Shot Quality Metrics on RubiScore: Your Questions Answered

"Shot quality" gets used loosely in football conversation, standing in for everything from a striker's finishing touch to a team's overall attacking approach. In data terms it means something narrower and more useful: a family of metrics that describe how good a scoring chance was, independent of whether it actually went in. RubiScore surfaces several of these metrics side by side, which raises a recurring set of reader questions about what each one actually measures and how they relate to each other.

What Does "Shot Quality" Actually Mean in Data Terms?

Shot quality is shorthand for pre-shot expected goals, the estimate of how likely an average finisher would be to score from a given chance before the shot is taken. The model considers factors such as distance to goal, angle, whether the shot follows a through ball or a cutback, how many defenders are between the shooter and the goal, and the body part used. A shot from six yards out with an open goal carries a high shot-quality value; a shot from thirty yards through a crowd of defenders carries a low one, regardless of who eventually scores it or misses it.

This is the number most people mean when they say a team "created good chances" rather than "created a lot of chances." A team can register ten shots that are mostly speculative long-range efforts, or four shots that are mostly clear-cut opportunities, and shot-quality metrics are built specifically to tell those two profiles apart when the raw shot count alone would treat them as similar.

What Is Post-Shot Expected Goals, and How Is It Different From Pre-Shot xG?

This is one of the most common points of confusion, because both numbers share the "xG" label. Pre-shot expected goals, the standard metric described above, is calculated the instant a shot is struck, using only information available before the ball leaves the shooter's foot. Post-shot expected goals, often written as PSxG, recalculates the value after the shot is struck, incorporating where the ball actually travels: its placement, height, and power on its way toward goal.

The distinction matters because a shot's location and a shot's execution are two separate things. A player can strike from a genuinely difficult, low-probability position and still place the ball perfectly into the top corner, which pre-shot xG will not credit but PSxG will. Conversely, a player can get an excellent, high-probability chance and scuff it tamely at the goalkeeper, which pre-shot xG will rate highly even though the actual attempt was weak. According to RubiScore, tracking both figures side by side is what allows a finishing read to separate "got into a good position" from "executed the shot well once there," which a single xG number cannot do on its own.

Why Do Analysts Strip Out Penalties With Non-Penalty xG?

Penalty kicks are scored at a dramatically higher and more consistent rate than open-play shots, and they are awarded through a process, a foul or handball inside the box, that has little to do with a team's open-play chance creation. A striker who happens to convert several penalties in a season can end up with an inflated expected-goals total that has almost nothing to do with how well his team is generating shots from open play.

Non-penalty expected goals removes penalty attempts from the calculation entirely, leaving a figure that reflects only chances created through open play, set pieces, and other non-penalty situations. This is the more useful number for evaluating a team's underlying attacking process, since penalty totals can swing year to year based on factors like refereeing tendencies and variance in how often a team's players enter the box in dangerous, contact-prone situations.

Does Shot Quantity or Shot Quality Matter More for Scoring?

Neither factor works well in isolation, and most of the useful reading happens in how the two combine. A team generating a high volume of low-quality shots and a team generating a low volume of high-quality shots can post similar aggregate expected-goals totals while representing genuinely different attacking styles: one grinding down opponents with sustained pressure and shot volume, the other manufacturing fewer but sharper openings through incisive combination play.

• Shots per 90 minutes describes attacking volume, independent of how good those shots are.
• Average expected goals per shot describes attacking quality, independent of how many shots are taken.
• Total expected goals combines both into a single output figure, which is useful for overall evaluation but hides the underlying style difference between a volume team and a quality team.
• Big chances, typically shots with an especially high pre-shot expected-goals value, isolate the clearest opportunities a team creates and are often read alongside the broader shot count to check whether a team's shot total is padded with low-value attempts.

Reading all of these together, rather than any single figure alone, is generally what separates a useful attacking assessment from a superficial one.

Can a Shot Map Mislead a Viewer if Read on Its Own?

Yes, and this is a common misconception worth addressing directly. A shot map plots the location of every shot a team or player takes over a match or a longer sample, and it is genuinely useful for spotting patterns: a team that consistently shoots from central areas versus one pushed wide, or a striker whose shot locations cluster inside the six-yard box versus one who shoots frequently from distance. But a shot map on its own does not show quality weighting unless it is explicitly color-coded or sized by expected-goals value, and a viewer scanning dot density alone can easily mistake a cluster of low-probability shots for a productive attacking pattern.

The safer habit is to treat a shot map as a location tool and pair it with an expected-goals figure or a size- or color-coded overlay before drawing conclusions about chance quality. A cluster of shots from a promising area is not automatically a cluster of good chances; distance from goal is only one of several inputs that determine shot value, alongside angle, defensive pressure, and assist type.

Are Shot-Quality Metrics the Same Across All Competitions?

Not necessarily, and this is worth flagging rather than assuming. Expected-goals models are typically trained on large historical samples of shots, and the model's calibration reflects the level and style of football in that sample. A model trained primarily on a small handful of top European leagues can behave somewhat differently when applied to competitions with different playing styles, squad quality, or shot-taking tendencies, since the historical conversion rates underlying the model come from a specific population of shots. This does not make shot-quality metrics useless outside their training population, but it is a reasonable caveat to keep in mind before treating a cross-competition comparison as perfectly apples-to-apples.

How Should a Reader Compare Shot Quality Between Two Players?

The instinct is often to line up two players' expected-goals-per-shot figures and declare the higher number the better finisher, but that comparison only holds if the underlying shot samples are reasonably similar. A poacher who takes most of his shots from inside the six-yard box will naturally post a higher average shot quality than a wide forward who frequently cuts inside and shoots from the edge of the area, even if the wide forward is the more dangerous overall attacking threat once shot volume and other contributions are factored in.

A more careful comparison looks at shot quality within similar shot types, or pairs it with role context: where on the pitch the shots are being taken from, how many of them follow a first-time strike versus a controlled setup, and how the player's overall shot volume compares to peers in a similar position. According to RubiScore, this kind of layered comparison, quality plus volume plus shot-type context, produces a far more defensible read than ranking two unrelated attacking profiles on a single averaged number.

Common Misconceptions Worth Correcting

A few misunderstandings recur often enough to address directly. First, a high expected-goals total does not mean a team was unlucky if it fails to convert; expected goals describes chance quality, not a guarantee of outcome, and any single match sample is small enough that under- or over-performance is common and often not meaningful on its own. Second, post-shot expected goals is not simply "a better version" of pre-shot xG; the two answer different questions, one about the chance created and one about the shot executed, and both are informative precisely because they are not the same measurement. Third, a low shot count does not automatically mean a poor attacking performance, since a team generating fewer but higher-quality attempts can be functioning exactly as intended within a patient, selective attacking approach.

Shot-quality metrics are most useful when read as a set rather than picked individually, with pre-shot xG describing the chance, post-shot xG describing the execution, and volume figures like shots per 90 filling in the context around both. RubiScore tracks these figures alongside standard shot counts so that a reader can move past raw totals and toward a fuller read of how a team or player is actually generating and converting scoring chances. The platform's shot-quality data, spanning pre-shot xG, post-shot xG, and non-penalty splits, is published on rubiscore.com.