Get an introduction to football analytics and learn how expected goals can enhance your football experience
Football analytics is a rapidly growing field that uses data to gain insights into the game. One of the key metrics in football analytics is expected goals which measures the likelihood of a shot resulting in a goal. Expected goals takes into account various factors such as the location of the shot, the type of shot, and the opponent’s defense.
Understanding expected goals can help casual fans appreciate the game more. By analyzing expected goals fans can identify which teams and players are performing well and which are not. xG xGA xA and shot quality are some of the key terms used in football analytics.
Ultimo aggiornamento: 1 settembre 2026
This guide explains what xG is, how to read a match dashboard, ways to avoid common stat traps, where to find free data, and how to apply insights to assess team form.
What are expected goals?
What are expected goals? Expected goals is a metric that measures the likelihood of a shot resulting in a goal. It takes into account various factors such as the location of the shot, the type of shot, and the opponent’s defense. Expected goals is calculated using a complex algorithm that analyzes data from thousands of shots. In most models, xG for a single shot is expressed on a 0–1 scale, where higher values indicate a greater chance of scoring; team totals sum these values across shots to reflect
How to read match dashboards
How to read match dashboards A match dashboard is a visual representation of a football match that shows various metrics such as expected goals shots on target and pass completion percentage. To read a match dashboard, fans need to understand what each metric means and how it relates to the game. Match dashboards can help fans identify which teams and players are performing well and which are not. As a rule of thumb, a team xG around 2.4 suggests it created chances worth roughly two to three goals over the match, while xGA reflects the quality of chances conceded.
Avoiding common stat traps
Avoiding common stat traps When analyzing football data, it’s essential to avoid common stat traps such as confirmation bias and cherry-picking. Confirmation bias occurs when fans only look at data that confirms their pre-existing beliefs, while cherry-picking involves selecting only the data that supports a particular argument. To avoid these traps, fans need to look at the data objectively and consider multiple perspectives. Combine xG with context like shot locations, game state, and lineup changes to maintain an evidence-based view.
Free data sources
Free data sources There are several free data sources available that provide football analytics data. These sources include Opta Sports FiveThirtyEight and Whoscored. Fans can use these sources to access data on expected goals shots on target and other metrics. Coverage and methodologies can vary, so compare definitions and notes on how each provider constructs xG models to interpret numbers with consistency.
Applying insights to understand team form
Applying insights to understand team form By applying insights from football analytics, fans can gain a deeper understanding of team form. Team form refers to a team’s performance over a specific period. By analyzing expected goals and other metrics, fans can identify which teams are performing well and which are not. This can help fans make informed decisions about which teams to support and which players to watch. Track rolling averages of xG and xGA to separate sustainable performance from short-term variance in goals and results.
