Soccer Data Analysis: Meaning and Significance of In-Games Statistics(EN)

Hello, soccer fans! I’m DataStriker, and today I want to talk about “Soccer Data Analysis: Meaning and Significance of In-Games Statistics”. Soccer is a beautiful sport in its own right, but delving deeper into the game through data analysis can help us better understand matches and evaluate the performance of players and teams.

Players Statistics vs. Team Statistics

Firstly, soccer data can be broadly categorized into two main types: player statistics and team statistics. Player statistics track the individual performance of players, while team statistics represent the overall performance of a team.

Examples of Player Statistics

1. Shots: The number of times a player attempts to score.
2. Assists: The number of times a player creates scoring opportunities.
3. Goal Scored: The total goals scores by a player.
4. Yellow Cards: The number of warnings a player receives.
5. Red Cards: The number of times a player is sent off.
6. Offsides: The number of times a player is called offside.
7. Corner Kicks: The number of corner kicks earned.
8. Penalties: The number of penalties earned or taken

Examples of Team Statistics

1. Victories: The number of matches won by the team.
2. Draws: The number of matches where the team ended in a tie.
3. Defeats: The number of matches lost by the team.
4. Clean Sheets: Matches where the team or goalkeeper did not concede any goals.
5. Key Passes: The number of passes that create scoring opportunities.
6. Through Balls: Passes that break through the opponent’s defense.

 

Why is Soccer Data Analysis Important?

Soccer data analysis holds importance for various reasons. It helps identify the strengths and weaknesses of both teams and players, enabling the improvement of strategies. Moreover, it enhances the understanding and enjoyment of the game for fans and assists clubs and national teams in player recruitment and tactical adjustments.

Diverse Classification of Statistical Data

Through data analysis, a variety of statistical data can be classified to understand different aspects of the game.

• Playing Time: The time a player spends on the field during a match.
• xG (Expected Goals): An expected goal metric, indicating the likelihood of each shot resulting in a goal.
• xA (Expected Assists): Expected assist metric, showing the likelihood of each pass resulting in an assist.
• Saves: The number of times a goalkeeper stops the ball from entering the goal.
• Fouls Committed / Fouls Suffered: The number of fouls committed by a player and the number suffered.
• Dribble Completed / Attempted Dribbles: The number of successful dribbles and attempted dribbles.
• Duels Won / Duels Lost: The number of duels (1v1 confrontations) won and lost.

In conclusion, soccer is a good of numbers and data. Data analysis enables a deeper understanding of player and team achievements, making the viewing experience more enjoyable. Expect more insights into soccer data in the future!