Shot maps have become a staple of football analysis, with dots scattered across a pitch diagram showing where each attempt came from. Paired with expected goals, they tell a story that the scoreline alone cannot: which team created better chances, whether a win was deserved, and where a team's attacking problems lie. Learning to read them takes a few minutes and transforms how you understand a match.

A basic shot map plots every shot on a pitch diagram, usually marking goals differently from missed or saved shots, and sometimes sizing each dot by the shot's expected goals value. Shots from inside the six-yard box appear close to goal, shots from the penalty spot cluster centrally, and long-range efforts scatter outside the box. At a glance, a team whose shots cluster centrally and close to goal created better chances than a team whose shots are mostly from distance or wide angles.

Advanced maps split shots by team, by half, or by player, and some show the sequence leading to the shot, such as whether it came from open play, a set piece, or a counter-attack. The most informative maps include expected goals values, so a dot's size reflects chance quality, not just location. A large dot close to goal represents a gilt-edged chance, while a small dot from thirty yards represents a speculative effort.

Expected goals assigns each shot a probability of being scored, based on distance, angle, body part, defensive pressure, and whether it followed a through ball or a cross. A penalty is typically worth around 0.76 to 0.79 xG, a close-range header from a corner much less, and a long-range shot often below 0.05. Adding up a team's xG gives a sense of how many goals its chances would typically produce, which can be compared with the actual score.

On a map, look for the total xG and its distribution: a team with high xG from a few big chances played differently from a team with similar xG spread across many low-value shots. The first team was clinical in creating clear openings; the second relied on volume. Both approaches can work, but they suggest different strengths and weaknesses for future matches.

Shot maps do not show chances that were not taken: a player who chose the wrong pass instead of shooting, a promising attack that broke down before a shot, or a defensive error that did not lead to an attempt. They also do not fully capture shot quality factors such as goalkeeper positioning or a defender's block, though modern xG models increasingly account for pressure and defensive actions.

A shot map from a single match can also mislead through randomness: a team can dominate the map yet lose to a deflected goal, or win despite creating little. Shot maps are most reliable over many matches, where patterns emerge: a team consistently creating high-xG chances is usually a good team, even if results lag temporarily.

Next time you see a shot map after a match, start with three questions: where were the shots taken from, how big were the biggest chances, and did the scoreline match the chance quality? If a team lost despite a far better map, it was probably unlucky or wasteful in finishing. If a team won despite a worse map, it was probably clinical or fortunate. Either way, the map adds context the scoreline hides.

Shot maps will not replace watching football, but they are one of the quickest ways to check whether your impression of a match matches the underlying chances. A glance at the dots often explains a result better than a page of possession stats.

Football shot maps plot every attempt by location, and xG maps weight each shot by scoring probability, revealing which team created better chances. Read them for chance location and quality, not just shot counts, and use them over many matches to spot genuine patterns rather than single-game luck.