A FIN-learning machine M receives successive values of the function f it is learning; at some point M outputs CONJECTURE which should be a correct index of f. When n machines simultaneously learn the same function f and at least k of these machines output correct indices of f, we have SYMMETRIC TEAM LEARNING denoted [k,n]FIN. [Daley, 1992] shows that sometimes a team or a probabilistic learner can simulate another one, if their probabilities (or team success ratios k/n) are close enough. Accordingly to [Daley, 1992] the critical ratio closest to 1/2 from the left is 24/49; paper [Daley, 1996] provides other unusual constants. These results are complicated and provide a full picture of comparisons only for FIN-learners with success ratio above 12/25. We generalize [k,n]FIN teams to ASYMMETRIC TEAMS [Smith, Apsitis COLT'1997]. We introduce a two player game on two 0-1 matrices defining two asymmetric teams. The result of the game reflects the comparative power of these asymmetric teams. Hereby we show that the problem for any a,b,c,d to determine whether [a,b]FIN is a subset of [c,d]FIN is algorithmically solvable. We also show that the set of all critical ratios is well-ordered. Simulating asymmetric teams with probabilistic machines provides an insight about the origin of the unusual constants like 24/49.