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Research Article

Relationship between key offensive performance indicators and match running performance in the FIFA Women’s World Cup 2023

ORCID Icon, , , , , & show all
Received 04 Nov 2023, Accepted 19 Mar 2024, Published online: 27 Mar 2024

Figures & data

Table 1. Variable descriptions.

Table 2. Offensive variables and running match distance from 110 cases of performance of the 2023 FIFA Women’s World Cup.

Table 3. Mixed linear model from offensive variables and team total distance covered (km).

Figure 1. Decision tree from running distance covered (km) and offensive variables. Risk estimates = 25.28 ± 3.34 (SE).

Figure 1. Decision tree from running distance covered (km) and offensive variables. Risk estimates = 25.28 ± 3.34 (SE).

Table 4. Mixed linear model from offensive variables and team high-speed distance covered.

Figure 2. Decision tree from high-speed running distance covered (km) and offensive variables. Risk estimates = 0.32 ± 0.04 (SE).

Figure 2. Decision tree from high-speed running distance covered (km) and offensive variables. Risk estimates = 0.32 ± 0.04 (SE).

Figure 3. Clusters of offensive variables and running distance different letters represent statistical differences between clusters.

Figure 3. Clusters of offensive variables and running distance different letters represent statistical differences between clusters.

Figure 4. Clusters association with 2023-WC final classification. Different letters for the same cluster represent statistical differences for the z-test with Bonferroni correction.

Figure 4. Clusters association with 2023-WC final classification. Different letters for the same cluster represent statistical differences for the z-test with Bonferroni correction.