Jobs4football Data Analysis Definitions

In modern football, data is an essential tool for understanding performance and shaping tactical decisions. From scouting players to evaluating team metrics, analysts rely on a shared framework of terms to interpret the numbers behind the game. Below is a beginner guide glossary of key performance analysis definitions used across football today, with explanations and practical examples:

Total

Total refers to the overall sum of specific metrics, showing cumulative performance across a given period. For example, a team that scores 10 goals during a tournament has a total of 10 goals for that competition.

Ranking

Ranking assigns players or teams a position within a dataset based on their performance in a particular metric. Rankings show how one compares to others—such as a team being ranked first for most goals scored.

Average (Mean)

Average, or mean, represents the standardised measure of performance, calculated by dividing the total value by the number of entries. If a side nets 18 goals in six matches, their average is 3 goals per game.

Difference from the average (Mean)

This measures how far a player or team’s figures sit from the average value in a dataset. For instance, if the average goals scored at a competition is 4 and a team records 6, their difference from the average is +2.

Median

The median is the middle value when numbers are arranged in order. It gives a clearer picture when results are skewed by outliers. For example, a team scoring 1, 2, 2, 2 and 8 goals has a median of 2, offering a more balanced view than the mean of 3, which is inflated by the 8-goal match.

Percentile

Percentile shows the percentage of performances below or equal to a specific value, placing achievements in comparative context. If a player’s scoring sits in the 90th percentile, their output equals or exceeds 90% of all others in that group.

Standard Deviation

Standard deviation measures how spread out values are around the average. High deviation suggests large differences in performance, while low deviation indicates consistency. For example, passes per player will have a higher deviation due to varied roles and possession levels.

Per Match

Per match metrics normalise statistics to a single game, allowing fair comparisons across teams that play different numbers of matches. A team with 12 goals from six games, for instance, averages 2 goals per match.

Per 90 minutes

Per 90 minutes normalises data based on standard match duration, accounting for different playing times. A player scoring four goals in 180 minutes across several games would average 1.8 goals per 90 minutes—offering a more accurate measure than goals per match.

Per 60 minutes ball-in-play time

Since the ball is typically in play for about 60 minutes of a match, this measure normalises actions during active play. A player completing 30 passes in a game with 50 minutes of ball-in-play time achieves 36 passes per 60 minutes, giving better context for actual activity levels.

Per 30 minutes in / Out Of Possession

This adjusts statistics based on possession phases, typically split around 30 minutes for each team in a balanced game. It allows analysts to evaluate styles and intensity, such as pressing frequency. If a player makes 15 presses during 20 out-of-possession minutes, they pressed 22.5 times per 30 minutes out of possession.

Completion rate

Completion rate tracks success efficiency, expressed as a percentage. If a team completes 340 out of 400 attempted passes, their pass completion rate stands at 85%.

Proportion

Proportion measures the ratio of a specific event type to all events within a category. A player completing 3 lofted passes from a total of 10 would have 30% of passes categorised as lofted.

Percent / Proportion Of Time

This metric looks at how time is distributed across different phases of play, giving insight into style and control. If a side is in possession for 20 minutes, out of possession for 25, and in contest for 5, the split is 40%, 50%, and 10% respectively.

Understanding these core data analysis terms empowers scouts, coaches, and clubs to make smarter decisions on and off the pitch. At Jobs4Football, we’re committed to helping football professionals harness the power of data to unlock new opportunities, improve performance, and shape the future of the game.

Whether you’re evaluating talent, building a squad, or refining your club’s strategy, these metrics are your foundation for success.