranked in the top 10 (2 through 10) or while ranked
in the top 25. The results were very similar in these cases. For example, the correlation
between the individual rankings when we use the comparison group “all holes with ranking >1”
and when we use the comparison group “all holes with ranking 2-10” is 0.95.
28
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31
32
0.5
11.5
2
-1 -.5 0 .5 1
Notes: histogram displays correlations between rank at start of hole and score (relative to par) on holefor 32,955 unique player-tournament combinations. Dataset includes 244 such combinations with novariation in either rank or rtp score.
Figure 3: Correlations between Rank and RTP Score(Player-Tournament Level)
33
RTP Score Birdie Par Bogey Double Bogey
All Ranks -0.0016 19.2% 62.8% 15.4% 1.8%
Rank 1 -0.0153 18.8% 64.2% 15.0% 1.3%
Ranks 2 to 5 -0.0228 19.6% 63.6% 14.7% 1.5%
Ranks 6 to 10 -0.0151 19.2% 63.7% 14.9% 1.6%
Rank 11 and Worse 0.0015 19.2% 62.6% 15.6% 1.9%
Observations 597,582
Players 960
Tournaments 448
Holes 8,064
Years 2003 to 2015
Table 1: Summary Statistics
Notes : Each observation represents outcome of a hole for a given player. Sample includes play from fina l (fourth) round of each
tournament. RTP measures score relative to par for a hole. For this variable, a va lue of 0 indicates a player achieved par for the
hole, a va lue of -1 indicates a birdie, a va lue of +1 indicates a bogey, and so on. Scores of better than birdie and worse than
double bogey, combined, make up less than 1 percent of scores .
34
(1) (2) (3) (4) (5) (6) (7)
Rank at Start of Hole 0.0004*** -0.0006*** 0.0004*** -0.0006*** -0.0241*** -0.0237*** -0.0191
[0.0000] [0.0002] [0.0000] [0.0002] [0.0002] [0.0004] [0.0166]
(Rank at Start of Hole)2 X 10-2 0.0013*** 0.0013*** -0.0006 0.0001
[0.0002] [0.0002] [0.0005] [0.0002]
Hole Fixed Effects X X X X X X X
Player Fixed Effects X X
Player-Par Fixed Effects X X
Player-Tournament Fixed Effects X X
Instrumental Variable X
Observations 597,582 597,582 597,582 597,582 597,582 597,582 597,582
Adjusted R-squared 0.112 0.112 0.113 0.113 0.163 0.163
Table 2: Rank and Performance for All Players
Notes : Results from estimating regress ion models where dependent variable i s score on hole relative to par (RTP). For this variable, a va lue of 0 indicates a
player achieved par for the hole, a va lue of -1 indicates a birdie, a va lue of +1 indicates a bogey, and so on. Standard errors , clustered at the hole level ,
displayed in brackets . ***indicates s igni ficance at 1% level .
35
(1) (2) (3)
Rank 2 -0.0386*** -0.0378*** -0.1593***
[0.0092] [0.0093] [0.0126]
Rank 3 -0.0250*** -0.0257*** -0.2428***
[0.0093] [0.0093] [0.0133]
Rank 4 -0.0273*** -0.0269*** -0.3283***
[0.0095] [0.0095] [0.0140]
Rank 5 -0.0273*** -0.0262*** -0.3934***
[0.0094] [0.0094] [0.0144]
Rank 6 -0.0287*** -0.0277*** -0.4480***
[0.0096] [0.0097] [0.0148]
Rank 7 -0.0215** -0.0186* -0.5038***
[0.0095] [0.0095] [0.0150]
Rank 8 -0.0241** -0.0236** -0.5735***
[0.0097] [0.0097] [0.0153]
Rank 9 -0.0277*** -0.0247** -0.6381***
[0.0099] [0.0099] [0.0159]
Rank 10 -0.0343*** -0.0332*** -0.7015***
[0.0098] [0.0098] [0.0161]
Hole Fixed Effects X X X
Player Fixed Effects X
Player-Par Fixed Effects X
Player-Tournament Fixed Effects X
Observations 91,684 91,598 90,838
Adjusted R-squared 0.121 0.122 0.163
Table 3: Rank and Performance for Players in Top 10
Notes : Results from estimating regress ion models where dependent variable i s score on hole relative to par (RTP). For this variable, a
va lue of 0 indicates a player achieved par for the hole, a va lue of -1 indicates a bi rdie, a va lue of +1 indicates a bogey, and so on.
Standard errors , clustered at the hole level , displayed in brackets . Sample i s restricted to players ranked in top 10 for tournament at time
hole i s s tarted. Independent variables are dummies for rank at s tart of hole for each player. Omitted category i s rank 1. ***indicates
s igni ficance at 1% level , ** indicates s igni ficance at 5% level , * indicates s igni ficance at 10% level .
36
(1) (2) (3)
Rank 42 0.0051 0.0043 -0.0362***
[0.0109] [0.0110] [0.0118]
Rank 43 0.0084 0.0076 -0.0724***
[0.0112] [0.0112] [0.0124]
Rank 44 0.0002 0.0018 -0.1213***
[0.0112] [0.0113] [0.0126]
Rank 45 -0.0116 -0.0113 -0.1732***
[0.0112] [0.0113] [0.0125]
Rank 46 0.0093 0.0091 -0.1986***
[0.0113] [0.0113] [0.0127]
Rank 47 0.0146 0.0145 -0.2335***
[0.0112] [0.0112] [0.0129]
Rank 48 0.0116 0.0128 -0.2647***
[0.0112] [0.0113] [0.0131]
Rank 49 0.0099 0.0104 -0.3054***
[0.0114] [0.0115] [0.0132]
Rank 50 -0.0104 -0.0092 -0.3675***
[0.0113] [0.0114] [0.0137]
Hole Fixed Effects X X X
Player Fixed Effects X
Player-Par Fixed Effects X
Player-Tournament Fixed Effects X
Observations 81,249 81,075 79,380
Adjusted R-squared 0.113 0.113 0.166
Table 4: Rank and Performance for Players Ranked 41 to 50
Notes : Results from estimating regress ion models where dependent variable i s score on hole relative to par (RTP). For this variable, a
va lue of 0 indicates a player achieved par for the hole, a va lue of -1 indicates a bi rdie, a va lue of +1 indicates a bogey, and so on.
Standard errors , clustered at the hole level , displayed in brackets . Sample i s restricted to players ranked between 41 and 50 for
tournament at time hole i s s tarted. Independent variables are dummies for rank at s tart of hole for each player. Omitted category i s rank
41. ***indicates s igni ficance at 1% level .
37
(1) (2) (3) (4) (5)
All Holes Holes 1-6 Holes 7-12 Holes 13-18 Last 3 Holes
Rank 1 0.0139** 0.0117 0.0011 0.0269** 0.0434**
[0.0064] [0.0107] [0.0110] [0.0116] [0.0170]
Rank 2 -0.0172*** -0.0199* -0.0041 -0.0259** -0.0354**
[0.0066] [0.0111] [0.0118] [0.0118] [0.0167]
Rank 3 -0.0082 -0.0159 -0.0053 -0.0071 -0.0229
[0.0066] [0.0112] [0.0114] [0.0118] [0.0171]
Rank 4 -0.0076 -0.0006 -0.0162 -0.0087 -0.0037
[0.0071] [0.0121] [0.0117] [0.0130] [0.0176]
Rank 5 -0.0054 -0.0066 -0.0151 0.0035 0.0049
[0.0068] [0.0118] [0.0116] [0.0122] [0.0172]
Rank 6 to 10 -0.0026 -0.0000 -0.0028 -0.0076 0.0041
[0.0034] [0.0059] [0.0058] [0.0061] [0.0086]
Rank 11 to 20 -0.0002 0.0043 0.0016 -0.0069 -0.0050
[0.0026] [0.0046] [0.0045] [0.0047] [0.0068]
Hole Fixed Effects X X X X X
Player-Par Fixed Effects X X X X x
Observations 479,484 159,509 159,665 159,567 79,523
Adjusted R-squared 0.112 0.111 0.103 0.122 0.128
Table 5: Rank and Performance throughout Final Round
Notes : Results from estimating regress ion models where dependent variable i s score on hole relative to par (RTP). For
this variable, a va lue of 0 indicates a player achieved par for the hole, a va lue of -1 indicates a birdie, a va lue of +1
indicates a bogey, and so on. Standard errors , clustered at the hole level , displayed in brackets . Sample includes a l l
players in tournament. Sample only includes tournaments in which a l l players play holes in same sequence.
Independent variables are dummies for rank at s tart of hole for each player. Omitted category i s those ranked 21 or
worse. ***indicates s igni ficance at 1% level , ** indicates s igni ficance at 5% level , * indicates s igni ficance at 10% level .
38
(1) (2) (3) (4) (5)
Rank 1 lead >=2
RTP RTP Birdie Par Bogey
Rank 1 -0.0125 0.0639*** -0.0474*** 0.0268* 0.0253**
[0.0277] [0.0220] [0.0111] [0.0147] [0.0119]
Rank 2 -0.0109 -0.0636*** 0.0173 0.0218 -0.0323***
[0.0237] [0.0241] [0.0150] [0.0178] [0.0121]
Rank 3 -0.0123 -0.0319 0.0135 -0.0050 0.0031
[0.0245] [0.0248] [0.0131] [0.0165] [0.0140]
Rank 4 -0.0178 0.0025 0.0006 0.0030 0.0055
[0.0267] [0.0238] [0.0138] [0.0172] [0.0123]
Rank 5 -0.0172 0.0168 -0.0179 0.0178 0.0077
[0.0265] [0.0233] [0.0130] [0.0172] [0.0136]
Rank 6 to 10 -0.0023 0.0066 -0.0166** 0.0176** 0.0034
[0.0130] [0.0117] [0.0067] [0.0083] [0.0064]
Rank 11 to 20 -0.0005 -0.0093 -0.0083 0.0135** 0.0020
[0.0103] [0.0093] [0.0054] [0.0068] [0.0049]
Hole Fixed Effects X X X X X
Player-Par Fixed Effects X X X X X
Observations 36,372 42,830 42,830 42,830 42,830
Adjusted R-squared 0.126 0.131 0.120 0.043 0.045
Table 6: Rank and Performance on Last 3 Holes of Tournament
Rank 1 lead <=1
Notes : Results in columns 1 and 2 from estimating regress ion models where dependent variable i s score on hole relative to par (RTP).
For this variable, a va lue of 0 indicates a player achieved par for the hole, a va lue of -1 indicates a bi rdie, a va lue of +1 indicates a
bogey, and so on. Results in columns 3 to 5 from estimating l inear probabi l i ty models where dependent variable i s hole outcome
indicated. Standard errors , clustered at the hole level , displayed in brackets . Sample includes a l l players in tournament. Sample only
includes tournaments in which a l l players play holes in same sequence. Independent variables are dummies for rank at s tart of hole
for each player. Omitted category i s those ranked 21 or worse. ***indicates s igni ficance at 1% level , ** indicates s igni ficance at 5%
level , * indicates s igni ficance at 10% level .
39
(1) (2) (3)
Rank 1 0.0126** 0.0264*** 0.0957***
[0.0062] [0.0081] [0.0338]
Career Earnings (millions of $) 0.0010***
[0.0003]
Rank 1 * Career Earnings -0.0010***
[0.0004]
Player Age 0.0005
[0.0030]
Rank 1 * Player Age -0.0025**
[0.0010]
Hole Fixed Effects X X X
Player-Par Fixed Effects X X X
Observations 580,410 580,410 580,410
Adjusted R-squared 0.113 0.113 0.113
Table 7: Impact of Being Ranked First by Player Characteristics
Notes : Results from estimating regress ion models where dependent variable i s score on hole relative to par (RTP). For this variable, a
va lue of 0 indicates a player achieved par for the hole, a va lue of -1 indicates a birdie, a va lue of +1 indicates a bogey, and so on.
Standard errors , clustered at the hole level , displayed in brackets . Sample includes a l l players in tournament for which age and career
earnings information was avai lable. ***indicates s igni ficance at 1% level , ** indicates s igni ficance at 5% level .
40
Rank Name Rank 1 Std Error Holes Observed
1 Love III, Davis -0.2637*** (0.002) 2,502
2 Els, Ernie -0.1518** (0.021) 2,214
3 Slocum, Heath -0.1189 (0.145) 3,006
4 Singh, Vijay -0.1079** (0.010) 3,510
5 Horschel, Billy -0.1068 (0.250) 1,170
6 Snedeker, Brandt -0.1017 (0.173) 2,214
7 Walker, Jimmy -0.1002 (0.212) 2,160
8 Piercy, Scott -0.0966 (0.288) 1,692
9 Choi, K.J. -0.0797 (0.215) 2,916
10 Matteson, Troy -0.0689 (0.440) 2,106
11 Johnson, Zach -0.0654 (0.304) 3,294
12 Perry, Kenny -0.0641 (0.244) 2,214
13 Mickelson, Phil -0.0635 (0.135) 2,646
14 Stallings, Scott -0.0620 (0.507) 828
15 Day, Jason -0.0570 (0.417) 1,638
16 Kuchar, Matt -0.0501 (0.463) 2,952
17 Kim, Anthony -0.0497 (0.580) 1,062
18 Baddeley, Aaron -0.0321 (0.698) 2,790
19 Sabbatini, Rory -0.0303 (0.674) 3,006
20 Leonard, Justin -0.0271 (0.726) 3,024
21 Verplank, Scott -0.0246 (0.762) 2,232
22 Woods, Tiger -0.0171 (0.656) 1,800
23 Laird, Martin -0.0154 (0.844) 1,728
24 Pettersson, Carl -0.0122 (0.859) 3,780
25 Dufner, Jason -0.0081 (0.923) 2,034
26 Scott, Adam -0.0078 (0.897) 1,998
27 Haas, Bill 0.0018 (0.981) 2,682
28 Holmes, J.B. 0.0023 (0.978) 2,232
29 Reed, Patrick 0.0048 (0.958) 882
30 Simpson, Webb 0.0077 (0.908) 1,890
31 Furyk, Jim 0.0078 (0.855) 3,168
32 Stricker, Steve 0.0102 (0.858) 2,232
33 McIlroy, Rory 0.0133 (0.834) 972
34 Cink, Stewart 0.0248 (0.766) 3,186
35 Clark, Tim 0.0266 (0.761) 2,466
36 Crane, Ben 0.0421 (0.620) 2,844
37 Toms, David 0.0495 (0.459) 2,664
38 Weir, Mike 0.0497 (0.523) 1,818
39 Mahan, Hunter 0.0532 (0.514) 3,222
40 Watney, Nick 0.0567 (0.452) 2,790
41 Goosen, Retief 0.0656 (0.456) 2,196
42 Donald, Luke 0.0671 (0.364) 2,430
43 Palmer, Ryan 0.0757 (0.325) 2,844
44 Rollins, John 0.0900 (0.325) 2,970
45 Watson, Bubba 0.0946 (0.125) 2,106
46 Rose, Justin 0.0970 (0.119) 2,628
47 Garcia, Sergio 0.1064 (0.113) 2,214
48 O'Hair, Sean 0.1337 (0.121) 2,628
49 Howell III, Charles 0.1628** (0.033) 4,014
50 Johnson, Dustin 0.2042** (0.022) 1,584
Table 8: Individual Player Rankings - Relative Performance when Ranked First in Tournament
Notes : Results from estimating individual regress ion models (by player) where dependent variable i s score on hole
relative to par (RTP). For this variable, a va lue of 0 indicates a player achieved par for the hole, a va lue of -1 indicates a
bi rdie, a va lue of +1 indicates a bogey, and so on. Coefficient and s tandard error reported for indicator variable equal
to 1 i f the player began the hole ranked fi rs t in the field, and 0 otherwise. Regress ions a lso include variable for scoring
average on hole. Sample i s restricted to players with at least 60 holes played whi le ranked fi rs t overa l l . ***indicates
s igni ficance at 1% level , ** indicates s igni ficance at 5% level .
