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 Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Data analytics in sports is crucial to evaluate the performance of singleplayers and the whole team. The literature proposes a number of tools for bothoffence and defence scenarios. Data coming from tracking location of players,in this respect, may be used to enrich the amount of useful information. Inbasketball, however, actions are interleaved with inactive periods. This paperdescribes a methodological approach to automatically identify active periodsduring a game and to classify them as offensive or defensive. The method isbased on the application of thresholds to players kinematic parameters, whosevalues undergo a tuning strategy similar to Receiver Operating Characteristiccurves, using a ground truth extracted from the video of the games.

Detecting and classifying moments in basketball matches using sensor tracked data