In recent years, the “counter-press”, “geggenpress” and “counter-attack” employed by Pep Guardiola’s, Jurgen Klopp’s and Jose Mourinho’s teams respectively, have been in vogue due to their ability to create good scoring chances by effectively overloading the team that has just lost the ball. These fast-paced, aggressive, and direct transitions provide some of the best opportunities for scoring, and are a potent strategy when executed effectively. Despite the importance around transitions in soccer, in terms of analytics, no quantitative measures have emerged. There are two prime reasons for this: i) obtaining the precise onset and offset time-stamp of a counter-attack is extremely challenging as the task is subjective and fine-grained, and ii) measuring the structural patterns and movements of a team is equally subjective and challenging for a human to annotate. Here we leverage supervised and unsupervised machine learning techniques to automatically and objectively detect these transition situations. First, we learn the formation and playbook of a team via hierarchical clustering. Next, from event sequences and team information we construct a measure of “offensive threat” to further classify threatening and non-threatening plays. Finally, our game-state specific templates enable us to quantify the “defensive disorder” of a team as they transition from offense to defense. From this analysis we are able to detect counter-attacks directly from the player-tracking data, without any human labels, and then use this to quantify the value and impact of execution on the counter-attack, both offensively and defensively, and identify teams with similar transition styles.