In this paper a complex network model is used to analyze the game of cricket. The nodes of this network are individual players and edges are placed between players who have scored runs in partnership. Results of these complex network models based on partnership are compared with performance of teams. Our study examines Test cricket, One Day Internationals (ODIs) and T20 cricket matches of the Indian Premier League (IPL). We find that complex network properties: average degree, average strength and average clustering coefficient are directly related to the performance (win over loss ratio) of the teams, i.e., teams having higher connectivity and well-interconnected groups perform better in Test matches but not in ODIs and IPL. For our purpose, the basic difference between different forms of the game is duration of the game: Test cricket is played for 5-days, One day cricket is played only for a single day and T20 is played only for 20 overs in an inning. In this regard, we make a clear distinction in social network properties between the Test, One day, and T20 cricket networks by finding relationships between average weight with their end point's degrees. We know that performance of teams varies with time - for example West Indies, who had established themselves as the best team during 1970s now is one of the worst teams in terms of results. So we have looked at evolution of team's performances with respect to their network properties for every decade. We have observed that, the average degree and average clustering coefficient follow similar trends as the performance of the team in Test cricket but not in One day cricket and T20. So partnership actually plays a more significant role in team performance in Test cricket as compared to One day cricket and T20 cricket. © Springer International Publishing Switzerland 2013.
CITATION STYLE
Tripathy, R. M., Bagchi, A., & Jain, M. (2013). Complex network characteristics and team performance in the game of cricket. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8302 LNCS, pp. 133–150). https://doi.org/10.1007/978-3-319-03689-2_9
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