The use of multiple sources of background information to drive selection of

identified in Table 8.1.

Table 8.1

Parameters found to have significant relationships or differences in biomechanical studies Parameter Maximal (Study 2) Preferred vs. Non-preferred (Study 3) Accuracy Speed-accuracy (Study 4)

Support hand position X X

Hand path X

Shoulder path X X

Elbow ROM X

Shoulder ROM X

Elbow angular velocity (max) X X

Shoulder speed X

Shoulder angular velocity X X X

Step-angle X

Separation angle X

Time to max upper-trunk rotation velocity X

Shoulder angle X X X

Forearm ROM X X

Lower-trunk ROM X X

Upper-trunk ROM X X

Forearm angular velocity X X

Upper arm angular velocity X X X

Elbow angular velocity (at BC) X X X

Maximum lower-trunk rotation velocity X X

8.2.2 The use of multiple sources of background information to drive selection of parameters

In previous research, some authors have failed to explain why certain parameters were chosen for analysis (e.g. Dichiera et al., 2006), while other authors have based their decision on only one source of information (e.g. previous findings, Sachlikidis & Salter, 2007). In comparison, this thesis used four sources of input to guide the choice of parameters. First, previous literature was eliminated as a viable source of information upon consideration that Australian football literature has addressed kicking, but no literature on handballing was available to guide parameter selection. Thus, the four sources of input included (a) a deterministic model (Appendix A), (b) coaching cues

from coaching literature, (c) coach feedback on what was considered important, and finally, (d) the in-game performance analysis (Parrington et al., 2013b [Chapter 2 of this thesis]). The use of multiple sources of input to drive the selection of key parameters for investigation considers both theoretical and evidence-based approaches and, therefore, provided a strong rationale for the parameters chosen throughout this thesis.

Deterministic models are paradigms based on Newtonian physics used to help determine the relationship between independent movement parameters and dependent outcome parameters (Chow & Knudson, 2011). Chow and Knudson recommend the use of deterministic models to assist the provision of a theoretical basis for sport

biomechanics research. Conversely, other researchers have indicated that deterministic models are restricted with respect to the information provided concerning ‘technique’ (Glazier & Robins, 2012; Glazier & Wheat, 2013). In this thesis, the deterministic model was used to gain an initial understanding and aid in the derivation of underpinning mechanical factors of the Australian football handball. The process provided a useful tool, which aided the process of parameter selection, but was not the sole source of input.

The second source of input used in this thesis included deriving parameters from coaching literature. This method involved assessment of the key qualitative coaching instructions and translation of these instructions into measurable parameters (Table 8.2). Table 8.2

Coaching instruction and parameter derived

Instruction (McLeod & Jaques, 2006) Parameter

1 Grip ball with stationary support hand Support hand position / support hand position from pelvis

2 Punch ball from support hand using clenched fist

Hand velocity

3 Step forward to gain power and distance Step length, knee angle, linear velocity (trunk/ shoulder/ hip)

4 Follow through with punching hand in a motion upward and toward the target

In addition to the use of coaching literature, coaching information was also obtained through consultation with coaching staff from one of the AFL clubs. Here, the coaches were asked what facets of handballing performance were important. In

particular they were asked to describe using coaching terms the type of movement the ‘good handballers’ possessed. The coaches described terms such as ‘being square’, ‘good backswing’, ‘striking through, not across the ball’, and ‘taking a step forward and knees bent’ as features of good handballing. Some of these parameters had obvious and direct links with terms used in the deterministic model (e.g. the hand path at ball contact with ‘striking through and not across the ball’) while some were more specific to what could be observed but not directly related (e.g. ‘being square’ might be connected with the position of the upper and lower trunk with respect to the passing target). Terms that were closely linked were associated with parameters in the deterministic model, while others were translated into new technical parameters (Table 8.3).

Table 8.3

Coaching cue and parameter derived

Coaching cue Parameter

1 Being square Upper and lower trunk orientation Separation angle

Upper and lower trunk range of motion Trunk / shoulder / hip path toward target 2 Good backswing Shoulder range of motion

Elbow range of motion 3 Striking through, not across the ball Hand path

Lateral deviation of the striking hand 4 Taking a step forward and knees bent Step length

Step direction Knee angle (flexion)

Qualitative observations from video footage of two players selected as the top handballers at the club were used to support the coaches’ descriptions (Figure 8.1). This method provided a useful applied approach toward research of a motor-skill. In

addition, conducting analysis of parameters based upon coaching cues allows evaluation of these cues and helps to provide evidence for or against their use in practice.

2 hand contact Onset of backswing

Maximum backswing/ onset of

downswing

Ball contact Follow through

Figure 8.1: Handball performed by a player described as one of two top handballers at the club at the time of testing.

The skill-focussed performance analysis used in Study 1 (Chapter 2), was the final source of parameter input. The identification of critical features relating to live match performance is believed to be good common practice (Hughes & Bartlett, 2002) and has been suggested to compliment biomechanical and decision-making evaluation (Ball & Horgan, 2013; Glazier, 2010). It is beneficial to use as a precursor to further analysis because of the ability to categorise predominant skill executions and separate physical and cognitive components of the motor-skill to retrieve a greater insight into performance (Ball & Horgan, 2013). Knowing the common skill executions is useful to direct further testing, such that the testing conditions can reflect the typical skill

performance (Ball & Horgan). The implementation of findings from the performance analysis is provided in Table 8.4.

Table 8.4

Implementation of parameters from skill-focussed performance analysis

Parameter Finding Implementation 1 Being square More commonly

performed when square Higher efficiency when square

Assess whether upper and lower trunk orientation with respect to target increases accuracy (Parrington et al., 2012).

2 Player stance/ motion

Stationary passes occur more often than in motion

Running and ‘knees- bent’ stance more efficient

Influenced starting position for

biomechanical testing. Players started from a stationary position and were able to take a step (or shuffle) forward.

Kinematic assessment of knee-angle and linear speed of the hip and shoulder. 3 Pass direction Forward passes most

common and most efficient

Biomechanical testing focussed on forward passes.

Assess hand path (Parrington et al., 2012). 4 Pass distance Short passes (6m or less)

more common

Biomechanical assessment of passes performed at similar distance. 5 Number of options One to two passing

options in clear support of ball carrier

Studies 5 and 6 used one valid option and one invalid option. Study 5 included one level of two but competing valid options. 6 Time to dispose ball Between one and three

seconds most common but time to dispose did not influence efficiency

Player instructed to perform under game intensity, but not constrained by time.

Finally, the decision of which biomechanical parameters to include in Study 6 was made through the assessment of Studies 2 – 4. Parameters that were significant in the maximal speed and accuracy based studies (Study 2 and 4) were included in the analysis, and any parameters found to differ between the preferred and non-preferred hand (Study 3) were excluded from analysis. Other parameters were removed based on the potential of the task causing differences in parameters that could not be controlled for (e.g. the position of the target would effect trunk rotation).

Using multiple sources of input, including theoretical models and evidence- based coaching information and game analysis, was a robust approach to select

parameters. It is important to consider multiple sources of input as each source might introduce a new parameter, or may provide additional support for the collection of a particular parameter.