Summary: Complex network analysis is an area of network science and part of graph theory that can be used to rank things, one of the most famous examples of which is the Google PageRank algorithm. But it can also be applied to sport. Cricket is a sport in which it is difficult to rank teams (there are three forms of the game, the various countries do not play each other very often etc.), whilst it is notoriously difficult to rank individual players (for how the ICC do it, see Ep 107: Ranking Cricketers). Satyam Mukherjee at Northwestern University became a bit famous when The economist picked up his work (more famous than when we picked it up!) and he has published extensively on complex network analysis as applied to cricket rankings. I had a very interesting chat with Satyam about his various works concerning the evaluation of cricket strategy, leadership, team and individual performance, and the papers we discuss in the podcast are listed below. One of the more interesting findings was that left-handed captains and batsmen are generally ranked higher than their right-handed counterparts, whilst this is not true for left-handed bowlers. Tune in to this episode here: Songs in the podcast: Loveshadow / CC BY-NC 3.0 Speck / CC BY-NC 3.0 Zep Hurme / CC BY 2.5 Stefan Kartenberg / CC BY-NC 3.0 References: Satyam Mukherjee (2013). Ashes 2013 - A network theory analysis of Cricket strategies arXiv arXiv: 1308.5470v1 Satyam Mukherjee (2013). Left handedness and Leadership in Interactive Contests arXiv arXiv: 1303.6686v1 Satyam Mukherjee (2012). Quantifying individual performance in Cricket - A network analysis of Batsmen and Bowlers arXiv arXiv: 1208.5184v2 Satyam Mukherjee (2012). Complex Network Analysis in Cricket : Community structure, player's role and performance index arXiv arXiv: 1206.4835v4 Satyam Mukherjee (2012). Identifying the greatest team and captain - A complex network approach to cricket matches arXiv arXiv: 1201.1318v2
