Data Analytics in Football: Understanding the Game Through Numbers

In modern football, data analytics — analyzing the game through numbers and statistics — has created a new revolution. The change shown in the movie 'Moneyball' for baseball is happening in football today as well.

What data is collected?

Player statistics: Goals, assists, touches, pass accuracy, number of sprints, distance covered.

Team statistics: Possession percentage, shots on goal, dangerous attacks, corner kicks.

Advanced metrics:

- xG (Expected Goals) — the quality of a shot opportunity. A score from 0 to 1 — the closer to 1, the higher the probability of a goal.

- PPDA (Passes Per Defensive Action) — an indicator of pressing intensity.

- Progressive Passes — the number of forward-oriented passes.

The concept of xG

xG is the most important indicator in modern football analytics. Every shot opportunity is evaluated: distance, angle, number of opponents, type of shot. For example: penalty = 0.76 xG (76% expected to be a goal), shot from 30 meters = 0.03 xG (3%).

If a team creates 2.5 xG but scores only 1 goal — they were unlucky. If they score 2 goals with 0.5 xG — they were lucky.

How is analytics used?

Transfers: Liverpool purchased Salah based on analytics — his numbers were ideal.

Tactics: Identifying the opponent's weaknesses through numbers.

Player development: Determining the strengths and weaknesses of every player.

Analytics in amateur football

You can also keep simple statistics: goals, assists, win/loss ratio. The Playmatch app saves game history — this data is useful for tracking your progress.

Write the result, goals, and the best player in your Telegram group after every game — even this simple analytics increases group motivation.