Draft:Composite Rating Method (CRM)

Composite Rating Method (CRM) is an evaluation metric developed in 2024 to assess individual and team performances in basketball. Drawing inspiration from established metrics such as Player Efficiency Rating (PER), ⁣Offensive and Defensive ratings (sometimes referred to as NET rating), and notably ELO, CRM aims to provide a comprehensive measure of player effectiveness within a team context.

Unlike ELO, which is commonly utilized in e-sports and similar events where players frequently switch teams, CRM is tailored specifically for professional basketball leagues where team rosters tend to remain static due to contractual obligations. The primary objective behind CRM's development was to devise a method capable of accurately estimating individual players' contributions while considering their interactions within the team dynamics.

CRM integrates the strengths of PER and NET ratings while adopting a scoring mechanism akin to ELO. By leveraging these methodologies, CRM facilitates the assessment of a team's potential strength even in scenarios where one or more players are sidelined due to injury or other factors.

Furthermore, similar to ELO's predictive capabilities, CRM proposes to forecast game outcomes through the utilization of a Sigmoid function, providing insights into the likely victor based on the calculated ratings.

Initially focused on the evaluation of player performances within the context of European basketball, specifically in leagues such as the Euroleague, Eurocup, and Basketball Champions League, CRM offers a versatile framework adaptable to various basketball competitions worldwide. With its emphasis on both individual prowess and team cohesion, CRM represents a significant advancement in the realm of basketball analytics.

Rating might have potential limitations, e.g., might not work well for NBA due to different rules and highly different basketball style. Moreover, as a new rating system it can still be improved by adding national leagues statistics, leading to more reliable statistical evaluation.