Are classical tests of repeated sprint ability in football externally valid

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Are “classical” tests of repeated-sprint ability
in football externally valid? A new approach to
determine in-game sprinting behaviour in elite
football players
Jan Schimpchen, Sabrina Skorski, Stephan Nopp & Tim Meyer
To cite this article: Jan Schimpchen, Sabrina Skorski, Stephan Nopp & Tim Meyer (2015):
Are “classical” tests of repeated-sprint ability in football externally valid? A new approach to
determine in-game sprinting behaviour in elite football players, Journal of Sports Sciences,
DOI: 10.1080/02640414.2015.1112023
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Are “classical” tests of repeated-sprint ability in football externally valid? A new
approach to determine in-game sprinting behaviour in elite football players
Jan Schimpchena
, Sabrina Skorskia
, Stephan Noppb
and Tim Meyera
a
Institute of Sports and Preventive Medicine, Saarland University, Saarbrücken, Germany; b
Institute of Cognitive and Team/Racket Sport Research,
German Sport University, Cologne, Germany
ABSTRACT
The aim of this study was to investigate the occurrence of repeated sprinting bouts in elite football.
Furthermore, the construct validity of current tests assessing repeated-sprint ability (RSA) was analysed
using information of sprinting sequences as they actually occurred during match-play. Sprinting
behaviour in official competition was analysed for 19 games of the German national team between
August 2012 and June 2014. A sprinting threshold was individually calculated based on the peak
velocity reached during in-game sprinting. Players performed 17.2 ± 3.9 sprints per game and during
the entire 19 games a total of 35 bouts of repeated sprinting (a minimum of three consecutive sprints
with a recovery duration <30 s separating efforts). This averages one bout of repeated sprinting per
player every 463 min. No general decrement in maximal sprinting speed was observed during bouts
with up to five consecutive sprints. Results of the present study question the importance of RSA as it is
classically defined. They indicate that shorter accelerations are more important in game-specific situa-
tions which do not reach speeds necessary to qualify them as sprints. The construct validity of classic
tests of RSA in football is not supported by these observations.
ARTICLE HISTORY
Accepted 2 October 2015
KEYWORDS
Physical performance;
soccer; high-intensity
running; individualised
thresholds; fitness testing;
team sports
Introduction
High-intensity running and sprint-type activities in football
(soccer) have been heavily researched in the past two decades
due to the hypothesis that they are crucial to performance
(Carling, Le Gall, & Dupont, 2012). This has also been under-
lined by Faude, Koch, and Meyer (2012), who identified
straight sprinting as the single most frequent action in goal
situations by either the scoring player or the assisting player.
However, previous studies have shown conflicting evidence
whether or not high-intensity running distance alone can
discriminate between players of different standards (Bradley
et al., 2013; Mohr, Krustrup, & Bangsbo, 2003). Possibly, a mere
focus on total sprint distance – albeit being useful – does not
provide sufficient information about the football-specific phy-
sical demands.
Due to the high-intensity intermittent nature of football
(Bradley, Di Mascio, Peart, Olsen, & Sheldon, 2010; Stølen,
Chamari, Castagna, & Wisløff, 2005), players may be required
to reproduce maximal or near-maximal sprints of short dura-
tion (1–7 s), with only brief recovery periods (consisting of
complete rest or low- to moderate-intensity activity), over an
extended period of time (90+ min) (Bishop, Spencer, Duffield,
& Lawrence, 2001; Carling & Dupont, 2011; Girard, Mendez-
Villanueva, & Bishop, 2011). This property is classically termed
repeated-sprint ability (RSA) and is widely considered a critical
element of football (Rampinini et al., 2007). It has been argued
that games can potentially be won or lost on occasions where
repeated sprinting is required from one or more players and
that the ability to resist fatigue during these bouts of repeated
sprinting may be of great relevance to the outcome of the
game (Girard et al., 2011). Consequently, a number of perfor-
mance tests have been developed in order to evaluate RSA in
football players with the aim to identify potential weaknesses
and prescribe individualised training protocols tailored speci-
fically to the demands of different positional roles (Bush,
Barnes, Archer, Hogg, & Bradley, 2015; Di Salvo et al., 2007).
Typically, these tests incorporate sprint distances of between
15 and 40 m with 3–15 repetitions, while recovery periods last
for 15–30 s (Haugen, Tonnessen, Hisdal, & Seiler, 2014).
While RSA has been widely accepted as a critical element of
physical fitness in football and a number of studies have
focused on the topic, research on the exact nature and occur-
rence of repeated-sprint sequences during elite level football
competition is limited (Buchheit, Mendez-Villanueva, Simpson,
& Bourdon, 2010; Buchheit, Simpson, & Mendez-Villanueva,
2013; Carling et al., 2012). Additionally, the construct validity
of present RSA tests as predictors of high-intensity running
performance in football has not been extensively investigated
(Carling et al., 2012). In order to provide construct validity,
measures of sprinting behaviour during competitive matches
have to be taken into account so that the football-specific
demands can be identified and analysed with regards to
their exact nature, i.e. number of sprints within the bout,
changes in maximal speed obtained throughout the sprints
or recovery time between sprints (Meckel, Machnai, & Eliakim,
2009). Designing RSA tests using information of in-game
sprinting behaviour as it actually occurs in professional
CONTACT Jan Schimpchen jan.schimpchen@uni-saarland.de
JOURNAL OF SPORTS SCIENCES, 2015
http://dx.doi.org/10.1080/02640414.2015.1112023
© 2015 Taylor & Francis
Downloadedby[ChineseUniversityofHongKong]at14:1021November2015

football is necessary to provide essential construct validity
(Currell & Jeukendrup, 2008).
Physical performance during matches is commonly
assessed using semi-automated video systems. These systems
utilise standardised absolute speed thresholds to classify dif-
ferent types of running behaviour, not taking into account
inter-individual differences in maximal sprinting speeds.
However, Abt and Lovell (2009) explained that due to the
highly individualised nature of the exercise intensity conti-
nuum, there is a need for an individualisation of speed thresh-
olds, since the stress produced by a given speed will vary
considerably between athletes. While the use of absolute
speed thresholds in football can be justified to a certain
degree because of the imminent direct competition with
opponents (Buchheit et al., 2013), an individual characterisa-
tion of players’ physical load and performance during a game
can only be obtained using individualised velocity thresholds.
Hence, it is desirable to establish a system which defines
sprinting thresholds individually for each player, allowing for
a more precise analysis of sprinting performance.
Consequently, the aims of this study were threefold: (1) to
identify and characterise sprinting patterns in elite football
players with respect to the demands specific to positional
role; (2) to identify an approach to individualise sprinting
speed thresholds that appropriately reflects inter-individual
differences in maximal sprinting speed without the necessity
to conduct further testing sessions with players; and (3) to
assess the construct validity of tests commonly used to iden-
tify RSA among football players taking into account require-
ments for repeated sprinting exercise as they occur in match-
play situations in elite football players.
Methods
Participants and match sample
Sprinting behaviour in official competition was analysed for 19
games (11 home, 8 away) of the German national team
between August 2012 and June 2014. Eight of these games
were played as part of the qualification round for the FIFA
World Cup 2014 in Brazil, while 11 games were friendly
matches. In total, 48 different outfield players made appear-
ances for the German team. In order to derive individualised
sprinting thresholds from maximal speed obtained during in-
game sprinting, an inclusion criterion for players was defined
as having played at least two full 45 min periods throughout
data collection. This was necessary to be reasonably sure that
players experienced a situation where (near) maximal sprint-
ing velocity was achieved. Only 30 players met this inclusion
criterion and were thus included for further analysis. Of these
30 players, seven were classified as central-defenders (CD), five
as fullbacks (FB), nine as holding-midfielders (HM), six as wide-
midfielders (WM), one as attacking-midfielder (AM) and two as
centre-forwards (CF), according to the follow-up tactical ana-
lysis of kicker online (kicker online, http://www.kicker.de,
Olympia-Verlag GmbH, Nuremberg). Occasionally, players
lined up in different positional roles. This was identified on a
game-to-game basis and considered for the analysis process.
Unfortunately, due to the occurrence of tactical changes that
could not be understood without a more in-depth analysis
using a more qualitative approach, three games were
excluded from analysis for position specific differences. This
decision was made in accordance with the scouts of the team.
Therefore, whenever positional differences are discussed
herein, this is based on a sample of 16 games.
Approval for data collection and analysis was granted and
done in cooperation with the German Football Association
(DFB) in the process of capturing match physical performance
data. All performance data were anonymised to ensure com-
plete player confidentiality. All measurements were taken as
part of the match analysis within the German national team.
Due to the local ethical guidelines an ethical approval from
the ethics committee was not mandatory since there were no
further interventions or additional diagnostics performed.
Only data from routine performance diagnostics were
collected.
Data collection and analysis
Data was collected using a semi-automatic computerised
player tracking system (Mastercoach, which is part of the
ProzoneTM
– Amisco group). Reliability and validity for the
system has previously been quantified to verify the capture
process and data accuracy (Carling, Bloomfield, Nelsen, &
Reilly, 2008; Di Salvo et al., 2007; Randers et al., 2010).
Sampling at 10 Hz, the software records players’ movements
using a multiple camera system positioned at roof level within
the relevant stadia overseeing the entirety of the pitch. Data
for high-intensity runs (>21 km · h−1
for >1 s) and sprints
(>24 km · h−1
for >1 s), as pre-defined by the Mastercoach
software, were derived and exported to Microsoft ExcelTM
(Microsoft Office 2013, Microsoft Corporation, Redmond,
USA) for further analysis.
Individualised sprinting thresholds were calculated as a
percentage of peak running velocity (PV) reached during in-
game sprinting (Buchheit et al., 2010). Since the majority of
recent research uses an absolute sprinting threshold set at
25.2 km · h−1
(Bush et al., 2015; Di Salvo et al., 2010;
Ingebrigtsen, Dalen, Hjelde, Drust, & Wisløff, 2015) this velocity
was taken as a reference point. Thus, to individualise sprinting
thresholds as a percentage the below equation was used:
25:2=In À game PVð ÞÃ100
From this, a group mean percentage was calculated and
applied to each player’s in-game PV in order to define their
individualised sprinting thresholds. For instance, player A had
an in-game PV of 34.4 km · h−1
and the group mean percen-
tage was 74.8%. Hence, his individualised sprinting threshold
was set at 25.7 km · h−1
. Runs with a maximal velocity higher
than individual thresholds were defined as sprints (Table 1).
Repeated sprinting bouts were defined as a minimum of
three consecutive sprints with a recovery duration equal to or
less than 30 s. These parameters were chosen in order to
represent the minimum amount of sprints performed in an
RSA test as well as a typical recovery duration chosen for the
tests. The bouts were investigated with regards to the amount
of sprints performed, recovery duration between sprints as
2 J. SCHIMPCHEN ET AL.

well as maximum velocity obtained during each sprint. While
research employing acceleration data might also be of interest
for the determination of match physical load, the focus of this
study was on actual sprint speed to be consistent with the
concept of repeated-sprint (i.e. actual maximal running speed)
sequences (Buchheit et al., 2010).
Furthermore, sprints were classified into four categories
according to preceding recovery time: <30 s, 30.1–60 s, 60.1–
120 s, >120 s (Balsom, Seger, Sjodin, & Ekblom, 1992).
Preceding recovery time in this case was defined as the time
(in seconds) elapsed since the previous sprint. Therefore, each
player’s first sprint per halftime or after substitution was not
included in this analysis.
Statistical analysis
Statistical analyses were conducted using IBM SPSS Statistics
20 (IBM, Armonk, NY, USA) and Microsoft ExcelTM
. Results are
reported as means ± standard deviation (SD) unless otherwise
stated. Differences in the amount of sprints performed accord-
ing to preceding recovery time across positional roles were
calculated using a χ2
test of independence. Post-hoc proce-
dures were undertaken using the adjusted residuals method
with α-values that were adjusted for the number of contrasts
being considered in order to account for type I error rates in
post-hoc χ2
tests (MacDonald & Gardner, 2000). Throughout
the analysis, an α-level of P < 0.05 was accepted as statistically
significant.
Results
The mean PV for the 30 players included for analysis was
33.7 ± 1.6 km · h−1
(CD: 34.3 ± 1.0; FB: 34.2 ± 1.3; HM:
32.2 ± 1.3; WM: 34.6 ± 1.3; AM: 36.4; and CF: 33.9 ± 0.6).
Consequently, 25.2 km · h−1
was equal to 74.8% of the
group mean. Individualised sprinting thresholds were calcu-
lated accordingly, resulting in thresholds ranging from
22.9 km · h−1
to 27.2 km · h−1
(Table 1 & Figure 1).
On average, players performed 17.2 ± 3.9 sprints per game.
Differences in the mean number of sprints per game were
evident between positional roles, with WM performing the
most and CD the fewest sprints (Table 2).
Positional differences were also apparent with regards to
recovery duration between consecutive sprints. For all positions,
the most commonly observed recovery duration between con-
secutive sprints was >120 s (64.9 ± 13.6%). A χ2
test of indepen-
dence examined the relationship between positional role and
amount of sprints performed according to preceding recovery
time. The relationship between these variables was statistically
significant, χ2
(15, n = 2514) = 88.33, P < 0.001. Using the cellwise
adjusted residual method with an adjusted α- level of P ≤ 0.002, it
was found that CDs performed significantly fewer sprints with a
preceding recovery time between 30.1–60 s (χ2
(1) = 9.80,
P = 0.002) and significantly more sprints with a preceding recov-
ery time of more than 120 s (χ2
(1) = 11.70, P < 0.001) than
expected. Conversely, HM performed more sprints with a preced-
ing recovery time of 30 s or less (χ2
(1) = 67.90, P < 0.001) and
significantly fewer sprints with a preceding recovery time of more
than 120 s (χ2
(1) = 21.72, P < 0.001) than expected (Table 3).
Throughout 19 games or 13,881 min of game play for players
that met the inclusion criterion, there were 35 incidences where
a repeated sprinting bout was performed. This averages a bout
of repeated sprinting per player approximately every 463 min, or
1.8 ± 1.7 bouts per game for all outfield players combined.
Thirteen of the 30 players (43.3%) included for analysis did not
perform a single bout of repeated sprinting throughout data
collection. In total, there were four incidences (FB = 1; HM = 3)
where one player performed two bouts of repeated sprinting
within the same match and one incidence (HM = 1) where a
player performed even three bouts of repeated sprinting.
The few observations of bouts of five repeated sprints
(n = 4) showed no decrement in maximal sprinting speed
throughout consecutive sprints. In fact, the maximal speed
reached during the last of five consecutive sprints was the
highest on average (Table 4). Similarly, no decrement in sprint-
ing speed was found across three bouts of repeated sprinting
within one game by the same player. The first bout (three
consecutive sprints) occurred 12 min into the game and was
performed with an average of 81.6 ± 3.7% of PV. The second
bout (five consecutive sprints) occurred after 57 min of game-
play and reached a mean of 79.5 ± 4.8% of PV, while the third
bout (three consecutive sprints) was performed after 85 min
and reached a mean of 81.7 ± 6.7% of PV.
Discussion
To the authors’ knowledge, this study is the first to investigate
repeated sprinting patterns in elite football players on a
Table 1. An overview of each player’s positional role, peak velocity (PV), 25.2 km
· h−1
expressed as a percentage of their PV as well as their individualised
sprinting thresholds.
Player ID Position
In-game PV
in km · h−1
25.2
km · h−1
as
% of PV
74.8% of PV –
Individualised threshold in
km · h−1
1 CD 34.4 73.3 25.7
2 HM 32.3 78.0 24.2
3 HM 30.6 82.4 22.9
4 CD 36.1 69.8 27.0
5 FB 32.2 78.3 24.1
6 CD 33.7 74.8 25.2
7 WM 35.3 71.4 26.4
8 FB 33.9 74.3 25.4
9 HM 31.9 79.0 23.9
10 CD 35.1 71.8 26.3
11 CD 34.2 73.7 25.6
12 FB 35.7 70.6 26.7
13 HM 34.7 72.6 26.0
14 CF 34.3 73.5 25.7
15 HM 31.3 80.5 23.4
16 HM 33.9 74.3 25.4
17 CF 33.4 75.4 25.0
18 FB 34.5 73.0 25.8
19 CD 33.7 74.8 25.2
20 WM 35.6 70.8 26.6
21 CD 32.9 76.6 24.6
22 AM 36.4 69.2 27.2
23 WM 34.2 73.7 25.6
24 HM 31.5 80.0 23.6
25 WM 35.3 71.4 26.4
26 HM 31.7 79.5 23.7
27 FB 34.7 72.6 26.0
28 WM 34.7 72.6 26.0
29 HM 31.9 79.0 23.9
30 FB 32.2 78.3 24.1
Mean ± SD 33.7 ± 1.6 74.8 ± 3.5 25.2 ± 1.2
JOURNAL OF SPORTS SCIENCES 3

national team level using individualised sprinting thresholds
tailored to each player’s peak velocity. One major finding was
that the mean recovery time between consecutive sprints
differs to a great extent between playing positions, indicating
that the physical load inflicted upon players by sprinting is
greatly affected by positional role. However, even though HMs
on average showed the shortest breaks between consecutive
sprints and performed one in five sprints with a recovery time
of 30 s or less, the extensive mean recovery duration of 233 s
indicates that the physical strain induced upon players by
sprints reaching (near) maximal velocities alone can generally
be considered very moderate. Balsom et al. (1992) showed
that a recovery period of at least 120 s provides sufficient
recovery to repeat 15 × 40 m sprints without a significant
decline in performance, while even a recovery period of only
60 s does not lead to a significant change in sprinting speed
for up to 30 m. Considering that players on average performed
80.9% of their sprints with a preceding recovery time of more
than 60 s and that the vast majority of sprint displacements
are shorter than 20 m (Burgess, Naughton, & Norton, 2006;
Gabbett & Mulvey, 2008; Vigne, Gaudino, Rogowski, Alloatti, &
Hautier, 2010), it is reasonable to assume that sprinting per-
formance does not deteriorate throughout a game due to an
accumulation of fatigue induced by prior sprinting. However,
Figure 1. Visualisation of the individualised nature of sprinting thresholds in contrast to an absolute threshold fixed at 25.2 km · h−1.
Table 2. Average amount of sprints per player and game across positional roles.
Game CD FB HM WM AM CF
1 9.5 16.0 19.5 16.5 12.0 8.3
2 7.5 14.5 9.1 17.5 5.0 16.5
3 9.5 16.5 19.0 19.2 4.0 20.0
4 14.8 19.0 15.0 30.0 17.0 24.0
5 8.5 19.0 21.5 21.0 17.0 2.0
6 14.5 16.5 25.3 21.3 28.4 29.6
7 16.7 35.3 46.7 25.0 30.0 24.7
8 13.5 19.0 9.0 18.5 17.0 12.0
9 9.5 16.0 10.0 24.0 23.0 13.0
10 10.0 19.0 18.5 20.0 11.0 17.0
11 12.5 13.5 13.0 21.0 9.0 15.0
12 6.5 11.5 16.5 19.5 12.0 17.0
13 11.5 14.0 32.0 21.0 n/a* 19.0
14 6.0 17.0 13.0 17.5 11.0 21.0
15 15.0 25.0 19.0 14.5 12.0 25.0
16 7.5 13.3 15.0 25.5 14.0 14.0
Mean ± SD 10.8 ± 3.3 17.8 ± 5.7 18.9 ± 9.6 20.8 ± 3.9 14.8 ± 7.5 17.4 ± 6.9
Note: * Players appearing in the role of an attacking midfielder did not meet the inclusion criterion and were therefore not included. Mean amount of sprints as well
as SD for this position were calculated using a 15 game sample.
Table 3. Mean recovery duration between sprints and frequency of recovery periods based on the time elapsed between consecutive sprints in relation to positional
role.
Recovery
duration
All players
(n = 2514) CD (n = 296) FB (n = 559) HM (n = 518) WM (n = 655) AM (n = 223) CF (n = 263)
Mean recovery
time (s)
274.3 ± 287.4 415.8 ± 402.1 275.1 ± 264.2 232.8 ± 262.5 243.7 ± 237.9 299.2 ± 326.0 255.7 ± 248.4
% <30 s (n) 12.3 ± 9.8 (313) 7.1 ± 5.3 (20) 11.3 ± 10.8 (47) 20.0 ± 13.2 (114)* 9.8 ± 2.1 (65) 10.1 ± 6.7 (18) 12.7 ± 6.5 (29)
% 30.1–60 s (n) 6.9 ± 4.3 (173) 4.7 ± 3.5 (7)* 6.9 ± 6.3 (40) 5.3 ± 4.5 (29) 6.3 ± 4.4 (54) 6.8 ± 3.1 (13) 8.8 ± 7.1 (23)
% 60.1–120 s (n) 16.0 ± 5.2 (429) 15.5 ± 4.1 (48) 15.9 ± 4.7 (92) 14.1 ± 8.1 (79) 18.4 ± 4.3 (109) 16.7 ± 3.8 (36) 14.1 ± 5.4 (37)
% >120 s (n) 64.9 ± 13.6 (1725) 72.7 ± 9.3 (221)* 65.9 ± 13.0 (380) 60.6 ± 20.3 (296)* 65.6 ± 3.4 (427) 66.4 ± 10.5 (156) 64.4 ± 14.0 (174)
Note: * Indicates a significant difference from the expected count (P < 0.002).

it has to be noted that this study did not assess physical
activity during recovery periods. Therefore, it is not possible
to make more specific statements about the physical nature of
the time spent between consecutive sprinting efforts and to
assess fatigue resulting from running at speeds below the
individual sprinting thresholds.
Another result of this study was that by using an individua-
lised sprinting threshold, it was found that players on average
performed 17.2 ± 3.9 sprints per game. Other studies investi-
gating average numbers of sprints per game report consistent
findings. Ingebrigtsen et al. (2015) showed that in an elite
Norwegian team, players performed 16.6 ± 7.9 sprints per
game, while a sprinting analysis of elite football players during
European Champions League and UEFA Cup matches
throughout 2002–2006 (Di Salvo et al., 2010) revealed that
players on average performed 27.2 sprints per game. With
regards to positional differences, Di Salvo et al. (2010) found
that WMs performed significantly more sprints than any other
position group, followed by attackers, wide-defenders, central-
midfielders and CDs. These findings are in accordance with the
results of the present study, if HMs and AMs are combined in
one category. Interestingly, the present study reveals that HM
perform about 30% more sprints compared to AMs, while also
performing significantly more sprints with short recovery dura-
tions of 30 s or less. Therefore, it might be advisable for future
research in this area to differentiate central midfielders into
these two sub-categories, allowing for a more comprehensive
evaluation of positional demands.
Somewhat surprisingly, the amount of sprinting reported
here is far lower compared to recent research investigating
the evolution of physical and technical performance para-
meters in the English Premier League (EPL) (Barnes, Archer,
Hogg, Bush, & Bradley, 2014), which reported an average of
57 ± 20 sprints per player and game for the 2012–13 season.
Even though Barnes and colleagues used a fixed sprinting
threshold (>25.1 km · h−1
) for each player, a certain level of
comparability to the methodology employed in this study
should remain due to our way of individualising sprinting
thresholds. Therefore, it is interesting to observe the extent
to which the average amount of sprints performed in the EPL
exceeds the amount performed in the German national team.
While it has been argued that the EPL is renowned for its
physicality (Barnes et al., 2014; Bradley et al., 2009), it is ques-
tionable whether a difference of that magnitude can solely be
attributed to a perceived difference in playing style. One of
the factors that might explain part of the difference is the level
of opposition for the German team during data collection. The
qualification round for the FIFA World Cup 2014 in Brazil
included games against teams such as the Faroe Islands or
Kazakhstan, ranked 154th and 139th at the time in the FIFA
world ranking, respectively. Indeed, previous studies have
shown that opponent level influences running behaviour.
Players of more successful teams have been found to cover
less total distance as well as less distance in high-intensity
running (running speed > 14 km · h−1
) and very high-intensity
running (running speed > 19 km · h−1
) compared to players of
less successful teams (Rampinini, Impellizzeri, Castagna,
Coutts, & Wisløff, 2009). Additionally, it has been shown that
players cover greater distances sprinting (running speed >
24 km · h−1
) against top-level opponents compared to bot-
tom-level opponents (Castellano, Blanco-Villasenor, & Alvarez,
2011). However, comparing the number of sprints in the game
against the lowest ranked opponent (Faroe Islands, 154th in
the FIFA ranking, 16.8 sprints) with the game against the
highest ranked opponent (Netherlands, 7th, 19.5 sprints) dis-
plays a rather minor difference, indicating that the level of
opposition might influence sprinting behaviour, but not to a
large extent. Moreover, even when singling out the game with
the highest amount of sprints performed (USA, 28th, 29.7
sprints per player), it shows that this still only equals about
half of what was reported by Barnes et al. (2014).
Repeated sprinting bouts during match-play were investi-
gated in order to gather information on periods with the most
intense sprinting demands throughout a game. Contrary to the
popular belief that RSA is a crucial physical component of team-
sport performance (Rampinini et al., 2007), it was shown that
bouts of repeated sprinting occurred only on a very sporadic
basis. On average, all German outfield players combined per-
formed 1.8 ± 1.7 bouts of repeated sprinting per game.
Previous research on repeated sprinting behaviour shows
similar results, even though there are methodological
differences that have to be considered. Carling et al. (2012)
analysed high-intensity running performance in 20 French
League 1 football players and reported 1.1 ± 1.1 high-intensity
bouts per player and game. This number is higher than the
number presented in this study. However, high-intensity
running was defined as runs performed at velocities
>19.8 km · h−1
over a minimum duration of 1 s, while a
high-intensity bout was defined as a minimum of three con-
secutive high-intensity runs with a mean recovery time ≤20 s
Table 4. Presentation of repeated sprinting bouts as they occurred during match-play, comparing maximum speed reached within single sprints, what percentage
of PV that speed reflects, as well as the time elapsed between consecutive sprints.
Three consecutive sprints (n = 30) (CD = 2;
FB = 4; HM = 15; WM = 3; AM = 1; CF = 5)) Four consecutive sprints (n = 1) (FB = 1) Five consecutive sprints (n = 4) (HM = 4)
Number of
sprint within
bout
Maximum speed
reached (km · h−1
) % of PV
Time
between
sprints (s)
Maximum speed
) % of PV
Time between
sprints (s)
Maximum speed
) % of PV
Time
between
sprints (s)
1 27.6 ± 2.2 83.8 ± 6.1 26.8 83.2 24.8 ± 1.1 77.9 ± 3.5
2 27.4 ± 2.1 83.4 ± 7.3 7.3 ± 7.7 32.2 100.0 3.8 26.6 ± 2.6 83.6 ± 8.6 4.4 ± 4.5
3 27.0 ± 2.1 81.9 ± 5.9 8.8 ± 7.7 31.7 98.4 2.2 25.3 ± 1.6 79.3 ± 4.1 2.3 ± 1.1
4 24.7 76.7 12.2 26.1 ± 2.8 81.9 ± 8.2 1.4 ± 0.4
5 27.0 ± 2.0 84.7 ± 7.7 4.6 ± 4.2
Mean 27.4 ± 2.2 83.1 ± 11.7 8.1 ± 7.6 28.9 ± 3.7 89.6 ± 11.4 6.1 ± 5.4 25.9 ± 2.1 81.5 ± 6.6 3.2 ± 3.1
Note: The repeated sprinting bouts are analysed according to the number of consecutive sprints within bouts.

separating efforts. The mean speed threshold employed for
analysis in the present study was 5.4 km · h−1
higher than the
one used by Carling et al. (2012), making it difficult to draw
comparisons between repeated high-intensity running beha-
viour in the French League 1 and repeated sprinting patterns
in the German national team. Nevertheless, results of both
studies suggest that players were generally able to reproduce
sprinting performance when it was required by the situational
context. Similarly, an investigation of repeated sprint
sequences during youth football matches found low occur-
rences of bouts of repeated sprinting, even when between-
sprint recovery time was set at 60 s (Buchheit et al., 2010).
Given the overall low occurrence of repeated sprinting
bouts during match-play it is questionable whether current
field tests designed to assess RSA in football players are a
worthwhile implement into training or testing routines. The
results of the present study in combination with previous
research (Buchheit et al., 2010, 2013; Carling et al., 2012)
seem to suggest that the construct validity of present tests
of RSA is not empirically supported. Buchheit et al. (2013)
found that changes in the occurrence of repeated sprinting
bouts were independent of those in physical capacities, indi-
cating that physical fitness might not directly limit sprinting
performance in match-play. Players’ activity patterns might be
more affected by game tactical and strategical requirements,
which shows that the relationship between physical fitness
and match running performance should not be viewed in a
simplistic manner (Buchheit et al., 2013). However, considering
that Barnes et al. (2014) reported a much higher amount of
sprints for EPL games, it remains questionable whether the
importance of RSA is competition specific.
Taken collectively, the results of the present study add to
the already existing body of literature questioning the impor-
tance of RSA in elite football. Even though some players
might experience short-term deteriorations of sprint perfor-
mance due to limitations in energy supply when the rest
periods between consecutive sprints are shorter than 30 s
(Spencer, Bishop, Dawson, & Goodman, 2005), an analysis of
maximum speed obtained throughout all efforts of repeated
sprinting bouts with five consecutive sprints revealed no
general decrement in sprinting performance. In-game sprint-
ing load appears to be more affected by shorter repeated
accelerations/decelerations. Therefore, it can be argued that
allocating training resources to specifically address perfor-
mance levels in situations where repeated maximal sprinting
is required might not be warranted based on analyses of
actual match-play performance patterns. Instead, a training
regimen that concurrently improves aerobic fitness and max-
imal acceleration and sprinting capacities appears to be bet-
ter suited to prepare players for the intermittent nature of
competitive match-play in elite football (Buchheit et al.,
2010). Notably the development of maximal acceleration
and sprinting speed might lead to improvements that are
more practically relevant to game-type situations. It has been
argued that a 30–50 cm difference, which equates to about
0.04–0.06 s over 20 m, is probably enough to be the decisive
factor in one-on-one duels (Haugen et al., 2014), thus making
it a critical element to the outcome of a game since most
goal scoring actions are preceded by straight-line sprints
(Faude et al., 2012). Therefore, it would seem reasonable to
occasionally incorporate specific fitness tests into the training
routine that assess aerobic fitness levels as well as accelera-
tion and maximal sprinting capabilities.
The main limitation to this study was that match sprinting
performance was analysed in isolation from other contextual
factors, such as running intensity below sprinting threshold,
mental fatigue, pacing strategies, current result, tactical con-
siderations or a combination of mutually inclusive factors.
Interpreting data without further context might potentially
draw a flawed picture of match physical performance
(Bradley & Noakes, 2013). While the primary goal of this
study was to quantify sprinting behaviour with regards to
RSA in elite football players, a more detailed approach could
have led to the detection of factors influencing repeated
sprinting performance in a more comprehensive manner.
However, while desirable, a more multi-dimensional approach
would have been beyond the scope of this study. Also, the
number of players as well as the number of games analysed
for the present study was relatively low. All players came from
the same team, making it difficult to draw assumptions for
different teams or different competitions. Nevertheless, this
team would go on to win the FIFA World Cup 2014 in Brazil,
indicating that the players included for analysis did indeed
play at the highest level possible.
Conclusion
In summary, this study provides an insight into repeated
sprinting characteristics in the highest level of football
match-play. It was shown that sprinting behaviour varied
across positional roles and that holding midfielders (HMs)
were required to perform more sprints with a short recovery
time preceding the sprint compared to other positions. On
average, national players of the 2014 FIFA World Cup winning
team performed a bout of repeated sprinting (defined as
exceeding 74.8% of one’s maximal velocity) every 463 min
and that even the most extreme situations of repeated sprint-
ing did not result in a decrement in performance across multi-
ple bouts or sprints. The present results indicate that players
only occasionally obtained near maximal speeds during in-
game sprinting. Therefore, the importance of RSA in its classi-
cal definition is questionable in elite football match play and
results indicate that current tests as well as conditioning pro-
grammes that aim to assess and improve the general or posi-
tion-specific ability to perform repeated sprinting work might
not be adequate to reflect game demands. It appears more
likely that shorter accelerations not reaching speeds necessary
to be recorded as sprints with potentially only short recovery
times between consecutive accelerations/decelerations might
be more important in game specific situations. While it has
been shown that training interventions emphasising an RSA-
specific protocol can effectively induce football specific train-
ing adaptations (Ferrari Bravo et al., 2008), it is doubtful
whether RSA is a critical component of team performance
and ultimately of team success.
Future research should focus on the interdependency of
physical and contextual parameters. As outlined above, it is
too simplistic to assume that physical capacity is inevitably the

limiting factor for match physical performance. With regards
to Barnes et al. (2014), it also seems worth investigating
whether the occurrence of bouts of repeated sprinting is
competition-specific.
Disclosure statement
The authors declare that there are no conflicts of interest.
Funding
No funding was provided which contributed to the development of this
manuscript.
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