During a cricket match, the commentary is the important thing which keeps the viewers entertained and updated about the live game. The commentary can be made interesting by narrating relevant stories related to live scenario of the sports game. But the knowledge of commentators, however wide for humans, is relatively less compared to that of the story details available about individual players. Here a system is presented called iSCoReS for individuals that can automatically suggest stories for commentators to tell during the live game. The iSCoReS for individuals is equipped with some scored examples of story-game state pairs which learns offline to connect sports stories of individual players to game states. Features describing the viewer would be input into the system, and stories could be selected partly based on these features. The problem addressed is to find and match the interesting stories as live commentary to a sports game for the individuals. Copyright © 2018 Inderscience Enterprises Ltd.