  Devising player-specific strategies in cricket necessitates a meticulous
understanding of each player's unique strengths and weaknesses. Nevertheless,
the absence of a definitive computational approach to extract such insights
from cricket players poses a significant challenge. This paper seeks to address
this gap by establishing computational models designed to extract the rules
governing player strengths and weaknesses, thereby facilitating the development
of tailored strategies for individual players. The complexity of this endeavor
lies in several key areas: the selection of a suitable dataset, the precise
definition of strength and weakness rules, the identification of an appropriate
learning algorithm, and the validation of the derived rules. To tackle these
challenges, we propose the utilization of unstructured data, specifically
cricket text commentary, as a valuable resource for constructing comprehensive
strength and weakness rules for cricket players. We also introduce
computationally feasible definitions for the construction of these rules, and
present a dimensionality reduction technique for the rule-building process. In
order to showcase the practicality of this approach, we conduct an in-depth
analysis of cricket player strengths and weaknesses using a vast corpus of more
than one million text commentaries. Furthermore, we validate the constructed
rules through two distinct methodologies: intrinsic and extrinsic. The outcomes
of this research are made openly accessible, including the collected data,
source code, and results for over 250 cricket players, which can be accessed at
https://bit.ly/2PKuzx8.
