An OWA-Based Model for Talent Enhancement in Cricket

Abstract

Cricket being an extremely competitive game; players are always under pressure to perform and improve continuously. Talent enhancement in cricket is conventionally achieved through practice under coach's supervision and analysis using costly assistive technology. Although this approach is workable, but it is constrained due to nonavailability of quality coaches, equipment, and in many cases the limited domain knowledge of the coaching staff. This constraint is significant for a country like India where 55,000 matches and 1,210,000 players play cricket daily. The literature review did not reveal any algorithm/model/framework focused on the issue of talent enhancement. In this paper, we present an alternate/supporting approach based on comparison of the players' overall talent class viz-a-viz the corresponding performances of the player against the 28 aspects of talent from cricketing perspective. The Ordered Weighted Averaging Aggregation OWA operator was used to aggregate the opinion of experts for assessment of talent classes in cricket. The normative data obtained for a cricket enthusiast was then compared using normalized adequacy coefficient with aggregated opinions of experts. This resulted in identification of the talent class of the cricketer. Subsequently, the weaknesses were identified by comparing the outcomes of the identified parametric tests for this cricket enthusiast with the value corresponding to his/her talent class. The algorithm was validated using two-sided t test. A case showing implementation of algorithm is also explained.