A Report on Putting Green Performance
Characteristics
Micah Woods, Ph.D.*
Chief Scientist | Asian Turfgrass Center
www.asianturfgrass.com
August 2012*

I would like to thank all of the golf course superintendents, greenkeepers, course
managers, and friends who welcomed me to their golf courses during a busy time andwho not only allowed me to collect the data, but provided invaluable support in collecting
the data. I would also like to thank Mr. Yukio Ueno for translating this document into
Japanese.

1Abstract
This report provides a summary of data collected on putting green
playability and growing environment characteristics from August 2011
to May 2012. In total, measurements were made on 210 greens1 on 79
golf courses. Data were collected from eight species of grass growing on putting greens in seven countries. Green speed as measured
with the stimpmeter ranged from 173 to 370 cm, with a mean value of
263 cm. Surface hardness measured with a 500 g Clegg impact hammer ranged from 57 to 137 gravities, with a mean of 88.3. Volumetricsoil moisture at the 0 to 6 cm depth as measured with a Theta-probe
ranged from 4.1 to 62.4% with a mean of 26.7%. Soil temperature atthe 5 cm depth ranged from 10.6°C to 33°C with a mean of 24.5°C.
Surface temperature of the grass leaves ranged from 8.4°C to 37.8°C
with a mean of 25.4°C.

1

Not all data were collected from all greens. The report provides speciﬁcs for each typeof measurement.

2

Contents
1

What data were collected, and why

42

Grass types

53

Data collected

54Green speed
4.1 The Brede equation . . . . . . . . . . . . . . . . . . . . . . . .

7
7

5Green hardness or ﬁrmness
5.1 Clegg hammer vs. Yamanaka tester . . . . . . . . . . . . . . .

13
17

6

Soil moisture

20

7

Soil and surface temperatures20

8Conclusion

24

3

1

What data were collected, and why

Why did I collect all this data? What was the purpose? It started with two
speciﬁc ideas at the start of this past summer. One idea was that I wanted tocollect data on the range of temperatures and soil moisture content underwhich creeping bentgrass is grown in Japan during the summer. I wanted
to see how the soil moisture and the temperatures were related to turfgrass
quality. The second idea was that I wanted to compare the surface ﬁrmness
measurements of the Clegg hammer2 and the Yamanaka tester3 . Once I
began collecting the data, I realized that it would be interesting to also havedata on green speed, and that it would be interesting to collect data froma wide range of courses from different geographic regions, with different
grass types, and different management practices.
I ended up collecting data at more courses than I had originally planned,
because I came to realize that these data are quite interesting for three reasons.
Because it hasn’t been done yet The range of values for green speed andsurface ﬁrmness are not known in exact detail across East and Southeast Asia, yet golfers and turfgrass managers often speak of soft greensor hard greens, fast greens, or slow greens. I wanted to start to put
a dataset together that will include these data for a range of courses
and grass types in Asia.
Benchmarking Many individual golf courses are interested in how theirsurfaces are compared with other golf courses around the region oraround the world. By collecting and sharing this information, turfgrass managers will have better information.
Better grass and better playing conditions Turfgrass managers can use thisinformation to adjust maintenance practices to reduce stress on thegrass, making for healthier plants, and also to ensure that playing
surfaces are at or above the desired standard.
If turfgrass managers know what the conditions are, and what work
has been done to get the playing surfaces to a certain level, they then are ina better position to adjust or maintain the playing surfaces, and are able toeither reduce stress on the turf, or to adjust the playing surfaces more towards the desired condition. With these data, we move toward a situation
where maintenance work can be at its highest level of efﬁciency.
2
3

Clegg Impact Soil Tester, SD Instrumentation, http://sdinst.com/
Yamanaka soil hardness tester, Fujiwara Seisakusho, Ltd.

4

Grass typeSpecies Name

Creeping bentgrass
Bermudagrass
Seashore paspalum
Korai
Annual bluegrass
Bent-poa
Fine fescue
Serangoon grass

Agrostis stolonifera
Cynodon dactylon & Cynodon hybrids
Paspalum vaginatum
Zoysia matrella & Z. paciﬁca
Poa annua
A. stolonifera + P. annua
Festuca rubra
Digitaria didactyla

Percent (%)
40
23
16
12
3
3
2
1

Table 1: Percentage of measurements taken from different grass types for
stimpmeter, Clegg Hammer, and temperature data

2

Grass types

A large number of measurements were collected from creeping bentgrass
greens at Japan. Table 1 shows the percentage of measurements collectedfrom various species of grass.
“Korai” greens are usually Z. matrella at Japan and are Z. matrella orZ. paciﬁca in Southeast Asia. “Bent-poa” greens are a mixture of creeping
bentgrass and annual bluegrass on the same greens, and “serangoon” grassis also called “blue couch.”

3

Data collected

Data presented in this report were collected from the following areas inJapan: Hokkaido, Ibaraki, Tochigi, Saitama, Chiba, Kanagawa, Shizuoka,
Yamanashi, Osaka, Hyogo, Wakayama, Hiroshima, Shimane, and Okinawa.
Data were also collected at India, Philippines, Sri Lanka, Thailand, Vietnam, and in the United States at California and Oregon.
Green speed was measured using a USGA stimpmeter. As a generalrule, three separate measurements of green speed were made on an individual green. Flat areas on greens were chosen for the measurement ofspeed wherever possible. When ﬂat areas were not available, measurements were made in the uphill and downhill direction, avoiding cross slopes,
and the equivalent stimpmeter reading was calculated according to themethod described by Brede (1991). Srixon Z-STARxv golf balls were usedin the measurement of green speed.
The Brede formula for calculating the corrected green speed on surfaces
5with a uniform slope less than 6% is

S=

2ab
a+b

(1)

where,
S = ball roll distance (corrected green speed)a = ball roll distance uphillb = ball roll distance downhill
Note that equation (1) is not the same as the standard equation (2) for
calculating ball roll distance which is the arithmetic mean of the uphill anddownhill direction, limited to a maximum difference between the two directions of 45 cm:

S=

a+b
2

(2)

where,
S = ball roll distancea = ball roll distance uphillb = ball roll distance downhill
In practice it is difﬁcult to ﬁnd ﬂat areas on putting greens with a difference of less than 45 cm in roll, and thus the Brede formula is quite useful.
All green speeds in this report, unless otherwise noted, were calculatedusing equation (1).
Green ﬁrmness was measured using a Clegg hammer from SDi with a
500 g domed end probe (golf probe). Nine readings were taken from eachgreen, with one reading being a single drop of the hammer through a guidetube from a height of 60 cm. Readings are a measurement of the peak deceleration of the hammer’s impact with the surface and are given in unitsof gravities, represented in this report as Gmax. Whenever possible, readings at the same location on the green were also made with a Yamanaka
soil hardness tester, and those results are in mm and correspond to a force
in kg cm-2 .
Soil moisture to a depth of 6 cm was measured with a Theta-probe from
Delta-T Devices4 . Nine readings were made per green, generally in a location close to the Clegg hammer readings.
Soil temperatures were measured at three locations on each green with
4

Theta-probe, Delta-T Devices, http://www.delta-t.co.uk/

6

a digital thermometer5 inserted to a 6 cm depth. Surface temperatures were
measured with an infrared thermometer6 , held at approximately 50 cm
above the green surface to measure the integrated temperature of a circle
on the green surface approximately 5 cm in diameter.
Data were analyzed using R software (R Development Core Team, 2012),
graphs were generated by the ggplot2 package (Wickham, 2009), and theEnglish version of this report was generated by the knitr package (Xie,
2012) with ﬁnal typesetting done in XELATEX.

4Green speed

Figure 1 shows all the measurements of green speed. Of the 623 measurements, there was a range from 173 to 370 cm, with a mean value of 263 cm.
Table 2 shows the equivalent speed in feet and inches for the range of green
speeds discussed in this report.
The boxplots in Figure 1 show the distribution of the data collected foreach grass type. The horizontal line at the center of the boxplot for eachspecies is the median green speed for that grass species. Each individual
point represents one measurement of green speed, with its value on thex-axis corresponding to the species of grass, and the value on the y-axis
showing the green speed calculated through equation (1) for that measurement.
Data for creeping bentgrass, bermudagrass, seashore paspalum, andkorai were all collected from eight or more different golf courses. Data forbent-poa, fescue, Poa annua, and serangoon grass were all collected fromthree or fewer golf courses. We can look at the data for the ﬁrst set ofgrass species as being somewhat representative of the species. The data
for the second set of species, taken from a limited number of sites, may be
more representative of the maintenance practices used at the golf courses
from which the data were collected, rather than an indication of the normal
range for the species.

4.1

The Brede equation

The Brede equation (1) is extremely useful. On most golf course putting
greens, it is difﬁcult to ﬁnd ﬂat areas to make a stimpmeter reading. In thisproject, I collected green speed data from 210 greens and even in places5

Taylor Precision Products model 9842 waterproof digital
http://www.taylorusa.com/
6
Fluke 62 mini infrared thermometer, http://www.ﬂuke.com/

7

thermometer,

Speed (cm)

Speed (feet)

170
180
190
200
210
220
230
240
250
260
270
280
290
300
310
320
330
340
350
360
370

5’7”
5’11”
6’3”
6’7”
6’11”
7’3”
7’7”
7’10”
8’2”
8’6”
8’10”
9’2”
9’6”
9’10”
10’2”
10’6”
10’10”
11’2”
11’6”
11’10”
12’2”

Table 2: Green speed in cm shown with the equivalent in feet

8

350

Green Speed (cm)

300

250

200

bent

bent−poa

bermudafescuekorai

paspalumpoa

serangoonSpeciesFigure 1: 617 measurements of green speed, separated in boxplots for eachspecies9Error in green speed (cm), calculated by USGA equation − Brede equation

200

100

0
0

200

400

600

800

Difference in roll distance (cm) between uphill and downhill direction

Figure 2: Error in stimpmeter readings increases rapidly when the difference between the two roll directions is more than 45 cm

10where the green seemed to be relatively ﬂat, the difference in roll between
the uphill and downhill direction was often more than 45 cm. When green
speed readings are made, a location at the edge or corner of a green is often selected, not because it is a representative area of the green, but simply
because that corner or ridge is ﬂat enough to get a valid reading. Alternatively, slightly sidehill rolls are sometimes made, so that the difference
between the two rolls is less than 45 cm, even though the sidehill roll causesa curve that introduces error into the reading.
In Figure 2 we see the error that comes into the stimpmeter readingwhen measurements are made on a slope such that the difference in rollbetween the uphill and downhill direction is more than 45 cm. The blueline marks the 45 cm breaking point. With a difference less than 45 cm
between the two directions, equation (2) can be used. With any difference
greater than 45 cm, the error in the reading is too great, and we cannot
make a stimpmeter reading using the standard method.
But wouldn’t it be great if we could obtain an accurate stimpmeter measurement on sloped areas where the difference in roll between the uphilland downhill direction is more than 45 cm? Figure 3 shows that we can dothat by using the Brede equation (1).
By making multiple measurements of green speed on each green, thedata can be organized to compare the green speed within greens. Figure 3
shows that for each of those paired measurements, meaning two measurements taken at different locations on the same green, the speed on thesloped area, even when the difference in roll between the uphill and thedownhill direction was 500 cm, once corrected using equation (1), was able
to predict accurately the speed measured at a ﬂat (difference between uphill and downhill < 45 cm) location on that same green. In effect, the resultof the Brede equation is to remove the error that was shown in Figure 2.
What about that scatter around the 1:1 line in Figure 3? Is that normal,
or should we expect that two measurements from the same green should
always have exactly the same value? If the stimpmeter reading is identicalat the two paired areas, it would be shown in Figure 3 right on top of theblue 1:1 line. What we see in the ﬁgure is a little bit of scatter around that
line, both for the sloped vs. ﬂat measurements (in orange) and the ﬂat vs.
ﬂat measurements (in blue).
Even within one green, it is natural that the speed varies somewhat
from point to point. Just because we measure a stimpmeter reading of acertain value at the back of the green, it does not mean that the front of thatgreen, or the middle of that green, will be exactly the same speed. To investigate this in more detail, I compared paired measurements from two ﬂatareas on the same greens (n = 36). I also compared paired measurements
11

Actual green speed from paired reading on same green in flat area (cm)350

Predictor(x−axis)difference
in cm(downhill− uphill)

300

0
100
200
300

250

400
500

200

200

250

300

350

Predicted green speed from Brede equation (cm)Figure 3: 212 paired measurements of green speed shown in orange use themeasurement from a sloped area (x-axis value) to predict the green speed
from a ﬂat area (roll difference < 45 cm) on the same green; 36 paired measurements of green speed shown in blue use the green speed of a ﬂat area
to predict the green speed of another ﬂat area on the same green; the size ofthe circles on the graph represent the difference in roll distance between the
uphill and downhill direction for the predictor green speed on the x-axis

12

of a sloped area with a ﬂat area on the same green (n = 212).
In the paired measurements of two ﬂat areas on the same green, themedian difference between stimpmeter readings was 9. In the paired measurements of a sloped area to a ﬂat area on the same green, the mediandifference between stimpmeter readings was 9.9. Based on these results, I
would expect that on a typical green, the difference in speed from one area
to the next may be about 9 cm. And it appears that including measurements from sloped areas only introduces additional variability of about 1
cm.
To put this variability into context, Karcher et al. (2001) demonstrated
that golfers are not able to detect a difference in green speed of less than
15 cm. Repeated measurements of green speed on the same research plots
found variability among stimpmeter measurements to be less than 15 cm(Richards et al., 2009) with that variability being attributed to factors suchas varying wind speed and direction, non-uniform surface conditions, anddifferences in leaf blade orientation due to grain or mowing patterns.
A look at the probability density functions (Figure 4) for the sloped vs.
ﬂat measurements (orange) and for the ﬂat vs. ﬂat measurements (blue)shows a lot of overlap, although the ﬂat vs. ﬂat measurements have fewer
differences above 20 cm. For practical purposes, the distribution of thedifferences between the paired stimpmeter readings are similar, suggesting that use of the Brede equation (1) on sloped areas of a green produces a
stimpmeter reading that is functionally equivalent to the stimpmeter reading on a ﬂat area of the same green.

5

Green hardness or ﬁrmness

The hardness of the putting surface is another important performance characteristic. Baker et al. (1996) used a 500 g Clegg hammer to measure thesurface hardness of 148 greens on 74 golf courses in Great Britain. Theirmeasurements of Gmax mostly fell within the range of 70 to 150; on greensless than ﬁve years old the mean Gmax was 126, with that value decreasing to 108 for courses ten or more years old. Figure 5 shows a histogramof the 2029 measurements I made of Gmax. The most frequent measurements were from 80 to 90, and the range was from 57 to 137 with a meanof 88 gravities.
Figure 6 shows a boxplot of all the Gmax measurements, separated by
species. We notice that the warm-season grasses tend to have harder surfaces than do the cool-season grasses, although there is a wide range forall species at which data were collected at more than one course. Data for
13

0.03

density

0.02

0.01

0.00
−25

0

25

50

Difference between paired stimpmeter readings on the same green (cm)Figure 4: In orange, the probability density function for the difference in
212 paired measurements of Brede equation measurements from sloped
areas and a measurement from a ﬂat area on the same green; in blue, theprobability density function for the difference in 36 paired measurementsof stimpmeter readings from two ﬂat areas on the same green

14

400

Count

300

200

100

0
50

75

100

125

Gmax

Figure 5: Histogram of 2029 Clegg hammer measurements of hardness(Gmax) on putting greens

15

140

Clegg Hammer (Gmax)

120

100

80

60

bent

bent−poa

bermudafescuekorai

paspalumpoa

serangoonSpeciesFigure 6: Clegg hammer measurements of green hardness (Gmax), separated in boxplots for each species

16bent-poa, fescue, poa, and serangoon grass should be evaluated with carein comparing to other species because those data were collected from asmall set of courses, thus the data may represent more about the management of the turf at those courses than about the grass species itself.
I ﬁnd it interesting that seashore paspalum greens tended to be ﬁrmer
than did bermudagrass greens. This is probably because seashore paspalum greens tend to be newer, thus probably having less organic matterin the soil, and consequently would be ﬁrmer. The korai greens tended tobe softer, and I suspect this is for three reasons:
1. Korai greens tend to be older than bermuda or seashore paspalum
greens, thus having more organic matter in the soil
2. Korai is a particularly well-adapted grass to East and Southeast Asia
where data were collected, so we expect that the grass may naturally
produce a large amount of organic matter compared to other grasses
in this type of climate
3. It is rare that korai greens receive as much maintenance as do greens
planted to another species
We saw in Figure 1 that the speed of korai greens can match the speedof other warm-season grasses; the data in Figure 6 show that korai greenstend to be softer than those of other warm-season grasses. But korai greenstend to receive less maintenance than other grasses. How good can korai
greens be, especially in Southeast Asia, with increased frequency of sandtopdressing and increased use of growth regulators? Based on these data
and the surprisingly high average green speed of korai, the answer may
be “surprisingly good”.

5.1

Clegg hammer vs. Yamanaka tester

At Japan the Yamanaka soil hardness tester is often used to measure theﬁrmness of a green. One of the objectives of this study was also to evaluate
the relationship between the Clegg hammer and the Yamanaka tester.
Figure 7 shows the scatterplot of Clegg and Yamanaka data taken fromthe same points. This is a rather small dataset, representing 39 greens, 8
courses, and 345 paired measurements, in total. There is more sensitivity tochanges in surface hardness when measured with the Clegg hammer thanwhen measured with the Yamanaka tester. The range of measurements in
345 samples was only 9, from a low of 16 to a high of 25, for the Yamanaka
17

25

Yamanaka Tester (mm)

22

20

18

70

80

90

100

110

120

Clegg Hammer (Gmax)

Figure 7: Scatterplot of Clegg hammer and Yamanaka tester, with the blueline showing the mean value of the Yamanaka tester for any value of Gmax

18

tester. With the Clegg, there was a range of 48 units, from a low of 71 to ahigh of 119.
We see in Figure 7 that there is a lot of scatter in the middle of the plot.
When Gmax is in the range from 80 to 100, which we saw in Figure 5 represents the typical green condition, there is very little correlation between
the Clegg hammer reading and the Yamanaka tester reading.
1.0

Density, scaled to 1

0.8

0.5

0.2

0.0
70

80

90

100

110

120

Clegg Hammer (Gmax)
1.0

Density, scaled to 1

0.8

0.5

0.2

0.0
18

20

22

25

Yamanaka Tester (mm)These small plots show a scaled probability density function for the
Clegg hammer and Yamanaka tester data. The Clegg hammer at left is inyellow and the Yamanaka tester data is in white at right. The curve for the
Clegg hammer data are wider, indicating more sensitivity in the measurements, while the data for the Yamanaka tester are more closely clusteredaround the values of 20, 21, and 22. For greens that are quite soft, or greensthat are quite hard, the Yamanaka tester captures that information. In the
middle range of green ﬁrmness, however, this limited comparison of the19Clegg hammer and the Yamanaka tester suggests that the Gmax data froma Clegg hammer would be more sensitive to the variability in surface hardness.

6

Soil moisture

There were 1193 measurements of soil moisture, representing most but not
all of the grasses tested. Figure 8 summarizes the soil moisture content ina boxplot by species. We note that bermuda and korai had the lowest average soil moisture content, which is what we would expect; warm season
grasses use water more efﬁciently than do cool season grasses and thus
can be maintained at lower levels of soil moisture. It is interesting to notethat the mean of all the 1193 soil moisture measurements was 26.7%. These
data were collected, overwhelmingly, from sand-based USGA rootzones.
The USGA recommendations for putting green construction (United States
Golf Association Green Section Staff, 2004) specify a capillary porosity atconstruction of 15 to 25%. On average, the greens that I tested were at or
above that level.
There is some indication that soil moisture content has an inﬂuence on
surface hardness. Figure 9 shows that there are hard greens associated withlow moisture content and soft greens associated with soil moisture content
higher than 35%. Above 35% soil moisture, turfgrass managers may lose
the ability to keep surfaces at a ﬁrm level of 100 (Gmax) or above.

7

Soil and surface temperatures

Figure 10 summarizes the temperatures by country, with surface temperatures measured at the time I collected the data shown as green circles, andthe soil temperatures at the time I collected data shown as orange triangles.
There are obviously a lot of seasonal variations in these temperatures, atleast in temperate countries. At Thailand the temperatures near sea level
are relatively consistent throughout the year.
It is interesting to note that in Japan, at the hottest time during the summer, the surface temperatures and soil temperatures equal or exceed thetemperatures measured at tropical locations in Thailand, the Philippines,
and Sri Lanka.

20Volumetric Water Content (%)

60

40

20

bent

bent−poa

bermudakorai

paspalumpoa

SpeciesFigure 8: Volumetric soil water content in the top 6 cm of the soil, measured
at 1193 points on golf course putting greens, separated in boxplots for eachof the six species from which data were collected

21

140

Clegg Hammer (Gmax)

120

100

80

60

20

40

60

Volumetric Water Content (%)

Figure 9: The relationship between soil moisture content measured to a
6 cm depth and surface hardness for 1193 paired samples on golf course
putting greens of multiple species

22Soil (orange triangle) or Surface (green circle) temperature (°C)

30

20

10

India

Japan

Philippines

Sri Lanka

ThailandUSA

Vietnam

Country

Figure 10: Soil temperature at a 6 cm depth and surface temperature for all
the greens from which data were collected23

8Conclusion

The collection of these data and the preparation of this report turned into amuch larger and longer project than I had anticipated. This larger project,however, is a more useful one than what I set out to do looking at soil andsurface temperatures of some golf course greens in Japan during the summer. For that, I am indebted to the many friends who helped me to collectthis data and to the Golf Course Committee of The R&A who provided oneof the meters used in this study.
These data, with which I have been wrangling for untold hours, havecertainly helped me to learn more about putting green performance andsome of the playability characteristics for different grass species and in different countries. I’ve prepared this report in an attempt to provide a basic
summary of the data, to show where the data from your course lie in relation to all the other data I collected, and in a few cases, to show some of
the things that I think are really interesting (like the Brede equation!) andto delve into those data in more detail.
I hope you will have found these data as interesting as I have and that
this information can be of some use to you in the management of yourfacility.

References
S.W. Baker, P.D. Hind, T.A. Lodge, J.A. Hunt, and D.J. Binns. A survey ofgolf greens in Great Britain. IV. playing quality. J. Sports Turf Res. Inst.,
72:9–21, 1996.
A.D. Brede. Correction for slope in green speed measurement of golf course
putting greens. Agronomy Journal, 83:425–426, 1991.
D. Karcher, T. Nikolai, and R. Calhoun. Golfer’s perceptions of green
speeds vary: over typical stimpmeter distances, golfers are only guessing when ball-roll differences are less than 6 inches. Golf Course Management, 69(57-60), 2001.
R Development Core Team. R: A Language and Environment for StatisticalComputing. R Foundation for Statistical Computing, Vienna, Austria,
2012. URL http://www.R-project.org. ISBN 3-900051-07-0.
Jacob S. Richards, Douglas E. Karcher, Thomas A. Nikolai, Jason J. Henderson, and John C. Sorochan. A comparison of two devices used to24measure golf course putting green speed. Applied Turfgrass Science, online(doi:10.1094/ATS-2009-0724-02-RS), 2009.
United States Golf Association Green Section Staff. USGA recommendations for a method of putting green construction. USGA World Wide
Web Site, March 2004.
Hadley Wickham.
ggplot2:elegant graphics for data analysis.
Springer New York, 2009.
ISBN 978-0-387-98140-6.
URL
http://had.co.nz/ggplot2/book.
Yihui Xie. knitr: A general-purpose package for dynamic report generation in
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25