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8.3.4.1 Aim

The aim of this investigation is to analyse the overall movement patterns of elite male field hockey players. Furthermore, to compare and contrast the difference in movement patterns with a players position.

8.3.4.2 Hypothesis Null hypothesis (H₀)

There will be no significant difference in the movement patterns of field hockey players in relation to their position.

Experimental hypothesis (H₁)

There will be a significant difference in the movement patterns of field hockey players in relation to player position.

8.3.4.3 Devising the method

The analysis of team games has tended to be more of a subjective and qualitative nature, and is characterized by observational techniques relying on the coaches’

evaluation of the game. An objective and quantitative paradigm should lead to a greater insight into the physiological demands of invasive games.

There are indicators of the physiological demands of different sports, including heart rate response (Carter 1996; Boddington et al. 2001), distance covered and work-rest ratios (Lothian and Farrally 1994; O’Donoghue and Parker 2001).

The most interesting time–motion analysis studies have been in the movement patterns of different positional roles (Reilly and Thomas 1976; Herbert and Tong 1996).

According to Hughes and Franks (2004), any statistics being gathered from a dynamic environment, like field hockey, can be difficult to obtain. Therefore, any quantitative analysis must be structured, for example, a flowchart. Franks and

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Goodman (1984) produced a hierarchical structure for initiating a notational system (cited in Hughes and Franks 2004), (Figure 8.14).

8.3.4.4 Pilot study

A pilot study was carried out for approximately 10 min. From conducting the pilot study, strengths and weaknesses of the system could be identified. It dem-onstrated that although the system collected great amounts of data, the system was too complex. The difficult decision is knowing when the limitations of a hand notation are acceptable within the terms of reference of the desired data collection. The hand notation was revised and evaluated, thus allowing a more effective methodology to be utilized.

8.3.4.5 Finalized method

The male hockey match recorded (n = 1) was of international standard, between Spain and Germany in the semi-final of the World Cup 2006. The location of the camera is unknown, but assumed its location is approximately the half way line. The game was viewed once for each discipline (n = 3; 1 forward, 1 midfield and 1 defence) for a total of three players and for the duration of 20 min.

Figure 8.14 Hierarchical structure representing events that take place in team games (Franks and Goodman 1984)

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Due to lack of technological equipment, movement classifications were timed with a stop watch and, by a random sampling method, Spain were selected to be the team of focus. In addition, the recording of data required two operators and movements patterns were operationally defined.

By adopting both Hughes and Franks (2004) and Frank and Goodman’s (1984) theories, a simple flowchart was devised appropriate for this investigation (Figure 8.15).

Figure 8.15 Hierarchical model of time–motion analysis in field hockey Table 8.11 Short-hand symbols

Movement Short-hand symbol

Stationary O

Walking W

Jogging J

Shuffling ∆

Sprinting *

Game-related activity G

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8.3.4.6 Limitations

There will be limitations placed upon this investigation such as a time and word limit, and lack of technological equipment, which prevents the investigation going into too great a depth.

Former type error occurs when an operator unintentionally enters incorrect data.

For example, uses the wrong short-hand symbol. The chance of error possibilities occurring is heightened within this study due to there being two operators.

Due to the continuous nature of running, it is difficult to strictly classify its movement into such discrete categories such as sprinting and jogging. There-fore, the study is subjective, thus affecting the reliability and repeatability of this notation.

8.3.4.7 Operational definitions Stationary

Standing, sitting, lying or stretching – activity that portrays little exhaustive movement.

Walking (forwards, backwards, lateral)

The back foot does not leave the floor until the front foot makes contact with the ground. This movement is slow in nature.

Running (forwards, backwards)

This is a slow running movement without obvious exhaustive effort.

Sprinting

Explosive movement that involves rapid extension of the hip and knee. There is obvious acceleration.

Game-related activity

This is any time during the game where the player is either in contact with the ball or attempting to become in contact with the ball; hitting, dribbling, tackling, channelling, side line balls, free hits, passing, etc.

8.3.4.8 Reliability

The repeatability and accuracy of a study is a central facet to notational analysis.

Hughes et al. (2004) found that within analysing 72 research papers, 70 per cent

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of investigations did not report any reliability study. A simple inter-operator test for reliability was performed using an equation suggested by Hughes et al.

(2004):

(Σ (mod [V1–V2] / Vmean) × 100%

V1 and V2 are variables, Vmean is their mean, mod = modulus, and Σ means ‘sum of’. The calculation will give a percentage error for each variable and operator.

Significance level was set at 5. If a value is p>5 it is assumed that the test is not significant, therefore not reliable.

From the results illustrated in both Table 8.12 and Figure 8.16, it can be identified that the overall reliability of the system was 1.01 per cent, suggesting that the system is reliable. However, when analysing the discrete values, there are large discrepancies within the data. For example, the least significant value is 19 per cent for shuffling. The most significant value was 4.7 per cent for walking. This emphasizes the subjective nature of the investigation. Also, dynamic activities such as shuffling and sprinting are more arduous to identify than walking and stationary movements. This heightens the importance of operational definitions.

Using computerized methods such as the CAPTAIN system (McLaughlin and O’Donoghue 2004) may increase the reliability of future time–motion analysis systems.

Figure 8.16 The overall data from the reliability study

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8.3.4.9 Results

From both Table 8.13 and Figure 8.17, it is illustrated that a forward position in hockey spent 5.57 min (26 per cent) of the 20 min of performance sprinting.

This is followed by 5.23 min running and 5.07 min walking.

It can be assumed that a forward may sprint and run more often during a game as it is their short duration bursts of activities into space that create through balls, and therefore attacking opportunities. In contrast, only 1.28 min of the game was spent doing game-related activity. This suggests how the results may be influ-enced by the state of play. For example, if Spain had less possession, or more defensive play.

Table 8.14 and Figure 8.18 show the results of the movement patterns of a spe-cific midfield hockey player. In comparison, the midfield player spent a longer duration in a wider range of activities than the forward. This may be because of Table 8.12 Data from a field hockey game notated once by two operators for 20 min, presented as an inter-reliability analysis