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Women’s Participation in Competitive Disc Golf
Nicholas Blahna, PDGA #53,133
Saint Mary's University of Minnesota
Schools of Graduate & Professional Programs
Quantitative Methods GM 630
Professor Mary Thomas
April 27th, 2014
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Women’s Participation in Competitive Disc Golf
Introduction
This article examines participation levels of women in the Professional Disc Golf
Association (PDGA). Specifically, this paper examines the percentage of women members of
the total historic PDGA membership and contrasts that information with the percentage of
women participating in PDGA sanctioned events. A Simple Random Sample (SRS) was
composed for this study consisting of participation information for close to 12,600 unique events
that occurred between 2002 and 2013.
This article contains an explanation of the growth of the PDGA membership base over
the past twelve years, specifically focusing on the differences between the male and female
halves of the membership population. Two hypotheses are tested against the demographic
information proposed by the PDGA. Information related to selecting the sample and selecting
the statistical analysisis techniques is included. After exploring the findings of the statistical
analysis of those hypotheses, conclusions are drawn with regard to the data. Finally, a personal
reflection of this study is included.
Background
A brief history. The Professional Disc Golf Association (PDGA) was founded in 1975,
by “Steady” Ed Headrick of Wham-O, and gifted to the players of the sport in 1983. The
purpose of the PDGA is to increase player participation, develop competitive play, increase
spectator participation, maintain the rules and competitive standards, and perform general
outreach (PDGA, 2009). To date, the PDGA has had over 64,000 individuals from all over the
world enroll as members, however the vast majority of those members have been men. Figure 1
shows the breakdown of the total historical membership numbers in terms of their percentage. It
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isn’t difficult to see how far off the membership numbers are from the global percentages of men
and women, which is roughly a 50/50 split.
Total Historic PDGA
Memberships
Unspecefied
0.05%
Women
8.41%
Men
91.54%
Figure 1. Total Historic PDGA Memberships from inception in 1975 to March, 2013.
Where we are today. Industry representatives such as Steve Dodge (2013) from Vibram
Disc Golf and John G. Duesler (2014) of Disc Golf Planet TV (DGPtv) have hypothesized about
numerous initiatives to help “grow the sport.” However, niether Dodge nor Duesler focused on
the glaring gender gap in participation.
Figure 2 shows that every year since 2002, when the PDGA really began to track
population metrics related to active membership numbers, the active population of both men and
women have been positive. In fact, more often than not, the population percentage changes for
women have been greater than the population percentage gains experienced by the men.
However, Figure 3 is significantly more telling of how wide the gender gap is in terms of active
PDGA membership numbers. Despite appearances, the growth of the population of women
members in the PDGA is increasing. While the numbers associated with active women PDGA
members seem to be experiencing linear growth, the population of active men seems to be
experiencing exponential growth.
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Percent of Change in Membership
Numbers
Positive Change in Active Membership
Numbers between, 2002-2012
20%
18%
16%
14%
12%
10%
8%
6%
4%
2%
0%
-2%
-4%
2002
Men
Women
2004
2006
2008
2010
2012
Figure 2. Positive change in active membership numbers between 2002-2012. Gathered from
year-end demographic data provided by the PDGA (2002-2013).
Trends in Active Membership Populations
20000
Membership Numbers
18000
16000
14000
12000
10000
Men
8000
Women
6000
4000
2000
0
2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
Year
Figure 3. Trends in active membership populations, 2002-2013. For Men R2=0.9938;
For Women R2=0.9571, where y=-159543.962+79.948x (p<0.0001). If the male population is
actually experiencing linear growth, R2 drops to 0.97896, where y= -2115804.615+1059.93x
(p<0.0001). Gathered from year-end demographic data provided by the PDGA (2002-2013).
The issue examined. I believe women’s participation is a bellweather the for health of
disc golf. As stated above, women represent roughly 50% of the global population, and I believe
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through increasing women’s participation in disc golf, the sport will grow at a faster rate. As the
popularity of the sport increases, new opportunities are created for the businesses such as Vibram
Disc Golf, Disc Golf Planet TV, the PDGA, and other new and existing organizations that
support (and are supported by) disc golfers, globally. The population of PDGA members is
clearly growing for both men and women; however, are women participating in PDGA events at
a rate consistent with the recorded population demographics?
The Study
Hypotheses
This article seeks to test two hypotheses related to women’s participation in PDGA
sanctioned events.
H1: Women participate in competitive disc golf events at a rate different than the rate of
overall representation in the PDGA’s total historic membership.
This hypothesis will examine participation rates of women in a SRS of the total
population of PDGA sanctioned events compared to the percentage of women in the overall,
historic PDGA membership (8.41%).
H 2 : As disc golf has become more popular over the years, women are participating at
competitive, PDGA sanctioned events at a greater rate.
This hypothesis will examine participation rates of women from multiple SRSs of PDGA
sanctioned events broken out into populations based on the year those events took place. The
purpose of this hypothesis is to see if womens participation has increased, decreased, or
remained the same since 2002.
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Sample Design
The sample used to examine H 1 is a stratified random sample. The total population of
PDGA sanctioned events is almost 12,600 events, and the sample consists of 5% of each event
from the years between 2002 and 2013. In total, the total sample consists of 630 individual
events. The sample of PDGA sanctioned events used in this study consists of National Tour, Atier, B-tier, C-tier, and X-tier events; ProAm, Pro, and Am only events; as well as events from
across the world. The samples used to test H 2 are simply the strata from the total sample used to
examine H 1 . After determining the sample size from each year, the annual population of events
was selected at random with Microsoft Excel.
Reducing Bias. Because the total population of events was available at the time this
study was conducted, a stratified random sample was used in this study. Using strata consisting
of 5% of the total population of PDGA sanctioned events from every year helps to ensure more
statistically significant and consistent results from the data. The resulting sample is highly
representative of the total population.
Methods
Analysis for this test will be conducted using Microsoft Excel and CrunchIt! provided by
W.H. Freeman & Company. The statistical techniques used in this study revolve around t
procedures. As figure 4 shows us, the total sample has a right skew, and we can assume that the
individual strata are similar. Furthermore, the sample (and each strata) consists of only 5% of
the total population of PDGA events from 2002 through 2013. Another reason behind using oneway t-tests is that they should be used when the data is “significantly larger than the sample, say
20 times larger,” (Moore, Knotz, & Flinger, 2013, p. 350). Therefore, according to Moore,
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Knotz, and Flinger (2013), one-way t tests will be the best tool to measure each distribution’s
mean against the percentage of women members of the PDGA.
ANOVA would have been used to test H 2 , if each distribution were normal (each with
the same standard deviation). However, as figure 4 shows us, there isn’t a normal distribution in
the total sample. Furthermore, ANOVA is only reliable if more that 20% of the cells contain
values greater than five. As this study is only interested in percentages, no cell contains a value
greater than one.
Figure 4. A histogram depicting the distribution of women’s participation in PDGA events,
calculated from the total sample.
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Table 1
Descriptive Statistic Information
Sample
n
Sample
Mean
Standard
Deviation
Min
Q1
Median
QI
Max
Total
Sample
630
0.06798
0.09932
0
0.01754
0.05263
0.08889
1
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
21
26
32
38
40
47
54
60
65
71
85
95
0.04891
0.1084
0.06432
0.06972
0.05872
0.06870
0.05737
0.08700
0.05400
0.05383
0.09399
0.05327
0.03062
0.1890
0.05480
0.04436
0.04904
0.07086
0.04659
0.1417
0.04837
0.05545
0.1784
0.05608
0
0
0
0
0
0
0
0
0
0
0
0
0.02412
0.03704
0.03653
0.03922
0.02731
0.01230
0.01887
0.01365
0.007576
0
0.01473
0
0.05556
0.07275
0.05409
0.06235
0.05379
0.05410
0.05203
0.05988
0.04651
0.04688
0.04082
0.04040
0.06671
0.1000
0.09970
0.09524
0.07387
0.1014
0.08000
0.1004
0.08990
0.08434
0.08422
0.08108
0.1020
1
0.2581
0.1935
0.1884
0.3000
0.1875
1
0.2269
0.2727
1
0.2553
Note: Adapted from data provided by the PDGA.
Results
Descriptive Statistics
Table 1 contains all the information related to the descriptive statistics of this study. It
includes the mean values and standard deviations, as well as the five number distributions for the
total sample and each individual stratum. This information was used to compile the boxplots in
Figure 5.
These boxplots make it easier to see the distribution of these stratum. According to Table
1, every stratum featured at least one event with 0% female participation. On the other end of
the spectrum, Figure 5 shows us that there are numerous years where the distribution featured
events with women’s participation as high as 100%. Although those events appear as outliers, it
is important to note that these were likely Women’s Championship events, or Women’s Global
Events. Unfortunately, this information doesn’t help to determine the validity of the hypotheses.
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Figure 5. Boxplots for each stratum. Compiled using information from the PDGA, analysed in
CrunchIT!
Inferential Statistics
Table 2 contains all the information related to the inferential portion of this study, and the
bulk of the answers to our questions. These results are based on the following hypotheses:
H o : The population mean = 0.0841 (8.41%, the percent of women in the total PDGA
population)
H a : The population mean is not 0.0841
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Table 2
One-way t-test Results.
Sample
n
Sample Mean
Standard Error
df
t Statistic
P-value
95% Confidence
Interval
Total
Sample
630
0.06798
0.003957
629
-4.073
<0.0001
(0.06021, 0.07575)
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
21
26
32
38
40
47
54
60
65
71
85
95
0.04891
0.1084
0.06432
0.06972
0.05872
0.06870
0.05737
0.08700
0.05400
0.05383
0.09399
0.05327
0.006682
0.03707
0.009687
0.007196
0.007754
0.01034
0.006340
0.01829
0.006000
0.006581
0.01935
0.005754
20
25
31
37
39
46
53
59
64
70
84
94
-5.267
0.6556
-2.042
-1.998
-3.273
-1.490
-4.216
0.1585
-5.017
-4.600
0.5111
-5.358
<0.0001
0.5181
0.04976
0.05307
0.00223
0.1431
<0.0001
0.8746
<0.0001
<0.0001
0.6106
<0.0001
(0.03497, 0.06285)
(0.03206, 0.1847)
(0.04456, 0.08408)
(0.05514, 0.08430)
(0.04304, 0.07440)
(0.04789, 0.08951)
(0.04465, 0.07009)
(0.05040, 0.1236)
(0.04201, 0.06599)
(0.04071, 0.06695)
(0.05551, 0.1325)
(0.04185, 0.06469)
Note: Adapted from data provided by the PDGA
The data from Table 2 confirms H 1 to be true, that Women participate in competitive disc
golf events at a rate different than the rate of overall representation in the PDGA’s total historic
membership. According to the total sample, there is a 95% confidence that women participate at
a level 2.39% to .84% below their overall representation.
In examining H 2 , we find that in five out of the 12 years studied, that a 95% confidence
rate cannot rule out the probability that women are represented at 8.41% participation at PDGA
sanctioned events. However, the data for the remaining seven years suggests that on average,
women’s participation is below 8.41%. These years include, 2002, 2003, 2004, 2006, 2008,
2010, 2011, and 2013. The p-values for 2003 and 2004 indicate that the null hypothesis cannot
be proved, but only by a miniscule amount. 2003, 2012 and 2009 appear to be the years where
women’s participation levels were highest. According to the data in Table 2, H 2 is proved to be
false, with only 25% of the years studied reporting means greater than the 8.41% test mean.
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Conclusions
This study has confirmed my suspicions that women participate in PDGA sanctioned
events at a rate lower than their overall representation in the population of PDGA members. It
should be stated that these results could be due to any number of variables. I believe that
initiatives such as the Women’s Global Event can be quite powerful in manipulating the
averages. Of the many outliers indicated in Figure 5, four of the largest five were Women’s
Global events. Outliers are good for bringing averages up; however, they don’t create
widespread change across a population the same way adjusting averages upward across the board
(Moore, Knotz, & Flinger, 2013). With that fact in mind, I still believe that events like the
Women’s Global event and “women only” events are still incredibly beneficial to introduce
women to the sport and fuel competitive drive.
Despite the level of attention that disc golf receives in the European market, I didn’t
observe any obvious differences with Women’s participation levels from the United States.
Although in working with the data, repeatedly I observed that the events from Japan seemed to
garner higher levels of women participation. Further study should be given to this trend. It may
be that tournament directors from Japan are providing a more female friendly environment than
western events have historically provided; though it could be that lurking variables (such as the
total number of events hosted in Japan) create an environment that spurs participation. It may be
a situation where low availability creates higher demand.
Further study in this area should also be completed based on the size of events. I believe
that large events such as National Tour and A-tier events attract larger women’s fields because of
the visibility of those events. It should be expected that Women’s fields will be larger at big
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events, but do those highly visible events garner Women’s fields that are significantly larger than
B-tier or C-tier events?
Personal Reflection
This assignment proved to be much more enjoyable than I expected it to be. Choosing a
topic that I’m passionate about was much more rewarding than pouring over data from work.
However, I wish I had found my hypotheses to be true. Furthermore, it was relatively painless to
put the lessons I’ve learned over the past eight weeks into practice. At a certain point, I realized
that the data I was gathering and compiling was dictating a lot of the decisions I made in terms of
which tools to use, and how to make my statistical comparisons.
The CrunchIt! software played a big part in completing this project. As I used Excel on
the Mac platform, it proved to be even more complicated and “clunky” than on the Windows
platform. I believe that having dedicated statistics software is necessary if I’m going to be
performing further statistical analyses in the office, or in my free time. Despite my experience, I
can honestly say that I’ve learned quite a bit about the functionality (and downfalls) of Excel.
In the future, I hope that I can create opportunities to examine more trends in disc golf. It
isn’t very often that I can combine my responsibilities to work or school with the passions I
enjoy in my free time. Though this wasn’t quite as fulfilling as enjoying the sunshine and hitting
huge putts, I think this is as close as I can get while remaining behind my computer.
In closing, I want to include a big ‘Thank You’ to Steve Ganz, the technology director for
the PDGA. Without his patience towards this project, his generosity with PDGA data, and his
swift correspondence time, I’m sure I wouldn’t have had the opportunity to examine
participation trends of women in PDGA events. Thanks so much, Steve!
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References
Disc Golf Association (DGA). (ND). Disc Golf History. Retrieved on April 26th, 2014, from
http://www.discgolf.com/how-to-play-disc-golf/disc-golf-history/.
Dodd, S. (2013). Grow the Sport. Retrieved on March 28th, 2014, from
http://www.vibramdiscgolf.com/grow-the-sport.html.
Duesler, J. G. (March 27th, 2014). DGPtv Conference Series 01--"Disc Golf Will Experience
Sudden, Rapid Growth Soon". Retrieved on March 28th, 2014, from
https://www.youtube.com/watch?v=fMQ9YE9UaCI.
Moore, Knotz, Flinger. (2013). Essential Statistics, 2nd Edition. New York: W.H. Freeman &
Company.
Professional Disc Golf Association (PDGA). (2002). 2002 PDGA Year End Demographics.
Retrieved on April 25th, 2014, from http://www.pdga.com/documents/2002-pdga-yearend-demographics.
PDGA. (2004). 2004 PDGA Year End Demographics. Retrieved on April 25th, 2014, from
http://www.pdga.com/documents/2004-pdga-year-end-demographics.
PDGA. (2005). 2005 PDGA Year End Demographics. Retrieved on April 25th, 2014, from
http://www.pdga.com/documents/2005-pdga-year-end-demographics.
PDGA. (2006) 2006 PDGA Year End Demographics. Retrieved on April 25th, 2014, from
http://www.pdga.com/documents/2006-pdga-year-end-demographics.
PDGA. (2007). 2007 PDGA Year End Demographics. Retrieved on April 25th, 2014, from
http://www.pdga.com/documents/2007-pdga-year-end-demographics.
PDGA. (2008). 2008 PDGA Year End Demographics. Retrieved on April 25th, 2014, from
http://www.pdga.com/documents/2008-year-end-demographics.
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PDGA. (2009). 2009 PDGA Year End Demographics. Retrieved on April 25th, 2014, from
http://www.pdga.com/pdga-documents/2009-pdga-year-end-demographics.
PDGA. (2009). Mission Statement. Retrieved on April 26th, 2014, from
http://www.pdga.com/files/09%20PDGA%20Mission%20Statement%20%20050109_1.pdf.
PDGA. (2010). 2010 PDGA Year End Demographics. Retrieved on April 25th, 2014, from
http://www.pdga.com/pdga-documents/2010-pdga-year-end-demographics.
PDGA. (2011). 2011 PDGA Year End Demographics. Retrieved on April 25th, 2014, from
http://www.pdga.com/pdga-documents/demographics-ye/2011-pdga-year-enddemographics.
PDGA. (2012). 2012 PDGA Year End Demographics. Retrieved on April 25th, 2014, from
http://www.pdga.com/node/28636.
PDGA. (2013). 2013 PDGA Year End Demographics. Retrieved on April 25th, 2014, from
http://www.pdga.com/pdga-documents/demographics-current/2013-pdga-and-disc-golfdemographics.
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Appendix A – Descriptive Statistics and t Procedures Outputs from CrunchIt!
Total Sample
n
Sample Mean
Standard Deviation
Min
Q1
Median
Q3
Max
630
0.06798
0.09932
0
0.01754
0.05263
0.08889
1
Total
Sample
Null hypothesis:
Population mean = 0.0841
Alternative hypothesis:
Population mean is not 0.0841
n:
630
Sample Mean:
0.06798
Standard Error:
0.003957
df:
629
t statistic:
-4.073
P-value:
<0.0001
n:
630
Sample Mean:
0.06798
Standard Error:
0.003957
df:
629
95% ConfInt:
(0.06021, 0.07575)
2002 Sample
2002
n
Sample Mean
Standard Deviation
Min
Q1
Median
Q3
Max
21
0.04891
0.03062
0
0.02412
0.05556
0.06671
0.1020
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Null hypothesis:
Population mean = 0.0841
Alternative hypothesis:
Population mean is not 0.0841
n:
21
Sample Mean:
0.04891
Standard Error:
0.006682
df:
20
t statistic:
-5.267
P-value:
<0.0001
n:
21
Sample Mean:
0.04891
Standard Error:
0.006682
df:
20
95% ConfInt:
(0.03497, 0.06285)
2003 Sample
2003
n
Sample Mean
Standard Deviation
Min
Q1
Median
Q3
Max
26
0.1084
0.1890
0
0.03704
0.07275
0.1000
1
Null hypothesis:
Population mean = 0.0841
Alternative hypothesis:
Population mean is not 0.0841
n:
26
Sample Mean:
0.1084
Standard Error:
0.03707
df:
25
t statistic:
0.6556
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P-value:
0.5181
n:
26
Sample Mean:
0.1084
Standard Error:
0.03707
df:
25
95% ConfInt:
(0.03206, 0.1847)
2004 Sample
Sample
n
Standard Deviation
Min
Q1
Median
Q3
Max
0.05480
0
0.03653
0.05409
0.09970
0.2581
Mean
2004
32
0.06432
Null hypothesis:
Population mean = 0.0841
Alternative hypothesis:
Population mean is not 0.0841
n:
32
Sample Mean:
0.06432
Standard Error:
0.009687
df:
31
t statistic:
-2.042
P-value:
0.04976
n:
32
Sample Mean:
0.06432
Standard Error:
0.009687
df:
31
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95% ConfInt:
(0.04456, 0.08408)
2005 Sample
Sample
n
Standard Deviation
Min
Q1
Median
Q3
Max
0.04436
0
0.03922
0.06235
0.09524
0.1935
Mean
2005
38
0.06972
Null hypothesis:
Population mean = 0.0841
Alternative hypothesis:
Population mean is not 0.0841
n:
38
Sample Mean:
0.06972
Standard Error:
0.007196
df:
37
t statistic:
-1.998
P-value:
0.05307
n:
38
Sample Mean:
0.06972
Standard Error:
0.007196
df:
37
95% ConfInt:
(0.05514, 0.08430)
2006 Sample
2006
n
Sample Mean
Standard Deviation
Min
Q1
Median
Q3
Max
40
0.05872
0.04904
0
0.02731
0.05379
0.07387
0.1884
Null hypothesis:
Population mean = 0.0841
Alternative hypothesis:
Population mean is not 0.0841
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n:
40
Sample Mean:
0.05872
Standard Error:
0.007754
df:
39
t statistic:
-3.273
P-value:
0.002232
n:
40
Sample Mean:
0.05872
Standard Error:
0.007754
df:
39
95% ConfInt:
(0.04304, 0.07440)
2007 Sample
2007
n
Sample Mean
Standard Deviation
Min
Q1
Median
Q3
Max
47
0.06870
0.07086
0
0.01230
0.05410
0.1014
0.3000
Null hypothesis:
Population mean = 0.0841
Alternative hypothesis:
Population mean is not 0.0841
n:
47
Sample Mean:
0.06870
Standard Error:
0.01034
df:
46
t statistic:
-1.490
P-value:
0.1431
n:
47
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Sample Mean:
0.06870
Standard Error:
0.01034
df:
46
95% ConfInt:
(0.04789, 0.08951)
2008 Sample
2008
n
Sample Mean
Standard Deviation
Min
Q1
Median
Q3
Max
54
0.05737
0.04659
0
0.01887
0.05203
0.08000
0.1875
Null hypothesis:
Population mean = 0.0841
Alternative hypothesis:
Population mean is not 0.0841
n:
54
Sample Mean:
0.05737
Standard Error:
0.006340
df:
53
t statistic:
-4.216
P-value:
<0.0001
n:
54
Sample Mean:
0.05737
Standard Error:
0.006340
df:
53
95% ConfInt:
(0.04465, 0.07009)
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2009 Sample
2009
n
Sample Mean
Standard Deviation
Min
Q1
Median
Q3
Max
60
0.08700
0.1417
0
0.01365
0.05988
0.1004
1
Null hypothesis:
Population mean = 0.0841
Alternative hypothesis:
Population mean is not 0.0841
n:
60
Sample Mean:
0.08700
Standard Error:
0.01829
df:
59
t statistic:
0.1585
P-value:
0.8746
n:
60
Sample Mean:
0.08700
Standard Error:
0.01829
df:
59
95% ConfInt:
(0.05040, 0.1236)
2010 Sample
2010
n
Sample Mean
Standard Deviation
Min
Q1
Median
Q3
Max
65
0.05400
0.04837
0
0.007576
0.04651
0.08990
0.2269
Null hypothesis:
Population mean = 0.0841
Alternative hypothesis:
Population mean is not 0.0841
n:
65
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Sample Mean:
0.05400
Standard Error:
0.006000
df:
64
t statistic:
-5.017
P-value:
<0.0001
n:
65
Sample Mean:
0.05400
Standard Error:
0.006000
df:
64
95% ConfInt:
(0.04201, 0.06599)
2011 Sample
2011
n
Sample Mean
Standard Deviation
Min
Q1
Median
Q3
Max
71
0.05383
0.05545
0
0
0.04688
0.08434
0.2727
Null hypothesis:
Population mean = 0.0841
Alternative hypothesis:
Population mean is not 0.0841
n:
71
Sample Mean:
0.05383
Standard Error:
0.006581
df:
70
t statistic:
-4.600
P-value:
<0.0001
n:
71
FINAL PAPER
23
Sample Mean:
0.05383
Standard Error:
0.006581
df:
70
95% ConfInt:
(0.04071, 0.06695)
2012 Sample
n
Sample Mean
Standard Deviation
Min
Q1
Median
Q3
Max
0.09399
0.1784
0
0.01473
0.04082
0.08422
1
8
2012
5
Null hypothesis:
Population mean = 0.0841
Alternative hypothesis:
Population mean is not 0.0841
n:
85
Sample Mean:
0.09399
Standard Error:
0.01935
df:
84
t statistic:
0.5111
P-value:
0.6106
n:
85
Sample Mean:
0.09399
Standard Error:
0.01935
df:
84
95% ConfInt:
(0.05551, 0.1325)
2013 Sample
FINAL PAPER
2013
24
n
Sample Mean
Standard Deviation
Min
Q1
Median
Q3
Max
95
0.05327
0.05608
0
0
0.04040
0.08108
0.2553
Null hypothesis:
Population mean = 0.0841
Alternative hypothesis:
Population mean is not 0.0841
n:
95
Sample Mean:
0.05327
Standard Error:
0.005754
df:
94
t statistic:
-5.358
P-value:
<0.0001
n:
95
Sample Mean:
0.05327
Standard Error:
0.005754
df:
94
95% ConfInt:
(0.04185, 0.06469)