Football Analytics

Set-Piece Analytics Explained

Set-piece analytics measures how teams create and prevent danger from corners, free kicks and throw-ins by separating delivery, first contact, shot quality and outcomes.

GoalIQAIx

30 Jul 2026 • 12 min read

Set-piece analytics is the structured analysis of how football teams create and prevent danger from corners, free kicks, throw-ins and other restarts. It measures the complete process rather than judging teams only by set-piece goals.

Useful measures include delivery location, first-contact rate, shot creation, shot quality, second-ball recovery and expected goals. Tracking and video data can add information about player movement, marking systems, blocking, goalkeeper positioning and the space a routine creates.

This matters because goals are relatively rare and can misrepresent performance over short periods. A team may execute dangerous routines without scoring, while another may convert several weak chances. Set-piece analytics helps separate repeatable process from finishing and short-term variance.

What Counts as a Set Piece?

A set piece is a restart during which the ball begins from a stationary or prescribed position. The attacking and defending teams have time to organise, although the amount of preparation varies by restart.

The main categories are:

Corners.
Direct free kicks.
Indirect free kicks.
Long attacking throw-ins.
Penalties.
Kick-offs and other rehearsed restarts.

Analysts normally separate penalties from other set pieces. A penalty is a highly valuable shot with a distinctive conversion probability, while a corner or indirect free kick requires several actions before a shot is produced.

Direct free kicks should also be distinguished from crossed free kicks. Shooting directly at goal tests the taker’s striking ability and the goalkeeper. A delivered free kick depends on delivery, movement, aerial contests, marking and second actions.

These categories should not automatically be combined. A team can be excellent at corners but ordinary at indirect free kicks, or dangerous from long throws without creating much threat from conventional deliveries.

Why Set Pieces Should Be Analysed Separately

Open play and set pieces create chances through different processes.

Open-play attacks emerge from a continuously changing game. Corners and free kicks allow teams to place players deliberately, rehearse movements and target known defensive weaknesses. They therefore involve a more controlled tactical starting point.

Separating the two helps answer important questions:

Is a team’s attacking output driven by open play or dead-ball situations?
Does it possess a repeatable set-piece advantage?
Is an apparent defensive weakness concentrated at corners rather than present throughout matches?
How dependent is the team on particular takers or aerial targets?
Would a change in personnel materially alter its threat?

A season total of 50 goals does not reveal how those goals were created. Two teams with the same overall output could have very different attacking profiles if one generates most of its threat in open play and the other relies heavily on restarts.

This is one reason analysts must ask which football statistics are relevant to the question being investigated. Overall numbers can conceal important differences between phases of play.

The Set-Piece Analytics Funnel

A useful framework follows each set piece from opportunity to outcome:

The team wins a set-piece opportunity.
The taker delivers the ball towards a target area or teammate.
An attacking player makes first contact or receives the ball.
The team retains or recovers the next action.
The sequence produces a shot.
The shot carries a particular expected-goals value.
The chance results in a goal or another outcome.

This funnel identifies where a routine succeeds or fails. A team may win first contact frequently but direct headers into low-value areas. Another may make fewer initial contacts but create better shots when it does.

Judging only the final goal total collapses all these stages into one noisy result. A more useful analysis measures the conversion between each stage.

Core Set-Piece Metrics

Set-piece opportunities

Before measuring execution, analysts should establish how many opportunities a team receives.

A high total of corner goals may partly reflect the team winning more corners rather than using each one more effectively. Strong attacking sides often spend more time near the opposition goal and therefore generate more restarts.

Useful opportunity measures include:

Corners won per match.
Attacking free kicks won by zone.
Long throws taken in advanced areas.
Set-piece opportunities per possession or final-third entry.

Opportunity volume and execution quality should be reported separately.

Delivery location and accuracy

Delivery analysis records where the ball was intended to arrive and where it actually arrived.

Corner deliveries might be classified as:

Near-post.
Central six-yard area.
Far-post.
Penalty-spot area.
Edge of the box.
Short corner.

The distinction between intention and outcome matters. A corner reaching the near post may reflect the planned target, an under-hit delivery or an interception by the first defender.

Analysts can evaluate delivery consistency by comparing the intended target with the ball’s actual trajectory and landing zone. Video remains important because event data may record the destination without explaining whether the routine was executed as designed.

First-contact rate

First-contact rate measures how often an attacking or defending player reaches the initial contested ball.

For an attacking team:

Attacking first-contact rate = attacking first contacts ÷ eligible deliveries

This is more informative than goals alone because winning the first contact is one necessary component of many routines. It can reveal whether movements are creating separation or whether the delivery is reaching the intended area.

However, first contact does not guarantee a useful outcome. A defender may allow a weak header from a difficult angle because it carries little danger. Analysts must therefore consider the location, direction and quality of the contact.

Shot-creation rate

Shot-creation rate measures how frequently a set piece leads to an attempt within a defined sequence or time window.

Shot-creation rate = set pieces producing a shot ÷ total set pieces

Definitions can vary. One provider may count only the first phase, while another includes shots after a clearance is recovered and the ball is returned to the penalty area.

The rule should be stated and applied consistently. Otherwise, comparisons between teams or data providers may be misleading.

Expected goals per set piece

Set-piece expected goals measures the quality of shots produced from dead-ball situations.

It can be expressed as:

Total set-piece xG.
Set-piece xG per match.
xG per corner or free kick.
xG per set-piece shot.

Expected goals estimates the probability that a shot will be scored based on comparable attempts. Set-piece xG therefore gives more credit to a close-range free header than to a pressured attempt from a narrow angle.

It should still be interpreted carefully. Different providers may classify set-piece sequences differently, and an ordinary shot-based xG model may not fully represent goalkeeper obstruction, defensive pressure or the trajectory of the delivery.

Expected goals per opportunity

Expected goals per opportunity combines shot frequency and shot quality:

Set-piece xG per opportunity = total set-piece xG ÷ total set-piece opportunities

This can distinguish volume from efficiency.

Suppose two teams each generate 5.0 set-piece xG:

Team A required 100 corners, producing 0.05 xG per corner.
Team B required 70 corners, producing approximately 0.071 xG per corner.

Their total output is equal, but Team B extracted more expected threat from each opportunity.

Second-ball recovery

Many successful set pieces do not lead directly from delivery to shot. The first contact may be cleared into a predictable area, where the attacking team recovers possession and begins a second phase.

Second-ball analysis can measure:

Which team controls the next action.
Where the clearance lands.
Whether the attacking structure protects the edge of the box.
How quickly the ball is returned to a dangerous area.
Whether the defensive line can move out effectively.

A routine may therefore be successful even when the initial delivery is cleared. If the movements influence where that clearance travels, the second phase can form part of the design.

Counterattack risk

Committing players to attack a set piece creates risk if possession is lost. An analysis that credits attacking threat without measuring defensive exposure is incomplete.

Teams can record:

Counterattacks conceded after attacking set pieces.
Opposition entries into the defensive half.
Shots or xG conceded during the transition.
The number and positioning of players retained behind the ball.

The best attacking routine is not necessarily the one producing the most immediate xG if it also creates excessive transition risk.

Why Goals Alone Are a Weak Measure

Set-piece goals are important outcomes, but they are infrequent. This makes short-term conversion highly vulnerable to randomness.

A team could score from three of its first 20 corners and then fail to score from the next 50. Another could repeatedly create strong headers that are saved or narrowly missed.

Goals combine several factors:

The quality of the opportunity.
Execution of the routine.
Finishing.
Goalkeeping.
Deflections and rebounds.
Refereeing decisions.
Random variation.

Analysts should therefore examine goals alongside first contacts, shots, expected goals and repeatable patterns. The same principle applies when interpreting a shot map and the quality of the attempts behind its headline total.

How Analysts Evaluate Corner Routines

A corner routine is more than the delivery and final header. It is a coordinated attempt to manipulate defenders and open a valuable area.

An analyst can break a routine into five elements.

Starting structure

Record where each attacker and defender begins. Relevant details include clustered attackers, players positioned around the goalkeeper, short-corner options and protection against counterattacks.

Movement

Track who attacks the ball, who clears space and who moves to receive a second ball. A player can contribute without touching the ball by dragging a marker away from the intended target.

Defensive response

Identify whether the opponent uses zonal, man-to-man or hybrid marking. The same attacking routine may work differently against each structure.

Delivery

Record the taker, foot, inswing or outswing, height, speed and target zone. Delivery quality should be separated from whether the receiver converts the chance.

Outcome and process

Record first contact, shot, xG, second-ball recovery and transition outcome. Video can then explain why the numerical result occurred.

For example, a near-post run might pull two zonal defenders forward while the main target moves into the central area. The decoy runner has created value even if conventional event data assigns that player no action.

Attacking and Defensive Set-Piece Analysis

Attacking and defensive statistics are related but not perfect mirror images.

Attacking analysis asks whether a team can:

Deliver consistently into valuable areas.
Create favourable match-ups.
Win first contact.
Generate shots with limited pressure.
Recover clearances and sustain the attack.
Protect itself against counterattacks.

Defensive analysis asks whether a team can:

Prevent clean delivery.
Protect the most valuable zones.
Track movement and avoid harmful mismatches.
Win first contact or force weak attempts.
Clear the second ball.
Move out without leaving opponents unmarked.

A high defensive first-contact rate is positive only if those contacts remove danger. Weak clearances into central areas can create more valuable second shots than the original delivery.

Goalkeeper behaviour also matters. Analysts can examine starting position, willingness to claim crosses, contact under pressure and coordination with the defensive system.

How Free Kicks and Throw-Ins Are Analysed

Corner analytics receives considerable attention because corners have consistent starting locations. Other restarts require separate frameworks.

Direct free kicks

Relevant variables include distance, angle, wall position, taker foot, shot trajectory and goalkeeper position. The analyst should distinguish direct attempts from disguised passes and deliveries.

Indirect free kicks

These resemble corners but originate from more varied locations. Delivery angle and the position of the defensive line become especially important.

Throw-ins

The purpose of a throw-in is not always to create an immediate shot. Success may mean retaining possession, escaping pressure, progressing up the pitch or finding a player in space.

StatsBomb’s discussion of measuring throw-in success illustrates why xG, possession value, retention and space can all be relevant depending on the objective.

Long attacking throws can be analysed more like corners, including delivery area, first contact, flick-ons, second balls and transition protection.

How Tracking Data Improves Set-Piece Analysis

Traditional event data records actions such as the delivery, aerial duel and shot. Tracking or freeze-frame data can also record where players were positioned around those actions.

This allows analysts to study:

Starting distances between attackers and markers.
Runs and changes of direction.
Space created by decoy movements.
Goalkeeper access to the flight of the ball.
Numerical advantages in target zones.
Defensive line shape.
The positioning of players protecting against transitions.

Research can extend beyond description. TacticAI, developed by Google DeepMind with Liverpool FC and published in Nature Communications, used geometric deep learning to analyse corner configurations, predict outcomes and suggest positional adjustments.

This does not mean artificial intelligence can independently design a perfect routine. Model outputs still depend on the available data, the outcome being optimised and whether a recommendation can be executed by the players involved.

The broader lesson is that set-piece analysis can move from counting outcomes towards modelling the relationships between players, space and tactical objectives.

Using Set-Piece Data for Opposition Analysis

Opposition analysis aims to identify repeatable tendencies rather than merely replay recent goals.

Analysts may ask:

Which zones does the opponent target most often?
Does it prefer inswinging, outswinging or short corners?
Who is the main target?
Which players act as decoys or blockers?
How does it change routines against zonal and man-marking systems?
Where do its clearances and second balls usually travel?
How many players remain in defensive positions?

Defensive weaknesses can be analysed in the same way. A club may discover that an opponent consistently loses contact with runners attacking the far post or leaves an unfavourable aerial match-up in a particular zone.

The numbers narrow the search. Video establishes whether the pattern has a plausible tactical explanation and remains relevant to the expected line-ups.

Set-Piece Analytics and Player Recruitment

Set-piece data can support recruitment when a club needs a particular capability rather than simply a player with several headed goals.

Potential attributes include:

Delivery accuracy into defined target zones.
Aerial-duel ability.
Timing and direction of penalty-area runs.
Ability to make first contact under pressure.
Heading accuracy and shot quality.
Defensive clearance quality.
Goalkeeper command of crossed balls.

Context remains essential. A player may win more headers because the team deliberately targets them, because their opponents are weak aerially or because the delivery repeatedly creates favourable situations.

StatsBomb has demonstrated how aerial metrics, event data and player-location data can support set-piece strategy and match-up decisions. Such measures should be treated as evidence for further investigation rather than universal player rankings.

How Clubs Turn the Analysis Into Decisions

Set-piece data creates value only when it changes preparation or decision-making.

A practical workflow is:

Define the tactical question.
Use data to identify a pattern or match-up.
Confirm the pattern through video.
Design or select an appropriate response.
Practise the routine with the expected players.
Record whether it was executed as intended.
Review both the process and outcome.

This reflects the wider process through which football clubs combine data, expert judgement and implementation. The analyst can identify an opportunity, but coaches and players must translate it into an executable plan.

A highly complex routine may appear attractive in a model but fail if there is insufficient training time or if the required movements do not suit the available players.

Set Pieces and Game State

Set-piece behaviour changes with the score and match situation.

A team chasing a late equaliser may send its goalkeeper forward, commit more players to the penalty area and accept increased counterattack risk. A side protecting a lead may prioritise keeping the ball in the corner or using a short routine rather than delivering immediately.

Competition format matters too. The risk accepted during a league match can differ from the final minutes of a knockout tie.

Analysts should therefore record:

The score when the set piece occurred.
The time remaining.
Whether the team needed to score.
The number of players committed forward.
Any red cards or substitutions affecting the match-up.

GoalIQAI’s guide to game state in football analytics explains why statistics must be interpreted alongside the incentives shaping team behaviour.

The Small-Sample Problem

Set-piece analysis is especially vulnerable to small samples. A team may take only a few corners in each match, while a particular routine or defensive match-up may appear much less frequently.

The problem becomes more severe when data is divided by:

Set-piece type.
Delivery zone.
Taker.
Target player.
Opposition marking system.
Match state.

A team that scores four goals from one routine may have found a repeatable advantage. It may also have benefited from exceptional finishing, poor goalkeeping or a small number of unusually favourable opponents.

Analysts should:

Use multiple seasons where personnel and tactics remain comparable.
Place more weight on recent data when routines have changed.
Compare goals with shots, xG and first-contact rates.
Review whether the same mechanism appears repeatedly on video.
Avoid treating small differences in conversion as meaningful.

The guide to sample size in football analytics provides a broader framework for judging whether an apparent pattern is sufficiently supported.

Common Set-Piece Analysis Mistakes

Ranking teams only by goals

Goals matter, but short-term finishing can make an ordinary process look exceptional or conceal a strong process that has not yet converted.

Treating every corner as equivalent

Short corners, inswinging deliveries, outswingers and routines against different marking systems represent different tactical events.

Ignoring opportunity volume

A team taking many corners can accumulate strong total numbers without being especially efficient per opportunity.

Crediting only the final player

The scorer and taker are visible, but decoy runs, blocks, screens and second-ball positioning may create the opportunity.

Ignoring failed routines

Reviewing only goals creates selection bias. The same movement may have been attempted several times without producing a shot.

Using data without video

Event data can identify patterns, but it may not explain the intended target, defensive scheme or contribution of off-ball movement.

Copying a successful routine without considering personnel

A routine designed around dominant aerial players and precise delivery may not transfer to a team with different strengths.

Key Takeaways

Set-piece analytics studies the complete process behind corners, free kicks, throw-ins and other restarts.
Set-piece performance should be separated from open play and divided by restart type.
Goals alone are too noisy to measure the quality of set-piece execution over short periods.
Useful metrics include opportunity volume, delivery location, first-contact rate, shot-creation rate, expected goals and second-ball recovery.
Expected goals per opportunity helps separate total production from efficiency.
Tracking data reveals movement, marking, spacing and off-ball contributions that event data may miss.
Attacking analysis should also measure counterattack risk after possession is lost.
Opposition analysis combines quantitative patterns with video and expected line-ups.
Set-piece data can support recruitment, tactical preparation and player-role decisions.
Small samples, changing personnel and provider definitions require careful interpretation.

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