ISOPAR: A Performance Analysis Project on theShotLink™ Database

Michael StöcklDepartment of Sport Science, University of Vienna, Vienna, Austria

michael.stoeckl@univie.ac.at

Peter LambSchool of Physical Education, Sport and Exercise Sciences, University of Otago, Dunedin, New Zealand

peter.lamb@otago.ac.nz

January 12, 2017Original version: June 27, 2011

Abstract

ISOPAR is a method for modelling playing characteristics of golf holes and allows the performanceof shots to be analyzed. The method is based on the ball locations provided by ShotLink™ and thesubsequent number of shots required to hole out from each respective location. ISOPAR values arecalculated which represent the number of shots the field would require to hole out. These ISOPARvalues can, a) be visualized on an ISOPAR map and, b) lead to a new performance indicator calledShot Quality, which is the difference between the ISOPAR values of the starting position and finishingposition, respectively. The Shot Quality score can also be used to determine how many shots were savedper shot, or per type of shot, with respect to the performance of the field.

1 Introduction

In performance analysis, characteristics of a process which describe how an outcome was achieved are used

to assess the performance itself (Hughes & Bartlett, 2002) and are referred to as performance indicators.

Classical performance analysis techniques in golf have focused on classes of golf shots (James, 2007),

such as driving distance, approach shot accuracy and putting average (James & Rees, 2008). Measures

like greens in regulation, average putts per green and driving distance are intended to describe players’

abilities to perform certain types of shots, yet these abilities are not actually assessed. For example, the

beginning position of a putt is the result of the approach shot to the green. So a good putting average

describes not only putting ability but also all previous shots on the hole – it is a composite measure.

Therefore, if independent measures for different types of golf shots existed then strengths and weaknesses

of a player’s game could be assessed (Ketzscher & Ringrose, 2002). Currently, golf performance analysis

lacks performance indicators which reflect the influence one shot has on the next. For example, on each

hole there is a chain of events which starts on the tee and ends once the ball is holed. Each shot represents

an event and the final position of shot n determines the starting position for shot n+1. A model preserving

the playing characteristics of the environment (for example, physical contours, playing conditions, etc.)

and the stroke sequence is more suitable than simply an analysis of frequencies of discrete events.

2 Background

Cochran and Stobbs (1968) had the idea to manually collect shot data (ball locations) and to analyze

performance based on these data. They wanted to measure the performance of professional golfers in

different aspects of the game and figure out which aspects of the game the leading golfers are better at than

the rest of the field. In the context of this study Cochran and Stobbs developed a model for calculating

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probabilities and the average number of remaining shots for holing out from certain (ranges of) distances.

At the time of their study the lack of modern technology prevented them from collecting more data and

enhancing their approach. Landsberger (1994) built on the work of Cochran and Stobbs by refining the

approach. Landsberger’s Golf Stroke Value System (GSVS) provided a starting point for more recent work

on establishing independent measures of performance.

Recent projects have emerged which have looked to further advance the shot value idea (Broadie, 2012;

Fearing, Acimovic, & Graves, 2011; Minton, 2011)1. Broadie (2008, 2012) developed statistical models to

calculate probabilities of holing out and derived benchmarks as average number of remaining shots from

the probabilities. One model provides benchmarks for holing out on the green based on the distance to the

hole and another model computes benchmarks for holing out off the green, which additionally includes a

classification of the ball location. Using these benchmarks Broadie has demonstrated a more valid method

for describing the performance of individual shots, called Strokes Gained. Strokes gained can be used to

explain the contribution of each shot to the total score. Based on the shot value idea of Broadie (2008),

Fearing et al. (2011) came up with a similar approach which is limited to the green. They applied various

regression models to achieve the probability of making a putt and a prediction of the distance remaining

after a missed putt. In addition to the distance to the hole used by Broadie, Fearing et al. (2011) consider

the strength of the field and the difficulty of the green. From this they illustrate the use of these benchmarks

to assess performance to individual shots using the same shot value idea as Broadie (2012). The PGA

TOUR uses this approach as a measure for individual shots, which is called Strokes Gained - Putting. Both

approaches provide very sophisticated models of putting performance with respect to the distance from the

hole.

In the absence of independent measures of individual shot performance, several studies (Clark III, 2004;

James, 2007; James & Rees, 2008; Scheid, 1990) have looked at the temporal variance of consecutive

golf scores – both hole scores and round scores. Analyses of round scores showed very low correlations

between scores of consecutive rounds when considered with respect to external influences on performance

(i.e. weather conditions and course setup). Analyses of hole scores also showed low correlations between

successive holes, again considering external influences like hole par and difficulty. Aside from the

obvious fact that good players tend to shoot good scores and poor players tend to shoot poor scores, these

1see PGA TOUR Academic Data Program page, available at: http://www.pgatour.com/stats/academicdata/ for detailedexplanations of these projects.

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results suggest that performance in golf is not subject to “streakiness”. In other words, the nature of the

performance of individual shots which make up hole and round scores seems not to be well understood.

In summary, consecutive round scores do not depend on one another, and consecutive hole scores do not

depend on one another. However, individual shots played on the same hole present a different scenario;

these shots make up a continuous chain of events so that the finishing position of shot n represents the

starting position for shot n+1. Although shots on the same hole are related, one would expect the same

lack of “streakiness” that has been demonstrated in the literature. This means that although a well played

shot tends to set up an advantage on the ensuing shot compared to a poorly played one, a well played shot

will not likely predict the performance of the ensuing shot.

3 The ISOPAR method

3.1 Framework

The previous described approaches of Broadie (2012) and Fearing et al. (2011), on which first measures for

individual shots are based, are statistical and developed to predict performance and to compare performance

to benchmarks of expected performance. Our approach is different from that and is specifically aimed at

characterizing the performance of individual shots based on their location and the relevant factors affecting

the shot, rather than just the distance to the hole. The framework of the ISOPAR project comes from a

systems perspective and has been empirically applied to many levels of analysis of human movement and

performance (e.g. Davids, Glazier, Araujo, & Bartlett, 2003; Kelso, 1995; Mayer-Kress, Liu, & Newell,

2006). The central concept is that neurobiological systems behave as complex systems and theories from

physical sciences, e.g. dynamical systems theory, are appropriate for understanding and modeling human

performance. Accordingly, golf performance is an emergent property of self-organizing dynamics and

the confluence of constraints influencing the golfer (Newell, 1986). We have subsequently applied this

perspective to golf performance on the PGA TOUR measured by ShotLink™. The underlying assumption

is that each shot a player faces, represents a new set of constraints and the player must adapt to the

constraints associated with the shot, which can be divided up into three main categories: environment,

organism, task (Newell, 1986). The constraints do not necessarily prescribe one particular response (e.g.

shot type), instead they guide the response selection of the golfer by excluding certain responses (Kugler,

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Kelso, & Turvey, 1980). Stöckl and Lames (2011) have demonstrated the ISOPAR method for visualizing

constraints in putting. A player’s putting performance is determined by a combination of environmental

constraints (e.g. slope of the green, distance to the hole, green speed, weather conditions), organism

constraints (e.g. psychological influences on the player, player’s green reading ability, player’s putting

ability) and task constraints (striking the golf ball with a club so that it rolls into the hole). The idea of

visualizing the confluence of constraints off the green, by which the performance of players is determined,

can be extended to entire holes to illustrate difficulty on a hole represented by the number of remaining

shots – since each shot is part of a player’s shot sequence. Off the green, a player’s performance is also

guided by the interaction of environmental, organism, and task constraints, however, their details may

differ. For example, environmental constraints are the hole design (straight hole compared to a doglegged

fairway), ball lie (e.g. fairway, rough, sand), line to the green (e.g. are there trees or other objects blocking

the line to the green?), or weather conditions (e.g. wind, rain); organismic constraints can be psychological

influences affecting the player, player’s perception of the best tactics, picking the ‘right’ club, or the

player’s ability to execute the shot; task constraints are similar to those for putting, in that the player hits

the ball with a club with the intention of the ball finishing close to, or in, the hole. However, the degrees

of freedom involved with the swings used from off the green introduce a wider range of task specific

constraints (e.g. achieving clean contact, club path into the ball, etc.). The coordination pattern used for

the swing must be more adaptable for off-green shots because of the increased degrees of freedom involved

with the movement itself as well as the increased variability in the results of the swing.

3.2 The concept

Here we present two analogies to help explain the following methods. In meteorology, lines of equal

barometric pressure are plotted on geographical maps. These maps are called isobar maps and the lines are

isobar lines. The term isobar (iso - meaning equal and bar - meaning pressure) is used appropriately as the

isobar map shows lines of equal pressure. Small diameter, closed lines represent minima and maxima by

which, areas of low-pressure and high-pressure can be identified. Densely packed isobar lines indicate a

steep gradient of air pressure. Meteorologists can therefore make weather predictions using isobar maps.

Our second analogy is to contour maps used in geography to show elevation. Similar to isobar lines, lines

of equal elevation are plotted on geographical maps. Here, densely packed lines represent steep ascents

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and descents. In both analogies, lines that are relatively close together represent steep changes in the map’s

z value. Likewise, lines that are relatively widely spaced represent areas of little change in pressure or

elevation.

For golf, we have developed the ISOPAR method for calculating a gradient of difficulty for a golf hole.

The output can then be plotted on a map of the golf hole to visualize the difficulty of certain areas. We call

these maps ISOPAR maps and a detailed explanation of how they are calculated is provided below.

3.3 Development and testing

The ISOPAR method was originally developed for visualizing difficulty on the green based on the

performance of the field on the green (Stöckl, Lamb, & Lames, 2011; Lamb, Stöckl, & Lames, 2011).

Since we have the opportunity to use the ShotLink™ database we can also calculate ISOPAR values and

maps for entire holes. The calculation of ISOPAR values for entire holes is based on the same algorithm

which will be described for greens in this section. To reduce computational complexity ISOPAR values are

only calculated in a non-convex area in which ball locations were recorded. In this section the development

and testing of the method is described for this application.

The three-dimensional spatial coordinates (x,y,z) of the green gives the first of three sets of triplets,

(xg,yg,zg), where g represents the number of measuring points. When available, this set of triplets can be

used for plotting the physical contour of the green.

For each ball position, (x,y), the corresponding number of strokes, z, required for the player to hole

out are used in the calculation. This gives our second of three sets of triplets (xp,yp,zp). For example, if a

player took four shots on a hole, that player contributed four data points to our dataset: the x,y coordinates

from the location of the first shot with a corresponding z value of 4 and the x,y coordinates from the second

shot and a corresponding z value of 3 and so on.

3.3.1 Computing ISOPAR values and maps

Before explaining the details of the algorithm for computing the ISOPAR values and maps, a rough

overview of the steps involved in calculating an ISOPAR map for a green is given (see Stöckl et al., 2011):

1. Assign a grid to the green (Figure 1).

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2. Calculate the ISOPAR value of every grid point subject to all measuring points with a modified

application of the exponential smoothing algorithm.

3. Compute a surface out of the ISOPAR values of the grid points using a smoothing spline interpolation

(Fahrmeir, Kneib, & Lang, 2009) to finely remove rough edges.

4. Calculate the ISOPAR map which consists of ISOPAR lines.

The following explains the steps for computing ISOPAR values and maps of greens in detail which we use

for the calculation of entire holes as well. All computations were performed in MATLAB (The Mathworks,

Inc.).

Assign grid to green: A grid with a specified mesh size is assigned to the green (Figure 1). The ISOPAR

values are computed at the grid nodes. For positions which lie between grid nodes the ISOPAR values

must be estimated. Therefore, a grid with an extremely small mesh size represents the data very well,

while a very large mesh size does not. However, there is a trade-off between representational power and

computational intensity. A mesh size which optimizes this trade-off should be used.

Exponential smoothing algorithm: From Step 1, coordinates (xi j,yi j) were assigned to the grid nodes.

The corresponding zi j values which represent the ISOPAR values were then calculated; this gives the final

set of triplets, (xi j,yi j,zi j), i = 1, . . . ,m, j = 1, . . . ,n.

The algorithm used here is a well known smoothing algorithm; however, our application of the

algorithm differs slightly from most applications. Typical applications of the exponential smoothing

algorithm are in time-series analyses and based on pairs (xk,yk),k = 1, . . . , t, from which the value yt+1 at

time xt+1 is computed. The modified application of this algorithm for calculating ISOPAR values is based

on the measuring points (xp,yp,zp), p = 1, . . . ,q (q = number of sample points). The ISOPAR values zi j

are computed based on these triplets.

To use the exponential smoothing algorithm, which is based on pairs, we transformed the triplets into

two-dimensional pairs, respectively. Since ball locations on the opposite side of the hole when computing

the ISOPAR value at a specific grid node, we introduced a constraint for the usage of ball locations in

order to calculate the ISOPAR value at a grid node. We empirically decided that ball locations which are

considered for computing an ISOPAR value need to be in an area of 60 degrees left and 60 degrees right

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Ball Location

Hole

Grid Node

Line Grid Node−Hole

60° Line

Used Data

j

i

(xij,y

ij)

Figure 1: The mesh grid shown on the green. Green line represents the edge of the green. (xi j,yi j)represents coordinates for a grid point, blue dots represent ball positions, and red dots represent ballpositions which are used for calculating the ISOPAR value at (xi j,yi j). The black, solid lines form a 60◦

angle which marks the boundary within which ball locations are used in the calculation.

from the straight line between the pin location and the respective grid node (the red data points in Figure

1). The transformation for every grid node was achieved by ordering the measuring points in ascending

order (the nearest point first) with respect to the Euclidean distance

di jp =√

(xi j− xp)2 +(yi j− yp)2 (1)

to the measured ball positions. This allowed the triplets from above to be written as pairs (di jp,zp).

With the pairs sorted as described, we could use the exponential smoothing algorithm to calculate the

ISOPAR values. In these pairings, (di jr,zr) represents the ball position with the shortest distance to the

respective grid node and (di j1,z1) represents the ball position with the largest distance to the grid node.

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(a) 6th hole (b) 18th hole

Figure 2: The ISOPAR maps for (a) the 6th hole at Bay Hill in the fourth round of the 2009 tournamentand (b) the 18th hole in the fourth round of the 2008 tournament. The green line represents the edge of thegreen, the flag position is shown as a black dot. iso2.0 is shown in magenta.

The exponential smoothing is calculated by

zi j = α

r−2

∑k=0

(1−α)kzr−k +(1−α)r−1z1, (2)

where 0≤ α ≤ 1 is the smoothing parameter (Hamilton, 1994).

The ISOPAR lines are calculated from the ISOPAR values (Figure 2). The ISOPAR lines, similar to

the isobar lines used in our meteorological analogy, are the lines of intersection between planes which are

parallel to the x,y plane in certain intervals and the surface which is calculated with the triplets (xi j,yi j,zi j).

The result is a contour map which empirically characterizes how many strokes “the field" took from each

position on the green. Each line on the contour map is one of these lines of intersection, thus we argue that

the ISOPAR lines give a visual representation of the difficulty of any shot on the green.

Smoothing spline interpolation: Because of the space between the grid nodes, the grid surface must be

smoothed. Figure 3 shows the difference between the raw surface and the smoothed surface using a cubic

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(a) (b)

Figure 3: Example of a portion of the grid surface (a) without smoothing and (b) with smoothing splineinterpolation.

smoothing spline interpolation (Fahrmeir et al., 2009).

minf

β

n

∑i=1

m

∑j=1

(zi j− f (vi j))2 +(1−β )λ

"(D2 f (x,y))2dxdy (3)

where

D2 =∂ 2

∂ 2x+2

∂ 2

∂x∂y+

∂ 2

∂ 2y,

vi j denotes the vector with entries(xi j

yi j

), λ = 1 in our case and β is the smoothing parameter. When β = 1,

f is a natural spline interpolant – the cubic spline interpolant; when β = 0, f is a least square fit surface

and as β → 1, the data remain relatively similar to the input.

Calculating the ISOPAR map: The ISOPAR lines are lines of intersection between the smoothed

surface (calculated in the previous subsection) and planes which are parallel to the x,y-plane in certain

intervals. For implementing the ISOPAR method we used intervals of 0.2, however, this value is not

critical. The value for the interval should depend on the objectives of and resources available to the user.

3.3.2 The performance indicator: Shot Quality

Shot Quality (SQ) is a post-hoc assessment of a shot taken. Similar to the shot value concept of Broadie

(2008), Shot Quality is determined as the difference in ISOPAR value at the starting position (IPVbe f ore)

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and the ISOPAR value at the finishing position (IPVa f ter) of the shot is calculated.

SQ = IPVbe f ore− IPVa f ter (4)

Shot Quality, as its name implies, represents the quality of a shot played. A shot of average performance,

with respect to the data set (in this case the ShotLink™ database), receives, by definition, a Shot Quality

score of 1 (proof shown below). A shot with a Shot Quality higher than 1 is considered a well played shot

and likewise, a shot with a Shot Quality score of less than 1 is a poorly played shot.

Like the additivity property of the model of Broadie (2012), a unique property of Shot Quality allows

consecutive shots, which are performed in sequence (1, . . . ,np) ending with the ball being holed, by a

given player p to be weighted so that the sum of their Shot Quality scores (SQ j) equals the ISOPAR value

of the beginning position (IPV1) of the sequence:

np

∑j=1

SQ j(4)=

np−1

∑j=1

(IPVj− IPVj+1)+ IPVnp −0

= IPV1− IPV2 + IPV2− IPV3 + . . .+ IPVnp−1 − IPVnp + IPVnp −0

= IPV1. (5)

We have included 0 in the the final term, IPVnp −0, to make clear that it represents the Shot Quality of the

final shot played on the hole (zero shots are required once the ball is holed).

Consider a hypothetical sequence of two putts on a green which starts from a position with an ISOPAR

value of 2.1. If the first putt missed, leaving a putt with an ISOPAR value of 1.1, the Shot Quality scores

must be 1.0 for the first putt and 1.1 for the second, which adds up to the beginning ISOPAR value of the

sequence. If the first putt had been much worse, the holed second putt would necessarily have a higher

value, the first lower, and then still add up to 2.1. If the second putt were missed, we now have a three shot

sequence and these three Shot Quality scores then add up to 2.1. This concept applies to a sequence of

shots of any length including the sequence of all shots played on a hole, as long as the final shot in the

sequence results in the ball being holed. To follow this example, no matter the player’s score on the hole,

the values of the Shot Quality scores will add up to the ISOPAR value of the starting point of the sequence:

the ISOPAR value at the tee (IPVTee). This leads us to another interesting property of Shot Quality. In

the ShotLink™ database all tee shots recorded on the same hole (and the same round) are assigned the

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same x,y coordinates – a single point. For this reason, we use the average score (Save) for the hole as the

ISOPAR value at the tee

IPVTee = Save =1p

p

∑j=1

S j, (6)

where S j are the hole scores for all p different players on the hole. Therefore, the sequence of all Shot

Quality scores for each player must add up to the average score for the hole. For example, another

hypothetical golfer might score a birdie on a par 4 which has an average score of 3.92 which might involve

a series of shots as follows: a good drive (SQ = 1.20), a slightly better than average approach from that

position (SQ = 1.05) and a very good putt (SQ = 1.67).

As mentioned above, the average Shot Quality of all shots played on a hole (SQave) must be 1 and can

now be shown by

SQaveTotal =1

p

∑j=1

S j

·p

∑j=1

n j

∑i=1

SQi

(5)=

1p

∑j=1

S j

·p

∑j=1

IPVTee

=1

p

∑j=1

S j

· p · IPVTee

(6)=

1p

∑j=1

S j

· p ·

p

∑j=1

S j

p

= 1, (7)

where p is the number of different players on the hole and n j is the number of shots played on the hole by

each player.

Additionally, a new concept can be derived from Shot Quality. Similar to Strokes Gained (Broadie,

2012; Fearing et al., 2011), already in use by the PGA TOUR, we assess the advantage gained relative to

the average by a well played shot (or vice versa). As with Strokes Gained (Broadie, 2012), Shots Saved is

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defined as

Shots Saved = SQ−SQave, (8)

where SQave denotes the average Shot Quality of certain shot types (SQaveType), e.g. drives, or the average

Shot Quality of all shots (SQaveTotal).

4 Applying the ISOPAR method to ShotLink™ data

While the methods of Fearing et al. (2011), Broadie (2012) and Minton (2011) can be used to make very

good generalizations about the expected outcome of a shot based on its distance, the ISOPAR method

is useful for answering a slightly different question. Given the factors which directly contribute to the

performance of the field, how were certain shots performed with respect to the performance of the field?

4.1 Reading ISOPAR maps

In Stöckl et al. (2011) the concept of ISOPAR maps was originally described for on-green performance of

amateur golfers. In this section we applied the ISOPAR method to on-green performance of PGA TOUR

golfers and extended the idea of calculating and visualizing difficulty to off-green performance of PGA

TOUR golfers.

According to Stöckl and Lames (2011) ISOPAR maps are suitable for identifying unique areas on the

green. The iso-lines represent different levels of difficulty according to the number of remaining shots

required to hole out. For example, if iso-lines were spread out evenly and circularly, we could conclude

that all the constraints which influence performance were evenly distributed. Yet, we know that many

factors (e.g. distance to the hole, angle of approach, distance of the hole from the front of the green, speed

and hardness of greens, etc.) directly influence performance, but they also indirectly influence performance.

In other words, having a tree blocking the line to the pin constrains the kind of shot a player can play.

This is an example of a factor directly affecting performance. An example of a factor indirectly affecting

performance is a situation in which a player tries to play strategically, by aiming away from a hazardous

area. The shot might result in a long remaining putt but the intention was to eliminat the possibility of

going in the hazard. The strength of the ISOPAR method is its capability of accounting for all the factors

that influence performance. To illustrate this idea further, imagine all the players on a hole aim at a certain

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area of the green rather than the pin because the pin is close to a penalizing hazard. This would affect

the distance-based statistical benchmarks and would make it look like everyone performed more poorly

than expected. Accordingly, the benchmark could then be adjusted so that it is only representative of

shots played on that specific hole. To take this example one step further though, imagine a situation in

which long hitters give themselves such an advantage off the tee that they can play to the pin whereas

the rest of the field still feel wise to play more conservatively. This situation represents a non-linear

relationship between players’ strategies. The same example is easy to think of on risk reward holes such as

drivable par-4s and tricky par-5s. The ISOPAR method is capable of modeling these non-linearities in

the data because it is a measure of performance rather than a predictor. The ISOPAR maps are capable of

identifying how advantageous shots were (e.g. a drive that provides a good angle for the approach shot) as

well as how hazardous hazards (e.g. rough, bunkers, trees, etc.) were.

In the following subsections we will explain ISOPAR maps and how they can be interpreted. To read

the ISOPAR maps we use the naming convention isoN to represent the ISOPAR line with value N.

4.1.1 On-green Performance

The iso2.0 line is of importance, beyond iso2.0, 3-putts exist more frequently than 1-putts. Figure 4 shows

the distribution of 1-, 2- and 3-putts on the 18th green at Bay Hill in 2008. The iso2.0 line is a result of the

3-putts shown in the figure.

Lorensen and Yamrom (1992), and later Penner (2002), modeled the difficulty of putting with different

amounts of break and elevation change and from different distances. The authors showed that, not

surprisingly, much more precision was required by the player as putting distance, break and elevation

change increased. The ISOPAR maps visualize these factors as well as many other subtle factors which

affect putting performance.

Since putting distance obviously increases outward from the hole, linearly and equally in all directions,

the iso-lines should be circular on a flat green. However, since slopes are not symmetrically distributed

across the green, the shape of the iso-lines can be useful in identifying easy or more difficult areas from

which to putt. Useful characteristics of iso-lines are their a) circularity, b) density and c) their distance

from the hole. If the map consists of circularly patterned iso-lines one can conclude that shot difficulty

does not depend on the direction from which the shot is played. As the iso-lines become more elliptical,

certain areas of the green must be considered more favorable to putt from. The spread, or density, of

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Figure 4: The distribution of iso-lines, 1-, 2- and 3-putts on the 18th green in the 4th round of the ArnoldPalmer Invitational in 2008.

iso-lines can be used to identify the severity of the gradient of difficulty on the green. A steep gradient is

expected to coincide with undulated areas of the green but has not been empirically shown with ShotLink™

data yet. The distance of the iso-lines from the hole can of course also be used to indicate difficulty of

a putt. Reference values could be used as a comparison to provide context to the value of the iso-lines

(e.g. Broadie, 2008; Cochran & Stobbs, 1968; Fearing et al., 2011; Tierney & Coop, 1998).

4.1.2 Off-green Performance

Figure 5 shows an ISOPAR map of the 18th hole in the 4th round of the AT&T Pebble Beach in 2011. The

18th hole at Pebble Beach is a unique par-5 because there is a tree in the middle of the fairway potentially

blocking shots to the green. Furthermore, this hole is located directly on the coastline, which is the border

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Figure 5: The distribution of iso-lines on the 18th hole in the 4th round of the AT&T Pebble Beach in2011.

on the left side.

Figure 6 is a zoomed in view of the ISOPAR map for the landing area of the drives. In the middle of

the fairway is the tree, on the left side of the fairway is just a small strip of rough before the coastline

starts, and on the right hand side of the fairway is a bunker. Looking at the iso-lines we notice that it

was advantageous for the field to hit their drives on the fairway left of the tree where there was a local

minimum of difficulty represented by the small, closed iso3.6 line. In contrast, the area behind the tree (the

tree was blocking the player’s line to the green) and close to or even in the bunker on the right hand side

was much more difficult shown by the iso4.0 and iso4.2 lines. Hence, players whose drives ended up behind

the tree or in the bunker had to take about half a shot more on this hole than players who were able to pass

the tree with their drives or were able to keep their drives on the fairway to the left of the tree. Among

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Figure 6: Zoomed in view of the ISOPAR map of the landing area in the fairway of 18th hole in roundfour at AT&T Pebble Beach in 2011. Red lines represent iso-lines

other constraints that influenced the players, this tree constrained the field’s play significantly.

4.2 Performance analysis

In this section we will show specific applications of the performance indicators Shot Quality and Shots

Saved in order to analyze performance.

4.2.1 Performance analysis based on ISOPAR maps on greens: Bay Hill in 2008 and 2009

As an initial demonstration of ISOPAR method, we used the ShotLink™ data from the Arnold Palmer

Invitational presented by MasterCard in 2008 and 2009. In both years the tournament was won by Tiger

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Woods sinking dramatic putts on the final hole. These tournaments give us an opportunity to demonstrate

performance analysis of the field using the ISOPAR method, as well as an analysis of the performance of

Woods in both years.

In this section Shot Quality and Shots Saved are used as performance indicators. The analyses in this

section are based on ISOPAR values calculated for greens only. In order to analyze shots based on all

shots, Shots Saved represents the difference between the Shot Quality of any shot and Shot Quality of the

average shot, which represents the field and has been shown to be exactly 1, and is called Shots Savedtotal

Shots Savedtotal = SQ−1. (9)

The Shots Savedtotal definition matches the definition of the Strokes Gained concept (Broadie, 2012;

Fearing et al., 2011). Shots Savedtotal represents the contribution of one shot by a player to that player’s

total score with respect to the field’s performance2.

Individual putts The ISOPAR method, because it is based on shot locations, can give Shot Quality

scores to individual shots. Table 1 shows the top-ten putts for the Arnold Palmer Invitational in 2008 and

2009, respectively. Notably, the putts with the highest Shot Quality scores are not necessarily the longest

putts. For example, in 2009 Daniel Chopra made a 31.2 foot putt on the 15th hole in the first round which

had the highest Shot Quality score of all putts in that year’s tournament, despite putts of more than double

the length being holed by other players.

Tiger Woods’ winning putts in each year are shown in Table 1 in bold face (see also Figure 7 in

Appendix A). In 2008, the winning putt was the best putt by Woods of the week and the 42nd best putt out

of over 11,000 putts in the entire tournament. In 2009, Woods’ winning putt was not his best of the week,

his best was on the 13th hole in the first round,which was the 40th best putt of the week. His winning putt

was the 203rd best putt of the week, again, out of just over 11,000 putts. This reveals exactly how well

Tiger Woods performed on his final putt of the tournament, with the tournament on the line, two years in a

row. One can, of course, argue that the winning putt was just one of 270 shots played in the tournament,

and they all contributed equally to the outcome. However, we must acknowledge, first that the preceding

shots in the tournament were played sufficiently well so that Woods had a chance to make a winning putt

2In section 4.2.2 a slightly different application of Shots Saved will be introduced based only on shot types

17

Table 1: Top-ten putts measured by Shot Quality for the Arnold Palmer Invitational in 2008 and 2009.

2008 SQ Hole Round Distance (ft)

1. Davis Love III 1.99 7 1 43.62. Bill Haas 1.99 2 1 61.13. D.A. Points 1.98 15 2 33.04. Charley Hoffman 1.98 3 1 36.85. Shaun Micheel 1.97 1 2 36.36. Mark Wilson 1.97 15 1 29.87. Tom Pernice Jr. 1.95 11 4 22.48. Brian Davis 1.95 6 2 23.09. Billy Mayfair 1.92 12 1 27.1

10. Kenneth Ferrie 1.91 2 1 41.042. Tiger Woods 1.83 18 4 24.2

n = 11,107

2009 SQ Hole Round Distance (ft)

1. Daniel Chopra 2.12 15 1 31.22. J. J. Henry 2.11 8 1 55.73. D. J. Trahan 2.11 1 2 33.74. Zach Johnson 2.09 7 3 35.95. Ben Curtis 2.03 9 2 45.06. Fred Couples 2.01 11 2 37.97. Brian Gay 2.00 11 3 29.88. Aaron Baddeley 2.00 10 3 25.39. Heath Slocum 1.99 9 3 73.3

10. Jerry Kelly 1.95 18 4 38.140. Tiger Woods 1.84 13 1 16.4

203. Tiger Woods 1.69 18 4 15.9

n = 11,116

on the last hole; and second, the final putt is not like the rest because the consequences are known. In this

sense we must appreciate the performance of Woods on these specific shots.

Shots Saved on and off the green Table 2 shows the top putting performers of the tournament and their

off-green performance. On-green performance is calculated as above, however, calculating Shot Quality

scores off the green were still under development as of paper version from June 27, 2011 (developments

are introduced in the next section). Therefore, the off-green Shots Savedtotal can be calculated based on the

average score of the field (hole, round or tournament) and the on-green score, which is already calculated.

For example, in 2008, Tiger Woods’ score of 270 was 13.73 strokes better than the average score of 283.73.

18

Of the 13.73 stroke margin between his score and the field we calculated that he gained 1.13 on the green

and, as a result, the remaining 12.60 strokes must have been from off the green.

In Table 2, the Shots Savedtotal performance indicator is introduced and shows a large discrepancy

between Shots Savedtotal on the green and Shots Savedtotal off the green. At first glance it appears as

though putting, because of the low Shots Savedtotal values, is much less important than shots played off

the green: this may in fact be the case but the topic requires some discussion first.

Table 2: Top-ten putters in the Arnold Palmer Invitational in 2008 and 2009.

2008 Shots Savedtotal Shots Savedtotalon the green off the green

1. Ken Duke 1.48 8.242. Tiger Woods 1.13 12.603. Hunter Mahan 0.79 8.944. Mark Wilson 0.47 -0.745. Carl Pettersson 0.06 7.676. Woody Austin -0.46 6.197. Ian Poulter -0.47 1.208. Nick Watney -0.66 5.399. Frank Lickliter II -0.92 6.65

10. Joe Ogilvie -1.08 4.81

n = 71

2009 Shots Savedtotal Shots Savedtotalon the green off the green

1. Brad Faxon 1.32 -1.462. Lee Janzen 1.19 5.673. Lucas Glover 1.06 6.804. Daniel Chopra 0.58 8.285. Padraig Harrington 0.54 7.326. Zach Johnson 0.48 10.387. Tiger Woods -0.03 13.898. Ben Crane -0.06 7.929. Paul Goydos -0.34 3.20

10. Cliff Kresge -0.40 4.26

n = 73

Shot Quality and consequently, Shots Savedtotal , are independent measures of performance because

the same metric is used for all shots. This means that any shot played can be directly compared to any

other shot played. With that in mind, a speculative explanation for the discrepancy between on-green and

19

off-green performance involves two factors: 1) There is a greater range of possible Shot Quality scores for

off-green shots compared to on-green shots. 2) a PGA TOUR player will typically take more off-green

shots than on-green shots, so the number of elements in the off-green sum is greater than the elements in

the on-green sum. Combined, these two factors may explain the discrepancy between Shots Savedtotal on

and off the green.

Anecdotally, one might notice in Table 2 that Brad Faxon is at the top of the Shots Savedtotal on the

green list in 2009 (he was not in the field in the 2008 tournament). Each year on the PGA TOUR, no matter

how it is measured (number of putts, putts per GIR, or just how smooth the stroke looks to an expert eye),

Brad Faxon is always among the best putters. In the 2009 tournament Faxon was the best putter and ranked

fourth worst off the green. Clearly, Faxon was only able to make the cut in this particular tournament

because of superior putting. As mentioned, Faxon is usually one of the best putters on the PGA TOUR

according to conventional statistics. These conventional statistics (e.g. putts per GIR), as we have already

mentioned, are a composite of previous shots played on the hole. If independent measures of performance,

such as the ISOPAR method, had been available we may have noticed that Faxon was an even better putter

than previously thought because of the disadvantage of relatively poor off-green performance cost him.

In Table 3, the leaders’ on- and off-green performance is shown. In both years, Woods performed, as

the winner should, well on and off the green. He ranked 2nd in putting in 2008 and 7th in 2009. Off the

green he ranked 9th in 2008 and 5th in 2009. Combined, his performance on and off the green were good

enough for him to win.

As exemplified by Vijay Singh, Niclas Fasth, Alex Cejka and Tom Pernice Jr. in 2008 and by Jason

Gore in 2009, it is possible to finish high in the tournament standings with relatively poor putting, if

off-green performance is exceptional. The converse situation, in which poor off-green performance is

balanced by excellent putting seems less profitable (for further context, see Table 9 in Appendix B which

shows the bottom ten players each year).

Using Shots Savedtotal , we were able to rank all the players in the field according to their on-green

and off-green performance. The correlation (Spearman’s rank) between tournament rank and putting rank

was ρ = .28 in 2008 and ρ = .44 in 2009. The correlation between tournament rank and off-green rank

in 2008 was ρ = .79 and ρ = .70 in 2009. These correlations are compelling evidence that off-green

performance contributes to overall performance more than on-green performance. Of course, we should

not discount the importance of putting, since it also is strongly correlated with overall performance. We

20

Table 3: Top-ten finishers in the Arnold Palmer Invitational in 2008 and 2009.

2008 Putting Shots Savedtotal Off-green Shots Savedtotalrank on the green rank off the green

1. Tiger Woods 2 1.13 9 12.602. Bart Bryant 11 -1.10 5 13.83

T3. Cliff Kresge 43 -4.70 2 15.42T3. Vijay Singh 54 -6.26 1 16.98T3. Sean O’Hair 16 -1.66 10 12.39T6. Ken Duke 1 1.48 30 8.24T6. Hunter Mahan 3 0.79 26 8.94T8. Niclas Fasth 57 -6.82 3 14.55T8. Alex Cejka 51 -5.33 7 13.06T8. Carl Pettersson 5 0.06 34 7.67T8. Tom Pernice Jr. 55 -6.59 4 14.32T8. Tom Lehman 30 -3.59 16 11.32T8. Bubba Watson 34 -3.68 15 11.41

n = 71

2009 Putting Shots Savedtotal Off-green Shots Savedtotalrank on the green rank off the green

1. Tiger Woods 7 -0.03 5 13.892. Sean O’Hair 32 -2.58 1 15.443. Zach Johnson 6 0.48 21 10.38

T4. Pat Perez 15 -0.91 20 10.77T4. John Senden 22 -1.76 11 11.62T4. Scott Verplank 25 -2.04 10 11.90T4. Nick Watney 27 -2.20 9 12.06T8. Daniel Chopra 4 0.58 30 8.28T8. Jason Gore 55 -6.24 2 15.10T8. Kenny Perry 28 -2.35 15 11.21

n = 73

mentioned in the Shots Savedtotal on and off the green section that there is a discrepancy between the

sum of Shots Savedtotal on the green and the sum of Shots Savedtotal off the green; and here show that

off-green performance contributed more to overall performance than on-green performance. It should be

noted that the discrepancy between on- and off-green Shots Savedtotal is not what implies the importance

of off-green performance, rather the rankings in off-green performance. Those who were among the best

off-green performance stood a better chance of doing well in the tournament. Indeed, good off-green

performance must be accompanied by on-green performance if one is to beat the best players in the world.

These findings simply suggest that off-green performance is likely more important than previously thought.

21

4.2.2 Performance analysis based on ISOPAR maps of entire holes from 2011

In this subsection we use the performance indicator Shots Saved to analyze players’ performances with

respect to different shot types for PGA TOUR tournaments in 2011. We calculated ISOPAR values and

maps for 2,754 holes played in 153 rounds from the 38 PGA TOUR tournaments measured by ShotLink™.

A variation of Shots Saved, called Shots Saved(type), is introduced here to compare shots of the same

type.

Shots Savedtype = SQ−SQaveType. (10)

Since shots are extracted from their original shot sequence in order to make this comparison, SQaveType

does not necessarily equal 1. Shots Saved(type) represents the quality of a shot in context of a certain shot

type with respect to the field’s performance. We defined five different shot types: Drives, long approach

shots, short approach shots, around the green shots, and putts (see Stöckl, Lamb, & Lames, 2012). In the

following, lists of the top-ten golfers for each shot type are presented and compared to the most similar

performance indicators currently used by the PGA TOUR.

Table 4: Top-ten putters in 2011 ranked by Shots Saved(type) per round. Last column contains rank inPGA performance indicator Strokes Gained - Putting.

Rank Name Rounds Shots Saved(type) Shots Saved(type) Strokes Gainedmeasured per Round total Rank (PGA)

1. Bryce Molder 71 0.773 54.863 32. Luke Donald 52 0.751 39.043 13. Charlie Wi 77 0.734 56.506 44. Steve Stricker 53 0.716 37.972 25. Kevin Na 68 0.591 40.159 86. Fredrik Jacobson 70 0.573 40.090 67. Brandt Snedeker 67 0.555 37.174 108. Greg Chalmers 79 0.553 43.668 59. Hunter Mahan 76 0.551 41.869 13

10. Jason Day 59 0.534 31.497 7

n = 202

Putting In table 4 the top-ten golfers with respect to putting in 2011 are ranked by their Shots Saved(type)

values per round. In the last column of this table the golfers’ ranks in the PGA TOUR performance

indicator Strokes Gained - Putting are listed. We calculated the Spearman rank correlation between the

22

two putting rankings (ρ = .94), which shows a striking similarity in rankings. Comparing the ranks of

the Shots Saved(type) ranking and the Strokes Gained - Putting ranking of the top-ten putters we can

see that these players are also identified as the best putters by the Strokes Gained - Putting performance

indicator. The small differences can be explained by the fact that the strokes gained method is based on

benchmarks considering the distance to the hole, an indicator for the difficulty of the green, and the field

strength in putting (Fearing et al., 2011; Broadie, 2012) whereas the ISOPAR method implicitly considers

all constraints which affect performance.

Table 5: Top-ten drivers in 2011 ranked by Shots Saved(type) per round. Last two column contain ranksin PGA performance indicator Total Driving and Driving Distance.

Rank Name Rounds Shots Saved(type) Shots Saved(type) Total Driving Driving Distancemeasured per Round total Rank (PGA) Rank (PGA)

1. J. B. Holmes 47 0.991 46.588 90 12. Dustin Johnson 54 0.904 48.827 30 33. Gary Woodland 70 0.834 58.382 23 54. Robert Garrigus 69 0.787 54.317 94 45. Bubba Watson 59 0.780 46.003 35 26. Adam Scott 45 0.710 31.972 5 247. Jhonattan Vegas 70 0.595 41.644 84 88. Martin Laird 62 0.572 35.435 26 139. Bill Haas 72 0.566 40.745 76 48

10. Kyle Stanley 80 0.558 44.653 18 9

n = 202

Driving Table 5 shows the top-ten drivers in 2011 ranked by their Shots Saved(type) values per round.

We considered drives to be all tee shots taken on par-4s and par-5s. The players in table 5, we argue are

popularly and anecdotally known as good drivers. We compared the Shots Saved(type) ranking to the

Total Driving performance indicator of the PGA TOUR which is intended to best account for a player’s

driving ability. Many of the good drivers with respect to Shots Saved(type), like J.B. Holmes or Robert

Garrigus, are not ranked well in the Total Driving performance indicator. The Spearman’s rank correlation

between the Shots Saved(type) ranking and the Total Driving ranking (ρ = .60) shows that these two

rankings are not coupled tightly. Total Driving is a combination of two performance indicators for driving,

the Driving Distance and the Driving Accuracy. The correlation between the Shots Saved(type) ranks

of the golfers and their Driving Distance rank (ρ = .74) is much stronger than the correlation between

23

Shots Saved(type) and Total Driving. Hence, Driving Accuracy, which is a binary measure of whether a

drive ends up in the fairway, seems to over-influence the Total Driving performance indicator.

Approach shots Table 6 and Table 7 show approach shot rankings. We distinguished between long and

short approach shots because we argue that players perform different types of swings for each of these two

shot types. Generally, shots longer than 100 yds require full swings, the specific distances can be gauged

by club selection. For approaches under 100 yds, players are usually hitting a wedge of some sort and

scale the distance the ball travels by adapting their swing. The ability to scale one’s swing according to

the shot (more common in short approaches), we argue, is a qualitatively different skill than performing

full swings which rely less on swing modifications. ShotLink™ also indicates whether a shot is a short or

long approach. Approach shots were defined by ShotLink™ as all shots taken from further than 30 feet

from the edge of the green (except tee shots), which ended up on or around the green (within 30 feet).

According to a benchmark used by the PGA TOUR an approach shot is a long approach shot if it is taken

from further away than 100 yards. Alternatively, an approach shot is a short approach shot if it is taken

from closer than 100 yards.

Table 6: Top-ten long approach shot players in 2011 ranked by Shots Saved(type) per round. Last columncontains rank in PGA performance indicator Approaches from >100 yards.

Rank Name Rounds Shots Saved(type) Shots Saved(type) >100 yardsmeasured per Round total Rank (PGA)

1. Phil Mickelson 58 0.691 40.087 822. Rory Sabbatini 61 0.535 32.641 733. Bubba Watson 59 0.443 26.135 1144. Luke Donald 52 0.380 19.741 10

T5. Sergio Garcia 45 0.378 17.031 93T5. Kris Blank 83 0.378 31.390 10

7. Chris DiMarco 82 0.372 30.493 1088. Jonathan Byrd 72 0.355 25.589 1149. Robert Garrigus 69 0.338 23.355 8

10. Alex Cejka 47 0.309 14.543 4

n = 202

Table 6 shows the top-ten long approach shot players with respect to Shots Saved(type) per round

in 2011. Most of the players ranked highly are again popularly known for their good long game or ball

striking. We compared our ranking with the PGA TOUR performance indicator Approaches from >100

24

yards by computing Spearman’s rank correlation (ρ = .53). The two performance indicators are only

correlated moderately. One reason for this is likely because the PGA TOUR performance indicator does

not take into account the difficulty of the starting position of an approach shot - only how close to the

hole the approach shot ends up. For example, an approach shot taken from 105 yards in the middle of

the fairway which ends up 8 feet from the hole is assessed the same quality as an approach shot taken

from 150 yards in the rough which ends up 8 feet from the hole according to the conventional performance

indicator. In contrast, the ISOPAR method considers the difficulty of a shot and assesses the more difficult

shot a higher Shot Quality value and consequently a higher Shots Saved(type) value. Because of this we

argue that Shots Saved better assesses the quality of the shot played.

Table 7: Top-ten short approach shot players in 2011 ranked by Shots Saved(type) per round. Last columncontains rank in PGA performance indicator Approaches from <100 yards.

Rank Name Rounds Shots Saved(type) Shots Saved(type) <100 yardsmeasured per Round total Rank (PGA)

1. Nick Watney 55 0.227 12.495 12. Paul Goydos 72 0.218 15.693 53. Brian Gay 58 0.194 13.159 224. Steve Stricker 51 0.189 9.617 25. Stephen Ames 51 0.184 9.391 156. Camilo Villegas 42 0.182 7.638 287. Justin Rose 51 0.151 7.693 118. Luke Donald 41 0.149 6.125 99. Chris Kirk 67 0.141 9.444 19

10. Scott Piercy 51 0.136 6.955 3

n = 202

Table 7 shows the top-ten short approach shot players with respect to Shots Saved(type) per round in

2011. Once again, we suggest that these golfers are well known as good short game players. We compared

this ranking to the PGA performance indicator Approaches from <100 yards. These two performance

indicators are also only correlated moderately (Spearman’s rank correlation ρ = .68), however slightly

stronger than Shots Saved(type) and Approaches from >100 yards. The difference in these two rankings

can be explained the same way as with the long approaches. The classical performance indicators do not

take into account the difficulty of a shot and only focus on the outcome of the shot, the remaining distance

to the hole, whereas the ISOPAR method accounts for a shot’s difficulty.

25

Furthermore, we can recognize that the top players’s Shots Saved(type) per round values are much

smaller in this shot category than in all other shot categories which were studied because short approach

shots are played far less frequently compared to the other shot types.

Table 8: Top-ten around the green shot players in 2011 ranked by Shots Saved(type) per round. Lastcolumn contains rank in PGA performance indicator Scrambling.

Rank Name Rounds Shots Saved(type) Shots Saved(type) Scramblingmeasured per Round total Rank (PGA)

1. Chris Riley 68 0.465 31.639 202. Kevin Na 68 0.422 28.709 93. Bio Kim 58 0.418 24.261 1424. Charles Howell III 91 0.380 34.577 5

T5. Justin Rose 63 0.337 21.260 95T5. Brian Gay 74 0.337 24.912 7

7. Webb Simpson 83 0.327 27.150 168. Marc Leishman 80 0.311 24.852 719. Rod Pampling 60 0.308 18.500 10

10. Alex Cejka 47 0.290 13.625 111

n = 202

Around the green shots Table 8 shows the top-ten around the green players ranked by Shots Saved(type)

per round in 2011. Around the green shots are defined as shots taken from an area within 30 feet around

the green, a variable which is collected by ShotLink™. These golfers are also subjectively known for

good short game performance. We compared Shots Saved(type) around the green to the PGA TOUR’s

Scrambling performance indicator although we admit it is not a completely valid comparison. The

Scrambling performance indicator represents how often a player saves par when missing the green in

regulation. So to do well in this statistic players could a) leave themselves easy around the green shots, b)

play their around the green shots very well or c) make a lot of par saving putts. With any combination of

these being possible it is difficult to make a meaningful analysis of performance using Scrambling. The

Shots Saved(type) ranking is only moderately correlated (ρ = .54) with the PGA TOUR’s performance

indicator Scrambling because this happens to be a category in which the performance of an individual shot

is lost.

An interesting example to illustrate this point is that of Bio Kim in 2011. Kim was ranked third

according to Shots Saved(type) but 142nd in Scrambling. Since Kim’s putting was fairly in 2011

26

(Shots Saved(type) rank = 99th), Kim likely played many good shots around the green but was unable to

make the ensuing par putt. This hurts his Scrambling rank but his ability to play ‘around the green’ shots

is accurately reflected in the Shots Saved(type) performance indicator.

5 Final remarks

The results presented in this working paper are specific to PGA TOUR tournaments. The results in section

4.2.1 in particular, which were outcomes of an earlier state of the project, are specific to the Arnold Palmer

Invitational in 2008 and 2009. Furthermore, the application of the ISOPAR method relies on calculating

ISOPAR values and maps for each hole in each round based on all shots (all putts in section 4.2.1) taken

by the participating players. Sometimes there are areas on a hole or a green where there are only a few

isolated ball locations. Since the ISOPAR method models the performance of the participating players, the

performance indicators Shot Quality and consequently Shots Saved may not assess the ‘real’ quality of

performances of those shots. Further work is needed to address this shortcoming.

Finally, we welcome any feedback from readers to help us improve and find new uses for the ISOPAR

method.

References

Broadie, M. (2008). Assessing golfer performance using golfmetrics. In D. Crews & R. Lutz (Eds.),

Science and Golf V: Proceedings of the 2008 World Scientific Congress of Golf (pp. 253–262). Mesa,

AZ: Energy and Motion Inc.

Broadie, M. (2012). Assessing golfer performance on the PGA TOUR. Interfaces, 42(2), 146–165.

Clark III, R. D. (2004). Streakiness among professional golfers: Fact or fiction. International Journal of

Sport Psychology, 34, 63–79.

Cochran, A., & Stobbs, J. (1968). The search for the perfect swing. Grass Valley, CA: The Booklegger.

Davids, K., Glazier, P., Araujo, D., & Bartlett, R. (2003). Movement systems as dynamical systems. Sports

Medicine, 33(4), 245–260.

Fahrmeir, L., Kneib, T., & Lang, S. (2009). Regression. Berlin: Springer.

27

Fearing, D., Acimovic, J., & Graves, S. (2011). How to catch a Tiger: Understanding putting performance

on the PGA Tour. Journal of Quantitative Analysis in Sport, 7(1), article 5.

Hamilton, J. D. (1994). Time series analysis. Princeton: Princeton University Press.

Hughes, M. D., & Bartlett, R. M. (2002). The use of performance indicators in performance analysis.

Journal of Sports Sciences, 20, 739–754.

James, N. (2007). The statistical analysis of golf performance. International Journal of Sports Science

and Coaching, 2(suppl. 1), 231–248.

James, N., & Rees, G. D. (2008). Approach shot accuracy as a performance indicator for US PGA Tour

golf professionals. International Journal of Sports Science and Coaching, 3(suppl. 1), 145–160.

Kelso, J. A. S. (1995). Dynamic patterns: The self-organization of brain and behavior. Camebridge, MA:

MIT Press.

Ketzscher, R., & Ringrose, T. J. (2002). Exploratory analysis of European Professional Golf Association

statistics. Journal of the Royal Statistical Society: Series D, 51, 215–228.

Kugler, P. N., Kelso, J. A. S., & Turvey, M. T. (1980). On the concept of coordinative structures as

dissipative structures: I. Theorectical line. In G. E. Stelmach & J. Requin (Eds.), Advances in

psychology: Tutorials in motor behavior (pp. 3–48). Amsterdam: North-Holland.

Lamb, P., Stöckl, M., & Lames, M. (2011). Performance analysis in golf using the ISOPAR Method.

International Journal of Performance Analysis in Sport, 11(1), 184–196.

Landsberger, L. (1994). A unified golf stroke value scale for quantitative stroke-by-stroke assessment.

In A. J. Cochran & M. R. Farrally (Eds.), Science and Golf II: Proceedings of the World Scientific

Congress of Golf (pp. 216–221). London: E & FN Spon.

Lorensen, W., & Yamrom, B. (1992). Golf green visualization. IEEE Computer Graphics & Applications,

12, 35–44.

Mayer-Kress, G., Liu, Y., & Newell, K. M. (2006). Complex systems and human movement. Complexity,

12(2), 40–51.

Minton, R. (2011, Retrieved May 2). Tigermetrics [Working Paper]. Roanoake College, Salem, Virginia.

Retrieved from http://www.pgatour.com/stats/academicdata/adp-roan1.pdf

Newell, K. (1986). Constraints on the development of coordination. In M. Wade & H. Whiting (Eds.),

Motor development in children: Aspects of coordination and control (pp. 341–361). Amsterdam:

Nijhoff.

28

Penner, A. R. (2002). The physics of putting. Canadian Journal of Physics, 80(2), 83–96.

Scheid, F. J. (1990). On the normality and independence of golf scores, with various applications. In

A. J. Cochran (Ed.), Science & Golf: Proceedings of the World Congress of Golf (pp. 147–152).

London: E & FN Spon.

Stöckl, M., Lamb, P., & Lames, M. (2011). The ISOPAR method - a new approach to performance analysis

in golf. Journal of Quantitative Analysis in Sport, 7(1), article 10.

Stöckl, M., Lamb, P., & Lames, M. (2012). A model for visualizing difficulty in golf and subsequent

performance rankings on the PGA Tour. International Journal of Golf Science, 1, 10–24.

Stöckl, M., & Lames, M. (2011). Modeling constraints in putting: The ISOPAR Method. International

Journal of Computer Science in Sport, 10, 74–81.

Tierney, D. E., & Coop, R. H. (1998). A bivariate probability model for putting proficiency. In

M. R. Farrally & A. J. Cochran (Eds.), Science and Golf III: Proceedings of the World Scientific

Congress of Golf (pp. 385–394). London: Human Kinetics.

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Appendices

A ShotLink™, Google Earth and ISOPAR maps

(a) 2008

Figure 7: ISOPAR map for the 18th hole at Bay Hill during the Arnold Palmer Invitational presented byMasterCard in 2008. Orange lines are shown at intervals of 0.2 ISOPAR value, ball positions are shown asyellow dots and the winning putt by Tiger Woods was taken from the red ‘X’, the hole is shown as a blackdot.

30

(b) 2009

Figure 7: ISOPAR map for the 18th hole at Bay Hill during the Arnold Palmer Invitational presented byMasterCard in 2009. Orange lines are shown at intervals of 0.2 ISOPAR value, ball positions are shown asyellow dots and the winning putt by Tiger Woods was taken from the red ‘X’, the hole is shown as a blackdot.

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B Shots Saved for the lowest finishers

Table 9: Shots Saved on and off the green for the lowest ten finishers of the 2008 and 2009 Arnold PalmerInvitational.

2008 Shots Saved Shots Savedon the green off the green

T62. George McNeill -4.67 2.40T62. Davis Love III -3.66 1.39T64. Paul Goydos -4.47 1.20T64. Steve Elkington -3.00 -0.27T64. Andrew Magee -2.97 -0.30T64. Fred Couples -1.77 -1.50T68. Robert Gamez -7.88 2.61T68. Marc Turnesa -7.44 2.17

70. Steve Lowery -4.27 -4.0071. Heath Slocum -9.39 -0.88

n = 71

2009 Shots Saved Shots Savedon the green off the green

T64. Boo Weekley -11.36 8.22T64. Luis Oosthuizen -1.10 -2.04T66. Skip Kendall -6.20 2.06T66. Richard Johnson -4.48 0.74T66. Kevin Streelman -3.52 -0.62T66. Aaron Baddeley -2.99 1.15T70. Oliver Wilson -8.63 2.49T70. Brian Davis -5.08 -1.06

72. Woody Austin -4.81 -2.3373. Bart Bryant -4.49 -5.65

n = 73

32