People new to the world of soccer/football analytics (or soccermetrics) will encounter a bit of trouble as this site will have a lot of abbreviated statistical terms. This page is meant to display all definitions and full forms of stats.
Fact file: You may be wondering, why are these terms abbreviated? Well, most fanalysts communicate through Twitter. Stats are abbreviated to fit into the 140-character limit.
Shots On Target (SOT)
Used as both player and team stats, it serves as an alternative to basic shot numbers, as it measures the number of accurate shots taken. Also works the other way around, that is, Shots On Target Conceded.
Shooting Accuracy (Shooting%)
It measures how well a team shoots. It’s a percentage of how many shots were on target. For example, if Chelsea have shot 20 times and 15 of them were on target, Chelsea’s ShAcc is 75%.
Goal Conversion (GConv)
100(Goals/Shots). A percentage that measures efficiency.
Total Shots Ratio (TSR)
It expresses the dominance of a team on the basis of shots taken. TSR is useful, as it has a high correlation with goal difference and points. Also, the TSR of a team in, let’s say, Season 1 correlates well with the TSR of the same team in Season 2. If 20 shots were taken in a match, and Chelsea took 8 shots while Arsenal took 12, Chelsea’s TSR is 8 divided by 20, which is 0.4 or 40%. Arsenal’s TSR is 12 divided by 20, which is 0.6 or 60%. On the flipside, Chelsea’s TSR against is 0.6 or 60%, while Arsenal’s TSR against is 0.4 or 40%.
However, TSR is biased. It only counts shot quantity, and not shot quality. For example, Arsenal shot a lot against Chelsea, but did Arsenal produce better shots than Chelsea? Some teams produce a low number of high-quality shots while some teams produce a high number of low-quality shots. Hence, Expected Goals are used more often.
It doesn’t stand for anything. It’s the online handle of Brian King, the maker of the stat who used it in hockey, who used those letters as his gamer tag in Counter-Strike. This is a different kind of stat. It measures luck. The peculiarity of PDO is that PDO numbers regress to the mean. A high score means your luck will soon run out and a low score means you’ll get better soon. The way to measure PDO is 10(Sh%+ Save %). Simple. The average PDO is 1000, and all teams’ PDO regresses to that number.
PDO, however is extremely flawed and most analysts don’t use it, as this stat completely destroys what I just explained earlier. In this stat, all shots are equal. However, there are people who use PDO, and think it is useful (like me). Some clever fanalysts have combined PDO with Expected Goals, like Sander Ijtsma.
Expected Goals (xG or ExpG)
Football’s favourite stat. Usually, it is shortened to ExpG or xG. It is a model that assigns a numerical value to a shot taken. The outcome of a shot will always be 1 goal or 0 goals, but xG quantifies a scoring opportunity. For example, a shot from the six-yard box will have a higher chance of going in compared to a shot from 30 yards out. So the Expected Goals for a shot from the six-yard box will be higher than the one taken from 30 yards out.
Many people have their own xG model. Although shot location is the most commonly used criterion, different people use different criteria, such as whether the shot was a header, whether it came from a cross, game state, whether it came from a counter-attack and so on. The most popular is Michael Caley’s, which also accounts for league average attack speed. Paul Riley, Colin Trainor and Constantinos Chappas, and Sander Ijtsma have good xG models of their own as well.
The main reason for which the stat is a favourite is because it takes into account not only shot quantity, but quality as well. xG is also versatile, as it is used for prediction, evaluating forwards, and evaluating goalkeepers. An xG number measures how well teams shoot from good scoring positions. A team’s/striker’s efficiency can be measured by dividing their/his xG score by the number of shots taken.
Individual player stats
Per 90 minutes (p90)
Used instead of per game or totals in stats. Most young players get less game time compared to players in their later twenties. So, dividing player statistics by games played will favour the older players. It’s better to divide the stats by the number of minutes played and then multiplying that by 90. This gives a more accurate measure of skill.
A key pass is a shot assist. If you only look at goal assists, a brilliant playmaker who plays behind a terrible striker will rank low. So, we look at passes that led to shots.
A non-penalty goal is a goal which wasn’t from a penalty. Why should we look at NPG90? A penalty shot has an 80% chance of becoming a goal. Though some players are ‘penalty specialists’, like Franck Ribery, Mario Balotelli, and Lionel Messi, many players can score from the penalty spot. If we use goals to evaluate players, we are also using the easy penalty goals that anyone can score. Hence, we use non-penalty goals.
Possession-Adjusted Defensive Statistics (Padj)
The problem with defensive stats is – okay there are a lot of problems with individual defensive stats. But one of them is the fact that teams that keep the ball better will score less on on-the-ball defensive actions like interceptions and tackles. So, first a base possession rate of 50% possession should be divided by the amount of possession the team in which the defender we’re looking at conceded. Then, that number should be multiplied by the number of tackles or interceptions he made. So if a team controlled 60% of the ball, and one of their center-backs completes 10 tackles a game, his possession-adjusted tackles number is 12.5.
(This page may be updated once in a while).