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European Journal of Sport Science
ISSN: (Print) (Online) Journal homepage: https://www.tandfonline.com/loi/tejs20
Effect of bio-banding on physiological and
technical-tactical key performance indicators in
youth elite soccer
Dennis Lüdin, Lars Donath, Stephen Cobley & Michael Romann
To cite this article: Dennis Lüdin, Lars Donath, Stephen Cobley & Michael Romann (2021): Effect
of bio-banding on physiological and technical-tactical key performance indicators in youth elite
soccer, European Journal of Sport Science, DOI: 10.1080/17461391.2021.1974100
To link to this article: https://doi.org/10.1080/17461391.2021.1974100
© 2021 The Author(s). Published by Informa
UK Limited, trading as Taylor & Francis
Group
Published online: 19 Sep 2021.
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ORIGINAL INVESTIGATION
Effect of bio-banding on physiological and technical-tactical key performance
indicators in youth elite soccer
Dennis Lüdin
a
, Lars Donath
b
, Stephen Cobley
c
and Michael Romann
a
a
Department of Elite Sport, Swiss Federal Institute of Sport Magglingen, Magglingen, Switzerland;
b
Department of Intervention Research in
Exercise Training, German Sport University Cologne, Cologne, Germany;
c
Department of Exercise and Sport Science, University of Sydney,
Sydney, Australia
ABSTRACT
Bio-banding has been introduced to reduce the impact of inter-individual differences due to
biological maturation among youth athletes. Existing studies in youth soccer have generally
examined the pilot-testing application of bio-banding. This is the first study that investigated
whether bio-banded (BB) versus chronological age (CA) competition affects reliable
physiological and technical-tactical in-game key performance indicators (KPIs) using a
randomized cross-over repeated measures design. Sixty-five youth elite soccer players from the
under-13 (U
13
) and under-14 (U
14
) age category and with maturity offsets (MO) between −2.5
and 0.5 years, competed in both a BB and CA game. For statistical analysis, players were divided
into four sub-groups according to CA and MO: U
13
MO
low
(CA ≤12.7, MO ≤−1.4), U
13
MO
high
(CA ≤12.7, MO > −1.4), U
14
MO
low
(CA > 12.7, MO ≤−1.4), U
14
MO
high
(CA > 12.7, MO > −1.4). The
two-factor mixed ANOVA revealed significant (p< .05) interactions between competition format
and sub-group for the KPIs high accelerations (
h
2
p= .176), conquered balls (
h
2
p= .227) and attack
balls (
h
2
p= .146). Especially, U
13
MO
high
(i.e. early maturing players) faced a higher physiological
challenge by having more high accelerations (|d| = 0.6) in BB games. Notably, U
14
MO
low
(i.e. late
maturing players) had more opportunities to show their technical-tactical abilities during BB
games with more conquered balls (|d| = 1.1) and attack balls (|d| = 1.6). Affected KPIs indicate
new challenges and learning opportunities during BB competition depending on a player’s
individual maturity status. Bio-banding can beneficially be applied to enhance the talent
development of youth elite soccer players.
KEYWORDS
Talent development; talent
identification; maturation;
relative age; football
Introduction
Successful talent development and identification are of
utmost importance for sports federations and clubs
(Vaeyens, Lenoir, Williams, & Philippaerts, 2008). In the
context of high-performance soccer, the systematic
development and promotion of promising athletes is
the foundation for financial sustainability and success
for many elite soccer clubs (Ford et al., 2020; Williams
& Reilly, 2000). It is, therefore, necessary to have an
effective process of talent development and identifi-
cation in place, if youth athlete potential is to success-
fully transfer into the elite level. However, this process
appears challenging as maturity-related individual
differences affect a youth’s performance and evaluation
of their potential (Guellich & Cobley, 2017; Unnithan,
White, Georgiou, Iga, & Drust, 2012).
Young athletes are commonly grouped into Chrono-
logical Age (CA) groups based on their age relative to
cut-offdates. Annual-age categories, predominantly
used in youth sport (including soccer), are meant to
help provide a more appropriate developmentally
matched environment for participation, promoting
equal opportunity and fair competition (Musch &
Grondin, 2001). Young athletes pursue sports to pro-
gress and in turn expect a fair and supportive environ-
ment (Vaeyens et al., 2008). However, the CA of players
can still differ by up to one year within the same CA cat-
egory. Thus, on average, relatively older players born
earlier in a CA group are more advanced in their physical
and cognitive development compared to their relatively
younger counterparts born at the end of the same CA
group (Musch & Grondin, 2001). These CA related differ-
ences manifest in terms of Relative Age Effects (RAEs),
where an overrepresentation of relatively older players
is apparent across youth sport and athlete development
programmes (Cobley, Baker, Wattie, & McKenna, 2009;
Smith, Weir, Till, Romann, & Cobley, 2018). Further,
large inter-individual variations in the process of
© 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group
This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-
nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built
upon in any way.
CONTACT Dennis Lüdin dennis.luedin@baspo.admin.ch
EUROPEAN JOURNAL OF SPORT SCIENCE
https://doi.org/10.1080/17461391.2021.1974100
maturation around the age of puberty (i.e. 11–16 years)
can both substantiate CA difference, or mitigate against
them depending on the individuals maturity (Malina,
Bouchard, & Bar-Or, 2004). Earlier maturing athletes
exhibit anthropometric and physiological advantages,
which provide performance advantages within respect-
ive CA categories. For instance, earlier maturing
players show superior physical strength (Lefevre,
Beunen, Steens, Claessens, & Renson, 1990; Till, Cobley
S, Cooke, & Chapman, 2014), speed (Malina, Eisenmann,
Cumming, Ribeiro, & Aroso, 2004), agility (Towlson,
Cobley, Parkin, & Lovell, 2018) and aerobic capabilities
(Malina, Beunen, Lefevre, & Woynarowska, 1997;
Towlson et al., 2018) compared to CA group matched
later maturing peers. Thus, if talent identification and
development programmes do not carefully consider
these inter-individual developmental differences within
organized CA groups, they are likely vulnerable to over-
looking and excluding late maturing, but potentially
talented players (Unnithan et al., 2012).
To address the problem of developmental differences
within talent identification and development, research
has identified several approaches, for example adopting
a broad longitudinal and multi-dimensional perspective
on athlete development (Sarmento, Anguera, Pereira, &
Araujo, 2018; Vaeyens et al., 2008) as well as delaying
selection and differentiation until beyond maturation
(Cobley, Till, O’Hara, Cooke, & Chapman, 2014).
In this line of reasoning, bio-banding has been intro-
duced in order to account for maturity-associated vari-
ation (Cumming, Lloyd, Oliver, Eisenmann, & Malina,
2017). The aim of bio-banding is to reduce the impact
of inter-individual maturational differences by helping
both late and early-maturing youth to potentially partici-
pate and compete on a more developmentally matched
basis (Malina et al., 2019). This is achieved via the process
of banding youth athletes according to other maturity-
related characteristics than just CA.
Existing studies in youth soccer have generally
examined pilot-testing application and effects of bio-
banding from a predominantly qualitative perspective
(Bradley et al., 2019; Cumming et al., 2018; Reeves,
Enright, Dowling, & Roberts, 2018; Towlson et al.,
2020). Reflecting across these studies, player interviews
of those who experienced a bio-banded (BB) soccer
tournament provided positive evaluative assessments.
Early maturing players perceived bio-banding as pro-
viding more of a physical challenge, indicating they
had to adapt their game toward greater technical and
tactical skill use. Late maturing players highlighted
they had more opportunities to use their technical,
physical and psychological competencies, due to less
physiological demands.
To date, only pilot data on the quantitative evalu-
ation of bio-banding in soccer are available. Over a
small, hence practically applicable age range (12–14
years) the effect of bio-banding on the overall game
play indicated a reduced physical demand while the
number of duels and set-piece situations increased
compared to the traditional CA competition format.
Ball possession analysis revealed a more equal game
between the teams with a quicker change of match
play situations when playing with BB teams (Romann,
Lüdin, & Born, 2020). It is assumed that these results
can be explained through a higher involvement of
late maturing players in the BB competition format. A
preliminary analysis of the effects of a BB competition
format on the physical and technical performance of
youth elite soccer players dependent on their maturity
status showed that early maturing players had a higher
rate of perceived exertion (RPE), more short passes and
less dribbles during bio-banding, whereas late matur-
ing players had more tackles and less long passes
(Abbott, Williams, Brickley, & Smeeton, 2019). But not
only late or early maturing players adapt their game
play: Average maturing players had more short
passes, less long passes and dribbled more often in
BB games. Unfortunately, these two existing studies
did not consistently analysed Key Performance Indi-
cators (KPIs) –referring to players’physiological and
technical-tactical in-game conditioning relevant to
talent development. Further, neither study provided
reliability assessments for their technical-tactical
indices applies.
Given the broader research and practical need to
improve the efficacy and effectiveness of talent devel-
opment and identification processes, and the more
specific need to address the influence of inter-individ-
ual maturational associated developmental differences
within youth sport competition and athlete evaluation,
the present study had two main purposes. Firstly,
using a randomized cross-over repeated measures
design, the present study aimed to examine bio-
banding effects relative to normative (CA group
based) youth soccer competition more extensively
and reliably by assessing multiple physiological, as
well as technical-tactical player’s in-game KPIs in a
sample of elite Swiss youth soccer players. The
second purpose was to provide practitioners and
talent development systems alike with information
about how and when bio-banding could be effectively
applied based on a player’s individual developmental
need. We considered that such a study would help
provide a more substantive quantitative evaluation of
BB games, as well as help anticipate the impact
upon individual talent development.
2D. LÜDIN ET AL.
Methods
Participants
The study was conducted in accordance with the guide-
lines of the Declaration of Helsinki. Following insti-
tutional ethical approval (Swiss Federal Institute of
Sport; Nr. 2019/079), eighty-one youth elite soccer
players between 11.7 and 13.7 years of age from either
the Under-13 (U
13
) or the Under-14 (U
14
) age category
who played at one of four Swiss elite youth soccer
clubs participated. Clubs were part of the national
youth development programme of the Swiss Football
Association. The programme promotes the “Top 2000”
U
13
and “Top 1300”U
14
players respectively (Knäbel,
2020). At the time of participation, players had been
officially licensed at a soccer club for 4.2 ± 0.7 years. All
Goalkeepers and eight injured players at the time of
data-collection were excluded, resulting in sixty-five
players being taken forward for data-analysis.
Study design
A randomized cross-over repeated measures design was
applied. Each participant played two games of 9v9 on a
natural grass pitch (56 × 67 m). The 9v9 game is the tra-
ditional format in Switzerland for the U
13
and U
14
age-
groups (Bruggmann & Moulin, 2020). A game consisted
of two 35-minutes halves separated by 10 min for half-
time. One game was played adhering to a BB compe-
tition format and the other adhering to the CA compe-
tition format. Games were played on two match days
separated by one week. Each match day, two clubs
with their respective two teams each played their
games against each other. To control for familiarization
effects, the first pair of clubs played their first game
under the CA format, while the second club pair
played their first games in the BB format. Playing pos-
itions were fixed and matched across games for each
player to ensure paired comparisons.
Maturity assessment
Maturity was assessed by anthropometric measure-
ments using the equation for predicting maturity
offset (MO) (Mirwald, Baxter-Jones, Bailey, & Beunen,
2002). MO is defined as years between the age at the
point in time of the measurement and the predicted
age at peak height velocity. The equation required
body weight, standing height and sitting height, which
were measured using a body scale (Seca 876, Seca,
Hamburg, Germany) and a stadiometer (Seca 217,
Seca, Hamburg, Germany). The measurements were
standardized, and measured according to protocol by
the same trained sport scientist two weeks prior to the
first games. Although some limitations of the method
exist, it is known to be especially accurate the closer sub-
jects are estimated to their MO (Malina & Koziel, 2014).
The participating soccer players between 11.7 and 13.7
years of age in this study are very close to the most accu-
rate time in point for the application of the equation.
Bio-banding procedure
The bio-banding procedure was based on the players
MO. Players with an individual MO lower than the MO
median were assigned to the lower bio-band (MO
low
).
Players with an individual MO higher than the MO
median were assigned to the higher bio-band (MO
high
).
Players in the MO
low
had an individual MO ≤−1.4
years. Players in the MO
high
had an individual MO >
−1.4 years. The clubs played the BB games with their
players categorized based on the MO (i.e. MO
low
and
MO
high
). For the CA games, the players were grouped
according to their CA (U
13
;CA≤12.7 and U
14
;CA>
12.7). For the statistical analysis, players were divided
into four sub-groups, taking into account CA and MO:
U
13
aged players in the lower bio-band (U
13
MO
low
), U
13
aged players in the higher bio-band (U
13
MO
high
), U
14
aged players in the lower bio-band (U
14
MO
low
) and U
14
aged players in the higher bio-band (U
14
MO
high
).
Descriptive statistics on maturity-related characteristics
for the four sub-groups are summarized in Table 1.
Key performance indicators (KPIs)
Table 2 provides a summary list and definitions of KPIs.
The list of KPIs was identified based on a combination
of previous bio-banding studies (Abbott et al., 2019;
Table 1. Descriptive maturity-related participants’characteristics
for the four sub-groups.
Sub-group
U
13
MO
low
(n= 20)
U
13
MO
high
(n= 12)
U
14
MO
low
(n= 11)
U
14
MO
high
(n= 22)
Variable
Chronological
age [yr]
12.2 ± 0.2 12.4 ± 0.2 13.0 ± 0.2 13.3 ± 0.3
Maturity
offset [yr]
−1.9 ± 0.3 −1.1 ± 0.2 −1.7 ± 0.3 −0.9 ± 0.4
Stature [cm] 147.9 ± 3.7 158.1 ± 5.5 147.4 ± 3.6 157.4 ± 6.9
Sitting height
[cm]
76.3 ± 2.4 81.6 ± 1.7 75.3 ± 2.1 80.5 ± 3.4
Body mass
[kg]
38.7 ± 4.5 45.9 ± 4.2 37.3 ± 3.0 44.4 ± 6.3
Notes: Values are Mean ± Standard Deviation. U
13
MO
low
= Under-13 players
in the lower bio-band; U
13
MO
high
= Under-13 players in the higher bio-
band; U
14
MO
low
= Under-14 players in the lower bio-band; U
14
MO
high
=
Under-14 players in the higher bio-band.
EUROPEAN JOURNAL OF SPORT SCIENCE 3
Bradley et al., 2019; Cumming et al., 2018; Romann et al.,
2020; Towlson et al., 2020) and a group of soccer experts
with 24.3 ± 9.5 years of experience in Swiss youth elite
soccer. KPIs were divided into Physiological and Techni-
cal-Tactical KPI types.
In-game player positional data were measured using
a Local Position Measurement system (LPM; inmotiotec
GmBH, Regau, Austria) with 24 Hz (Frencken, Lemmink,
& Delleman, 2010; Stevens et al., 2014). Using the LPM,
the following physiological indicators were determined:
Total distance covered, High-speed running
(≥15.8 km·h
–1
) distance covered, and the Number of
high accelerations (≥2.4 m·s
–2
). Commonly applied
speed zones and thresholds for acceleration were used
and adjusted for age (Bradley et al., 2009; Harley et al.,
2010). Due to the failure of the connection of transpon-
ders to the base station during the matches, data from
12 players could not be integrated into the data set.
For technical-tactical KPIs, all player data were
derived from game video-recording and coded based
on the Team Sports Assessment Procedure (TSAP) (Gré-
haigne, Godbout, & Bouthier, 1997). Each game was
filmed using two mounted video cameras placed on
the side line of each pitch half, guaranteeing full area
and match play coverage. The game recording was
oriented toward ball-following without zoom. These
recordings were used to manually code all technical-tac-
tical KPIs by one of three experts using an iPad with the
application Dartfish Note (Dartfish, Fribourg, Switzer-
land). Intra-class correlation coefficient (ICC) analysis
identified good (.75 ≤ICC < .90) to excellent (ICC ≥.90)
intra-rater reliability of the three experts (two-way
mixed effects, absolute agreement, single measurement)
(Koo & Li, 2016): .825, .902, and .900, respectively. Inter-
rater reliability of the collection of technical-tactical indi-
cators was moderate (.50 ≤ICC < .75) to good (.75 ≤ICC
< .90) (two-way mixed effects, absolute agreement,
single rater) (Koo & Li, 2016).
Statistical analysis
The collected KPIs were checked for homogeneity of the
error variances and the covariances using Levene’s test
(p> .05) and Box’s test (p> .05), respectively. For all
KPIs except “attack balls”, there were homogeneity of
the error variance and covariances, respectively. The
assumption of homogeneity of the error variances and
covariances was met after the KPI “attack balls”was
box–cox transformed (Box & Cox, 1964). KPIs were ana-
lysed using two-factor mixed analysis of variance
(ANOVA). Competition format (BB, CA) served as
within-subject factor and sub-group (U
13
MO
low
,
U
13
MO
high
,U
14
MO
low
,U
14
MO
high
) as the between-sub-
jects factor. Partial eta squared (
h
2
p) were calculated to
analyse the effect size (ES) of the interactions.
h
2
pwas
regarded as a trivial (
h
2
p< .01), small (.01 ≤
h
2
p< .06),
medium (.06 ≤
h
2
p< .14) or large (
h
2
p≥.14) ES (Cohen,
1988). Bonferroni post-hoc tests for pairwise compari-
sons within competition formats. Statistical significant
α-level was set at a p-value < .05. Additionally, to
support the interpretations of detected interactions,
mean ± standard deviation for sub-groups separated
by competition format, as well as Cohen’sdES and cor-
responding 95% confidence intervals for the differences
between the competition formats within each sub-
group were calculated. Cohen’sdwas regarded as a
trivial (|d| < 0.2), small (0.2 ≤|d| < 0.5), medium (0.5 ≤|d|
< 0.8) or large (|d|≥0.8) ES (Cohen, 1988). Main effects
for sub-group were excluded from statistical analysis as
they were not subject to the purpose of the study. Statisti-
cal calculations were conducted using a statistic software
(IBM SPSS Statistics, Version 25.0, Armonk, NY, United
States).
Results
No significant main effects were identified for the com-
petition format. A summary of the results of the inter-
actions between competition format and sub-group of
Table 2. Definition and description of KPIs.
KPIs Description
Physiological KPIs
Total distance [m] Total distance covered
High-speed running
[m]
Distance covered ≥15.8 km·h
–1
High accelerations [n] Number of accelerations ≥2.4 m·s
–2
Technical-Tactical KPIs
Conquered balls [n]
(ICC = .805)
Intercepting a pass from an opponent or
Stealing the ball form an opponent or
Causing a missed pass from an opponent
Attack balls [n]
(ICC = .789)
Passing the ball to a teammate in direction of
the opponent’s goal or
Taking the ball to a free space in direction of
the opponent’s goal or
Protecting the ball in a small space or
Taking the ball past an opponent or
Shooting the ball on the opponent’s goal
Neutral balls [n]
(ICC = .598)
Passing the ball to a teammate across or in
direction of the own goal or
Taking the ball across or in direction of the
own goal
Lost balls [n]
(ICC = .859)
Losing the ball without having scored a goal
(shots on the opponent’s goal excluded)
Volume of play on the
ball [n]
(ICC = .713)
Receiving the ball from a teammate or
Intercepting a pass from an opponent or
Stealing the ball from an opponent
Volume of play offthe
ball [n]
(ICC = .551)
Putting the opponent under pressure
Efficiency on the ball
[%]
Share of conquered and attack balls on the total
number of conquered, attack, neutral and lost
balls
Notes:KPIs = Key Performance Indicators; ICC = Intra-class correlation
coefficient.
4D. LÜDIN ET AL.
the two-factor mixed ANOVA on Key Performance Indi-
cators (KPIs) are presented in Table 3. Mean ± standard
deviation for the sub-groups depending on the compe-
tition format, as well as Cohen’sdES and corresponding
95% confidence intervals for the differences between
the competition formats within each sub-group are pre-
sented in Table 4.
Physiological KPIs
The two-factor mixed ANOVA revealed a statistically sig-
nificant interaction between competition format and
sub-group for high accelerations (F
(3,49)
= 3.495, p
= .022,
h
2
p= .176). Pairwise post-hoc tests showed signifi-
cantly more high accelerations for U
14
MO
high
compared
to U
13
MO
high
only in the CA competition format (p
= .016). Cohen’sdES indicated a higher number of
high accelerations for U
13
MO
high
during BB games with
a medium ES (|d| = 0.6). Other sub-groups showed a
lower number of high accelerations with small or
medium ES during BB games compared to the CA com-
petition format (U
13
MO
low
:|d| = 0.4, U
14
MO
low
:|d| = 0.6,
U
14
MO
high
:|d| = 0.5). No significant interactions
between competition format and sub-group for the
total distance and high-speed running were present.
Related to these indicators, trivial or small ES were
evident for the difference between competition
formats within all sub-groups.
Technical-tactical KPIs
The two-factor mixed ANOVA revealed statistically sig-
nificant interactions between competition format and
sub-group for conquered balls (F
(3,61)
= 5.964, p= .001,
h
2
p= .227) and attack balls (F
(3,61)
= 3.466, p= .022,
h
2
p
= .146). Pairwise post-hoc tests for conquered balls
revealed no significant differences between the sub-
groups in any competition format. Cohen’sdES
however indicated a higher number of conquered balls
specifically for U
14
MO
low
during BB games with a large
ES (|d| = 1.1). U
13
MO
high
had less conquered balls with
a medium ES (|d| = 0.6), whereas U
13
MO
low
and
U
14
MO
high
showed more conquered balls in BB games
compared to the CA competition format with a small
and a trivial ES, respectively. Pairwise post-hoc tests
showed significantly more attack balls for U
14
MO
high
compared to U
14
MO
low
(p= .034) and U
13
MO
high
(p
= .043) only in the CA competition format. A higher
number of attack balls was specifically evident for
U
14
MO
low
during BB games with a large ES (|d| = 1.6).
Additionally, medium
h
2
pES demonstrated a trend
towards interactions between competition format and
sub-group for volume of play on the ball (F
(3,61)
=
2.513, p= .067,
h
2
p= .110) and efficiency on the ball
(F
(3,61)
= 2.646, p= .057,
h
2
p= .115). No significant post-
hoc tests were evident for these two KPI. However, a
higher volume of play on the ball, specifically for
U
14
MO
low
during BB games, was showed with a large
ES (|d| = 0.8). Further, only U
14
MO
low
showed a higher
efficiency on the ball compared to the CA competition
format with a large ES (|d| = 0.9). No significant inter-
actions between competition format and sub-group
for neutral balls, lost balls and volume of play offthe
ball were revealed. Related to these indicators, trivial
or small ES were evident for the difference between
competition formats within all sub-groups.
Discussion
To the best of our knowledge, this is the first study that
systematically examined bio-banding in elite youth
soccer players based on quantitative data. A randomized
crossover trial was applied to analyse the effects of bio-
banding on participating sub-groups depending on
maturity status and CA. The data mainly showed signifi-
cant interactions between competition format and sub-
group for the KPI high accelerations, conquered balls
and attack balls. Specifically, U
13
MO
high
showed more
high accelerations with a medium ES in their BB com-
pared to their CA game. In addition, in their BB game
U
14
MO
low
had more conquered balls and attack balls
with a large ES compared to their CA game.
Physiological KPIs
Previous qualitative studies on bio-banding effects
based on player’s perceptions indicated a greater phys-
iological challenge during BB format for early maturing
players compared to the traditional CA competition
format (Bradley et al., 2019; Cumming et al., 2018). The
Table 3. Summary of interactions between competition format
and sub-group of the ANOVA on KPIs.
KPIs df
Num
df
Den
F
h
2
pp-value
Physiological KPIs
Total distance 3 49 0.579 .034 .632
High speed running 3 49 0.998 .058 .402
High accelerations 3 49 3.495 .176
†
.022*
Technical-tactical KPIs
Conquered balls 3 61 5.964 .227
†
.001*
Attack balls 3 61 3.466 .146
†
.022*
Neutral balls 3 61 0.088 .004 .966
Lost balls 3 61 0.286 .014 .835
Volume of play on the ball 3 61 2.513 .110
††
.067
Volume of play offthe ball 3 61 0.119 .006 .948
Efficiency on the ball 3 61 2.646 .115
††
.057
Notes: KPIs = Key performance indicators; df
Num
= Degrees of freedom for
the numerator; df
Den
= Degrees of freedom for the denominator; F= F stat-
istic;
h
2
p= partial eta squared;
†
indicates
h
2
p≥.14;
††
indicates .06 ≤
h
2
p
<.14;
*
indicates p< .05.
EUROPEAN JOURNAL OF SPORT SCIENCE 5
Table 4. Descriptive results of KPIs and Cohen’sdES of the difference between competition formats within sub-groups.
Sub-group
U
13
MO
low
U
13
MO
high
U
14
MO
low
U
14
MO
high
KPIs CA BB ES
95% CI
Lower;
Upper CA BB ES
95% CI
Lower;
Upper CA BB ES
95% CI
Lower;
Upper CA BB ES
95% CI
Lower;
Upper
Physiological KPIs
Total distance [m] 3983 ±
316
4074 ±
320
−0.3 −0.8; 0.1 3910 ±
266
4005 ±
279
−0.4 −1.1; 0.3 4119 ±
404
4018 ±
478
0.2 −0.5; 0.9 4245 ±
386
4324 ±
565
−0.1 −0.7; 0.5
High speed running [m] 501 ± 141 452 ±
133
0.4 0.0; 0.9 498 ±
111
471 ± 93 0.2 −0.5; 0.9 515 ±
200
524 ±
179
−0.1 −0.8; 0.6 571 ±
163
604 ±
249
−0.2 −0.8; 0.4
High accelerations [n] 62 ± 23 50 ± 17 0.4 0.0; 0.9 47 ±13* 64 ± 25 −0.6 −1.3; 0.1 57 ± 17 48 ± 13 0.6 −0.1; 1.3 75 ± 24 62 ± 19 0.5 −0.1; 1.1
Technical-Tactical KPIs
Conquered balls [n] 7.7 ± 3.8 8.4 ± 2.6 −0.3 −0.7; 0.2 9.0 ± 4.3 7.1 ± 3.2 0.6 −0.1; 1.2 5.8 ± 2.1 9.3 ± 2.7 −1.1 −1.8; −0.4 9.4 ± 4.0 9.5 ± 3.5 0.0 −0.5; 0.4
Attack balls [n] 10.4 ±
5.2*
10.7 ±
3.6
−0.1 −0.5; 0.4 13.5 ±
6.7
12.3 ±
4.8
0.3 −0.4; 0.9 9.1 ±
3.2*
14.4 ±
4.4
−1.6 −2.3; −1.0 15.3 ±
6.3
15.3 ±
6.6
0.0 −0.5; 0.4
Neutral balls [n] 9.7 ± 4.1 10.7 ±
4.8
−0.2 −0.6; 0.3 8.6 ± 3.0 9.3 ± 2.7 −0.3 −0.9; 0.3 11.7 ±
5.9
12.2 ±
4.7
−0.1 −0.8; 0.6 12.5 ±
4.7
12.7 ±
4.6
0.0 −0.5;0.4
Lost balls [n] 7.5 ± 2.6 7.3 ± 2.5 0.0 −0.4; 0.5 7.8 ± 2.1 7.3 ± 2.3 0.2 −0.5; 0.8 6.6 ± 2.6 7.4 ± 1.8 −0.3 −0.9; 0.4 7.4 ± 3.2 7.3 ± 2.9 0.0 −0.4; 0.5
Volume of play on the
ball [n]
23.3 ± 7.8 25.5 ±
7.3
−0.2 −0.7; 0.2 25.3 ±
7.4
23.6 ±
5.7
0.4 −0.2; 1.0 22.5 ±
5.8
28.0 ±
6.9
−0.8 −1.5; −0.1 27.4 ±
8.2
27.1 ±
6.6
0.0 −0.4; 0.5
Volume of play offthe
ball [n]
13.1 ± 4.1 14.0 ±
5.7
−0.2 −0.6; 0.3 12.2 ±
5.1
12.6 ±
4.0
−0.1 −0.7; 0.5 12.6 ±
5.0
12.3 ±
5.7
0.0 −0.6; 0.7 12.7 ±
4.0
12.8 ±
5.2
0.0 −0.5; 0.4
Efficiency on the ball [%] 50.4 ± 8.8 51.6 ±
8.1
−0.1 −0.6; 0.4 55.9 ±
8.8
53.5 ±
9.5
0.3 −0.4; 0.9 45.9 ±
8.9
55.1 ±
9.5
−0.9 −1.5; −0.2 54.3 ±
9.1
54.4 ±
10.3
0.0 −0.5; 0.4
Notes: Values are Mean ± Standard Deviation per 35 min; KPIs = Key performance indicators; U
13
MO
low
= Under-13 players in the lower bio-band; U
13
MO
high
= Under-13 players in the higher bio-band; U
14
MO
low
= Under-14
players in the lower bio-band; U
14
MO
high
= Under-14 players in the higher bio-band; CA = Chronological age competition format; BB = Bio-banded competition format; ES = Effect size (Cohen’sd); 95% CI = 95% Confi-
dence interval, *indicates significantly lower values than U
14
MO
high
(p< .05).
6D. LÜDIN ET AL.
player’s perceived higher physiological demands were
underlined by a preliminary analysis of bio-banding
during youth soccer competition, where only early
maturing players produced higher rates of perceived
exertion (RPE) in BB games compared to CA games
(Abbott et al., 2019). Still, the same study did not show
a simultaneous significant increase in any of the other
measured quantitative physiological indicators that
could have mediated the higher RPE (i.e. total distance
covered, high-speed running distance covered and
explosive distance covered). In line with those findings,
the present study only revealed trivial or small ES of
bio-banding on the total or the high-speed running dis-
tance covered for any sub-group. However, a significant
interaction between competition format and sub-group
for the number of high accelerations was evident with a
large ES. Specifically, U
13
MO
high
had more high accelera-
tions with a medium ES. Throughout the other sub-
groups, lower numbers of high accelerations were
found in their BB game compared to their CA game.
This finding provides a mediation for the higher per-
ceived physiological demand of early maturing players
when playing BB matches and hence confirms obser-
vations in previous studies (Abbott et al., 2019; Bradley
et al., 2019; Cumming et al., 2018). The higher physio-
logical challenge for players who are relatively advanced
in maturation is seen as a positive influence on their
development, as it prepares them for future adult age
competitions against physically more equal opponents
(Cumming et al., 2018).
Technical-tactical KPIs
Regarding current studies analysing the effects of bio-
banding on technical-tactical indicators, affected
players reported different experiences depending on
their maturity status in interviews (Bradley et al., 2019;
Cumming et al., 2018). On the one hand, early maturing
players pointed out that they can no longer solely rely
on their physical strength during BB games, but more
often have to resort to their technical-tactical abilities
to compete. On the other hand, late maturing players
enjoyed a greater opportunity to prove their technical-
tactical skills in BB compared to CA games. Overall, it
can be assumed that bio-banding leads to a more
balanced and technically-tactically challenging game
with more duels and unsuccessful passes (Romann
et al., 2020). Indeed, previous results showed significant
interactions between competition format and maturity
group on certain technical-tactical indicators (Abbott
et al., 2019). Less dribbles of early maturing players
confirm the reduced physical advantage during BB
matches. Late maturing players were able to successfully
conduct a tackle more often during BB compared to CA
games. Present findings showed significant interactions
between competition format and sub-group for con-
quered balls and attack balls. In addition, for volume of
play on the ball and efficiency on the ball, moderate
h
2
pES demonstrated a trend towards interactions
between competition format and sub-group. Specifi-
cally, during BB games only U
14
MO
low
had more con-
quered and attack balls, as well as a higher volume of
play and efficiency on the ball compared to their CA
game, with a large ES. Thus, new learning stimuli with
more opportunities to show technical-tactical abilities
during bio-banding were evident, especially for
U
14
MO
low
. Also, it is assumed that a higher technical-tac-
tical involvement and the associated greater influence
on the match play supports the psychological develop-
ment of players playing in a lower CA category (e.g.
taking on more responsibility and leadership or increas-
ing confidence) (Cumming et al., 2018). Further, in BB
games U
13
MO
high
had less conquered balls and volume
of play on the ball compared to their CA game, with
medium ES. While they were physically rather superior
in the CA competition format, they were provided with
a challenging environment and had to adapt their
style of play to more equal opponents in the BB game.
A variety of challenges has been indicated to impact
the psychological development of athletes and help
them reach their potential (e.g. developing mental
toughness and resilience) (Collins & MacNamara, 2012).
For completeness, not all collected technical-tactical
KPI were affected by bio-banding. For the KPI neutral
balls, lost balls and volume/time of play offthe ball,
there were only trivial or small ES in differences
between competition formats within sub-groups with
no significant interaction between competition format
and sub-group.
Limitations
The study has some limitations that need to be
addressed. The application of bio-banding in the
current study includes the U
13
and U
14
age categories
only. As such, findings are based on study participants
between 11.7 and 13.7 years of age. Applying the
results to other or wider age ranges should be used
with caution. Playing positions were fixed and
matched across games for each player. However, the
sample size did not allow an analysis specified for
playing positions. Future studies should therefore try
to evaluate the effects of bio-banding specified for
each playing position, as it would provide a more
detailed understanding of bio-banding. The individual
and short-term variability is a feature of youth soccer
EUROPEAN JOURNAL OF SPORT SCIENCE 7
matches. Hence, more studies, especially longitudinal
studies, would be desirable and valuable to evaluate
the effects of bio-banding even more thoroughly and
precisely.
Practical implications
Practitioners such as coaches and staffshould consider
and be aware of the effects bio-banding can have on
different sub-groups involved. The present study
allows estimation of the effects and their extent on tech-
nical-tactical and physiological KPIs when implementing
bio-banding for U
13
and U
14
youth elite soccer players
and the corresponding four sub-groups. According to
the KPIs measured, players who did not change their tra-
ditional CA category (i.e. U
13
MO
low
and U
14
MO
high
)
experienced trivial to small changes due to bio-
banding compared to CA competition. However,
players changing their usual CA category (i.e.
U
13
MO
high
and U
14
MO
low
) encountered more substantial
effects. On the one hand, players from U
13
MO
high
face a
higher physiological challenge and can no longer rely
solely on their physical advantage. Whereas players
from U
14
MO
low
had more opportunities to use and
demonstrate their technical-tactical skills. Thus, by pro-
viding participating players with a new learning environ-
ment and considering their individual developmental
needs, bio-banding has the potential to enhance the
process of talent development. Bio-banding can be
applied to modify the competition environment to
benefit the development for both types of players,
offering them new stimuli for technical-tactical skill
acquisition and maturity-adjusted physical challenges.
Further, coaches and staffare given the chance to mini-
mize the loss of potential talents by evaluating player’s
potential in an environment adapted to their matura-
tional development.
Conclusion
Practitioners within talent development systems should
be aware of the revealed effects of bio-banding and con-
sider them when implementing bio-banded (BB) compe-
tition. In summary, present results showed that bio-
banding affects in-game Key Performance Indicators of
youth elite soccer players, depending on individual
maturity status. Compared to chronological age (CA)
competition, in BB competition late maturing players
are provided with more opportunities to show techni-
cal-tactical skills, such as conquering the ball or
playing an attack ball. Early maturing players face a
higher physiological challenge manifested by more
high accelerations in BB compared to CA competition.
To conclude, present findings show the potential of
bio-banding to improve the process of talent develop-
ment and help apply bio-banding more focused and
adjusted to the player’s developmental needs.
Acknowledgements
The authors would like to thank Markus Frei, Marco Bernet and
Adrian Elvedi for their contribution to the data collection
process. Also, we thank coaches, staffand players of the
clubs for their participation and effort during the match
days. Furthermore, we want to acknowledge Stefan Brunner,
Hannes Schäfer and Raphael Kern of the Swiss Football Associ-
ation for their support and cooperation during the project.
Disclosure statement
No potential conflict of interest was reported by the author(s).
ORCID
Dennis Lüdin http://orcid.org/0000-0002-7960-8160
Stephen Cobley http://orcid.org/0000-0001-6099-392X
Michael Romann http://orcid.org/0000-0003-4139-2955
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EUROPEAN JOURNAL OF SPORT SCIENCE 9
