The most important striker metric isn’t the one clubs are buying.
By David Findlay, Founder of KiqIQ.
Striker Metrics Questioned as 3D Data Redefines Value
Brentford owner Matthew Benham told delegates at the MIT Sloan Sports Analytics Conference 2026 that a player’s ability to get into scoring positions is “way, way more informative than finishing.” The remark reflects a growing shift inside elite football analytics, where traditional striker metrics such as goals per 90 minutes are increasingly viewed as incomplete indicators of performance.
Despite that, transfer market behaviour has yet to fully adjust. Clubs continue to commit significant fees to forwards based largely on output statistics, even as new tracking systems suggest those figures may be secondary signals rather than primary indicators of value.
What the Data Shows
The analytical gap identified by Benham is now measurable at scale. Since the start of the 2025/26 season, the Deutsche Fußball Liga (DFL) has implemented full 3D tracking across Bundesliga matches, generating approximately 3.5 million data points per game via Sportec Solutions.
This data captures off-ball movement, spatial occupation and pressing triggers as continuous, timestamped vectors rather than isolated events. For recruitment and performance teams, it enables a shift from evaluating outcomes to analysing the conditions that produce them.
Former Liverpool director of research Ian Graham highlighted a related inefficiency at Sloan. In level game states late in matches, the expected value of pursuing a win — worth three points — exceeds the return of protecting a draw. However, teams frequently adopt conservative tactics, underscoring a persistent gap between analytical models and decision-making.
For clubs, that gap extends to recruitment. A forward who consistently generates high-quality opportunities may appear less effective on traditional dashboards than one outperforming expected goals over a short sample. The underlying movement data, now available through 3D tracking, challenges those surface-level comparisons.
What 3D Tracking Changes
Unlike 2D positional data, which records player location on the pitch, 3D tracking incorporates vertical movement. This includes jump timing, aerial duel structure and pressing angles on balls played over defensive lines.
For strikers, the distinction is significant. Instead of measuring whether a player scored, analysts can evaluate whether they consistently created scoring conditions — through positioning, timing and movement.
The DFL’s automated event detection system integrates tracking data with broadcast footage at frame level, enabling large-scale analysis without manual tagging. This increases both the speed and volume of insights available to clubs.
At SportsInnovation 2026 in Düsseldorf, DFL CEO Dr. Steffen Merkel identified training and performance analysis, coaching support and personalised content as key AI application areas. While all three are advancing simultaneously, clubs face the challenge of ensuring performance analytics investment is not diluted by commercial or broadcast-driven priorities.
A Market Adjustment Still Pending
The implication is a potential valuation blind spot. Transfer decisions remain heavily anchored to observable outputs such as goals, shots and expected goals (xG), even as tracking data highlights the importance of underlying movement patterns.
3D tracking does not resolve this automatically. As DFL Vice President of Product and Innovation Dominik Scholler noted, “We still need the human in the loop.” Converting large-scale tracking data into actionable recruitment models remains a specialist capability.
Clubs that develop those models ahead of wider adoption may gain a temporary competitive advantage as 3D tracking infrastructure expands across top leagues.
Frequently Asked Questions
What is off-ball positioning in football analytics?
Off-ball positioning refers to a player’s movement and spatial decisions without possession. Advanced tracking systems measure these as continuous vectors — including position, speed and direction — rather than discrete events like passes or shots.
How does 3D tracking differ from GPS or 2D tracking?
GPS provides general location data but lacks precision in congested match environments. 2D optical tracking captures horizontal (x/y) coordinates. 3D tracking adds the vertical axis, enabling analysis of aerial duels, jump timing and pressing angles.
Why are goals-per-90 metrics considered incomplete?
Goals-per-90 captures outcomes but not the processes that lead to them. It can be influenced by short-term variance in finishing, whereas positioning and chance creation are more stable indicators of performance.
What is Sportec Solutions?
Sportec Solutions is the DFL’s data subsidiary, responsible for collecting and processing Bundesliga match data, including the 3D tracking and automated event detection systems.
What is off-ball positioning in football analytics?
Off-ball positioning refers to a player’s movement, spacing, and spatial decisions when they don’t have the ball. Advanced tracking systems measure these as vectors — location, direction, and speed — rather than as discrete events like passes or shots.
How does 3D tracking differ from GPS or 2D optical tracking?
GPS gives location data but lacks the precision for crowded pitch scenarios. 2D optical tracking captures x/y coordinates. 3D tracking adds the vertical axis, enabling analysis of aerial duels, jump timing, and true pressing geometry — critical for forwards and defenders operating in aerial contest zones.
Why does the win/draw points ratio matter for in-game tactics?
In most football competitions, a win yields 3 points versus 1 for a draw. When tied late in a game, the expected-value return of playing aggressively — accepting more defensive risk to pursue a winner — is mathematically superior to defending a draw. Analytics consistently shows teams underweight this incentive.
What is the DFL’s Sportec Solutions?
Sportec Solutions is the DFL’s data subsidiary, responsible for collecting, processing, and distributing Bundesliga match data. It supplies the Match Facts analytical feed and operates the automated event detection system now running on 3D tracking infrastructure.