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.