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TOP 100 European football clubs by enterprise value

Football is more than just a sport – it’s a massive business. The latest analysis of the Top 100 European football clubs by enterprise value (2021-2024) reveals fascinating trends and shifts at the top of the financial rankings. While the English Premier League (EPL) continues its dominance, clubs like Nottingham Forest and RC Lens have seen extraordinary growth. However, not all clubs are on an upward trajectory – Schalke 04 and Borussia Dortmund face financial challenges.

Biggest Winners: Who Has Grown the Most?

Manchester City has claimed the top spot with an astonishing €5.94 billion enterprise value, driven by smart investments, global branding, and consistent sporting success. Arsenal FC recorded a 154.8% increase, making it one of the fastest-growing clubs in the top 10. Nottingham Forest saw a 419% revenue increase after being promoted to the Premier League, highlighting the power of TV rights and sponsorship deals.

League Dominance – EPL vs. The Rest of Europe

The English Premier League continues to grow and now generates over €7 billion annually, almost twice as much as La Liga. Their commercial model, combined with global popularity, makes them the financial powerhouse of football.

New Entrants & Club Strategies

Besides financial power, innovative strategies are shaping the football business:

Brighton & Hove Albion and Brentford FC use data analytics and a “Moneyball” approach to maximize squad value.
RC Lens and Cádiz CF invest in technology and digital strategies to enhance fan engagement.

Conclusion

In modern football, financial strength often translates into success on the pitch. Clubs that effectively balance investments, strategy, and global influence are emerging as long-term leaders.

TOP 100 European football clubs by enterprise value Download

Hamstring injuries in professional football: Summary of research that tracked causes through 21 seasons.

A research paper titled “Hamstring injuries in professional men’s football: a 21-season review” by Ekstrand et al. The paper investigates the incidence, burden, and characteristics of hamstring injuries in professional men’s football over a 21-season period from 2001/2002 to 2021/2022. The study aims to provide updated information on the trends and patterns of football hamstring injuries and identify potential risk factors and prevention strategies for these injuries. The research team collected exposure and injury data from professional football clubs across Europe and analyzed the data to determine the incidence, burden, and characteristics of hamstring injuries. The study used the Munich muscle injury classification system to classify hamstring injuries as either structural or functional and to describe the affected muscles. The research paper provides valuable information to coaches, medical staff, and football organizations to help prevent and manage hamstring injuries in professional men’s football.

What are the main risk factors for hamstring injuries in professional football players?

According to this research the main risk factors for hamstring injuries in professional football players include the intensity and volume of high-risk activities such as running and sprinting, which have increased over time. The crowded player calendar, with more international team travel and matches, also compounds the pressure on hamstrings associated with football intensity. Additionally, previous hamstring injury is a strong predictor of future injury.

How can coaches and medical staff prevent and manage hamstring injuries in their teams?

Autors recommends that coaches and medical staff discuss the risk of hamstring injuries with players and implement appropriate programs to prevent and manage them. These programs may include load management in training and matches, making all parties aware of the risk, and ensuring players complete their rehabilitation diligently.

Which was the mechanism of the injury in percentages?

According to the information provided in the paper the most common mechanism of hamstring injury in professional men’s football was running/sprinting, accounting for 62% of structural injuries and 51% of functional injuries. Other mechanisms of injury included kicking, jumping, and stretching. The frequency of injury mechanisms differed significantly between structural and functional injuries (p<0.001).

Conclusion

Based on the information provided in the paper it can be concluded that hamstring injuries are a significant problem in professional men’s football, with increasing incidence and burden over the past 21 seasons. The main risk factors for hamstring injuries include the intensity and volume of high-risk activities such as running and sprinting, which have increased over time, and previous hamstring injuries. Coaches and medical staff can implement appropriate programs to prevent and manage hamstring injuries, but there is currently no evidence-based program to prevent hamstring recurrence.

Reference:
https://pubmed.ncbi.nlm.nih.gov/36588400/#:~:text=Between%202014/15%20and%202021,CI%201.2%25%20to%2018.3%25

Example of scouting report

Kyogo-4 Download

1. Manchester City

Manchester City is a team which bases its game on a set defence and many passes before the end of the action. Over the years, their coach Pep Guardiola has created a game structure in attack whose primary focus is the prevention of the opponent’s counterattacks. With this objective, the role of full back players, often playing in half spaces at different heights and who are complemented by attacking midfielders in the occupation of these spaces, is crucial.In general, City have somewhat passive movements of the full backs in the half spaces after leading the ball to the stopper, and sometimes in situations when the ball is in front of them, most often with a winger.Also, the movements of the full back players in the wing position on the open ball from the central zones can be much more active, where the side players very cautiously conquer the field in depth and thus prevent progressive passes that throw out many opposing players. Such problems are especially visible when stoppers (Stones and Ake) play in the position of the full backs. Such attacks, when they end with a loose ball, open larger spaces between the back and midfield lines because the wing players stand too deep, thus opening spaces between the lines for a second or two before they can close them. It is especially important to emphasize that these problems are much smaller when City uses the overload tactic (positioning more players on one side of the field) because they very well set the opponent players on desired position and then use quick side changes, which to a good extent cancels the above problems.As their game is based on controlling the game with the ball, the role of the last passes in the prevention of the opponent’s counterattacks is important. Many final City passes end up with balls in the air – usually parabolic balls from half space with good control of the zone under the ball with five players, most often in a 2-3 structure. Passes in the air make it difficult for the opponent to open counters because they are much more difficult to control and play accurately.As City narrows the field in attacking the opponent’s goal, they have many shots that end up in the opponent’s block, and therefore the matches in which they use the width of the field better and get more open shots are a good indicator of their victories.Another important factor in choosing to play the opponent’s system is the choice of passing and moving into the middle or wing zone. City occupies the central zones better and overloads them, than it does in the wing zones. Their system is primed to control the middle of the pitch and if they are allowed any space in the central zone they will use it very well, which cannot be said for all situations on the wing.The most effective actions in the wing positions are the dribbles towards the centre of the field by the wing players, to which they have very good movements of the players in front of the ball (further wing, 8 and striker). They do most of the dribbling in the back third with wings and 8s, especially in second attacks (after winning loose or nobody’s ball). The entire structure of the game is designed to prevent and allow frontline players to overwork, so it’s no surprise that dribbling percentage is such a good predictor of their success.City is a team that plays pressing for a large part of the game and tries to take the ball away from the opponent in the shortest possible interval. The biggest problems they have are due to the large number of pressing actions on the opponent’s goalkeeper, where they lose their compactness and because of this, often the second ball after the opponent’s long ball. In this way, the opponent enters the final third and creates opportunities and breaks – usually corners.In general, they are too vertical in pressing and it would be good for them to wait a little longer for pressing triggers (the moment when they can direct the opponent’s attack), because the lines below could close the spaces more compactly.They have the biggest problems with long balls and overloading of the back line. The back line tries to defend such attacks with a high line and only a small mistake in closing is enough to open a deep pass for the opponent. They react especially poorly to the overload in the middle of the field in the jump, where their side players remain isolated in the game 1vs1.Another potential disadvantage is the control of long balls, which they try to keep in their possession in a risky way (passing and dribbling) and in this way they provide opportunities for the opponents.The same problem is also reflected in breaks, and the most problems are created by teams that attack all free kicks within 60 m from their goal with many players. The constant attempts to keep the back line high and the weaker pressing reset after the second ball leaves them exposed against this type of action.

2. Liverpool

Liverpool is a team that bases its game much more on the control of transitions, long balls, and other balls.In their attack, the emphasis is on parabolic deep balls behind the back line, crosses from wing positions and half space, quick short combinations in central zones and diagonal balls.They especially use diagonal balls towards the left side to Roberston, for whom the winger opens space in the wing position with his deep movements, and there is almost no game in which they are offensively efficient, without that segment of the game functioning at a high level.Their attacking structure is still not as clean as City’s. They rely more on the dynamics of actions and excellent counter pressing.The largest number of players are brought into the 16m when the opponent allows them depth to the side players, and then the occupation of the 16m follows with four to five players while the players below control the bounced balls very well after the centre kick.Liverpool creates the most successful attacks after the first and second lost ball, by trying to play the final pass in the inferiority of two to three players. When it seems that the lines below will not compactly accompany such an attack, there is a strong movement towards the ball and a counter-pressing that seeks to take the ball through the duel, cutting the passing lines and taking advantage of the direction of the opponent’s attacks. Since their game is based so much on that segment, the number of won defensive duels is crucial in this phase of the game as well.Transitions work in the same way, where they like to play deep on two or three players in the front line and if the deep ball does not go through, they have an excellent movement of the formation in front of the ball, which provides them with the possibility of counter pressing.It is interesting that they use the same principle with their own and the opponent’s long balls.The only situations when they are not so vertical is when there is an open diagonal ball towards the far side, where they create an excellent surplus in front of the ball with the side players and attack the opponent’s penalty area beautifully in waves.Due to the aforementioned style of play, it is clear that aggressive control of the lost ball plays a key role for them in all phases of the game, and because of this, yellow cards are a good predictor of their results = when the prevention structure moves well under the ball, they win duels and take balls cleanly, and when they are late they fall out and receive an increased number of yellow cards.It should be emphasized that in pressing and counter-pressing actions, they have a great focus on moving towards the ball, and when the situation is played out, they compensate with extremely good descent and defence of the penalty area. The back pressing of the attacking and midfield lines after they have been played is perhaps the best in the world and it is very difficult to play back passes against them when the opposing team enters the final third.

3. Arsenal

When we analyse Arsenal, we must divide the 2021/22 season into two parts. The first part of the season was very bad, and they were looking for a style of play that suits them. In the first 10 rounds, they were so bad that they were close to the relegation zone.From the 4-2-3-1 system, the team usually had a very weak occupation of the central zones, a lack of width due to premature entry of the wings towards the middle, very high positioned full backs who opened spaces for the counter and risky combinations in the central zones.In the defensive phase, the team had weak pressure on the ball, poorly controlled the far side and especially poorly defended the half spaces because of the back line that passively guarded the goal and because of which the wings and the near midfielders closed those spaces in an undefined and bad way.Because of all the above-mentioned characteristics and transitions, they were disconnected and isolated, and everything pointed to the dismissal of coach Arteta.From the period around the New Year, they found the game in a 4-3-3 formation and since then they have been at the very top of the league in terms of points won. They started playing with the full backs set up in half space, false nine and high 8s as the general setting of the game. The game began to resemble that of City where Arteta worked as an assistant coach before taking over at Arsenal.The main difference is that in attack Arsenal is more focused on playing the wing zones with triangles on the sides that form a side-8 and the wing, and more attack turns through the near midfielder. On both wings, they have wings on opposite feet and in the new system of the game they can reach the wings much more easily, who dribble towards the middle and create excess and play diagonal passing lines in front of them.The moment they started playing that way, they started to control the middle of the field and win many balls from counter pressing. From that moment on, the number of defensive duels won, became a great predictor of their success.A segment that has also taken them to a whole new level is moving towards long balls. Before, they played long balls against the settled defence and counters against isolated players of the offensive line, while now they even anticipate the zones of the second ball with players under the ball, which significantly increased the number of offensive duels won.They use many crosses from half space after the return ball, so that both, they, and City lack width in the attacking phase, which is why they have an increased number of blocked shots because they shoot at the goal in situations when the opponent is compact in width in front of their goal.They have taken the pressing game to a completely new level, they manage to initiate pressing with strikers, wingers and 8s across different widths of the pitch, which was never the case before. In this way, they significantly increased the number of balls won in the opponent’s half, along with, of course, the counter-pressing phase, which we explained in the previous part of the text. On the other hand, they showed problems in pressing actions against teams that play with five players in the back line, where they either opened too much space in the intermediate lines or became too passive and focused on compactly defending their goal without pressing the ball.As their game became more connected, their counters became much better and there they introduced a segment that Liverpool has, which is the arrival of the full backs from the second plan with overlapping and underlapping, but in addition they added the arrival of 8 in the central areas of the field in the end.However, when they have less time on the ball per won ball, they still know how to play too vertically and do not use the third man enough to play off the opponent’s pressure towards the far side.The phase of the game that they brought to an exceptional level is the attacking throw in, where they developed excellent actions to open up the far side and created a large number of entries into the final third in that way.

Difference between Arsenal, Liverpool and Manchester city on the pitch in the 2021/2022 season through the statistics.

After analysing nine leagues in Europe (TOP5+Austria, Belgium, Croatia, and Poland) (https://www.linkedin.com/pulse/do-leagues-generally-differ-between-themselves-pitch-slaven-marasovi%C4%87) based on a survey conducted on LinkedIn I have analysed three clubs from Premier League in the season 2021/22: Manchester City, Liverpool and Arsenal. So, how do these three clubs differ on the pitch based on the statistical data and analysis? Could you read the playing style from it? Their strength and weaknesses. What could be the benefits of this kind of analysis? Could clubs use it to improve their strengths and reduce their liabilities or to recognize it in opponents’ games? Could players use it for identifying the best clubs for their signings? Maybe it could be interesting for journalists to analyse matches…?

To answer those questions, I have used four criteriums:

1. What are the key factors to create a chance in the individual club?

2. What are the key factors on the pitch for scoring a goal in the individual club?

3. What are the key factors on the pitch for not losing the game in the individual club?

4. What are the key factors on the pitch for winning a match in an individual club?

To describe those four criteriums I have used:

1. frequency (what is most used in each league),

2. statistical correlation and

3. statistical regression.

As we stated in the prior article, the frequency of 104 observed aspects of the game will tell us which aspect of the match is happening the most. That will not help us understand how to reach each of the criteriums.

On the opposite, the statistical correlation will tell us what correlates with each of the criteriums and help us start understanding each of criteriums per country. So, more interesting will be those aspects of the game which have a stronger correlation with each of the criterium.

Regression models will indicate us what is specific for reaching each of criteriums per each country and help us better understand each of criteriums. For the best understanding of each country will be summarized correlations and regression models as prepared for interpretation.

If you want to read more about regression models just go on the above-mentioned article https://www.linkedin.com/pulse/do-leagues-generally-differ-between-themselves-pitch-slaven-marasovi%C4%87 .

I will provide you insights and interpretation will live for you. Also, I can announce the interpretation of this data by an expert @Goran Rosanda, so, stay tuned.

In the following heatmap of correlations of different aspects of the game are presented. So, let’s start with correlations between “creating a chance”, “scoring a goal”, “not losing a match” and “winning a match” and different aspects of the game per each club. It was very interesting to go through each correlation and notice the differences, so I advise you, if you find this topic interesting, to go through it. That’s why I will provide all correlations to you (without xG related). Each aspect of the game is interesting and if you combined one aspect of the game with another you can have interesting conclusions. For the practical side and size of this text, I will focus just on the obvious differences in the game between the clubs.

The yellow card’s most negative influence on the Liverpool game and correlation is much (negative) higher than for the other two clubs, while the red card’s most negative influence has on the Manchester City game. Manchester City is having the most negative correlation with offsides (have a look at Liverpool in the “not defeat” category”). The percentage of successful actions is most important for Manchester City, while the number of shots and shots on target is for Arsenal. Again, per cent of shots on target is having the most positive influence on Manchester City’s game. Further on, blocked shots have a negative influence on the Manchester City game. There is an interesting difference between Liverpool and the other two clubs relating to key passes accurate. While Arsenal and Manchester City have an extremely high positive correlation with creating chance (+0.7), Liverpool does not have any correlation at all (0.0). For goals, Manchester City and Arsenal are having very positive correlation (0.4) while Liverpool is having very negative (-0.4). In “not losing the game” and “winning the match” Liverpool is having most positive correlation.

Per cent of accurate passes is most important for Manchester City games, and crosses, challenges and challenges won are having the most negative influence on the same club. On the other side per cent of the challenges won is having a positive influence on Arsenal’s game. Also, defensive challenges and defensive challenges won are having a mostly negative influence on Manchester City games. This does not mean that winning challenges in defence is bad but keeping Manchester City defending and under-pressing is negatively influencing their performance. It is not easy to do, obviously, but that’s the description of how they have been not winning and losing games.

Manchester City also does not like football “in the air” since they are having very different correlation in air challenges and air challenges won than Arsenal and Liverpool. In Manchester City, it is very negative. On the other side, Arsenal is having a positive influence in winning air challenges with victory and not losing the match. Successful tackles are having the most negative influence on the Manchester City game again as same as lost balls. Ball recoveries are having a most positive influence on Arsenal’s game, same as ball recoveries in the opponent’s half, team pressing, team pressing successfully and per cent of pressing efficiency. Ball possession quantity is having the most negative influence on the Manchester City game while the average duration of ball possession is having the most positive influence on the Manchester City game. This might indicate that in games in which Manchester City had a lot of possession whit small average possession of the ball possession and with a lot of interruption in actions from the opposite team. Attacks on the left flank are most negative for Manchester City and most positive for Liverpool. The positional attack is the most negative for the Manchester City game the same as per cent of efficiency of throw-in actions. Liverpool “does not like” free kicks with shots because it has a negative influence on their game. Penalties have a positive influence on Arsenal’s game.

Table 1.: Heatmap of correlations between chances and different aspects of game per each club

The following models will indicate to us what is specific for reaching each of the criteriums and for each club. In each of the tables for regression models, green will be marked aspects of the game which have a statistically positive significant correlation, red will be marked aspects of the game which have a statistically negative significant correlation and those aspects of the game which don’t have any statistically significant correlation will not be marked.

It is interesting that some clubs’ models have fewer independent variables and in some more. It might be that in clubs with fewer variables, independent variables are more influential on dependent variables than in models with more independent variables in the model. It means that experts would have fewer variables to focus on if they would use this model to improve chances, and goals, not being defeated or winning. Also seeing variables in chances, one can see what each club is focused on. It might be interesting for planning matches or for opponent teams playing games against such clubs.

Regression models for “create a chance”

Adjusted R square is very strong (very high) for the “create chance” criterium, for all clubs. It goes from 0,808 for Liverpool, over 0.877 for Manchester City till 0.926 for Arsenal. Only key passes – accurate and penalties are present in all models.

For creating chances for Arsenal, it is important to have efficient pressing, ball recoveries in the opponent’s half, counter attacks among others. Arsenal has attacks with shots – set pieces attacks while Liverpool has entrance to the penalty box with shots on target and Manchester City has set pieces attacks, dribbles, key passes accurate and shots on target. It might be observed from the perspective of distance from goal and from the perspective of how they “materialize” chances. It seems that Liverpool and Manchester City are more similar than Arsenal is with these two clubs. It could be argued that the difference between Liverpool and Manchester City is by “principle” how they “approach” the chance. It might be that Liverpool is reaching the penalty box by actions before the shot while Manchester City also use dribbles before chance not just from the penalty box.

Regression models for “score a goal”

To score a goal is more difficult to predict than to make a chance. Still, the adjusted R square is strong above 0,600. On the other side, in models for “goals” is much more variables with a negative significant Pearson correlation. Have in mind to see those variables as those which should be reduced in the potential game plan. There is no single variable which is common for all clubs.

It is interesting to see that in this criteria, Arsenal is also having more “distance” from the other two clubs. While Arsenal is scoring from a more “defensive” position using ball recoveries in the opponent’s half and counter-attacks in Liverpool and Manchester City there is no such case. Both Liverpool and Manchester City are more “pressing oriented” and focused on the “ground”, avoiding the ball in the air.

Regression models for “not defeat”

Not to be defeated is the most difficult to predict of all four observed variables and it is especially radical in Liverpool’s case. Adjusted R square is very weak, just above 0,100. Models for “not defeat” have the least variables per model and again, especially for Liverpool. There is no single variable present in all models.

For Arsenal model consists of similar variables while the situation is changed in Liverpool and Manchester City case. In Liverpool’s case, there is just one variable, shots on post/bar which should be reduced. It might indicate that in a number of matches which they have lost, they had shot in post/bar. There was no single other variable which was significant for the model for not losing the match. Manchester City is very sensitive on red cards (the other two clubs are not), tackles and crosses and Manchester should reduce those variables in the game.

Regression models for victory

Although all of the models have their purpose in planning the game, and it might be that is best to observe them all in planning, victory might be the most interesting because victory brings the most to the team, players, coach, and the club. Adjusted R square is between moderate and strong, from 0,598 to 0,673. There is no single variable which is present in all models

There is a difference between the model in this criterion and another in the Arsenal case. In all other criteria, counterattacks were part of the model, while the “victory” model is not. On the other hand, there are two new variables in the model, per cent of pressing efficiency and defensive challenges won. It might be argued that when Arsenal has successful pressing and defensive challenges won that it indicates a victory for them. In Liverpool’s case, per cent of efficiency of attacks through the left flank is very important. On the other hand, if they are “forced” on yellow cards it indicates not winning the match. Obviously, a lot of shots on post/bar. And while the defence won the challenges, chances for victory are better. In Manchester City’s case, if they succeed in not being “interrupted” you might lose the match against them. If they succeed to dribble their way in and have efficiency in shooting the target be aware.

Compared to the last, what changed in Arsenal, Manchester City and Liverpool this season?

This analysis was made on the first 17 rounds of the Premier league. Besides this season, the highest changes are identified and presented. Also, the biggest changes which have significant statistical correlation this season is also given. Analysis was made based on the criterium “victory”. It means that all presented data all in relation to victory. If it is positive, it means that when the frequency is higher, victory is more likely to happen (e.g., more shots more possibility for victory), and vice versa (e.g., more lost balls, less possibility for victory). It is interesting to notice that there are not just minor differences in correlations between clubs. Also, it is very indicative to see where the highest changes in correlation with victory happened this season compared to last.

Statistical correlation goes from -1 to +1. -1 means that if something happens, the other variable definitely will not happen, while +1 implies that if something happens, the other analysed variable will definitely happen.

Correlation in season 2022/23

Arsenal’s „shots on target“ have a moderate correlation with victory in opposition to two others clubs which means that they are more precise or have better-prepared situations for shooting on goal or better decision–making in execution. On the other side shots wide have a negative correlation with victory for Arsenal while Liverpool has an almost moderate positive correlation. It might mean that Liverpool achieves victory through the high intensity of attacks; on a higher amount of attacks with shots comes a higher amount of victories. On the other side, very moderate, near-strong negative correlation Manchester City has with blocked shots. Liverpool doesn’t have it negatively at all, but Arsenal does have it, almost weaker than Manchester City.

here is an indicative difference between Arsenal on one side and Liverpool and Manchester City on the other side in crosses. Arsenal has a weak but positive correlation while Arsenal and Manchester City have between weak and moderate and negative correlation.

In all types of challenges, Arsenal has no stronger significant correlation with victory, but Manchester City and Liverpool do. It means that challenges do not influence the victory of Arsenal but do influence negatively the victories of Manchester City and Liverpool. This is very important in understanding the game styles of those three clubs. It is especially noticeable in air challenges where Arsenal has a weak to moderate positive correlation with the victory while Liverpool and especially Manchester City, have a very strong negative correlation.

Ball recoveries and ball interceptions have a positive weak correlation with victory in Arsenal’s case but don’t have for others.

Liverpool has a strong – very strong negative correlation between lost balls and victory. Manchester City has weak negative and Arsenal doesn’t have a significant correlation at all between lost balls and victory. Obviously, Arsenal had a better response on lost balls in the first 17 rounds, while Liverpool has the worst and even generally poor reaction on lost balls when we are analysing the influence on victory.

An interesting difference between Arsenal and Manchester City is that Arsenal has a weak – moderate negative correlation with ball recoveries in the opponent’s half while Manchester City has a moderate positive correlation with victory. So, for Manchester City is, generally, very important to play on the opposite side of the field while Arsenal does not respond positively in such a situation.

All teams have a similar moderate positive correlation between team pressing and victory, with Manchester City having the strongest and Liverpool the weakest.

One more interesting difference between Arsenal and the other two teams is in the opponent’s passes per defensive action. Arsenal doesn’t have a significant correlation while Manchester City and Liverpool do have a moderate – strong positive correlation. It means they actually do more opponent’s passes per defensive action while for Arsenal is irrelevant. Manchester City prefers the right flank while Arsenal is balanced with a little more flavour on both flanks than in the centre.

Liverpool does like the left flank in case they succeed to have a shot in such an action. If not, it might be dangerous for them. Also, they like attacks through the centre.

Neither of the clubs has a positive significant influence on positional attacks with victory. Obviously, they need to use some other mechanisms to win a match. Even more, all clubs have a significant negative correlation with victory, Arsenal is weak, and Manchester City and Liverpool strong negative correlation between positional attacks and victory.

All teams, if they succeed to shoot have a positive significant correlation between counterattacks with victory in which Arsenal have a moderate correlation, the best of all three teams, while Liverpool and finally Manchester City, have a weak positive correlation.

Liverpool like free-kick attacks with shots but doesn’t like throw-ins. For the other two teams, there is no significant correlation.

It seems that there is a positive, weak, but significant correlation between penalties and victories in the Manchester City case.

The biggest difference in correlation from season 2021/22 to 2022/23 with victory

In this paragraph, I’m analysing which segments of the game made the highest change from last season. It is measured in statistical correlation with victory.

Arsenal made the highest increase in significant correlation with victory in fouls (an increase of 0.41) and air challenges (an increase of 0.35). On the other side highest decrease in significant correlation Arsenal made with: ball recoveries in the opponent’s half (-0.78), blocked shots (-0.67), counter-attacks (-0.57), shots wide (-0.56), positional attacks with shots (-0.55) and efficiency for attacks through the left flank, % (-0.54). It seems that Arsenal is more capable to create successful actions from “scratch” this season than last and they have more options in the creation of successful actions which might be more direct this season than the last one. It does not mean necessarily that Arsenal has weaker ball recoveries, blocked shots and other above-stated variables, but because of the change in game style, they are using it less.

For Liverpool highest increase was in the opponent’s passes per defensive action (0.85), counter-attacks (0.45) and free-kick attacks with shots (0.40). The highest decrease in correlation with a victory was in: ball possessions, quantity (-0.66), positional attacks (-0.64) and total actions (-0.56). It is obvious that Liverpool is having decreased in the creation of successful actions from “scratch”, but they perform well if the ball is in the opponents’ possession and combined with counter-attacks as well, but it does not bring more victories to them.

Manchester City had the highest increase in correlation with victory in opponent’s passes per defensive action (0.74), tackles successful (0.52), team pressing (0.48), red cards (0.46) and the team pressing successfully (0.46). On the other side highest decrease with a victory was in attacks – centre (-0.59) and attacks with shots – centre (-0.49). It might be that Manchester City has increased intensity in team pressing this year but it might be over-aggressive which results in red cards, tackles and team pressing itself. Also, they have started to use both sides as well as centres for attacks. Or maybe more precisely, they are much more expected from opponents in attacks in the centre while flanks are more open. If Manchester persists in attacks in the centre, its lower chances for victory. Manchester should spread attacks on both sides as well.

Highest statistically significant correlations among the biggest difference in correlation from season 2021/22 to 2022/23 with victory

An increase in Arsenal with fouls and air challenges has a 0.28 strength of positive correlation which is a weak correlation. The strongest negative correlation is with shots wide (-0.42), Ball recoveries in the opponent’s half (-0.35) and blocked shots (-0.33) which are moderate negative correlations. Arsenal obviously has problems if they lose the ball on the opponent’s side and they should stream to finish each action rather than be interrupted in the field which has a negative influence on victory. It might be that the next step in the development of their game will be in reaction to lost balls and preventing counter-attacks.

For Liverpool highest positive correlation among those with the highest change in correlation with victory is with: the opponent’s passes per defensive action (0.50) – strong/moderate, counter-attacks (0.26) and free-kick attacks with shots (0.21) – weak. The strongest negative correlation among those which decreased significantly is with: ball possessions, quantity (-0.60), and positional attacks (-0.53). Liverpool is not using possession of the ball efficiently, and they achieve their victories more through counterattacks and opponents’ instability after losing the ball in the field. There might be more reasons for this including motivation and intensity of the general performance.

In Manchester City’s case strongest positive correlation with victory, among those correlations which increased most is with: Opponent’s passes per defensive action (0.69), team pressing (0.53) and the team pressing successfully (0.51) which are all strong correlations. The highest negative significant correlation among those which decreased most in Manchester is with: attacks – centre (-0.63) – strong and attacks with shots – centre (-0.37) – moderate. Manchester City is very efficiently using team pressing and this segment is something their opponents should be aware of. On the other side, when Manchester is in possession of the ball, this year, opponents are prepared for their actions through the centre and Manchester should change something or move to flanks as well to create more time for their players to use more available space than if they use just centre.

Do leagues generally differ between themselves on the pitch by different game aspects and how?

Have you ever wondered do leagues between themselves differ between each other based on the overall performance on the pitch? I mean of course they differ, if you have team with 100 times more values than team in another league, but how do they differ? And how would you identify the differences between leagues?

I have tested nine leagues in last season (2020/2021) by different aspects of the game: England, Spain, German, Italy, France, Belgium, Austria, Croatia and Poland. And I have used four criteriums to identify those differences:

1. What are key factors to create a chance in the individual league?

2. What are key factors on the pitch for scoring a goal in the individual league?

3. What are key factors on the pitch for not losing the game in the individual league?

4. What are key factors on the pitch for winning a match in individual league?

To identify those goals, I have used:

frequency (what is most used in each league),
statistical correlation and
statistical regression.

The frequency of 104 observed aspects of the game will tell us which aspect of the match is happening the most. That will not help us understanding how to reach each of criterium.

On the opposite, statistical correlation will tell us what correlate with each of criterium and help us started understanding each of criteriums per country. So, more interesting will be those aspects of the game which have stronger correlation with each of criterium.

Regression models will indicate us what is specific for reaching each of criteriums per each country and help us better understand each of criteriums. For best understanding for each country will be summarized correlations and regression models as preparation for interpretation.

For all observed dependent variables (criteriums) aim was to get the highest possible R number with the same methodology for all observed dependent variables and for all leagues. R number explains the power of the model. It lies between 0 and 1. Higher the number more precise model. It actually explains the number of variations in the dependent variable which are explained with the independent variables. Adjusted R square is R number multiplied with adjustment factor created by comparison different regression models with different independent variables.

All variables in model are statistically significant, and those which are positively statistically significant in Pearson correlation are marked green and those which are negatively statistically significant in Pearson correlation are marked red. Those which are not statistically significant in Pearson correlation are not marked. Mostly VIF number is below 5, almost all bellow 10, and rarely above 10.

Finally, I will leave each of you to give “sweet” interpretation of results. And would like to hear your thoughts about interpretation from your side 😉

In each of following heatmap and highlight map correlation of different aspects of game are presented in tables. So, let’s start with correlations between “creating a chance” and different aspects of game per countries.

Table 1.: Heatmap of correlations between chances and different aspects of game per countries

CHANCES	England	Spain	France	Germany	Italy	Austria	Belgium	Croatia	Poland
Attacks – left flank	0.178	0.158	0.160	0.078	0.197	0.199	0.098	0.252	0.145
Attacks with shots – left flank	0.413	0.400	0.350	0.364	0.465	0.386	0.414	0.421	0.485
Efficiency for attacks through the left flank, %	0.361	0.374	0.289	0.353	0.415	0.363	0.398	0.316	0.456
Attacks – center	0.201	0.121	0.233	0.257	0.111	0.264	0.196	0.088	0.108
Attacks with shots – center	0.438	0.343	0.423	0.422	0.383	0.489	0.380	0.240	0.370
Efficiency for attacks through the central zone, %	0.373	0.300	0.348	0.333	0.360	0.397	0.342	0.222	0.332
Attacks – right flank	0.267	0.118	0.069	0.122	0.131	0.192	0.178	0.278	0.113
Attacks with shots – right flank	0.450	0.390	0.323	0.392	0.411	0.421	0.450	0.427	0.389
Efficiency for attacks through the right flank, %	0.380	0.354	0.321	0.375	0.377	0.414	0.399	0.338	0.386
Positional attacks	0.354	0.240	0.229	0.235	0.213	0.311	0.229	0.324	0.176
Positional attacks with shots	0.579	0.547	0.512	0.547	0.532	0.558	0.568	0.505	0.537
% of efficiency for positional attacks	0.538	0.516	0.479	0.532	0.504	0.536	0.543	0.428	0.527
Counter-attacks	0.122	0.032	0.085	0.099	0.169	0.188	0.179	0.159	0.159
Counter-attacks with a shot	0.375	0.307	0.319	0.342	0.420	0.384	0.371	0.332	0.439
% of efficiency for counterattacks	0.354	0.304	0.307	0.333	0.367	0.309	0.314	0.293	0.401
Set pieces attacks	0.296	0.199	0.329	0.154	0.317	0.247	0.209	0.408	0.258
Attacks with shots – Set pieces attacks	0.429	0.327	0.381	0.384	0.448	0.393	0.359	0.496	0.397
% of efficiency for set-piece attacks	0.292	0.261	0.167	0.317	0.293	0.287	0.253	0.282	0.280
Free-kick attacks	-0.022	-0.054	0.073	-0.036	0.032	-0.039	-0.008	0.129	0.010
Free-kick attacks with shots	0.170	0.111	0.170	0.170	0.203	0.147	0.137	0.231	0.182
% of efficiency for free-kick attacks	0.192	0.166	0.148	0.214	0.201	0.155	0.151	0.187	0.232
Corner attacks	0.420	0.338	0.339	0.359	0.420	0.404	0.359	0.459	0.368
Corner attacks with shots	0.411	0.319	0.319	0.363	0.419	0.372	0.358	0.449	0.364
% of efficiency for corner attacks	0.162	0.141	0.107	0.151	0.208	0.158	0.165	0.218	0.149
Throw-in attacks	-0.058	-0.063	0.070	-0.172	-0.068	-0.001	-0.091	0.054	-0.076
Throw-in attacks with shots	-0.018	0.002	0.023	-0.058	-0.019	0.054	-0.003	0.079	-0.025
% of efficiency for throw-in attacks	0.003	-0.019	-0.007	-0.029	-0.037	0.063	0.020	0.039	-0.026
Free-kick shots	0.168	0.078	0.166	0.070	0.179	0.213	0.057	0.102	0.170
Goals – Free-kick attack	0.056	0.037	0.115	0.118	0.080	0.126	0.060	0.082	0.023
% scored free kick shots	0.032	0.042	0.111	0.110	0.071	0.076	0.059	0.053	0.028

If we highlight corelations stronger than +/- 0,400 than we have different table:

Table 2.: Highlight of correlations stronger than +/- 0,400 between chances and different aspects of game per countries

CHANCES	England	Spain	France	Germany	Italy	Austria	Belgium	Croatia	Poland
Attacks with shots – left flank	0.413	0.400	0.350	0.364	0.465	0.386	0.414	0.421	0.485
Efficiency for attacks through the left flank, %	0.361	0.374	0.289	0.353	0.415	0.363	0.398	0.316	0.456
Attacks with shots – center	0.438	0.343	0.423	0.422	0.383	0.489	0.380	0.240	0.370
Attacks with shots – right flank	0.450	0.390	0.323	0.392	0.411	0.421	0.450	0.427	0.389
Efficiency for attacks through the right flank, %	0.380	0.354	0.321	0.375	0.377	0.414	0.399	0.338	0.386
Positional attacks with shots	0.579	0.547	0.512	0.547	0.532	0.558	0.568	0.505	0.537
% of efficiency for positional attacks	0.538	0.516	0.479	0.532	0.504	0.536	0.543	0.428	0.527
Counter-attacks with a shot	0.375	0.307	0.319	0.342	0.420	0.384	0.371	0.332	0.439
% of efficiency for counterattacks	0.354	0.304	0.307	0.333	0.367	0.309	0.314	0.293	0.401
Set pieces attacks	0.296	0.199	0.329	0.154	0.317	0.247	0.209	0.408	0.258
Attacks with shots – Set pieces attacks	0.429	0.327	0.381	0.384	0.448	0.393	0.359	0.496	0.397
Corner attacks	0.420	0.338	0.339	0.359	0.420	0.404	0.359	0.459	0.368
Corner attacks with shots	0.411	0.319	0.319	0.363	0.419	0.372	0.358	0.449	0.364

Highest number of correlations stronger than +/- 0,400 have Italy (9), while least France, Germany and Belgium (3). While there are some common correlations between all or most countries, like for “Positional attacks with shots” (no exclusion), “% of efficiency for positional attacks” (no exclusion), “Attacks with shots – left flank” (France, Germany and Austria excluded), “Attacks with shots – centre” (Spain, Italy, Belgium, Croatia and Poland excluded) and “Corner attacks” (Spain, France, Germany Belgium and Poland excluded). On the other side, there are some specifies for single of few countries. “Efficiency for attacks through the left flank, %” is specific for Italy and Poland, “Efficiency for attacks through the right flank, %” is specific just for Austria, “Counter-attacks with a shot” is specific just for Italy and “Set pieces attacks” just for Croatia.

Among all correlations strongest is correlation in England with “Positional attacks with shots” (0,579**).

When we look at correlations between “score a goal” and different aspects of game we have generally weaker correlations.

Table 3.: Heatmap of correlations between goals and different aspects of game per countries

GOALS	England	Spain	France	Germany	Italy	Austria	Belgium	Croatia	Poland
Attacks – left flank	0.009	-0.006	-0.055	-0.037	-0.032	0.023	-0.030	0.085	-0.028
Attacks with shots – left flank	0.238	0.201	0.126	0.209	0.240	0.234	0.241	0.215	0.214
Efficiency for attacks through the left flank, %	0.274	0.220	0.151	0.241	0.274	0.266	0.270	0.197	0.248
Attacks – center	0.185	0.025	0.111	0.141	0.062	0.065	0.039	0.051	0.042
Attacks with shots – center	0.394	0.234	0.297	0.279	0.229	0.299	0.170	0.175	0.226
Efficiency for attacks through the central zone, %	0.322	0.222	0.258	0.233	0.224	0.297	0.180	0.168	0.228
Attacks – right flank	0.044	-0.093	-0.116	-0.108	-0.040	0.030	-0.064	0.050	-0.089
Attacks with shots – right flank	0.238	0.174	0.148	0.158	0.230	0.254	0.226	0.220	0.147
Efficiency for attacks through the right flank, %	0.271	0.221	0.217	0.222	0.263	0.277	0.268	0.209	0.193
Positional attacks	0.113	-0.046	-0.057	-0.029	-0.012	0.008	-0.078	0.097	-0.110
Positional attacks with shots	0.371	0.242	0.211	0.248	0.250	0.278	0.228	0.227	0.187
% of efficiency for positional attacks	0.410	0.281	0.267	0.289	0.281	0.323	0.289	0.226	0.241
Counter-attacks	0.047	-0.023	0.017	0.046	-0.008	0.141	0.079	0.040	0.132
Counter-attacks with a shot	0.277	0.244	0.250	0.283	0.292	0.335	0.278	0.288	0.328
% of efficiency for counterattacks	0.280	0.248	0.272	0.300	0.349	0.263	0.281	0.304	0.292
Set pieces attacks	0.016	-0.069	0.017	-0.094	-0.024	-0.054	-0.029	0.105	-0.038
Attacks with shots – Set pieces attacks	0.167	0.057	0.162	0.123	0.110	0.083	0.144	0.240	0.113
% of efficiency for set-piece attacks	0.217	0.146	0.163	0.268	0.188	0.172	0.218	0.286	0.174
Free-kick attacks	-0.090	-0.164	-0.047	-0.117	-0.142	-0.198	-0.086	-0.038	-0.054
Free-kick attacks with shots	0.033	-0.062	0.057	0.048	-0.017	-0.100	0.009	0.124	0.048
% of efficiency for free-kick attacks	0.093	0.074	0.082	0.144	0.059	0.017	0.075	0.184	0.078
Corner attacks	0.092	0.028	0.007	0.066	0.044	0.070	0.035	0.176	0.003
Corner attacks with shots	0.152	0.058	0.091	0.119	0.087	0.140	0.154	0.181	0.087
% of efficiency for corner attacks	0.121	0.069	0.122	0.111	0.104	0.136	0.158	0.153	0.130
Throw-in attacks	-0.085	-0.082	0.033	-0.212	-0.064	-0.058	-0.078	-0.079	-0.098
Throw-in attacks with shots	-0.042	-0.030	0.037	-0.123	-0.037	-0.050	-0.062	-0.030	-0.086
% of efficiency for throw-in attacks	0.000	-0.033	-0.013	-0.083	-0.034	-0.037	-0.039	-0.046	-0.119
Free-kick shots	0.009	-0.055	0.073	0.019	0.007	-0.091	0.006	0.041	0.061
Goals – Free-kick attack	0.038	0.058	0.171	0.211	0.173	0.077	0.080	0.151	0.161
% scored free kick shots	0.033	0.062	0.186	0.203	0.169	0.084	0.080	0.104	0.153

If we highlight corelations stronger than +/- 0,275 than we have different table:

Table 4.: Highlight of correlations stronger than +/-0,275 between goals and different aspects of game per countries

GOALS	England	Spain	France	Germany	Italy	Austria	Belgium	Croatia	Poland
Attacks with shots – center	0.394	0.234	0.297	0.279	0.229	0.299	0.170	0.175	0.226
Efficiency for attacks through the central zone, %	0.322	0.222	0.258	0.233	0.224	0.297	0.180	0.168	0.228
Efficiency for attacks through the right flank, %	0.271	0.221	0.217	0.222	0.263	0.277	0.268	0.209	0.193
Positional attacks with shots	0.371	0.242	0.211	0.248	0.250	0.278	0.228	0.227	0.187
% of efficiency for positional attacks	0.410	0.281	0.267	0.289	0.281	0.323	0.289	0.226	0.241
Counter-attacks with a shot	0.277	0.244	0.250	0.283	0.292	0.335	0.278	0.288	0.328
% of efficiency for counterattacks	0.280	0.248	0.272	0.300	0.349	0.263	0.281	0.304	0.292
% of efficiency for set-piece attacks	0.217	0.146	0.163	0.268	0.188	0.172	0.218	0.286	0.174

There is similarity between correlations with “create a chance” and “score a goal”. Again, England (7) and Austria (6) have most correlations above +/- 0,275, while least France (2) and Poland (2). Corelations which are most common are “% of efficiency for positional attacks” (Spain, France, Croatia and Poland excluded), “Counter-attacks with a shot” (Spain and France excluded) and “% of efficiency for counterattacks” (Spain and Austria excluded). On the other side there are some specifies like “Efficiency for attacks through the central zone, %”, “Efficiency for attacks through the right flank, %” and “Positional attacks with shots” for England and Austria and “% of efficiency for set-piece attacks” for Croatia.

Even weaker correlations are with “not defeat”.

Table 5.: Heatmap of correlations between not defeat and different aspects of game per countries

NOT DEFEAT	England	Spain	France	Germany	Italy	Austria	Belgium	Croatia	Poland
Attacks – left flank	0.057	0.011	-0.003	0.024	0.014	0.017	-0.013	0.095	-0.024
Attacks with shots – left flank	0.209	0.105	0.057	0.132	0.183	0.152	0.121	0.198	0.165
Efficiency for attacks through the left flank, %	0.198	0.096	0.060	0.146	0.189	0.157	0.146	0.160	0.192
Attacks – center	0.172	0.004	0.090	0.099	0.111	0.028	0.064	0.096	0.084
Attacks with shots – center	0.283	0.126	0.167	0.155	0.185	0.127	0.091	0.070	0.175
Efficiency for attacks through the central zone, %	0.221	0.128	0.155	0.106	0.162	0.122	0.077	0.023	0.132
Attacks – right flank	0.088	0.005	-0.059	0.012	0.035	-0.014	0.025	0.207	-0.126
Attacks with shots – right flank	0.187	0.084	0.128	0.130	0.151	0.148	0.164	0.179	0.093
Efficiency for attacks through the right flank, %	0.176	0.081	0.159	0.142	0.142	0.187	0.171	0.121	0.131
Positional attacks	0.144	-0.003	-0.011	0.034	0.042	-0.048	-0.019	0.193	-0.141
Positional attacks with shots	0.265	0.098	0.120	0.167	0.170	0.140	0.099	0.181	0.114
% of efficiency for positional attacks	0.260	0.097	0.144	0.172	0.176	0.186	0.120	0.126	0.163
Counter-attacks	0.108	0.041	0.049	0.118	0.127	0.156	0.151	0.119	0.211
Counter-attacks with a shot	0.258	0.178	0.161	0.153	0.255	0.201	0.221	0.192	0.276
% of efficiency for counterattacks	0.220	0.158	0.147	0.118	0.240	0.140	0.215	0.169	0.211
Set pieces attacks	0.073	-0.071	0.043	-0.046	0.037	-0.011	-0.008	0.124	-0.009
Attacks with shots – Set pieces attacks	0.172	0.030	0.096	0.113	0.098	-0.007	0.099	0.229	0.032
% of efficiency for set-piece attacks	0.174	0.082	0.055	0.204	0.100	0.034	0.134	0.222	0.073
Free-kick attacks	-0.049	-0.097	-0.027	-0.106	-0.060	-0.130	-0.074	-0.035	-0.053
Free-kick attacks with shots	0.029	-0.040	0.044	0.005	0.017	-0.129	0.013	0.082	-0.012
% of efficiency for free-kick attacks	0.049	0.050	0.092	0.068	0.051	-0.043	0.068	0.105	0.026
Corner attacks	0.134	-0.040	0.035	0.034	0.081	0.095	0.035	0.164	0.039
Corner attacks with shots	0.183	0.039	0.045	0.118	0.071	0.092	0.089	0.200	0.073
% of efficiency for corner attacks	0.139	0.070	0.011	0.092	0.050	0.092	0.095	0.137	0.085
Throw-in attacks	-0.040	-0.016	0.050	-0.051	-0.034	-0.041	-0.013	0.029	-0.045
Throw-in attacks with shots	-0.018	-0.007	0.018	0.014	-0.001	-0.087	-0.007	0.038	-0.107
% of efficiency for throw-in attacks	0.013	-0.014	-0.040	0.027	-0.005	-0.110	0.020	-0.011	-0.135
Free-kick shots	0.023	-0.077	0.029	-0.012	0.043	-0.050	0.008	-0.017	0.034
Goals – Free-kick attack	0.052	-0.021	0.095	0.065	0.047	0.025	0.024	0.058	0.054
% scored free kick shots	0.056	-0.013	0.085	0.053	0.045	0.039	0.024	0.038	0.048

If we highlight correlations stronger than +/- 0,200 we will see that overall number of correlations is smaller than for previous two criteriums.

Table 5.: Highlight of correlations stronger than +/- 0,200 between not defeat and different aspects of game per countries

NOT DEFEAT	England	Spain	France	Germany	Italy	Austria	Belgium	Croatia	Poland
Attacks with shots – left flank	0.209	0.105	0.057	0.132	0.183	0.152	0.121	0.198	0.165
Attacks with shots – center	0.283	0.126	0.167	0.155	0.185	0.127	0.091	0.070	0.175
Efficiency for attacks through the central zone, %	0.221	0.128	0.155	0.106	0.162	0.122	0.077	0.023	0.132
Positional attacks with shots	0.265	0.098	0.120	0.167	0.170	0.140	0.099	0.181	0.114
% of efficiency for positional attacks	0.260	0.097	0.144	0.172	0.176	0.186	0.120	0.126	0.163
Counter-attacks	0.108	0.041	0.049	0.118	0.127	0.156	0.151	0.119	0.211
Counter-attacks with a shot	0.258	0.178	0.161	0.153	0.255	0.201	0.221	0.192	0.276
% of efficiency for counterattacks	0.220	0.158	0.147	0.118	0.240	0.140	0.215	0.169	0.211
Attacks with shots – Set pieces attacks	0.172	0.030	0.096	0.113	0.098	-0.007	0.099	0.229	0.032
% of efficiency for set-piece attacks	0.174	0.082	0.055	0.204	0.100	0.034	0.134	0.222	0.073
Corner attacks with shots	0.183	0.039	0.045	0.118	0.071	0.092	0.089	0.200	0.073

England is having most correlations (7) while Spain, France and Austria have no single correlation stronger than +/- 0,200. “Counter-attacks with a shot” (England, Italy and Poland) and “% of efficiency for counterattacks” (England, Italy, Belgium and Poland) is most common aspect of game in correlations. Strongest correlation is and “% of efficiency for counterattacks” in Poland (0,276).

Finally, are presented correlations with “victory” which are stronger than correlations with “not defeat”.

Table 7.: Heatmap of correlations between victory and different aspects of game per countries

VICTORY	England	Spain	France	Germany	Italy	Austria	Belgium	Croatia	Poland
Attacks – left flank	0.021	-0.014	-0.011	-0.002	-0.014	-0.020	-0.029	0.118	-0.054
Attacks with shots – left flank	0.221	0.130	0.086	0.177	0.179	0.143	0.131	0.211	0.116
Efficiency for attacks through the left flank, %	0.235	0.146	0.080	0.201	0.191	0.169	0.161	0.174	0.151
Attacks – center	0.154	0.070	0.086	0.095	0.076	0.045	0.051	0.088	0.049
Attacks with shots – center	0.281	0.173	0.158	0.164	0.196	0.206	0.071	0.174	0.214
Efficiency for attacks through the central zone, %	0.228	0.143	0.135	0.125	0.180	0.200	0.060	0.139	0.213
Attacks – right flank	0.056	-0.065	-0.130	-0.072	-0.019	0.033	0.001	0.101	-0.115
Attacks with shots – right flank	0.183	0.131	0.084	0.122	0.195	0.127	0.176	0.186	0.115
Efficiency for attacks through the right flank, %	0.179	0.161	0.162	0.167	0.206	0.133	0.184	0.166	0.169
Positional attacks	0.115	-0.018	-0.074	-0.006	0.010	-0.065	-0.021	0.155	-0.139
Positional attacks with shots	0.287	0.146	0.113	0.188	0.183	0.107	0.108	0.185	0.099
% of efficiency for positional attacks	0.296	0.171	0.166	0.215	0.197	0.148	0.133	0.153	0.155
Counter-attacks	0.042	0.000	0.075	0.036	0.022	0.228	0.068	0.074	0.122
Counter-attacks with a shot	0.228	0.225	0.161	0.174	0.277	0.326	0.208	0.319	0.325
% of efficiency for counterattacks	0.228	0.222	0.149	0.179	0.300	0.214	0.221	0.316	0.311
Set pieces attacks	-0.002	-0.101	-0.013	-0.114	-0.055	-0.037	-0.086	0.044	-0.066
Attacks with shots – Set pieces attacks	0.142	0.011	0.050	0.059	0.036	-0.001	0.065	0.194	0.004
% of efficiency for set-piece attacks	0.195	0.085	0.039	0.194	0.122	0.023	0.157	0.264	0.076
Free-kick attacks	-0.135	-0.200	-0.051	-0.112	-0.156	-0.169	-0.098	-0.075	-0.099
Free-kick attacks with shots	0.003	-0.088	0.009	0.047	-0.029	-0.115	0.006	0.068	-0.047
% of efficiency for free-kick attacks	0.085	0.049	0.027	0.135	0.056	-0.018	0.074	0.112	0.017
Corner attacks	0.113	0.006	-0.022	-0.006	0.035	0.109	-0.025	0.117	0.010
Corner attacks with shots	0.144	0.030	-0.005	0.018	0.032	0.073	0.070	0.199	0.044
% of efficiency for corner attacks	0.097	0.022	0.000	0.027	0.041	0.035	0.087	0.198	0.080
Throw-in attacks	-0.099	-0.059	0.025	-0.148	-0.083	-0.081	-0.087	-0.066	-0.099
Throw-in attacks with shots	0.005	-0.022	0.015	-0.053	-0.044	-0.067	-0.072	-0.085	-0.103
% of efficiency for throw-in attacks	0.045	-0.017	-0.023	-0.030	-0.056	-0.082	-0.033	-0.130	-0.126
Free-kick shots	0.054	-0.088	0.039	0.045	-0.019	-0.103	0.024	0.017	0.000
Goals – Free-kick attack	0.055	-0.030	0.089	0.146	0.087	0.072	0.045	0.075	0.100
% scored free kick shots	0.046	-0.027	0.113	0.131	0.090	0.079	0.052	0.038	0.094

If we highlight correlations stronger than +/- 0,200 we will see that overall number of correlations is higher than for previous criterium.

Table 7.: Highlight of correlations between victory and different aspects of game per countries

VICTORY	England	Spain	France	Germany	Italy	Austria	Belgium	Croatia	Poland
Attacks with shots – left flank	0.221	0.130	0.086	0.177	0.179	0.143	0.131	0.211	0.116
Efficiency for attacks through the left flank, %	0.235	0.146	0.080	0.201	0.191	0.169	0.161	0.174	0.151
Attacks with shots – center	0.281	0.173	0.158	0.164	0.196	0.206	0.071	0.174	0.214
Efficiency for attacks through the central zone, %	0.228	0.143	0.135	0.125	0.180	0.200	0.060	0.139	0.213
Efficiency for attacks through the right flank, %	0.179	0.161	0.162	0.167	0.206	0.133	0.184	0.166	0.169
Positional attacks with shots	0.287	0.146	0.113	0.188	0.183	0.107	0.108	0.185	0.099
% of efficiency for positional attacks	0.296	0.171	0.166	0.215	0.197	0.148	0.133	0.153	0.155
Counter-attacks	0.042	0.000	0.075	0.036	0.022	0.228	0.068	0.074	0.122
Counter-attacks with a shot	0.228	0.225	0.161	0.174	0.277	0.326	0.208	0.319	0.325
% of efficiency for counterattacks	0.228	0.222	0.149	0.179	0.300	0.214	0.221	0.316	0.311
% of efficiency for set-piece attacks	0.195	0.085	0.039	0.194	0.122	0.023	0.157	0.264	0.076

Again, England have most correlations (8), while France has no correlations stronger than +/- 0,200. Most common correlations are with “Counter-attacks with a shot” and “% of efficiency for counterattacks” (France and Germany excluded for both). Strongest correlation is with “Counter-attacks with a shot” in Austria.

Models for observed criteriums

Following models will indicate to us what is specific for reaching a criterium. For each criterium, models are presented for each country. In each of tables for regression models, green will be marked aspects of the game which have statistically positive significant correlation, red will be marked aspects of the game which have statistically negative significant correlation and those aspects of the game which don’t have any statistically significant correlation will not be marked.

It is interesting that in some country’s models have fewer independent variables and in some more. It might be that in countries with less variables, each of those independent variables are more influential on dependent variable than in models with more independent variables in the model. It means that experts would have less variables to focus if they would use this model to improve chances, goals, not being defeated or winning. Also seeing variables in chances, one can see where clubs from certain country are generally focused to create chances from. It might be interesting for planning matches against such clubs. Of course, it is not the same for all clubs from certain league since it can oscillate significantly but still it might open some new perspectives on playing style from clubs of observed leagues.

Regression models for “create a chance”

Adjusted R square is strong (very high) for “create chance” criterium, for all countries above 0,800. It goes from 0,810 in France till 0.851 in Belgium. Although some variables are unique present in all models, there are differences between leagues. All leagues uniquely have key passes accurate and shots on target in the model, almost all have entrance to the penalty box (Germany don’t) and shots on post/bar (England don’t).

England is country with 10 variables in the model. It has one variable which have negative significant correlation with chances in England, red cards and it should be reduced to achieve more chances logically. Also, it is uniqueness of their model together with blocked shots and defensive challenges won.

England 2021/22

Adj. R sq. 0,824

Key passes accurate

Shots

Blocked shots

Shots wide

Attacks with shots – Set pieces attacks

Free-kick shots

Entrance to the penalty box

Shots on target

Red cards

Defensive challenges won

In England highest correlation with “create a chance” have “Positional attacks with shots” (0.579) and “% of efficiency for positional attacks” (0,538). Highlighted correlation in England are “Attacks with shots – left flank”, “Attacks with shots – center”, “Attacks with shots – right flank”, “Attacks with shots – Set pieces attacks”, “Corner attacks” and “Corner attacks with shots”. Only “Attacks with shots – Set pieces attacks” are present both in model and significant correlation. In this model there is “red card” aspect coloured in red which means this aspect should be reduced to reach a criterium in opposite to green ones which should be intensified.

Spain is country with 15 variables in the model. Uniqueness of their model is attacking challenges won despite big number of variables.

Spain 2021/22

Adj. R sq. 0,817

Key passes accurate

Shots

Shots on target

Entrance to the penalty box

Shots on post / bar

Attacks with shots – Set pieces attacks

Shots wide

Attacking challenges won

Free ball pick ups

% scored free kick shots

Free-kick shots

Free-kick attacks with shots

Corner attacks with shots

Throw-in attacks with shots

Ball recoveries in opponent’s half

In Spain highest correlation with “create a chance” have “Positional attacks with shots” (0.547) and “% of efficiency for positional attacks” (0,516). Highlighted correlation in Spain are “Attacks with shots – left flank”, “Attacks with shots – center”, “Attacks with shots – right flank”, “Attacks with shots – Set pieces attacks”, “Corner attacks” and “Corner attacks with shots”.

France is country with 13 variables in the model and the smallest adjusted R square number. Uniqueness of their model are % of efficiency for corner attacks and % of efficiency for throw-in attacks.

France 2021/22

Adj. R sq. 0,810

Key passes accurate

Shots on target

Entrance to the penalty box

Attacks with shots – Set pieces attacks

Shots wide

Shots on post / bar

Goals – Free-kick attack

Crosses

% scored free kick shots

% of efficiency for throw-in attacks

Tactics

Ball recoveries in opponent’s half

% of efficiency for corner attacks

In France highest correlation with “create a chance” have “Positional attacks with shots” (0.512) and “% of efficiency for positional attacks” (0,479). Highlighted correlation in France is just one more – “Attacks with shots – center”.

Germany is country with 12 variables in the model. Uniqueness of their model are dribbles successful and fouls. Fouls are having negative significant Pearson correlation with chances.

Germany 2021/22

Adj. R sq. 0,834

Key passes accurate

Shots

Shots on target

Shots on post / bar

Attacks with shots – Set pieces attacks

Dribbles successful

Key passes

Accurate crosses, %

Free ball pick ups

Fouls

Goals – Free-kick attack

Free-kick attacks

In Germany highest correlation with “create a chance” have “Positional attacks with shots” (0.547) and “% of efficiency for positional attacks” (0,532). Highlighted correlation in Germany is, as in France just one more – “Attacks with shots – center”.

Italy is country with most variables (18) in the model. Uniqueness of their model are crosses accurate, corner attacks, counter-attacks with a shot and ball recoveries.

Italy 2021/22

Adj. R sq. 0,841

Key passes accurate

Shots on target

Entrance to the penalty box

Shots on post / bar

Attacks with shots – Set pieces attacks

Free ball pick ups

Corner attacks

Counter-attacks with a shot

Key passes

Crosses

Crosses accurate

Free-kick shots

Free-kick attacks with shots

Corner attacks with shots

Throw-in attacks with shots

Shots wide

Ball recoveries in opponent’s half

In Italy highest correlation with “create a chance” have “Positional attacks with shots” (0.532) and “% of efficiency for positional attacks” (0,504). Highlighted correlation in Spain are “Attacks with shots – left flank”, “Efficiency for attacks through the left flank, %”, “Attacks with shots – right flank”, “Counter-attacks with a shot“, “Attacks with shots – Set pieces attacks”, “Corner attacks” and “Corner attacks with shots”. Only “Attacks with shots – Set pieces attacks” and “Counter-attacks with a shot” are present both in model and significant correlation.

Austria has less variables than average (9) and have uniqueness in Ball possession, sec., Throw-in attacks and Ball possession, %.

Austria 2021/22

Adj. R sq. 0,822

Key passes accurate

Shots

Shots on target

Free-kick shots

Entrance to the penalty box

Shots on post / bar

Ball possession, sec

Throw-in attacks

Ball possession, %

In Austria highest correlation with “create a chance” have “Positional attacks with shots” (0.558) and “% of efficiency for positional attacks” (0,536). Highlighted correlation in Austria are “Attacks with shots – center”, “Attacks with shots – right flank”, “Efficiency for attacks through the right flank, %” and “Corner attacks”.

Belgium is country with least variables (8) in the model and highest adjusted R square. There is no uniqueness in their model. They have tactics as well as France.

Belgium 2021/22

Adj. R sq. 0,851

Key passes accurate

Shots on target

Entrance to the penalty box

Shots on post / bar

Attacks with shots – Set pieces attacks

Tactics

Crosses

Shots wide

In Belgium highest correlation with “create a chance” have “Positional attacks with shots” (0.568) and “% of efficiency for positional attacks” (0,543). Highlighted correlation in Belgium are “Attacks with shots – right flank”, “Set pieces attacks”, “Attacks with shots – Set pieces attacks”, “Corner attacks” and “Corner attacks with shots”. Only “Attacks with shots – Set pieces attacks” are present both in model and significant correlation.

Croatia is country with 10 variables in the model. Uniqueness of their model is tackles, air challenges won, % and positional attack.

Croatia 2021/22

Adj. R sq. 0,829

Key passes accurate

Shots on target

Shots on post / bar

Entrance to the penalty box

Goals – Free-kick attack

Attacks with shots – Set pieces attacks

Tackles

Accurate crosses, %

Air challenges won, %

Positional attacks

In Croatia highest correlation with “create a chance” have “Positional attacks with shots” (0.505) and Attacks with shots – Set pieces attacks” (0,496). Highlighted correlation in Croatia are “Attacks with shots – right flank”, “Set pieces attacks”, “% of efficiency for positional attacks”, “Corner attacks” and “Corner attacks with shots”. Only “Attacks with shots – Set pieces attacks” are present both in model and significant correlation.

Poland is country with 12 variables in the model. Uniqueness of their model is key passes, attacks with shots – left flank, % of efficiency for set-piece attacks and challenges in attack won, %.

Poland 2021/22

Adj. R sq. 0,820

Key passes accurate

Shots on target

Shots

Shots on post / bar

Entrance to the penalty box

Free-kick shots

Crosses

Key passes

Counter-attacks

Attacks with shots – left flank

% of efficiency for set-piece attacks

Challenges in attack won, %

In Poland highest correlation with “create a chance” have “Positional attacks with shots” (0.537) and Attacks with shots – Set pieces attacks” (0,527). Highlighted correlation in Poland are “Attacks with shots – left flank”, “Efficiency for attacks through the left flank, %”, “Counter-attacks with a shot” and “% of efficiency for counterattacks”.

Regression models for “score a goal”

To score a goal is more difficult to predict than to make chances. Still, adjusted R square is still moderate to strong around 0,500. On the other side, in models for “goals” is much more variables with negative significant Pearson correlation and every model have at least one of such variables, so, have in mind to see those variables as those which should be reduced in potential game plan. Variable which is present in all models is shots on target.

England has 11 variables in the model. Uniqueness of their model are ball recoveries in opponent’s half and crosses accurate.

England 2021/22

Adj. R sq. 0,497

Shots on target

Crosses

Key passes accurate

Lost balls

Defensive challenges won

Corners

Ball recoveries in opponent’s half

Opponent’s passes per defensive action

Crosses accurate

Offsides

Free-kick shots

In England highest correlation with “score a goal” have “% of efficiency for positional attacks” (0.410). Highlighted correlation in England are “Attacks with shots – center”, “Efficiency for attacks through the central zone, %”, “Efficiency for attacks through the right flank, %”, “Positional attacks with shots”, “Counter-attacks with a shot” and “% of efficiency for counterattacks”. In this model there is “lost balls” and “offsides” red marked. So, if you want to score a goal, reduce lost balls and offsides if you are playing in England. Let’s see is are the same aspects of the game indicative in the other countries.

Spain has 17 variables in the model. Uniqueness of their model are entrance to the penalty box and accurate passes.

Spain 2021/22

Adj. R sq. 0,498

Shots on target

Crosses

Key passes accurate

Shots

Opponent’s passes per defensive action

Defensive challenges won

Free-kick attacks

Ball possession, sec

Ball possession, %

Entrance to the penalty box

Corners

Positional attacks

Ball recoveries

Lost balls

Accurate passes, %

Accurate passes

% scored free kick shots

In Spain there is no significant correlation stronger than +/- 0,275. Still be aware of red ones in the model 😉 and enhance green ones.

France has 13 variables in the model. Uniqueness of their model are team pressing, challenges won, % and efficiency for attacks through the central zone, %.

France 2021/22

Adj. R sq. 0,463

Shots on target

Crosses

Key passes accurate

% scored free kick shots

Yellow cards

Lost balls

Corners

Opponent’s passes per defensive action

Team pressing

Challenges won, %

Shots on target, %

Efficiency for attacks through the central zone, %

Defensive challenges won

In France highest correlation with “score a goal” have “Attacks with shots – center” (0.297). Highlighted correlation in France is “% of efficiency for counterattacks”. “Lost balls”, “Crosses” and “yellow cards” are to be reduced.

Germany has 15 variables in the model and highest adjusted R square number. Uniqueness of their model are attacking challenges and average duration of ball possession, min.

Germany 2021/22

Adj. R sq. 0,525

Shots on target

Crosses

Key passes

Attacking challenges

Goals – Free-kick attack

Shots on target, %

% of efficiency for set-piece attacks

% of efficiency for counterattacks

Ball possession, %

Ball possession, sec

Average duration of ball possession, min

Ball possessions, quantity

Ball recoveries

Offsides

Yellow cards

In Germany highest correlation with “score a goal” have “% of efficiency for counterattacks” (0.300). Highlighted correlation in Germany are “Attacks with shots – center”, “% of efficiency for positional attacks” and “Counter-attacks with a shot”. “Ball possessions, quantity”, “Crosses” and “yellow cards” are to be reduced.

Italy has 20 variables in the model and highest adjusted R square number. Uniqueness of their model are set pieces attacks, challenges in attack won, %, free ball pick ups, throw-in attacks, tackles won, %, efficiency for attacks through the left flank, % and efficiency for attacks through the right flank, %.

Italy 2021/22

Adj. R sq. 0,483

Shots on target

Set pieces attacks

Goals – Free-kick attack

Key passes

Crosses

% of efficiency for counterattacks

Shots wide

Entrance to the penalty box

Dribbles

Challenges in attack won, %

Opponent’s passes per defensive action

Free ball pick ups

Corner attacks

Free-kick attacks

Throw-in attacks

Tackles won, %

Shots on target, %

Efficiency for attacks through the left flank, %

% of efficiency for set-piece attacks

Efficiency for attacksthrough the right flank, %

In Italy highest correlation with “score a goal” have “% of efficiency for counterattacks” (0.349). Highlighted correlation in Germany are “% of efficiency for positional attacks” and “Counter-attacks with a shot”. Interesting, red marked in Italy are “Free ball pick ups” and “Free-kick attacks”.

Austria has 15 variables in the model and smallest adjusted R square. Uniqueness of their model are ball recoveries in opponent’s half, total actions, and dribbles.

Austria 2021/22

Adj. R sq. 0,441

Shots on target

Positional attacks

Free-kick shots

Key passes accurate

Throw-in attacks with shots

Ball recoveries in opponent’s half

Goals – Free-kick attack

Opponent’s passes per defensive action

Ball possessions, quantity

Total actions

Ball recoveries

Dribbles

Ball possession, %

Free-kick attacks

Ball possession, sec

In Austria highest correlation with “score a goal” have “Counter-attacks with a shot” (0.335). Highlighted correlation in Austria are “Attacks with shots – center”, “Efficiency for attacks through the central zone, %”, “Efficiency for attacks through the right flank, %”, “Positional attacks with shots” and “% of efficiency for positional attacks”. “Free-kick attacks” are to be reduced.

Belgium has most variables in the model (21). Uniqueness of their model are ball recoveries in opponent’s half, total actions and dribbles.

Belgium 2021/22

Adj. R sq. 0,473

Shots on target

Crosses

Lost balls

Key passes

Corner attacks

Yellow cards

Team pressing successful

Accurate passes, %

Free-kick attacks

Ball possession, %

Ball possession, sec

Ball recoveries

Opponent’s passes per defensive action

Free ball pick ups

Shots wide

% of efficiency for corner attacks

% of efficiency for counterattacks

Blocked shots

% of efficiency for positional attacks

Attacks with shots – Set pieces attacks

Throw-in attacks with shots

In Belgium highest correlation with “score a goal” have “% of efficiency for positional attacks” (0.289). Highlighted correlation in Belgium are “Counter-attacks with a shot” and “% of efficiency for counterattacks”. “Crosses”, “Lost balls”, “Yellow cards” and “Free-kick attacks” are to be reduced.

Croatia has the least number of variables (9) in the model. Uniqueness of their model is air challenges.

Croatia 2021/22

Adj. R sq. 0,449

Shots on target

Key passes accurate

Crosses

Goals – Free-kick attack

Air challenges

% of efficiency for set-piece attacks

Shots wide

% of efficiency for counterattacks

Fouls

In Croatia highest correlation with “score a goal” have “% of efficiency for counterattacks” (0.304). Highlighted correlation in Croatia are “Counter-attacks with a shot” and “% of efficiency for set-piece attacks”. “Crosses” is to be reduced.

Poland has 12 variables in the model. Uniqueness of their model are % of efficiency for throw-in attacks and counter-attacks with a shot.

Poland 2021/22

Adj. R sq. 0,465

Shots on target

Crosses

Key passes accurate

Shots wide

Lost balls

Ball recoveries

Goals – Free-kick attack

Corners

% of efficiency for throw-in attacks

% of efficiency for corner attacks

Counter-attacks with a shot

Fouls

In Poland highest correlation with “score a goal” have “Counter-attacks with a shot” (0.328). Highlighted correlation in Poland is “% of efficiency for counterattacks”. “Crosses”, “Lost balls” and “% of efficiency for throw-in attacks” is to be reduced.

Regression models for “not defeat”

Not to be defeated is most difficult to predict of all four observed variables. Adjusted R square is weak, just beneath 0,300 with few exceptions. Models for “not defeat” have least variables per model. In this models, negative significant Pearson correlation is present except for Croatia. Have in mind to see those variables as those which should be reduced in potential game plan. There is no single variable present in all models.

England has 15 variables in the model and highest adjusted R square. Uniqueness of their model are entrance to the penalty box, defensive challenges won, offsides, tackles successful, yellow cards and average duration of ball possession, min.

England 2021/22

Adj. R sq. 0,299

Shots on target

Opponent’s passes per defensive action

Crosses accurate

Key passes accurate

Entrance to the penalty box

Crosses

Ball interceptions

Ball recoveries in opponent’s half

Lost balls

Defensive challenges won

“

Offsides

Tackles successful

Yellow cards

Average duration of ball possession, min

In England highest correlation with “not defeat” have “Attacks with shots – center” (0.283). Highlighted correlation in England are “Attacks with shots – left flank”, “Efficiency for attacks through the central zone, %”, “Positional attacks with shots”, “% of efficiency for positional attacks”, “Counter-attacks with a shot” and “% of efficiency for counterattacks”. In this model red marked are “Opponent’s passes per defensive action”, “Lost balls”, “Lost balls” and “Offsides”. So, if you want not to be defeated, reduce them.

Spain has 13 variables in the model. Uniqueness of their model are ball possessions – quantity, attacks – centre, defensive challenges and attacking challenges.

Spain 2021/22

Adj. R sq. 0,231

Shots on target

Opponent’s passes per defensive action

Crosses

Key passes

Ball recoveries

Ball possessions, quantity

Fouls

Attacks – center

Defensive challenges

Attacking challenges

Dribbles successful

Shots wide

Shots on target, %

In Spain there is no significant correlation above +/- 0,200. In this model red marked are “Opponent’s passes per defensive action” and “Crosses”.

France has least number of variables (6) in the model. This model has no uniqueness.

France 2021/22

Adj. R sq. 0,165

Shots on target

Challenges won, %

Crosses

Ball interceptions

Key passes accurate

Entrance to the penalty box

In France there is also no significant correlation above +/- 0,200. In this model there red marked is “Crosses”.

Germany has most variables (18) variables in the model and smallest adjusted R square. It has most unique variables of all models for “not defeat”. Uniqueness of their model are set pieces attacks, challenges, total actions, shots on post / bar, accurate passes, shots and entrances to the opposition half.

Germany 2021/22

Adj. R sq. 0,292

Shots on target

Opponent’s passes per defensive action

Set pieces attacks

Key passes

Accurate passes, %

Successful actions, %

% of efficiency for set-piece attacks

Challenges

Total actions

Challenges won

Shots on post / bar

Accurate passes

Ball recoveries

Shots

Entrances to the opposition half

Positional attacks

Free-kick attacks with shots

Air challenges won, %

In Germany there is just one significant correlation with “not defeat” – “% of efficiency for set-piece attacks” (0.204). In this model red marked is “Opponent’s passes per defensive action”.

Italy has 10 variables in the model. This model has no uniqueness.

Italy 2021/22

Adj. R sq. 0,228

Shots on target

Opponent’s passes per defensive action

% of efficiency for counterattacks

Challenges won, %

Crosses

Key passes

Red cards

Ball recoveries in opponent’s half

Lost balls

Ball recoveries

In Italy there are two significant correlation with “not defeat” – “Counter-attacks with a shot” (0.255) and “% of efficiency for counterattacks”. In this model red marked are “Opponent’s passes per defensive action” and “Red cards”.

Austria has 13 variables in the model. Uniqueness of their model are % of efficiency for throw-in attacks, team pressing successful and air challenges.

Austria 2021/22

Adj. R sq. 0,282

Key passes accurate

Fouls

Shots on target, %

% of efficiency for throw-in attacks

Challenges won, %

Free-kick attacks with shots

Crosses accurate

Team pressing successful

Lost balls

Ball recoveries

Accurate passes, %

Successful actions, %

Air challenges

In Austria there is no significant correlation above +/- 0,200. In this model red marked are “% of efficiency for throw-in attacks”, “Free-kick attacks with shots” and “Lost balls”.

Belgium has 12 variables in the model. Uniqueness of their model is pressing efficiency, %.

Belgium 2021/22

Adj. R sq. 0,231

Shots on target

Ball interceptions

Key passes

Crosses

Air challenges won, %

% of efficiency for counterattacks

Ball recoveries

Dribbles successful

Pressing efficiency, %

Lost balls in own half

Challenges won, %

Shots on target, %

In Belgium there is just one significant correlation with “not defeat” – “% of efficiency for counterattacks” (0.215). In this model red marked is “Crosses”.

Croatia has 7 variables in the model. Uniqueness of their model are air challenges won and efficiency for attacks through the left flank, %.

Croatia 2021/22

Adj. R sq. 0,221

Shots on target

Air challenges won

% of efficiency for set-piece attacks

Key passes accurate

Shots wide

Ball recoveries in opponent’s half

Efficiency for attacks through the left flank, %

In Croatia highest correlation with “not defeat” have “Attacks with shots – Set pieces attacks” (0.229). Highlighted correlation in Croatia are “% of efficiency for set-piece attacks” and “Corner attacks with shots”. In this model there is no red marked aspect of the game.

Poland has 10 variables in the model. Uniqueness of their model are lost balls in own half, free-kick attacks and tactics.

Poland 2021/22

Adj. R sq. 0,248

Shots on target

Positional attacks

Ball recoveries

Lost balls in own half

% of efficiency for throw-in attacks

Challenges won

Key passes accurate

Crosses

Free-kick attacks

Tactics

In Poland highest correlation with “not defeat” have “Counter-attacks with a shot” (0.276). Highlighted correlation in Croatia are “Counter-attacks” and “% of efficiency for counterattacks”. In this model red marked aspects of the game are “Positional attacks”, “Lost balls in own half”, “% of efficiency for throw-in attacks” and “Crosses”.

Regression models for victory

Although all of models has it purpose in planning the game, and it might be that is best to observe them all in planning, victory might be the most interesting because victory brings most to the team, players, coach, and the club. Adjusted R square is between moderate and weak, from 0,225 to 0,364. There is no single variable which is present in all models. So, lets see “how to win a match in England”

England has 17 variables in the model. Uniqueness of their model are successful actions, defensive challenges won, total actions, entrances to the final third and challenges.

England 2021/22

Adj. R sq. 0,355

Shots on target

Crosses

Successful actions

Air challenges won, %

Free-kick attacks

% of efficiency for throw-in attacks

Lost balls in own half

Red cards

Defensive challenges won

Key passes accurate

Total actions

Tackles successful

Entrances to the final third

Crosses accurate

Challenges

Opponent’s passes per defensive action

Team pressing

In England highest correlation with “victory” have “% of efficiency for positional attacks” (0.296). Highlighted correlation in England are “Attacks with shots – left flank”, “Efficiency for attacks through the left flank, %”, “Attacks with shots – center”, “Efficiency for attacks through the central zone, %”, “Positional attacks with shots “, “Counter-attacks with a shot” and “% of efficiency for counterattacks”. In this model red marked are “Free-kick attacks”, “Lost balls in own half”, “Red cards”, “Crosses accurate” and “Opponent’s passes per defensive action”. So, if you want to win the match, reduce them and enhance the green ones.

Spain has the most variables (19) of all models. Uniqueness of their model are air challenges won, entrances to the opposition half, offsides and shots.

Spain 2021/22

Adj. R sq. 0,331

Shots on target

Crosses

Key passes

Opponent’s passes per defensive action

Free-kick attacks

Air challenges won

Ball interceptions

Successful actions, %

Fouls

Red cards

Dribbles successful

Attacking challenges won

Counter-attacks with a shot

Entrances to the opposition half

Offsides

Set pieces attacks

Ball recoveries

Lost balls

Shots

In Spain highest correlation with “victory” have “Counter-attacks with a shot” (0.225). Highlighted correlation in Spain is “% of efficiency for counterattacks”. In this model red marked are “Set pieces attacks” and “Lost balls”.

France has 9 of all models. Uniqueness of their model are corner attacks with shots and yellow cards.

France 2021/22

Adj. R sq. 0,241

Shots on target

Crosses

Key passes accurate

Challenges won, %

Ball interceptions

Red cards

Tactics

Corner attacks with shots

Yellow cards

In France there is no significant aspect with more strength than +/- 0,200. In this model red marked are “Crosses”, “Yellow cards” and “Red cards”.

Germany has least variables (6) of all models and weakest model. Uniqueness of their model are challenges in defence won – %, goals – Free-kick attack and shots on post / bar.

Germany 2021/22

Adj. R sq. 0,225

Shots on target

Set pieces attacks

Key passes accurate

Crosses

Challenges in defence won, %

Opponent’s passes per defensive action

Goals – Free-kick attack

Shots on post / bar

In Germany there is just two aspects of the game with correlation with “victory” above +/- 0,200 – “% of efficiency for positional attacks” (0.215) and “Efficiency for attacks through the left flank, %”. In this model red marked are “Set pieces attacks”, “Crosses” and “Opponent’s passes per defensive action”.

Italy has 12 of all models. Uniqueness of their model are accurate passes – %, free ball pick ups – %, and scored free kick shots.

Italy 2021/22

Adj. R sq. 0,301

Shots on target

Crosses

Key passes

Successful actions, %

Opponent’s passes per defensive action

Accurate passes, %

% of efficiency for counterattacks

Free-kick attacks

Air challenges

Free ball pick ups

% scored free kick shots

Tackles successful

In Italy highest correlation with “victory” have “% of efficiency for counterattacks” (0.300). Highlighted correlation in Italy are “Efficiency for attacks through the right flank, %” and “Counter-attacks with a shot”. In this model red marked are “Crosses”, “Opponent’s passes per defensive action”, “Free-kick attacks” and “Free ball pick ups”.

Austria has 12 variables in the model. Uniqueness of their model are shots on target – %, accurate crosses – %, and attacks – centre.

Austria 2021/22

Adj. R sq. 0,304

Counter-attacks with a shot

Key passes accurate

Shots on target, %

Crosses accurate

Red cards

Accurate crosses, %

Tactics

Lost balls in own half

Ball recoveries

Lost balls

Fouls

Attacks – center

In Austria highest correlation with “victory” have “Counter-attacks with a shot” (0.326). Highlighted correlation in Austria are “Attacks with shots – center”, “Efficiency for attacks through the central zone, %”, “Counter-attacks” and “% of efficiency for counterattacks”. In this model red marked is “Red cards”.

Belgium has 12 variables in the model. Uniqueness of their model are challenges in defence won – %, tackles and attacks with shots – centre.

Belgium 2021/22

Adj. R sq. 0,265

Shots on target

Crosses

Key passes

Team pressing

Set pieces attacks

Challenges in defence won,%

Dribbles successful

Tackles

% of efficiency for counterattacks

Attacks with shots – center

Attacking challenges won

Tackles successful

In Belgium highest correlation with “victory” have “% of efficiency for counterattacks” (0.221). Highlighted correlation in Belgium is “Counter-attacks with a shot”. In this model red marked are “Set pieces attacks” and “Crosses”.

Croatia has 10 variables in the model and strongest model. Uniqueness of their model are % of efficiency for set-piece attacks and ball recoveries in opponent’s half.

Croatia 2021/22

Adj. R sq. 0,364

Shots on target

Key passes accurate

% of efficiency for counterattacks

Crosses accurate

% of efficiency for set-piece attacks

% of efficiency for throw-in attacks

Ball interceptions

Ball recoveries in opponent’s half

Shots wide

Air challenges won, %

In Croatia highest correlation with “victory” have “Counter-attacks with a shot” (0.319). Highlighted correlation in Croatia are “Attacks with shots – left flank”, “% of efficiency for counterattacks” and “% of efficiency for set-piece attacks”. In this model red marked is “% of efficiency for throw-in attacks”.

Poland has 12 of all models. There is no uniqueness of their model.

Poland 2021/22

Adj. R sq. 0,306

Shots on target

Crosses

Counter-attacks with a shot

Shots wide

Team pressing

Challenges won, %

“Ball interceptions

Lost balls

Ball recoveries

% of efficiency for throw-in attacks

Air challenges

Free-kick attacks

In Poland highest correlation with “victory” have “Counter-attacks with a shot” (0.325). Highlighted correlation in Croatia are “Attacks with shots – center”, “Efficiency for attacks through the central zone, %” and “% of efficiency for counterattacks”. In this model red marked are “Crosses”, “Lost balls”, “% of efficiency for throw-in attacks” and “Free-kick attacks”.

Check out this article on LinkedIn: https://www.linkedin.com/pulse/do-leagues-generally-differ-between-themselves-pitch-slaven-marasovi%C4%87/

1. What are key factors to create a chance in the individual league?

2. What are key factors on the pitch for scoring a goal in the individual league?

3. What are key factors on the pitch for not losing the game in the individual league?

4. What are key factors on the pitch for winning a match in individual league?

To identify those goals, I have used:

frequency (what is most used in each league),
statistical correlation and
statistical regression.

The frequency of 104 observed aspects of the game will tell us which aspect of the match is happening the most. That will not help us understanding how to reach each of criterium.

Finally, I will leave each of you to give “sweet” interpretation of results. And would like to hear your thoughts about interpretation from your side 😉

Table 1.: Heatmap of correlations between chances and different aspects of game per countries

CHANCES	England	Spain	France	Germany	Italy	Austria	Belgium	Croatia	Poland
Attacks – left flank	0.178	0.158	0.160	0.078	0.197	0.199	0.098	0.252	0.145
Attacks with shots – left flank	0.413	0.400	0.350	0.364	0.465	0.386	0.414	0.421	0.485
Efficiency for attacks through the left flank, %	0.361	0.374	0.289	0.353	0.415	0.363	0.398	0.316	0.456
Attacks – center	0.201	0.121	0.233	0.257	0.111	0.264	0.196	0.088	0.108
Attacks with shots – center	0.438	0.343	0.423	0.422	0.383	0.489	0.380	0.240	0.370
Efficiency for attacks through the central zone, %	0.373	0.300	0.348	0.333	0.360	0.397	0.342	0.222	0.332
Attacks – right flank	0.267	0.118	0.069	0.122	0.131	0.192	0.178	0.278	0.113
Attacks with shots – right flank	0.450	0.390	0.323	0.392	0.411	0.421	0.450	0.427	0.389
Efficiency for attacks through the right flank, %	0.380	0.354	0.321	0.375	0.377	0.414	0.399	0.338	0.386
Positional attacks	0.354	0.240	0.229	0.235	0.213	0.311	0.229	0.324	0.176
Positional attacks with shots	0.579	0.547	0.512	0.547	0.532	0.558	0.568	0.505	0.537
% of efficiency for positional attacks	0.538	0.516	0.479	0.532	0.504	0.536	0.543	0.428	0.527
Counter-attacks	0.122	0.032	0.085	0.099	0.169	0.188	0.179	0.159	0.159
Counter-attacks with a shot	0.375	0.307	0.319	0.342	0.420	0.384	0.371	0.332	0.439
% of efficiency for counterattacks	0.354	0.304	0.307	0.333	0.367	0.309	0.314	0.293	0.401
Set pieces attacks	0.296	0.199	0.329	0.154	0.317	0.247	0.209	0.408	0.258
Attacks with shots – Set pieces attacks	0.429	0.327	0.381	0.384	0.448	0.393	0.359	0.496	0.397
% of efficiency for set-piece attacks	0.292	0.261	0.167	0.317	0.293	0.287	0.253	0.282	0.280
Free-kick attacks	-0.022	-0.054	0.073	-0.036	0.032	-0.039	-0.008	0.129	0.010
Free-kick attacks with shots	0.170	0.111	0.170	0.170	0.203	0.147	0.137	0.231	0.182
% of efficiency for free-kick attacks	0.192	0.166	0.148	0.214	0.201	0.155	0.151	0.187	0.232
Corner attacks	0.420	0.338	0.339	0.359	0.420	0.404	0.359	0.459	0.368
Corner attacks with shots	0.411	0.319	0.319	0.363	0.419	0.372	0.358	0.449	0.364
% of efficiency for corner attacks	0.162	0.141	0.107	0.151	0.208	0.158	0.165	0.218	0.149
Throw-in attacks	-0.058	-0.063	0.070	-0.172	-0.068	-0.001	-0.091	0.054	-0.076
Throw-in attacks with shots	-0.018	0.002	0.023	-0.058	-0.019	0.054	-0.003	0.079	-0.025
% of efficiency for throw-in attacks	0.003	-0.019	-0.007	-0.029	-0.037	0.063	0.020	0.039	-0.026
Free-kick shots	0.168	0.078	0.166	0.070	0.179	0.213	0.057	0.102	0.170
Goals – Free-kick attack	0.056	0.037	0.115	0.118	0.080	0.126	0.060	0.082	0.023
% scored free kick shots	0.032	0.042	0.111	0.110	0.071	0.076	0.059	0.053	0.028

If we highlight corelations stronger than +/- 0,400 than we have different table:

Table 2.: Highlight of correlations stronger than +/- 0,400 between chances and different aspects of game per countries

CHANCES	England	Spain	France	Germany	Italy	Austria	Belgium	Croatia	Poland
Attacks with shots – left flank	0.413	0.400	0.350	0.364	0.465	0.386	0.414	0.421	0.485
Efficiency for attacks through the left flank, %	0.361	0.374	0.289	0.353	0.415	0.363	0.398	0.316	0.456
Attacks with shots – center	0.438	0.343	0.423	0.422	0.383	0.489	0.380	0.240	0.370
Attacks with shots – right flank	0.450	0.390	0.323	0.392	0.411	0.421	0.450	0.427	0.389
Efficiency for attacks through the right flank, %	0.380	0.354	0.321	0.375	0.377	0.414	0.399	0.338	0.386
Positional attacks with shots	0.579	0.547	0.512	0.547	0.532	0.558	0.568	0.505	0.537
% of efficiency for positional attacks	0.538	0.516	0.479	0.532	0.504	0.536	0.543	0.428	0.527
Counter-attacks with a shot	0.375	0.307	0.319	0.342	0.420	0.384	0.371	0.332	0.439
% of efficiency for counterattacks	0.354	0.304	0.307	0.333	0.367	0.309	0.314	0.293	0.401
Set pieces attacks	0.296	0.199	0.329	0.154	0.317	0.247	0.209	0.408	0.258
Attacks with shots – Set pieces attacks	0.429	0.327	0.381	0.384	0.448	0.393	0.359	0.496	0.397
Corner attacks	0.420	0.338	0.339	0.359	0.420	0.404	0.359	0.459	0.368
Corner attacks with shots	0.411	0.319	0.319	0.363	0.419	0.372	0.358	0.449	0.364

Among all correlations strongest is correlation in England with “Positional attacks with shots” (0,579**).

When we look at correlations between “score a goal” and different aspects of game we have generally weaker correlations.

Table 3.: Heatmap of correlations between goals and different aspects of game per countries

GOALS	England	Spain	France	Germany	Italy	Austria	Belgium	Croatia	Poland
Attacks – left flank	0.009	-0.006	-0.055	-0.037	-0.032	0.023	-0.030	0.085	-0.028
Attacks with shots – left flank	0.238	0.201	0.126	0.209	0.240	0.234	0.241	0.215	0.214
Efficiency for attacks through the left flank, %	0.274	0.220	0.151	0.241	0.274	0.266	0.270	0.197	0.248
Attacks – center	0.185	0.025	0.111	0.141	0.062	0.065	0.039	0.051	0.042
Attacks with shots – center	0.394	0.234	0.297	0.279	0.229	0.299	0.170	0.175	0.226
Efficiency for attacks through the central zone, %	0.322	0.222	0.258	0.233	0.224	0.297	0.180	0.168	0.228
Attacks – right flank	0.044	-0.093	-0.116	-0.108	-0.040	0.030	-0.064	0.050	-0.089
Attacks with shots – right flank	0.238	0.174	0.148	0.158	0.230	0.254	0.226	0.220	0.147
Efficiency for attacks through the right flank, %	0.271	0.221	0.217	0.222	0.263	0.277	0.268	0.209	0.193
Positional attacks	0.113	-0.046	-0.057	-0.029	-0.012	0.008	-0.078	0.097	-0.110
Positional attacks with shots	0.371	0.242	0.211	0.248	0.250	0.278	0.228	0.227	0.187
% of efficiency for positional attacks	0.410	0.281	0.267	0.289	0.281	0.323	0.289	0.226	0.241
Counter-attacks	0.047	-0.023	0.017	0.046	-0.008	0.141	0.079	0.040	0.132
Counter-attacks with a shot	0.277	0.244	0.250	0.283	0.292	0.335	0.278	0.288	0.328
% of efficiency for counterattacks	0.280	0.248	0.272	0.300	0.349	0.263	0.281	0.304	0.292
Set pieces attacks	0.016	-0.069	0.017	-0.094	-0.024	-0.054	-0.029	0.105	-0.038
Attacks with shots – Set pieces attacks	0.167	0.057	0.162	0.123	0.110	0.083	0.144	0.240	0.113
% of efficiency for set-piece attacks	0.217	0.146	0.163	0.268	0.188	0.172	0.218	0.286	0.174
Free-kick attacks	-0.090	-0.164	-0.047	-0.117	-0.142	-0.198	-0.086	-0.038	-0.054
Free-kick attacks with shots	0.033	-0.062	0.057	0.048	-0.017	-0.100	0.009	0.124	0.048
% of efficiency for free-kick attacks	0.093	0.074	0.082	0.144	0.059	0.017	0.075	0.184	0.078
Corner attacks	0.092	0.028	0.007	0.066	0.044	0.070	0.035	0.176	0.003
Corner attacks with shots	0.152	0.058	0.091	0.119	0.087	0.140	0.154	0.181	0.087
% of efficiency for corner attacks	0.121	0.069	0.122	0.111	0.104	0.136	0.158	0.153	0.130
Throw-in attacks	-0.085	-0.082	0.033	-0.212	-0.064	-0.058	-0.078	-0.079	-0.098
Throw-in attacks with shots	-0.042	-0.030	0.037	-0.123	-0.037	-0.050	-0.062	-0.030	-0.086
% of efficiency for throw-in attacks	0.000	-0.033	-0.013	-0.083	-0.034	-0.037	-0.039	-0.046	-0.119
Free-kick shots	0.009	-0.055	0.073	0.019	0.007	-0.091	0.006	0.041	0.061
Goals – Free-kick attack	0.038	0.058	0.171	0.211	0.173	0.077	0.080	0.151	0.161
% scored free kick shots	0.033	0.062	0.186	0.203	0.169	0.084	0.080	0.104	0.153

If we highlight corelations stronger than +/- 0,275 than we have different table:

Table 4.: Highlight of correlations stronger than +/-0,275 between goals and different aspects of game per countries

GOALS	England	Spain	France	Germany	Italy	Austria	Belgium	Croatia	Poland
Attacks with shots – center	0.394	0.234	0.297	0.279	0.229	0.299	0.170	0.175	0.226
Efficiency for attacks through the central zone, %	0.322	0.222	0.258	0.233	0.224	0.297	0.180	0.168	0.228
Efficiency for attacks through the right flank, %	0.271	0.221	0.217	0.222	0.263	0.277	0.268	0.209	0.193
Positional attacks with shots	0.371	0.242	0.211	0.248	0.250	0.278	0.228	0.227	0.187
% of efficiency for positional attacks	0.410	0.281	0.267	0.289	0.281	0.323	0.289	0.226	0.241
Counter-attacks with a shot	0.277	0.244	0.250	0.283	0.292	0.335	0.278	0.288	0.328
% of efficiency for counterattacks	0.280	0.248	0.272	0.300	0.349	0.263	0.281	0.304	0.292
% of efficiency for set-piece attacks	0.217	0.146	0.163	0.268	0.188	0.172	0.218	0.286	0.174

Even weaker correlations are with “not defeat”.

Table 5.: Heatmap of correlations between not defeat and different aspects of game per countries

NOT DEFEAT	England	Spain	France	Germany	Italy	Austria	Belgium	Croatia	Poland
Attacks – left flank	0.057	0.011	-0.003	0.024	0.014	0.017	-0.013	0.095	-0.024
Attacks with shots – left flank	0.209	0.105	0.057	0.132	0.183	0.152	0.121	0.198	0.165
Efficiency for attacks through the left flank, %	0.198	0.096	0.060	0.146	0.189	0.157	0.146	0.160	0.192
Attacks – center	0.172	0.004	0.090	0.099	0.111	0.028	0.064	0.096	0.084
Attacks with shots – center	0.283	0.126	0.167	0.155	0.185	0.127	0.091	0.070	0.175
Efficiency for attacks through the central zone, %	0.221	0.128	0.155	0.106	0.162	0.122	0.077	0.023	0.132
Attacks – right flank	0.088	0.005	-0.059	0.012	0.035	-0.014	0.025	0.207	-0.126
Attacks with shots – right flank	0.187	0.084	0.128	0.130	0.151	0.148	0.164	0.179	0.093
Efficiency for attacks through the right flank, %	0.176	0.081	0.159	0.142	0.142	0.187	0.171	0.121	0.131
Positional attacks	0.144	-0.003	-0.011	0.034	0.042	-0.048	-0.019	0.193	-0.141
Positional attacks with shots	0.265	0.098	0.120	0.167	0.170	0.140	0.099	0.181	0.114
% of efficiency for positional attacks	0.260	0.097	0.144	0.172	0.176	0.186	0.120	0.126	0.163
Counter-attacks	0.108	0.041	0.049	0.118	0.127	0.156	0.151	0.119	0.211
Counter-attacks with a shot	0.258	0.178	0.161	0.153	0.255	0.201	0.221	0.192	0.276
% of efficiency for counterattacks	0.220	0.158	0.147	0.118	0.240	0.140	0.215	0.169	0.211
Set pieces attacks	0.073	-0.071	0.043	-0.046	0.037	-0.011	-0.008	0.124	-0.009
Attacks with shots – Set pieces attacks	0.172	0.030	0.096	0.113	0.098	-0.007	0.099	0.229	0.032
% of efficiency for set-piece attacks	0.174	0.082	0.055	0.204	0.100	0.034	0.134	0.222	0.073
Free-kick attacks	-0.049	-0.097	-0.027	-0.106	-0.060	-0.130	-0.074	-0.035	-0.053
Free-kick attacks with shots	0.029	-0.040	0.044	0.005	0.017	-0.129	0.013	0.082	-0.012
% of efficiency for free-kick attacks	0.049	0.050	0.092	0.068	0.051	-0.043	0.068	0.105	0.026
Corner attacks	0.134	-0.040	0.035	0.034	0.081	0.095	0.035	0.164	0.039
Corner attacks with shots	0.183	0.039	0.045	0.118	0.071	0.092	0.089	0.200	0.073
% of efficiency for corner attacks	0.139	0.070	0.011	0.092	0.050	0.092	0.095	0.137	0.085
Throw-in attacks	-0.040	-0.016	0.050	-0.051	-0.034	-0.041	-0.013	0.029	-0.045
Throw-in attacks with shots	-0.018	-0.007	0.018	0.014	-0.001	-0.087	-0.007	0.038	-0.107
% of efficiency for throw-in attacks	0.013	-0.014	-0.040	0.027	-0.005	-0.110	0.020	-0.011	-0.135
Free-kick shots	0.023	-0.077	0.029	-0.012	0.043	-0.050	0.008	-0.017	0.034
Goals – Free-kick attack	0.052	-0.021	0.095	0.065	0.047	0.025	0.024	0.058	0.054
% scored free kick shots	0.056	-0.013	0.085	0.053	0.045	0.039	0.024	0.038	0.048

If we highlight correlations stronger than +/- 0,200 we will see that overall number of correlations is smaller than for previous two criteriums.

Table 5.: Highlight of correlations stronger than +/- 0,200 between not defeat and different aspects of game per countries

NOT DEFEAT	England	Spain	France	Germany	Italy	Austria	Belgium	Croatia	Poland
Attacks with shots – left flank	0.209	0.105	0.057	0.132	0.183	0.152	0.121	0.198	0.165
Attacks with shots – center	0.283	0.126	0.167	0.155	0.185	0.127	0.091	0.070	0.175
Efficiency for attacks through the central zone, %	0.221	0.128	0.155	0.106	0.162	0.122	0.077	0.023	0.132
Positional attacks with shots	0.265	0.098	0.120	0.167	0.170	0.140	0.099	0.181	0.114
% of efficiency for positional attacks	0.260	0.097	0.144	0.172	0.176	0.186	0.120	0.126	0.163
Counter-attacks	0.108	0.041	0.049	0.118	0.127	0.156	0.151	0.119	0.211
Counter-attacks with a shot	0.258	0.178	0.161	0.153	0.255	0.201	0.221	0.192	0.276
% of efficiency for counterattacks	0.220	0.158	0.147	0.118	0.240	0.140	0.215	0.169	0.211
Attacks with shots – Set pieces attacks	0.172	0.030	0.096	0.113	0.098	-0.007	0.099	0.229	0.032
% of efficiency for set-piece attacks	0.174	0.082	0.055	0.204	0.100	0.034	0.134	0.222	0.073
Corner attacks with shots	0.183	0.039	0.045	0.118	0.071	0.092	0.089	0.200	0.073

Finally, are presented correlations with “victory” which are stronger than correlations with “not defeat”.

Table 7.: Heatmap of correlations between victory and different aspects of game per countries

VICTORY	England	Spain	France	Germany	Italy	Austria	Belgium	Croatia	Poland
Attacks – left flank	0.021	-0.014	-0.011	-0.002	-0.014	-0.020	-0.029	0.118	-0.054
Attacks with shots – left flank	0.221	0.130	0.086	0.177	0.179	0.143	0.131	0.211	0.116
Efficiency for attacks through the left flank, %	0.235	0.146	0.080	0.201	0.191	0.169	0.161	0.174	0.151
Attacks – center	0.154	0.070	0.086	0.095	0.076	0.045	0.051	0.088	0.049
Attacks with shots – center	0.281	0.173	0.158	0.164	0.196	0.206	0.071	0.174	0.214
Efficiency for attacks through the central zone, %	0.228	0.143	0.135	0.125	0.180	0.200	0.060	0.139	0.213
Attacks – right flank	0.056	-0.065	-0.130	-0.072	-0.019	0.033	0.001	0.101	-0.115
Attacks with shots – right flank	0.183	0.131	0.084	0.122	0.195	0.127	0.176	0.186	0.115
Efficiency for attacks through the right flank, %	0.179	0.161	0.162	0.167	0.206	0.133	0.184	0.166	0.169
Positional attacks	0.115	-0.018	-0.074	-0.006	0.010	-0.065	-0.021	0.155	-0.139
Positional attacks with shots	0.287	0.146	0.113	0.188	0.183	0.107	0.108	0.185	0.099
% of efficiency for positional attacks	0.296	0.171	0.166	0.215	0.197	0.148	0.133	0.153	0.155
Counter-attacks	0.042	0.000	0.075	0.036	0.022	0.228	0.068	0.074	0.122
Counter-attacks with a shot	0.228	0.225	0.161	0.174	0.277	0.326	0.208	0.319	0.325
% of efficiency for counterattacks	0.228	0.222	0.149	0.179	0.300	0.214	0.221	0.316	0.311
Set pieces attacks	-0.002	-0.101	-0.013	-0.114	-0.055	-0.037	-0.086	0.044	-0.066
Attacks with shots – Set pieces attacks	0.142	0.011	0.050	0.059	0.036	-0.001	0.065	0.194	0.004
% of efficiency for set-piece attacks	0.195	0.085	0.039	0.194	0.122	0.023	0.157	0.264	0.076
Free-kick attacks	-0.135	-0.200	-0.051	-0.112	-0.156	-0.169	-0.098	-0.075	-0.099
Free-kick attacks with shots	0.003	-0.088	0.009	0.047	-0.029	-0.115	0.006	0.068	-0.047
% of efficiency for free-kick attacks	0.085	0.049	0.027	0.135	0.056	-0.018	0.074	0.112	0.017
Corner attacks	0.113	0.006	-0.022	-0.006	0.035	0.109	-0.025	0.117	0.010
Corner attacks with shots	0.144	0.030	-0.005	0.018	0.032	0.073	0.070	0.199	0.044
% of efficiency for corner attacks	0.097	0.022	0.000	0.027	0.041	0.035	0.087	0.198	0.080
Throw-in attacks	-0.099	-0.059	0.025	-0.148	-0.083	-0.081	-0.087	-0.066	-0.099
Throw-in attacks with shots	0.005	-0.022	0.015	-0.053	-0.044	-0.067	-0.072	-0.085	-0.103
% of efficiency for throw-in attacks	0.045	-0.017	-0.023	-0.030	-0.056	-0.082	-0.033	-0.130	-0.126
Free-kick shots	0.054	-0.088	0.039	0.045	-0.019	-0.103	0.024	0.017	0.000
Goals – Free-kick attack	0.055	-0.030	0.089	0.146	0.087	0.072	0.045	0.075	0.100
% scored free kick shots	0.046	-0.027	0.113	0.131	0.090	0.079	0.052	0.038	0.094

If we highlight correlations stronger than +/- 0,200 we will see that overall number of correlations is higher than for previous criterium.

Table 7.: Highlight of correlations between victory and different aspects of game per countries

VICTORY	England	Spain	France	Germany	Italy	Austria	Belgium	Croatia	Poland
Attacks with shots – left flank	0.221	0.130	0.086	0.177	0.179	0.143	0.131	0.211	0.116
Efficiency for attacks through the left flank, %	0.235	0.146	0.080	0.201	0.191	0.169	0.161	0.174	0.151
Attacks with shots – center	0.281	0.173	0.158	0.164	0.196	0.206	0.071	0.174	0.214
Efficiency for attacks through the central zone, %	0.228	0.143	0.135	0.125	0.180	0.200	0.060	0.139	0.213
Efficiency for attacks through the right flank, %	0.179	0.161	0.162	0.167	0.206	0.133	0.184	0.166	0.169
Positional attacks with shots	0.287	0.146	0.113	0.188	0.183	0.107	0.108	0.185	0.099
% of efficiency for positional attacks	0.296	0.171	0.166	0.215	0.197	0.148	0.133	0.153	0.155
Counter-attacks	0.042	0.000	0.075	0.036	0.022	0.228	0.068	0.074	0.122
Counter-attacks with a shot	0.228	0.225	0.161	0.174	0.277	0.326	0.208	0.319	0.325
% of efficiency for counterattacks	0.228	0.222	0.149	0.179	0.300	0.214	0.221	0.316	0.311
% of efficiency for set-piece attacks	0.195	0.085	0.039	0.194	0.122	0.023	0.157	0.264	0.076

Models for observed criteriums

Regression models for “create a chance”

England 2021/22

Adj. R sq. 0,824

Key passes accurate

Shots

Blocked shots

Shots wide

Attacks with shots – Set pieces attacks

Free-kick shots

Entrance to the penalty box

Shots on target

Red cards

Defensive challenges won

Spain is country with 15 variables in the model. Uniqueness of their model is attacking challenges won despite big number of variables.

Spain 2021/22

Adj. R sq. 0,817

Key passes accurate

Shots

Shots on target

Entrance to the penalty box

Shots on post / bar

Attacks with shots – Set pieces attacks

Shots wide

Attacking challenges won

Free ball pick ups

% scored free kick shots

Free-kick shots

Free-kick attacks with shots

Corner attacks with shots

Throw-in attacks with shots

Ball recoveries in opponent’s half

France is country with 13 variables in the model and the smallest adjusted R square number. Uniqueness of their model are % of efficiency for corner attacks and % of efficiency for throw-in attacks.

France 2021/22

Adj. R sq. 0,810

Key passes accurate

Shots on target

Entrance to the penalty box

Attacks with shots – Set pieces attacks

Shots wide

Shots on post / bar

Goals – Free-kick attack

Crosses

% scored free kick shots

% of efficiency for throw-in attacks

Tactics

Ball recoveries in opponent’s half

% of efficiency for corner attacks

Germany is country with 12 variables in the model. Uniqueness of their model are dribbles successful and fouls. Fouls are having negative significant Pearson correlation with chances.

Germany 2021/22

Adj. R sq. 0,834

Key passes accurate

Shots

Shots on target

Shots on post / bar

Attacks with shots – Set pieces attacks

Dribbles successful

Key passes

Accurate crosses, %

Free ball pick ups

Fouls

Goals – Free-kick attack

Free-kick attacks

Italy is country with most variables (18) in the model. Uniqueness of their model are crosses accurate, corner attacks, counter-attacks with a shot and ball recoveries.

Italy 2021/22

Adj. R sq. 0,841

Key passes accurate

Shots on target

Entrance to the penalty box

Shots on post / bar

Attacks with shots – Set pieces attacks

Free ball pick ups

Corner attacks

Counter-attacks with a shot

Key passes

Crosses

Crosses accurate

Free-kick shots

Free-kick attacks with shots

Corner attacks with shots

Throw-in attacks with shots

Shots wide

Ball recoveries in opponent’s half

Austria has less variables than average (9) and have uniqueness in Ball possession, sec., Throw-in attacks and Ball possession, %.

Austria 2021/22

Adj. R sq. 0,822

Key passes accurate

Shots

Shots on target

Free-kick shots

Entrance to the penalty box

Shots on post / bar

Ball possession, sec

Throw-in attacks

Ball possession, %

Belgium is country with least variables (8) in the model and highest adjusted R square. There is no uniqueness in their model. They have tactics as well as France.

Belgium 2021/22

Adj. R sq. 0,851

Key passes accurate

Shots on target

Entrance to the penalty box

Shots on post / bar

Attacks with shots – Set pieces attacks

Tactics

Crosses

Shots wide

Croatia is country with 10 variables in the model. Uniqueness of their model is tackles, air challenges won, % and positional attack.

Croatia 2021/22

Adj. R sq. 0,829

Key passes accurate

Shots on target

Shots on post / bar

Entrance to the penalty box

Goals – Free-kick attack

Attacks with shots – Set pieces attacks

Tackles

Accurate crosses, %

Air challenges won, %

Positional attacks

Poland is country with 12 variables in the model. Uniqueness of their model is key passes, attacks with shots – left flank, % of efficiency for set-piece attacks and challenges in attack won, %.

Poland 2021/22

Adj. R sq. 0,820

Key passes accurate

Shots on target

Shots

Shots on post / bar

Entrance to the penalty box

Free-kick shots

Crosses

Key passes

Counter-attacks

Attacks with shots – left flank

% of efficiency for set-piece attacks

Challenges in attack won, %

Regression models for “score a goal”

England has 11 variables in the model. Uniqueness of their model are ball recoveries in opponent’s half and crosses accurate.

England 2021/22

Adj. R sq. 0,497

Shots on target

Crosses

Key passes accurate

Lost balls

Defensive challenges won

Corners

Ball recoveries in opponent’s half

Opponent’s passes per defensive action

Crosses accurate

Offsides

Free-kick shots

Spain has 17 variables in the model. Uniqueness of their model are entrance to the penalty box and accurate passes.

Spain 2021/22

Adj. R sq. 0,498

Shots on target

Crosses

Key passes accurate

Shots

Opponent’s passes per defensive action

Defensive challenges won

Free-kick attacks

Ball possession, sec

Ball possession, %

Entrance to the penalty box

Corners

Positional attacks

Ball recoveries

Lost balls

Accurate passes, %

Accurate passes

% scored free kick shots

In Spain there is no significant correlation stronger than +/- 0,275. Still be aware of red ones in the model 😉 and enhance green ones.

France has 13 variables in the model. Uniqueness of their model are team pressing, challenges won, % and efficiency for attacks through the central zone, %.

France 2021/22

Adj. R sq. 0,463

Shots on target

Crosses

Key passes accurate

% scored free kick shots

Yellow cards

Lost balls

Corners

Opponent’s passes per defensive action

Team pressing

Challenges won, %

Shots on target, %

Efficiency for attacks through the central zone, %

Defensive challenges won

Germany has 15 variables in the model and highest adjusted R square number. Uniqueness of their model are attacking challenges and average duration of ball possession, min.

Germany 2021/22

Adj. R sq. 0,525

Shots on target

Crosses

Key passes

Attacking challenges

Goals – Free-kick attack

Shots on target, %

% of efficiency for set-piece attacks

% of efficiency for counterattacks

Ball possession, %

Ball possession, sec

Average duration of ball possession, min

Ball possessions, quantity

Ball recoveries

Offsides

Yellow cards

Italy 2021/22

Adj. R sq. 0,483

Shots on target

Set pieces attacks

Goals – Free-kick attack

Key passes

Crosses

% of efficiency for counterattacks

Shots wide

Entrance to the penalty box

Dribbles

Challenges in attack won, %

Opponent’s passes per defensive action

Free ball pick ups

Corner attacks

Free-kick attacks

Throw-in attacks

Tackles won, %

Shots on target, %

Efficiency for attacks through the left flank, %

% of efficiency for set-piece attacks

Efficiency for attacksthrough the right flank, %

Austria has 15 variables in the model and smallest adjusted R square. Uniqueness of their model are ball recoveries in opponent’s half, total actions, and dribbles.

Austria 2021/22

Adj. R sq. 0,441

Shots on target

Positional attacks

Free-kick shots

Key passes accurate

Throw-in attacks with shots

Ball recoveries in opponent’s half

Goals – Free-kick attack

Opponent’s passes per defensive action

Ball possessions, quantity

Total actions

Ball recoveries

Dribbles

Ball possession, %

Free-kick attacks

Ball possession, sec

Belgium has most variables in the model (21). Uniqueness of their model are ball recoveries in opponent’s half, total actions and dribbles.

Belgium 2021/22

Adj. R sq. 0,473

Shots on target

Crosses

Lost balls

Key passes

Corner attacks

Yellow cards

Team pressing successful

Accurate passes, %

Free-kick attacks

Ball possession, %

Ball possession, sec

Ball recoveries

Opponent’s passes per defensive action

Free ball pick ups

Shots wide

% of efficiency for corner attacks

% of efficiency for counterattacks

Blocked shots

% of efficiency for positional attacks

Attacks with shots – Set pieces attacks

Throw-in attacks with shots

Croatia has the least number of variables (9) in the model. Uniqueness of their model is air challenges.

Croatia 2021/22

Adj. R sq. 0,449

Shots on target

Key passes accurate

Crosses

Goals – Free-kick attack

Air challenges

% of efficiency for set-piece attacks

Shots wide

% of efficiency for counterattacks

Fouls

Poland has 12 variables in the model. Uniqueness of their model are % of efficiency for throw-in attacks and counter-attacks with a shot.

Poland 2021/22

Adj. R sq. 0,465

Shots on target

Crosses

Key passes accurate

Shots wide

Lost balls

Ball recoveries

Goals – Free-kick attack

Corners

% of efficiency for throw-in attacks

% of efficiency for corner attacks

Counter-attacks with a shot

Fouls

Regression models for “not defeat”

England 2021/22

Adj. R sq. 0,299

Shots on target

Opponent’s passes per defensive action

Crosses accurate

Key passes accurate

Entrance to the penalty box

Crosses

Ball interceptions

Ball recoveries in opponent’s half

Lost balls

Defensive challenges won

“

Offsides

Tackles successful

Yellow cards

Average duration of ball possession, min

Spain has 13 variables in the model. Uniqueness of their model are ball possessions – quantity, attacks – centre, defensive challenges and attacking challenges.

Spain 2021/22

Adj. R sq. 0,231

Shots on target

Opponent’s passes per defensive action

Crosses

Key passes

Ball recoveries

Ball possessions, quantity

Fouls

Attacks – center

Defensive challenges

Attacking challenges

Dribbles successful

Shots wide

Shots on target, %

In Spain there is no significant correlation above +/- 0,200. In this model red marked are “Opponent’s passes per defensive action” and “Crosses”.

France has least number of variables (6) in the model. This model has no uniqueness.

France 2021/22

Adj. R sq. 0,165

Shots on target

Challenges won, %

Crosses

Ball interceptions

Key passes accurate

Entrance to the penalty box

In France there is also no significant correlation above +/- 0,200. In this model there red marked is “Crosses”.

Germany 2021/22

Adj. R sq. 0,292

Shots on target

Opponent’s passes per defensive action

Set pieces attacks

Key passes

Accurate passes, %

Successful actions, %

% of efficiency for set-piece attacks

Challenges

Total actions

Challenges won

Shots on post / bar

Accurate passes

Ball recoveries

Shots

Entrances to the opposition half

Positional attacks

Free-kick attacks with shots

Air challenges won, %

Italy has 10 variables in the model. This model has no uniqueness.

Italy 2021/22

Adj. R sq. 0,228

Shots on target

Opponent’s passes per defensive action

% of efficiency for counterattacks

Challenges won, %

Crosses

Key passes

Red cards

Ball recoveries in opponent’s half

Lost balls

Ball recoveries

Austria has 13 variables in the model. Uniqueness of their model are % of efficiency for throw-in attacks, team pressing successful and air challenges.

Austria 2021/22

Adj. R sq. 0,282

Key passes accurate

Fouls

Shots on target, %

% of efficiency for throw-in attacks

Challenges won, %

Free-kick attacks with shots

Crosses accurate

Team pressing successful

Lost balls

Ball recoveries

Accurate passes, %

Successful actions, %

Air challenges

In Austria there is no significant correlation above +/- 0,200. In this model red marked are “% of efficiency for throw-in attacks”, “Free-kick attacks with shots” and “Lost balls”.

Belgium has 12 variables in the model. Uniqueness of their model is pressing efficiency, %.

Belgium 2021/22

Adj. R sq. 0,231

Shots on target

Ball interceptions

Key passes

Crosses

Air challenges won, %

% of efficiency for counterattacks

Ball recoveries

Dribbles successful

Pressing efficiency, %

Lost balls in own half

Challenges won, %

Shots on target, %

In Belgium there is just one significant correlation with “not defeat” – “% of efficiency for counterattacks” (0.215). In this model red marked is “Crosses”.

Croatia has 7 variables in the model. Uniqueness of their model are air challenges won and efficiency for attacks through the left flank, %.

Croatia 2021/22

Adj. R sq. 0,221

Shots on target

Air challenges won

% of efficiency for set-piece attacks

Key passes accurate

Shots wide

Ball recoveries in opponent’s half

Efficiency for attacks through the left flank, %

Poland has 10 variables in the model. Uniqueness of their model are lost balls in own half, free-kick attacks and tactics.

Poland 2021/22

Adj. R sq. 0,248

Shots on target

Positional attacks

Ball recoveries

Lost balls in own half

% of efficiency for throw-in attacks

Challenges won

Key passes accurate

Crosses

Free-kick attacks

Tactics

Regression models for victory

England has 17 variables in the model. Uniqueness of their model are successful actions, defensive challenges won, total actions, entrances to the final third and challenges.

England 2021/22

Adj. R sq. 0,355

Shots on target

Crosses

Successful actions

Air challenges won, %

Free-kick attacks

% of efficiency for throw-in attacks

Lost balls in own half

Red cards

Defensive challenges won

Key passes accurate

Total actions

Tackles successful

Entrances to the final third

Crosses accurate

Challenges

Opponent’s passes per defensive action

Team pressing

Spain has the most variables (19) of all models. Uniqueness of their model are air challenges won, entrances to the opposition half, offsides and shots.

Spain 2021/22

Adj. R sq. 0,331

Shots on target

Crosses

Key passes

Opponent’s passes per defensive action

Free-kick attacks

Air challenges won

Ball interceptions

Successful actions, %

Fouls

Red cards

Dribbles successful

Attacking challenges won

Counter-attacks with a shot

Entrances to the opposition half

Offsides

Set pieces attacks

Ball recoveries

Lost balls

Shots

France has 9 of all models. Uniqueness of their model are corner attacks with shots and yellow cards.

France 2021/22

Adj. R sq. 0,241

Shots on target

Crosses

Key passes accurate

Challenges won, %

Ball interceptions

Red cards

Tactics

Corner attacks with shots

Yellow cards

In France there is no significant aspect with more strength than +/- 0,200. In this model red marked are “Crosses”, “Yellow cards” and “Red cards”.

Germany has least variables (6) of all models and weakest model. Uniqueness of their model are challenges in defence won – %, goals – Free-kick attack and shots on post / bar.

Germany 2021/22

Adj. R sq. 0,225

Shots on target

Set pieces attacks

Key passes accurate

Crosses

Challenges in defence won, %

Opponent’s passes per defensive action

Goals – Free-kick attack

Shots on post / bar

Italy has 12 of all models. Uniqueness of their model are accurate passes – %, free ball pick ups – %, and scored free kick shots.

Italy 2021/22

Adj. R sq. 0,301

Shots on target

Crosses

Key passes

Successful actions, %

Opponent’s passes per defensive action

Accurate passes, %

% of efficiency for counterattacks

Free-kick attacks

Air challenges

Free ball pick ups

% scored free kick shots

Tackles successful

Austria has 12 variables in the model. Uniqueness of their model are shots on target – %, accurate crosses – %, and attacks – centre.

Austria 2021/22

Adj. R sq. 0,304

Counter-attacks with a shot

Key passes accurate

Shots on target, %

Crosses accurate

Red cards

Accurate crosses, %

Tactics

Lost balls in own half

Ball recoveries

Lost balls

Fouls

Attacks – center

Belgium has 12 variables in the model. Uniqueness of their model are challenges in defence won – %, tackles and attacks with shots – centre.

Belgium 2021/22

Adj. R sq. 0,265

Shots on target

Crosses

Key passes

Team pressing

Set pieces attacks

Challenges in defence won,%

Dribbles successful

Tackles

% of efficiency for counterattacks

Attacks with shots – center

Attacking challenges won

Tackles successful

Croatia has 10 variables in the model and strongest model. Uniqueness of their model are % of efficiency for set-piece attacks and ball recoveries in opponent’s half.

Croatia 2021/22

Adj. R sq. 0,364

Shots on target

Key passes accurate

% of efficiency for counterattacks

Crosses accurate

% of efficiency for set-piece attacks

% of efficiency for throw-in attacks

Ball interceptions

Ball recoveries in opponent’s half

Shots wide

Air challenges won, %

Poland has 12 of all models. There is no uniqueness of their model.

Poland 2021/22

Adj. R sq. 0,306

Shots on target

Crosses

Counter-attacks with a shot

Shots wide

Team pressing

Challenges won, %

“Ball interceptions

Lost balls

Ball recoveries

% of efficiency for throw-in attacks

Air challenges

Free-kick attacks

Check out this article on LinkedIn: https://www.linkedin.com/pulse/do-leagues-generally-differ-between-themselves-pitch-slaven-marasovi%C4%87/

TOP 75 EUROPEAN CLUBS BY BRAND VALUE

After calculating enterprise value for TOP100 clubs (https://managementoffootball.com/top-100-european-clubs-by-enterprise-value/) in 31 European leagues, I have calculated Brand value of TOP75 clubs from the same leagues.

What is brand value? Brand value is value stated in money somebody would be ready to buy your brand for. It is defined through logo, brand name, and everything what is perceived as indivisible part of your brand identity.

Why is it important? “A brand encompasses the name, logo, image, and perceptions that identify a product, service, or provider in the minds of customers. It takes shape in advertising, packaging, and other marketing communications, and becomes a focus of the relationship with consumers.” Further on, Wikipedia states, that brand equity, defined in marketing as “the worth of a brand in and of itself – i.e., the social value of a well-known brand name” is important for determination of price structure.

By Wikipedia, Interbrand classifies these uses of brand valuation in three categories:

Financial applications (e.g. mergers and acquisitions, balance sheet valuation, investor relations)
Brand management applications (e.g. brand portfolio management, resource allocation)
Strategic / Business case applications (e.g. brand architecture, brand repositioning)

When we talk about methodology, there are different methodologies for calculating brand equity by Wikipedia:

The cost approach (amount of money which someone would use to create the equivalent of observed brand),
The market approach (it is based on comparison of market price of other brands)
The income approach:
1. Price premium method (price premium it generates comparing with non-branded product similar quality)
1. Volume premium method (volume premium it generates comparing with non-branded product similar quality)
1. Income split method
1. Multi-period excess earnings method
1. Incremental cash flow method or Excess Margin
1. Royalty relief method

*More about specific brand methodologies you can find in (Clark, R. (2013): Measuring the Value of Brands) on link: http://www.valcoronline.com/PDFs/Valuing%20Brand%20Equity_10-7-13_vFINAL(rc).pdf

The same logic used in calculations of enterprise value is used here as well. A number of “things” might influence on brand, or if you want strength of brand influence on number of “things”. And those “things” make “footprints” for brand value in the world around us. So, first goal was to identify as many “footprints” as possible and collect data about them. Data I have been using were data which are not data from financial reports. Instead, we have been using a spectrum of external (not from club documents or reports) and exclusively publicly available data.

Same as in enterprise value, the final goal was to find the algorithm in the standardized model for the spectrum of clubs from the lowest brand value in Europe to clubs with the highest enterprise value in Europe (from 0mil € or something less to 1500mil€). For both, enterprise value and brand value, the second goal was to find an algorithm which is time resistant as long as possible. Finally, the algorithm was found using different analyses in inferential statistics and I have made the list of TOP75 clubs in Europe by brand value (MoF further in text).

Same testing was used for brand value as for enterprise value calculation. Before release, an algorithm in a standardized model was tested in several ways. First, algorithm results were checked by logical brand value in reality for the whole span of club values, from lower valued clubs as well as high valued clubs (span should go from 0 to 1500mil€ in the logical sense). The second quality check was confirmed by statistical analysis rules. The third was a comparison with brand value calculated by KPMGs spin-off, Football Benchmark (https://www.footballbenchmark.com/library/football_clubs_valuation_the_european_elite_2022); comparing results of enterprise value with KPMGs spin-off, Football Benchmark we receive 97% of statistically positive significant correlation between results. Forth was using data from years before instead of one, last year.

I have been looking on 11 external areas as “footprints” of what’s happening within the club for a period of time. Those areas are 1. success in European competitions (UEFA), 2. investment in the team, 3. social networks, 4. team structure, 5. value of the team, 6. Sale-buying, 7. stadium, 8. attendance, 9. domestic competition, 10. general environment and 11. football environment. Through different stages of research, we have been using in total more than 170 different variables to find the final standardized algorithm. Also, we have been using data about single variables from 5 years before and for some variables even 10 years before.

We have been analyzing 227 clubs from 36 European leagues. 31 leagues are from the 1st national tier and 5 leagues are from the 2nd national tier:

1	ESP – La Liga
2	ESP – La Liga 2
3	ENG – Premier league
4	ENG – Championship
5	GER – Bundesliga
6	GER – 2. Bundesliga
7	FRA – Ligue 1
8	FRA – Ligue 2
9	ITA – Serie A
10	ITA – Serie B
11	TUR
12	POR
13	NED
14	SCO
15	SVI
16	ROU
17	UKR
18	BEL
19	RUS
20	GRE
21	POL
22	SER
23	CRO
24	AUT
25	HUN
26	CZE
27	DEN
28	NOR
29	SWE

In the analysis were included all clubs from the Big 5 leagues and the best clubs from other listed leagues. More clubs per league were selected from the following leagues: Netherlands, Portugal, Turkey, Belgium, English Championship and Russia (*this year’s results are not affected by the war in Ukraine for Ukraine and Russian clubs)

Nr.	Club	State	BV mil €
1	Real Madrid	SPA	1377
2	FC Barcelona	SPA	1348
3	Manchester United	ENG	1303
4	Liverpool FC	ENG	1273
5	Bayern Munich	GER	1183
6	Manchester City	ENG	1143
7	Chelsea FC	ENG	1013
8	Paris Saint-Germain	FRA	918
9	Tottenham Hotspur	ENG	820
10	Arsenal FC	ENG	767
11	Juventus FC	ITA	678
12	Borussia Dortmund	GER	612
13	Atlético de Madrid	SPA	533
14	Inter Milan	ITA	501
15	AC Milan	ITA	378
16	Leicester City	ENG	317
17	Everton FC	ENG	313
18	SSC Napoli	ITA	305
19	AS Roma	ITA	280
20	RB Leipzig	GER	277
21	Olympique Lyon	FRA	263
22	Bayer 04 Leverkusen	GER	251
23	AFC Ajax Amsterdam	NED	233
24	Olympique Marseille	FRA	215
25	Valencia CF	SPA	208
26	FC Porto	POR	188
27	Aston Villa	ENG	187
28	West Ham United	ENG	183
29	AS Monaco	FRA	180
30	Real Sociedad	SPA	175
31	Wolverhampton Wanderers	ENG	172
32	SL Benfica	POR	170
33	Newcastle United	ENG	168
34	SS Lazio	ITA	157
35	Sevilla FC	SPA	153
36	Southampton FC	ENG	148
37	Galatasaray A.S.	TUR	146
38	Atalanta BC	ITA	143
39	LOSC Lille	FRA	136
40	Real Betis Balompié	SPA	134
41	ACF Fiorentina	ITA	130
42	Athletic Bilbao	SPA	129
43	Sporting CP	POR	125
44	FC Schalke 04	GER	124
45	Fenerbahce SK	TUR	123
46	Crystal Palace	ENG	122
47	Eintracht Frankfurt	GER	122
48	Borussia Mönchengladbach	GER	115
49	TSG 1899 Hoffenheim	GER	112
50	PSV Eindhoven	NED	108
51	Brighton & Hove Albion	ENG	105
52	Villarreal CF	SPA	103
53	Stade Rennais FC	FRA	96
54	VfL Wolfsburg	GER	95
55	OGC Nice	FRA	92
56	Leeds United	ENG	86
57	Fulham FC	ENG	86
58	AFC Bournemouth	ENG	84
59	Hertha BSC	GER	83
60	FC Zenit	RUS	82
61	Red Bull Salzburg	AUT	81
62	Watford FC	ENG	79
63	Feyenoord Rotterdam	NED	79
64	VfB Stuttgart	GER	77
65	FC Girondins Bordeaux	FRA	76
66	SV Werder Bremen	GER	75
67	West Bromwich Albion	ENG	74
68	Rangers FC	SCO	72
69	Sheffield United	ENG	70
70	Torino FC	ITA	70
71	Club Brugge KV	BEL	70
72	Trabzonspor	TUR	69
73	Stoke city	ENG	68
74	AS Saint-Étienne	FRA	67
75	Burnley FC	ENG	66
	TOTAL:	22,161 bil €

The total value of TOP75 clubs is 22,161 bil. € coming from 12 European leagues with 6 clubs from the 2nd national tier:

	Number of clubs in TOP75	Total brand value of clubs per league	Percent of brand value of clubs from single league in TOP 75	Average brand value of clubs per league
ENG	23	8649	39%	376
SPA	9	4159	19%	462
GER	12	3125	14%	260
ITA	9	2642	12%	294
FRA	9	2042	9%	227
POR	3	483	2%	161
NED	3	419	2%	140
TUR	3	338	2%	113
RUS	1	82	0%	82
AUT	1	81	0%	81
SCO	1	72	0%	72
BEL	1	70	0%	70

In the above table, values of clubs from a single league were summarized to identify differences between leagues in TOP75 brand value. The highest brand value of clubs from a single league in TOP75 is having English Premier league (8649mil€) followed by Spain La Liga (4159mil€), German Bundesliga (3125mil€), Italy Serie A (2642mil€) and French Ligue 1 (2042mil€). Mosta valuable club from second tier is Fulham (86 mil €). English Premier league is pointed out with more than double as value as Spain La Liga.

If we look at the column “Percent of brand value of clubs from single league in TOP 75” we will see again that the English Premier league pointed out. 85% of clubs from the Premier league, are part of TOP75, followed by German Bunesliga (67%), Italy Serie A (45%), Spain La Liga (45%) and France Ligue 1 (45%).

The column “Average brand value of clubs per league” is the value from all clubs from one league in TOP 75 divided by the number of clubs in the observed league. Leagues with a higher number of clubs will have less average if they have the same number of clubs in the TOP 75 as leagues with fewer clubs in it. Spain La Liga is having is having the highest average (462mil€) followed by Premier league (376mil€), Italian Serie A (294mil€), German Bundesliga (260mil€), and France Ligue 1 (227mil€). In Enterprise value, on first position was Premier league while second was Spanish La Lia.

Those two columns (“Percent of brand value of clubs from single league in TOP 75”, and “Average brand value of clubs per league”) might indicate the concentration of overall brand strength.

If we compare results from the MoF BV list and results from KPMGs spin-off Football Benchmark, clubs which fall of the KPMGs spin-off Football Benchmark list are 1. FC Köln, Celtic FC. On the other side FC Porto, AS Monaco, Real Sociedad, SS Lazio and others came in.

Again, in Brand value as in Enterprise value, it is noticeable that generally Italian clubs are generally higher valued in the Mof BV algorithm than in KPMG’s spin-off, Football Benchmark.

The characteristic of this method is that at any moment value of any club in Europe can be calculated based on publicly available data. Although results were tested with KPMG’s spin-off Football Benchmark and sales which really happened, using financial reports for another step of testing the model would be beneficial. Characteristic of this model is that it is standardized and not influenced by any possible misleading in financial reports.

Model by itself reveals what from reality influences most on the brand value of the clubs and as such, they can be used for setting relevant and measurable goals for the top management of the club, and for setting KPIs for the top management of the clubs regarding either brand or enterprise value.

With enterprise value and brand value research finished, we are moving to next research:

The quality of human capital in club management is interesting as well as sports staff especially. So, on the list of next research is a calculation of:
1. standardized model of quality of management of clubs and
1. standardized model of quality of sport management of the club..

Moving from management of the club to the performance on the pitch, research which is almost to be released is about differences per TOP5+4 leagues, trying to identify models how to win, not to lose, score goals or create chances.

If you have any comments, we would be grateful to talk about this, what do you think about the possibility of this kind of calculations of brand value, what do you think about results and do you have maybe any idea about improving this research (combining with financial reports would, of course, be very useful but here is an idea to use date, not from financial research) and about further research. Special thanks goes to all guys from www.managementoffootall.com

Check out this article on LinkedIn: https://www.linkedin.com/pulse/calculated-top75-european-clubs-brand-value-2021-slaven-marasovi%C4%87/

TOP 100 EUROPEAN CLUBS BY ENTERPRISE VALUE

publicly traded clubs included) without taking into consideration of market capitalization, debt, and cash? Yes, TOP 100 European clubs by enterprise value calculated!

After four months of research, I and my team were dealing with calculations of the enterprise value of TOP 100 European clubs using statistical analysis to find an algorithm that does not use data about market capitalisation, debt, and cash, but data from financial reports.

So, what is enterprise value? It is a value you would pay for business if you were aiming to purchase it or, a value you would receive if you would sell your business. The following formula is used to determine the enterprise values of publicly traded businesses:

Enterprise Value = Market Capitalization + Debt (short and long term) – Cash (and cash equivalents)

But what if you want to buy a club which is not publicly traded? There are several methods how for calculating it:

1. Comparable Company Analysis (CCA)

2. Discounted Cash Flow (DCF) method

3. First Chicago Method

4. Asset-based method

All of these methods are in more detail explained on the following link (https://corporatefinanceinstitute.com/resources/knowledge/valuation/private-company-valuation/ ), but one can find a lot of literature on the web.

I will just quote snapshots from definitions:

Ad1. The Comparable Company Analysis (CCA) method operates under the assumption that similar firms in the same industry have similar multiples.

Ad2. The Discounted Cash Flow (DCF) method takes the CCA method one step further. As with the CCA method, we estimate the target’s discounted cash flow estimations, based on acquired financial information from its publicly traded peers.

Ad3. The First Chicago Method is a combination of the multiple-based valuation method and the discounted cash flow method. The distinct feature of this method lies in its consideration of various scenarios of the target firm’s payoffs. (Usually, this method involves the construction of three scenarios: a best-case (as stated in the firm’s business plan), a base-case (the most likely scenario), and a worst-case scenario. A probability is assigned to each case.)

Ad 4. The asset-based method is used when is more profitable to liquidate a firm than continue with business because with liquidation firm will have better cash flow than if it continues with business.

If you don’t know which method to use, help could also be found at „The International Valuation Standards Council” – IVSC or national agencies for evaluation of firms.

In this research, the goal was to assess publicly and non-publicly traded clubs to calculate their enterprise value. Since there is no share price for non-traded clubs, we have been looking for the club’s overall environment which would influence on share price to go up or down. There is a lot of literature about reasons for share price fluctuation generally, but also specifically for the football industry as well. Joao Duque and Nuno Abrantes Ferreira from Instituto Superior Economia e Gestao, Universidade Tecnica de Lisbao have found positive relationship between sporting performance and share price. José Allouche from Université de Paris 1 Panthéon-Sorbonne, Paris and Sébastien Soulez from Université Lumiere Lyon 2, Lyon have found that sporting results positively influence on share price after victory and vice versa. Further on, they have found that managerial decisions about running and investing in sporting facilities, sponsoring and positive financial results influence positively the share price and in case of unfavourable financial results influence negatively the share price. Also, they have found the relationship between human resources (players) and share price. Similar is found in Turkey regarding winning (https://www.jstor.org/stable/41343433), where winning in the European cup does not affect club stock share returns while winning in domestic matches does influence positively.

However, a number of “things” might influence share price, and those “things” make “footprints” for enterprise value in the world around us. So, our first goal was to identify as many “footprints” as possible and collect data about them. Data we have been using were data which are not data from financial reports, not data about share prices. Instead, we have been using a spectrum of external (not from club documents or reports) and exclusively publicly available data.

The final goal was to find the algorithm in the standardized model for the spectrum of clubs from the lowest enterprise value in Europe to clubs with the highest enterprise value in Europe (from 0mil € or something less to 3200mil€). The second goal was to find an algorithm which is time resistant as long as possible. Finally, the algorithm was found using different analyses in inferential statistics and we have made the list of TOP100 clubs in Europe by enterprise value (MoF further in text).

Before release, an algorithm in a standardized model was tested in several ways. First, algorithm results were checked by logical enterprise value in reality for the whole span of club values, from lower valued clubs as well as high valued clubs (span should go from 0 to 3200mil€ in the logical sense). The second quality check was confirmed by statistical analysis rules. The third was a comparison with enterprise value calculated by KPMGs spin-off, Football Benchmark (https://www.footballbenchmark.com/library/football_clubs_valuation_the_european_elite_2022); comparing results of enterprise value with KPMGs spin-off, Football Benchmark we receive 99.4% of statistically positive significant correlation between results. The fourth was sales which happened in 2021 in the UEFA report (https://editorial.uefa.com/resources/0272-145b03c04a9e-26dc16d0c545-1000/master_bm_high_res_20220203104923.pdf). Fifth was using data from years before instead of one, last year.

We have been looking on 11 external areas as “footprints” of what’s happening within the club for a period of time. Those areas are 1. success in European competitions (UEFA), 2. investment in the team, 3. social networks, 4. team structure, 5. value of the team, 6. Sale-buying, 7. stadium, 8. attendance, 9. domestic competition, 10. general environment and 11. football environment. Through different stages of research, we have been using in total more than 160 different variables to find the final standardized algorithm. Also, we have been using data about single variables from 5 years before and for some variables even 10 years before.

We have been analysing 227 clubs from 36 European leagues. 31 leagues are from the 1st national tier and 5 leagues are from the 2nd national tier:

1	ESP – La Liga
2	ESP – La Liga 2
3	ENG – Premier league
4	ENG – Championship
5	GER – Bundesliga
6	GER – 2. Bundesliga
7	FRA – Ligue 1
8	FRA – Ligue 2
9	ITA – Serie A
10	ITA – Serie B
11	TUR
12	POR
13	NED
14	SCO
15	SVI
16	ROU
17	UKR
18	BEL
19	RUS
20	GRE
21	POL
22	SER
23	CRO
24	AUT
25	HUN
26	CZE
27	DEN
28	NOR
29	SWE

Of all those clubs, the TOP 100 clubs by enterprise value are:

	Club	State	EV – mil€
1	Real Madrid	SPA	3139
2	FC Barcelona	SPA	3019
3	Manchester United	ENG	2867
4	Liverpool FC	ENG	2847
5	Bayern Munich	GER	2727
6	Manchester City	ENG	2540
7	Chelsea FC	ENG	2299
8	Paris Saint-Germain	FRA	2053
9	Tottenham Hotspur	ENG	1761
10	Arsenal FC	ENG	1623
11	Juventus FC	ITA	1558
12	Borussia Dortmund	GER	1347
13	Atlético de Madrid	SPA	1116
14	Inter Milan	ITA	1052
15	AC Milan	ITA	766
16	Leicester City	ENG	670
17	SSC Napoli	ITA	655
18	Everton FC	ENG	633
19	RB Leipzig	GER	625
20	Bayer 04 Leverkusen	GER	582
21	AS Roma	ITA	544
22	Olympique Lyon	FRA	542
23	AFC Ajax Amsterdam	NED	469
24	AS Monaco	FRA	451
25	Valencia CF	SPA	431
26	FC Porto	POR	401
27	SL Benfica	POR	377
28	Real Sociedad	SPA	356
29	Olympique Marseille	FRA	350
30	Wolverhampton Wanderers	ENG	346
31	Atalanta BC	ITA	344
32	Aston Villa	ENG	341
33	SS Lazio	ITA	326
34	Borussia Mönchengladbach	GER	312
35	Galatasaray A.S.	TUR	309
36	Southampton FC	ENG	305
37	Sevilla FC	SPA	292
38	TSG 1899 Hoffenheim	GER	274
39	Sporting CP	POR	274
40	ACF Fiorentina	ITA	272
41	West Ham United	ENG	259
42	Eintracht Frankfurt	GER	244
43	Crystal Palace	ENG	242
44	Fenerbahce SK	TUR	241
45	Newcastle United	ENG	239
46	Villarreal CF	SPA	237
47	VfL Wolfsburg	GER	233
48	LOSC Lille	FRA	232
49	PSV Eindhoven	NED	226
50	Athletic Bilbao	SPA	222
51	FC Schalke 04	GER	221
52	Red Bull Salzburg	AUT	207
53	AFC Bournemouth	ENG	204
54	OGC Nice	FRA	202
55	Real Betis Balompié	SPA	198
56	Shakthar Donetsk	UKR	184
57	FC Zenit	RUS	178
58	Torino FC	ITA	177
59	Brighton & Hove Albion	ENG	174
60	Stade Rennais FC	FRA	174
61	US Sassuolo	ITA	168
62	Fulham FC	ENG	168
63	Hertha BSC	GER	161
64	Watford FC	ENG	155
65	Club Brugge KV	BEL	149
66	PFC CSKA Moskva	RUS	141
67	FC Girondins Bordeaux	FRA	140
68	Getafe CF	SPA	139
69	Celtic FC	SCO	136
70	FC Spartak Moskva	RUS	136
71	RSC Anderlecht	BEL	135
72	VfB Stuttgart	GER	134
73	Burnley FC	ENG	133
74	Celta de Vigo	SPA	132
75	SV Werder Bremen	GER	130
76	Feyenoord Rotterdam	NED	129
77	SC Braga	POR	125
78	Udinese Calcio	ITA	123
79	AS Saint-Étienne	FRA	122
80	GNK Dinamo Zagreb	CRO	122
81	1.FSV Mainz 05	GER	120
82	Cagliari Calcio	ITA	119
83	West Bromwich Albion	ENG	119
84	Dynamo Kyiv	UKR	118
85	Bologna FC 1909	ITA	118
86	SC Freiburg	GER	116
87	Leeds United	ENG	116
88	UC Sampdoria	ITA	111
89	Stoke city	ENG	108
90	RCD Espanyol Barcelona	SPA	106
91	Olympiacos Piraeus	GRE	106
92	KRC Genk	BEL	104
93	FC Nantes	FRA	103
94	Swansea city	ENG	102
95	AZ Alkmaar	NED	101
96	Norwich City	ENG	101
97	Montpellier HSC	FRA	99
98	Trabzonspor	TUR	97
99	Lokomotiv Moscow	RUS	96
100	FC Basel 1893	SVI	93
TOTAL	50 414 mil€

The total value of TOP 100 clubs is 50,414 bil. € coming from 18 European leagues with two leagues from the 2nd national tier (English Championship and German 2. Bundesliga):

		Tot value clubs-TOP100	% of league clubs-TOP100	Average value-TOP100
1	ENG – Premier league	18349	95%	882
2	SPA	9386	60%	469
3	GER – Bundesliga	7226	67%	382
4	ITA	6332	70%	317
5	FRA	4468	55%	223
6	POR	1177	22%	65
7	NED	925	22%	51
8	ENG – Championship	700	21%	29
9	TUR	647	15%	32
10	RUS	551	25%	34
11	BEL	387	17%	22
12	GER – 2. Bundesliga	351	11%	20
13	UKR	302	13%	19
14	AUT	207	8%	17
15	SCO	136	8%	11
16	CRO	122	10%	12
17	GRE	106	7%	8
18	SUI	93	10%	9

In the above table, values of clubs from a single league were summarized to identify differences between leagues in TOP 100 enterprise value. The highest enterprise value of clubs from a single league in TOP100 is having English Premier league (18349mil€) followed by Spain La Liga (9386mil€), German Bundesliga (7726mil€), Italy Serie A (6332mil€) and French Ligue 1 (4468mil€). English Premier league is pointed out with almost double as value as Spain La Liga. Further on, English Championship is the 8th league (700mil€) in Europe by the value of clubs in TOP100 (German 2. Bundesliga is the 12th league by value (351mil€) of clubs from one league in TOP100).

If we look at the column “% of league clubs-TOP100” we will see again that the English Premier league pointed out, that 95% of clubs from the league, or 19 (Brentford is out) are part of TOP100, followed by Italy Serie A (70%), German Bundesliga (67%), Spain La Liga (60%) and France Ligue 1 (55%).

The column “Average value-TOP100” is the value from all clubs from one league in TOP 100 divided by the number of clubs in the observed league. Leagues with a higher number of clubs will have less average if they have the same number of clubs in the TOP 100 as leagues with fewer clubs in it. Again, the English Premier League is having the highest average (882mil€) followed by Spain’s La Liga (469mil€), German Bundesliga (382mil€), Italian Serie A (317mil€), and France Ligue 1 (223mil€).

Those two columns (“% of league clubs-TOP100”, and “Average value-TOP100”) might indicate the concentration of overall competition and quality of the observed league. A higher percent of league clubs and average might mean higher quality and competition in the league.

If we compare results from the MoF EV list and results from KPMGs spin-off Football Benchmark, the biggest positive differences are with the following clubs (MoF EV – KPMG EV):

	Club	MoF-KPMG (EV)
1	Liverpool FC	291
2	FC Barcelona	205
3	AC Milan	188
4	SSC Napoli	172
5	Leicester City FC	144
6	AS Roma	131

It is noticeable that generally Italian clubs are generally higher valued in the Mof EV algorithm than in KPMG’s spin-off, Football Benchmark with the highest percent of the difference in EV from KPMGs (SSC Napoli 36%, AC Milan 32%, and AS Roma 32%).

Biggest negative differences (MoF EV – KPMG EV):

	Club	MoF-KPMG (EV)
1	Tottenham Hotspur FC	-151
2	Atletico de Madrid	-118
3	Atalanta BC	-110
4	Sevilla FC	-98
5	Paris-Saint Germain FC	-79
6	Villareal CF	-66

The list of 32 clubs from Football Benchmark is different from Mof EV list. Clubs which are on Mof EV list but not on Football Benchmark one, are RB Leipzig, Bayer 04 Leverkusen, AS Monaco, Real Sociedad, Olympique Marseille and Wolverhampton Wanderers.

Model by itself reveals what from reality influences most on the enterprise value of the clubs and as such, they can be used for setting relevant and measurable goals for the top management of the club, and for setting KPIs for the top management of the clubs.

Although enterprise value is important when one is investing in the club, estimating the potential of an individual club and the gap which it can fill is of big importance as well. It depends not just on clubs’ inner potential (financial, human resources, community, infrastructure …) but also on football’s external environment and general environment and based on other research results that are interesting in combination with EV research.

Next research…

The quality of human capital in club management is interesting as well as sports staff especially. So, on my list of next research is a calculation of standardized model of quality of management of clubs and standardized model of quality of sport management of the club. Besides that, two research on the “to do” list is research about brand and research on talents.

If you have any comments, we would be grateful to talk about this, what do you think about the possibility of this kind of calculations of enterprise value, what do you think about results and do you have maybe any idea about improving this research (combining with financial reports would, of course, be very useful but here is an idea to use date, not from financial research) and about further research. Special thanks go to Nikica Krnić, Konstantin Kornakov and Josip Korda.

Check out this article on LinkedIn:

WHAT IS ESSENTIAL FOR RUNNING A SUCCESSFUL ACADEMY?

As I have been analyzing in last articles, most important of 12 quality areas are Cognitive care, Human capital, Productivity and Talent identification.
You can find articles on following links:
1. How we perceive the physical and „decision making“ characteristics of players, how we can measure them and develop them? Click to read more .
2. About what is “produced” in the academies and what we could “produce” as well. STAVITI LINK NA WEBU_BLOG GDJE SE NALAZI 3. What do we about „digital life“ of kids in an academy, do we do anything about it and what could we do. Click to read more.

4. Relation between academies and player agents. Click to read more.
5. What academies across Europe do most often and what do they the least? Click to read more.

Most important quality areas were detected over working processes used on daily basis in youth academies. You will find all this quality areas in most used, least used, most correlated working process and in analysis of interviews.

Those working processes are separated in 17 quality areas. Among them most important are Youth academy – players, Youth academy – Coaches and Youth academy – Staff.

Finally, in development of youth academy, among 17 working areas and 12 quality areas, focus should be on coaches, staff, and players from one side and human capital, cognitive care, productivity and TID from the other side. Finally, ingredients are club buy-in and strategic importance for academy. That’s the path to follow to get to maximum of your potentials. Having this you will develop other quality and working areas as well!

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