What is a baseline
A baseline can be any number that serves as a reasonable and defined starting point for comparison purposes. It may be used to evaluate the effects of a change, track the progress of an improvement project, or measure the difference between two periods of time.
Whilst the above ‘Team Data’ can be a good source to help aid decision surrounding tactics and performance it isn’t the ideal starting place to carry out a deep dive when looking at individual performances against metrics, (stating the obvious).
They do however provide us with a narrative which will reflect on the player deep dive. Briefly looking at the above team attacking and defending performance charts I know the following.
- Attacking players will likely to perform above xG per 90 average for the league due to the overperformance of the XG per game value
- Strikers will likely to have a decent conversion rate due to the high goals scored per game and shots on target ratio
- Defenders will have a high headers won percentage, we have conceded lower than average per game and clearances, blocks, interceptions, and tackles attempted are not outperforming the league average. How are we succeeding in stopping the opposition, aerially.
The above data fails to provide us with a fixed point of reference for individuals, which can be used in future for comparative purposes.
The ‘Player Data’ is a great place to explore this. However, in my opinion there is one pretty big problem with the way in which the data in presented. It cannot be manipulated, I will be writing a blog after the end of the first season for View From The Touchline to show you how Tableau can be used to aid data manipulation.
The big problem is with this is different tactics will have different roles and these roles will have certain roles within the tactic. For example, as discussed in my earlier blog ‘Game Model’ width in the 4–2–2–2 is provided by the wing-backs and therefore I want to know how much distance mine are travelling along with how many times they win possession per 90 minutes. This in the data hub…not possible my friend, hopefully in FM24 the data hub will be customisable with drop downs to compare metrics of choice.
Anyways, what I have done below is pulled off the player outputs into Google Sheets and documented who is performing the best at each along with a Villarreal average. Knowing that we want to improve the squad year on year, we will need to make sure the average is improved, providing us with a much needed baseline.
The role of the Goalkeeper within the tactic is to keep the ball out of the net, simple enough. Gerónimo Rulli is doing a great job at this, he not only has a save percentage of 86%, but also is preventing an expected goal per 90 figure of 0.56. Knowing that a clear cut chance is worth 0.15 xG, he is stopping on average three clear cut chances per game.
Our baseline taking forward is that we expect all goalkeepers to have a save percentage of 83% which is three percent above the La Liga average.
Ok, so we play the ball out from the back and need our defenders to be good in the air due to having a trigger press set to ‘Trap Outside’.
Jorge Cuenca is the defender with the most passes completed per 90 with 90.08 and also progresses the ball the most per 90 (6.16). Cuenca is often played against weaker opponents to provide Pau Torres a rest, which could put his values under question…just a little context if you haven’t been reading through the blog on the SI Forum.
Pau Torres is leading by example in the air winning 89% of his aerial duels, whilst Cuenca is winning the most possession for us, at least 23 times per game.
When comparing to the La Liga average all of our centre-backs are performing either at average or above for hearers won, pass completion, and are losing the ball less than others.
We will want centre-backs to have a high pass completion 93% and at the same time win the ball back 20.79 times per 90, along with a high headers won value of 83%.
As previously stated we need our wing-backs to provide our width, consistently. Therefore there is a need to have an exceptional cardio-vascular system, the engine.
Johan Mojica is leading the way, running 14km per 90 minutes and all except Sergio Carreira is running over the Villarreal average of 13.5km.
If the wing-backs are providing the width, they will need to contribute play in possession across all phases of play. Therefore, the need to be a good passer of the ball to retain possession is also a must. Juan Foyth is our best ball player, with 89% pass completion. The La Liga average is for defenders only and not wing/full-backs so the value is going to be higher, knowing that many centre-backs like to play those short/safe horizontal passes to draw on the press.
One of the main weaknesses of the 4–2–2–2 is that teams are vulnerable down the flank, so you guest it, our wide players need to also be good at winning back possession. Juan Foyth again leads the way with 16.32 times per game.
The central midfielders in our system form one half of the ‘Magic Rectangle’, their purpose is to protect the defensive line and also operate under the more advanced players to support recycling the ball when in the attacking third.
We need our central midfielders to be able to move the ball without turning over possession, just like everyone else. Francis Coquelin holds the best pass completion rate at 89%, whilst all players are performing above the La Liga average. The Frenchman along with Dani Parejo and Etienne Capoue are also winning the ball at the same rate as the league average.
The specialists, these players are operating in the half-space and are expected to not only create but also contribute to scoring.
Yeremy Pino is excelling in this area, the Spaniard has the highest non-penalty xG value of 0.33 and also playing at least two key passes per 90. This may not seem high but when looking at his clear cut chances per 90, he is not far off creating a clear cut chance every two key passes.
Alex Baena has the lowest sample size in terms of minutes played, yet he is also producing 2.03 open play key passes with a higher clear cut chance value than Yeremy…food for thought in terms of his usage.
Samuel Chukwueze is completing over double the La Liga average dribbles and nearly all players are performing above the Villarreal average for Pressures completed, showing the impact these so called luxury players are having at winning the ball back on average 7.23 times per match each.
The Advanced Forward carries the teams main goalscoring threat on his shoulders, Fer Nino is proving that this added responsibility is nothing but lightwork!
The striker leads pack, he is outperforming his XG value by 6.33 goals and the highest conversion rate in the squad 28%. He has the highest xG per shot value 0.19, his average is above the clear cut chance xG value, crediting his ability to get his shots away in positions of significant threat, (this to me shows his over performance is less to do with luck and more down to his quality).
Gerard Moreno is completing the most passes per 90, no surprise given he played a large proportion of his games as the DLF, prior to changing his role to CF on support.
If we wanted to go more direct Fer Nino is also winning the most aerial duels, whilst Gerard is winning the most possession and completing the most pressures.
The Villarreal baseline (the average figures for all metrics) will be saved onto a separate excel spreadsheet so we can use them as a reference point when recruiting players.
I will then populate these on a yearly basis as the save progresses with a similar supporting narrative, like the above.
I just wanted to write this post to show you all how I use the in game data within my saves, specifically looking at KPI’s. Remember, as stated earlier in the post, each tactic will have its own pro’s and cons. You will need to understand the tactic if you want to gain the most added value from your analysis.