Identifying the greatest team and captain - A complex network approach to cricket matches
Satyam Mukherjee

TL;DR
This paper introduces a network-based ranking method using PageRank to evaluate cricket teams and captains based on match quality, providing a new, data-driven approach to assess success in cricket.
Contribution
It presents a novel application of diffusion-based PageRank on cricket match networks to rank teams and captains independently of external criteria.
Findings
Australia is identified as the top team in both Test and ODI cricket.
Steve Waugh is ranked as the best Test captain.
Ricky Ponting is ranked as the best ODI captain.
Abstract
We consider all Test matches played between 1877 and 2010 and One Day International (ODI) matches played between 1971 and 2010. We form directed and weighted networks of teams and also of their captains. The success of a team (or captain) is determined by the 'quality' of wins and not on the number of wins alone. We apply the diffusion based PageRank algorithm on the networks to access the importance of wins and rank the teams and captains respectively. Our analysis identifies {\it Australia} as the best team in both forms of cricket Test and ODI. {\it Steve Waugh} is identified as the best captain in Test cricket and {\it Ricky Ponting} is the best captain in the ODI format. We also compare our ranking scheme with the existing ranking schemes which include the Reliance ICC Ranking. Our method does not depend on `external' criteria in ranking of teams (captains). The purpose of this…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
