The data divide: Why aren't national teams using it as much?
Last summer’s European Championships demonstrated something unsophisticated about modern international soccer. Europe’s most prolific soccer nations faced off every few days with an abundance of world-class talent on display. Yet the end product often felt somewhat underwhelming. Games involving many of the tournament’s favorites felt cluttered, disorganized and rigid compared to the highest quality of matches played in the Champions League.
Match winners frequently relied on an oppositional lapse in concentration, error or fatigue, rather than their quality to proceed through the tournament. England, for example, colorlessly trudged their way to the final despite arguably failing to produce a performance resembling their star-studded, experienced squad until the semi-final. Similar critiques exist for France, Portugal and Germany, each of whom limped out in the quarter or semi-finals without providing much inspiration.
Teams were often selected arbitrarily rather than with a consistent, understood system in mind. England tested Trent Alexander-Arnold, Bukayo Saka and Phil Foden in unfamiliar roles. They desperately attempted to squeeze all their stars into a starting line-up. In doing so, England failed to select a fit left-back to start the tournament.
Premier League clubs such as Brighton, Brentford and Liverpool have demonstrated the effectiveness of data-driven transfer approaches. They have built balanced squads that outperform their available resources. At an international level, it was astonishing to witness so many nations select a squad without a clear intention.
It isn’t necessary to field 11 world-class players
Club teams that win leagues, progress deeply into continental tournaments or vastly outperform their expectations rarely have a world-class player in each position. After all, Bayern Munich reached last season’s Champions League semi-final with Eric Dier as their starting center-back and Borussia Dortmund made it to the final with few household names.
Essentially, a club or national team’s success in major tournaments is not dependent on having eleven of the world’s best players on the pitch at any given time. Instead, systems, player compatibility and familiarity have a growing prevalence in determining the beautiful game’s winners and losers.
Major international nations have the luxury of possessing several world-class players to formulate their team’s core. But these world-class players shouldn’t litter the team at the expense of a fixed identity, cohesion and fluidity.
The most successful nations establish a core of four to six players. That includes Spain’s 2008, 2010 and 2012 European Championship and World Cup-winning team. These types of teams then intelligently select the rest of the team to emphasize their strongest attributes.
Data could drive smarter decision-making for national teams
In a data-centric era of soccer, the lack of sophisticated analysis relating to international squad selection is even more dumbfounding. The resources are available for coaching teams to build squads on data metrics that access player compatibility. The aforementioned Premier League overachievers are evidence of that. The question remains: why do international teams still appear so randomized?
One of the few nations known to incorporate data as one of their primary squad selection tools is Morocco. The North African nation narrowly missed out on upsetting FIFA’s elite. It topped Portugal on its way to progressing to a first World Cup semi-final in 2022.
Last summer’s tournament concluded with a few teams gaining a clearer picture of what their strongest team looks like or how they play together most effectively. Perhaps the world’s strongest nations will have to take inspiration from Morocco’s data-driven approach. That may be the only way to realize their potential at the 2026 World Cup.
PHOTOS: IMAGO