The contribution of a bowler plays a key role in the outcome of a match, and hence, it is a very critical issue for consideration in team formation. However, state-of-the-art works have primarily focused on individual performances rather than a comparative analysis of bowlers throughout their careers. A new framework for combining Association Rule Mining (ARM) and a modified version of the Capital Asset Pricing Model (CAPM) technique is presented in this paper. The proposed methodology is applied to individual Indian bowlers’ career statistics to extract several effective statistics impacting the players’ performances. Expected measures of bowling performance have been developed by analyzing the risk of including the bowler in the squad. In that scenario, we have worked with around 6000 rules in the One-Day International (ODI) format, 6800 rules in the T-20 format, and 2500 rules in the Test format generated for bowlers by the Apriori Algorithm. The relations among various intrinsic factors have been analyzed to prepare a bowler pool and to plan strategies accordingly. Finally, we compared our bowler ranking results to recent state-of-the-art works and rankings published by the International Cricket Council (ICC) and obtained satisfactory results.