World Cup 2026 Offside Technology: AI, Computer Vision, and the Connected Ball

Short clips showing the connected-ball context around the adidas Trionda and offside decision support.

At 500Hz, the system receives one sample every 2 milliseconds. A kick, header, deflection, or touch creates a characteristic change in acceleration and rotation. That signal helps identify the candidate kick point.

The ball does not decide offside. Its main job is timestamping.

Once the system estimates the touch time, the optical tracking layer can ask: where were all relevant body parts at that exact instant?

In engineering terms, this is sensor synchronization:

ball sensor event time -> synchronized tracking frame -> offside geometry

That timestamp is the bridge between ball physics and player geometry.

What the Ball Sensor Actually Measures

It is tempting to say the ball sends “motion data,” but that phrase hides the interesting engineering.

A connected match ball typically uses an inertial measurement unit, or IMU. An IMU is a small electronics package that measures motion without needing an external camera. In many systems, it contains at least:

Accelerometer: measures linear acceleration along three axes, usually written as ax, ay, and az. In a football, this can reveal sudden impacts because a kick creates a large acceleration spike.

Gyroscope: measures angular velocity around three axes, usually written as wx, wy, and wz. This tells the system how fast the ball is spinning and around which axes.

Clock and radio interface: timestamp the measurements and transmit them to the review system. Some reported connected-ball systems also use ultra-wideband, or UWB, a radio technology that can support high-precision ranging and localization by measuring signal timing.

The exact production signal-processing pipeline is proprietary, but a useful mental model is:

accelerometer spike + gyroscope change + synchronized timestamp = candidate ball touch

An accelerometer alone can detect a large impact, but it may not fully describe the event. The gyroscope adds rotational information. For example, a glancing touch may produce a different acceleration and spin pattern from a clean kick through the center of the ball.

There is also a useful derived quantity called jerk, which is the rate of change of acceleration. If acceleration changes very sharply, jerk is high. Touch detection systems often look for abrupt changes in the inertial signal rather than only the raw acceleration value.

A simplified IMU sample might look like this:

time_ms, ax, ay, az, wx, wy, wz 124500.000, 0.3, -0.1, 9.7, 0.02, 0.01, 0.05 124502.000, 38.5, 14.2, -6.8, 8.10, 2.40, 5.70

The units depend on the sensor system, but conceptually the accelerometer reports translational acceleration and the gyroscope reports spin rate. The important part for offside is not the exact force of the kick. It is the timestamp of the first relevant contact, because the player positions must be evaluated at that instant.

Layer 3: Player Digital Twins

The 2026 system also adds more realistic player-specific 3D models.

Earlier offside animations commonly used generic human figures. They were useful for explaining a decision, but they did not represent every player’s exact proportions. In close calls, that distinction can matter. Two players can have similar heights but different leg length, shoulder width, boot size, and running posture.

Reports around the 2026 system describe each World Cup player being scanned to create a physically accurate 3D model. These models are used to personalize the visualization and help the tracking system represent the player’s body more realistically.

A player-specific digital twin gives the tracking system a more realistic body model than a generic skeleton. Front and side views help represent proportions, limb lengths, torso shape, and extremities that matter in close offside decisions.

From a vision perspective, this is an important idea: skeletal keypoints are sparse. A digital twin helps turn sparse keypoints into a more complete estimate of the surface and extremities of a specific body.

For example:

A hip, knee, ankle, and foot keypoint can describe leg pose.

A player-specific model helps estimate where the boot actually extends.

Shoulder and torso dimensions can improve the boundary between playable body and arm.

The same skeleton can be rendered in a way fans can identify on broadcast replays.

This does not make the 3D animation raw evidence by itself. The animation is still a reconstruction from sensor estimates. But player-specific geometry can make both measurement and explanation better.

Layer 4: VAR and Human Judgment

The word “automated” can be misleading. FIFA describes SAOT as a support tool for video match officials and on-field officials. Its purpose is to make offside decisions faster, more reproducible, and more accurate.

The important 2026 upgrade is Advanced SAOT. FIFA’s own explainer says the advanced system additionally sends an automated audio alert to on-field match officials for clear offside situations.

That is different from saying the software has final authority.

In straightforward cases, the system can detect that an attacker who receives the ball was clearly beyond the offside line when the teammate played it. In those cases, the assistant referee can receive information sooner.

But many situations still require human review:

Did an offside-positioned player interfere with the goalkeeper’s line of vision?

Did the attacker challenge a defender for the ball?

Was a defender’s touch a deliberate play or only a deflection?

Was there a rebound from the goalkeeper, post, crossbar, or match official?

Did a player in an offside position actually become involved in active play?

Those questions are legal and contextual, not just geometric.

How an Offside Check Works

The exact production software is proprietary, but the functional sequence is clear enough from FIFA’s public explainers and technical reporting.

1. A teammate plays the ball

The ball’s IMU produces high-frequency accelerometer and gyroscope readings. A sharp acceleration spike, a sudden change in angular velocity, or a high-jerk event marks a likely touch, kick, header, or deflection.

1. The system retrieves the synchronized player state

The tracking platform aligns the IMU event timestamp with the camera-derived player positions. Because the cameras and ball sensor are synchronized to a common clock, the system can estimate the 3D pose of every player at the relevant time. If the exact touch falls between two camera frames, the system can interpolate the player positions between adjacent tracking samples.

1. The offside line is computed

The line is normally defined by the second-last opponent’s nearest legal body part to the goal line. If the ball is nearer to the goal line than the second-last opponent, the ball defines the line instead.

1. Attacker body parts are compared

The system compares each attacker’s legal body parts against the line. Hands and arms are excluded under Law 11, so the measurement must distinguish between playable body regions such as the head, torso, legs, and feet, and non-playable regions such as the arms.

1. The system waits for involvement

Being in an offside position is not automatically an offence. In the clearest positional cases, the relevant trigger is the attacker receiving or touching the ball.

1. Officials are alerted or VAR reviews the incident

Clear cases can produce an audio alert for match officials. More complex cases remain with the VAR and referee.

1. A 3D visualization is generated

Once the decision is confirmed, the same calibrated 3D coordinates can be rendered into a 3D animation for broadcast and stadium screens. The animation is a visualization of the tracked geometry, not a separate camera recording.

The Computer Vision Pipeline

If we ignore the legal part for a moment, the vision system has a clean engineering shape.

Camera Calibration

Every camera must know how its pixels map to the pitch. In computer-vision terms, this includes intrinsic parameters, extrinsic parameters, distortion correction, and a consistent world coordinate frame.

Without calibration, a line drawn on a broadcast image is only a graphic. With calibration, the line corresponds to a real plane on the field.

In a simplified setup, this is the difference between drawing a line as a visual overlay and projecting a real 3D pitch line into the image with a camera matrix and pose estimate. The first is just a graphic. The second is geometry.

Multi-View Detection and Pose Estimation

Each player is visible from several angles. The system detects the player and estimates body keypoints in each view. These 2D keypoints are then associated across cameras and lifted into 3D.

This is where modern pose estimation and tracking matter. Offside is not asking “where is the person?” It is asking “where is the farthest legal body part of this person at a specific instant?”

This requires more than bounding boxes. A bounding box around a player includes empty background and usually includes the arms, which are not relevant for offside. Keypoints are more precise: ankle, knee, hip, shoulder, head, and foot landmarks give the system a way to reason about playable body parts.

Temporal Tracking

Raw per-frame keypoints are noisy. Players occlude each other. The ball moves fast. A production system needs temporal filtering and identity tracking so that player A does not become player B when they cross paths.

This is where tracklets and filters become important. A tracklet is a short sequence of observations believed to belong to the same object or player. A filter, such as a Kalman-filter-style estimator, combines the current measurement with the previous state to reduce jitter and predict where the player is likely to be in the next frame.

Ball Event Detection

The connected ball supplies high-frequency IMU samples. The system detects candidate touch events from acceleration spikes, angular-velocity changes, and possibly derived features such as jerk. It then synchronizes that event with camera data.

If the ball also provides UWB-style radio measurements, those can support ball localization or validation. The key point is that the ball sensor gives the review system an independent high-rate signal that is not limited by video frame rate.

Geometric Decision Support

Once all coordinates are in the same frame, the geometric test is conceptually simple:

attacker_legal_body_part_x > offside_line_x

In real systems, the details are harder: pitch orientation, attacking direction, calibration uncertainty, visibility, body-part eligibility, and synchronization all matter. A good system also needs an uncertainty model. For example, if the estimated attacker position has a few centimeters of uncertainty, the software should treat that very differently from a case where the attacker is clearly beyond the defender by a large margin.

Visualization

Finally, the system renders the offside line, the involved players, and the key instant as a 3D animation. This is not the measurement itself. It is an explanation layer built from the measured coordinates.

A Simplified Version

Here is a deliberately simplified sketch of how you might think about the pipeline if you were prototyping a toy version with pose estimation models.

This is not production SAOT. It is a conceptual bridge for computer vision engineers.

Conceptual pseudo-code, not production SAOT.

1. Calibrate cameras into the same pitch coordinate system.

cameras = load_calibrated_cameras("stadium_calibration.json")

2. Keep a short rolling buffer of tracked 3D player poses.

tracking_buffer = PoseTrackingBuffer(seconds=3.0)

3. The ball stream contains IMU samples:

accelerometer: ax, ay, az

gyroscope: wx, wy, wz

ball_event_detector = BallTouchDetector( acceleration_threshold=30.0, angular_velocity_threshold=6.0, jerk_threshold=120.0, )

for timestamp, camera

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