an investigation into the use of a movement ...

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AN INVESTIGATION INTO THE USE OF A MOVEMENT ASSESSMENT

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PROTOCOL FOR UNDER 14 RUGBY LEAGUE PLAYERS IN A TALENT

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DEVELOPMENT ENVIRONMENT

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David Morley, Daniel Pyke and Kevin Till,

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Leeds Metropolitan University

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ABSTRACT

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This study investigated the use of a movement assessment protocol for under 14 rugby league

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players by evaluating the relationships between chronological age, maturation, and

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anthropometry, and fitness and qualitative movement assessments (QMA) of 84 rugby league

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players within a talent development environment. A one-way ANOVA showed Quartile 1

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players were more mature, taller (173.0±7.4 vs 165.0±8.0 cm and heavier (72.558.7kg) than

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Quartile 4 players, with no difference evident for fitness or QMA measures. Earlier maturing

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players had significantly greater upper body power (5.39±0.46 vs 4.42±0.68 m) , 20m speed

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(3.48±0.14 vs 3.65±0.19s) and power pass QMA (13.88±2.18 vs 12.00±1.98) than later

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maturing players. Body mass was positively related to power pass fitness (r=0.50)and QMA

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(r=0.22) scores, with negative relationships found for vertical jump performance (r=-0.24),

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sprint QMA (r=-.29) and turn off either foot QMA (r=-0.26). There is a need to educate

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coaches about the use of both fitness testing and qualitative movement assessments to identify

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talented U14 rugby league players, which potentially reduces relative age and maturational

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biases.

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INTRODUCTION

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The Rugby Football League (RFL) is the governing body for Rugby League football in the

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UK, with almost 250,000 people currently involved as players, coaches, match officials or

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volunteers and 90,000 children taking part in the Champion schools knock-out competition

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each year [1]. The development of young players is of great importance to the game and it is

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believed that by facilitating a smooth transition at the adolescent stage, the quality of players

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progressing from junior to senior level can be increased [2]. In alignment with the recognition

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of the necessity to ensure a ‘smooth transition’, the RFL piloted a talent development

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intervention, named Embed the Pathway (EtP) within the Under 14 age category. A purpose

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of EtP was to assess player ‘movement’ and ‘physical’ capabilities within a 6-panel talent

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development framework used by the National Governing Body [3]. The assessment of the

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Under 14 age group was of particular interest to the RFL as this was a key period of

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maturation for the players in terms of transiting through Peak Height Velocity [4] and players

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were about to be formally selected onto the first exclusive segment of the RFL talent pathway.

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This study explored the relationship between the results of fitness and movement assessments,

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relative age, maturation and anthropometric characteristics of Under 14 junior Rugby League

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players, within the context of a talent development intervention. A ‘dualistic’ approach was

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selected for the Under 14 players that relied upon a quantitative approach, drawing from

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fitness assessments, traditionally associated with talent identification practices in rugby

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league across junior and senior levels [5, 6], and a qualitative approach that aimed to assess

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movement competency by analysing the movement process rather than performance outcome

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[7].

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By combining these two approaches, and assessing any relationships with pre-determined

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developmental characteristics, the study aimed to inform the development of a subsequent

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assessment protocol to be used on the wholesale delivery of EtP, nationally. The majority of

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assessment methods typically employed in these environments are reliant upon quantitative

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measurements as the main identifying feature for talent selection [8]. This study challenges

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this dominance through a consideration of the benefits and limitations in such a one

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dimensional approach when used with an adolescent age group, in which relative age effects

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as well as biases towards the early maturing, physically superior player may be prevalent [9].

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At junior level, with rugby league players aged 6-14 years, player development has become

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increasingly focused on the qualitative development of movements specific to the game

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[10,11]. In contrast, the majority of players within the England Talent Pathway and

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professional rugby league clubs are commonly assessed using traditional fitness testing

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batteries (e.g. Nike SPARQ Rugby protocol, [12]). Aligned to this, previous researchers

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predominantly explored the use of fitness measures including speed, agility, power, strength

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and aerobic capacity [9,13,14,15]. This assessment continuum disregards the suggestion that

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movement development should be viewed from a lifespan perspective and should continue

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through adolescence alongside physical development [7].

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Developing a dualistic Movement Assessment Protocol

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Meylan et al. [8] acknowledged that a one-dimensional approach using physiological and

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anthropometrical assessment to identify talent, despite being traditional, is misleading and

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outdated. It has also been suggested that in order to develop high levels of physical literacy a

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player should develop competence in a variety of movements, applying them in a range of

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different environments [16]. In order to track development and identify talent accurately, it is

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important to use assessments of competency appropriate to the age group and the sport. With

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these developmental perspectives in mind, the RFL proposed the use of a movement

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assessment protocol (MAP) consisting of two testing batteries to be used within the EtP

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programme; a qualitative movement assessment (QMA) and a fitness assessment.

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The QMA measures specialised skills, typically found in the game of rugby league, that

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involve the combination and refinement of three categories of movement (locomotor,

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manipulative and stability) [7]. For example, the hop, stick and grip assesses all three

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movement categories typically encountered by a player being tackled (Table 1).

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The physical assessment draws from a range of previously validated measures established to

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assess physical fitness in relation to speed, agility and whole body power [17]. The physical

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assessment was also designed in recognition of the recommendation that physical

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conditioning of pre-pubescent players should be avoided, with an alternative focus on the

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development of motor skills deemed more appropriate [18,19]. Due to the nature and duration

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of the assessment it has also been advised that pre-pubescent players’ performance could be

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metabolically limited [20]. Based on these perspectives, the use of assessments of anaerobic

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and aerobic capacity is questionable. This is not the case with respect to the physical

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assessments that were selected for use within the MAP, with maturation having both positive

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and negative effects on speed, agility, strength and power tests [21,22,23,24,25,26].

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Relative Age Effects, Maturation and Anthropometrics

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Youth rugby is divided into annual age groups to ensure fair and safe competition takes place

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between players. However, inequalities favouring the player born earlier in the year have been

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evident with regards to participation in the game and subsequent selection to representative

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squads [13,27]. A relatively older player can often measure higher anthropometrically than

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their younger counterparts [13]. This may then lead to bias in their selection over their

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younger peers due to the coaches’ perceptions of positive links between greater size and

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increased performance within the game [27]. However, other evidence suggests relatively

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high anthropometric measurements do not always lead to increased physiological

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performance [13].

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These inequalities in participation and selection have been termed relative age effects (RAE)

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and have been attributed to individual differences in maturational status [28]. Evidence of the

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RAE has been found in a range of sports [27], including rugby league [13]. This is not the

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case in sports where movement skills are suggested to play a more important role than

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physiological fitness [29,30]. These findings strengthen the need for the evaluation of a

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dualistic approach to reduce the risk of RAE in a talent identification environment.

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During maturation, whole body development occurs leading to increased muscle mass and

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subsequently strength and power [21]. This again creates inequalities when the early maturing

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player is more likely to be selected to talent squads and subsequently exposed to higher

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quality training and coaching [27], particularly when considering the notion that players do

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not maintain this advantage into adulthood [31]. Therefore, this study explored the

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relationship between the results of fitness and movement assessments, relative age,

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maturation and anthropometric characteristics of Under 14 junior Rugby League players,

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within the context of a talent development intervention.

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METHOD

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Participants

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Eighty four players (age M = 14.14 years, SD = 0.29, range 13.6-14.6 years) from rugby

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league community clubs were invited to attend one of three development days held locally to

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them at different regions within the UK.

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Institutional ethical approval was granted for the study and parental consent and player assent

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was sought and provided prior to the commencement of data capture.

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Anthropometrics

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Anthropometric measurements were taken for height (M = 169.9 cm, SD = 7.9, range 147-

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187 cm), sitting height (M = 83.5 CM, SD = 5.1, range 67-98cm) and body mass (M = 65.6

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kg, SD = 13.6 kg, range 39-109 kg). Height and sitting height were recorded to the nearest

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0.1cm using a Seca Alpha stand. Body mass was measured using calibrated Seca Alpha

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(mode 770) scales.

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Relative Age

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The date of birth for each player was recorded and categorized into one of 4 birth quartiles

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(Q), as previously documented [13,27] (Q1 = September-November, Q2 = December-

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February, Q3 = March-May, Q4 = June-August) based on the selection start date of 1st

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September used for creating chronological annual-age groups in UK rugby league.

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Maturation

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Age at peak height velocity (PHV) is the most common measurement of maturational status

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and was calculated using an age at PHV prediction equation [32]. This method used a gender-

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specific multiple regression equation including height, sitting height, leg length, body mass,

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chronological age, and their interactions to estimate age at PHV [33]. Years from PHV

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(YPHV) was then calculated by subtracting age at PHV from chronological age. Following

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the estimation of YPHV, participants were categorised into one of three groups (i.e. Earlier,

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Average, Later) based on their YPHV [9]. The boundaries between the three groups were

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established by subtracting and adding 0.5 to the mean YPHV (0.1 +/- 0.78). Players were

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categorised into later (-0.4 years or below, n=21), average (between -0.39 and 0.59 years,

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n=42) and earlier maturing (0.6 years and above, n=21) groups. This resulted in a minimum of

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1 year difference in maturation between the later and earlier-maturing groups.

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Movement Assessment Protocol

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Participants were advised to wear appropriate clothing and footwear for testing. All

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participants were provided with a standardised warm up approximately 15 minutes in

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duration. The warm-up included a range of dynamic movements followed by moderate to

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high intensity sprints. Participants were split into groups and performed the tests on a rotation

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system. Tests were performed on a range of surfaces both indoor and outdoor and were all

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completed within a two hour period on the same day.

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The MAP involved a combination of fitness and qualitative movement assessments, as

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follows:

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Fitness tests

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Sprint speed was measured over 20 metres using Brower Timing Systems (IR Emit, Draper,

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Utah, USA). Players began in their own time from a staggered standing start and times were

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recorded to the nearest 0.01s. A previous study using the same test and equipment has

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displayed high reliability (intraclass correlation coefficients were r=0.852, p0.80 for

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each of the assessments. The QMA was conducted in the afternoon with groups starting off at

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different positions and then rotating in the following order: (i) 20m Sprint, (ii) Zigzag Agility,

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Hop Stick and Grip and (iii) Superman, Squat.

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Data analysis

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Data was analysed using SPSS Statistics Software Version 20. Means and standard deviations

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were calculated for all variables. In order to investigate differences between relative age

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quartiles and maturational groups, a one-way analysis of variance (ANOVA) was used. Post-

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hoc Bonferroni tests were also performed to highlight which individual groups displayed

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significant differences. To investigate the relationships between height and body mass with

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MAP variables, a Pearson product correlation coefficient was conducted. Statistical

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significance was set at p3>4

Years from PHV

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10.55

4

3.33

0.02

1>4

5.20

0.002

1,3>4

4.03

0.01

1>4

0.99

0.40

0.87

0.46

1.20

0.32

2.57

0.06

0.36

0.78

0.15

0.93

0.30

0.83

1.20

0.32

0.97

0.41

0.18

0.91

0.23

0.87

0.07 0.57 ±

0.08 18

0.60 Height (cm)

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172.96

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Body Mass (kg)

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85.35 ±

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169.44 ±

18

83.44 ±

28

15.40

62.24 ±

15

170.18

28

84.25 ±

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8.16

65.87 ±

165.00 ± 8.01

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5.03 28

-0.69 ± 0.84

± 8.20

4.26 18

0.14 ±

0.09

0.69

6.53

3.88 72.49 ±

28

0.59

± 7.44 Sitting Height (cm)

0.08 ±

0.07

79.33 ± 5.95

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12.02

58.71 ± 14.77

Fitness Tests Vertical Jump (cm)

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Medicine ball throw (m)

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20m Sprint (s)

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47.58 ±

18

8.40 5.01 ±

18

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11.98 ±

5.00 ±

18

3.53 ±

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1.00

11.75 ±

15

4.84 ±

26

3.57 ±

15

0.77

12.40 ±

4.72 ± 0.61

15

0.23 26

45.17 ± 5.11

0.62

0.22 18

46.50 ± 7.48

0.63

0.23 Zigzag Agility (s)

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6.21

0.65 3.52 ±

49.20 ±

3.65 ± 0.16

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0.72

12.28 ± 0.82

QMA Vertical Jump Score

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Power Pass Score

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14.22 ±

18

2.70 13.30 ±

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13.61 ±

18

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Superman Score

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12.70 ±

18

17

17

17.18 ±

17

3

20

15.05 ± 4.14

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13.76 ±

15.47 ±

16

17.75 ±

25

15.33 ± 4.40

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14.08 ±

14.76 ±

25

15.20 ±

15

17.52 ±

13

15.58 ± 1.87

13.92 ± 3.62

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15.69 ± 2.66

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3.33 24

13.93 ± 1.75

2.24 21

13.33 ± 2.06

3.57

4.25 18

13.15 ±

13.87 ± 2.07

1.38

2.07

3.36 Hop, Stick, Grip Score

13.78 ±

15

2.48

4.10

1.23 Squat Score

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1.83

3.28 16.28 ±

13.61 ±

13.59 ± 2.50

2.22

2.19 Turn off Either Foot Score

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2.87

2.18 Sprint Score

14.28 ±

16.82 ± 3.06

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14.73 ± 1.83

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Table 3: Comparisons between Maturation Group and Anthropometric Characteristics,

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Fitness and QMA scores Characteristics

Earlier Maturing (E)

Average Maturing (A)

N

M (SD)

N

M (SD)

Later Maturing (L) N M (SD)

Chronological Age (Years)

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14.28 ±

42

14.16 ±

21

Years from PHV

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0.28 1.00 ±

0.27 42

0.40 Height (cm)

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178.33

42

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Body Mass (kg)

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89.14 ±

21

42

84.07 ±

21

12.05

65.51 ±

Post-hoc

8.47

L

-0.96

163.65

A>L

160.62 ±

69.35

A>L

127.57

A>L

31.39

A>L

1.79

0.17

17.22

A>L

3.29

0.04

EL

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Table 4: Correlations (r) and 95% Confidence Intervals (CI) between Height and Body Mass

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with Fitness and QMA Scores Variable

Height

Body Mass

20m Sprint

-0.31 (-0.51 - -0.13)**

0.12 (-0.11 – 0.25)

Vertical Jump

0.20 (0.01 – 0.50)

-0.24 (-0.37 – 0.14)*

Zigzag Agility

-0.18 (-0.39 – 0.08)

0.09 (-0.12 – 0.32)

Medicine Ball Throw

0.53 (0.42 – 0.70)***

0.50 (0.37 – 0.74)***

Vertical Jump QMA

0.20 (-0.05 – 0.45)

0.05 (-0.08 – 0.37)

Power Pass QMA

0.32 (0.08 – 0.54)**

0.22 (-0.06 – 0.45)*

Sprint QMA

-0.17 (-0.51 – 0.05)

-0.29 (-0.52 – 0.03)**

Turn Off Either Foot QMA

-0.07 (-0.39 – 0.18)

-0.26 (-0.51 - -0.00)*

Superman QMA

0.11 (-0.13 – 0.41)

0.16 (-0.06 – 0.48)

Squat QMA

-0.09 (-0.31 – 0.08)

-0.20 (-0.45 – 0.02)

Hop, Stick and Grip QMA

0.08 (-0.02 – 0.46)

-0.02 (-0.25 – 0.31)

Fitness

QMA

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Significant correlations between variables: *P