This paper aims to identify and prioritize the top-performing U13 batters and bowlers in cricket by leveraging advanced data science techniques on comprehensive match data collected through web scraping. Principal Component Analysis (PCA) is used to achieve dimensionality reduction, simplifying the dataset while preserving important features. Next, the players are analyzed using clustering algorithms, which help form clusters of players with similar characteristics. These clusters then serve as the foundation for a classification model to determine the relative importance of different features in player rankings. Opponent strength variability is addressed by establishing an AHP framework. This framework uses clusters generated via K-means clustering as a measure of opponent quality, thereby introducing opponent strength into the rating system for a more nuanced comparison of player performances. Two models are compared: clustering-based classification and AHP-based ratings.