Einkommen bundesligaspieler

einkommen bundesligaspieler

Okt. mit Phillip Lahm, Mats Hummels, Jerome Boateng, Manuel Neuer und Thomas Müller logischerweise die anderen fünf Bundesliga-Spieler. Wichtig: Die Übersicht und die Umfragen sollen keinerlei Wertung über die Gehälter der Bundesligaspieler sein. Als Profifussballer verdient man natürlich. Dez. In England werden daher die Einkommen der Spieler am liebsten auf eine Davon können die allermeisten Bundesliga-Spieler nur träumen.

bundesligaspieler einkommen -

Kabinenpredigt Kein Grund durchzudrehen! Das macht einen durchschnittlichen Jahresverdienst von rund 1,9 Millionen Euro pro Spieler. Aber ich habe ein paar Bedenken deswegen. Aber was soll er machen? Wie lange braucht ein Muskelfaseriss zum abheilen? Allerdings gibt es dabei zwei Dinge zu bedenken: In Seiferts Reich wäre alles prima, wenn er nicht selbst merken würde, wie schnell sich das Rad mittlerweile dreht — und er derjenige ist, der es in Schwung halten muss.

bundesligaspieler einkommen -

Hier Trennen sich nämlich Dimensionen! Kann ich mit einer leichten Erkältung Sport treiben? So geht es nicht. Und wieso werden die z. Hat auch auf dem Ich denke Sie können aber einen guten Eindruck über das Gehaltsgefüge in Vereinen geben. Hinterlasse eine Antwort Antwort verwerfen. Liga werden Gehälter bezahlt, die absolut nicht nachvollziehbar sind. Zurück Bundesliga - Übersicht Spielpläne.

Einkommen bundesligaspieler -

Auch Borussia Dortmund zahlt gut, wie Die meisten der rund Spieler aus der ersten deutschen Liga sind Millionäre oder werden es bald sein. Zurück Politik - Übersicht Meinung. Wie seht ihr das? In Seiferts Reich wäre alles prima, wenn er nicht selbst merken würde, wie schnell sich das Rad mittlerweile dreht — und er derjenige ist, der es in Schwung halten muss. Ab wann wird es gefährlich? Zurück Sicher leben - Übersicht Kriminalitätsprävention. Ihre Daten werden verschlüsselt übertragen. Was Bundesligaspieler im Monat verdienen. Und wieso werden die z. Zurück Hilfe - Übersicht Fragen zu noz. Das steht auf dem Speiseplan der Spieler. In einer Parkanlage sind zwei Treppen mit gleichen Stufenhöhen geplant. Das lässt vermuten, dass dort mehr Spieler mit einem geringeren Gehalt versammelt sind, während die Topleute nicht weniger verdienen dürften als bei der Konkurrenz. Danach steht er ohne Studium oder Ausbildung, im schlimmsten Falle gar ohne Abitur aber mit kaputtem Körper im Nichts. Eine ähnliche Summe soll auch Bundesliga-Absteiger SC Paderborn in seinen Kader investiert haben - für die es jetzt aber im Gegensatz zu den Leipzigern nicht zurück in die Bundesliga ging, sondern eine Liga weiter nach unten. Stelle ich mir gerade schwierig vor, das alles unter einen Hut zu bekommen ohne, dass die Spieler bei dem Stress sich verletzen.

Auch diese basieren meist auf Schätzungen. In den veröffentlichen Quellen werden die Gehälter in der Regel als Bruttogehalt angegeben. Die Übersicht und die Umfragen sollen keinerlei Wertung über die Gehälter der Bundesligaspieler sein.

Ich behalte es mir vor nicht sachliche Kommentare zu entfernen. Dieser enthält nämlich auch Prämien etc.

Personalkosten, Etat, Personaletat oder Lizenzspieleretat müssen nicht das Gleiche sein. Hier findet ihr mehr zum Thema Schalke K-P Boatengs Gehalt scheint wesentlich höher als hier beschrieben.

Hinterlasse eine Antwort Antwort verwerfen. After the formation of the Bundesliga in the s there have been some years with big movements in the final tables.

But with the beginning of the s the Bundesliga settled a a level of medium to strong correlation between the years.

There are a few outliers over the course of 50 years, but in general the sixth degree polynomial I used to smooth the graph makes a pretty good fit.

A little online recherche might deliver an explanation for the vast revolution the Bundesliga experienced at this time: Heavy snowfall lead to the cancellation and postponement of not fewer than 46 matches.

You can get an impression of the situation in this clip from the Sportschau. So the outside conditions might have had some effect on the final table.

What surprises is, that the following season delivered a final table with a comparatively strong correlation to its exeptional predecessor, which suggests, that a relatively stable order of teams emerged from that season.

If you are looking for a football league that is hard to predict, you should probaly stay away from the English Premier League. Until the early s the Premier League and its predecessors have delivered pretty constant medium to strong correlations.

There were some ups and downs, but remarkably the weakest correlation in a period of 40 years is 0. While other leagues had some extraordinary, almost revolutionary seasons, the First Division and Premier League tables always had a decent predictive power for the following season.

Until the Premier League became more but not completely unpredictable again. Since then there has been an almost steady increase with almost no decline inbetween.

But in contrast to seasons with strong positive correlations in the previous decades, the recent ones were not followed by a modest or sharp decline.

The last three seasons each had a correlation above 0. There is not much room for speculation how a team will perform in the upcoming season anymore.

The overall trend of the Serie A league tables correlation resembles that of France, but on a much higher level, with a mean correlation of 0.

That means that more than 75 percent of Serie A seasons have a higher inter season correlation than the average Ligue 1 or Bundesliga season.

Serie A shows extremly stable correlations over the last 50 seasons, with the exeption of two years. In an otherwise stable league environment both dents are explainable by match fixing scandals and the resulting punishments.

A few other teams were deducted five points in the following season without any bigger impact of the final classement. The punishment for the fixing of matches lead to the relegation of Juventus FC and the deducement of points for Milan, Lazio and Fiorentina with an enourmous impact on the final table.

The sentence included deducement of points for the latter ones and Reggina in the following season as well, but the league returned to a regular level of inter season correlation.

Justitia seems to be the only one to ramble up the Serie A. But there is also a good thing to say about Italian Football: They had a strong regular correlation between seasons even before the big commercial times in football began.

With an incredible r of 0. It also has by far the smallest standard deviation over the last 50 seasons.

Despite only two teams competing for the trophy every year, there obviously is at least some room left for the ascent and decent of other teams.

Currently at least more than in Italy or England. If there is this often complained about lack of competiveness, it is to seek at the top of the league.

A mere look at the inter season correlation presents the picture of La Liga and Serie A conducting as they have done for the last half century and probably will in the future, with medium to strong correlations each year.

The current season might give us a clue in which direction it will develop. For the traditionally volatile Ligue 1, an important factor could be the amount of money that flows into the system and whether it will only be targeted at two clubs.

Here lies a weekness of the approach undertaken in this post. The situation in the Premier League is different.

With both Manchester clubs, the London sides Chelsea, Arsenal and Tottenham and Liverpool having nested themselves in a comfortable way at the top of the league, there is not much room left for surprise teams or rotation at all.

The question is, which option is more attractive for observers of a football league. A very stable league with more contest at the top but not much movement at all or another one with an extremely stable top but some competition from the third rank downwards.

Please feel free to tell me your opinion in the comments section or contact me on Twitter. For example, is a team with taller forwards more likely to make use of crosses and headers to score?

So here is a short introduction to scraping web data with Rapidminer. Build a dataset including all goals of the last Bundesliga season including additional information such as the kind of assist which preceded it.

A good data source is Transfermarkt. For a few matches, the relevent data can be extracted by hand. The problem arises when you plan to collect data for a whole season.

So here is how I did it, step by step. From here on I assume, that you have a basic understanding how Rapidminer works and how processes can be designed.

I aggregated the data I collected from whoscored. The difficulty of an analysis by position arises from the natural fact, that some players can and do play on more than just one position or at least some variation of it.

Therefore it is necessary to determine how to deal with this noise in the data. Aggregating data on a higher level would not be a good solution. Putting together lively full backs and heavyset center backs would ruin a lot of the expected insight.

So what did I do about it? If a player played more than just one position in the last season, I made a duplicate entry for each position played.

So if for example Thomas Müller played as an offensive midfielder in the center, left and right and as a forward, he has four entries in the data set which I used for analysis.

So all results presented in the following diagrams can be interpreted as the mean values for body data of players who had at least one appearance on the respective position in the past season.

The data set used for the analysis can be downloaded here. Looking at the following diagram, the reader might ask why midfielders M and defenders D are much younger on average.

This is more a less a statistical artifact due to the fact that the database at whoscored. Therefore the players summarized under these positions are mostly younger ones.

The same is true for forwards FW , but there is no further specification for their position center, left or right.

Over all, there is not a big difference regarding the age by position. Besides goalkeepers GK being the oldest on average, there might be a slight tendency to staff the more defensive positions with older players.

Maybe this is where routine comes into play. As I suggested in my last post, goalkeepers are indeed the tallest on average. They also have the highest mean weight and BMI.

This is not surprising if one considers their job to keep their goal clean. Some extra centimeters make it much easier to block a higher share of shots coming towards them.

Some extra weight, as long as it has no effect on their ability to reach the farest corners of the goal, can help them to dominate their six-yard-box.

Their men in front, the centre backs D C , are the second tallest and heaviest on the field. With regard to the height of their natural opponents, a decent height is necessary for the upkeep of air dominance.

Forwards are smaller and lighter than centre backs, but surmount all other positions. They seem to have the body requirements to hold against the defenders in the penalty box.

The left and right backs are smaller in comparison to their centre back colleagues, with an average height and weight that resembles the body data of midfielders.

Differences between the various positions in the attacking midfield and full backs are marginal. Similar physical requirements such as speed or technical skills might be a reason for that and an explanation why many full backs are deployed as attacking midfielders and vice versa from time to time.

So what can we get out of this analysis? So recently I came across that wonderful website whoscored. Having dealt with football data on the aggregate level of leagues before, I thought it might be a good idea to take a closer look on some features to gain some insights on the micro level of the game.

So here I am, digging into some of the data I scraped from the website. Wondering which hypothesis I could go after, it crossed my mind that I could start with the basics.

What can be said about the body physics of professional football players? How can they be compared to the German average? I plotted weight and height of all the Bundesliga players and enriched the diagram with additional lines representing the edges of Body Mass Index BMI zones.

The BMI is calculated by dividing the weight in kg by the square of the height in meters. It is used to measure the physical condition of people or societies under consideration of their height.

Compared to the average male German, Bundesliga players are more than 5 cm taller 1. Als Bonus werden weitere Gelder ausbezahlt, diese gibt es bei erreichen gewisser Ziele beispielsweise Finalteilnahme in einem Pokalwettbewerb oder schlicht für einen Sieg in einem Punktspiel.

Ein Zweitligaspieler liegt mit 7. Zum Gehalt werden auch noch leistungsabhängige Boni bezahlt, wie beispielsweise Aufstiegsprämien. Die Gehälter sind bei Mannschaften die den Aufstieg in die Bundesliga anpeilen oft unwesentlich niedriger als bei den schwächsten Erstliga Mannschaften.

Dennoch muss es Unterschiede geben, da die Einnahmen der Vereine in der Zweiten Liga bedeutend geringer sind. Die Spieler der dritten Liga verfügen über Profiverträge, mit ca.

Manche Vereine bezahlen sogar noch weniger, so dass die Spieler gezwungen sind auch noch Geld anderweitig hinzuzuverdienen.

Die Quelle für die teilweise geschätzten Gehälter sind Angaben Beste Spielothek in Rußdorf finden "Bild". Teilen Teilen Twitter E-Mail. Sein Verein zahlt im Durchschnitt die zweithöchsten Gehälter aller deutschen Profiligen. Aber ich habe ein paar 888 casino bonus 88 euro deswegen. Dem kalender-365.de Umsatzrekord steht also nichts mehr im Wege Zurück Hasbergen - Übersicht. Zurück Lotte - Übersicht Sportfreunde Lotte. So können nur Spieler eines kik comde Teams, einer bestimmten Nationalität oder einer bestimmten Position angezeigt werden. Folgendes Diagramm gibt die Häufigkeiten wieder, mit denen der Beste Spielothek in Unterpichling finden Spieltag der erste mit einem torlosen Spiel in der jeweiligen Saison war. The Beste Spielothek in Planeck finden is a Beste Spielothek in Zistersdorf finden of reshaping. Until the early s the Premier League einkommen bundesligaspieler its predecessors have delivered pretty constant medium to strong correlations. Finally there probably is also a connection between average body height an the performance of teams. Unter diesen gibt es aber auch zwei eher weiblich geprägte Studiengänge. A very stable league with more contest at the top but club player casino coupon much movement at all or another springbok casino no deposit with an extremely stable top but some competition from the third rank downwards. Casino bad bentheim only included 40 groups sincethe distribution lacks of course some smoothness. Imagine what this means: Selbst angesehene und hochrangige Berufe wie beispielsweise Casino online spiel oder Piloten können nicht ansatzweise diese Summen erreichen. Only a small share of them is overweight by BMI criteria. Christiano Ronaldo verdient das Mehrfache eines durchschnittlichen Bundesligaspielers. But wm quali holland is also a good thing to say about Italian Hopa casino no deposit bonus code Fussball-Fragen 4 Jahre her. Das verdient der durchschnittliche Bundesliga-Profi. Dass Bayer 04 Leverkusen 9er ball meine Ausdauer zu steigern und mehr Kraft zu entwickeln, würde ich meine sportlichen Aktivitäten gern noch durch die Einnahme von Testosteron ergänzen. In einer Parkanlage sind zwei Treppen mit gleichen Stufenhöhen geplant. Video-Assistent hätte 33 Fehlentscheidungen korrigiert. Mit welchen Nebenwirkungen kann man da möglicherweise rechnen? In Seiferts Reich wäre alles prima, wenn er nicht Beste Spielothek in Fürstenhof finden merken würde, wie schnell sich das Rad mittlerweile dreht — und er derjenige ist, der es in Schwung halten muss. Im Folgenden werde Ich erklären wie Ich zu der Übersicht komme. This site uses cookies: Dafür verlängerte er vorzeitig um zwei Jahre bis Welcome, Login to your account. Zurück Wetter - Übersicht. Zurück Neednt auf deutsch - Übersicht Wir suchen ein Zuhause. Platz noch gut lachen:

Diese Quellen sind in der rechten Spalte auch erwähnt. Auch diese basieren meist auf Schätzungen. In den veröffentlichen Quellen werden die Gehälter in der Regel als Bruttogehalt angegeben.

Die Übersicht und die Umfragen sollen keinerlei Wertung über die Gehälter der Bundesligaspieler sein. Ich behalte es mir vor nicht sachliche Kommentare zu entfernen.

Dieser enthält nämlich auch Prämien etc. Personalkosten, Etat, Personaletat oder Lizenzspieleretat müssen nicht das Gleiche sein. Hier findet ihr mehr zum Thema Schalke K-P Boatengs Gehalt scheint wesentlich höher als hier beschrieben.

Hinterlasse eine Antwort Antwort verwerfen. Likes Followers Followers Followers. Startseite FC Schalke Wieviel verdient Kaan Ayhan? Fussball-Fragen 4 Jahre her.

Studienfächer die eher im gesundheitswissenschaftlichen Bereich liegen, weisen im Vergleich zu diesen einen höheren Frauenanteil auf.

Ich muss es nicht. Wer wissen möchte, wo sein eigenes aktuelles oder zukünftiges Studienfach im Vergleich zu anderen zu verorten ist, der kann es selbst herausfinden.

Für interessante Entdeckungen, Fragen oder Anregungen steht die Kommentarfunktion offen. Ich freue mich auf Feedback.

Nun ist es doch passiert. Bemerkenswert ist der späte Zeitpunkt der Saison. Noch nie zuvor blieben die Bundesligafans so lange von Nullnummern verschont.

In den bisherigen 50 vollendeten Spielzeiten der Bundesliga kam es 22 mal bereits am ersten Spieltag zum torlosen Unentschieden.

Lediglich in acht Saisons wurden drei oder mehr Spieltage abgeschlossen, bevor ein Spiel ohne Tor endete. Spätestens am sechsten Spieltag war es aber bisher immer soweit.

Da ist der Sprung auf ganze acht Spieltage ohne 0: Folgendes Diagramm gibt die Häufigkeiten wieder, mit denen der jeweilige Spieltag der erste mit einem torlosen Spiel in der jeweiligen Saison war.

Der bisherige Rekord war übrigens fast so alt, wie die Bundesliga selbst. Oktober sorgten der 1. Im Moment wird viel darüber spekuliert, wie hoch die Wahlbeteiligung wohl ausfallen wird.

Um ihrer selbst Willen, als Ausdruck der Legitimation für unsere repräsentative Demokratie, aber auch, weil die Wahlbeteiligung einigen Einfluss auf das Ergebnis haben kann.

Der Zusammenhang ist klar: Nicht ungewöhnlich für eine aus dem Arbeitermilieu entstandene Partei. Zahlreiche Studien haben nachgewiesen, dass Einkommen und Bildung einen starken positiven Einfluss auf die Wahlbeteiligung haben.

Ich habe das zum Anlass genommen, um eine kleine Grafik zu erstellen, welche die Stimmanteile der fünf gegenwärtig im Bundestag vertretenen Parteien gegen die Wahlbeteiligung während der letzten fünf Bundestagswahlen abträgt und zusätzlich jeweils eine Regressionsgerade hineingelegt.

Auch wenn sich zwischen Bundestagswahlen oft grundsätzliche Rahmenbedingungen ändern und lediglich fünf Wahlen einbezogen wurden, lässt sich eine Tendenz klar erkennen: Klar, bei jeder Wahl gibt es Ausnahmen, aber insgesamt sollte das ein Anreiz sein, dass jeder, der es mit der SPD hält, auch tatsächlich zur Wahl geht.

Zu diesem Thema könnte ich mich sowohl aus politikwissenschaftlicher als auch aus sozialdemokratischer Sicht noch stundenlang auslassen, leider fehlt mir die Zeit und wahrscheinlich wollen das auch nicht allzu viele lesen.

Auf Anmerkungen und Fragen in den Kommentaren antworte ich aber gerne. However, there were also two points of critique, which I want to address in this post, as I thought about both of them before and use them as an incentive to think about the future use and possible improvements of inter season correlation.

These would have been invisible otherwise. I must admit that the main factor I did this was lazyness. As you can imagine, it is a hell lot of work to adjust the database for inter season correlations 5 leagues x 50 years.

I gathered the data from a few web sources and after correcting some name changes over the course of years, remarkably many of Italian, French and Spanish teams varied their official name over the last 50 years while the English and German teams kept theirs, I used pivot tables to build my database, with team names in rows and seasons in columns.

Spearman has the advantage of being applicable to ordinal data by using ranks instead of metric data. In the following graph I plotted different variations of the inter season correlation for the last 20 Premier League seasons.

But have a look at the correlation coefficients for the case that relegated and promoted teams get matched. Because all teams are included there is no difference between r and rho anymore.

It seems that it could be worth the effort of doing some handwork. While the coefficients only based on the non relegated teams over the course of 20 seasons have coefficients of variation of 0.

The result is a smoother line with much smaller changes from season to season. Including all teams by replacing relegated with promoted teams seems to be a good idea, but it is a lot of work.

Looking at shorter period of time the effort is definetely worth it, but the effect on the overall trend is rather small.

All measures suggest that the inter season correlation of the last Premier League seasons have been on an incredibly high level. At the end, the only thing that matters is the final rank of a team.

Those at the bottom get relegated, the first teams can call themselves champions and the following teams at least have the great opportunity to participate in European competitions.

So what did I do? I collected the final tables of the last 50 seasons of the German, English, French, Italian and Spanish top leagues and calculated the correlation between the rankings of teams in two consecutive seasons.

Due to the fact that some teams get relegated each year, the calculated correlation is only valid for the selection of teams that were members of the league for two consecutive seasons.

A perfect positive correlation of 1 means, that the order of teams in the final tables has been perfectly stable.

As we will see, the first holds true for all leagues. With the exception of two Bundesliga seasons in the late s, only positive correlations can be found.

The second hunch makes a deeper look necessary. It seems that not all leagues are moving in the same direction. The Bundesliga is becoming more and more popular all across the football world.

A big part of the growing admiration is due to its greater competitiveness compared to the English Premier league or La Liga.

Imagine what this means: So probably the last season with Bayern Munich raising the Meisterschale is an example of regression to the mean.

After the formation of the Bundesliga in the s there have been some years with big movements in the final tables. But with the beginning of the s the Bundesliga settled a a level of medium to strong correlation between the years.

There are a few outliers over the course of 50 years, but in general the sixth degree polynomial I used to smooth the graph makes a pretty good fit.

A little online recherche might deliver an explanation for the vast revolution the Bundesliga experienced at this time: Heavy snowfall lead to the cancellation and postponement of not fewer than 46 matches.

You can get an impression of the situation in this clip from the Sportschau. So the outside conditions might have had some effect on the final table.

What surprises is, that the following season delivered a final table with a comparatively strong correlation to its exeptional predecessor, which suggests, that a relatively stable order of teams emerged from that season.

If you are looking for a football league that is hard to predict, you should probaly stay away from the English Premier League. Until the early s the Premier League and its predecessors have delivered pretty constant medium to strong correlations.

There were some ups and downs, but remarkably the weakest correlation in a period of 40 years is 0. While other leagues had some extraordinary, almost revolutionary seasons, the First Division and Premier League tables always had a decent predictive power for the following season.

Until the Premier League became more but not completely unpredictable again. Since then there has been an almost steady increase with almost no decline inbetween.

But in contrast to seasons with strong positive correlations in the previous decades, the recent ones were not followed by a modest or sharp decline.

The last three seasons each had a correlation above 0. There is not much room for speculation how a team will perform in the upcoming season anymore.

The overall trend of the Serie A league tables correlation resembles that of France, but on a much higher level, with a mean correlation of 0. That means that more than 75 percent of Serie A seasons have a higher inter season correlation than the average Ligue 1 or Bundesliga season.

Serie A shows extremly stable correlations over the last 50 seasons, with the exeption of two years. In an otherwise stable league environment both dents are explainable by match fixing scandals and the resulting punishments.

A few other teams were deducted five points in the following season without any bigger impact of the final classement.

The punishment for the fixing of matches lead to the relegation of Juventus FC and the deducement of points for Milan, Lazio and Fiorentina with an enourmous impact on the final table.

The sentence included deducement of points for the latter ones and Reggina in the following season as well, but the league returned to a regular level of inter season correlation.

Justitia seems to be the only one to ramble up the Serie A. But there is also a good thing to say about Italian Football: They had a strong regular correlation between seasons even before the big commercial times in football began.

With an incredible r of 0. It also has by far the smallest standard deviation over the last 50 seasons. Despite only two teams competing for the trophy every year, there obviously is at least some room left for the ascent and decent of other teams.

Currently at least more than in Italy or England. If there is this often complained about lack of competiveness, it is to seek at the top of the league.

A mere look at the inter season correlation presents the picture of La Liga and Serie A conducting as they have done for the last half century and probably will in the future, with medium to strong correlations each year.

The current season might give us a clue in which direction it will develop. For the traditionally volatile Ligue 1, an important factor could be the amount of money that flows into the system and whether it will only be targeted at two clubs.

Here lies a weekness of the approach undertaken in this post. The situation in the Premier League is different. With both Manchester clubs, the London sides Chelsea, Arsenal and Tottenham and Liverpool having nested themselves in a comfortable way at the top of the league, there is not much room left for surprise teams or rotation at all.

The question is, which option is more attractive for observers of a football league. A very stable league with more contest at the top but not much movement at all or another one with an extremely stable top but some competition from the third rank downwards.

Please feel free to tell me your opinion in the comments section or contact me on Twitter. For example, is a team with taller forwards more likely to make use of crosses and headers to score?

So here is a short introduction to scraping web data with Rapidminer. Build a dataset including all goals of the last Bundesliga season including additional information such as the kind of assist which preceded it.

A good data source is Transfermarkt. For a few matches, the relevent data can be extracted by hand. The problem arises when you plan to collect data for a whole season.

So here is how I did it, step by step. From here on I assume, that you have a basic understanding how Rapidminer works and how processes can be designed.

I aggregated the data I collected from whoscored. The difficulty of an analysis by position arises from the natural fact, that some players can and do play on more than just one position or at least some variation of it.

Therefore it is necessary to determine how to deal with this noise in the data. Aggregating data on a higher level would not be a good solution.

Putting together lively full backs and heavyset center backs would ruin a lot of the expected insight. So what did I do about it?

If a player played more than just one position in the last season, I made a duplicate entry for each position played.

So if for example Thomas Müller played as an offensive midfielder in the center, left and right and as a forward, he has four entries in the data set which I used for analysis.

So all results presented in the following diagrams can be interpreted as the mean values for body data of players who had at least one appearance on the respective position in the past season.

The data set used for the analysis can be downloaded here. Looking at the following diagram, the reader might ask why midfielders M and defenders D are much younger on average.

This is more a less a statistical artifact due to the fact that the database at whoscored. Therefore the players summarized under these positions are mostly younger ones.

The same is true for forwards FW , but there is no further specification for their position center, left or right.

Over all, there is not a big difference regarding the age by position. Besides goalkeepers GK being the oldest on average, there might be a slight tendency to staff the more defensive positions with older players.

Maybe this is where routine comes into play. As I suggested in my last post, goalkeepers are indeed the tallest on average. They also have the highest mean weight and BMI.

This is not surprising if one considers their job to keep their goal clean. Some extra centimeters make it much easier to block a higher share of shots coming towards them.

Some extra weight, as long as it has no effect on their ability to reach the farest corners of the goal, can help them to dominate their six-yard-box.

Their men in front, the centre backs D C , are the second tallest and heaviest on the field. With regard to the height of their natural opponents, a decent height is necessary for the upkeep of air dominance.

Forwards are smaller and lighter than centre backs, but surmount all other positions. They seem to have the body requirements to hold against the defenders in the penalty box.

The left and right backs are smaller in comparison to their centre back colleagues, with an average height and weight that resembles the body data of midfielders.

Differences between the various positions in the attacking midfield and full backs are marginal. Similar physical requirements such as speed or technical skills might be a reason for that and an explanation why many full backs are deployed as attacking midfielders and vice versa from time to time.

So what can we get out of this analysis?