Tuesday, 11 March 2014

Ep 153: Complex Network Analysis in Cricket



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:
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

2 comments:

  1. If you're reading Satyam, a question or two:

    1) When you are ranking individual bowlers, the link between the bowler and the batsman is a function of the number of times the bowler has dismissed the batsmen, and their averages. It would seem to favour bowlers who played a lot of games (Murali, Warne, Walsh etc) as they are quite likely to dismiss batsmen more often simply through having played a lot. Are the links normalised to take this into account?

    2) Could you do individual bowlers in a similar way to the way you do batsmen? You'd only have data from 2001, but what do you think you'd get if instead of using the number of times the batsmen was dismissed by the bowler, you used the bowler's average against that particular batsman (or the inverse I guess as you want a small average but strong link)? At the moment, if Murali had dismissed Ponting 15 times in his career, you'd have the same result whether or not his average against Ponting was 11 or 61. I imagine someone like Steyn would rise to the top because of his strike rate.

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  2. 1) No the links are not normalized. First of all playing a lot of matches indicates the bowler is good, else he wouldn't be playing in the team for long. Secondly normalization by number of matches played has its own problem, since it would give advantage to one time wonders. Probably normalizing with number of times bowler faced the batsman would is possibility - although my guess is it won't affect the ranking too much

    2) You could do that. But evaluating bowlers since 1877 is more fascinating in my opinion :) Data from 2001 has restricted the analyses for batsmen - whether Bradman was the greatest is still an open debate.

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