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Has player development in men's tennis really changed? An historical rankings perspective

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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.
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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,
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
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Has player development in mens tennis really changed? An historical
rankings perspective
Sport Science and Medicine Unit, Tennis Australia, Richmond South, Australia,
Institute of Sport, Exercise and Active
Living, Victoria University, Footscray, Australia,
Biomechanics and Performance Analysis, Australian Institute of Sport,
Bruce, Australia and
School of Sport Science, Exercise and Health, University of Western Australia, Crawley, Australia
(Accepted 26 February 2014)
Tennis federations are regularly faced with decisions regarding which athletes should be supported in nancial terms, and
for how long. The nancial 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 playerscareers, 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 rst professional ranking point
and entry into the Top 100 signicantly 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 50100 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
Identication 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 nancial
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.
Journal of Sports Sciences, 2014
Vol. 32, No. 15, 14771484,
© 2014 Taylor & Francis
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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 signicant, 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 rst 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 playerstran-
sition into professional tennis, as well as the time
spent within the Top 100 (Churn Time) and time
between rst point and nal exit from Top 100
(Career Longevity) were identied 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.
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 dened as Top 10 (where
career peak rank is 10), Top 1150 (where career
peak rank is 1150) and Top 51100 (where career
peak rank is 51100). 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-
tied. 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
rst 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 1150
and Top 51100 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 rst ranking point. A random sample of 30
athletes was selected and manually checked for accu-
racy. 100% of these data were veried 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 variablesin the careers
of elite tennis athletes were proposed. The rst
career milestone to be investigated was the age at
which athletes achieved their rst 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 rst point
(Development Time). The time between an athletes
rst entry into and nal exit from the Top 100
(Churn Time) was measured as the third milestone
variable (Churn Time). Finally, the time taken from
the rst ranking point to the nal 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 athletes
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 athletes
rst 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
ts the exponential curves to the data; however, a
rst order Taylor series expansion, eax 1þax, was
invoked to obtain linear approximations to these
ln E YijxðÞ½¼Wx þMRBiþI(1a)
ijxðÞ¼exp Wx þMRBiþIðÞ
¼exp WxðÞexp MRBiþIðÞ
ijxðÞ1þWxðÞexp MRBiþIðÞ(1c)
Here Yand xare the dependent and independent
variables, respectively, and W, MRB and Irelate to
the tted coefcients 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
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.
Average age of Top 100 athletes
The mean ages of the 10 athletes ranked 110, the
40 athletes ranked 1150 and the 50 athletes ranked
51100 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 rst ranking
OLS regressions were performed, probing the
response of Age of First Ranking to the week of
achievement of rst point and minimum ranking
band, and no signicant 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 signicant with F(2, 270) =
9.89, P< 0.0001. Post-hoc analysis revealed all
groups to be signicantly different at α= 0.05
= 1.03 to 0.17 years and p
= 0.0062,
= 1.57 to 0.67 years and
< 0.0001,
= 0.93 to
0.11 years and p
= 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 1150 and Top 51100
athletes, respectively.
Figure 1. The mean age of the 10 athletes ranked 110, the 40 athletes ranked 1150 and the 50 athletes ranked 51100 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 gure.
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Development time
OLS Poisson regressions were performed, probing
the response of Development Time to the week of
achievement of rst 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 signicant 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 signicant.
Independent samples t-tests revealed all groups
were signicantly different at α=0.0001
= < 0.0001, p
= < 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 1150 and Top 51100 athletes, respectively.
It is of note that the approximation made in
Equation (1c) is valid, since the magnitude of the
tted 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 1150 and Top 51100
athletes, respectively.
Churn time
SincemanyTop51100 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 conrmed 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 rst ranking for athletes of different minimum ranking bands.
1480 M. K. Bane et al.
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cohort required transformation, all three were tted
in separate OLS regressions, probing the response
of Churn Time to the week of achievement of the
rst point. As before, over-dispersion was apparent
in these data, and a quasi-Poisson regression model
was required. Only the Top 1150 cohort exhibited
asignicant 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 1150 athletes were considered, which is sig-
nicant 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 1150 and Top 51100 athletes, respec-
tively. For Top 51100 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 rst point and minimum ranking
band. No signicant overall effects for Week, or
interaction between Week and minimum ranking
band were observed; however, visual inspection
showed Top 51100 athletes exhibited a positive
trend (see below). Therefore we performed a one-
way ANOVA grouped by minimum ranking band
(see Figure 5a), which was signicant with
F(2, 270) = 60.40, P< 0.0001. Independent sam-
ples t-tests revealed all groups to be signicantly
different (
= 100 to 51 weeks and
< 0.0001,
= 134 to 235
weeks and p
< 0.0001,
105 to 46 weeks and p
< 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 1150
and Top 51100 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, Top1150 and Top
51100 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 51100 cohort
between career longevity, week and minimum ranking band.
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Supplementary OLS Poisson regressions involving
only data regarding the Top 51100 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 signicant 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
Professional mens tennis has undergone signicant
change since the mid-1980s. Perhaps the most inu-
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).
milestones in an athletes 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 1150 athletes, whereas a statistically
signicant increase was noted among the Top 51
100 athletes. It is important to note that the nd-
ings for the Top 10 cohort were not inconsistent
with the previous ndings (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 110
and Top 1150 and Development Time for Top
51100 athletes. Development time has generally
increased over time; however, Churn Time has
not, therein necessitating that the portion of an
athletes career spent within the Top 100 has
diminished. Signicantly though, Career
Longevity does not encompass the entirety of an
athletes career, and beyond what this study has
operationally dened 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 nancial 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 coefcient, a
measure of the disparity between a populations rich-
est and poorest members, of the current ATP prize-
money allocation is 0.428 (Robson, 2012a), compar-
able to Nigerias and Moroccos economies (World
Bank Web site, 2012). This is up from 0.324 in 1990
(Robson, 2012a), which is comparable to
Switzerlands and Canadas 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 nancial 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,
Top 10 and Top 1150 spend far greater portions
of their career at a higher earning capacity compared
to Top 51100 athletes. Only Top 51100 athletes
are showing increasing Career Longevity and, in
part, this unique trend may be the product of these
athletesneed to compete for longer for the above-
mentioned nancial 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, &
Hannan, 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 rst point increased between 1985
and 2010 was rejected. Approximately 70% (i.e. ±1
std. devs.) of Top 10, Top 1150 and Top 51100
athletes achieve their rst point between the ages of
1618.2, 16.319.1 and 16.419.7, respectively.
There was a signicant 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 rst ATP ranking between the
ages of 14 and 16. Our results show that most Top
100 athletes are likely to achieve their rst ATP point
signicantly later in their career; however, this fails
to account for any nuance associated with a players
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-nals, 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.
The results of the study indicate that athlete devel-
opment time has signicantly increased between
1985 and 2010. This appears to be at the expense
of time spent within the Top 100 for Top 1150
athletes. The career length of Top 51100 athletes
has increased, possibly owing to improving sport
science, medicine and training practices, as well as
a perceived nancial 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 nancially 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 nancially viable pro-
fessional tennis careers. That is, as few as approxi-
mately 130 athletes currently earn sufcient prize
money to be self-sufcient, yet in 2010, the top 5
athletes earned a sum of $30 million, enough to
theoretically sustain 150 athletes.
Association of Tennis Professionals Web site. (2011). Singles rank-
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Association of Tennis Professionals Web site. (2012). Australian
open offer $30M prize money.Retrievedfromhttp://www.atp-
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.
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... This variable, ranking level, created three ranking bands: Top 10, Top 11-50 and Top 51-100. This division into three parts, which has been used in previous studies, (17) represents a slight departure from the usual top 10, top 50 and top 100 breakdown. From the standpoint of statistics it has the advantage of providing three independent subsets. ...
... In a very unique study looking at pooled ranking data since the beginning of official ATP rankings (1973 to 31 December 2010) the mean ± standard deviations for an athlete to go from his first point till Top 100 was 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. (17) Our data for the TET (199.68 weeks + 98.80) to go from the Top 1000 ranking to a Top 100 ranking) for the current Top 100 players. This approximate fours year average timeline is rather similar to the same analysis recently performed on the men' s professional circuit over the same time period and using the same methodology. ...
... Also, this data highlights that it takes significantly more time to progress through the rankings compared to the historical male data analysis. (17) Multiple reasons exists why the Top 1000 is a better marker for starting a professional career when determining milestone related data. Achieving a Top 1000 ranking requires approximately 9 WTA ranking points in 2014. ...
... A career benchmark is the timing of a major career milestone. Following the approach adopted by Bane et al. (2014), the milestones investigated included the age and years to achieve a first ranking, a first top 100 ranking, and last top 100 ranking. Benchmarks were summarised by the mean across top 100 players. ...
... d = 1.7) at this milestone. These ages imply that the years taken to break into the top 100 ("development time" as in Bane et al., 2014) were 2.3, 3.4, 4.1, and 5.2 for top 10, top 11-20, top 21-50, and top 51-100 players, respectively (Figure 1). In a separate analysis, we found that the times to break into the top 100 were statistically equivalent to the tennis evolution time, defined by Kovacs et al. (2015a) as the transition time from a ranking of 1000 to 100. ...
... Previous work on the career progression of male tennis players also highlighted the importance of players distinguishing themselves among their age groups early on Bane et al. (2014). In contrast with findings in the men's game, we have shown that the characteristics of the ranking trajectories for female players were more distinct when described as a function of age rather than ranking year . ...
Official rankings are the most common measure of success in professional women’s tennis. Despite their importance for earning potential and tournament seeding, little is known about ranking trajectories of female players and their influence on career success. Our objective was to conduct a comprehensive study of the career progression of elite female tennis talent. The study examined the ranking trajectories of the top 250 female professionals between 1990 and 2015. Using regression modelling of yearly peak rankings, we found a strong association between the shape of the ranking trajectory and the highest career ranking earned. Players with the highest career peak ranking were the youngest when first ranked. For example, top 10 players were first ranked at age 15.5 years (99% CI = 14.8–15.9), 1.2 years (99% CI = 0.8–1.5) earlier than top 51–100 players. Top 10 players were also ranked in the top 100 longer than other players, holding a top 100 ranking until a mean age of 29.0 years (99% CI = 27.8–30.3) compared with age 24.4 years (99% CI = 23.7–25.2) for top 51–100 players. Ranking trajectories were more distinct with respect to player age than years from first ranking. The present study’s findings will be instructive for players, coaches, and administrators in setting goals and assessing athlete development in women’s tennis.
... This is connected to the research by Bane et al. (2014), who compared the career trajectories of athletes between 1985 and 2010. They detected differences between the points when the athletes achieved their first points on the circuit to the moment of getting into TOP 100. ...
... The previous research studying the factors affecting success in tennis (De Bosscher et al., 2003) also focused on the competitive environment based on the number of tournaments (Galenson, 1993;Crespo et al., 2001;Filipcic et al., 2013). The second factor was the level of quality the junior players must achieve to continue in their adult professional careers (Reid et al., 2006;Reid et al., 2009;Brouwers et al., 2012), followed by its trajectory (Guillaume et al., 2011;Reid & Morris, 2011;Reid et al., 2014;Bane et al., 2014;Kovalchik et al., 2017;). Other research works focused on social and economic factors affecting the athlete's career. ...
... In along -term programme of training a young player, apart from learning techniques and tactics, physical fitness should be alsodeveloped. The effectiveness of playing on acourt largely depends to a greatextent on the tennis player's motor skills and physical fitness [2][3][4][5]. ...
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Introduction: In order to reach the highest level of tennis it is essential to begin trainings in early childhood. In along-term training program of a young competitor, apart from learning techniques and tactics,physical fitness should be also developed. The effectiveness of the game on the court largely dependson motor abilities and the physical fitness of the tennis player. The aim of the study was an attempt to establish an interrelationships between the rates of motor abilities in general and special characteristics of tennis players aged 10-14. Material and method: The research was carried out on a group of 40 tennis players.For assessment of the level of general efficiency a European test of the physical fitness-Eurofit was used. However, special efficiency was assessed with four tennis tests. The results of the tests showed systematic development of general and special efficiency at certain rates and a continually significant diversity between training players of particular age groups, especially amongst the youngest tennis players. Conclusion: 1. Among the examined male and female tennis players, there was a significant variation at the level of general and special efficiency in particular age groups. 2. A statistically significant dependence was found between the results of general and special fitness tests. 3. There is a need to conduct general and special tests in order to program the training process properly for tennis players.
... 20 Some models of athletic development suggest that by age 13 years, the adolescent should decide whether to specialize in his favorite sport or to continue in playing sports recreationally. 21 It is not appropriate for an adolescent to engage in intense training before age 16 years, as it is after this period that athletes develop physically, cognitively, socially, and emotionally and therefore acquire the skills necessary for intense and specialized training. 22 Therefore, despite the models, possibly some of the youth tennis players investigated are not following the recommendations. ...
Objective: Investigate the relationship between the initiation age and practice time, in training and competition, and the ranking position of youth elite tennis players. Method: Participated 130 youth elite tennis players with a Brazilian ranking (102 boys and 28 girls) aged 13–18 years, selected in two international competitions. A Binary logistic regression was performed. Results: The results showed that tennis players who started earlier have a 28% better chance of reaching the top 20 ranking, and that each additional year of training increases the chance of a tennis player to reach the top 20 by 1.43 times. Also, each year of experience in competitions increases the chance of tennis players to reach the top 20 ranking by 1.41 times, and that an earlier start each year in participating in competitions increases the chances of an athlete to reach the top 20 by 20%. Conclusion: The initiation age of training and the experience in competitions are important factors there are related to better ranking positions of youth elite tennis players.
... The process of tournament actions grows in speed and dynamic because of offensive and aggressive style of playing. Contemporary rivalry in tennis characterises by possibility of playing of finishing hit from each position at the court [1,7,23,24]. conclusions 1. There is a tremendous variation among examined female tennis players at the level of overall and special efficiency in the group of female tennis players aged 9 and 10. ...
This study analysed the competition scheduling of future top 100 and 250 ranked tennis players from international tournament profiles at ages 13–18y. Retrospective tournament data were analysed for 165 future top 100 (T100) and top 250 (T250) males during their junior international tournament eligibility. Tournament/match volumes, days between tournaments and consecutive tournaments (<8 days between) were quantified for junior and professional events. A two-way (age x ranking) analysis of variance determined the effects of age and ranking group on tournament profiles. Significant interactions were observed for tournament volumes across junior and professional categories, with T100 players competing in professional tournaments earlier (p<0.05). No significant interactions were observed for volumes of junior or professional matches played (p>0.05). No significant interactions were observed for days between tournaments or consecutive tournaments played (p>0.05). Significant main effects were observed for age on tournament volume, with junior and professional volume increasing at age 15 and 17, respectively (p<0.05). Higher match volumes were observed for T100 players compared to T100-S players (p<0.05). Competition schedules intensify at age 15 compared with ages 13–14y through increased tournament and match volumes. Future T100 players’ transition to professional tournaments earlier, alongside greater engagement in higher quality junior tournaments.
The Official World Golf Ranking (OWGR) offers a rich data source that may be used by golf National Sporting Organisations (NSOs) to inform the allocation of human and financial capital. Golf has undergone many changes over the past few decades, thus before rankings data can be used for benchmarking purposes it is crucial to appraise its temporal stability. This study aimed to determine whether the ranking pathways of top 100 golfers have changed over time. Data were collected on 470 golfers who entered the top 100 between January 1990 and December 2018. Golfers were assigned to birth-year defined cohorts: Cohort 1 (1989–1999) (n = 79); Cohort 2 (1979–1988) (n = 153); Cohort 3 (1969–1978) (n = 174); and Cohort 4 (1959–1986) (n = 64). Descriptive statistics were reported for ranking milestones and one-way ANOVAs used to investigate temporal trends. Golfers from younger age cohorts reached milestones at significantly earlier ages and in less time than their older peers. For instance, the time taken to reach the OWGR top 100 for Cohort 1, 2, 3, and 4 was 3.55, 5.99, 7.72, and 10.23 years, respectively. Together, these findings highlight the temporal instability of golf rankings data and provide scientific data to inform athlete selection and investment decisions.
This review poses three key issues that will progress our understanding of the sport expertise literature and its translational scientific impact. Primarily relying on research conducted in interceptive sport tasks, and to a lesser extent team sports, we review the perceptual-cognitive skills of sports experts and explore the challenge of designing a sufficiently representative task to examine expertise. We focus on the methodological challenges presented by the reciprocal relationship between players’ action capabilities and their perceptual-cognitive skill. Second, we consider the need for a paradigm shift in the experimental approach used when examining the development of sport expertise. In short, a shift from traditional expert-novice designs to more prospective longitudinal designs that cross-sectionally track the development of expertise is discussed. The final issue considers how the volume of in situ data now collected provides a rich source of information that sport expertise researchers have only begun to consider and integrate with more traditional sport psychology research. We demonstrate how statistical approaches that have described the likely trajectories of expert performers on their journey toward expertise coupled with more traditional qualitative experimental approaches can provide useful insights into the development of psychological performance skills and more broadly sport expertise. © 2017
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Variants of Development of Sports Career of 11-Year-Old Female Swimmers Introduction. The development of a sporting career takes a course within a phased manner, the various stages are characterized by different goals and tasks. In competitive swimming there are many cases of athletes who achieved high sporting results in the category of children and youths and who soon after abandoned their interest in training. The aim of this study was to find the association between the sports achievements of 11-year-old girls competing in the 200 m backstroke events and their sports level in the subsequent years of their career. Material and methods. The results of the sporting careers of girls who ranked from 1 st to 20 th in the 200 m backstroke event in the Polish Correspondence Championships for 10 and 11-Year-Old Children in 2003 were presented. Their positions taken in subsequent editions of the summer Polish championship till 2009 were the criterion of their career development. An analysis of documents was used as a scientific method. The basis for the analysis were post-competition protocols listed on the official website of the Polish Swimming Federation. Results. Only 30% of the examined athletes took part in all main events intended for their age. Less than half of them participated in the Polish 17-18-Year-Old Junior Championships organized within the Nationwide Juvenile Olympics. 40% of the girls from the studied group ended their careers before the age of 15. Four basic variants of development of a sporting career were observed in the group. Conclusions. After analyzing the athletes' careers it can be stated that a high position in the national ranking of 11-year-olds does not guarantee significant sporting achievements in later years. In the studied group of girls the stroke and distance specialization had changed in most cases (80%).
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Despite a sports analytics research history that goes back more than 50 years and a recent dramatic rise in the level of scholarly interest in sports analytics, no prior research has attempted to identify its scope, scale, and growth in terms of the body of published refereed articles in the literature. Prior research has also not identified the "players" in the field: the journals and institutions that most commonly publish sports analytics research and are most commonly cited. To answer these questions, I examined 140 journals in operations research, statistics, applied mathematics, and applied economics, and identified 1,146 articles that address the application of analytics in sports. The results provide a picture of the size and nature of sports analytics research and its purveyors, and offer some perspective on the parameters of the field.
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Background: Anterior cruciate ligament (ACL) and meniscus injuries are common in female athletes participating in cutting and pivoting sports such as basketball. The epidemiological characteristics of injury in athletes seen at the Women's National Basketball Association (WNBA) combine and the effect of ACL reconstruction and meniscus surgery on longevity in the WNBA are unknown. Purpose: To evaluate the details and spectrum of injuries in athletes entering the WNBA combine and to assess the potential effect of specific injuries on the round drafted into the WNBA and career length. Study design: Descriptive epidemiology study. Methods: Demographic data and the documented collegiate injury profile were reviewed from the WNBA database for all players entering the WNBA combine in 2000-2008. The study included injury data on 506 athletes. Complete demographic data were available for 496 players. Results: Of the athletes taking part in the combine, 45.2% were guards, 33.7% were forwards, and 21.1% were centers. Ankle sprain (47.8% of players), hand injury (20.8%), patellar tendinitis (17.0%), ACL injury (15.0%), meniscus injury (10.5%), stress fracture (7.3%), and concussion (7.1%) were the most common injuries reported. Seventy-three athletes (14.4%) reported ACL reconstruction before entering the WNBA combine, and meniscus surgery was the next most common surgery (n = 50 players; 9.9%). There were no differences in ACL or meniscus surgery when analyzed by player position or round drafted. History of ACL or meniscus surgery did not affect career length in the WNBA. Excluding ACL and meniscus surgery, other reported surgical procedures were knee arthroscopic surgery (11.7%), ankle reconstruction (2.6%), and shoulder stabilization (2.0%). Conclusion: The ankle is the most common site of injury and ACL reconstruction is the most common surgery in elite female athletes participating in the WNBA combine. A history of injury or surgery did not affect the round drafted or career length.
Children’s reasons for participating in sport as well as their reasons for discontinuing involvement have been extensively studied over the last decade. However, a complete understanding of the underlying processes influencing these phenomena has been clouded by failing to consider a number of individual difference and contextual factors related to sport participation. These missing links include participant status group differences, program type, level of intensity, type of sport, particular reasons for attrition, multiple assessments across a season, developmental differences, and the social structure surrounding the sport experience. Future research possibilities and practical implications for pediatric educators are provided.
We use National Football League (NFL) data to analyze the impact of minimum salaries on an employee’s career length. The NFL has a salary structure in which the minimum salary a player can receive increases with the player’s years of experience. Salary schedules similar to the NFL’s exist in public education, federal government agencies, the Episcopalian church, and unionized industries. NFL data allows us to control for a player’s productivity. We find statistically significant evidence that minimum salaries shorten career length when they require teams to increase a player’s base salary or total compensation from year t to year t plus 1.
Talent identification at a young age is deemed essential for many national sporting organisations to increase the chances of success for their players on the international stage. Talent identification methods can be imprecise and national tennis associations and coaches often identify talent based on performances at youth tournaments and junior rankings. However, not much is known about the relationship between the international competition performances of young tennis players and later success. This relationship is explored in this study using comparisons based on: (a) the results of 3521 players at U14 youth tournaments; (b) the rankings of 377 junior players (U18) by the International Tennis Federation; (c) the rankings of 727 professional male players by the Association of Tennis Professionals; and (d) the rankings of 779 professional players by the Women's Tennis Association. Junior performances (U18) and performances at youth tournaments (U14) appear to have a low success rate in predicting later success. No distinct age was found at which players should start to perform in order to be successful at the professional level. It is concluded that even though good performances at young ages increase athletes’ chances to become elite players, they are not a precondition for achieving later success. Therefore, this study informs talent scouts, sport development officers, coaches and high performance managers of the role that performances at international youth competitions may play in talent identification in tennis.
Abstract In men's professional tennis, players aspire to hold the top ranking position. On the way to the top spot, reaching the top 100 can be seen as a significant career milestone. National Federations undertake extensive efforts to assist their players to reach the top 100. However, objective data considering reasonable ranking yardsticks for top 100 success in men's professional tennis are lacking. Therefore, it is difficult for National Federations and those involved in player development to give empirical programming advice to young players. By taking a closer look at the ranking history of professional male tennis players, this article tries to provide those involved in player development a more objective basis for decision-making. The 100 names, countries, birthdates and ranking histories of the top 100 players listed in the Association of Tennis Professionals (ATP) at 31 December 2009 were recorded from websites in the public domain. Descriptive statistics were reported for the ranking milestones of interest. Results confirmed the merits of the International Tennis Federation's junior tour with 91% of the top 100 professionals earning a junior ranking, the mean peak of which was 94.1, s=148.9. On average, top 100 professionals achieved their best junior rankings and earned their first ATP point at similar ages, suggesting that players compete on both the junior and professional tours during their transition. Once professionally ranked, players took an average 4.5, s=2.1 years to reach the ATP top 100 at the mean age of 21.5, s=2.6 years, which contrasts with the mean current age of the top 100 of 26.8, s=3.2. The best professional rankings of players born in 1982 or earlier were positively related to the ages at which players earned their first ATP point and then entered the top 100, suggesting that the ages associated with these ranking milestones may have some forecasting potential. Future work should focus on the change in top 100 demographics over time as well as the evaluation of the interaction between rankings and tournament play.
The medicalisation of aging and old age constructs ageing as first and foremost a biomedical event and as a process of inevitable decline. In sports science and sports medicine the functional decrements normally associated with ageing are being addressed. There is evidence reported in the scientific literature suggesting that certain exercise interventions can ‘reduc[e] or prevent[…] functional declines linked to secondary aging’ [Goggin, N.L., and Morrow, J.R. Jr. (2001). "Physical Activity Behaviors of Older Adults." Journal of Aging and Physical Activity 9, 58–66.].However a sociological critique is necessary. Whilst sports science seeks to position itself as a key player in the fight against ageing, it also opens the potential for the reconstruction of the ageing body as fit. However the evidence that exercise can fundamentally reshape older bodies is equivocal. A new frame is proposed which divorces exercise from anti-ageing purposes and uses the science of exercise to enable older people to recover a sense of physical competence as a creative pursuit in its own right.