For years, expected goals (xG) has been the benchmark for measuring attacking efficiency in football. It tells us how likely a shot was to become a goal based on historical data. Yet by 2026, analysts increasingly accept that a single xG value captures only the final action, not the process that made it possible. Modern performance departments in elite clubs now rely on expected threat (xT) and the so-called “chance chain” approach to understand how possession sequences create danger long before the shot occurs.
Why a Single xG Is No Longer Enough
xG remains useful: it evaluates shot quality based on factors such as distance, angle, body part and defensive pressure. However, it ignores the preceding passes, carries and positional movements that destabilised the defence. If a winger beats two opponents and squares the ball for a simple tap-in, the passer’s work is invisible in the xG model. The finisher receives statistical credit; the creator does not.
By 2026, clubs in the Premier League and Bundesliga combine event data with tracking data to address this gap. They analyse how players move defenders, manipulate space and shift defensive blocks before the decisive action. Without this context, performance assessment risks becoming reductionist and sometimes misleading.
There is also a tactical dimension. Teams that specialise in structured build-up, such as Manchester City under Pep Guardiola, generate sequences of short passes that gradually increase threat. Others rely on rapid transitions. Both styles may produce similar xG totals, yet the underlying attacking mechanisms differ significantly. Understanding those mechanisms requires metrics that track danger throughout the entire move.
The Limits of Shot-Based Evaluation
Shot-based models struggle with small sample sizes. A striker may score from two low-probability attempts and appear efficient, while another repeatedly finds high-value positions but misses narrowly. Over short periods, xG and actual goals can diverge sharply, leading to distorted narratives about form or finishing ability.
Moreover, defenders and deep-lying midfielders are almost absent from classic attacking metrics. A progressive pass breaking two lines may be the most important action of the sequence, yet it does not appear in shot-centred summaries. Recruitment departments in 2026 increasingly flag such blind spots when scouting creative midfield profiles.
Finally, xG does not reward territorial dominance unless it ends in a shot. A team can sustain pressure, pin the opponent back and repeatedly enter the penalty area without producing a clear attempt. Traditional metrics may classify the possession as sterile, even if it demonstrates structural superiority.
What Is Expected Threat (xT) and How It Works
Expected threat (xT) assigns value to every on-ball action based on how much it increases the probability of scoring during that possession. Instead of focusing only on shots, the pitch is divided into zones. Each zone carries a probability of eventually leading to a goal. When a player moves the ball from one zone to a more dangerous one, he adds positive xT.
In practice, if a full-back plays a forward pass from the halfway line into the inside channel near the box, the model calculates the change in scoring probability between those zones. The difference is credited to the passer. Over time, this reveals which players consistently move possession into threatening areas.
By 2026, enhanced versions of xT integrate tracking data, accounting for defensive positioning and speed of play. Clubs such as Liverpool and Arsenal reportedly combine proprietary xT variations with pressing metrics to evaluate how quickly threat is generated after regaining possession.
From Zones to Decision-Making Insight
xT is not merely a number; it reflects decision-making quality. A midfielder choosing a safe sideways pass may maintain possession but add minimal threat. Another who attempts a vertical ball between lines takes more risk but potentially increases xT significantly. Over a season, these patterns outline a player’s tactical profile.
The model also helps coaches refine training design. If data shows that threat accumulation stalls in specific zones, analysts can identify structural issues: poor spacing, limited third-man runs or insufficient width. Tactical adjustments then target those precise bottlenecks.
Importantly, xT rewards consistency. Players who repeatedly progress the ball into advanced half-spaces or central corridors accumulate value even without registering assists. This broader lens supports fairer performance evaluation, particularly for creative midfielders and attacking full-backs.
The “Chance Chain” Concept in Modern Analysis
The “chance chain” approach tracks every player involved in a possession sequence that ends in a shot. Rather than attributing value solely to the shooter or the final passer, the model distributes credit across the entire attacking move. Each contributor to the build-up is recognised.
This method is especially relevant in structured positional play. A centre-back initiating a line-breaking pass, a pivot receiving under pressure and a winger stretching the defensive line may all be essential links in the chain. Even if they never touch the ball inside the penalty area, their contribution shapes the opportunity.
As of 2026, several data providers combine chance chain metrics with possession value models, offering clubs layered reports that quantify both territorial progression and collective involvement. Recruitment teams use these reports to identify undervalued players in leagues where traditional statistics understate creative influence.
Collective Creation Over Individual Highlight
The chance chain perspective reinforces a fundamental truth: goals are rarely isolated acts. They are the product of coordinated movement, spacing and timing. By mapping the entire sequence, analysts can distinguish between systemic creation and individual improvisation.
For coaching staff, this is invaluable. If most high-quality chances stem from specific build-up patterns, those patterns can be reinforced. If, instead, threat depends heavily on one player’s individual brilliance, structural adjustments may be required to reduce tactical dependency.
Ultimately, combining xT with chance chain analysis provides a more complete narrative of attacking football. It shifts focus from the final shot to the evolving danger throughout possession. In the analytical landscape of 2026, this integrated approach represents not a replacement for xG, but its necessary evolution.
