Building football analytics basics without code, creating xG models with spreadsheets and no-code tools
Football analytics has become a crucial aspect of the sport, with teams and fans alike using data to gain a deeper understanding of the game. One key metric is expected goals, or xG, which measures the likelihood of a shot resulting in a goal.
xG models can be complex and require significant coding expertise, but it is possible to build a simple model using spreadsheets and no-code tools.
Importing match data
The first step in building an xG model is to import match data.
This can include information such as the location of shots, the type of shot, and the outcome of the shot. Match data can be sourced from a variety of places, including official league websites and data providers. Once the data has been imported, it can be cleaned and formatted to make it easier to work with.
Assembling the xG model
With the match data in hand, it is possible to start assembling the xG model. This involves using statistical techniques such as regression analysis to identify the factors that are most closely correlated with goal scoring. Regression analysis can be used to identify the relationship between different variables, such as the location of the shot and the likelihood of it resulting in a goal.
Validating the model
Once the xG model has been assembled, it is important to validate it to ensure that it is accurate. This can be done by testing the model against a dataset of historical matches and comparing the predicted xG values to the actual outcomes. Validation is a crucial step in the process, as it helps to identify any biases or errors in the model.
Visualizing chances
Finally, the xG model can be used to visualize chances and provide insights into team and player performance. This can be done using a variety of visualization tools such as heat maps and scatter plots. Heat maps can be used to show the locations on the pitch where a team is most likely to score, while scatter plots can be used to compare the xG values of different teams and players.
