In football, Big Data plays a vital role across various areas of analysis, from scouting potential talent to preparing for rivals.
However, one of the most important applications of data is understanding your own team’s performance and how it fits into the broader competitive environment.
By using data to evaluate everything from tactical decisions to individual player contributions, teams can enhance their game model and make data-driven improvements.
In this post, we’ll break down;
- how teams can use data to analyze their own performance,
- what key factors to consider, and
- how this insight can help optimize a team’s game model.
Step 1: Understanding Your Teams Competitive Environment
The first step in using data to analyze your own team is understanding its specific situation within the competitive environment.
This requires looking at whether the fundamentals of your team’s game model align with the demands of the competition.
For instance, if your team focuses on high pressing but plays in a league where direct play from the goalkeeper dominates, your tactics may be out of sync with the competition.
It’s essential to assess whether the team’s game model matches the realities of the league.
Analyzing your team in context involves understanding the predominant game styles of the competition—whether they are direct or combinative—and knowing the characteristics of rival teams, from their physical abilities to their tactical setups.
2: Analyze your Teams Performance with the Ball
When analyzing a team’s performance with the ball, certain key factors should be taken into account to ensure a complete understanding of the team’s offensive strategy. Here are some examples of what data can reveal:
- Risk-taking through passing: Analyze whether the team is taking risks in its own half and how often those risks lead to lost possession.
- Partnerships: Examine how players work together inside and outside passing channels and how effectively they create space for progression.
- Pass distance and support play: Identify patterns of short versus long passes and the extent to which players support each other with the ball.
- Shot conversion and progression: Analyze how often the team enters the opponent’s final third, the quality of shots taken, and the overall goal conversion rate.
Using data, coaches can break down these factors to improve decision-making on and off the ball, refining their tactical approach based on the strengths and weaknesses identified through analysis.
3. Analyse What Happens Without the Ball
On the defensive side, analyzing moments without the ball is equally important. Here, coaches focus on the team’s defensive positioning, how compact the lines are, and how effectively the team recovers the ball. Key data points to track include:
- Defensive line positioning: Is the team maintaining a low, medium, or high block based on the opposition’s setup?
- Ball recoveries: How many times does the team win the ball in the opponent’s half? Does the team press in pairs, threes, or a collective unit?
- Aerial duels and second plays: Track the number of aerial duels won, particularly by center-backs and forwards, as well as how well the team handles second balls.
This detailed defensive analysis helps coaches make data-driven adjustments, whether it’s tightening the space between the defensive line and the goalkeeper or shifting to a higher press based on match context.
4. Tailoring the Game Model Over Time
A game model is a structured set of criteria that regulates how a team operates in various phases of play. However, it must be adaptable.
A team’s game model should evolve based on ongoing analysis of both the team and the competition.
Data helps identify when certain elements of the game model should be adjusted to better fit the competitive landscape without sacrificing the team’s overall philosophy.
For instance, tracking the possession-to-opportunity conversion rate can help determine whether a more direct style of play should be incorporated or whether maintaining a combinative passing approach yields better results in specific matchups.
5. Continuous Analysis Throughout the Season
To stay competitive, data analysis must be continuous throughout the season. By updating performance data after each match, teams can maintain a real-time understanding of how they stack up against their rivals.
Using tools like Tableau Public, clubs can create visualizations that offer insights into the team’s evolutionary or cumulative performance, allowing for immediate adjustments to the training process and tactical setups.
The ability to compare your own game model to that of other teams in the league not only helps identify areas for improvement but also provides valuable insight into the broader trends within the competition.
Conclusion
Analyzing your own team with Big Data provides a detailed, objective view of how well your game model fits within the competitive environment.
By tracking performance both with and without the ball, teams can refine their tactics, improve player coordination, and ensure that the team’s game model evolves based on real-time insights.
With continuous analysis, teams can adapt and grow, making data an essential tool in staying competitive throughout the season.