YAKSHITA PRATHA MODEL (YPM ): AN ALTERNATIVE TO DUCKWORTH LEWIS STERN (DLS) METHOD

Abstract

The Yakshita Pratha model (YPM) is an innovative machine learning model designed to set accurate, revised target scores for limited over cricket matches that are interrupted by inclement weather, floodlight failure or some crowd issues. While the former Duckworth Lewis- Stern (DLS) method operates on static mathematical framework centered primar ily on resource preservation (wickets left and overs remaining ), the YPM captures modern cricketing dynamics . This model is grounded in a series of robusts data-driven assump tions regarding a team’s evolving behavioural patterns, scoring potentials and a highly non-linear impact of losing wickets as the match progresses. The core philosophy of this research is the transition away from the static models to wards a highly adaptive , observe scoring paradigm. In contrast to the traditional method that rely heavily on generalized baseline decay curves, the YP model is based on mod ern shifts in playing style . It accounts for contemporary strategic changes, including deep batting lineup, aggressive data driven strategies and multi phased power hitting . This model treats match scoring as interconnected, path dependent process, the proposed model conditions, predictions based on how a team has used their resources in previous matches and real time execution context , rather than theoretical remaining capacity.