  Indian Premier League (IPL) is a tournament of twenty over cricket matches.
Teams of this tournament are selected via an auction from a pool of players.
Each team employs a think-tank to build the best possible team. Few studies
have been performed to automate the process of team selection. However, those
studies mostly concentrate either on the current form of the players, or their
long term performance. In this paper, we have (i) selected traditional features
as well as determined some derived features, which are generated from the
traditional features, for batsmen and bowlers, (ii) formulated heuristics for
clustering batsmen into openers, middle-order batsmen and finishers, (iii)
formulated heuristics for relative ranking of batsmen and bowlers considering
the current performance as well as the experience of each player, and (iv) have
proposed two greedy algorithms for team selection where the total credit point
of the team and the number of players in each cluster is fixed. Our proposed
ranking scheme and algorithm not only determines the best possible team but can
also determine the best alternate player if one of the target players is
unavailable.
