An approach to tactical match analysis and the use of statistics in football

The number of analysts in football has largely grown throughout the last few years. The density of teams performing at a high level has increased due to information transfer through globalisation. As a consequence, clubs need to improve their performances on the pitch on a daily basis and cannot afford to overlook mistakes. Analysts, therefore, are supposed to analyse the teams tactical performances.

In this article, we will show you how to analyse a football match in order to draw conclusions which are not solely based on the result of the game.

What is a match analysis?

A match analysis is defined as the examination of a game in a structured way. Match analysis is supposed to provide feedback for team and coach about the success of their plan and performance.

In order to avoid sole reliance on numbers and statistics, match analysis is mainly divided into two categories, the qualitative analysis and the quantitative analysis.

Quantitative analysis

Quantitative analysis describes the analysis of a game through numbers and statistics. Therewith, quantitative analysis is completely objective. The most common example in football is the possession rate. Other frequently used statistics are won duels, the pass completion rate, the running distance and many more, but what conclusions can we draw from those values?

The answer to that question is disillusioning. Quantitative analysis is very limited in its validity. Only a few of the statistics offer a significant correlation with the result of a match. Possession rates, for example, do not correlate with the result of the game. However, there is a small positive correlation between the possession rate of possession-based teams and the result as well as a negative correlation between the possession rate of defensive-minded sides focusing on counter-attacking and the result.

Nevertheless, statistics can help us to back up the results of our qualitative analysis. One could, for example, prove the success of a high press with a relatively low pass completion rate of the opposition defenders.

To provide statistics with a higher validity, values such as expected Goals were invented. The so-called xG calculates the predictability of a goal to be scored from certain areas of the pitch in certain situations. Thereby, it is possible to calculate how many goals a team would have normally scored in a match with an average conversion of chances. This statistic solves the issue of the law of large numbers in football. Since the number of goals in football is relatively low, the result can completely differ from the actual performances of both teams. The xG values of the Serie A game between Atalanta and Empoli displayed below bear witness to this assumption.

Every statistic is lent a different significance. Obviously the bigger the correlation between the event and the result of the match, the bigger the significance of the statistic. The number of shots on target is, for instance, more significant than the number of completed passes.

When bearing the mentioned aspects in mind, quantitative analysis can be useful to make predictions and back up our qualitative analysis.

Qualitative a

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