Desporto - Ténis (Movement Characteristics of Elite Tennis Players on Hard Courts with Respect to the Direction of Ground Strokes)

(Personal underlines)

 ©Journal of Sports Science and Medicine (2013) 12, 275-281

http://www.jssm.org

Research article

Movement Characteristics of Elite Tennis Players on Hard Courts with Respect

to the Direction of Ground Strokes

Rafael Martínez-Gallego 1 , José F.Guzmán 1, Nic James 2, Janez Pers 3, Jesús Ramón-Llin 1 and

Goran Vuckovic 4

1 Faculty of Physical Activity and Sport Sciences, University of Valencia, Spain; 2 Sport Institute, University of Middle-

sex ,London, England; 3 Faculty of Engineering, University of Ljubljana, Ljubljana, Slovenia; 4 Faculty of Sport, Uni-

versity of Ljubljana, Ljubljana, Slovenia

Abstract

Previous studies of movement characteristics in tennis have

considered the effect of playing surface but have assumed that

playing strategies are simply determined by the surface as op-

posed to being under an individual’s control. This study consid-

ered the selection of cross court or down the line ground strokes

as being indicative of playing strategy and measured the out-

come of playing these shots in terms of the opponent’s move-

ments. Matches (N = 8) at the 2011 ATP tournament 500 Valen-

cia were recorded and analysed using SAGIT, a computer vision

tracking system that allowed both players’ movements to be

tracked automatically, albeit with operator supervision. The data

was split into (N = 188) games for analysis purposes and these

lasted a median 174.24 seconds with active time (ball in play) a

median proportion of 34.89% (IQR = 10.64%) of total time.

During the active time losers of games tended to cover less

distance (median = 80.17 m), move quicker (median = 1.38 m·s-

1), spend more time in the defensive zones (median = 14.24 s)

and less in the offensive zones (median = 44.74 s). These results

suggested that game winners tended to dominate game losers,

forcing them to exhibit behaviors typically associated with a

defensive strategy. Defensive and offensive strategy are not well

defined currently and future investigations should consider

movements in relation to individual shots, in particular their

velocities, at the rally level and by different individuals to better

understand successful performance.

Key words: Motion analysis, tactics, winners, losers, differ-

ences.

Introduction

Tennis is a dynamic and complex game in which players

repeatedly make decisions regarding positioning and shot

selection (O’Donoghe and Ingram, 2001). Notational

analysis allows these dynamic and complex situations to

be measured objectively, in a consistent and reliable man-

ner, so that critical events can be quantified during tennis

competition (Gillet et al., 2009; Hughes and Barlett,

2007). This type of analysis has been widely applied to

racket sports in four main areas, tactical evaluation, tech-

nical evaluation, movement analysis and for creating

databases and modelling (Hughes et al., 2007).

A significant number of studies have addressed dif-

ferent performance indicators in tennis such as timing

factors (Hughes and Clarke, 1995; O’Donoghue and Lid-

dle 1998), rally length (O’Donoghue and Ingram, 2001),

point profiles (O’Donoghue, 2006), serve and serve-return

performance (Gillet et al., 2009; Hizan et al., 2011;

Loffing et al., 2009), patterns of play (Hughes and Clarke,

1995) and distance covered (Fernandez-Fernandez et al.,

2009; Filipcic et al., 2006; Suda et al., 2003).

Other studies about elite tennis strategies deter-

mined the influence of the gender of the player and court

surface on elite tennis strategy showing that both vari-

ables have a significant influence on the nature of singles

tennis at Grand Slam tournaments in terms of rally types

(baseline or net) and length (O’Donoghue and Ingram,

2001). In a similar manner O’Donoghue (2006) analysed

the influence of the type of game (normal or tie-break) on

the type of point (net or baseline) for male and female

players and found that female players play more cau-

tiously during tie-breaks than during normal games by

staying at the baseline to a greater extent, however male

players did not have a significant reduction in net points

during tie-breaks.

Previous movement analysis studies have shown

that the winner of a professional singles match of 66 min-

utes duration covered 3705 meters in comparison to the

loser who covered 3045 meters (Martínez-Gallego et al.,

2012). Suda et al. (2003) analyzed a female singles match

of 82 minutes duration and found that one of the players

covered 6932 meters. Filipčič et al. (2006) analyzed the

distance covered by male and female young players,

comparing the two genders and the winners with losers,

and found no significant differences in any comparison.

On the other hand, Fernandez-Fernandez et al. (2009) in a

comparative study on activity profiles and physiological

demands of advanced and recreational level veteran play-

ers, analyzed the distances travelled by both groups of

players, found significantly higher values for distance

travelled by the advanced level players.

Whilst the importance of the direction of ground-

stroke shots has been recognized by coaches and players,

the scientific literature has not adequately investigated

this aspect of the game. The literature on anticipation in

racket sports has recognised that players are able to de-

termine where a shot is to be played prior to the shot

being played (for a review see James and Patrick, 2004).

However, James and Bradley (2004) maintained that

much of the anticipatory behavior seen in studies of play-

er movements in racket sports was a consequence of play-

ers’ assessments of situational probabilities based on

previous knowledge of likely shot selection. In other

words shot selection was thought to be determined, to

Received: 31 August 2012 / Accepted: 27 November 2012 / Available online: 11 April 2013 / Published: 01 June 2013276

some extent, by the previous shot and court location of

the two players. To date there is also a dearth of literature

providing information about the distance covered by elite

players. Consequently, the aim of this research was to

analyze the distance covered in relation to the direction of

groundstrokes and to establish whether there are differ-

ences between winners and losers.

Methods

Sample of matches and participants

Matches were recorded at the ATP tournament 500 Va-

lencia (n = 8) in 2011 containing 11 professional players

(age 24.8 ± 2.9) ranked between 5 and 113 on the ATP

ranking list during the tournament. A university ethics

committee granted ethical approval and informed written

consent was obtained from the organizing committee of

the tournament.

Procedure

During the competition all matches were recorded with

two IP cameras (Bosch Dinion IP 455, Germany) that

were attached to the ceiling above the court with each one

covering half of the court (Figure 1). The cameras were

then connected to a laptop (located outside the court) to

save all video footage in mpeg-4 format.

Figure 1. Camera locations and video image captured.

The final digital images had a resolution of 384 x

288 pixels at 25 frames per second to avoid video

interlacing problems. A flash from a digital camera was

used to synchronise the two video footages prior to each

match. Digital images were processed by the SAGIT

tracking system that allowed both players’ movements to

be tracked automatically, albeit with operator supervision,

using a computer vision method (Perš et al., 2002; Vučk-

ović et al., 2010). This procedure involved several stages

including calibrating the system to the empty court, a

tracking phase and manual notation of players’ activity

(stroke type, stroke outcome and start and end of rallies).

This activity information was added such that both the

movement and event data were synchronised, and hence

could be assessed in relation to each other when exported

into spreadsheet format. Specific data of interest e.g.

positions of players during a particular activity type (Perš

Tennis movement characteristics

et al., 2005) were identified through a combination of

SQL statements in Microsoft Access (Microsoft, Red-

mond, USA) and data sorting techniques in Microsoft

Excel.

Data treatment

All matches were divided into games for analysis, 188

games in total, to see if this would differentiate perform-

ance between winners and losers of games. This meant

that players could be classified as losers (game losers)

even if they subsequently won the match (since match

outcome was not used as a measure).

Figure 2. Offensive and defensive zones.

The software was programmed to divide each half

of the court into offensive (OZ) and defensive zones (DZ)

(Figure 2). The OZ comprised the whole court area from

1.5 m behind the baseline to the net, where it was thought

the player would be in a state of equilibrium with his

opponent or on the offensive and as such could take some

risk to attack the opponent if he decided to. When the

player was in the DZ it was thought that the aim of the

player would be to minimize risk and try to recover to the

centre of the court. By separating the court in this manner

in the software information could be collected regarding

players’ motion in each zone and all zone specific infor-

mation could be calculated as a proportion of total time

(ball in play (active) and ball not in play (passive)). All

shots were manually added using the frame-by-frame

playback facility in the software. All shots contained

information regarding shot type (serve, ground stroke,

volley, drop), outcome (rally continue, error, winner) and

direction (cross court or down the line). Only ground

strokes (cross court and down the line) were analyzed for

the purpose of this study.

Statistical analysis

All data was exported from SAGIT software to Microsoft

Excel and SPSS v18 for analysis. All data were examined

for normality (Shapiro-Wilks) and with some departures

from normality, multiple outliers and large differences in

variance noted, non-parametric tests and descriptive sta-

tistics were used. Wilcoxon signed ranks tests were used

to test for differences between game winners and losers.

Spearman’s rho was used to assess correlations between

variables.Martinez-Gallego et al. 277

Results

Games lasted a median 174.24 seconds (minimum = 50s,

maximum 716s) with active time (ball in play) a median

proportion of 34.89% (IQR = 10.64%) of total time. As

would be expected total match time correlated well with

active (r = 0.92) and passive time (r = 0.95). However the

relationship between the proportionate ball in play time

and total match time was very low (r = 0.16).

correlation between the players’ speeds reasonably strong

(r = 0.51).

Since a fundamental component of tennis is not on-

ly moving the opponent around the court but to try to put

the opponent into more defensive areas of the court (as

opposed to attacking areas) a further analysis was under-

taken on movement characteristics in the attacking and

defending zones during the active periods of the game.

Figure 3. Distance covered during active (ball in play) period

of a game.

Winners and losers of games unsurprisingly had

similar activity profiles during passive (ball out of play)

time covering between 21.3m and 392.38m at average

velocities between 0.40 m·s-1 and 1.12 m·s-1. However

losers tended to cover less distance (median = 80.17 m,

Figure 3) during the active phases than winners (median =

84.17m; z = 3.81, p < 0.001) with a very high correlation

between the distances run by the two players (r = 0.94).

Figure 5. Time spent in offensive and defensive zones during

active period of a game.

Both winners and losers tended to spend more time

in the offensive zones compared to the defensive zones

(Figure 5) although game losers tended to spend more

time (z = 5.89, p < 0.01) in the defensive zones (median =

14.24s) than the winners (median = 5.86s) and less time

in the offensive zones (median = 44.74s) than the winners

(median = 50.88s).

Figure 4. Average speed during active (ball in play) period of

a game.

However losers tended to move quicker (median =

1.38 m·s-1, Figure 4) during the active phases than win-

ners (median = 1.33 m·s-1; z = 4.39, p < 0.001) with the

Figure 6. Relationship between time in offensive zone by

winner with time in offensive zone by loser.

The time spent by game winners in the offensive

zone correlated more strongly with the time spent by278

Figure 7. Relationship between time in offensive zone by

winner with time in defensive zone by loser.

The time spent in a zone correlated very strongly

with the distance covered in that zone (correlations ranged

between 0.93 and 0.98). Game winners tended to cover a

more distance (median = 62.99m) than losers (median =

61.16m) in the offensive zones (z = 2.98, p < 0.01) and

their average speed was less (median=1.25m/s) than the

losers (median = 1.38 m·s-1; z = 5.93, p < 0.01). However,

game losers tended to cover more distance (median =

19.78 m) than the winners (median = 10.66m) in the de-

fensive zones (z = 4.67, p < 0.001) and their average

speed was also less (median = 1.50 m·s-1) than the win-

ners (median = 1.80 m·s-1; z = 2.22, p = 0.03). The aver-

age speed by a player in any zone did not strongly corre-

late with the time spent in that zone (correlations ranged

between -0.05 and 0.34) or distance covered in that zone

(correlations ranged between 0.03 and 0.45).

The incidence of down the line and cross court

shots (excluding serves, service return and volleys) were

analyzed to determine whether the time and movement

characteristics in the different zones could have been a

consequence of different shot selections. There was no

difference in the proportionate frequency of down the line

and crosscourt drives out of all shots between the winners

(median = 64.29%) and losers (median = 63.64%; z =

1.39, p = 0.16). However losers tended to play more

crosscourt shots (median = 7; Figure 8) than winners

(median = 6; z = 4.91, p < 0.001) and more down the line

shots (median = 4) than winners (median = 3; z = 5.18, p

< 0.001).

At the game level there was no relationship (r =

0.08) between the proportionate occurrence of cross court

shots by one player and the other (Figure 9).

Tennis movement characteristics

game losers in the offensive zone (r = 0.73; Figure 6) than

with the time spent by losers in the defensive zone (r =

0.61; Figure 7). Similarly the time spent by game winners

in the defensive zone correlated more strongly with the

time spent by losers in the offensive zone (r = 0.64) than

with the time spent by losers in the defensive zone (r =

0.15).

Figure 8. Frequency of down the line and cross court shots

by game winners and losers.

Discussion

In this study, winners covered more distance during the

active phases than losers which differs from previous

studies that have found no significant differences between

winners and losers in terms of distance covered

(Martínez-Gallego et al., 2012; Filipcic et al., 2006).

However, these previous studies analyzed total time,

rather than distinguishing between active and passive

time, like this study. This makes it unlikely that differ-

ences would be found since there is more passive time

(about 65% of total time) than active time and both play-

ers are likely to perform very similar activity profiles

during the passive time. Moreover the activity profile

during the passive phase is of limited interest with respect

to game play.

Figure 9. Tendency for game winners and losers to play

cross court shots.

The finding that game winners covered more dis-

tance during ball in play time than losers is maybe coun-

terintuitive but it is likely that a simple average value is

not particularly informative on this occasion. The dataMartinez-Gallego et al. 279

used here involved different players who were at times

both game winners and losers and hence particular types

of player e.g. defense oriented, may have affected the

results enough to change the overall findings. In order to

discriminate winner and loser differences it may be more

advisable to consider winners and losers at the rally level

rather than game level and consider individual player

profiles as opposed to average values for a number of

players.

Game winners spent less time in the defensive

zones than losers which was to be expected as the winner

of rallies in squash were shown to spend more time in the

offensive zone, which also allowed winners to move op-

ponents away from the center of the court (Vučković et

al., 2009).

Some previous studies have showed how players’ strate-

gies have a high influence on energy demands, which

were greater for players who used defensive or counterat-

tacking strategies (Martínez-Gallego et al., 2012;

Fernández et al., 2006; Smekal et al., 2001). Logically,

players who cover greater distances and/or at faster

speeds will have higher energy demands which might

indicate a more defensive strategy i.e. tending to chase

after balls rather than hitting aggressive shots. The losers

in this study tended to cover less distance but at higher

speeds supporting the hypothesis that winners tended to

force losers to adopt more defensive tactics. This conjec-

ture was also supported by the finding that game losers

tended to spend more time, and cover more distance, in

the defensive zones than the winners. The data supports

the contention that winners take the ball earlier more

often than the losers of games and the consequence of this

is that the opponent is forced to play the ball in the defen-

sive area more often. This is likely to be due to an accu-

rate shot by the game winner or an inaccurate one by the

game loser. Other factors such as the speed or recovery

ability of a player could also account for these differences

but this data sample contained the same mixture of play-

ers in both winner and loser categories.

A player’s movements are largely dictated by the

opponent’s choice of shot, which in turn is likely to be

influenced by the previous shot and the court location of

the two players (James and Bradley, 2004). Shot selec-

tions therefore determine player movements and may

confer information about a player’s strategy. On this basis

the direction of groundstrokes was analyzed and the re-

sults obtained show how shot types and shot locations are

insufficient indicators of strategy since ball velocity has

not been considered. It may be the case, for example, that

it is the pace of the shot that is more important than the

direction.

Playing strategy has also been shown to be a con-

sequence of court surface (O’Donoghue and Ingram,

2001; Fernández et al, 2006). Matches analyzed in this

study were played on an indoor hard surface, classified as

Category 3 (Medium) pace, in the International Tennis

Federation classification of court surfaces. On this basis

the results of this study have been expected as conven-

tional wisdom and coaches’ manuals (Crespo and Miley,

1999) suggests that more offensive strategies should be

applied on this type of surface. These results confirm that

the more successful performances tended to be more

typical of offensive strategies i.e. players covered less

distances and played shots from more forward positions.

The extent to which sampling procedures affected

the results of this study are difficult to gauge precisely but

caution must be taken when considering the external

validity of this study due to the small sample size. For

example some of the results here may have be outliers in

relation to a study with a larger sample. Whilst this may

or may not have been true, the finding that the percentage

of active time (in relation to total time) was a median of

34.89% (SD = 10.64%) was far higher than found in any

previous study (e.g. Christmass et al. (1998) found an

average of 23.3% (SD = 1.4%) for International players

on hard courts). It is logical to associate this high active

play value with longer point and game durations although

previous studies have found lower relative active times on

clay courts (the slowest surface) e.g. Fernández et al.

(2005) found an average percentage active time of 18.2%

(SD = 5.8%). Consequently it is suggested that whilst

court surface has a significant influence on match activity,

with a more defensive strategy associated with slower

surfaces like clay (Fernández et al., 2006), the results of

this study suggest that this is too simplistic a generaliza-

tion of playing strategy and what may be considered as

defensive and attacking strategies requires further classi-

fication. In particular the velocity of shots and the avail-

ability of time afforded the opponent would seem logical

variable to investigate.

Conclusion

This study has provided more detailed information of

players’ positioning in the court in respect to shot selec-

tion and shot effectiveness and related this to players’

motion during and between games. The inference that

shot information was indicative of playing strategy was

presented with reference to offensive and defensive zones

on the court. However these zones are likely to have been

too simplistic to produce a comprehensive and totally

accurate picture of a playing strategy. For example the

zones could be further split up with perhaps a neutral zone

between the offensive and defensive zones. The results

found here lead us to believe that there is a need to fur-

ther refine how defensive and offensive strategy are de-

fined and future investigations that consider movements

in relation to shots should consider also individual shots,

in particular their velocities, at the rally level and by dif-

ferent individuals to better understand successful per-

formance.

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Key points

• During the active time losers of games covered less

distance, moved quicker, spend more time in the de-

fensive zones and less in the offensive zones.

• These results suggested that game winners tended to

dominate game losers, forcing them to exhibit be-

haviors typically associated with a defensive strat-

egy.

• There are no differences between the proportion of

cross court shots and down the line shots played by

game winners and game losers.

• Future research should consider individual shots at

the rally level to better understand successful per-

formance and ultimately strategy.

AUTHORS BIOGRAPHY

Rafael MARTÍNEZ-GALLEGO

Employment

PhD student

Degree

Master Degree

Research interests

Performance Analysis in racket sports.

E-mail: ramargal@hotmail.com

José Francisco GUZMÁN LUJÁN

Employment

Professor at the University of Valencia,

Faculty of Sport Sciences and Physical Ac-

tivity

Degree

PhD

Research interests

Sports Psychology, Performance Analysis.

E-mail: Jose.F.Guzman@uv.es

Nic JAMES

Employment

Head of research for the London Sport Insti-

tute at Middlesex University, London.

Degree

Professor

Research interests

Performance Analysis - profiling perform-

ance, momentum, performance indicators,

reliabilty, automatic tracking of movement.

Sports Psychology - situation awareness,

anticipation, decision making, motor skills

Main sports studied - soccer, rugby, squash,

golf

E-mail: n.james@mdx.ac.uk

Janez PERŠ

Employment

Assistant Professor at the Faculty of

Electrical Engineering, University of

Ljubljana, Slovenia.

Degree

PhDMartinez-Gallego et al. 281

Research interests

Computer vision, pattern recognition, human

motion analysis.

E-mail: janez.pers@fe.uni-lj.si

Jesús RAMÓN-LLIN MAS

Employment

Technician at University of Valencia

Degree

Master degree, PhD student

Research interests

Performance Analysis in racket sports.

E-mail: jeramas@hotmail.com

Goran VUČKOVIĆ

Employment

Assistant Professor at the University of

Ljubljana, Faculty of Sport, Slovenia.

Degree

PhD

Research interests

Performance and Time-Motion Analysis in

different sports.

E-mail: goran.vuckovic@fsp.uni-lj.si

Rafael Martínez-Gallego

Faculty of Physical Activity and Sport Sciences, University of

Valencia, Spain