| While scoring strategies and player performance in cricket have
been studied, there has been little published work about the influence
of batting order with respect to One-Day cricket. We apply a mathematical
modelling approach to compute efficiently the expected performance
(runs distribution) of a cricket batting order in an innings. Among
other applications, our method enables one to solve for the probability
of one team beating another or to find the optimal batting order for
a set of 11 players. The influence of defence and bowling ability
can be taken into account in a straightforward manner. In this presentation,
we outline how we develop our Markov Chain approach to studying the
progress of runs for a batting order of non- identical players along
the lines of work in baseball modelling by Bukiet et al., 1997.
We describe the issues that arise in applying such methods to cricket,
discuss ideas for addressing these difficulties and note limitations
on modelling batting order for One-Day cricket. By performing our
analysis on a selected subset of the possible batting orders, we apply
the model to quantify the influence of batting order in a game of
One Day cricket using available real-world data for current players.
KEY
WORDS: One-day cricket, batting orders, mathematical modelling.
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