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Where did it all go wrong for Juventus last season, and what needs to change?

Comparing Juventus’ numbers to the top teams around Europe.

US Salernitana v Juventus FC - Serie A Photo by Gabriele Maricchiolo/NurPhoto via Getty Images

If you ask yourself “Why did Juventus not win the Scudetto in the 2021-22 season?” I am sure many potential reasons will come to mind, likely including injuries, Max Allegri’s coaching, lack of a deep bench, more injuries and so on. The actual answer to that question is complex — with all those factors interacting and playing a role, in addition to others, such as decisions made in the transfer market last season and in the past.

As a scientist, I see most of these as hypothetical factors that need to be tested in order to definitely say they are influencing the team’s performance and ability to win. To be able to test these things data are needed, and there is a lot out there related to soccer. For example, all that video tracking and player GPS data that is collected which can generate some really neat analytics. (See this figure which shows you a passing network from Juventus’ 2017/18 game against Napoli. Look how important Mattia De Sciglio was in that game!)

Anyway, I am not a soccer statistic analyst, nor do I have access to most of that type of data, so I have some limitations to fully exploring the whys of Juventus’ recent downward slide. But nevertheless, I set out to try to at least scratch the surface of the question using data that are available (freely) online.

Offensive Metrics of Success

To start, I read some recently published papers and analyses to determine some Key Performance Indicators, which are variables used to evaluate success at the player and team level. A study using data from the 2016-17 Serie A season found that total shots and shots on target were good indicators for a team making it into the top 3 of the league. So I downloaded those variables for Juventus from the 2021-22 season and the top two teams from Serie A, La Liga, the Premier League, and the top team from Ligue 1 and Bundesliga. I also grabbed the total expected goals since it is a popular metric. (See nice explanation of what this is here, which fbref gets from StatsBomb; and if you want to nerd out further, click here.)

Figure 1. The 2021/2022 season total shots, shots on target and expected goal (xG) comparing Juventus to the top teams in the Bundesliga, Premier League, La Liga, Ligue 1 and Serie A across 38 league games (*only 34 games in the Bundesliga). Data from

So, what does Figure 1 tell us?

Juve had the smallest total shots taken and the lowest shots on target (on average 54 less shots on target then the top teams) and expected goals in the 2021-22 season, although pretty similar to Barcelona. These results are not super surprising and seem a little circular — less shots of target = less goals = less wins = not winning the Scudetto — but do present something tangible that we will need to improve in the 2022-23 season.

Defensive Metrics of Success

The above statistics focus on offensive play, which is not the only important aspect of a game as there is of course the defensive side. What metrics best explain a good defense is something I will look into another day; however, The Analyst provides a nifty figure of the zones of control of Serie A teams from last season that I think are pretty informative. (To find it, scroll down to the blue, red and gray fields.)

As the figure caption explains, the blue zones of the field are where the team had the most control — that is, “gains more than 55% of total touches” — and the red zones are where the opposing team had the most control (gray zones are contested). Look at Juve’s defensive half of the field — only five of the 15 zones were typically in their control! Compare that to Milan and Inter — they controlled 12/15 and 13/15 of their halves, respectively.

This metric isn’t perfect — Fiorentina’s whole defensive half is blue and Juve were ahead of them in the table — but if you compare the top to the bottom teams, having control in defense, and midfield, looks to be a good predictor of success.

Speaking of midfield, Juve’s was pretty much gray — an apt color choice to describe the center of the park last season.

Depth of a Team’s Bench

Something I noticed in Juventus’ recent friendly against Real Madrid, when the La Liga champions made a nine-player substitution in the beginning of the second half, was how many of those subs were good, experienced players. In comparison, Juventus’ bench had a lot of young, inexperienced players. I had felt the same way when I would watch Inter Milan making substitutions last season.

So I wanted to see if I could compare Juve’s options substitution-wise to the top two Serie A teams (Milan and Inter), and, because I wanted to see how Juve compared to Real Madrid too, I also included the top La Liga teams (Real Madrid and Barcelona).

*I did not do any in-depth research of how one might do such a comparison, so I should preface the below with the caveat that I used the data I had available and came up with a method that I thought was meaningful — feel free to comment with other ideas. Also, I made some decisions that people could argue with on how to analyze the data, but used the same method for each team, so should relatively comparable across teams.

In order to compare substitute option across teams, I needed a single numeric metric to describe a player’s ability so I used ranking of players, which is a number between 0 and 10, 10 being very highly ranked. (For example here is the data from Juventus.)

First, I created a list of “Starters” for each team, made up of 11 players that had started the most games for each team and this ended up being 16 or more games in the 38-game season. If there was a tie for the 11th spot — that is, both players had the same number of games started, and this happened for two teams — I included the player that had played more games overall or that had the higher ranking if they were tied for overall games, too.

Then, I created a list of “Substitutes” that were all the remaining players that started less than 16 games, but I removed players that had played in less than four games. The decision to make the cutoff at four games was a bit arbitrary, but it is likely that when players played a very low number of games, their ranking would be more biased. (For example Inter backup goalkeeper Ionut Radu played only one game and got a score of 4.9 because of a keeper error that led to a goal, but if he had played in more games one would imagine that his score would go up.)

Figure 2 below shows the averages for the starters and substitutes. Nothing really stands out to say that Juve’s substitutes are any less capable than the other teams, and the all teams are pretty similar. If you squint, Juventus and Inter’s substitutes might have slightly lower rankings on average compared to Real Madrid, but it is marginal. The starters’ rankings for every team are significantly better than the substitutes as we would expect, with Juve and Barcelona being on average a bit lower than Inter and Real Madrid.

Figure 2. Average player rankings (+/- standard error) as calculated by for players that started in 16 or more games (“Starters) and players that started in less than 16 games (“substitutes”) comparing Juventus to top two teams in Serie A and La Liga. Only players that played in 4 or more games were included. Averages for “starters” were always across 11 players, while the number of “substitutes” varied by team (Juve = 16, Milan = 15, Inter = 12, Madrid = 13 and Barcelona = 19).

Note that within league comparisons are more meaningful since those teams played the same teams across the season from which the rankings were calculated (that is, the strength of schedule was the same for Juventus, Milan and Inter, but not the same for Juventus in comparison to Real Madrid and Barcelona).

Consistency of Formations and Starters

One major takeaway I had from the Women’s Euro tournament this summer was that the team that won in the final — and all their games — was the team that did not change its formation or a single starter from the first game to last.

Now, it would be impossible for Juventus to field the same starters across a 38-game season (with injuries and COVID-19 still being a thing), but I was curious to see if consistency of formations and starters might have influenced Juventus’ performance in the 2021-22 season compared to the top Serie A and La Liga teams.

Consistency in formations

When looking at the formations across the season, there was a clear difference between Juventus and the top two teams from Serie A and La Liga. Below are the numbers of games in different formations (not including formations that were just used once).

  • Juventus – 19 games in 4-4-2, 8 games in 4-3-3, 5 games in 4-2-3-1
  • Inter – 35 games in 3-5-2, 3 games in 3-5-1-1
  • Milan – 36 games in 4-2-3-1, 2 games in 4-3-3
  • Real Madrid – 34 games in 4-3-3
  • Barcelona – 33 games 4-3-3, 3 games 4-2-3-1

Juventus had the least consistency in formation — and by a large margin. This again is not surprising with Max Allegri coming back and the team being in a “transition” year. Nevertheless, these data suggest that consistency in formation is important for teams to do well in their leagues.

Consistency in starters

To give a numeric value the consistency of starters, I compared the average number of games that “Starters” started, the total number of games they played, and the number of games that “Substitutes” started versus came in as a sub. I used the same criteria for starters and substitutes as described above. The results are depicted in Figure 3 below. I realize it is not very straightforward. To hopefully better explain:

The average number of “Starter Total Appearances” refers to the number of games that starters played in (as either a starter or sub) compared to “Starter Starting” which refers to the number of games when they started. The difference between those averages gives you an idea of consistency. Inter, for example, was the most consistent team with their starters starting in ~31 games – so if they were available, they played. Juventus had the lowest average number of games when their starters started (24.5), while Inter had the highest (31.2).

Reasons for starters not starting could be that the team was playing in a different formation (which is only really likely for Juve given the above data about formations), but it could also be unrelated to tactical decisions, including that starters were injured, had COVID, or players that transferred into the team took their place.

The average number of “Subs Starting” refers to when a substitute player started a game compared to “Sub Subbing” which refers to the number games when they were a substitute. So a higher value for Subs Starting would be an indicator of less consistency, and Juve had the highest value for this metric (9.2 games) compared to Inter, which had the lowest (6.1).

Long story short, Juve need more consistency in their formation and starters.

Figure 3. Average number of appearances across 38 league games for players that were “Starters” (started in 16 or more games) and players that were “Substitutes” (started in less than 16 games) comparing Juventus to top two teams in Serie A and La Liga from the 2021/2022 season. Data from


I was planning on doing some analyses on injuries to test a couple hypotheses about Juve’s injury history. I know a lot is lamented on about J Medical and the Juventus training staff, so I thought it would be neat to see if there was evidence to support that they play a role in Juve’s injuries woes. If so, then Juve should have more injuries per player per game than other Serie A teams. And players that come to Juve should start to have more injuries than they had in the past, and players that leave Juve should (after a period of time) have fewer injuries than they had while at Juve.

My research was stymied by the fact that I could not find these types of historical datasets of injuries online. One study I read said they got their data from by looking at the injury history of each player … and yeah, there was no way I was going to click through hundreds of player profiles to get that data.

I can share that research shows that frequency of games played is a key predictor for injury (players are more likely to get injured if they are playing consecutive games with less than 4 days compared to greater than 6 days in between) and that this was also demonstrated specifically in Serie A after the COVID lockdown when games were played more frequently.

So given that Juve players play more games/minutes than most players in Serie A because they also play in the Champions League and Coppa Italia — and they are more likely to play for their national teams — that probably explains some of the reasons why Juve players have had a lot of injuries.