ANALYZING TABLE REPORTS

The initial step in data analysis involves consolidating data from various variables across the selected games into a single comprehensive table. Additionally, it is important to include a "per 90 minutes" metric to standardize the data.

Given the multitude of variables, it’s easy to become overwhelmed. Therefore, it’s highly recommended to focus on a select few variables of interest to streamline the analysis and avoid confusion.

Next, the selected variables are tracked and analyzed across the games of interest. Below, you can view some examples of the visuals I provide (these are team examples, same graphs are made with players).

Important Note: The visuals presented highlight the variables I consider most insightful. However, if a club requires analysis of additional variables (available from the data), these can be provided promptly upon request. Additionally, each visual is accompanied by clear instructions on how to read and interpret the data, ensuring you can draw the most accurate and insightful conclusions. 

The next step involves creating team radars. The variables used in these radars can be adjusted as needed; the example below illustrates some options. The purpose of this visual is to show each team’s performance in relation to other teams, focusing on key variables. 

e.g. Bayern had more touches in the opponent's box/game than 83% of teams in UCL 22/23.

Similar visuals can be created for individual players, but these are tailored to the positions they play. Since different positions have distinct roles, it’s not appropriate to compare forwards with defenders directly. 

We recognize that drawing conclusions from visualizations or interpreting graphs for the first time can be challenging. Therefore, if you need further explanation or have any questions, please feel free to reach out. 

These were some examples of analyzing data from table reports. However, since this data doesn't indicate where or under what circumstances the events occurred, it has its limitations. To achieve a higher standard of analysis, we recommend exploring event data analysis.