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Inter-individual differences in size, maturity status, function, and behavior among youth of the same chronological age (CA) have long been a concern in grouping for sport. Bio-banding is a recent attempt to accommodate maturity-associated variation among youth in sport. The historical basis of the concept of maturity-matching and its relevance to youth sport, and bio-banding as currently applied are reviewed. Maturity matching in sport has often been noted but has not been systematically applied. Bio-banding is a recent iteration of maturity matching for grouping youth athletes into ‘bands’ or groups based on characteristic(s) other than CA. The percentage of predicted young adult height at the time of observation is the estimate of maturity status of choice. Several applications of bio-banding in youth soccer have indicated positive responses from players and coaches. Bio-banding reduces, but does not eliminate, maturity-associated variation. The potential utility of bio-banding for appropriate training loads, injury prevention, and fitness assessment merits closer attention, specifically during the interval of pubertal growth. The currently used height prediction equation requires further evaluation.
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Vol.:(0123456789)
Sports Medicine
https://doi.org/10.1007/s40279-019-01166-x
REVIEW ARTICLE
Bio‑Banding inYouth Sports: Background, Concept, andApplication
RobertM.Malina1,7 · SeanP.Cumming2 · AlanD.Rogol3 · ManuelJ.Coelho‑e‑Silva4 ·
AntonioJ.Figueiredo4 · JanM.Konarski5 · SławomirM.Kozieł6
© Springer Nature Switzerland AG 2019
Abstract
Inter-individual differences in size, maturity status, function, and behavior among youth of the same chronological age (CA)
have long been a concern in grouping for sport. Bio-banding is a recent attempt to accommodate maturity-associated vari-
ation among youth in sport. The historical basis of the concept of maturity-matching and its relevance to youth sport, and
bio-banding as currently applied are reviewed. Maturity matching in sport has often been noted but has not been system-
atically applied. Bio-banding is a recent iteration of maturity matching for grouping youth athletes into ‘bands’ or groups
based on characteristic(s) other than CA. The percentage of predicted young adult height at the time of observation is the
estimate of maturity status of choice. Several applications of bio-banding in youth soccer have indicated positive responses
from players and coaches. Bio-banding reduces, but does not eliminate, maturity-associated variation. The potential utility
of bio-banding for appropriate training loads, injury prevention, and fitness assessment merits closer attention, specifically
during the interval of pubertal growth. The currently used height prediction equation requires further evaluation.
* Robert M. Malina
rmalina@1skyconnect.net
Sean P. Cumming
sc325@bath.ac.uk
Alan D. Rogol
adrogol@comcast.net
Manuel J. Coelho-e-Silva
mjcesilva@hotmail.com
Antonio J. Figueiredo
afigueiredo@fcdef.uc.pt
Jan M. Konarski
konarski@awf.poznan.pl
Sławomir M. Kozieł
slawomir.koziel@hirszfeld.pl
1 Department ofKinesiology andHealth Education,
University ofTexas atAustin, Austin, TX, USA
2 Sport, Health andExercise Science Research Group,
Department forHealth, University ofBath, Bath, UK
3 Department ofPediatrics, University ofVirginia,
Charlottesville, VA, USA
4 CIDAF (uid/dtp/0423), Faculty ofSport Science andPhysical
Education, University ofCoimbra, Coimbra, Portugal
5 Theory ofSports Department, University ofPhysical
Education, Poznań, Poland
6 Department ofAnthropology, Hirszfeld Institute
ofImmunology andExperimental Therapy, Polish Academy
ofSciences, Wrocław, Poland
7 10735 FM 2668, BayCity, TX77414, USA
1 Introduction
Bio-banding is a recent effort at grouping youth athletes
within a chronological age (CA) range, thus far 11–15years,
into ‘bands’ or groups based on characteristic(s) other than
CA for specific competitions and training. Percentage of pre-
dicted adult height attained at the time of observation is the
maturity indicator used for bio-banding [1].
Bio-banding serves as an adjunct to, and is not a replace-
ment for, CA-based groups. It attempts to address inter-
individual differences in biological maturity status during
adolescence. Maturity-associated variation among youth,
including athletes, is well-documented [27], but has
received more attention among boys than girls [8, 9]. Sport
is selective and exclusive during adolescence, and selectiv-
ity in many sports is based, in part, on maturity status [10].
R.M.Malina et al.
Key Points
Bio-banding is a recent iteration of the maturity-match-
ing concept.
Percentage of predicted adult height at the time of obser-
vation is a valid indicator of maturity status.
Both youth and their coaches have responded favorably
to bio-banded soccer tournaments and to training in bio-
banded groups.
Bio-banding in soccer appears to benefit both early- and
late-maturing youth by presenting new opportunities and
challenges.
Bio-banding reduces, but does not eliminate, maturity-
associated variation.
the gum line) and complete eruption of the specific teeth
was considered.
About 70years later in 1904, C. Ward Crampton [1417],
a physician in the New York City public schools, proposed
stage of pubic hair as an indicator of “physiological age” in
boys. Based on a survey of about 1200 first-year high school
boys 11–18years of age (majority 13–15years), three stages
were proposed: (1) pre-pubescent—no pubic hair is present;
(2) pubescent—pubic hair is present, initially fine and not
pigmented, and gradually becoming pigmented and changing
in texture; and (3) post-pubescent—pubic hair is kinked or
twisted (curled). The stages were also labeled, respectively,
immature, maturing, and mature [16]. Based on further study
of body size, muscular strength, and school performance of
boys aged 12–18years, “physiological age” was proposed as
an indicator of readiness for work: “the attempt to establish
an age—in the child labor movement—above which a child
may safely work and under which he may not, may well take
this fundamental fact into consideration” (p. 142) [17].1
Although Crampton was the Assistant Director of Physi-
cal Training of the New York Public Schools, sport per
se was not specifically mentioned, but application of the
method in medical, educational, social, and ethical contexts
was indicated. Crampton [17] did not apply the pubic hair
criteria to girls, but noted height and weight differences
between immature (pre-menarcheal) and mature (post-
menarcheal) high school girls aged 13–16years.
Soon after the discovery of X-rays by Wilhelm Röentgen
in 1895–1896, Rotch [18, 19], a contemporary of Cramp-
ton, proposed in 1908 the use of “anatomic age” based on
a radiograph of the carpals and epiphyses of the wrist, to
account for individual differences in the context of school,
child labor, and athletics and in both boys and girls. Noting
the dissociation of chronological and anatomic ages in chil-
dren < 14years, Rotch [19] emphasized that “…it does not
… mean that … [because a child] … is 10 or 12 chronologi-
cal years of age, [he/she] should … be grouped in athletics
with boys or girls of that chronological age…” (p. 619).
Thus, Rotch essentially questioned CA grouping in sport
early in the twentieth century!
Subsequently, the concepts of physiologic and anatomic
age were applied in several contexts. The importance of phys-
iological age in three aspects of the lives of adolescent boys
that were significantly influenced by body size and maturity
1 Although Crampton is given credit for coining the term “physi-
ological age”, he indicated that the credit “…properly belongs to …
Franz Boas, who, with G. Stanley Hall and Luther H. Gulick, gave
the author [Crampton] the encouragement of their approval and inter-
est” (p. 52) [92]. Boas was an anthropologist with a primary interest
in the study of growth and especially adolescence, Hall was a psy-
chologist who focused on adolescence, and Gulick was a physician
active in school physical education and physical training.
Given current interest in bio-banding, this review has sev-
eral objectives: first, to place the concept of bio-banding into
a historical context; second, to briefly summarize methods of
maturity assessment, CA grouping for sport, and associated
factors; third, to review current applications of bio-banding;
and fourth, to address biological variation within bio-bands.
2 Historical Background
Concern for inter-individual differences associated with CA
per se and biological maturity status has deep historical roots.
This surfaced during the industrial revolution in the mid-
nineteenth century when the utility of CA as an indicator of
the readiness of youth for factory work was questioned. The
Factory Act of 1833 in the UK set CA limits for youth work-
ers [11]. Two laws of the act are relevant: first, it was unlaw-
ful for a child to work in mills or factories (except silk mills)
unless he/she completed their ninth year of age, and second,
it was unlawful for youth to work more than 9h/day until he/
she completed their 13th year (though in silk mills, children
under 13years of age were permitted to work 10h/day).
Lack of birth certificates, especially among the poor, was
a problem, and the utility of CA estimates based on physi-
cal appearance was questioned. In 1837, Edwin Saunders
[12, 13] proposed permanent dentition as an indicator of
CA among children in the context of readiness for work.
Using observations of about 1000 children and youth, age-
related variation in the eruption of the permanent dentition
between 9 and 13years was emphasized. Eruption of the
central and lateral incisors and the first molars was proposed
as indicative of the ninth year, and eruption of the canines
(cuspids) and second molars as indicative of the 13th year. It
was not clear whether the interval between initial (piercing
Bio-Banding in Youth Sports
status—work, social affairs, and athletics—were highlighted
in 1910 [20], and the distributions of pubic hair stages among
boys and the physiological maturity status of girls aged
10–21years participating in the Baltimore Public Athletic
League were described in 1916 [21]. The criteria for girls
included menstrual flow, enlargement the breasts, “appear-
ance of subcutaneous fat”, and axillary hair. Application of
physiological age to physical training in both sexes was also
noted: “It would be justifiable to arrange physical training
schedules in schools on the basis of physiological age, giving
boys or girls of the same physiological age similar types of
exercise” (p. 196) [22]. Applications to “mental maturation”,
school progress and promotion, industrial work, social adjust-
ment, and “moral and religious awakenings” were also noted.
Pubertal assessments were used in studies of physical
activity, body size, and performance of adolescent boys
12–16 years of age in the 1930s [23, 24]. Of interest, the
focus was variation in height, weight, strength, and motor
ability associated with pubertal status within single-year CA
groups, which contrasted earlier efforts and also the reason-
ably common current practice of grouping youth by stage
of pubertal development without considering potentially
independent effects of CA.
The use of hand–wrist radiographs was extended to studies
of epiphyseal union in 1910 [25], and was applied to adoles-
cents in the context of physical immaturity among youth on
entry into the United States Naval Academy: “It is common
knowledge that a boy of 16 may be so immature that he shows
no signs of puberty; yet it is possible, and has indeed hap-
pened, that such a youth is admitted to a class in which some
of his fellows are completely matured men, and he undertakes
necessarily to equal their accomplishments in work and athlet-
ics” (p. 7–8) [26]. It was also suggested that admission to the
Academy should be standardized by “anatomic age”.
Application of the principles of skeletal maturity of the
hand and wrist on a wider basis, including sport, did not
occur until publication of T. Wingate Todd’s Atlas of Skel-
etal Maturation in 1937 [27]. Maturity-associated variation
in competitive sports for youth was noted in a 1952 report
on Desirable Athletic Competition for Children [28]. The
report included a comment from Wilton M. Krogman, pro-
fessor of physical anthropology in the Graduate School of
Medicine at the University of Pennsylvania and Director
of the Philadelphia Center for Research in Child Growth,
which highlighted a comparison of the skeletal maturation
of high school boys in Cleveland, Ohio (n = 1028) classified
as athletes and non-athletes; the former were biologically
advanced by about 2years.2
Implications of maturity-associated variation among
younger sport participants were subsequently highlighted
in youth baseball. Little League baseball was founded by
Carl Stotz in 1939, and expanded rapidly with economic
prosperity and expansion of urban populations into the
suburbs after World War II [29]. Among 112 players aged
10–12years in the 1955 Little League World Series [30],
42 (37%) were pre-pubescent, 19 (17%) pubescent, and 51
(46%) post-pubescent. Among 55 players aged 11–13years
in the 1957 World Series [31], skeletal ages (SAs) [27]
were average (within ± 1.0year of CA) in 25 (45%) and
advanced by more than + 1.0year in 25 (45%) players, but
were delayed by more than – 1.0year in only five (10%)
players. The distribution prompted the following comment:
“The successful Little League ball player is old for his age
… biologically advanced. This boy succeeds, it may be
argued, because he is more mature, biologically more sta-
ble, and structurally and functionally more advanced…” (p.
55) [31]. This observation has been replicated in studies of
male youth athletes in several sports: American football,
soccer, ice hockey, basketball, swimming, and track and
field (except for distance runners)—the maturity-related
advantage generally emerges circa 12–13years of age [10,
32, 33].
3 Maturation andMaturity Assessment
Three processes—growth, maturation, and development—
characterize the lives of children and youth between birth
and adulthood. Growth refers to the increase in body size,
changes in proportions and body composition, and changes
in specific systems associated with body size. Matura-
tion refers to progress towards the biologically mature
stature, which varies among systems—skeletal, reproduc-
tive, somatic, neuromuscular, neuroendocrine, dental, etc.
Development refers to the acquisition and refinement of
the cognitive, social, emotional, moral, motor, and other
behaviors expected of the culture within which the indi-
vidual is reared. The three processes occur concurrently
and interact. Interactions are especially prominent during
adolescence, influence self-concept, self-esteem, perceived
competence, etc., and play a significant role in the develop-
ment of sport talent. Inter-individual differences in biologi-
cal maturation also play a central role in the latter as they
impact body size, strength, power and motor performances,
and influence behaviors, especially during adolescence [7].
Awareness of methods for the assessment of maturity sta-
tus—state of maturation at the time of observation—and
maturity timing—age at which specific maturational events
occur—is thus essential. A related factor is the tempo or
rate of maturation.
2 Krogman worked under the direction of T.W. Todd at Western
Reserve University in Cleveland in the 1930s; skeletal maturity was
likely assessed with the atlas method of Todd [27], which was in the
process of development at this time.
R.M.Malina et al.
3.1 Maturity Status
Two indicators have traditionally been used to assess matu-
rity status, SA and secondary sex characteristics. Methods
of assessment, variation among methods and limitations of
SA [7, 10, 3436], and methods, specific criteria, reliability,
and limitations of pubertal status assessments [3, 7, 3638]
have been described previously. Dentition is another indica-
tor [7], although the teeth have not been used as a maturity
indicator in studies of youth athletes. Percentage of predicted
near adult height attained at the time of observation [39] is
an indicator of maturity status that is increasingly used in
studies of youth athletes [40]. This protocol is the indicator
of maturity status used in applications of bio-banding (see
Sect.5).
3.2 Tempo ofMaturation
Tempo refers to the rate at which the process of matura-
tion progresses in different systems. Longitudinal data are
required to measure tempo. Limited evidence suggests that
progress in SA (gains in SA relative to CA) and in each
secondary sex characteristic (intervals between stages of
puberty) varies considerably within and among individuals
[36].
3.3 Maturity Timing
The two commonly used indicators of maturity timing are
age at peak height velocity (PHV) and age at menarche [7,
36]. Both require longitudinal data spanning adolescence.
Ages at peak velocity of growth in specific body dimensions,
and ages at attaining a specific SA, stage of puberty, and/or
percentage of adult height can also be estimated provided
longitudinal data are available [7, 4143]. Ages at PHV for
youth athletes are limited due in large part to the selectivity
of samples, which results in longitudinal height records that
do not span the interval of the growth spurt [44]. Many of
the studies started too late and/or concluded too early.
More recently, equations for predicting maturity offset
(defined as time before PHV) and in turn predicted age at
PHV (CA minus offset) [45] are increasingly used in studies
of youth athletes [40]. The sex-specific equations require
CA, height, sitting height, estimated leg length, and weight.
Modified equations limited to CA and height or CA and
sitting height in boys and CA and height in girls are also
available [46]. Validation studies of the prediction equa-
tions in longitudinal samples of Polish [4749] and US [50]
youth have indicated significant intra-individual variation
in predicted ages at PHV depending upon CA at prediction,
and major limitations with early and late maturing youth of
both sexes. Ages at PHV were later than observed in early
maturing individuals and earlier than observed in late matur-
ing individuals; the equations had utility within a narrow CA
range in average maturing boys close to the time of observed
age at PHV.
4 Chronological Age inYouth Sports
Youth sports are largely organized by CA within each sex.
CA at registration for a sport or a specific competition is a
function of date of birth and the prescribed cut-off date for
age groups and seasons. In addition to CA, other groupings
include skill (novice, experienced, select, pre-Olympic, etc.),
weight categories (judo, wrestling), and weight and position
limitations (American football).
Cut-off dates vary among sports and have varied over
time. For example, the upper CA limit was 12years of
age (i.e., not yet 13years) at the start of the Little League
baseball season (30April in 2017). Among teams who pro-
gressed through the play-offs, many boys were 13years of
age by the time of the Little League World Series in August.
The cut-off date for the 2018 season was thus changed to
31August so that all players would be 12years or under at
the time of the World Series [51].
In European soccer, the competitive season generally
begins in September and CA of youth players as of the 31st
of December of the competitive season defines specific age
groups. Thus, a player classified as under (U) 12 will not
have attained his 13th birthday by 1 January or a player
classified as U14 will not have attained his 15th birthday
by 1 January of the competitive season. Since the season
continues through the spring in Europe, some 12-year-old
players will be 13years and some 14-year-old players will be
15years of age during the second half of the season.
Grouping youth by maturity status for a sport, often
labeled maturity matching, has often been mentioned but
has not been systematically applied [8, 9]. Primary objec-
tives of maturity grouping include equalizing competition,
enhancing opportunity for success, prevention of injury as a
result of size mismatches, individualization of training, and
talent development. The latter are apparent in efforts to ‘pro-
tect’ talented later maturing youth by ‘holding them back’ to
avoid mismatches in size and athleticism in their competitive
CA group. This challenge is explicit in the comments of Sir
Alex Ferguson (p. 260) [52]:
“The biggest risk was that we had erred in our assess-
ment of a particular boy and could have used his slot
to work with a more talented youngster. We had to
wait a little longer to see the real potential in some
boys, because not everyone’s physique develops at the
same rate.”
Bio-Banding in Youth Sports
Individual differences in maturity status and timing are
also implicit in discussions of injury prevention. Status
implies size mismatches, while timing implies susceptibil-
ity to certain injuries associated with differential timing
of growth. A survey of injuries in youth players from 38
soccer academies indicated no clear association with the
adolescent years, with two exceptions [53]: Sever’s disease
(heel, calcaneal apophysitis) and Osgood–Schlatter’s dis-
ease (knee, inflammation of patellar tendon of the anterior
quadriceps muscle at the tibial tuberosity) accounted for
only 5% of all injuries, but the former occurred most often
among U10–U14 players (peak U11 players), while the latter
occurred most often among players U12–U16years (peak
U13–U14 players) [53]. In the general population, CAs of
boys with Sever’s disease ranged from 9 to 15years with a
median of 12years [54], while CAs of boys with Osgood-
Schlatter’s disease ranged from 12 to 15years [55]. Both
conditions were also more prevalent among youth active
in sport, and may be related to the differential timing of
growth in segments of the lower extremity. The adolescent
spurt occurs first in the distal segments [foot and ankle, fol-
lowed by the lower leg (tibia) and the proximal segment
(femur)]; the growth spurt of the trunk follows that of the
lower extremities [7].
‘Playing up’ a grade in school sport in the USA is a
related issue, especially in middle or junior high school (7th
and 8th grades). The New York State Public High School
Athletic Association includes maturity status among several
criteria for an athlete in the 7th or 8th grade to ‘move up’
and compete with those in higher grades in interscholastic
sport [56, 57]. In addition to approval of parents and the
local board of education, the protocol calls for evaluation
of general medical status, pubertal status (in boys, pubic
hair; in girls, menarcheal status), height and weight, prior
experience in the sport, physical fitness, coach rating of pro-
ficiency in skills, and a try-out for the team. Guidelines for
other states, in contrast, focus on CA. Some permit a 7th or
8th grader who is “old for his grade” to play up according to
his CA [58], while some do not permit 7th and 8th graders
to play up [59]. As the population in some rural areas of the
USA declines, the number of students in school districts also
declines, meaning the issue of ‘playing up’ may increase in
significance in an effort to field teams in some sports.
Two issues merit attention when addressing the potential
utility of grouping youth by maturity status for sport, train-
ing, and/or specific competitions: variation in maturity status
within a competitive age group, and variation in CA and
other characteristics among individuals of the same biologi-
cal maturity status. The first is illustrated in Table1 among
soccer players in two competitive age groups, while the sec-
ond is illustrated in Table2 for the total sample of players
grouped by stages of pubic hair. Variation in CA, body size,
current height as a percentage of predicted adult height, skel-
etal maturity status, functional capacities, and sport-specific
skills within competitive age groups and among players of
the same pubertal status is considerable.
Behavioral implications of maturity-matching strategies
should not be overlooked. Youth athletes should be viewed
in a holistic perspective, recognizing variation in psycho-
social and technical/tactical characteristics, in size and
maturity-related characteristics, and their potential interac-
tions. Strategies should be in place to prepare and support
players who are ‘playing up’ or ‘playing down’ a CA group
to increase the likelihood that they possess the necessary
skills to adapt to the challenges. Asking a player to com-
pete against younger, physically matched peers, may create
a perception that the athlete is less able or does not have
the potential to succeed at the adult level. Such concerns
can be allayed through education and by highlighting the
fact that several successful late-developing soccer players
have played down an age group at various stages of their
development, for example Alex Oxlade-Chamberlain, Jesse
Lingaard, and Danny Welbeck.
Table 1 Variation (minimum and maximum values) in chronologi-
cal age, skeletal age, pubertal stages, body size, functional capacities,
and sport-specific skills among youth soccer players in two competi-
tive age groupsa
CMJ counter movement jump, U under
a Calculated from data reported in Figueiredo etal. [81]. Observations
were made early in the season for U12 and late in the season for U14
players; several thus attained their 15th birthday. Functional and skill
tests: sprint—fastest of 7 sprints (35m with a slalom), agility—10 × 5
shuttle run, CMJ—ergo-jump protocol, yoyo—intermittent endurance
test level 1, dribbling—slalom dribble, passing—wall pass
b Times are inverted as a better performance is a lower time
Characteristic U12 soccer, n = 87 U14 soccer, n = 72
Chronological age (years) 11.0–12.9 13.3–15.2
Skeletal age (Fels method)
[years]
8.3–14.6 12.0–17.7
Pubic hair Stages 1–3 Stages 2–5
Height (cm) 132.2–160.7 142.8–182.9
Height (% predicted adult
height)
77.2–92.4 87.1–100
Weight (kg) 26.5–55.0 34.0–77.5
Sprint (s)b9.96–7.45 8.91–7.02
Agility (s)b24.6–18.4 20.9–15.9
CMJ (cm) 6.7–38.3 22.4–46.1
Yoyo endurance run (m) 240–2880 320–3960
Dribbling (s)b23.1–13.1 16.7–11.7
Passing (points) 8–23 11–30
R.M.Malina et al.
5 Bio‑Banding
Bio-banding refers to the grouping of youth athletes within
a given CA range, 11–15years, into ‘bands’ or groups based
on estimated biological maturity status for specific competi-
tions and/or training. Percentage of predicted adult height
attained at the time of observation is the maturity indicator
presently used in bio-banding. It is a recent application and
extension of the concept of ‘maturity matching’ [8, 9].
5.1 Height Prediction
Height prediction equations used clinically include SA
among the predictors [6066], although a height prediction
protocol without SA was recommended as a non-invasive
indicator of maturity status [39]. The suggestion was based
on the common clinical premise that mid-parent height
(average of the heights of the biological mother and father)
provides a target range within which the adult height of a
youngster will likely fall. Sex-specific prediction equations
without SA were subsequently developed [67] and are used
in current applications of bio-banding. The equations were
developed on participants in the Fels Longitudinal Study,
all of whom were of European ancestry (White) and from
families of middle and upper socioeconomic status in south-
west Ohio, USA; families of low socioeconomic status were
under-represented in the sample [68].
The sex-specific prediction equations [67], commonly
called the Khamis–Roche method, include age-specific con-
stants from 4.0 to 17.5years for height, weight, and mid-parent
height. The protocol requires the CA, height, and weight of the
youngster and mid-parent height. The height of the youngster
at the time of observation is then expressed as a percentage
of his predicted adult height. This is the indicator of matu-
rity status at the time of observation. Youth are grouped into
specific bands based on percentage of predicted adult height,
e.g., ≥ 85.0% and < 90.0% or 90.0% and < 95.0% [6973].
The bands are assumed to span the adolescent spurt; however,
bands are not fixed and may be modified as needed.
Measurement variability in height and weight (inter-
and intra-observer technical errors of measurement) may
influence predictions. This also applies to parental heights,
although reported parental heights adjusted for the tendency
to overestimate height have generally been used. Neverthe-
less, “…the inability to predict the timing or the intensity of
the adolescent growth spurt…” is a major source of error in
height predictions (p. 4) [74].
Across the age range of the Khamis-Roche prediction
equations for males, the mean error ± standard deviation
at the 50th percentile was 2.2 ± 0.6cm; the corresponding
mean error at the 90th percentile was 5.3 ± 1.4 cm [67].
Focusing on the age range in which bio-banding has been
applied, 11–15years, median age group-specific errors at
the 50th percentiles ranged from 2.4 to 2.8cm, while cor-
responding errors at the 90th percentiles ranged from 5.5
to 7.3cm. The median and 90th percentile errors translate
to approximately 1.5% and 3.0% of predicted height for the
average male. Given the errors associated with height pre-
dictions, some players may be in or out of a band due to
errors associated with predictions.
Table 2 Variation (minimum
and maximum values) in
chronological age, skeletal age,
body size, functional capacities,
and sport-specific skills among
U12 and U14 soccer players
grouped by stage of pubic hair
developmenta
CA chronological age, CMJ counter movement jump, SA skeletal age, U under
a Calculated from data reported in Figueiredo et al. [81]. Observations were made early in the season for
U12 and late in the season for U14 players; several thus attained their 15th birthday. Functional and skill
tests: sprint—fastest of 7 sprints (35m with a slalom), agility—10 × 5 shuttle run, CMJ—ergo-jump proto-
col, yoyo—intermittent endurance test level1, dribbling—slalom dribble, passing—wall pass
b Times are inverted as a better performance is a lower time
Stages of pubic hair development
Characteristic 1 2 3 4/5
n47 43 35 32/2
CA (years) 11.0–12.8 11.0–14.1 11.3–15.2 13.5–15.2
SA (Fels method) [years] 8.3–14.3 11.1–14.4 10.7–16.5 13.1–17.7
Height (cm) 132.2–152.6 136.1–165.4 143.0–177.1 151.1–182.9
Height (% predicted adult height) 77.2–87.0 82.3–95.7 84.8–98.2 91.0–100
Weight (kg) 26.5–41.0 31.0–61.0 35.5–70.0 45.0-77.5
Sprint (s)b9.09–7.45 9.96–7.41 8.91–7.15 8.56–7.02
Agility (s)b24.1–18.4 24.6–17.9 20.9–15.9 20.2–17.3
CMJ (cm) 6.7–37.2 10.1–44.0 18.3–46.1 22.9–42.7
Yoyo (m) 400–2880 240–3720 320–3840 480–3960
Dribbling (s)b19.5–13.3 23.1–12.6 17.3–11.7 16.3–12.1
Passing (points) 8–23 10–23 14–16 11–30
Bio-Banding in Youth Sports
Nevertheless, the bio-banding grouping strategy appar-
ently reduces maturity-associated variation compared to
that observed in competitive CA groups and in competi-
tive bands. The latter is suggested in a box plot analysis
of percentage of predicted adult height for players in four
US Soccer developmental academies (Fig.1). In addition to
single-year CA groups, two methods were used to bio-band
male players by percentage of predicted adult height. The
first, quartile bio-banding, involved transferring players in
the most and least mature quartiles within a CA group up
or down an age group, respectively, i.e., the most mature
U13s were grouped with the U14s and the least mature
U14s were grouped with the U13s, and so on. The second
strategy grouped players within a maturity band which
restricted percentage of predicted adult height to a specific
range, ≥ 85.0–< 90.0%. For each CA group, the bio-banding
strategy reduced the range of variation in maturity status and
also reduced differences at the extremes of the maturity con-
tinuum [75]. Although variation was reduced, the inclusive
medians were very similar across the CA and bio-banded
groups.
5.2 Predicted Height andOther Indicators
ofMaturity Status
The relationship between estimated maturity status based on
percentage of predicted adult height at the time of observa-
tion and an established indicator of maturity status is rel-
evant. Among American football players 9–14years of age,
concordance between maturity status (late, average, early)
based on SA (Fels method [76]) and percentage of predicted
adult height was moderate: < 11years, 69%; 11–12years,
52%; ≥ 13years, 67% [77]. Similar moderate concordance
was noted between the two maturity indicators in soccer
players aged 11–12 (57%) and 13–14years (63%), while
Spearman rank order correlations between stage of pubic
hair and maturity status based on percentage of predicted
adult height were significant for 11–12 (rho = 0.34) and
13–14years (rho = 0.36), but at the low end of the moderate
range [78].
5.3 Applications ofBio‑Banding
Several studies have considered responses of youth soccer
players to bio-banded competitions. Players 11 through 14
years from four English Premier League (EPL) academies
with current heights ≥ 85.0% and < 90.0% of their predicted
adult heights participated in three 11-a-side games (25min
halves) on a regular size field [69, 70]. Sixteen players par-
ticipated in focus groups which emphasized player percep-
tions of their experiences competing in bio-banded groups,
i.e., competing with players of similar maturity status. Per-
ceptions of coaches were also considered.
Overall, the experiences were positive for all players.
Early maturing (younger boys ‘playing up’ with older boys
of the same maturity status) and late maturing (older boys
‘playing down’ with younger boys of the same maturity
status) players presented contrasting but favorable views of
the bio-banded games. Early maturing players viewed the
games as not as challenging physically compared with age
group competitions, but had to adapt their style of play to a
faster game and had to place more emphasis on tactics and
Fig. 1 Percentage of predicted
adult height attained at obser-
vation by chronological age
groups (U13, U14, U15) and by
groups based on quartile bio-
banding (QBB) and bands based
on percentage of predicted adult
height (%PAH) (drawn from
data reported in Cumming etal.
[75]). U under
R.M.Malina et al.
techniques. Late maturing players also viewed the games as
less challenging but liked the opportunity to demonstrate
and develop their technical, tactical, and physical skills, and
often adopted leadership and mentoring roles in the games.
Coaches noted that many early maturing players (younger
boys playing up) were getting by on their size and strength
alone, and were forced to modify their game to different
challenges, adopting a more technical, tactical, and team-
oriented style of play. In contrast, coaches of late maturing
players (older boys playing down) noted aspects of play not
ordinarily seen due to their dependence on their size/strength
advantage; from another perspective, coaches noted that the
late maturing boys could demonstrate their skills instead of
being physically dominated in age group competitions [69,
70]. Older late maturing boys were also more likely to step
into leadership roles, instructing, motivating, and mentoring
their younger yet physically matched peers. Consistent with
the players, the coaches agreed that the process of restrict-
ing maturity-related variation in body size and athleticism
results in a game that emphasizes technical and tactical apti-
tude over physicality.
Game demands of 13 EPL academy players 13–15years
of age (14.0 ± 0.4 years) with current heights 90.0%
and < 95.0% of their predicted adult heights were compared
in CA-based and bio-banded 11-a-side matches (40min
halves) [71, 72]. Percentage of time in different heart rate
intensity zones (via telemetry) based on percentage of maxi-
mal heart rate did not differ between CA-based (85.4 ± 4%)
and bio-banded (84.8 ± 4%) matches. Match performance
analysis, adjusted for playing time, suggested more passes
and touches, and slightly more shots on goal in the bio-
banded match.
Round robin bio-banded tournaments for four boys’
teams (CA 12.5–15.4years) and four girls’ teams (CA
12.3–15.2years) were recently hosted by US Soccer; each
team competed in three games (40min halves). Results par-
alleled observations in the EPL tournaments and were gener-
ally similar among males and females [79]. Early maturing
players of both sexes reported greater levels of physical chal-
lenge and described the process of playing up as a superior
learning experience. While late maturing players described
the experience as less physically and technically challenging,
they reported more opportunity to utilize and demonstrate
their physical and technical attributes and to assume posi-
tions of leadership. Both early and late maturing boys and
girls described the games as placing a greater emphasis on
creativity and technical competence over physicality, and
also described the tournament as beneficial to their develop-
ment, more enjoyable, and less stressful. The participants
also enjoyed the opportunity to play and compete with new
players.
6 Related Issues
Although the studies of bio-banded matches are promising,
questions regarding percentage of predicted adult height
as a maturity indicator and variation within bands merit
attention.
6.1 Changes inPercentage Adult Height withAge
Data for three longitudinal studies of the general popula-
tion and two cross-sectional studies of American football
and soccer players are summarized in Table3. The former
have a measure of near adult or young adult height, while
the latter are based on percentage of predicted adult height.
The data were reasonably concordant across the CA range
and those for athletes 13–14years were generally consist-
ent with advanced skeletal maturity status observed at these
ages [80, 81].
6.2 Bands andtheAdolescent Spurt
Applications of bio-banding [6972, 79] have used bands
described as spanning the adolescent spurt: percentages of
predicted adult height of ≥ 85.0% and < 90.0% or ≥ 90.0%
and < 95.0%. How do the bands relate to the adolescent
Table 3 Percentage of adult height attained at different chronologi-
cal ages (CA) in three longitudinal studies [Fels Longitudinal Study,
University of California, Berkeley Study (UCB), Wrocław Growth
Study (WGS)] and percentage of predicted adult height at the time of
observation in two cross-sectional studies of youth American football
(AmFB) and soccer playersa
a Percentage of measured young adult height in the Fels [39], UCB
[61] and WGS (Kozieł SM, unpublished data) studies and percentage
of predicted adult height in American football [80] and soccer [81])
players
% Adult height % Predicted adult
height
CA (years) Fels UCB WGS AmFB Soccer
9.0 75.6 75.6 74.8 75.4
9.5 77.2 77.2
10.0 78.6 78.4 78.2 79.3
10.5 79.8 80.3
11.0 81.6 81.3 80.6 82.3 82.4
11.5 82.5 83.5 83.6
12.0 84.9 84.0 83.4 85.1 84.9
12.5 85.4 87.4 85.8
13.0 88.7 87.3 87.4 88.9 90.0
13.5 89.2 91.3 91.2
14.0 92.7 91.0 91.4 94.1 93.9
Bio-Banding in Youth Sports
spurt, specifically percentage of adult height attained at
take-off (TO) and at PHV in longitudinal studies? Among
Polish boys [42], 80% of adult height was attained at
11.0 ± 0.8years, while TO occurred at 11.8 ± 1.2 years.
PHV was attained at about the same age as 90% of adult
height in Polish boys, 14.0 ± 1.2 and 13.9 ± 1.0years, respec-
tively [42], and in US boys, 13.8 ± 1.2 and 13.7 ± 1.0years,
respectively [41]. Recent analyses of two early longitudinal
series (Brush Foundation, Cleveland, OH, USA; University
of California, Berkeley, CA, USA) also noted occurrence of
PHV at about 90% of adult height in boys [82].
The preceding focused on central tendencies. Among
boys in the Zurich longitudinal study, median percentages
of adult height attained at TO and PHV and respective 10th
and 90th percentiles were reported [83]. The median per-
centage of adult height at TO (11.2 ± 1.1years) was 81.5%
(10th and 90th percentiles 78.3% and 84.5%) and at PHV
(14.0 ± 0.9years) was 91.5% (10th and 90th percentiles
89.8% and 92.9%). Using the 10th and 90th percentiles for
percentages of adult height attained at TO and at PHV, per-
centages of adult height ≥ 85.0% and < 90.0% would appear
to approximate the interval between TO and PHV.
6.3 Variation Within Dierent Bands
Using the observations for Swiss boys [83], Ameri-
can football players 11.0–14.2 years [77] and club soc-
cer players 11.0–15.2years [81] were grouped into four
bands based on percentages of predicted adult height at
the time of observation using the Khamis-Roche method
[67]: ≥ 78.0% and < 85.0%—at TO (at-TO); ≥ 85.0% and
< 90.0%—interval between TO and PHV (TO-to-PHV);
Table 4 Variation (minimum
and maximum values) for
chronological age (CA), skeletal
age (SA, Fels method), SA
minus CA (SA−CA), and body
size, and maturity status based
on SA of American football
players aged 11.0–14.2years
and soccer players aged 11.0–
15.2years, and maturity status
based on stage of pubic hair
(PH) in soccer players within
several bands of percentage of
predicted adult height at the
time of observationa
Criteria for maturity status classifications: Late, SA younger than CA by >1.0year; Average, SA within
±1.0year of CA; Early, SA older than CA by >1.0year
CA chronological age, PH pubic hair, PHV peak height velocity, SA skeletal age, TO take-off
a Calculated from data for American football players reported in Malina etal. [77] and for soccer players
reported in Figueiredo etal. [81]. No football player and one soccer player had a percentage of predicted
adult height < 78.0%. Stage of pubic hair was available only for soccer players
Bands based on percentage of predicted adult height
Characteristic At-TO
(≥ 78.0% and
< 85.0%)
TO-to-PHV
(≥85% and
<90.0%)
At-PHV
(≥90.0% and
<93.0%)
Post-PHV
(≥ 93.0%)
American football n = 29 n = 36 n = 18 n = 15
CA (years) 11.0–12.7 11.2–14.0 12.0–14.2 12.4–14.2
SA (years) 9.6–14.3 10.7–15.0 13.2–16.1 14.4–17.5
SA–CA (years) 2.7 to +2.7 – 1.7 to +2.8 – 0.3 to +2.8 0.5 to +3.3
Height (cm) 131.8–163.7 148.5–168.0 154.2–177.8 159.9–181.7
Weight (kg) 27.4–63.6 42.4–92.6 40.0–102.8 53.4–108.0
Maturity status
SA late 9 2 0 0
SA average 8 11 3 3
SA early 12 23 15 12
Soccer n = 55 n = 36 n = 20 n = 47
CA (years) 11.0–12.6 11.0–14.0 12.8–14.2 13.4–15.2
SA (years) 8.3–13.9 9.3–14.5 12.4–16.5 13.4–17.7
SA–CA (years) 4.2 to +2.7 – 3.1 to +3.4 – 1.7 to +2.8 0.9 to +2.7
Height (cm) 132.2–153.2 140.8–160.7 143.0–169.3 157.3–182.9
Weight (kg) 26.5–50.5 31.0–55.0 37.0–63.0 45.0–77.5
Maturity status
SA late 12 6 2 0
SA average 31 19 12 28
SA early 12 11 6 19
PH 1 39 7
PH 2 15 23 4 1
PH 3 1 6 14 14
PH 4 2 30
PH 5 2
R.M.Malina et al.
90.0% and < 93.0%—about the time of PHV (at-PHV);
and ≥ 93.0%—post-PHV.
Distributions of the youth athletes in each sport and asso-
ciated variation in CA, SA (Fels method), height, weight,
and maturity status within the respective bands are summa-
rized in Table4. Variation within and among bands should
be noted. Among youth American football and soccer play-
ers, CA was similar within three of the four bands; soccer
players in the post-PHV band were older. Proportionally
more American football players were advanced in skeletal
maturity in each band, while late maturing players were
absent in the at-PHV and post-PHV bands. Among soccer
players, the proportion of late maturing youth based on SA
declined across the four bands, while three stages of pubic
hair were represented in three bands and four stages were
represented in the post-PHV band. The range of heights
within each band was reasonably similar except for Ameri-
can football players in the group at-TO. However, the range
of weights was considerably greater among American foot-
ball than among soccer players in each of the bands, and
increased across bands.
6.4 Predicted Height With andWithout SA
Predictions of adult height that include SA among the pre-
dictors are generally viewed as more accurate than predic-
tions without SA. In this context, the concordance of bio-
banded groups based on two height prediction equations
developed on the Fels longitudinal sample—one using CA,
height, weight, and mid-parent height [67] and the other
using CA, height, weight, mid-parent height, and SA [64]—
were compared in the American football [77] and soccer
[81] players (Table 5). Bio-banded groups with the two
equations were concordant in 71% (70 of 98) of American
football players and in 82% (131 of 159) of soccer players.
Concordance of classifications was highest in the post-PHV
band in both sports; each had only one misclassification.
Concordance was generally the same in the other three bands
among American football players—about 67%. Among soc-
cer players, however, concordance declined systematically
across bands, from 80% (at-TO) to 75% (TO-to-PHV) and
to 65% (at-PHV).
Table 5 Concordance of
bio-banded groups based on
predicted adult height without
skeletal age [89] and with
skeletal age [87] in American
football players 11.0–14.2years
and soccer players 11.0–
15.2yearsa
Italicized numbers indicate concordant classifications
Chi square: American football = 139.78, p < 0.000; soccer = 274.11, p < 0.000
PHV peak height velocity, SA skeletal age, TO take-off
a Calculated from data for American football players reported in Malina etal. [77] and for soccer players
reported in Figueiredo etal. [81]. No football player and one soccer player had a percentage of predicted
adult height < 78.0%. SA was assessed with the Fels method
Bands based on predicted adult height without SA
Bands based on predicted adult
height with SA
At-TO
(≥78.0% and
<85.0%)
TO-to-PHV
(≥85% and
<90.0%)
At-PHV
(≥90.0% and
<93.0%)
Post-PHV
(≥ 93.0%)
Total
American football
At-TO (≥78.0% and <85.0%) 20 2 0 0 22
TO-to-PHV (≥85% and
<90.0%)
924 0 0 33
At-PHV (≥90.0% and
<93.0%)
0 10 12 1 23
Post-PHV (≥ 93.0%) 0 0 6 14 20
Total 29 36 18 15 98
Soccer
At-TO (≥78.0% and <85.0%) 44 7 0 0 51
TO-to-PHV (≥85% and
<90.0%)
11 27 4 0 42
At-PHV (≥90.0% and
<93.0%)
0 2 13 1 16
Post-PHV (≥ 93.0%) 0 0 3 46 49
Total 55 36 20 47 158
Bio-Banding in Youth Sports
Table 6 Characteristics of youth
soccer players 11.0–15.2 years
of age classified by skeletal
maturity status within bands
of percentage of predicted
adult height at the time of
observationa
Criteria for maturity status classifications: Late, SA younger than CA by >1.0year; Average, SA within
±1.0year of CA; Early, SA older than CA by >1.0year
A average, CMJ counter movement jump, E early, L late, PHV peak height velocity, SA skeletal age, SD
standard deviation, TO take-off
a Calculated from data reported in Figueiredo etal. [81]; see Table1 for details of the functional and sport-
specific tests
Bands based on percentage of predicted adult height
At-TO (≥ 78.0
to < 85%) [n = 55]
TO-to-PHV
(≥ 85% to < 90%)
[n = 36]
At-PHV (≥ 90%
to < 93%) [n = 20]
Post-PHV
(≥ 93%) [n = 47]
Characteristic NM SD NM SD NM SD NM SD
CA (years) L 12 11.8 0.5 6 13.0 0.7 2 14.1
A 31 11.6 0.4 19 12.6 0.8 12 13.6 0.4 28 14.5 0.5
E 12 13.4 0.3 11 11.9 0.5 6 13.3 0.3 19 14.2 0.6
SA (years) L 9.9 0.7 11.2 1.0 12.7
A 11.5 0.7 12.6 0.8 13.8 0.7 14.6 0.4
E 13.2 0.5 13.9 0.5 14.9 0.9 15.9 0.9
Height (cm) L 138.3 4.0 144.7 3.0 147.1
A 142.8 5.6 147.9 3.5 156.7 3.8 167.3 6.1
E 143.4 5.2 152.3 6.1 159.9 5.6 170.7 4.6
Weight (kg) L 33.3 3.8 35.8 2.3 40.0
A 36.5 4.7 39.9 4.9 47.0 5.0 56.8 7.6
E 37.2 5.2 46.8 5.6 49.3 7.2 62.1 7.7
Sprint (s) L 8.3 0.5 8.0 0.5 8.0
A 8.5 0.6 8.1 0.4 8.0 0.4 7.8 0.4
E 8.6 0.5 8.4 0.5 7.7 0.1 7.7 0.4
Agility (s) L 20.3 1.4 19.8 0.6 18.8
A 20.7 1.3 19.8 1.4 19.4 0.8 18.7 0.8
E 21.0 1.1 20.7 1.7 18.3 0.7 18.4 1.1
CMJ (cm) L 22.2 6.0 24.6 4.5 27.2
A 26.5 5.1 26.6 6.9 28.9 6.5 32.0 4.6
E 25.2 4.7 27.3 5.8 29.8 4.4 33.8 3.8
Yoyo (m) L 1673 721 2473 901 2980
A 1246 695 1827 809 1877 898 2661 933
E 987 756 1331 838 2447 529 2591 984
Dribbling (s) L 15.5 1.4 15.0 2.1 13.2
A 16.0 2.2 14.6 1.3 13.8 1.1 13.1 0.7
E 16.4 1.7 15.7 2.0 13.7 0.9 13.5 1.1
Passing (points) L 18.8 2.1 20.3 2.2 19.0
A 16.8 3.6 19.8 1.7 20.3 3.3 22.4 3.1
E 16.8 3.4 18.2 2.9 20.3 2.4 20.6 3.8
Potential L 3.3 1.2 3.5 1.0 4.0
A 2.8 1.2 3.4 1.1 2.5 0.9 3.6 1.1
E 3.1 1.1 3.2 1.4 3.5 1.5 3.3 1.2
Task L 4.3 0.5 4.5 0.2 4.1
A 4.2 0.5 4.3 0.5 4.3 0.5 4.1 0.6
E 4.5 0.3 4.2 0.4 4.1 0.4 4.1 0.7
Ego L 2.2 0.5 1.8 0.6 2.1
A 2.2 0.8 1.9 0.6 2.0 0.6 1.8 0.6
E 2.4 0.7 1.8 0.6 1.4 0.3 1.7 0.6
R.M.Malina et al.
6.5 Maturity Variation Within Bands
Characteristics of soccer players classified as late, average,
or early maturing (based on SA minus CA) within each
of the four bands spanning adolescence are compared in
Table6. Maturity-related differences in height and weight
in each band were consistent with the gradient noted in the
literature, i.e., early > average > late [7]. The counter move-
ment jump, an indicator of power, showed a similar gradient
in the TO-to-PHV, at-PHV, and post-PHV bands. In contrast,
endurance (yoyo shuttle run) in the bands at-TO and TO-to-
PHV showed, on average, a gradient of late > average > early,
although differences were rather small. A similar trend was
suggested for sprint and agility, but differences in endurance,
sprinting, and agility were not consistent between average
and early maturing players in the at-PHV and post-PHV
bands. In contrast, two soccer-specific skills, task and ego
orientation, and coach evaluations of potential did not vary
by maturity status within the four bands.
6.6 Other Considerations
As applications of the bio-banding concept increase, many
academies systematically measure heights at quarterly and
perhaps shorter intervals to monitor changes in height and
weight for the purpose of estimating changes in predicted
maturity status. Of relevance, changes in height and weight
during the course of the day (diurnal variation) and differen-
tial growth during the year (seasonal variation) require atten-
tion. Youngsters are tallest upon arising and ‘lose’ height,
especially in the first 3h [84], while weight tends to increase
during the day. Youth also should not be measured soon after
training. Seasonally, youth also grow more in height during
the spring and summer, and more in weight during the fall
and winter [85].
While it is possible to consider technical and psychologi-
cal skills in banding, the latter more likely follow variation
in CA and experience rather than maturity status. Maturity-
related contrasts in size, strength, and power are more sig-
nificant between 11 and 15years [7] and often attract the
attention of coaches, while technical and psychological skills
are likely more relevant in later adolescence. For example,
adolescent soccer players 13–14years of age selected by
trainers for regional teams were taller and heavier, advanced
in skeletal maturation, and performed better in power and
speed tests than unselected teammates [86]. In contrast, body
size did not differ between selected and not-selected late
adolescent professional players 16–18years of age, while
the former performed better in functional (shuttle sprint),
technical (dribbling), and tactical (positioning, deciding)
tasks [87].
Bio-banding is often mentioned in the context of the
relative age effect (RAE), the over-representation of players
born early within a competitive year. While it appears logical
to assume that the oldest players within a CA group would
be the most physically mature, evidence indicates that the
RAE and biological maturity status are independent [88,
89]. The RAE is present during childhood and generally
maintained through adolescence, whereas the maturity-
related bias emerges with the onset of puberty and increases
with CA and level of competition [90]. The RAE and matu-
rity status are functions of different factors: the calendar
(birth and cut-off dates) and genotype, respectively [7]. It is
entirely possible for a young athlete to be the oldest within
a CA cohort and also the least biologically mature, and vice
versa. Solutions to counter the RAE during youth should be
considered, including attributes associated with older CA
and experience within competitive groups. Other potential
considerations include competitions based on mean CA and
rotating cut-off dates, fourth-quarter trial days, and use of
visual cues to help coaches/scouts account for differences in
CA among players [91].
7 Conclusions: What Bio‑Banding Is andIs
Not
Bio-banding attempts to accommodate individual differ-
ences in size, strength, and power associated with variation
in maturity status by grouping youth athletes into specific
bands defined by percentage of predicted adult height at the
time of observation. It is neither a complete solution nor a
panacea; variation in maturity and its correlates, size and
power, are reduced but not entirely eliminated.
Bands are biologic constructs applied in specific contexts.
They are not fixed and can be modified in terms of absolute
values and widths. Applications of specific bands may also
be varied to provide experiences for all players to periodi-
cally play up or play down.
Bio-banding is an adjunct to and not a replacement for
CA groups. It has been applied only in the short term, e.g.,
experimental tournaments, training matches, training activi-
ties, etc.
Bio-banding is potentially useful for both identifying and
developing talent. It may facilitate the accommodation of
maturity variation when evaluating an individual’s talent and
also provide an appropriate environment and challenges in
which individuals varying in maturity status can optimally
develop.
Bio-banding has been applied in samples of varying back-
grounds and perhaps mixed-ancestry. The height prediction
equations used in the bio-banding protocol were developed
on better-off American children and adolescents of European
ancestry. The issue of potential ethnic variation in height
prediction merits attention.
Bio-Banding in Youth Sports
Research is required to better understand the potential
benefits and risks of bio-banding and how coaches, sport
psychologists, and parents can better support players to
ensure that such strategies contribute positively towards
youth development.
Compliance with Ethical Standards
Funding No funding was provided to any of the authors for this review.
The research on Portuguese youth soccer players used in several tables
was supported in part by Fundação para Ciência e a Tecnologia.
Conflict of interest Robert Malina, Alan Rogol, Manuel Coelho-e-
Silva, Antonio Figueiredo, Jan Konarski, and Sławomir Kozieł declare
that they have no conflicts of interest. Sean Cumming has worked in
research and consultancy roles with the Premier League, the English
Football Association, the Lawn Tennis Association, and British Gym-
nastics.
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This study focus is to develop a new model to estimate skeletal age (SA) as a function of the biological maturation status in male soccer players, and to propose cut-off points to classify the biological maturation status based on the percentage of adult height (PAH) in male soccer players. SA was determined in 747 Portuguese male soccer players, using the Tanner-Whitehouse (TW) 3 method, and PAH was predicted by TW3 (P-TW3) and Khamis-Roche (P-KR) methods. Subsequently, the sensitivity and specificity of the P-TW3 were estimated to classify late, on-time and early maturers to obtain cut-off points, by age; and to develop specific equations for each maturation stage. Both the model using P-TW3 and the model using P-KR showed a SA predictive capacity of 93%. The average differences were similar to zero. P-TW3 cutoff points were established by ROC curve analysis to identify late and early maturers according to their SA. Following, predictive models were developed to estimate SA according to maturity status. The predictive capacity of the models was 87.3% in late maturers, 92.3% in early maturers and 93.5% in early maturers. The prediction models are a reliable and cost-effective method to estimate SA in male soccer players.
Article
Students’ experiences of physical education (PE) are considered important for lifelong attitudes towards physical activity. Sex-related differences and the individualised tempo in anthropometric growth because of biological maturation lead to secondary school students within chronological age-ordered classes possessing vast differences in anthropometric and physical characteristics, which may negatively impact the PE experience for late- or early-maturing children. Therefore, the purpose of this review was to (i) critically discuss the influence of maturation on developmental factors related to PE and (ii) provide key stakeholders with guidance on how to manage this period of development more effectively and propose solutions to alleviate the confounding influence of biological maturity currently being implemented within sporting contexts. Secondary school children of different maturation status are often categorised using arbitrary, chronological age-ordered bandings, resulting in groups of children exhibiting large within-group variations in physical, behavioural, emotional, and educational development. This heterogeneity may lead to sub-optimal learning environments, which are confounded by complex and often negative developmental consequences for children who are at either extreme (late- or early-maturing) of the maturation continuum. This is particularly important within PE, where engagement, enjoyment, and resultant lifelong physical activity attitudes are influenced by perceived competence and relatedness, and where these needs may be thwarted because of considerable maturity-related variations. This paper posits that it is time for key stakeholders within child education to explore new ways to supplement current teaching practices and consider occasionally grouping children by maturation status (i.e. bio-banding) within secondary school PE to enhance students’ experiences.
Conference Paper
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Bio-banding is the grouping of youth athletes based on their level of maturation, rather than chronological age (CA). Within youth soccer, grouping players within CA groups for training and competition can result in large variability in the biological development of the players. This variability can have severe implications for performance. No quantitative data investigating the influence of bio-banding upon in-game performance indicators has been published. The aim of this study was to compare the physical and technical demands of elite youth soccer players competing in bio-banded and CA group matches. Participants (N=13) from an U14 age group at a category 3 academy voluntarily participated. Following University ethics approval, data was collected at two CA group matches, and at one bio-banded match. Maturation was assessed from % of predicted adult height (PAH), an estimation from parental height. The bio-banded match was played in-house, with players competing with/against a variety of ages (13-15), who had attained between 90-95% of PAH, creating a band which is representative of circa-pubertal/growth spurt. All games were recorded by a camcorder to enable the analysis of technical performance indicators, in addition to the measurements of heart rate (HR) and RPE. The average % max heart rate (MHR) was not significantly different between the bio-banded (84.8 ± 4.0%) and CA group matches (85.4 ± 4.7%) (P>0.05). Time spent within the HR intensity zone 60-69% was significantly higher during the CA group matches (4 ± 4.0 mins) compared to the bio-banded match (2 ± 3.3 mins) (P<0.05). Technical evaluation revealed that significantly more passes (542 v 259), particularly passes from within the players own half (351 v 150), and touches (623 v 318) were made in the bio-banded game compared to CA group games (P<0.05), with significantly more headers in the CA group matches (35 v 18) (P<0.05). All other technical performance indicators (number and accuracy of shots, passes, dribbles, tackles and blocks) reported no significant difference (P>0.05). Results suggest bio-banding and CA group matches may elicit a similar physiological load. However, the results also suggest bio-banding is more technically demanding, and may serve as a better format to develop a player’s technical ability. Further investigation is required to analyse the psycho-social aspect of bio-banding, and utilise new in-game monitoring equipment (e.g. GPS) to collect data from more matches to confirm these findings.
Article
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Purpose: To compare characteristics of club level male soccer players 11 and 13 years of age, and to evaluate playing status in soccer two and 10 years after baseline by birth quarter (BQ). Methods: Youth players 11 (n = 62, born 1992, observed December 2003) and 13 (n = 50, born 1990, observed April 2004) years were grouped by BQ. Baseline data included stature, weight, maturity status, functional capacities, soccer skills, goal orientation, and coach evaluation of potential. Playing status in soccer in 2006 and 2014 was also available. Baseline characteristics and subsequent playing status were compared by BQ. Results: Baseline characteristics did not differ by BQ except for age and percentage of predicted adult height. Though not significant, coaches tended to rate players in BQ1as higher in potential. For those competing in soccer as adults, BQ2 (4), BQ3 (5) and BQ4 (2) were represented among players11 years, and BQ1 (3), BQ2 (2), BQ3 (1) and BQ4 (4) among players 13 years. Conclusion: Although limited to small numbers, differences among players by BQ were inconsistent. The results indicate a need to extend potential explanations of the RAE to include behavioral variables, coaches, training environment, and perhaps the culture of the sport.
Article
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Humans are one of the few species undergoing an adolescent growth spurt. Because children enter the spurt at different ages making age a poor maturity measure, longitudinal studies are necessary to identify the growth patterns and identify commonalities in adolescent growth. The standard maturity determinant, peak height velocity (PHV) timing, is difficult to estimate in individuals due to diurnal, postural, and measurement variation. Using prospective longitudinal populations of healthy children from two North American populations, we compared the timing of the adolescent growth spurt’s peak height velocity to normalized heights and hand skeletal maturity radiographs. We found that in healthy children, the adolescent growth spurt is standardized at 90% of final height with similar patterns for children of both sexes beginning at the initiation of the growth spurt. Once children enter the growth spurt, their growth pattern is consistent between children with peak growth at 90% of final height and skeletal maturity closely reflecting growth remaining. This ability to use 90% of final height as easily identified important maturity standard with its close relationship to skeletal maturity represents a significant advance allowing accurate prediction of future growth for individual children and accurate maturity comparisons for future studies of children’s growth.
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Background: The Tanner-Whitehouse radius-ulna-short bone protocol (TW2 RUS) for the assessment of skeletal age (SA) is widely used to estimate the biological (skeletal) maturity status of children and adolescents. The scale for converting TW RUS ratings to an SA has been revised (TW3 RUS) and has implications for studies of youth athletes in age-group sports. Objectives: The aim of this study was to compare TW2 and TW3 RUS SAs in an international sample of male youth soccer players and to compare distributions of players by maturity status defined by each SA protocol. Methods: SA assessments with the TW RUS method were collated for 1831 male soccer players aged 11-17 years from eight countries. RUS scores were converted to TW2 and TW3 SAs using the appropriate tables. SAs were related to chronological age (CA) in individual athletes and compared by CA groups. The difference of SA minus CA with TW2 SA and with TW3 SA was used to classify players as late, average, or early maturing with each method. Concordance of maturity classifications was evaluated with Cohen's Kappa coefficients. Results: For the same RUS score, TW3 SAs were systematically and substantially reduced compared with TW2 SAs; mean differences by CA group ranged from - 0.97 to - 1.16 years. Kappa coefficients indicated at best fair concordance of TW2 and TW3 maturity classifications. Across the age range, 42% of players classified as average with TW2 SA were classified as late with TW3 SA, and 64% of players classified as early with TW2 SA were classified as average with TW3 SA. Conclusion: TW3 SAs were systematically lower than corresponding TW2 SAs in male youth soccer players. The differences between scales have major implications for the classification of players by maturity status, which is central to some talent development programs.
Article
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Individual differences in the growth and maturation have been shown to impact player performance and development in youth soccer. This study investigated Premier League academy players’ experiences of participating in a tournament bio-banded for biological maturation. Players (N = 66) from four professional soccer clubs aged 11 and 14 years and between 85–90% of adult stature participated in a tournament. Players competed in three 11 vs 11 games on a full size pitch with 25-min halves. Sixteen players participated in four 15-min focus groups and were asked to describe their experiences of participating in the bio-banded tournament in comparison to age group competition. All players described their experience as positive and recommended the Premier League integrate bio-banding into the existing games programme. In comparison to age-group competitions, early maturing players described the bio-banded games more physically challenging, and found that they had to adapt their style of play placing a greater emphasis on technique and tactics. Late maturing players considered the games to be less physically challenging, yet appreciated the having more opportunity to use, develop and demonstrate their technical, physical, and psychological competencies. Bio-banding strategies appear to contribute positively towards the holistic development of young soccer players.
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Background: Predicted maturity offset and age at peak height velocity are increasingly used with youth athletes, although validation studies of the equations indicated major limitations. The equations have since been modified and simplified. Objective: The objective of this study was to validate the new maturity offset prediction equations in independent longitudinal samples of boys and girls. Methods: Two new equations for boys with chronological age and sitting height and chronological age and stature as predictors, and one equation for girls with chronological age and stature as predictors were evaluated in serial data from the Wrocław Growth Study, 193 boys (aged 8-18 years) and 198 girls (aged 8-16 years). Observed age at peak height velocity for each youth was estimated with the Preece-Baines Model 1. The original prediction equations were included for comparison. Predicted age at peak height velocity was the difference between chronological age at prediction and maturity offset. Results: Predicted ages at peak height velocity with the new equations approximated observed ages at peak height velocity in average maturing boys near the time of peak height velocity; a corresponding window for average maturing girls was not apparent. Compared with observed age at peak height velocity, predicted ages at peak height velocity with the new and original equations were consistently later in early maturing youth and earlier in late maturing youth of both sexes. Predicted ages at peak height velocity with the new equations had reduced variation compared with the original equations and especially observed ages at peak height velocity. Intra-individual variation in predicted ages at peak height velocity with all equations was considerable. Conclusion: The new equations are useful for average maturing boys close to the time of peak height velocity; there does not appear to be a clear window for average maturing girls. The new and original equations have major limitations with early and late maturing boys and girls.
Book
Growth, Maturation and Body Composition documents one of the most remarkable and significant studies in the field of human biology. The Fels Longitudinal Study is the longest, largest and most productive serial study of human growth, maturation and body composition. This book shows how data collected from more than 1000 participants during the past 60 years have been analysed to test a wide range of hypotheses, and describes how the findings have led to the development of improved research methods. Topics covered include the management and analysis of data, prenatal, familial and genetic studies, physical growth, development and maturation, bones and teeth, body composition, and risk factors for cardiovascular disease. With more than 1000 specialized publications of Fels data, the present book provides a unique overview of this fascinating research programme, which will be of interest to a wide range of researchers, including those in the fields of physical anthropology, nutrition science, pediatrics, gerontology, epidemiology, endocrinology, human genetics, as well as statistics.
Book
The second edition of Growth, Maturation, and Physical Activity has been expanded with almost 300 new pages of material, making it the most comprehensive text on the biological growth, maturation, physical performance, and physical activity of children and adolescents. The new edition retains all the best features of the original text, including the helpful outlines at the beginning of each chapter that allow students to review major concepts. This edition features updates on basic content, expanded and modified chapters, and the latest research findings to meet the needs of upper undergraduate and graduate students as well as researchers and professionals working with children and young adults. The second edition also includes these new features: - 10 lab activities that encourage students to investigate subject matter outside of class and save teachers time - A complete reference list at the end of each chapter - Chapter-ending summaries to make the review process easy for students - New chapters that contain updates on thermoregulation, methods for the assessment of physical activity, undernutrition, obesity, children with clinical conditions, and trends in growth and performance - Discussions that span current problems in public health, such as the quantification of physical activity and energy expenditure, persistent undernutrition in developing countries, and the obesity epidemic in developed countries The authors are three of the world's foremost authorities on children's growth and development. In 29 chapters, they address introductory concepts and prenatal growth, postnatal growth, functional development, biological maturation, influencing factors in growth, maturation and development, and specific applications to public health and sport. In addition, secular trends in growth, maturation, and performance over the past 150 years are considered. You'll be able to recognize risk factors that may affect young athletes; you'll also be able to make informed decisions about appropriate physical activities, program delivery, and performance expectations. Growth, Maturation, and Physical Activity, Second Edition, covers many additional topics, including new techniques for the assessment of body composition, the latest advances in the study of skeletal muscle, the human genome, the hormonal regulation of growth and maturation, clarification of dietary reference intakes, and the study of risk factors for several adult diseases. This is the only text to focus on the biological growth and maturation process of children and adolescents as it relates to physical activity and performance. With over 300 new pages of material, this text expertly builds on the successful first edition.
Article
Background: Individual differences in biological maturation impact player selection and development in youth football. Aim: To evaluate players perceptions of competing in a football tournament where they were matched by maturity rather than chronological age. Subjects: Participants included male junior footballers from three professional academies (N = 115). Methods: The study employed multiple methods of analysis, including one sample means t-tests, equivalence tests, ANOVAs, and thematic analysis of qualitative data derived from open-ended questions. Results and Conclusions. Player’s perceived the bio-banding format as providing two main benefits. Early maturing players perceived greater physical and technical challenge, and in turn new opportunities and challenges. Late maturing players perceived less physical and technical challenge, yet greater opportunity to demonstrate technical and tactical abilities. The players reported that they enjoyed and understood the purpose of the bio-banded format, and perceived less risk for injury. Players in all three maturity groups reported more opportunity to engage in leadership behaviours, influence game-play, and express themselves on the ball in the bio-banded format. Bio-banding may facilitate development for both early and late maturing academy players by presenting new learning environments and challenges.
Article
Objectives: The main and interactive effects of biological maturity status and relative age upon self-regulation in male academy soccer players are considered. Consistent with the ‘underdog’ hypothesis, whereby relatively younger players may benefit from competitive play with older peers, it was predicted later maturing and/or relatively younger players would report more adaptive self-regulation. Design: Cross-sectional study. Method: Players (n = 171, aged 11–16 years) from four English professional soccer academies completed the modified Soccer Self-Regulation Scale. Date of birth, height, weight and parental height were obtained. Relative age was based on birth quarter for the selection year. Maturity status was based upon percentage of predicted adult height attained. Results: Linear regression models showed later maturation was inversely associated with adaptive self-regulation, while relative age was unrelated to self-regulation. Conclusions: In partial support of the underdog hypothesis, later maturing players appear to possess a psychological advantage.