Video-based time-motion analysis systems

The majority of time-motion investigations in hockey have used observational techniques to evaluate the overall physical activity associated with match-play by recording and analysing the many different activities for the players observed (Spencer, et al., 2004). Early approaches focused on the use of hand notation systems for the recording of activity patterns, where player movements were tracked on a scale plan of the pitch. More refined systems were then developed using coded commentaries of activities recorded on audio tape, in conjunction with measurements based on stride length and frequency taken from video recordings to evaluate the total distance (TD) covered for the duration of a match (Dobson & Keogh, 2007). Whilst the early studies that employed these methods revealed important information about the demands of hockey, the complexity and large amount of time required for coding, analysing and interpreting the output inhibited their use by performance analysts (Lythe & Kilding, 2011). Furthermore, these methods did not allow real-time analysis and were extremely labour-intensive in terms of the capture and analysis of data (Carling, Bloomfield, Nelsen, & Reilly, 2008). These original techniques were also

restricted to the analysis of a single player, therefore limiting the practical application of research projects.

A later method of filming and analysis utilised two or three fixed cameras (Reilly & Gilbourne, 2003). This allows for a combined view with the cameras covering the complete playing area and their fields of view overlapping facilitating the tracking of players from one camera’s view to the next. Player motion was then subjectively categorised while watching the video playback. This can be performed by one, but usually two operators. The first operator watching the video, calling changes in motion of a single player, with the second operator imputing the events manually into a computer with purpose built software (Edgecomb & Norton, 2006; Spencer, et al., 2004). Computer-based tracking (CBT) relies on ground markings and reference points that translate to markers on a miniaturized, calibrated version of the playing field (Edgecomb & Norton, 2006). This method utilises a stylus or movements of a mouse which correspond to the linear distance travelled by the player to estimate activity profiles. The validity and reliability of this method is discussed in the following section. Although each method used different techniques, they fundamentally measured the same variables, namely, the activity profile of players. Alternative methods for measuring activity profiles have included video footage taken from overhead views of the pitch for computer-linked analysis of the movements of the whole team and synchronized cameras positioned to overlook each half of the pitch; activities are then calculated using trigonometric principles (Edgecomb & Norton, 2006). Both notation and motion analysis techniques provide a valuable source of feedback to coaches and players, specifically regarding the physical requirements of match play.

Activity profiles of players within a team have been established according to the intensity, duration, and frequency of classified activities (e.g., walking, moving sideways or backwards, jogging, cruising, and sprinting). Movement classification systems were originally documented in the soccer literature (Reilly & Thomas, 1976) and recently modified for use in other team sports such as rugby (Deutsch, Maw, Jenkins, & Reaburn, 1998; Docherty, Wenger, & Neary, 1988) and hockey (MacLeod, et al., 2007; Spencer, et al., 2004). Each movement was coded as one of six speeds of locomotion and depending on the sport assessed, game specific movements and involvements were also identified (Duthie, et al., 2003a; Macutkiewicz & Sunderland, 2011). For example, hockey time-motion analysis studies have included lunging as part of the analysis (MacLeod, et al., 2007). Rugby time-motion analysis studies have also included game specific movements identifying three states of non-running intensive exertion (rucking / mauling, tackling, and scrimmaging), and three discrete activities (kicking, jumping, passing) (Sirotic, et al., 2009).

Whilst there is no strict consensus in time-motion research on motion categories, there are common modes of movement used in studies across different sports. In a study of elite men’s hockey using a video-based time-motion analysis system, player motion was coded into five distinct categories. These were defined as follows (Spencer, et al., 2004):

1. Standing: motionless.

2. Walking: motion, but with both feet in contact with the ground at the same time at some point during the gait cycle.

4. Striding: vigorous motion with airborne phase, higher knee lift than jogging (included skirmishing movements of rapid changes of motion, forwards / backwards/ laterally).

5. Sprinting: maximal effort with a greater extension of the lower leg during forward swing and a higher heel lift relative to striding.

Difficulties exist when comparing data from studies using different classification systems. For example, some studies have combined the motions of sprinting and striding (or high-intensity efforts) into one category and utilised different speed zones to define these high intensity categories. This may impact the distance recorded for these zones (Mayhew & Wenger, 1985; McKenna, Patrick, Sandstrom, & Chennells, 1988; Meir, Arthur, & Forrest, 1993). Furthermore, different methods have been used to document motion activity (i.e. manual charting, audio recording, video recording and computer tracking), which may influence the accuracy of results (Spencer, et al., 2004).