The previous nine articles in this 11-article series posed questions of coaches as to how they consider ‘what kind of club are we?’.

It has also explored how the decisions we make align with what is important within our environment, and provided some solutions we can all utilise in developing our players, our coaches - and ourselves.

Analysing how we are getting on can be a useful reflection of whether we are doing what we agreed we are committed to. 

In this sense, generic, universal and arbitrary data points are possibly a dangerous place to start.

In 2015, I wrote two connected articles in The FA’s Boot Room magazine, discussing the importance of aligning the decisions we make to the commitments agreed.

This encompassed the whole human player development system, and analysis was a critical part of this alignment.

The articles encouraged coaches to analyse both how the team is learning to play with how we coach, and connect data collection to these commitments.

This was to support player development programmes to measure what they valued, rather than be seduced by the perception that data objectifies the game of soccer. 

While data may provide some more objective reference points, it is very difficult - and possibly a fool’s errand - to attempt to completely objectify the game.

"Generic data can be misplaced if not aligned to the agreed intentions of the club..."

There are also problems with data points like expected goals (xG) and acute:chronic workload ratio (ACWR), which take different data points and, with a machine learning algorithm, distil them into a single number that seeks to determine whether or not a player should have scored, a team should have won or whether a player is likely to get injured.

These generic types of data collection, which the industry tends to use comparatively, can be misplaced if they aren’t aligned to the agreed intentions of any club, team or individual player.

There are also many other contextual factors that these algorithms find it challenging to consider. Being driven by a single data point - without considering the many contextual factors which data science can sometimes dismiss as being more subjective - can be risky.

While many experienced practitioners understand these contextual factors, and embed them into their coaching and development programmes, there is value in being purposeful in agreeing how we, more broadly, analyse how the players and the team are developing - aligned with what we are committed to - and the role our coaching plays in supporting this.

Previous articles have explored the integration of our playing approach with the individual players in our care, across their human systems, into our coaching. This particular one focuses on the analysis of this integration.

Data sits inside analysis, it isn’t alone. Coaches are increasingly working with people who have data scientist, performance analysis or insights roles to ensure their work is in harmony with the programme.

The first of my Boot Room articles from 2015 was entitled ‘Aligning Visions’. Linked to a Premier League club’s player-development programme, it focused on analysing the defending and pressing of our teams as a means of connecting our analysis to the ways the teams intended to play soccer.

As such, examining data points that connect to our intentions may be of value. The Passes Per Defensive Action (PPDA) metric was one we began to look more purposefully at, as a consequence of valuing pressing high and seeking to win the ball back early.

PPDA is calculated by dividing the number of passes allowed by the team out of possession, by the number of defensive actions.

A smaller PPDA value signifies a greater level of defensive or pressing intensity, as the defensive team has allowed a smaller number of uncontested passes to be made.

Teams that play a more passive style of play - defending nearer to their goal and then attempting to score with counter-attacks - may not see great value in a lower PPDA as their approach is, perhaps, less concerned about how intense their press is.

Similarly, a team who is down to 10 players, or who leads 3-0 and is happy to allow the opposition to have the ball and expend their energy, may also factor those critical contextual factors into their analysis.