They have better (compared to their average per game) pass success rate and dribble success rate in the final period. Only Montreal Impact have a better pass success rate during this period. No team comes close to the Red Bulls when we consider all three indexes together.

Therefore, New York Red Bulls are the fittest and most enduring team in MLS. They tailor their tactics to take full advantage of that strength; they make the game so fast and random that they can exhaust their opponent’s energy and destroy it as the game enters the final stage.

The power and the limitation of the analysis

The Red Bulls have a superficially strange relationship between their defensive pressure and their offensive performance. They are the best pressing team and hold the highest defensive line in MLS, but the higher and stronger they pressure the opponent, the worse chances (xG) they create. Most analysts argue that the pressing tactic prevents the opponent from entering the offensive phase. It traps the opponent in the transition phase, where it is most vulnerable for the counter-attack. Our correlation analysis does not support that the Red Bulls are maximizing these transitional opportunities to create chances. Instead, they use pressing to generate more back-and-forth possessions and exhaust the opponent’s stamina. The Red Bulls use their better endurance to bully the opponent. Their performance indexes in the last segment of the game support this tactic.

Deciphering the effect of such tactic would be difficult with traditional analysis; most tactical analyses focus on using the video highlight and the game flow to demonstrate what any team is trying to do in a match. Dissecting the tactical setup requires the demonstration of the positions of the players and the structure they produced. But the Red Bulls’ tactic doesn’t need a structure. In fact, they want to disrupt most structures to make the game as chaotic as possible. Any strategy/tactic merely is a method to gain an advantage, and we shouldn’t be surprised that some tactics require a lack of structure. A tactic like this will not be easily detected from the traditional tactical analysis.

Except for the Red Bulls, we also predict that for most teams in MLS, holding a higher defensive line or increasing the defensive pressure helps them to create better chances. But how does that interaction happen? This question raises many important issues for soccer analytics. First, how exactly does the position of the defensive line impact the creation of chances? We can merely argue that retrieving the possession closer to the opponent brings a team closer to the opponent’s goal. But how high a defensive line should a team hold to optimize chance creation? The difference between the team with the highest (New York) and the lowest (San Jose) defensive line is about 11 yards (10% of the pitch). That is the largest difference between any two teams in this measure. How can such a small difference impact the creation of chances? If we look at the number of tackles, the largest difference between the team with the most and least fewer than ten tackles with a normalized amount of possession. Again, a small difference has a mysteriously significant impact. And this butterfly effect applies to the other measures such as the long pass ratio or the short pass success.

Perhaps we are looking the game the wrong way if we consider all the actions or events together; the average number of passes is about 470 while the average number of tackles is about 19. That small number of tackles can’t have a massive influence on the performance of passes. But once we group all the passes into different possessions (or pass sequences), we reduce that number of groups into about 150 possessions per game, meaning we are increasing the influence of each tackle (which usually only happen once in a possession) more than three-fold. The importance of all those actions with small numbers now makes become obvious.

But we still have 150 possessions per team per game, and we don’t know how to measure the importance of most possessions; for example, the correlation analyses here pre-selects all the outcomes with a positive xG. Each team has an average of 19 shots per game, and therefore, a maximum of 19 possessions end with shots (some posessions contain multiple shots). Analyzing the performance of a team with xG means we are discarding the other 130 possessions. We are not looking at what a team is trying to do the majority of the time. We only measure its performance when it almost achieves its goal (to score). We don’t have a complete description of the game.

What about creating an xG value for all possessions based on where the location ends, but not whether it results in a shot or not? Doing that gives a better measurement of a team’s performance. Or even assigning an xG value for every action event. For instance, a tackle in the opponent’s box to regain the possession will have a stronger effect on chance creation than a tackle outside of the box to prevent an opponent’s shot. Separating events spatially will help, but assigning a value will give a quantitative measure of how they impact the game.