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Has player development in men’s tennis really
changed? An historical rankings perspective
Michael Kenneth Baneabc, Machar Reidad & Stuart Morganc
a Sport Science and Medicine Unit, Tennis Australia, Richmond South, Australia
b Institute of Sport, Exercise and Active Living, Victoria University, Footscray, Australia
c Biomechanics and Performance Analysis, Australian Institute of Sport, Bruce, Australia
d School of Sport Science, Exercise and Health, University of Western Australia, Crawley,
Australia
Published online: 22 Apr 2014.
To cite this article: Michael Kenneth Bane, Machar Reid & Stuart Morgan (2014) Has player development in men’s tennis
really changed? An historical rankings perspective, Journal of Sports Sciences, 32:15, 1477-1484
To link to this article: http://dx.doi.org/10.1080/02640414.2014.899706
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Has player development in men’s tennis really changed? An historical
rankings perspective
MICHAEL KENNETH BANE
1,2,3
, MACHAR REID
1,4
& STUART MORGAN
3
1
Sport Science and Medicine Unit, Tennis Australia, Richmond South, Australia,
2
Institute of Sport, Exercise and Active
Living, Victoria University, Footscray, Australia,
3
Biomechanics and Performance Analysis, Australian Institute of Sport,
Bruce, Australia and
4
School of Sport Science, Exercise and Health, University of Western Australia, Crawley, Australia
(Accepted 26 February 2014)
Abstract
Tennis federations are regularly faced with decisions regarding which athletes should be supported in financial terms, and
for how long. The financial investments can be considerable, given the cost of competing on tour has been estimated at a
minimum $121,000 per year and only the top 130 professionally ranked athletes earned enough prize money to cover this
cost in 2012. This study investigates key points of progression in tennis players’careers, to determine how these have
changed over time and how that evolution may inform talent development. Approximately 400,000 weekly rankings for 273
male professional tennis players between 1985 and 2010 were compiled, and historical trends in the time taken to reach
career milestones were investigated by least-squares regression. The time between earning a first professional ranking point
and entry into the Top 100 significantly increased over time for all considered athletes. This was at the detriment of time
spent within the Top 100 for some athletes. Career peak Top 50–100 athletes have shown an increase in longevity. These
results assist tennis federations in assessing the progress of developing athletes and highlight the evolving nature of the
competition for top players.
Keywords: trend analysis, athlete longevity, data mining, tennis, athlete development
Introduction
Identification and development of talent in athletes
is accepted as an important contributor to success in
sport at an international level, and if nations are
likely to succeed they must adopt more rigorous
methodologies (De Bosscher, Bingham, Shibli, van
Bottenburg, & De Knop, 2008). The financial
investments can be considerable, for example, the
Lawn Tennis Association of Great Britain and
Tennis Australia spent £12 million (Lawn Tennis
Association, 2012) and $24 million (Tennis
Australia Web site, 2012), respectively, on athlete
development in 2012. Tennis federations that under-
stand how athletes progress through their careers can
reduce the chance that potentially talented athletes
are severed from funding early, or that athletes who
are not likely to succeed are supported, at a cost of
approximately $121,000–$197,000 per year
(Quinlan, 2012), thereby draining resources that
may be utilised more effectively elsewhere.
Quantitative analyses are increasingly being
employed in assessing the likelihood of success of
developing athletes. The growth in sports analytics
is perhaps best summarised by Coleman (2012),
who refers to a recent exponential increase in the
number of research articles published. Some of
these empiricisms include the correlations between
performance in tests of physical capability and suc-
cess in future playing careers (Pyne, Gardner,
Sheehan, & Hopkins, 2005) in Australian football
players. Data science has also been employed in
tracking career attrition in baseball (Witnauer,
Rogers, & Saint Onge, 2007) and American football
(Ducking, 2012) athletes. A similar study in elite
swimming tracked the careers of 20 athletes between
the ages of 11 and 18, to determine the relationship
between future success and changes in their pre-
ferred strokes (Bielec, 2012).
In tennis, a study (Guillaume et al., 2011) ana-
lysed the Top 10 athletes and calculated the win/loss
ratios throughout the ascent and the inevitable
decline of their careers and found career length was
shorter for tennis players competing between 1985
and 2009 compared to those competing from 1973
to 1985 (Guillaume et al., 2011). Reid et al. used
Correspondence: Michael Kenneth Bane, Sport Science and Medicine Unit, Tennis Australia, Private Bag 6060, Richmond South, 3121 Australia.
E-mail: mbane@tennis.com.au
Journal of Sports Sciences, 2014
Vol. 32, No. 15, 1477–1484, http://dx.doi.org/10.1080/02640414.2014.899706
© 2014 Taylor & Francis
Downloaded by [Machar Reid] at 04:53 02 January 2015
year-end rankings data to establish ranking bench-
marks for athletes who reached a Top 100 ranking
(Reid & Morris, 2011). The transition between
junior and professional competition has been mod-
elled using linear regression, unearthing junior rank-
ing to be a statistically significant, albeit minor (5
and 13% of variance explained for boys and girls,
respectively), predictor of senior ranking for girls
(Reid, Crespo, & Santilli, 2009) and boys (Reid,
Crespo, Santilli, Miley, & Dimmock, 2007). More
recently, Brouwers, De Bosscher, and Sotiriadou
(2012) explored the link between success in junior
and professional competition, and found that while
junior success can be a predictor of senior success, it
is not necessarily a precondition.
An increase in the age and career longevity of com-
peting tennis players has been widely reported in
popular tennis media; however, the actual evidence
usually depends on an analysis of incomplete or
ambiguous data sets (Mallet, 2010) and/or hearsay
from “experts”(Branch, 2011; Clarey, 2010; Dunn,
2012;Garber,2012,2013; Lubinsky, 2010). Similar
age increases have been reported in other sports such
as football (Kuper, 2012), baseball (Witnauer et al.,
2007) and basketball (Galletti, 2010).
This study focuses on the careers of elite male
tennis players between 1985 and 2010, and investi-
gates the age of athletes in the Top 100 in addition to
the time taken to achieve key career milestones as a
function of the date at which athletes become pro-
fessionally ranked. The age at which players achieve
their first ranking point (obtained by winning ATP-
sanctioned matches) and the time taken to reach
Top 100 (Development Time), previously described
(Reid & Morris, 2011) as hallmarks of players’tran-
sition into professional tennis, as well as the time
spent within the Top 100 (Churn Time) and time
between first point and final exit from Top 100
(Career Longevity) were identified as key career
milestones. We hypothesise that the age of athletes
competing in the Top 100 and the time taken to
achieve the four career milestones has increased in
the era from 1985 to 2010.
Method
Rankings data ranging from the inception of the
ATP ranking to 31 December 2010 were obtained
from the ATP rankings website (Association of
Tennis Professionals Web site, 2011) and entered
into JMP Pro 10 for initial processing. Data obtained
related to the accumulated ranking points and abso-
lute rank an athlete achieved at a given rank-date
(ranks are generally calculated weekly by the ATP),
as well their names and country of origin. These
athletes were grouped into minimum ranking bands
based on the peak ATP ranking achieved over their
career. These classes were defined as Top 10 (where
career peak rank is ≤10), Top 11–50 (where career
peak rank is 11–50) and Top 51–100 (where career
peak rank is 51–100). Athletes who held rankings
after 31 December 2010 were not considered since
the minimum ranking they will achieve over their
career is yet unknown. Rankings for athletes who
did not make the Top 100 in their careers were not
otherwise considered.
Since data were obtained from the public domain
(Association of Tennis Professionals Web site,
2011), some erroneous and missing data were iden-
tified. To ensure integrity, rankings were included in
the analysis only if at least 99 of the Top 100 ranked
athletes were represented in a given rank-week. If
this criterion was not met, all data for the week
were rejected. All rankings prior to 1985 were
rejected from the analysis, since the vast majority of
those data did not satisfy this measure. The criterion
was met for all rank-dates between 01 January 1985
and 31 December 2010. Players who obtained their
first ranking before 1985 were not considered, since
the length and quality of their pre-1985 careers were
unknown. Overall 273 athletes were considered,
comprising 47, 121 and 105 Top 10, Top 11–50
and Top 51–100 athletes, respectively.
Date of birth information was also obtained from
the ATP rankings database (Association of Tennis
Professionals Web site, 2011). This was used to
calculate the average age of athletes competing in
the Top 100, and the age at which athletes achieved
their first ranking point. A random sample of 30
athletes was selected and manually checked for accu-
racy. 100% of these data were verified to be accurate.
The number of ranked athletes in a given ranking
week was also extracted from the data. The number of
athletes ranked in the week in which the most athletes
were ranked was determined for every calendar year
over the scope of the study and used asa measure of the
number of athletes competing in that year.
A series of key “milestone variables”in the careers
of elite tennis athletes were proposed. The first
career milestone to be investigated was the age at
which athletes achieved their first ATP ranking (Age
of First Ranking). Once athletes achieve this mile-
stone, they inevitably attempt to reach the subse-
quent ranking targets. The second milestone
considered was the time taken to reach the Top
100 from achievement of the first point
(Development Time). The time between an athlete’s
first entry into and final exit from the Top 100
(Churn Time) was measured as the third milestone
variable (Churn Time). Finally, the time taken from
the first ranking point to the final departure from the
Top 100 (Career Longevity) was measured. It
should be noted that Career Longevity is not inter-
changeable with the total duration of an athlete’s
1478 M. K. Bane et al.
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career, since many athletes will continue to compete
beyond the time they have departed the Top 100.
Ordinary least squares (OLS) regression analyses
were performed, using R statistical programming (R
Core Team, 2012), for each key milestone variable,
with week relative to 01 January 1985 of an athlete’s
first point (Week), minimum ranking band and the
between-effect interactions included as predictors. A
Poisson regression model was chosen for the
Development Time, Churn Time and Career
Longevity variables, to account for the count-like
nature of these data. The Poisson regression model
fits the exponential curves to the data; however, a
first order Taylor series expansion, eax 1þax, was
invoked to obtain linear approximations to these
curves.
ln E YijxðÞ½¼Wx þMRBiþI(1a)
EY
ijxðÞ¼exp Wx þMRBiþIðÞ
¼exp WxðÞexp MRBiþIðÞ
(1b)
EY
ijxðÞ1þWxðÞexp MRBiþIðÞ(1c)
Here Yand xare the dependent and independent
variables, respectively, and W, MRB and Irelate to
the fitted coefficients for Week, minimum ranking
band and Intercept, respectively. This was done so
that an easily interpretable average rate of increase/
decrease in the relevant milestone variables could be
determined.
If the week and interaction effects were insignif-
icant (i.e. no evidence to reject the null hypothesis),
suggesting no trend over time, a one-way analysis of
variance (ANOVA) grouped by minimum ranking
band was performed. Post-hoc between-minimum
ranking band independent samples t-tests were also
performed, for all career milestones.
Results
Average age of Top 100 athletes
The mean ages of the 10 athletes ranked 1–10, the
40 athletes ranked 11–50 and the 50 athletes ranked
51–100 were calculated for every week for which
data was available, and plotted temporally (See
Figure 1). Note that data is not segregated by mini-
mum ranking band in this section (although group
labels are the same), and athletes are free to move
between groups (or out of the data set if they exit the
Top 100) from week to week. A positive trend is
observed for all groups, lending credence to the
notion that the age of athletes is increasing.
Age of first ranking
OLS regressions were performed, probing the
response of Age of First Ranking to the week of
achievement of first point and minimum ranking
band, and no significant effects for Week, or inter-
action between Week and minimum ranking band,
were observed. Therefore, we performed a one-way
ANOVA grouped by minimum ranking band (see
Figure 2), which was significant with F(2, 270) =
9.89, P< 0.0001. Post-hoc analysis revealed all
groups to be significantly different at α= 0.05
(
95%
CI
T10,11–50
= 1.03 to 0.17 years and p
T10,11–50
= 0.0062,
95%
CI
T10,51–100
= 1.57 to 0.67 years and
p
T10,51–100
< 0.0001,
95%
CI
11–50,51–100
= 0.93 to
0.11 years and p
11–50,51–100
= 0.013). The mean ±
standard deviations of the Age of First Ranking were
determined to be 17.1 ± 1.1, 17.7 ± 1.4 and 18.1 ±
1.7 years for Top 10, Top 11–50 and Top 51–100
athletes, respectively.
Figure 1. The mean age of the 10 athletes ranked 1–10, the 40 athletes ranked 11–50 and the 50 athletes ranked 51–100 were calculated for
every week for which data was available, and plotted temporally. Note that data is not segregated by minimum ranking band in this figure.
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Development time
OLS Poisson regressions were performed, probing
the response of Development Time to the week of
achievement of first point and minimum ranking
band. Since the variance of these data was larger
than the mean (over-dispersion exhibited), a quasi-
Poisson model was required to ensure standard
errors were calculated correctly. The overall
model was determined with residual deviance of
12,273 on 269 degrees of freedom, with an over-
dispersion parameter of 46. A significant effect for
both Week (t=3.03,P= 0.0027) and minimum
ranking band was observed (see Figure 3). The
interaction between these terms was not significant.
Independent samples t-tests revealed all groups
were significantly different at α=0.0001
(p
T10,11–50
= < 0.0001, p
T10,51–100
<0.0001,p
11–
50,51–100
= < 0.0001). The approximate average
rate of increase of Development Time was found
to be 2.5, 3.9 and 5.2 weeks per year for Top 10,
Top 11–50 and Top 51–100 athletes, respectively.
It is of note that the approximation made in
Equation (1c) is valid, since the magnitude of the
fitted value of Wis small (<0.001). The mean ±
standard deviations in the Development Time were
134.0 ± 57.2, 209.5 ± 96.8 and 285.1 ± 129.2
weeks for Top 10, Top 11–50 and Top 51–100
athletes, respectively.
Churn time
SincemanyTop51–100 athletes spent very short
periods of time within the Top 100, the data were
far from normally distributed. To resolve this, a
cubed-root transform was performed on these
data. A Shapiro-Wilk test confirmed normality in
Figure 3. Plot summarising regression analysis between development time, Week and minimum ranking band.
Figure 2. Boxplots describing the quartiles of age of first ranking for athletes of different minimum ranking bands.
1480 M. K. Bane et al.
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thetransformeddata.SinceonlytheTop51–100
cohort required transformation, all three were fitted
in separate OLS regressions, probing the response
of Churn Time to the week of achievement of the
first point. As before, over-dispersion was apparent
in these data, and a quasi-Poisson regression model
was required. Only the Top 11–50 cohort exhibited
asignificant trend with Week, in a model deter-
mined with residual deviance of 8024 on 103
degrees of freedom, with an over-dispersion para-
meter of 78. A negative effect for Week (t=−1.82,
P= 0.071) was observed (see Figure 4)whenonly
Top 11–50 athletes were considered, which is sig-
nificant at α= 0.1. The approximate average rate of
decrease of Churn Time in these athletes was found
to be 9.3 weeks per year. The mean ± standard
deviation was determined to be 535.7 ± 158.3,
357.3 ± 169.3 and 114.1 ± 118.7 weeks for Top
10, Top 11–50 and Top 51–100 athletes, respec-
tively. For Top 51–100 athletes, the median Churn
Time was 58 weeks, indicating high positive skew.
Career longevity
OLS Poisson regressions were performed, probing
the response of Career Longevity to the week of
achievement of first point and minimum ranking
band. No significant overall effects for Week, or
interaction between Week and minimum ranking
band were observed; however, visual inspection
showed Top 51–100 athletes exhibited a positive
trend (see below). Therefore we performed a one-
way ANOVA grouped by minimum ranking band
(see Figure 5a), which was significant with
F(2, 270) = 60.40, P< 0.0001. Independent sam-
ples t-tests revealed all groups to be significantly
different (
95%
CI
T10,11–50
= 100 to 51 weeks and
p
T10,11–50
< 0.0001,
95%
CI
T10,51–100
= 134 to 235
weeks and p
T10,51–100
< 0.0001,
95%
CI
11–50,51–100
=
105 to 46 weeks and p
11–50,51–100
< 0.0001). The
mean and standard deviations of Career Longevity
were determined to be 669.7 ± 162.3, 566.7 ± 163.2
and 399.1 ± 152.1 weeks for Top 10, Top 11–50
and Top 51–100 athletes, respectively.
Figure 4. Plot summarising regression analysis between Churn Time, Week and minimum ranking band. Top 100 data was transformed,
regressed and then un-transformed to obtain the parameters used in the regression line. Data relating to Top 10, Top11–50 and Top
51–100 athletes are indicated by “◊”,“•” and “×”plot characters, respectively.
Figure 5. (a) Boxplot describing the quartiles for career longevity and (b) plot summarising regression analysis of Top 51–100 cohort
between career longevity, week and minimum ranking band.
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Supplementary OLS Poisson regressions involving
only data regarding the Top 51–100 cohort were
performed. A positive relationship with week (t=
2.34, P= 0.021) was observed for this subset of the
data (see Figure 5b), which is significant at α= 0.05.
The approximate average rate of increase was found
to be 6.8 weeks per year. Two unacceptably high
leverage observations were removed from this
model.
Discussion
Professional men’s tennis has undergone significant
change since the mid-1980s. Perhaps the most influ-
ential has been the development of racket material
technologies (see Cooper, 2013), which has led to
the rise of the power game (Stevegtennis Website,
2013). Tennis is now more commercialised and
prize money/corporate sponsorship has increased
(Association of Tennis Professionals Web site,
2012; ATP, 2013a,2013b,2013c; International
Tennis Federation Web site, 2013). Media attention
is greater today, and athletes increasingly require
good media and public-speaking skills to cope
(Stevegtennis Website, 2013). The past 30 years
have also seen the decline of powerhouse nations
such as the USA and Australia, and the rise of
Europe. Tennis has become more globalised, with
34 nations represented in the ATP Top 100 at the
time of writing this article (Association of Tennis
Professionals Web site, 2011). The number of sub-
tier tournaments played (Futures, Challengers) has
also expanded, thereby increasing the opportunity
for developing athletes to compete (International
Tennis Federation Web site, 2013).
Theaimsofthisstudyweretoinvestigatekey
milestones in an athlete’s career, and examine
whether the time taken to reach them has changed
over time. The results are generally contrary to our
hypotheses, yet still consistent with the widely
reported increase in age of Top 100 athletes. The
data indicate that there has been no change in the
age at which players become professionally ranked.
Career Longevity has also remained steady for Top
10 and Top 11–50 athletes, whereas a statistically
significant increase was noted among the Top 51–
100 athletes. It is important to note that the find-
ings for the Top 10 cohort were not inconsistent
with the previous findings (Guillaume et al., 2011),
since this study only captures athletes who were
ranked after 1984. The primary contribution to
Career Longevity was Churn Time for Top 1–10
and Top 11–50 and Development Time for Top
51–100 athletes. Development time has generally
increased over time; however, Churn Time has
not, therein necessitating that the portion of an
athlete’s career spent within the Top 100 has
diminished. Significantly though, Career
Longevity does not encompass the entirety of an
athlete’s career, and beyond what this study has
operationally defined as Churn Time. However,
Federations are presumably less likely to support
athletes who have peaked; thus, post-Churn Time
milestones were not investigated in this study.
Greater Development time presumably places
greater financial strain on individual athletes and
tennis federations. Tennis Australia estimates the
cost of travel to ~30 tournaments and to employ
coaching and support staff to be between $121,000
and $197,000 per year in the modern game
(Quinlan, 2012). Inspection of the ATP prize-
money allocation (Stevegtennis Website, 2011)
reveals that only the top 130 male athletes earned
this amount in 2010. The small number of athletes
who break even is due in part to an increasing skew
in the distribution of wealth among ATP competi-
tors (Robson, 2012a,2012b). The Gini coefficient, a
measure of the disparity between a population’s rich-
est and poorest members, of the current ATP prize-
money allocation is 0.428 (Robson, 2012a), compar-
able to Nigeria’s and Morocco’s economies (World
Bank Web site, 2012). This is up from 0.324 in 1990
(Robson, 2012a), which is comparable to
Switzerland’s and Canada’s economies (World
Bank Web site, 2012). It is alarming that
Development time has increased concomitantly
with a shift of wealth away from players of lower
ranking. These financial pressures are however start-
ing to be addressed, with all four grand-slam tourna-
ments recently increasing their total prize-money
allocation, and in some cases distributing greater
portions to early exiting players (Association of
Tennis Professionals Web site, 2012,2013a,
2013b,2013c).
Top 10 and Top 11–50 spend far greater portions
of their career at a higher earning capacity compared
to Top 51–100 athletes. Only Top 51–100 athletes
are showing increasing Career Longevity and, in
part, this unique trend may be the product of these
athletes’need to compete for longer for the above-
mentioned financial imperative. With advancements
in injury treatment, athletes are more likely to
recover from major injuries. For example, over the
past 30 years, advances in ACL reconstruction sur-
gery have reduced tissue trauma, recovery times and
also inconsistency of results (McCulloch,
Lattermann, Boland, & Bach, 2007). A recent
study (McCarthy, Voos, Nguyen, Callahan, &
Hannafin, 2013) of female basketball athletes com-
peting between 2000 and 2008 showed that athletes
who receive this surgery do not experience any
reduction in career length compared to the general
athlete population. Even anti-ageing has been the
focus of some research, in the hope that athletes
1482 M. K. Bane et al.
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can negate the deleterious effects of ageing (Tulle,
2008). Such long-term changes over the range of the
study may go some way to explain how athletes with
a desire to do so are able to achieve greater longevity.
The hypothesis that the ages at which athletes
achieve their first point increased between 1985
and 2010 was rejected. Approximately 70% (i.e. ±1
std. devs.) of Top 10, Top 11–50 and Top 51–100
athletes achieve their first point between the ages of
16–18.2, 16.3–19.1 and 16.4–19.7, respectively.
There was a significant overlap between groups,
and although starting earlier implies a greater chance
of achieving a higher minimum ranking band, it not
necessary for success. Tennis Australia, for example,
adheres to rigid development criteria (Tennis
Australia Web site, 2013), which encourages its ath-
letes to achieve their first ATP ranking between the
ages of 14 and 16. Our results show that most Top
100 athletes are likely to achieve their first ATP point
significantly later in their career; however, this fails
to account for any nuance associated with a player’s
country of origin. Athletes aged 18 or below are also
encouraged to achieve ranking goals in international
junior competition; however, high achievement share
mixed correlation with success in senior competition
(Brouwers et al., 2012; Reid et al., 2007), and
requirements for athletes to specialise early may
increase the risk of injury (Dalton, 1992) as well as
the likelihood of mental and physical burnout (Weiss
& Petlichkoff, 1989).
The number of ranked athletes has increased (see
Figure 6), and we may postulate that this could
result in increased competitiveness at the elite level
and thus a driving factor behind the evolution of
athlete careers. A more detailed inspection of the
data however provides evidence to the contrary.
Plotting the number of athletes with more than four
rankings points as a function of time shows the vast
majority of growth in number of players comes from
athletes who are only able to secure a handful of
points (see Figure 6). To put this into context, a
modern athlete can secure four points by reaching
two quarter-finals, or four round of 16 playoffs in
entry-level professional events, futures 10,000 tour-
naments, over a 12-month period. The number of
futures tournaments played increased from 212 in
1998 to 425 in 2010 (International Tennis
Federation Web site, 2013), and thus the number
of participating players has presumably increased
too. It could be argued that these “Journeyman”
athletes have little if any effect on the careers of
Top 100 tennis players.
Conclusion
The results of the study indicate that athlete devel-
opment time has significantly increased between
1985 and 2010. This appears to be at the expense
of time spent within the Top 100 for Top 11–50
athletes. The career length of Top 51–100 athletes
has increased, possibly owing to improving sport
science, medicine and training practices, as well as
a perceived financial imperative. Evidence to suggest
this is not related to the increased number of ranked
athletes is also put forward.
The following two practical recommendations
from the study can be advanced:
●Tennis federations should consider supporting
developing athletes for longer period of time,
given the increasing time taken to reach the
Top 100 and/or forge a financially viable profes-
sional tennis career. Interestingly, there is no
evidence to suggest they will also spend longer
within the Top 100.
●Governing bodies should explore ways in which
to increase the number of financially viable pro-
fessional tennis careers. That is, as few as approxi-
mately 130 athletes currently earn sufficient prize
money to be self-sufficient, yet in 2010, the top 5
athletes earned a sum of $30 million, enough to
theoretically sustain 150 athletes.
References
Association of Tennis Professionals Web site. (2011). Singles rank-
ing database. Retrieved from http://www.atpworldtour.com/
Rankings/Singles.aspx
Association of Tennis Professionals Web site. (2012). Australian
open offer $30M prize money.Retrievedfromhttp://www.atp-
worldtour.com/News/Tennis/2012/10/Features/Australian-
Open-Prize-Money.aspx
Figure 6. Maximum number of ranked athletes in a given year and
the maximum number of athletes in a given year holding more
than four rankings points.
Has men’s tennis really changed? 1483
Downloaded by [Machar Reid] at 04:53 02 January 2015
Association of Tennis Professionals Web site. (2013a). Roland
Garos prize money increases announced. Retrieved from http://
www.atpworldtour.com/News/Tennis/2013/04/16/Roland-
Garros-Prize-Money.aspx
Association of Tennis Professionals Web site. (2013b). US open
prize money to reach $50M. Retrieved from http://www.atp-
worldtour.com/News/Tennis/2013/03/12/US-Open-
Prizemoney.aspx
Association of Tennis Professionals Web site. (2013c).
Wimbledon announces 40 per cent prize money increase for 2013
championship.Retrievedfromhttp://www.atpworldtour.com/
News/Tennis/2013/04/17/Wimbledon-2013-Prize-Money-
Increase.aspx
Bielec, G. (2012). Variants of development of sports career of
11-year-old female swimmers: Careers of 11-year-old female
swimmers. Polish Journal of Sport and Tourism, 19,36–40.
Branch. (2011). Golden age for older women at the highest levels of
tennis. Retrieved from http://www.nytimes.com/2011/06/04/
sports/tennis/04iht-ages04.html?_r=0
Brouwers, J., De Bosscher, V., & Sotiriadou, P. (2012). An exam-
ination of the importance of performances in youth and junior
competition as an indicator of later success in tennis. Sport
Management Review, 15, 461–475.
Clarey, C. (2010). Youth movement stalls. Retrieved from http://
www.nytimes.com/2010/08/30/sports/tennis/30iht-TEEN.html
Coleman, B. J. (2012). Identifying the “players”in sports analytics
research. Interfaces, 42, 109–118.
Cooper, J. (2013) An evolutionary history of tennis racquets.
Retrieved from http://tennis.about.com/od/racquetsballsstring-
ing/a/evolmodracquet.htm
Dalton, S. (1992). Overuse injuries in adolescent athletes. Sports
Medicine, 13,58–70.
De Bosscher, V., Bingham, J., Shibli, S., van Bottenburg, M., &
De Knop, P. (2008). The global sporting arms race. An interna-
tional comparative study on sports policy factors leading to interna-
tional sporting success. Aachen: Meyer & Meyer.
Ducking, J. (2012). The effects of minimum salaries on career length:
Evidence from the National Football League. Retrieved from
http://economics.appstate.edu/sites/economics.appstate.edu/
files/Ducking.pdf
Dunn, E. (2012). College is killing American men’s tennis. Retrieved
from http://www.chatsports.com/tennis/a/College-Is-Killing-
American-Mens-Tennis-10-194-346-UT_GfNFrYzE
Galletti,A.(2010).Peak age in the NBA and the miracles of modern
medicine. Retrieved from http://arturogalletti.wordpress.com/
2010/07/13/peak-age-in-the-nba-and-the-miracles-of-modern-
medicine/
Garber, G. (2012). Thirty no longer a death sentence. Retrieved from
http://espn.go.com/tennis/usopen12/story/_/id/8311320/us-
open-thirty-no-longer-death-sentence-tennis
Garber, G. (2013). Roger Federer and 30-year-old cohorts thriving.
Retrieved from http://espn.go.com/sports/tennis/notebook/
_/page/secondservings02282013/tennis-roger-federer-30-year-
old-cohorts-thriving-second-servings
Guillaume, M., Len, S., Tafflet, M., Quinquis, L., Montalvan, B.,
Schaal, K., …Toussaint, J. F. (2011). Success and decline:
Top 10 tennis players follow a biphasic course. Medicine and
Science in Sports and Exercise, 43, 2148–2154.
International Tennis Federation Web site. (2013). Men’s history.
Retrieved from http://www.itftennis.com/procircuit/about-
pro-circuit/men’s-history.aspx
Kuper, S. (2012). The optimal age to be an athlete. Retrieved from
http://au.askmen.com/sports/fanatic_300/325_the-best-age-for-
athletes.html
Lawn Tennis Association. (2012). British annual review. Retrieved
from http://www.lta.org.uk/Footer/about-us/Structure-and-
Vision/LTA-Annual-Reports/
Lubinsky, A. (2010). The ATP’s aging, like a fine wine. Retrieved
from http://www.tennisgrandstand.com/2010/09/30/the-atp%
E2%80%99s-aging-like-a-fine-wine/
Mallet, J. (2010). Age and ranking part 1. Retrieved from http://
statracket.net/?view+articles/ageandranking1.html
McCarthy, M. M., Voos, J. E., Nguyen, J. T., Callahan, L., &
Hannafin, J. A. (2013). Injury profile in elite female basketball
athletes at the Women’s National Basketball Association com-
bine. The American Journal of Sports Medicine, 41, 645–651.
McCulloch, P. C., Lattermann, C., Boland, A. L., & Bach Jr., B.
R. (2007). An illustrated history of anterior cruciate ligament
surgery. Journal of Knee Surgery, 20,95–104.
Pyne, D. B., Gardner, A. S., Sheehan, K., & Hopkins, W. G.
(2005). Fitness testing and career progression in AFL football.
Journal of Science and Medicine in Sport, 8, 321–332.
Quinlan, G. (2012, May 29). The lay of the land: Prize-money in
professional tennis. Presentation at international tennis federa-
tion’s coaches commission, Paris.
R Core Team. (2012). R: A language and environment for statistical
computing. Vienna: R Foundation for Statistical Computing.
ISBN 3-900051-07-0. Retrieved from http://www.R-project.
org/
Reid, M., Crespo, M., & Santilli, L. (2009). Importance of the
ITF junior girls’circuit in the development of women profes-
sional tennis players. Journal of Sports Sciences, 27, 1443–1448.
Reid, M., Crespo, M., Santilli, L., Miley, D., & Dimmock, J.
(2007). The importance of the International Tennis
Federation’s junior boys’circuit in the development of profes-
sional tennis players. Journal of Sports Sciences, 25, 667–672.
Reid, M., & Morris, C. (2011). Ranking benchmarks of top 100
players in men’s professional tennis. European Journal of Sport
Science,13, 350–355.
Robson, D. (2012a). Occupy tennis? Income inequality grows on ATP
Tour. Retrieved from http://usatoday30.usatoday.com/sports/
tennis/story/2012-03-14/bnp-paribas-open-indian-wells-pay-
disparity-on-the-atp-tour/53538094/1
Robson, D. (2012b). Prize money distribution roils ATP tour.
Retrieved from http://usatoday30.usatoday.com/SPORTS/
usaedition/2012-03-15-USAEast1SptKTXT1ART_ST_U.htm
Stevegtennis Website. (2011). ATP prize money allocation for 12/27/
10. Retrieved from http://www.stevegtennis.com/2010-atp-ten-
nis-rankings/
Stevegtennis Website. (2013). How tennis has changed over the last
30 years. Retrieved from http://www.stevegtennis.com/2013/02/
how-tennis-has-changed-over-the-last-30-years/
Tennis Australia Web site. (2012). Annual report. Retrieved from
http://assets.tennis.com.au/annual_report/e-brochures/2011-
2012/
Tennis Australia Web site. (2013). Athlete development scholarship
criteria. Retrieved from http://www.tennis.com.au/players/
development/national-academies
Tulle, E. (2008). Acting your age? Sports science and the ageing
body. Journal of Aging Studies, 22, 340–347.
Weiss, M. R., & Petlichkoff, L. M. (1989). Children’s motivation
for participation in and withdrawal from sport: Identifying the
missing links. Pediatric Exercise Science, 1, 195–211.
Witnauer, W., Rogers, R., & Saint Onge, J. (2007). Major league
baseball career length in the 20th century. Population Research
and Policy Review, 26, 371–386.
World Bank Web site. (2012). Gini index. Retrieved from http://
data.worldbank.org/indicator/SI.POV.GINI/
1484 M. K. Bane et al.
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