  In this work, we extended a stochastic model for football leagues based on
the team's potential [R. da Silva et al. Comput. Phys. Commun. \textbf{184}
661--670 (2013)] for making predictions instead of only performing a successful
characterization of the statistics on the punctuation of the real leagues. Our
adaptation considers the advantage of playing at home when considering the
potential of the home and away teams. The algorithm predicts the tournament's
outcome by using the market value or/and the ongoing team's performance as
initial conditions in the context of Monte Carlo simulations. We present and
compare our results to the worldwide known SPI predictions performed by the
"FiveThirtyEight" project. The results show that the algorithm can deliver good
predictions even with a few ingredients and in more complicated seasons like
the 2020 editions where the matches were played without fans in the stadiums.
