English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

The aim of this exploratory study is (1) to determine the relationship between substitution network (Sub-N) parameters and teams’ standings and (2) to find out the key individual performance indicators that differentiated substitution groups of players, and explore the association between players’ percentages and team’s standing within the obtained substitution groups. A total of 574,214 substitution events during the last 10 NBA seasons were analysed to construct Sub-N for each team observation. Three different player groups were obtained after clustering their playing time, clustering coefficient and vulnerability. Team’s clustering coefficient, standard deviation of vulnerability and out-degree centrality of starters exhibited moderate to strong correlations with team’s standing during playoffs ( r  = 0.54–0.76). The regression models showed that defensive win share (beta = 0.54–0.67), turnovers (−0.15 to −0.25) and assists (0.12–0.26) were predictive for all players’ net ratings, and the role players who scored more points presented higher net ratings (0.34). Finally, players from top-playoff teams exhibited lower absolute value of vulnerabilities ( r  = 0.80). The findings demonstrate the feasibility of Sub-N for exploring the association between rotation and competitive performance, and provide quantitative reference for coaching staff to optimize substitution structures and rosters.

Exploring the relationship between basketball rotation and competitive performance using substitution network analysis