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Sports coaches today have an access to a wide variety of information sources that describe the performance of their players. Cricket match data is highly available and rapidly growing in size which far exceeds the human abilities to analyze. Our major intention is to model an automated framework to identify specifics and correlations among play patterns, so as to haul out knowledge which can further be represented in the form of useful information in relevance to modify or improve coaching strategies and methodologies to confine performance enrichment at team level as well as individual. With this information, a coach can assess the effectiveness of certain coaching decisions and formulate game strategy for subsequent games. Since real time cricket data is too complex , Object-relational model is used to employ more sophisticated structure to store such data. Frequent pattern evaluation is imperative for sports be fond of cricket match data which facilitates recognition of main factors accounting for variances in data. While using simple apriori for interrelationship analysis, it is less time efficient because the raw data set which is too large and complex. On integrating association mining with Principal Component Analysis, the efficiency of mining algorithm is improved provided that Principal Component Analysis generates frequent patterns through statistical analysis and summarization not by repeated searching like other frequent patterns generation techniques. As the size and dimension of annotation database is large, Principal Component Analysis proceeds as a compression mechanism. Then the frequent patterns are analyzed for their interrelationship in order to generate interesting and confident rules of association.