This working paper presents a new scoring algorithm for the SF-6D, one of the most popular preference-based health status measures. Previous algorithms suffer from a phenomenon called the floor effect (i.e., lack of sensitivity of the instrument for detecting health gains of individuals whose baseline health is poor). Our algorithm expands the range of utility scores in such a way that the floor effect vanishes. We get this wider range thanks to the use of a lottery equivalent method through which preferences are elicited from a representative sample of the Spanish general population.