Perhaps, or maybe the right data just isn't tracked / accessible yet.
Basketball is an instructive example --- as recently as 10-15 years ago, it was thought that this game couldn't be quantified/predicted nearly as well as baseball, that it had a lot of the same fluid properties of soccer and (US) football. Fast forward and a lot of work has been done to push basketball much closer to the baseball-side of the spectrum. Whose to say whether or not taking detailed data of every movement of every player in a soccer match might yield similar breakthroughs.
From what I have read (previous 538 blog post on Messi), they are already tracking a good deal of data about the games. I think one issue with soccer is that there is a lack of discrete, measurable outcomes in the game. I read a while back that one of the breakthroughs in basketball analysis came when they started tracking the total point differential during each player's time on court. Because so many points are scored in a game, and because so many games are played in a season, this stat was a fairly reliable and accurate picture of how a player would impact the team's performance (which allowed teams to measure the impact of players who may not rank high in the more traditional stats).
In soccer, you don't have a lot of data points to model against. The number of goals scored is typically low. Because of this, there is probably a higher level of uncertainty and variance in the outcomes of soccer games (and the prediction models as well).
You do have lots of data points. Each pass is a data point, each shot is a data point. Opta logs mores than 2000 events per game, each with an outcome and pitch coordinates. Yes, soccer is more complex than even basketball, but there's a lot more money involved and people watching. This stuff is being worked out right now, and it's an exciting field.
Often shots on goal or number of corners are used as a proxy variable because those events occur much more often than scored goals. But you're right that football is incredibly hard to model. For example, what would happen to the Argentinian team if Messi gets injured? Any pundit can tell you that it would probably be "really bad", but quantifying exactly how bad is currently impossible.
Basketball is an instructive example --- as recently as 10-15 years ago, it was thought that this game couldn't be quantified/predicted nearly as well as baseball, that it had a lot of the same fluid properties of soccer and (US) football. Fast forward and a lot of work has been done to push basketball much closer to the baseball-side of the spectrum. Whose to say whether or not taking detailed data of every movement of every player in a soccer match might yield similar breakthroughs.