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Original Research
Effects of Age on Physical Match Performance in
Professional Soccer Players
Ezequiel Rey,
1
Pablo B. Costa,
2
Francisco J. Corredoira,
1
and Alex Sal de Rell ´an Guerra
1
1
Faculty of Education and Sport Sciences, University of Vigo, Pontevedra, Spain; and
2
Human Performance Laboratory, Department of
Kinesiology, Center for Sport Performance, California State University, Fullerton, California
Abstract
Rey, E, Costa, PB, Corredoira, FJ, and Sal de Rell ´an Guerra, A. Effects of age on physical match performance in professional soccer
players. J Strength Cond Res XX(X): 000–000, 2019—This study aimed to evaluate the effects of age using a large-scale analysis of
match physical performance in professional soccer players. A total of 10,739 individual match observations were undertaken on
outfield players competing in the first and second divisions of the Spanish soccer professional leagues during the 2017–2018
season, using a computerized tracking system (TRACAB, Chyronhego, New York, USA). The players were classified into five
positions and into 5 age groups (,20 years, 20–24.9 years, 25–29.9 years, 30–34.9 years, and $35 years). The results showed that
(a) professional soccer players aged $30 years exhibit a significant decrease (p,0.01) in the total distance covered, medium-
speed running distance, high-speed running (HSR) distance, very HSR (VHSR) distance, sprint distance, and maximum running
speed compared with younger players (,30 years); (b) professional soccer players aged $35 years exhibit a significant decrease (p
,0.01) in the number of HSR, number of VHSR, and number of sprints compared with younger players (,35 years); and (c) all
playing positions reduced their physical performance; however, external midfielders were less affected by age effects. In con-
clusion, this study demonstrates players’ physical match performance reduces with increasing age. Such findings may help
coaches and managers to better understand the effects of age on match-related physical performance and may have the potential
to assist in decisions regarding recruitment and player list management within professional soccer clubs.
Key Words: age group, work-rate, aging, football, positional roles
Introduction
Professional soccer teams commonly involve players from a wide
age range (4). In the 4 major European professional soccer lea-
gues, Bundesliga (Germany), Premier League (England), Serie A
(Italy), and La Liga (Spain), most players are between the ages of
21 and 29 years, and fewer are aged 30 years and older (11).
Moreover, anecdotal evidence suggests that soccer performance
in male players peaks in the mid-to-late 20s (11). In addition,
previous studies found that an inverted U curve characterizes the
relationship between market valuation and age, with peak value
occurring in the 26–30 age range (20). Thus, the age of pro-
fessional soccer players seems to be an important variable of in-
terest to coaches, managers, and executives because it can affect
the soccer club’s personnel decisions, such as the kind of contract
they offer players and the fee they are willing to pay or accept for
a transfer (11). For this reason, it seems important to understand
how soccer players’performance changes with age. Examining
the influence of age on physical performance indicators could be
of interest for coaches and sports scientists by providing new
information on critical factors for the selection of training strat-
egies in player preparation.
Although match physical performance has been extensively
studied in relation to factors such as playing position, fatigue or
pacing, competitive standard, fixture congestion, or contextual
variables (7,10), to the best of our knowledge, no studies have
examined the age-related changes of physical match play
performance in professional soccer players. By contrast, age-
related performance changes in individual sports have been
broadly analyzed (1). Physical performance develops throughout
life through growth, maturation, aging, and training, and age
peak performance varies according to sport and motor skills in-
volved (1).
Although scientific evidence exists regarding the effect of age
on fitness in elite soccer players (4,18,35), this evidence is limited
and partially inconclusive. Haugen et al. (18) examined anaerobic
performance characteristics of male soccer players through dif-
ferent age stages. Overall, the results showed that sprint velocity
peaked in the age range 20–28 years, with significant decreases in
velocity thereafter. On the contrary, no differences were observed
in countermovement jump across the different age groups. More
recently, Botek et al. (4) compared different fitness determinants
of professional soccer players divided into different age groups,
reporting age-related decrements in maximal oxygen uptake and
maximal power output, as well as age-related increments in body
fat percentage in players aged .30 years. By contrast, Tonnessen
et al. (35) observed practically no differences in maximal aerobic
power across age groups. Although the aging process influences
the athletes’physical and mental development and, in turn, their
competitive performance (1), there are no scientific studies using
modern tracking techniques that have examined the effects of age
on competitive match performance in professional soccer players.
In the absence of previous evidence about age-related match
performance changes in elite soccer players, this study aimed to
evaluate the effects of age using a large-scale analysis of match
physical performance in professional soccer players. The research
hypothesis is that match performance decrease with age in
Address correspondence to Dr. Ezequiel Rey, zequirey@uvigo.es.
Journal of Strength and Conditioning Research 00(00)/1–6
ª2019 National Strength and Conditioning Association
1
Copyright © 2019 National Strength and Conditioning Association. Unauthorized reproduction of this article is prohibited.
professional soccer players. Moreover, the data would provide
relevant information on the extent to which playing positions are
more or less affected by age-related changes in performance, and
therefore guide coaches and managers on decisions concerning
individualizing training strategies and contract policies.
Methods
Experimental Approach to the Problem
In this cross-sectional study, physical match performance data
from the Spanish first and second division leagues from the
2017–2018 season were analyzed according to age group and
playing position. Data were collected only from players who
participated in the entire match (i.e., 90 minutes).
Subjects
In total, 10,739 individual match observations of 419 outfield
players were used. In line with previous studies (6,12), the players
were classified into 5 positions: central defenders (CD; match
observations 53,014), external defenders (ED; match observa-
tions 52,563), central midfielders (CM; match observations 5
2,844), external midfielders (EM; match observations 51,124),
and strikers (S; match observations 51,194). This classification
was performed based on the different activity on the pitch, and the
primary area in which this activity was performed. In line with
previous studies in soccer (4) and individual sports
(19,21–23,29–32), soccer players were divided into 5-year age
groups: G1 (,20 years), G2 (20–24.9 years), G3 (25–29.9 years),
G4 (30–34.9 years), and G5 ($35 years). Players’age was cal-
culated as the date of each official match minus the date of birth
according to the information provided by the official La Liga
website (https://www.laliga.es/en). All data were anonymized in
accordance with the principles of the Declaration of Helsinki to
ensure player and team confidentiality. As data used in this study
were collected as part of players’routine monitoring, ethic com-
mittee approval and informed consent were not required (36).
Procedures
Match performance was recorded using a multiple camera–
computerized optical tracking system TRACAB (Chyronhego,
New York, NY, USA), managed from the application Medicoach
(Mediapro & LFP, Madrid, Spain) that has a sampling frequency
of 25 Hz. This system consists of 8 cameras that identify players
by their movement, shape, and color information. Previous in-
dependent study has reported TRACAB is a valid and reliable
tracking system method and average measurement errors of 2%
for distances covered (28).
Total distance covered, medium-speed running (MSR) distance
(14–21 km·h
21
), high-speedrunning (HSR)distance (.21 km·h
21
),
very HSR (VHSR) distance (21–24 km·h
21
), sprinting speed
running distance (.24 km·h
21
), number of HSR efforts,number of
VHSR efforts, number ofsprints, and the peak speed reached during
the match defined as maximal running speed were collected for
each player.
Statistical Analyses
Results are reported as mean values and SDs (mean 6SD). The
Kolmogorov-Smirnov and Levene’s tests showed that all data
were normally distributed and displayed homogeneous variance.
Hence, a one-way analysis of variance (ANOVA) was used to
evaluate differences in match physical performance between the
different age groups (G1, G2, G3, G4, and G5). Two-way
ANOVAs (Age [G1 vs. G2 vs. G3 vs. G4 vs. G5] 3position [CD
vs. ED vs. CM vs. EM vs. S]) were used to analyze the effects of
age and position on player activity. In the event of a difference
being present, Bonferroni-adjusted post hoc tests were used to
identify specific effects. In addition, Cohen’sdeffect size (ES) was
reported for identified statistical differences. Effect sizes greater or
equal to 0.2, 0.5, and 0.8 were considered to represent small,
medium, and large differences, respectively (9). Statistical signif-
icance was set at p#0.05. All statistical analyses were conducted
using IBM SPSS Statistics 21 for Macintosh (IBM Co., New York,
NY, USA).
Results
Age Distribution
Figure 1 displays the age distribution of all players who partici-
pated in the study. The age of players who played at least one
entire match ranged from 17.3 to 38.3 years with an average of
27.3 64.0 years. There are fewer players in the G1 and G5, 3 and
4% of total sample, respectively. Meanwhile, a large number of
players were observed in G2 (28%), G3 (42%), and G4 (23%).
Age Differences in Players’ Physical Performance
There was a main effect for age (p,0.01) on all physical variables
(Table 1). Players in G3, G4, and G5 covered 1.8–2.3% less total
distance compared with players in G1 (p,0.01, ES 50.07–0.12)
and G2 (p,0.01, ES 50.09–0.13). The HSR and VHSR were
6.3–26.4% lower in G4 and G5 compared with G1 (p,0.01, ES
50.31–0.57), G2 (p,0.001, ES 50.28–0.68), and G3 (p,
0.01, ES 50.25–0.71). In addition, HSR and VHSR were
10.6–14.8% lower in G5 compared with G4 (p,0.01, ES 5
0.29–0.39). The sprint distance was 8.5–33.3% lower in G3, G4,
and G5 compared with G1 (p,0.01, ES 50.16–0.70) and G2 (p
,0.01, ES 50.17–0.68). Lower sprint distance was also covered
in G5 compared with G3 (p,0.01, ES 50.50) and G4 (p,0.01,
ES 50.37). The number of HSR, VHSR, and sprints were
17.4–37.5% fewer for G5 players compared with players in G1 (p
,0.01, ES 50.71, 0.75, and 0.92), G2 (p,0.01, ES 50.79,
0.80, and 0.93), G3 (p,0.01, ES 50.62, 0.56, and 0.56), and G4
(p,0.01, ES 50.53, 0.56, and 0.70). Finally, players in G1 and
G2 achieved 1.1–4.5% higher maximal running speeds compared
with players in G3 (p,0.01, ES 50.16–0.27), G4 (p,0.01, ES
50.32–0.43), and G5 (p,0.01, ES 50.64–0.75).
Age Differences in Players’ Physical Performance According
to Playing Positions
Central Defenders. Central defender in G3, G4, and G5 covered
3.6–38.8% less total distance (p,0.01, ES 50.53–1.01), MSR
distance (p50.034, ES 50.42–1.27), HSR (p,0.01, ES 5
0.28–1.37), VHSR distance (p,0.01, ES 50.29–1.49), and
sprint distance (p,0.01, ES 50.20–1.06) than G1 and G2.
Central defender in G3, G4, and G5 made 15.7–40.0% less
number of HSR (p,0.01, ES 50.36–1.26) and sprints (p,0.01,
ES 50.20–1.17) than G1 and G2. Central defender players in G5
achieved 3.3–4.8% lower maximal running speeds compared
with players in G1 (p,0.01, ES 51.27), G2 (p,0.01, ES 5
0.78), G3 (p,0.01, ES 50.64), and G4 (p,0.01, ES 50.45).
Age-Related Performance Changes in Soccer (2019) 00:00
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Players in G3 and G4 also achieved 1.1–2.5% lower maximal
running speeds than G1 (p,0.01, ES 50.37–0.59) and G2 (p,
0.01, ES 50.12–0.30).
External Defenders. External defender in G4 and G5 covered
2.1–5.6% less total distance than G1 (p50.003, ES 50.41–0.94),
G2 (p,0.01, ES 50.38–0.91), and G3 (p,0.01, ES 50.32–0.84).
G5 also covered 2.9% less total distance than G4 (p,0.05, ES 5
0.46). External defender in G5 players covered 8.7–18.2% less MSR
distance and HSR distance than G1 (p,0.01, ES 50.74 and 0.54,
respectively), G2 (p,0.01, ES 50.66 and 0.59, respectively), G3
(p,0.01, ES 50.73 and 0.53, respectively), and G4 (p,0.01, ES 5
0.42 and 0.25, respectively). External defender in G4 and G5 covered
13.4–31.6% less VHSR distance andsprintdistancethanG1(p,
0.01, ES 50.74–0.88), G2 (p,0.01, ES 50.82–0.83), and G3 (p,
0.01, ES 50.70–0.71). External defender in G5 made 12.0–27.7%
less number of HSR (p,0.01, ES 50.40–0.81), VHSR (p,0.01, ES
50.40–0.89), and sprints (p,0.01, ES 50.54–1.08) than G1, G2,
G3, and G4. External defender players in G3, G4, and G5 achieved
1.4–4.7% lower maximal running speeds compared with players in
G1 (p,0.01, ES 50.30–1.03) and G2 (p,0.01, ES 50.24–0.56).
Players in G5 also achieved 1.9–3.2% lower maximal running speeds
than G3 (p,0.01, ES 50.80) and G4 (p,0.01, ES 50.66).
Central Midfielders. Central midfielder in G1 and G3 covered
1.7–4.3% less total distance than G2 (p,0.05, ES 50.63 and
0.20, respectively), G4 (p,0.05, ES 50.69 and 0.23, re-
spectively), and G5 (p,0.05, ES 50–18 and 0.64, respectively).
G1 covered 2.2% less total distance than G3 (ES 50.23). Central
midfielder in G1 covered 11.8–18.9% less MSR distance than G2
(p,0.01, ES 50.72), G3 (p,0.01, ES 50.49), G4 (p,0.01, ES
50.95), and G5 (p,0.01, ES 50.69). Central midfielder in G5
covered 9.3–19.4% less VHSR distance, and sprint distance than
G1 (p,0.05, ES 50.22–0.29), G2 (p,0.05, ES 50.34–0.39),
G3 (p,0.05, ES 50.28–0.31), and G5 (p,0.05, ES 5
0.20–0.30). Central midfielder in G5 made 9.5–17.3% less
number of HSR and VHSR than G1 (p,0.05, ES 50.27 and
0.25, respectively), G2 (p,0.05, ES 50.53 and 48, respectively),
G3 (p,0.05, ES 50.50 and 37, respectively), and G4 (p,0.05,
ES 50.53 and 0.32, respectively). Central midfielder in G5 also
made 18.1% less number of sprints than G1 (p50.035, ES 5
0.36), G2 (p50.034, ES 50.36), and G3 (p50.038, ES 50.33).
Central midfielder players in G3, G4, and G5 achieved 1.7–4.1%
lower maximal running speeds compared with players in G1 (p,
0.05, ES 50.26–0.59) and G2 (p,0.01, ES 50.31–0.68).
Players in G5 also achieved 2.0% lower maximal running speeds
than G3 (p50.029, ES 50.32).
External Midfielders. External midfielder in G2 and G3 covered
10.1–18.2% more HSR distance than G4 (p,0.05, ES 50.32
and 0.59, respectively) and G5 (p,0.05, ES 50.32 and 0.59,
respectively). External midfielder players in G3 (p,0.05, ES 5
0.20 and 0.30, respectively), G4 (p,0.05, ES 50.41 and 0.39,
respectively), and G5 (p,0.05, ES 50.88 and 0.78, respectively)
covered 6.9–28.0% less VHSR distance and sprint distance than
G2. External midfielder in G4 and G5 made 11.5–17.3% less
number of VHSR than G2 (p,0.01, ES 50.39 and 0.71, re-
spectively). Central midfielder in G3, G4, and G5 also made
18.2–22.7% less number of sprints than G2 (p,0.01, ES 50.37,
0.53, and 0.75, respectively).
Strikers. Strikers in G1 and G4 covered 2.1–6.0% less total dis-
tance than G2 (p,0.05, ES 50.66 and 0.28, respectively), G3 (p
,0.01, ES 50.78 and 0.41, respectively), and G5 (p,0.01, ES 5
0.68 and 0.23, respectively). Strikers in G1 and G5 covered
10.6–24.1% less MSR distance than G2 (p,0.01, ES 50.83 and
0.60, respectively), G3 (p,0.01, ES 51.11 and 0.89, re-
spectively), and G4 (p,0.01, ES 50.67 and 0.44, respectively).
G1 covered 7.3% less total distance than G5 (p50.039, ES 5
0.31). Strikers in G4 and G5 covered 11.6–41.1% less HSR dis-
tance and VHSR distance than G2 (p,0.01, ES 51.02–1.67) and
G3 (p,0.01, ES 51.02–1.60). Strikers players in G5 covered
21.9–40.1% less sprint distance than G1 (p,0.01, ES 50.87), G2
(p,0.01, ES 51.24), G3 (p,0.01, ES 51.11), and G4 (p,0.01,
ES 50.72). Strikers in G4 covered 23.1% less sprint distance than
G2 (p,0.01, ES 50.67). Strikers in G5 and G4 made
19.0–38.1% less number of HSR, VHSR, and sprints than G2 (p,
0.01, ES 51.54–1.35) and G3 (p,0.01, ES 51.20–137). Strikers
in G5 also made 20.9–27.7% less number of VHSR and sprints
than G1 (p,0.01, ES 50.97 and 1.10, respectively). Strikers
players in G4 and G5 achieved 2.5–3.5% lower maximal running
speeds compared with players in G1 (p,0.01, ES 50.62 and 0.78,
respectively), G2 (p,0.01, ES 50.53 and 0.69, respectively), and
G3 (p,0.01, ES 50.53 and 0.69, respectively).
Figure 1. Distribution of players by age.
Age-Related Performance Changes in Soccer (2019) 00:00 |www.nsca.com
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Table 1
Players’ match activity according to different age groups depending on playing position.*
Variables
Independent of position Central defenders
<20 y 20–24.9 y 25–29.9 y 30–34.9 y ‡35 y <20 y 20–24.9 y 25–29.9 y 30–34.9 y ‡35 y
Total distance (m) 10,211 6784† 10,381 6990† 10,164 6914 10,156 61,091 10,142 6972 10,086 6796† 9,823 6695† 9,473 6627 9,431 6625 9,343 6646
MSR (m) 1,988 6495 2,141 6555† 2,054 6581 2,059 6601 1,987 6549 2,064 6390† 1,813 6408† 1,655 6349 1,588 6348 1,605 6347
HSR (m) 259 677 282 6102 266 6112 254 697‡ 227 688‡ 265 666‡ 213 666‡ 194 669 174 666 184 666
VHSR (m) 541 6194 568 6238 524 6242 491 6204‡ 418 6181‡ 548 6168† 409 6131† 372 6135 333 6133 338 6126
Sprint (m) 281 6145† 285 6161† 257 6154 236 6133‡ 190 6112‡ 250 6103‡ 196 694‡ 178 688 158 687 153 679
No. of HSR 24 67 25 68 24 69 23 68 19 67‡ 23 66‡ 19 65‡ 17 6616651665
No. of VHSR 39 612 41 615 38 616 36 613 30 612‡ 38 611 30 69 28 6925692569
No. of sprints 16 67 16 68 14 68 14 66 10 66‡ 15 66‡ 11 65‡ 10 65964964
Maximal speed
(km·h
21
)
31.0 61.9 30.8 61.9 30.5 61.9‡ 30.2 61.8‡ 29.6 61.8‡ 30.9 60.9† 30.6 61.6† 30.4 61.7 30.1 61.7 29.4 61.4‡
Variables
External defenders Central midfielders
<20 y 20–24.9 y 25–29.9 y 30–34.9 y ‡35 y <20 y 20–24.9 y 25–29.9 y 30–34.9 y ‡35 y
Total distance (m) 10,336 6671 10,315 6674 10,268 6653 10,049 6714‡ 9,757 6555‡ 10,577 6685‡ 11,035 6764 10,819 61,325‡ 11,063 6715 11,014 6688
MSR (m) 2,099 6368 2,057 6339 2,107 6396 1,986 6417 1,813 6401‡ 2,169 6518‡ 2,557 6551 2,460 6655 2,675 6529 2,500 6439
HSR (m) 315 6101 317 688 317 6104 283 681 259 6106‡ 239 677 266 692 262 6105 266 697 232 697
VHSR (m) 675 6216 688 6207 669 6223 579 6170║513 6220║430 6158 463 6176 453 6199 448 6183 390 6200‡
Sprint (m) 350 6165 370 6148 352 6147 296 6120║253 6132║191 6108 196 6107 191 6116 181 6108 158 6116‡
No. of HSR 25 67 28 67 28 67 25 67 22 68‡ 21 6723672368 23 681968‡
No. of VHSR 43 612 48 613 47 614 41 611 36 614‡ 32 611 35 612 34 614 33 612 29 613‡
No. of sprints 18 67 20 67§ 19 67 16 65 13 66‡ 11 6511651166 10 65966║
Maximal speed
(km·h
21
)
31.7 61.8 31.6 61.5 31.2 61.5‡ 30.8 60.9‡ 30.2 61.0‡ 29.8 61.9† 29.9 61.7† 29.3 61.9 29.1 61.6 28.7 61.8║
Variables
External midfielders Forwards
<20 y 20–24.9 y 25–29.9 y 30–34.9 y ‡35 y <20 y 20–24.9 y 25–29.9 y 30–34.9 y ‡35 y
Total distance (m) # 10,281 61,487 10,398 61,413 10,420 6914 10,266 6495 9,613 6715‡ 10,092 6738 10,231 6864 9,843 61,033‡ 10,032 6498
MSR (m) # 2,110 6607 2,214 6635 2,213 6468 2,103 6221 1,570 6428‡ 1,941 6456 2,068 6466 1,895 6530 1,693 6370‡
HSR (m) # 345 6115§ 345 6134§ 310 6105 282 698 261 683 327 696{326 695{288 683 242 667
VHSR (m) # 756 6257‡ 704 6268 654 6241 577 6130 570 6152 707 6218{698 6227{581 6156 417 6112‡
Sprint (m) # 410 6175‡ 359 6165 344 6166 295 6115 308 699 380 6151§ 372 6163 292 6109 228 683‡
No. of HSR # 30 610 30 611 28 68 26 66 25 67 30 68{29 68{26 672165
No. of VHSR # 52 616{50 618 46 615 43 68 43 611 51 614{50 615{42 610 34 67‡
No. of sprints # 22 68‡ 19 68 18 67 17 65 18 65 21 67§ 20 68 17 661364‡
Maximal speed
(km·h
21
)
# 31.4 62.3 31.1 62.2 31.2 61.8 31.6 61.5 31.7 61.4§ 31.6 61.5§ 31.6 61.5§ 30.8 61.5 30.6 61.4
*MSR 5medium-speed running; HSR 5high-speed running; VHSR 5very high-speed running.
†Significantly different (p,0.01) from 25 to 29.9 years, 30–34.9 years, and $35 years.
‡Significantly different (p,0.05) from all other age groups.
§Significantly different (p,0.01) from 30–34.9 years and $35 years.
║Significantly different (p,0.01) from ,20 years, 20–24.9 years, and 25–29.9 years.
{Significantly different (p,0.01) from ,20 years, 30–34.9 years, and $35 years.
#There are no observations in this age group.
Age-Related Performance Changes in Soccer (2019) 00:00
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Discussion
The current study revealed significant differences in physical
match performance across age categories. More specifically, there
was a trend toward poorer values among the 30- to 34.9- and
$35-year age categories compared with the ,20-, 20- to 24.9-,
and 25- to 29.9-year players. This trend was consistent across all
the analyzed playing positions, but less pronounced among EMs.
This is the first study to quantify physical performance of dif-
ferent age groups during professional soccer matches using
modern tracking techniques. The current results showed players
$30 years exhibit a significant decrease in the total distance
covered compared with the younger age groups. The total dis-
tance covered in a match has been used extensively to indicate the
overall physical demands during competition in soccer (25,34).
No direct comparisons can be made between the present findings
and previous studies because soccer is a complex activity influ-
enced by several contextual factors (26). However, it could be
argued the observed reduction of total distance covered may be
related with the decrease in aerobic function observed beyond the
third decade of life in athletic populations (approximately 10%
per decade) (1). Specifically in soccer players, Botek et al. (4)
confirmed this age-related decline in aerobic function and
reported significant 5% reductions of relative maximal oxygen
consumption in players .30 years compared with younger
players. However, these findings contrasts with those observed by
Tonnessen et al. (35) in Norwegian professional soccer players,
who found that there were no differences in maximal oxygen
consumption across age groups. Thus, future longitudinal studies
are required to provide conclusive evidence.
Match analysis has shown that sprintandhigh-intensityactionsare
the most important factors in soccer, due to the relationship with
training status and their ability to discriminate between different levels
of play (6,13). Similar to previous studies, we used the distance covered
and number of runs and sprints to describe high-intensity activities
(3,13,18,27). The present results show that the players $30 years (G4
and G5) covered significantly less HSR, VHSR, and sprint distance
than the younger age groups. Players $35 years (G5) also performed
significantly less number of HSR, VHSR, and sprints than the younger
age groups. This information is especially important as high-intensity
efforts are useful indicators of physical performance in soccer (5) and
seems to support the notion that elite players might have a specific age
window of highest performance. Present results seem to partially agree
with previous studies that have investigated age of peak performance
in athletic disciplines with high-explosive strength demands, showing
that powerful athletes typically reach peak performance in their mid-
to-late 20s with substantial decreases in short-term power around the
age of !30 years (15,17,24). From a physiological point of view, the
findings of this study could be partially explained by a loss of anaer-
obic performance with higher age, such as declined muscle strength,
declined anaerobic power, or progressive decrease in the cross-
sectional area of fast type II fibers (8,14,24). However, the absence of
data on fitness level in this study indicates additional future studies
analyzing the relationship between the decline of the physical match
performance and physical fitness are warranted.
Previous studies have stated that maximum running speed
achieved by players during a match is a determinant factor to
succeed in modern soccer (2,33). Data of this study demonstrated
a progressive decline of maximal running speed from G1 (,20
years) to G5 ($35 years), with the greatest impairment between
G4 and G5 (30–34.9 to $35 years). Despite direct comparisons
not being possible due to the absence of previous studies in soccer
players, the decrease in maximum running speed observed is in
agreement with previous investigations demonstrating age-
related impairments in physical capacities in professional soccer
players (18). These results, while necessary to be taken with
caution due to the complex and unpredictable nature of soccer
game, may have important practical implications due to the dif-
ferences observed in this capacity between positional roles (6).
Thus, it would be recommended to regularly monitor maximal
speed during training in professional soccer players because it
could provide important information regarding changes in per-
formance that could be of benefit for adjusting training loads,
especially in older players.
The scientific literature indicatesthephysicalandtechnicalper-
formance during a match varies among playing positions (6,12).
Results of this study indicate that, when analyzing the effects of age
for the different playing positions,age-relatedperformancedeclines
seem to be more pronounced among CD, ED, CM, and S than EM,
who are able to maintain match physical performance in terms of
total distance covered, MSR, number of sprints, and maximal speed
past the age of 30 years. These differences may be related to the
importance of maintaining the match work-rate for the EM in
comparison with other playing positions. That is, considering play-
ers’work-rate is significantly related to soccer performance, those
EM who are able to maintain their physical performance might be
able to extend their careers (20).
Findings of this study should be interpreted with caution
because this was a cross-sectional design; hence, results may
have been influenced by genetic and skill factors or lifestyle.
Thus, it is reasonable to assume players leave the sport when
their performance starts to decline, and that only the players
who are genetically gifted or highly skilled continue to par-
ticipate beyond a certain age and have longer careers. There-
fore, further studies with longitudinal designs are necessary to
confirm the results observed in the present investigation. Be-
cause of the considerable variability observed match-to-match
in professional soccer players (16), probably influenced by
different situational variables (i.e., match location, match
status, and quality of the team and opponent), a large sample of
players representing all playing positions and age groups was
needed. Thus, our data seem to provide a valid expression of
match running-performance comparisons across different age
groups.
Practical Applications
This study demonstrated that players’physical match per-
formance reduces with increasing age. Such findings may have
a great deal of practical implications and may help coaches
and managers to better understand the effects of age on match-
related physical performance and may have the potential to
assist in decisions regarding recruitment and player list man-
agement within professional soccer clubs. First, it is recom-
mended that fitness coaches consider the players’age and
positional role when analyzing match-related physical per-
formance to observe possible weak and strong performance
points. Second, present data may be useful for researchers
focusing on age-related performance changes in team sports
and could be used in future studies as reference. Considering
that the observed age-related physical performance impair-
ments probably cannot be avoided, coaches and athletes can
also benefit from the present data because it can help them
develop age-tailored training programs that focus on in-
creasing aerobic performance and maximal running speed.
Age-Related Performance Changes in Soccer (2019) 00:00 |www.nsca.com
5
Copyright © 2019 National Strength and Conditioning Association. Unauthorized reproduction of this article is prohibited.
ACKNOWLEDGMENTS
E.R. was supported by the program “Jos`
e Castillejo”from the
Spanish Ministry of Science, Innovation, and Universities.
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