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Performance Analysis

Discovering frequently recurring movement sequences in team-sport athlete spatiotemporal data

ORCID Icon, , &
Pages 2439-2445 | Accepted 09 Dec 2016, Published online: 09 Feb 2017

Figures & data

Figure 1. The relative frequency of clustered observations for (a) velocity, (b) angular velocity and (c) acceleration movement features.

Figure 1. The relative frequency of clustered observations for (a) velocity, (b) angular velocity and (c) acceleration movement features.

Table 1. Movement subunits, their percentage contribution to the wider data set and qualitative descriptor (this wider data set is inclusive of all players, matches and positions).

Table 2. The Minkowski distances for movement sequence distributions between netball playing positions including centre (C), goal attack (GA), goal defence (GD), goal keeper (GK), goal shooter (GS), wing attack (WA) and wing defence (WD).

Figure 2. The relative contribution of frequently recurring LCS sequences by netball playing position including centre (C), goal attack (GA), goal defence (GD), goal keeper (GK), goal shooter (GS), wing attack (WA) and wing defence (WD). The number of iterations of each movement subunit are represented by bN, for example K2 refers to KK and N5 refers to NNNNN.

Figure 2. The relative contribution of frequently recurring LCS sequences by netball playing position including centre (C), goal attack (GA), goal defence (GD), goal keeper (GK), goal shooter (GS), wing attack (WA) and wing defence (WD). The number of iterations of each movement subunit are represented by bN, for example K2 refers to KK and N5 refers to NNNNN.

Figure 3. A network analysis of movement sequence similarity between playing positions.

Figure 3. A network analysis of movement sequence similarity between playing positions.