This paper presents a simple algorithm for diagnosis of a graph-theoretical, self-diagnosis model. The algorithm is based on a ranking method. That is, the algorithm uses the analogy between the rule used in the ranking method: the player who was hit more players directly or indirectly should be ranked lower and the rule used in diagnosis : the unit which is identified as faulty by more other units directly or indirectly should be more strongly suspected as faulty. With this algorithm, faulty units are identified from a given syndrome by simple calculation of a matrix. Although another algorithm is proposed whose complexity is less than the algorithm proposed here, this algorithm has the following two features : (1) Simplicity : The algorithm uses only the matrix multiplication and further, the matrix is directly obtainable from syndromes. Thus the algorithm can be implemented easily as a computer program. (2) Universality : This algorithm can be used not only for self-diagnosis model of PMC type, but for other types of self-diagnosis models including probabilistic model with slight modifications. These features of the algorithm are investigated for the systems with design D1t (t1, 2). The modification of the algorithm for the probabilistic self-diagnosis model is also discussed. The approach seems to open a channel between theoretical works of ranking theory in the field of graph theory and syndrome decoding of models in system diagnosis.