Expected Goals (xG) has revolutionized how we assess attacking performance in football. It measures the likelihood of a shot resulting in a goal, given factors such as shot location, angle, and type of assist. But while xG tells us how good the chance was, it tells us nothing about how well the shot was executed. That’s where Expected Goals on Target (xGOT) comes in — a metric designed to measure the quality of finishing once the ball leaves the player’s foot.
What Is xGOT?
xGOT, or Expected Goals on Target, evaluates the probability of a shot becoming a goal after it has been struck and is on target.
It combines shot context (as in xG) with post-shot information such as:
- The precise placement of the shot within the goal
- The speed and trajectory of the ball
- The goalkeeper’s position, reaction, and reach
- The difficulty of the save
In simpler terms:
xG measures the potential of the chance.
xGOT measures the execution of the shot.
How It’s Calculated
While each data provider has its own proprietary model, the general method is similar.
Once a shot is on target, tracking data identifies the ball’s coordinates when it crosses the goal line or reaches the goalkeeper. Using historical data of thousands of similar shots, the model estimates the probability that this exact shot would result in a goal — factoring in:
- Ball placement (center, corners, high/low)
- Shot velocity and curve
- Distance and angle from goal
- Goalkeeper’s position and movement path
A well-placed, powerful strike into the top corner from 20 meters might only carry an xG of 0.05, but if struck perfectly, its xGOT could rise to 0.35 or more.
Conversely, a close-range effort with an xG of 0.80 could drop to 0.30 xGOT if it’s hit weakly toward the keeper.
Why It Matters
The difference between xG and xGOT tells a deeper story about player performance.
- When xGOT consistently exceeds xG, it suggests elite finishing: the player regularly strikes the ball better than expected.
- When xGOT lags behind xG, it signals inefficient finishing: the player reaches strong positions but struggles to convert.
This is particularly important for recruitment and player development. A striker who scores fewer goals than their xG indicates poor execution, but if their xGOT is high, it might instead reflect exceptional goalkeeping or bad luck.
Over time, xGOT helps separate players who simply find chances from those who create goals out of half-chances.
Beyond the Individual: Tactical Interpretation
While xGOT is often used for player evaluation, it can also reveal tactical trends at the team level.
Team Finishing Profile
Teams that generate high xG but low xGOT might dominate territorially yet lack precision in the final action. Their build-up and structure create chances, but the execution doesn’t match the intent.
Shot Selection
A side that takes many long-range or off-balance shots will usually record low xGOT. This can expose poor shot discipline or frustration in possession.
Goalkeeper Evaluation
Comparing xGOT to actual goals conceded allows analysts to measure goalkeeping performance.
- Conceding fewer goals than xGOT → outstanding saves or strong positioning.
- Conceding more → poor reactions, technical errors, or psychological lapses.
At the highest level, combining xGOT data with video analysis helps goalkeeping coaches pinpoint positioning flaws or recurring save difficulties.
Linking xG and xGOT
The relationship between xG and xGOT is central to understanding finishing quality.
- xGOT ≈ xG:
Indicates average finishing — the player executes the shot roughly as expected.
Example: A composed finish placed toward the corner. - xGOT > xG:
Reflects above-average execution — strong technique, precision, or deception.
Example: A curling effort into the top corner. - xGOT < xG:
Suggests below-average execution — poor contact or rushed decision.
Example: A weak shot straight at the goalkeeper.
By tracking these relationships over time, analysts can separate genuine finishing quality from short-term luck or small sample sizes.
Practical Use in Coaching and Recruitment
For analysts, the key is not just collecting the data — but applying it.
xGOT can be used to:
- Profile finishers for recruitment: identify strikers who consistently beat goalkeepers from limited service.
- Tailor finishing training: target specific weaknesses (e.g., shots across the keeper, composure under pressure).
- Assess tactical fit: a team that relies on quick transitions may prioritize players with high xGOT efficiency under speed and stress.
Limitations of xGOT
Like any model, xGOT has its limits. It doesn’t account for:
- Defensive proximity or pressure after the shot
- Deflections, rebounds, or unpredictable ball movement
- Psychological or contextual factors such as fatigue, scoreline, or crowd pressure
Therefore, xGOT must complement, not replace, qualitative video analysis.
Numbers indicate what happened; footage shows why it happened.
Conclusion
Expected Goals on Target (xGOT) bridges a crucial gap in performance analysis.
While xG quantifies chance creation, xGOT quantifies shot execution. Together, they offer a complete view of finishing performance.
For coaches, scouts, and analysts, xGOT highlights what separates good finishers from great ones — precision, composure, and timing. In a sport where margins are small, understanding and measuring finishing quality has never been more important.