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

Player classification in the game of cricket is very important, as it helps the coach and the captain of the team to identify each player’s role in the team and assign responsibilities accordingly. The objective of this study is to classify allrounders into one of the four categories in one day international (ODI) Cricket format and to accurately predict new all-rounders’. This study was conducted using a collection of 177 players and ten player-related performance indicators. The prediction was conducted using three machine learning classifiers, namely Naive Bayes (NB), k-nearest neighbours (kNN), and Random Forest (RF). According to the experimental outcomes, RF indicates significantly better prediction accuracy of 99.4%, than its counter parts.

Classification of All-Rounders in the Game of ODI Cricket: Machine Learning Approach