Data Explainers
Data Explainers
Some say there is only one set of numbers that matters in football – the scoreline.
However, at More Than A Game we like to delve into the data to bring you closer to the biggest stories, to explain exactly what a player offers or why something is happening on the pitch.
After all, if the world's most successful clubs consult data when recruiting players and coaches, why shouldn't fans hold an interest in numbers?
With data becoming increasingly visible in the football world, we compiled this handy explainer to tackle some of the more ambiguous terms being thrown around.
Never again will More Than A Game's readers enter a debate unprepared.
Expected Goals (xG)
Loved by some, loathed by others and misunderstood by many, expected goals is one of the most visible metrics in football.
It measures the quality of a chance, assigning a likelihood of it being scored – an xG value between zero and one – based on several factors, including shot position and angle, the type of shot (e.g., with the head or foot), type of assist (e.g., cross, cutback etc.) and pattern of play.
Opta – who first introduced xG in 2012 – say their model is "powered by hundreds of thousands of shots from historical data". In a nutshell, looking at whether certain types of chances were historically converted tells us how likely they are to result in a goal.
As well as measuring a team’s chance creation, xG is valuable when discussing player performance.
Those who post goal returns above their xG figures over a given period are said to be overperforming, those who do the opposite are underperforming.
Expected Assists (xA)
Expected assists (xA) measures the likelihood of any pass becoming an assist. Like xG, xA is based on historical pass data. The pattern of play, where a pass is played from and received, and the type of pass are among the factors impacting the chance of a pass becoming an assist.
As with xG, passes are assigned an xA value which can give an indication of a player's creativity.
For instance, Bruno Fernandes' 16.7 xA led all other Premier League players in 2022-23, though his actual return of eight assists suggests shortcomings from his Manchester United team-mates.
Expected goals on target (xGOT) and goals prevented
With xG and xA generally used to analyse attackers, won't somebody please think of the goalkeepers?
Expected goals on target (xGOT) data is collated in the same way as xG, but is a post-shot, rather than pre-shot, metric. In other words, it considers where a shot is placed within the goal.
While it can therefore indicate how well a player finishes chances, it is also commonly used to assess goalkeepers.
For instance, if a goalkeeper faces 40 xGOT throughout a season but only concedes 30 goals, they are said to have prevented 10 goals.
Shot-creating actions/goal-creating actions
A shot-creating action is one of the two last offensive actions in the lead-up to a shot. This can include passes, take-ons, fouls drawn, defensive actions, or even shots that lead to another shot. Goal-creating actions, as the name suggests, are actions leading to a goal.
Shot or goal-creating actions can also be narrowed down based on the type of action. For example, Kieran Trippier led the Premier League for dead-ball shot-creating actions in 2022-23 but ranked 19th for open-play passes leading to a shot.
Progressive passes/progressive carries
FBref.com defines a progressive pass as a completed pass that moves the ball at least 10 yards closer to the opposition's goal than at any point in the last six passes, or any successful pass into the opposing penalty area. Passes originating in defensive areas (the first 40% of the pitch) are excluded.
Progressive passes can be used to analyse passers and receivers – a high number of progressive passes received often suggests a tendency to be targeted by team-mates or an ability to find space.
A progressive carry, meanwhile, is any instance where a player moves – with the ball at their feet – at least 10 yards closer to the opposition's goal than at any point in the last six passes, or into the area. Carries ending in a player's own defensive half don't count.
Progressive passing/carrying distance
A simple one. Progressive passing or carrying distance is the total distance covered by a player's passes or carries towards the opposition goal, with backwards passes counting as zero.
Chances created
Chances created is a simple measure of the number of times a player assists a shot (including goals). It is sometimes referred to as key passes, though that measure traditionally excluded assists.
Passes per defensive action (PPDA)
Passes per defensive action is a metric developed in response to the increased prevalence of the high press, indicating how aggressively a team attempts to regain possession.
Dividing the opposition's number of passes by the amount of defensive actions – interceptions, tackles (successful and failed) and fouls – from an out-of-possession team gives them a PPDA figure.
The metric only focuses on actions in the 60% of the pitch closest to the in-possession team's goal, so the lower the PPDA, the more active a team is thought to be when pressing high.
High turnovers
According to Opta, a high turnover is any sequence which starts in open play and within 40 metres of the opponent's goal. The metric is thus used to analyse pressing styles.
A sequence is defined as any passage of play which "belongs to one team and is ended by a defensive action, a stoppage in play or a shot".
Shot-ending or goal-ending high turnovers – as their names suggest – are high turnovers that end in shots or goals.
The above definitions cover a tiny sample of the metrics used in football analytics. With data use continuing to expand and evolve, more will surely be added in the future.
Hopefully, however, they equip More Than A Game readers to venture into the minefield of football data, and better understand the sport we love.