The effects of situational variables on distance covered at various speeds

Abstract

The aim of this study was to examine the effect of match location, quality of opposition, and match status on distance covered at various speeds in elite soccer. Twenty-seven Spanish Premier League matches played by a professional soccer team were monitored in the 2005–2006 season using a multiple-camera match analysis system. The dependent variables were the distance covered by players at different intensities. Data were analysed using a linear regression analysis with three independent variables: match status (i.e. whether the team was winning, losing or drawing), match location (i.e. playing at home or away), and quality of the opponents (strong or weak). The top-class players performed less high-intensity activity (>19.1 km · h) when winning than when they losing, but more distance was covered by walking and jogging when winning. For each minute winning, the distance covered at submaximal or maximal intensities decreased by 1 m (P

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The effects of situational variables on distance covered at various speeds in
elite soccer
Carlos Lago a; Luis Casais a; Eduardo Dominguez a; Jaime Sampaio b
a Facultad de CC da Educacion e o Deporte, Universidad de Vigo, Pontevedra, Spain b Department of
Sport Sciences, University of Trás-os-Montes e Alto Douro, Vila Real, Portugal
Online publication date: 08 February 2010
To cite this Article Lago, Carlos, Casais, Luis, Dominguez, Eduardo and Sampaio, Jaime(2010) 'The effects of situational
variables on distance covered at various speeds in elite soccer', European Journal of Sport Science, 10: 2, 103 — 109
To link to this Article: DOI: 10.1080/17461390903273994
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ORIGINAL ARTICLE
The effects of situational variables on distance covered at various
speeds in elite soccer
CARLOS LAGO
1
, LUIS CASAIS
1
, EDUARDO DOMINGUEZ
1
, & JAIME SAMPAIO
2
1
Facultad de CC da Educacion e o Deporte, Universidad de Vigo, Pontevedra, Spain, and
2
Department of Sport Sciences,
University of Tra
´s-os-Montes e Alto Douro, Vila Real, Portugal
Abstract
The aim of this study was to examine the effect of match location, quality of opposition, and match status on distance
covered at various speeds in elite soccer. Twenty-seven Spanish Premier League matches played by a professional soccer
team were monitored in the 20052006 season using a multiple-camera match analysis system. The dependent variables
were the distance covered by players at different intensities. Data were analysed using a linear regression analysis with three
independent variables: match status (i.e. whether the team was winning, losing or drawing), match location (i.e. playing at
home or away), and quality of the opponents (strong or weak). The top-class players performed less high-intensity activity
(19.1 km ×h
1
) when winning than when they losing, but more distance was covered by walking and jogging when
winning. For each minute winning, the distance covered at submaximal or maximal intensities decreased by 1 m (PB0.05)
compared with each minute losing. For each minute winning, the distance covered by walking and jogging increased by
2.1 m (PB0.05) compared with each minute losing. The home teams covered a greater distance than away teams during
low-intensity activity (B14.1 km ×h
1
)(PB0.01). Finally, the better the quality of the opponent, the higher the distance
covered by walking and jogging. Our findings emphasize the need for match analysts and coaches to consider the
independent and interactive effects of match location, quality of opposition, and match status during assessment of
the physical component of football performance.
Keywords: Physical performance, contextual factors, work rate, soccer
Introduction
The physiological demands of soccer have been
studied intensively in male players. Timemotion
analysis research has demonstrated that elite players
typically cover distances of 914 km during a match
(Barros et al., 2007; Di Salvo et al., 2007; Mohr,
Krustrup, & Bangsbo, 2005; Rampinini, Coutts,
Castagna, Sassi, & Impellizzeri, 2007). The type of
exercise in soccer is intermittent, with a change in
activity every 46 s (Bangsbo, 1994; Mohr et al.,
2005). Thus, an international top-class player per-
forms approximately 1330 activities during a match,
including about 220 runs at high speed (Barros
et al., 2007; Di Salvo et al., 2007; Mohr et al., 2005;
Rampinini et al., 2007). Playing a high-level match
can elicit up to 75% of maximal oxygen uptake, with
the anaerobic system contributing greatly during
intense periods (Bangsbo, 1994; Mohr et al.,
2005). Several studies have shown decrements in
physiological performance during matches. In parti-
cular, it has been suggested that high-intensity
running and sprinting decrease from the first to the
second half, probably due to physical fatigue (Barros
et al., 2007; Mohr et al., 2005; Rampinini et al.,
2007; Rampinini, Impellizzeri, Castagna, Coutts, &
Wisløff, 2009).
Given that soccer is dominated by strategic
factors, it is reasonable to suggest that situational
variables may somehow influence the teams’ and
players’ activities. Empirical evidence suggests that
the situational variables of match location (i.e.
playing at home or away), match status (i.e. whether
the team was winning, losing or drawing), and the
quality of the opposition (strong or weak) are the
most important factors for soccer performances
(James, Mellalieu, & Holley, 2002; Jones, James, &
Correspondence: C. Lago, Facultad de CC da Educacion e o Deporte, Universidad de Vigo, Av. Buenos Aires s/n, 36002 Pontevedra,
Spain. E-mail: clagop@uvigo.es
European Journal of Sport Science, March 2010; 10(2): 103109
ISSN 1746-1391 print/ISSN 1536-7290 online #2010 European College of Sport Science
DOI: 10.1080/17461390903273994
Downloaded By: [B-on Consortium - 2007] At: 12:12 12 March 2010

Mellalieu, 2004; Lago & Martin, 2007; Taylor,
Mellalieu, James, & Shearer, 2008; Tucker, Mellalieu,
James, & Taylor, 2005).
According to Bloomfield and colleagues (Bloomfield,
Polman, & O’Donoghue, 2005a) and Taylor et al.
(2008), the importance of these situational factors is
reflected in changes in team strategies in response to
score-line. However, despite the importance of ac-
counting for match location, quality of opposition,
and match status during the assessment of tactical
aspects of soccer performance (Carling, Williams, &
Reilly, 2005; Taylor et al., 2008), very few studies
have examined the relationships between physical
performance during the match and these situational
variables (Bloomfield, Polman, & O’Donoghue,
2005b; Di Salvo, Gregson, Atkinson, Tordoff, &
Drust, 2009; O’Donoghue & Tenga, 2001; Rampinini
et al., 2009; Shaw & O’Donoghue, 2004).
Moreover, the effects of these situational factors
on distance covered at various speeds in elite soccer
are unclear, given that previous research used small
sample sizes and analysed situational variables in-
dependently, thereby neglecting to account for the
complex and dynamic nature of soccer performance
(McGarry & Franks, 2003; Reed & O’Donoghue,
2005). Given these shortcomings, the aim of the
present study was to examine the independent and
interactive effects of match location, quality of the
opposition, and match status on the distance covered
at various speeds in elite soccer.
Methods
Twenty-seven Spanish Premier League matches
played by a professional soccer team were monitored
during the 20052006 season using a multiple-
camera match analysis system (Amisco Pro†, ver-
sion 1.0.2, Nice, France). The movements of all 10
outfield players (goalkeepers were excluded) of the
sampled team were observed throughout matches by
means of eight stable, synchronized cameras posi-
tioned at the top of the stadium (sampling frequency
25 Hz). Only data for those players completing
entire matches (i.e. 90 min) were included in the
analysis. A total of 182 individual items of data from
19 players were used in the study (see Table I).
Signals and angles obtained by the encoders were
sequentially converted into digital data and recorded
on six computers for post-match analysis. Zubillaga
and colleagues (Zubillaga, Gorospe, Hernandez, &
Blanco, 2009) have recently evaluated the reliability
and validity of Amisco Pro†for quantifying dis-
placement velocities during match-related activities
relative to data obtained using timing gates. [For
previous applications of the Amisco system, see
Di Salvo et al. (2007; Di Salvo, Benito, Calderon
Montero, Di Salvo, & Pigozzi, 2008).] The sampled
match results (17 home and 10 away matches)
consisted of 7 wins, 7 draws and 13 losses, with 25
goals scored and 38 conceded by the sampled team.
The team’s overall record for the sampled season
was 10 wins, 11 draws, 17 losses, 36 goals scored,
and 56 goals conceded. We only analysed 27
matches because not all the teams of the Spanish
Premier League have the Amisco system. Moreover,
the sampled team did not use the Amisco system
until the third home match.
Voluntary informed consent was obtained from all
players before the study began. Ethics approval for
all experimental procedures was granted by the
University Human Research Ethics Committee.
Written permission from the sampled club was
received to record and analyse data.
From the stored data, the distance covered, the
time spent in five different intensity categories, and
the frequency of occurrence of each activity for
players in different positions were obtained by specially
developed software (Athletic Mode Amisco Pro†,
Nice, France). Match analyses were performed,
distinguishing between the following five intensity
categories (Di Salvo et al., 2007, 2008): 011 km ×
h
1
(standing, walking, jogging); 11.114.0 km ×h
1
(low-speed running); 14.119.0 km ×h
1
(moderate-
speed running); 19.123.0 km ×h
1
(high-speed
running); 23 km ×h
1
(sprinting). Outfield players
in this investigation were assigned to one of five
positional groups according to their activity on the
pitch: central defenders (n52), external defenders
(n41), central midfield players (n45), external
midfield players (n17), and forwards (n27).
Table I. Summary statistics for the observed outfield players
Player Positional role
Number of matches
observed
Player 1 EM 5
Player 2 EM 3
Player 3 CM 13
Player 4 ED 19
Player 5 CD 13
Player 6 F 5
Player 7 ED 4
Player 8 F 8
Player 9 F 11
Player 10 CD 21
Player 11 F 3
Player 12 CM 20
Player 13 CM 12
Player 14 EM 2
Player 15 EM 7
Player 16 CD 6
Player 17 ED 10
Player 18 ED 8
Player 19 CD 12
Note:CDcentral defender, EDexternal defender, CM 
central midfield player, EMexternal midfield player, F
forward.
104 C. Lago et al.
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Data analysis
To examine how much unique variance in the
dependent variable was explained by each indepen-
dent variable, a standard multiple regression was
used. When estimating the regression models, we
found no evidence of heteroscedasticity in residuals
or multicollinearity among regressors. Moreover, the
RESET test (Ramsey, 1969) did not reveal specifi-
cation problems. The detection of heteroscedasticity
was done according to White’s test. White’s test is
used to establish whether the residual variance of a
variable in a regression model is constant. To test for
constant variance, one regresses the squared resi-
duals from a regression model onto the regressors,
the cross-products of the regressors, and the squared
regressors. One then inspects the R
2
-value. Multi-
collinearity was checked using Klein’s rule, which
states that serious multicollinearity is present if the
R
2
-value of the regression of a predictor variable on
other predictor variables is higher than the R
2
-value
of the original regression.
When interpreting the statistical results, positive
or negative coefficients indicate a greater or lower
propensity to increase/decrease distance covered by
players. The independent variables were the situa-
tion variables:
1. Match status, measured as the total number of
minutes observed in each score-line state (win-
ning, losing or draw). The comparison group is
losing. This means that the panel match status in
the regression model presents two coefficients
from the comparison of drawing and losing and
from the comparison of winning and losing.
2. Match location, a dummy variable indicating if
the game was played at home or away. Playing
at home is the comparison group.
3. Quality of opposition, the difference in the final
ranking (in the current season) of the consid-
ered team and the opponent, i.e.
Quality of opposition P
A
P
B
where P
A
is the final ranking of the sampled team
and P
B
is the final ranking of the opponent.
Statistical analysis was performed using STATA
for Windows, version 10.0 (Stata Corp., Texas,
USA). For all analyses, statistical significance was
set at PB0.05.
Results
The distances covered at different work intensities
by players of different positional roles are presented
in Table II. The effects of match location, quality of
the opposition, and match status on distance
covered at various speeds in elite soccer are dis-
played in Table III.
Total distance covered
The total distance covered was explained by match
location (PB0.01) and quality of the opponent
(PB0.05). In essence, playing away reduced the
total distance covered by 262 m compared with
playing at home. Players covered a greater distance
when playing against better ranked teams. Each
position difference in the end-of-season ranking
between opposing teams increased the total distance
covered by 15 m. When all the independent variables
were zero that is, the team was losing throughout a
match played at home the distance covered by
players was 10,719 m.
Distances covered at submaximal or maximal intensities
The total distance covered at submaximal or max-
imal intensities (19.1 km ×h
1
) was explained by
match status. For each minute winning, the distance
covered at maximal intensity decreased by 0.95 m
(PB0.05) compared with each minute losing. For
example, if the team was losing for the whole 90 min,
the predicted distance covered at maximal intensity
would be 86 m higher than if winning throughout
the match. At submaximal intensity, for each minute
winning the distance covered decreased by 1.1 m
compared with each minute losing. When all the
independent variables were equal to zero, the dis-
tance covered by players was 302 m (maximal
intensity) and 618 m (submaximal intensity).
Distances covered at medium intensities
The total distance covered at medium intensities
(14.119.0 km ×h
1
) was not explained by the
situational variables. When all independent variables
were equal to zero, the distance covered by players
was 1677 m.
Distances covered at low intensities
The total distance covered at low intensities (B14.1
km ×h
1
) was explained by match status, match
location, and quality of the opponent. For each
minute winning, the distance covered walking and
jogging (011 km ×h
1
) increased by 2.2 m (PB0.05)
compared with each minute losing. Accordingly,
each minute winning increased by 2.1 m (PB0.01)
the distance covered at low-speed running (11.114.0
km ×h
1
) compared with each minute losing. Playing
away decreased the total distance covered walking and
jogging and at low-speed running by 144 m (PB0.01)
and 66 m (PB0.05), respectively. Finally, players
Situational variables in elite soccer 105
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Table II. Distance covered (m) at different work intensities by players of different positional roles (standard deviations in parentheses)
Positional role Total distance covered
Walking and jogging
(011 km ×h
1
)
Low-speed running
(11.114.0 km ×h
1
)
Medium intensities
(14.119.0 km ×h
1
)
Submaximal intensity
(19.123.0 km ×h
1
)
Maximal intensity
(23 km ×h
1
)
Central defenders 10 491 (496) 6864 (228) 1611 (181) 1441 (277) 388 (114) 188 (84)
External defenders 11 050 (482) 6791 (245) 1621 (175) 1735 (247) 576 (135) 327 (131)
Central midfielders 11 320 (610) 6941 (401) 1794 (210) 1903 (334) 502 (132) 179 (84)
External midfielders 11 425 (354) 6892 (261) 1671 (278) 1916 (161) 609 (117) 337 (94)
Forwards 10 686 (714) 6813 (251) 1378 (232) 1567 (336) 584 (116) 344 (112)
Table III. The influence of match location, quality of opposition, and match status on the total distance covered (m) during an entire match (standard errors in parentheses)
Total distance covered
Walking and jogging
(011 km ×h
1
)
Low-speed running
(11.114.0 km ×h
1
)
Medium intensities
(14.119.0 km ×h
1
)
Submaximal intensity
(19.123.0 km ×h
1
)
Maximal intensity
(23 km ×h
1
)
Variables Coeff. Beta Coeff. Beta Coeff. Beta Coeff. Beta Coeff. Beta Coeff. Beta
Match status
drawing 3.79 (2.48) 0.16 3.63** (1.10) 0.35 1.68* (0.84) 0.19 0.29 (1.28) 0.02 1.32* (0.55) 0.25 0.48 (0.51) 0.11
winning 2.10 (1.19) 0.10 2.18* (0.97) 0.23 2.13** (0.69) 0.28 1.65 (1.18) 0.01 1.09* (0.51) 0.23 0.95* (0.38) 0.24
Match location 262.47** (11.32) 0.19 143.93** (42.96) 0.23 66.06* (37.85) 0.13 18.27 (55.35) 0.03 19.02 (23.55) 0.06 15.20 (20.16) 0.06
Quality of opposition 15.47* (11.32) 0.12 16.81** (5.27) 0.33 4.99 (3.43) 0.12 2.49 (5.93) 0.04 4.29 (2.71) 0.16 1.54 (2.14) 0.07
Intercept 10719.91** (190.52) 6619.73** (102.98) 1508.53** (58.09) 1677.62** 95.58 617.94** (39.96) 302.07** (33.69)
R
2
0.38 0.37 0.36 0.32 0.42 0.44
Note: Betastandardized coefficients. ** PB0.01; * PB0.05.
106 C. Lago et al.
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covered more distance when playing against better
ranked teams. Each position difference in the end-of-
season ranking between opposing teams increased the
total distance covered walking and jogging by 17 m
(PB0.01). When all the independent variables were
equal to zero, the distance covered by players walking
and jogging was 6620 m and at low-speed running
1508 m.
To clarify the impact of the results presented in the
regression model, Table IV presents a simulated total
distance covered by players at different speeds under
different scenarios. What distance would be covered
by players when the evolving match status differs?
Is it similar when the team plays away against strong
opposition or plays at home against weak opposition?
In Table IV, different possibilities for each situation
variable are showm. For example, the expected dis-
tance covered at maximal intensity (23 km ×h
1
)by
players differs considerably according to match status
(by 31%). If the final result in a match were 10tothe
sampled team and they scored the goal in the first
minute (90 min winning), the distance covered by
players would be 195 m. If the opponent won 10and
scored the goal in the first minute, the distance covered
by players would be 280 m.
Discussion
The results of the present study appear to confirm
that the distance covered at various speeds by elite
soccer players is dependent on match contextual
factors. The results were always influenced by one or
more situational variables, especially match location
and match status. Thus, elite soccer players per-
formed less high-intensity activity when winning
than when they were losing. A 50% decline in the
distance covered at submaximal and maximal
intensities (19.1 km ×h
1
) when winning suggests
that players do not always use their maximal physical
capacity for the 90 min. In fact, given that winning is
a comfortable status for a team, it is possible that
players assume a ball contention strategy, keeping
the game slower, which results in lower speeds
(Bloomfield et al., 2005b). Accordingly, it is obvious
that players performed less low-intensity activity
when losing than when winning in an attempt to
recover from an unfavourable position.
Home teams covered a greater distance than
visiting teams at low intensity (B14.1 km ×h
1
),
but no differences were observed at medium, sub-
maximal or maximal intensities. Despite the fact that
home advantage in soccer is a well-known and well-
documented fact (Brown et al., 2002; Clarke &
Norman, 1995; Nevill & Holder, 1999; Pollard,
1986; Tucker et al., 2005), the precise causes and
their simple or interactive effects on performance
are still not clear. However, the most plausible
Table IV. Simulated distance covered (m) at different speeds depending on match location, quality of opposition, and match status
Home matches Away matches
Match status
Quality of
opposition Total
011
km ×h
1
11.114.0
km ×h
1
14.119.0
km ×h
1
19.123.0
km ×h
1
23
km ×h
1
To t a l
011
km ×h
1
11.114.0
km ×h
1
14.119.0
km ×h
1
19.123.0
km ×h
1
23
km ×h
1
Winning
90 min
Strong
(value 14)
11 125 7195 1770 1627 461 195 10 862 6911 1704 1609 442 180
Winning
90 min
Weak
(value 5)
10 925 6871 1673 1674 542 224 10 562 6587 1609 1656 523 209
Losing
90 min
Strong
(value 14)
10 930 6997 1678 1612 552 280 10 667 6713 1512 1594 533 265
Losing
90 min
Weak
(value 5)
10 636 6674 1595 1651 633 309 10 373 6390 1445 1633 614 294
Situational variables in elite soccer 107
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explanations are: crowd effects, travel effects, famil-
iarity, referee bias, territoriality, specific tactics, rule
factors, and psychological factors (Pollard, 2008).
The distance covered at the lowest intensities (011
km ×h
1
) was also explained by the variable quality of
the opponent. The better the quality of the opponent,
the higher the distance covered by walking and
jogging. These results are similar to the findings of
Mohr and colleagues (Mohr, Krustup, & Bangsbo,
2003) and Rampinini et al. (2009). They found that
more successful teams covered less distance at lower
intensities than players from less successful teams.
In conclusion, the results emphasize the impor-
tance of accounting for match location, quality
of opposition, and match status during the assess-
ment of the physical aspects of soccer performance
(Carling et al., 2005; Taylor et al., 2008). Physical
performance was influenced by the situational vari-
ables, either independently or interactively. The
importance of these factors is reflected in changes
in the teams’and players’activities as a response to
match status. The implications for match analysts
and coaches for evaluating performance and devel-
oping relevant training drills are obvious. Existing
recommendations suggest that the scouting of up-
coming opposition should be carried out under
circumstances that are reflective of the conditions
under which the future match will occur. However,
such procedures are unlikely to be practical due to
time and resource constraints. Consequently, by
establishing the impact of particular situational vari-
ables on performance, teams can be observed, when
possible, with appropriate adjustments being made to
analyses based on knowledge of such effects (Taylor
et al., 2008). Similarly, post-match assessments of
the technical, tactical, and physical aspects of per-
formance can be made more objective by factoring in
the effects of situational variables (Carling et al.,
2005; Kormelink & Seeverens, 1999). Finally, if a
notational analyst or coach has identified that the
technical, physical or tactical aspects of performance
are adversely influenced by specific situational vari-
ables, possible causes can be examined and match
preparation focused on reducing such effects.
References
Bangsbo, J. (1994). The physiology of soccer: With special
reference to intense intermittent exercise. Acta Physiologica
Scandinavica,619, 151.
Barros, R. M. L., Misuta, M. S., Menezes, R. P., Figueroa, P. J.,
Moura, F. A., Cunha, S. A., et al. (2007). Analysis of the
distances covered by first division Brazilian soccer players
obtained with an automatic tracking method. Journal of Science
and Medicine in Sport,6, 233242.
Bloomfield, J. R., Polman, R. C. J., & O’Donoghue, P. G. (2005a).
Effects of score-line on team strategies in FA Premier League
soccer. Journal of Sports Sciences,23, 192193.
Bloomfield, J. R., Polman, R. C. J., & O’Donoghue, P. G.
(2005b). Effects of score-line on intensity of play in midfield
and forward players in the FA Premier League. Journal of Sports
Sciences,23, 191192.
Brown, T., Van Raalte, J., Brewer, B., Winter, C., Cornelius, A., &
Anderson, M. (2002). World Cup soccer home advantage.
Journal of Sport Behaviour,25, 134144.
Carling, C., Williams, A. M., & Reilly, T. (2005). Handbook of
soccer match analysis: A systematic approach to improving perfor-
mance. Abingdon, UK: Routledge.
Clarke, S., & Norman, J. (1995). Home ground advantage of
individual clubs in English soccer. Statistician,44, 509521.
Di Salvo, V., Baron, R., Tschan, H., Calderon Montero, F. J.,
Bachl, N., & Pigozzi, F. (2007). Performance characteristics
according to playing position in elite soccer. International
Journal of Sports Medicine,28, 222227.
Di Salvo, V., Benito, P. J., Calderon Montero, F. J., Di Salvo, M.,
& Pigozzi, F. (2008). Activity profile of elite goalkeepers during
football match-play. Journal of Sports Medicine and Physical
Fitness,48, 443446.
Di Salvo, V., Gregson, W., Atkinson, C., Tordoff, P., & Drust, B.
(2009). Analysis of high intensity activity in Premier League
soccer. International Journal of Sports Medicine,30, 205212.
James, N., Mellalieu, S. D., & Holley, C. (2002). Analysis of
strategies in soccer as a function of European and domestic
competition. International Journal of Performance Analysis in
Sport,2,85103.
Jones, P. D., James, N., & Mellalieu, D. (2004). Possession as a
performance indicator in soccer. International Jour nal of Perfor-
mance Analysis in Sport,4,98102.
Kormelink, H., & Seeverens, T. (1999). Match analysis and game
preparation. Spring City, PA: Reedswain.
Lago, C., & Martin, R. (2007). Determinants of possession of the
ball in soccer. Journal of Sports Sciences,25, 969974.
McGarry, T., & Franks, I. (2003). The science of match analysis.
In T. Reilly, & A. M. Williams (Eds.), Science and soccer (pp.
265275). London: Routledge.
Mohr, M., Krustrup, P., & Bangsbo, J. (2003). Match perfor-
mance of high-standard soccer players with special reference to
development of fatigue. Journal of Sports Sciences,21, 519528.
Mohr, M., Krustrup, P., & Bangsbo, J. (2005). Fatigue in soccer:
A brief review. Journal of Sports Sciences,23, 593599.
Nevill, A., & Holder, R. (1999). Home advantage in sport: An
overview of studies on the advantage of playing at home. Sport
Medicine,28, 221236.
O’Donoghue, P., & Tenga, A. (2001). The effect of score-line on
work rate in elite soccer. Journal of Sports Sciences,19,2526.
Pollard, R. (1986). Game location as a determinant of team
performance in ACC basketball during 19001991. Journal of
Sports Sciences,4, 237248.
Pollard, R. (2008). Home advantage in football: A current review
of an unsolved puzzle. Open Journal of Sports Science,1,1214.
Rampinini, E., Coutts, A. J., Castagna, C., Sassi, R., &
Impellizzeri, F. M. (2007). Variation in top level soccer match
performance. International Journal of Sports Medicine,28, 1018
1024.
Rampinini, E., Impellizzeri, F. M., Castagna, C., Coutts, A. J., &
Wisløff, U. (2009). Technical performance during soccer
matches of the Italian Serie A league: Effect of fatigue and
competitive level. Journal of Science and Medicine in Sport,12,
227233.
Ramsey, J. B. (1969). Test for specification errors in classical lineal
least squares regression analysis. Journal of the Royal Statistical
Society,31, 350371.
Reed, D., & O’Donoghue, P. (2005). Development and applica-
tion of computer-based prediction methods. International
Journal of Performance Analysis in Sports,5,1228.
108 C. Lago et al.
Downloaded By: [B-on Consortium - 2007] At: 12:12 12 March 2010

Shaw, J., & O’Donoghue, P. (2004). The effect of scoreline on
work rate in amateur soccer. In P. O’Donoghue, & M. D.
Hughes (Eds.), Notational analysis of sport VI (pp. 8491).
Cardiff: UWIC.
Taylor, J. B., Mellalieu, S. D., James, N., & Shearer, D. (2008).
The influence of match location, quality of opposition and
match status on technical performance in professional associa-
tion football. Journal of Sports Sciences,26, 885895.
Tucker, W., Mellalieu, S. D., James, N., & Taylor, J. B. (2005).
Game location effects in professional soccer: A case study.
International Journal of Performance Analysis in Sports,5,2335.
Zubillaga, A., Gorospe, G., Hernandez, A., & Blanco, A. (2009).
Comparative analysis of the high-intensity activity of soccer players
in top level competition. In T. Reilly, & F. Korkusuz (Eds.),
Science and football VI (pp. 182185). London: Routledge.
Situational variables in elite soccer 109
Downloaded By: [B-on Consortium - 2007] At: 12:12 12 March 2010