I am 18 games deep into my save with Bologna FC, a save in which I am looking to replicate the tactical philosophy of Thiago Motta, as covered in his UEFA Pro Thesis, The Value of the Ball.

I will look to cover in this post my approach to making tactical tweaks in FM using the data available in game to help inform changes to the fluid playing system in place. I will be adopting the funnel approach, for those of you who are not familiar with this method, you simply start with broader aspects, and then gradually narrow the focus until you reach the specific aspect of the topic that will be addressed.

I wanted to start this post with a visualisation, our rolling expected goals performance. This would be one of the visualisations which would be on my wish list for Sports Interactive to add to the continually improving in-game data hub, I believe it offers a really good stating point when analysing performance across a wide sample of games.

 

Looking at the above, what does it tell me;

• We have only accumulated an xG of over two once in 18 games, this was against Udinese.
• We have conceded an xG of above two in six games.
• Lazio were the only side to restrict us to low quality goal scoring chances.
• We had four gams where we restricted our opponents to low quality goal scoring chances (Salernitana, Cremonese, Lazio, Sampdoria, and Spezia).

Averaging both metrics across the sample of games, we are generating 1.18expected goals per game, whilst conceding 1.36 goals per game. A cause for concern for any manager regardless of the results obtained in each match.

*FYI I will share with you the league table at the mid-point later in the post, for now it is irrelevant, we are conceding more chances than we are creating, we need to delve deeper.

 

My next stop is to attempt to ascertain one of two things, are we taking our shots from high value scoring positions and how effective are we when presented with chances. First stop, I will explore xG per shot.

xG per shot – a team with a higher xG per shot means that they have been more selective in teir shooting, taking shots in situations that were more likely to result in a goal.

Looking at this metric, you will note that we are not the most selective team in the league in terms of shot positioning, Napoli and Sassuolo are taking the majority of shots in positions with a higher goal outcome.

Now, this simple numerical fact can have different explanations. If a team decides to employ a low-block, counter-attacking strategy, if successful, they will take their shots against a disorganised and numerically disadvantaged defence, resulting in higher quality chances. On the flip side, when a team is controlling the tempo of the game and dominating the final third, shot selection strategies may translate into an attempt to move the ball closer to goal rather than shooting from range.

 

The above methodology is highlighted extremely well via the pitch tilt visual, with teams in the top left square, likely to be those adopting a lower block and looking to exploit the opposition on the counter, whilst the bottom right are those that adopt a possession based approach, controlling the tempo of the game in the final third.

What have I learned? Well, I don’t think there is any need to look to improve our xG per shot metric, especially when looking at this metric as a stand alone. However, as with anything there is always many moving pieces and therefore I want to look at a few more details, just to make sure.

Now this is where I will reveal our current position in Serie A, we are doing swimmingly well, currently in 8th place in the league. However, I wanted to draw your attention to the ‘Goal Output’ visual, this shows a teams performance against two metrics, expected goals for and against.

Switching between the two slides you will see there is a positive correlation between non penalty expected goals scored and league position, the only outliers in the top left quadrant are Roma (10th) and Juventus (14th), who are having a shocking season to date.

Every other team find themselves in the top seven, whilst both ourselves (8th) and Sassuolo (9th) conceding above average in terms of expected goals against. You could argue using the rule of averages that if we don’t improve either our goals scored or restrict our opponents to chances of lesser quality, our luck will run out and we will slip down the table.

 

So, we have established that we are not accumilating enough xG across 90 minutes and that our xG per shot is not exceptional, but wat about the frequency of shots and our ability to convert these into goals.

The attacking efficiency visual informs us the outcomes of the above questions, as you can see we are actually performing extremely well with respect to our conversion rate, the third highest in the league, and we are taking slightly less shots than average.

Taking the information from this visual and comparing it with the other sides with a similar pitch tilt to ours, we aren’t taking anywhere near as many shots as the company we share (Milan, Inter, Napoli, and Atalanta).

Taking us deeper down the rabbit hole, I want to look at the granular detail, specifically individuals who are taking the majority of our shots to see if there are any outliers/trends.

 

After analysing the above table, which shows our top six volume shooters and their supporting metrics, I have identified the following key headlines:

• Marko Arnautovic has the best xG per shot amongst our top six volume shooters, given he is our lone striker, playing as a DLF (support), this should come as no suprise, given he is the spearhead of the attack.
• Our two central midfielders have the lowest xG per shot
• Lewis Ferguson is taking the most shots per 90, whilst Nicolas Dominguez is taking the fewest.
• Musa Barrow is the best at working the oppositions goalkeeper, whilst Nicolas Dominguez is the worst.
• Three players have the best conversion rate at 18%, whist Lewis Ferguson finds himself bottom of the pile.

The key takeaway from this piece of analysis is that our biggest volume shooter, Lewis Ferguson, is by far the most wasteful. He is taking his shots from positions with the lowest scoring value, this is reflected in his non-penalty expected gaols per 90 (0,17), the lowest and his low conversion rate.

 

Lewis will be given the ‘Shoot Less Often’ player instruction, in the hope that this will instruct him to play a pass, to a team mate closer to goal, in turn helping us to raise our xG per match value.

Whilst I appreciate it looks as if a lot of time has been spent on analysis to come to small conclusion, I wanted to really highlight to you the importance of data. I think it is clear to see when reading this piece that I enjoy this element of the game and I am sure that this approach isn’t for everyone.

---

KEY PERFORMERS

Before I close this update, I wanted to show some of the key performers to date, this hopefully will give you a flavour for the key roles to date within the tactical system.

The below visualisations are taken from the Statman skin, they are comparison to players performing a similar role across the top 20 leagues and expressed as a percentile.

Nicolás Domínguez (CAR) – Serie A Average Rating 7.16

 

Riccardo Orsolini (IW) – Serie A Average Rating 7.13

 

Musa Barrow (IF) – Serie A Average Rating 7.12

 

Łukasz Skorupski (SK) – Serie A Average Rating 7.04

 

Lewis Ferguson (BBM) – Serie A Average Rating 7.04