Sports analytics is evolving nowadays in an amazing way, thanks to automated or semi-automated sensing technologies that provide high-fidelity data streams extracted from every game. In my talk, I present two approaches to show that the analysis of these wealth of data can boost the understanding of the patterns of success in team sports. In the first approach, from observational data of soccer games I extract a set of pass-based performance indicators and summarize them in the H indicator. We observe a strong correlation among the H indicator and the success of a team, and therefore perform a simulation on the four major European championships (78 teams, almost 1500 games). We found that the final rankings in the simulated championships are very close to the actual rankings in the real championships, and show that teams with high ranking error show extreme values of a defense/attack efficiency measure, the Pezzali score. In the second approach, we propose the structure of a mathematical model to simulate soccer games. Based on a wide set of features capturing the playing style of soccer teams, the model generates synthetic events occurring on the pitch and a distribution of possible results of a game, taking into account the playing styles of the two facing teams.