In superscalar architectures, branch prediction techniques are necessary to handle control dependences, boosting the instruction fetch and increasing the number of available useful instructions for parallel execution. Nowadays, most of branch predictors use a kind of table containing branch histories and target addresses. These histories generate different patterns that appear many times with probabilities that depend on the program execution flow. The PPM (prediction partial matching) predictor, which works with branch pattern probabilities, was analyzed and used as base for the development of a more aggressive model, denominated TDPP (Transition Dependent Probability Predictor). This new model was analyzed and evaluated on the SimpleScalar Tool Set Platform. The results obtained in the SPEC 2000 benchmarks reached average hit rates about 98% for 16-bits history sizes. The TDPP model was more efficient than PPM and appropriate for real implementation in the near future.