In the first post of a series of posts on this topic, we take an in-depth look at the top player agencies in the English Premier League over the past 5 years.

Data

- All data is taken from the website Transfermarkt.

- The agent information is not available for about 38.9% of the total number of players. However they accounted for only 15.7% of the overall market value.
This number includes players

• whose market value is low
• who have retired over the past 5 years and
• who have agents but the information is not available

- We have excluded the set of data with no agent info unless explicitly stated.

Assumption

- Players rarely change agents. They change teams much more often.

 Methodology

-          Histograms of Agent-Club linkage based on:

• # of players of a team having the same agent
• Total market value of players of a given agent who play for the same team

-          Visualized Agent-Club relationship using heat maps

Figure 1:  Histograms – Breakdown by Market Value per Agent (in Mil)

Figure 2: Histogram – Total player per agent

Histogram of % of players per agent

Figure 3: Histogram of player % and market value %

Observations

• More than 50% of the players and the market value of the transfer market are controlled by a handful of agents.
• 20 agencies control 50% of the player transfer market. The other 50% is made up of 279 agencies.
• Some agents have very few clients but who are super stars. The % of market value is disproportionate to the # of players in their clientele.A good example of this is Gestifute, the agency run by Jorge Mendes, the agent of the likes of Cristiano Ronaldo, Nani et al. Gestifute is ranked 5^th in market value map but only 11^th in the player count map. See the histogram of player % & market value %. Another example is Pinhas Zahavi, agent of Carlos Tevez.

Figure 4: Heat Map of Agent-Club by player count

Figure 5: Heat Map of Agent-Club by market value

Figure 6: Heat map by % of players of a team with an agent. (Players whose agent info is unavailable has been included in this calculation)

Figure 7: Player Percentage per team sorted by the highest % of players of a team with the same agent.

Observations

• 10 – Maximum number of players of the same club with the same agent. Sunderland & Stellar Football. (Figure 4 – C)
• € 145.5m – The highest amount of market value of players of a team linked to the same agent. Manchester United and Gestifute. (Figure 5 – E)
• 27 – Stellar Football’s players played in 27 of the 29 EPL teams over the last 5 years. This is the maximum number. (Figure 4 – D)
• 103 – Total number of Stellar Football’s players who played in EPL over the past 5 years (Figure 4 – D)
• 6 – Arsenal has the highest # & % of players without an agent among the players whose agent info is available. There could be more players with no agents among the players who agent info is not available. (Figure 4- A,6)
• 14 – EPL clubs dealt with a 14.15 agents on an average over the last 5 years. The Median is 14. (Figure 7)
• 22 – Arsenal, Tottenham Hotspur and Portsmouth
  have the highest diversity of agents over the past 5 seasons. Fulham is a close second with 21. (Figure 7 – G)
• 5 – Norwich City and Swansea City with 6 have the least diversity of agents among their player ranks. Both teams were promoted to the EPL at the beginning of this season. (Figure 7 – H)
• The differences between the market value heat map to player count heat map are due to the fact that some agencies (e.g.: Gestifute, Pinhas Zahavi) have a lot of average-to-good players as clients’ vs. others who have only a few clients but are superstars. (Figure 4 – B, 5 – E, 5 – F)
• In the player count heat map the relationship between Gestifute & Chelsea appears very strong. When Mourinho was the coach at Chelsea, all the Portuguese players at Chelsea (e.g.: Carvalho, Paulo Ferreira, Deco) were represented by Jorge Mendes’s agency. (Figure 4 – B)
• On a similar note Gestifute has a strong relationship with Manchester United in the market value heat map. This is due to the astronomical market value of Cristiano Ronaldo. (Figure 5 – E)

In the next post we will further analyze the linkage between clubs & agents and also look at other leagues.

Glossary

Top Agents in EPL:

Stellar Football Ltd – David Manasseh, Jonathan Barnett, Ertan Göksu

Base Soccer Agency – Frank Trimboli, Leon Angel           

James Grant Sports Management – Craig Sharon, Lyle Yorks

World in Motion – Andy Evans, Bill Pethybridge, James Lippett, Freddy Akehurst et al

Key Sports Management – John Colquhoun

Gestifute – Jorge Mendes, Luis Correia

A few weeks ago when City played Arsenal at the Emirates, there was this banner:

With the Manchester Derby looming on Monday, there are a slew of articles centered on arguments like buying titles and class.

I don’t know how to quantify “class”. However, I wanted to analyze how Manchester City’s spending stacks up with the rest of the contenders in the Premier League.

Methodology:

1. Compared the inflation adjusted spending numbers from 1999-2011 of United, City, Arsenal, Spurs and Liverpool.

2. I used the Consumer Price Index based inflation numbers of the GBP for the first round of analysis.

But Football transfer fee inflation is hard to measure.  It can fluctuate much more because unlike CPI based inflation (which is based on the price changes of a basket of goods), Football transfers form a very niche segment in a niche industry.

3. I did another view of the data using the definition of inflation based on the average annual transfer fee in the Premier League from the site Transfer Price Index
A quote from the TPI article summarizes why CPI based inflation rate might not be a good indicator of the football player transfer fee inflation
“The cumulative Transfer Price Index is running at 730% for the 20 year history of the Premier League compared to a Bank of England cumulative Consumer Price Index of 77.1%.”

4. I overlaid the spending patterns of Real Madrid & FC Barcelona who are two very successful clubs in Europe and regularly buy top players.

5. I also looked at the Deloitte Money League rankings over the past 10 years to visualize the size of Manchester City before and after the takeover by Sheikh Mansour.

Data:

1. All the transfer price data is taken from the site www.transfermarkt.com. All prices in millions of Euros.
2. The CPI inflation numbers are taken from the Bank of England.
3. Used the average transfer fee chart from the Transfer Price Index
4. Deloitte Money League rankings of the past 10 years from Deloitte website via  Sarah Rudd

Play with the Interactive Visualization of the TPI & CPI based transfer spend from 1999 to 2011.

TPI based transfer spend 1999-2011

CPI based transfer spend 1999-2011

Play with the Interactive Visualization of the TPI & CPI based transfer spend from 1999 to 2011.

Observations:

• City spent a net total of € 679 mil on transfers from 1999 to 2011, higher than everyone else except Chelsea.

• However before City got taken over the Abu Dhabi United Group their overall spending is significantly less than everyone except Arsenal.
• The average end-of-season league position of City from 1999-2007 was 14.7. After the takeover in 2008, the average league position of City is 5 (including 2011-12). An impressive improvement in such a short span of time.
• Teams like Manchester United, Liverpool and Spurs have a longer history of spending. This makes City’s spending in a compressed time-frame look exaggerated.
• Chelsea did something similar between 2002 and 2005 to break into the top 4.

Comparing City to United

There is no doubt that City has spent a lot more than United between 1999 and 2011.

However if you discount the sales of extraordinary* sales of Cristiano Ronaldo & David Beckham to Real Madrid, the overall numbers will be lot closer. (*extraordinary sales are explained below)

Here is a list of top transfers of United between 1999 and 2011 with inflation adjusted prices.
Criteria: TPI adjusted price greater than or equal to 30 mil euros.

In contrast there are only very few big sales that they have made a lot of money off of.

• The Cristiano Ronaldo’s sale is an extraordinary sale as was Beckham deal on its day. In both cases the buyer was Real Madrid under Florentino Perez.

• Beckham’s price was driven-up because of Perez openly touting his “Galactico policy” of signing the hottest player on the market each year during his tenure.

• Cristiano Ronaldo’s price was driven up because one of the election promises of Perez was to sign Ronaldo. This meant Manchester United had all the leverage during the negotiations.

These are extraordinary scenarios that don’t happen on a regular basis.

Here is a list of top transfers of City over this period of time.
Criteria: TPI adjusted price greater than or equal to 30 mil euros.

Conclusions:

1. City has spent a lot but the compressed time-frame of the spending makes it look exaggerated.
2. City made up almost 10 positions in their average league finish from 14.7 to 5 after the takeover.
3. Apart from Arsenal, all other top 4 contenders have been spending regularly over a longer period of time.
4. Sheikh Mansour’s Abu Dhabi United Group took over Manchester City in August of 2008. But Man City was ranked thrice in the top 20 of the Deloitte Money League even before the takeover. This shows that they have always had a sound financial base and fan support.
   
   Deloitte Money League rankings of City from 2001-201

Other observations:

1. Chelsea’s total spending curve is a surprise. It is common knowledge that Abramovich had spent a lot in early 2000s but the total amount is staggering.They are on par with Real Madrid over the 12 years. The only difference being the steep slope between 2002 and 2005 vs. a fairly linear spending pattern of Real Madrid.
2. Arsenal is the only club that seems to be consciously balancing the books year after year. Their curve oscillates year to year.
3. Similar to the steep slope in Chelsea’s curve between 2002 and 2005 is the steep slope in City’s curve between 2006 and 2010 but not nearly as steep.
4. Real Madrid and FC Barcelona spend a lot of money annually, especially the former.
5. For all the hype surrounding “La Masia”, FC Barcelona spent as much as Manchester City between 1999 and 2011.

The New York Times recently did an excellent job illustrating the qualitative aspects of Jeremy Lin’s performances.  The sports media has done a great job covering what Jeremy Lin has done, but this New York Times piece goes into how Lin is accomplishing what he has and why he is a good point guard, all with 3 simple animations.  It reminded me a lot of this video which calls for exactly this type of analysis in soccer.  The closest I’ve found are the brilliant videos that AllasFCB2 puts together.

Why do I bring it up?  After much hype about the goal glut in the Premier League this season, things are starting to quiet down.  Goals per match dropped from 3.3 in October to 2.87 in November which is expected based on previous years’ data.  If this season continues to be like others, we can expect the dip to continue through February.

Goals per match by month for the Premier League from 2005-present. Orange marks are for the current season. The grey area represents one standard deviation from the mean.

Looking at how total goals are progressing, this season isn’t much different from previous seasons.

Running total of goals in the Premier League. Orange is this season.

We have a decent idea of what is going on here (goal scoring pace slows in the middle of the season) but we don’t know why.  Is fatigue and squad rotation responsible?  It certainly is an interesting theory to investigate.

First Leg

Completed passes by position for the first leg of Seattle Sounders FC - Real Salt Lake

When I looked at the first leg of Seattle Sounders FC – Real Salt Lake, one thing that was immediately apparent was the inability of Seattle’s midfield to have an impact on the match.  In the above diagram, the same thing can be seen as depicted by moments when the orange band becomes very narrow.  The wider the band, the more passes completed and Seattle’s midfield wasn’t getting it done.  One thing that wasn’t obvious at first was how much Seattle’s forwards were involved either side of half time.  Seattle enjoyed a bit more possession during this time.  Most of the passes by Seattle’s forwards during this time were near midfield.  Did Seattle enjoy more possession because their forwards tracked back and provided extra numbers in the middle?

Completed passes in the final third for the first leg of Seattle Sounders FC - Real Salt Lake

Filtering the data set down to just passes in the final third, Seattle’s early problems are again apparent.  What was interesting was that even though Real Salt Lake was dominating the first 30 minutes, their fullbacks didn’t seem to venture too far forward.  The above graph is just for completed passes, but looking at all passes, there was only 1 pass attempted by a defender in the final third in the first 28 minutes. Later in the game, RSL’s fullbacks were able to get more forward and during this time, RSL scored two goals.

Second Leg

Completed passes for the second leg of Seattle Sounders FC - Real Salt Lake

Seattle had a 3 goal deficit to overcome in the second leg and needed to press early on.  Again there were moments where Seattle’s midfield were not very involved, but this is somewhat expected given that by the 21st minute they had already replaced 2 midfielders due to injuries.  What’s interesting in this graph is Seattle’s heavy reliance on their defenders to move the ball, particularly towards the end of the match when they were looking for the equalizer.

Completed passes in the final third for the second leg of Seattle Sounders FC - Real Salt Lake

The above graph is completed passes in the final third.  Seattle got their fullbacks forward early and often while Real Salt Lake parked the bus.  RSL really had no intention of trying to score and rarely ventured very far forward. Also of note is the absence of passes in the final third by forwards towards the end of the game as RSL successfully defended the long ball approach.

Summary

This visualization technique allows us to look at several dimensions of data over time.  When data is presented in summary form, a lot of the context about the momentum of the match is lost.  By looking at the stacked timeseries, the ebbs and flows of each team become apparent.  If you were to overlay key events the data could be even more revealing.

The diagrams above were produced using Processing and data from Opta’s MLS Chalkboards.

Total Goals Scored in the Premier League are increasing at an alarming rate...of less than 1 goal per matchday

Looking at total goals scored in the Premier League (and estimating the total for this season based on the current rate) we do see an upward trend.  However, this only works out to a rate of less than one additional goal per match day.  Are expensive new signings the reason for this?  It’s hard to say, but a variation this small could also be explained by a change of tactics by a single club. There seems to be a trend of some newly promoted teams playing a more open style rather than playing for the draw and hoping for survival.  Granted, the sample size is extremely small, but if you look at Blackpool last season and compare the goals involved in their matches versus other teams that were relegated in season’s past, the difference is about 30 goals (with the exception of Burnley in 2009/2010 who conceded a whopping 82 goals).  Put in this context, a difference of 30 goals per season for the entire league isn’t very substantial.

Another factor to consider is where we are in the season.  For baseball, pitchers and batters “get fit” at different rates.  It’s not uncommon for a batter to be on target for a ridiculous number of home runs early in the season, only for them to cool off as pitchers get more fit.  Is there a similar phenomena with goal scoring in the Premier League?

Average goals per match peaks in October before bottoming out in Jan/Feb.

Looking at Goals Per Game in each month over the last 6 seasons, we see pretty considerable variation across months.  The peak is in October before it bottoms out after the busy Christmas period (January and February) and finally picks up a bit before the end of the season.  Certainly, the fact that October is the highest scoring month and that this season was the highest rate for an October in the last 6 years adds to the perception that there is a substantial increase in goals.  In reality, August and September’s rates were within a standard deviation of the mean, and we expect the rate to decrease, so is this year really any different from years past?

Running total of goals scored throughout the season. This season is within the normal range of past seasons.

If we look at the running total of goals scored over the course of the season, this season (highlighted in orange) is still within the normal range of past seasons.  There really doesn’t seem to be much abnormal about the goal scoring rate of this season — just an abnormally high number of goals in the highest scoring month.

Omar Chaudhuri from 5 Added Minutes asked why there was this downward trend in the middle of the season and to be honest, I haven’t a clue.  There could be several factors: different fitness levels of different positions, squad rotations, fixture congestion, getting used to new teammates after the transfer windows.  Regardless of the reason, it will be interesting to see if the scoring rate cools down this season or if they can buck the trend and we really are experiencing a goal glut.

If you chat with me about the statistical analysis of soccer, one of the first phrases out of my mouth is probably “I hate passing percentage”.  I still do (because often the numbers are quoted without context and used to “prove” one team is superior to another), but I am going to use some passing stats here to illustrate some points.

Passing Momentum

Total Attempted Passes for each team over time

For the first 30 minutes, Seattle clearly struggled to control the ball and allowed Real Salt Lake to maintain possession and pass the ball around.  Why is this important?  Seattle is a team that has been competing in 3 tournaments and is playing at altitude that it isn’t accustomed to.  Chasing the ball for 30 minutes to start the game is sure to be taxing on already tired legs.  It wasn’t until around the75th minute that Seattle started to see a sustained advantage in passes completed, however, that wasn’t so much because of their improved play but because RSL shut it down and tried to protect their two goal lead.

Total Attempted Passes in the final third

Looking at passes just in the final third, again Seattle was the inferior team, failing to get much penetration early on while having to absorb lots of pressure from Real Salt Lake.  Seattle had some opportunities towards the end of the first half, but failed to capitalize.  Towards the end of the match, Seattle was again getting opportunities in the final third, but their inability to complete a pass really let them down.

Passing Distance

Distribution of passing distances for Seattle Sounders and Real Salt Lake

Why did Seattle have such a hard time completing passes? Whether it was that Real Salt Lake did a good job of closing down the passing channels or Seattle failing to move off the ball and provide options for their teammates is hard to say without going back and rewatching (something I can’t stomach).  What is apparent, is that Seattle had to revert to attempting much longer passes than Real Salt Lake.  The above graph shows the quartiles of attempted pass distances for each team in 15 minute increments.  Throughout the game, but in particular early on, Seattle’s passes were much longer than Real Salt Lake’s.  Seattle definitely struggled playing out of the back, with defenders often trying to play the ball down field to alleviate pressure, but failing to connect with a teammate.

Passing Out of the Back

Passing completion in the defensive third. Weight of the line is the average distance of the passes.

There’s a lot going on in the graph above, but basically for the defensive third it shows passing completion and the average distance of complete/incomplete passes.  Seattle’s passing completion out of the back is very low with the incomplete passes tending to be much longer than the completed passes.

Midfield Battle

Pass selection for Seattle midfielders

Pass selection for Real Salt Lake midfield

Not surprisingly, RSL’s midfielders were able to complete a high number of short passes while Seattle’s midfield attempted longer passes with little success.  Of particular note is that there were long stretches of time where Alvaro Fernandez, Brad Evans and Lamar Neagle failed to complete a pass (hard to tell in the graph, but if there isn’t a dot on the line, there is no pass attempted for that time period and the software just connects points where there where there was data.  It’s not just that the Sounders midfield didn’t complete as many passes as Real Salt Lake, it’s that they didn’t see enough of the ball.

Shots

Shot Distances by Type of Shot

While Seattle only managed 5 Shots On Target, they were pretty even with Real Salt Lake in terms of shots taken from 18 yards or less.  RSL’s dominance in Shots On Target comes mostly from long distance shots.  Seattle was a little unlucky with the goals they conceded and had they been a little more clinical, the scoreline could have been a little more favorable.  I like that Seattle was selective in the shots they took and waited for good opportunities while (for the most part) restricting RSL to shooting from the outside.

Summary

The passing stats for the Sounders are atrocious.  They allowed Real Salt Lake to dominate possession early on, causing themselves to chase the ball and wear themselves out.  Long passes out of the back caused them to bypass the midfield and more often than not return the ball back to Real Salt Lake.  Seattle was able to absorb a lot of the RSL pressure and keep them shooting from the outside.  The abscence of the two first-choice center backs for RSL plus the possible return of Mauro Rosales bodes well for Seattle.  The Sounders are no strangers to scoring three but will find it tough since Real Salt Lake can put 11 behind the ball and protect their 3 goal lead.

Box Plot of Minutes Played in MLS regular season for the 10 teams that qualified for the playoffs. Teams participating in CONCACAF Champions League are in red.

One way to limit fatigue in the squad is to rotate players and spread out the playing time more evenly.  A more sophisticated model would weight recent minutes played more heavily, but to start we can look at the minutes played in the MLS Regular season.  This will under represent minutes played for players on teams like Dallas and Seattle because it ignores the US Open Cup and CCL but it gives us a level playing field to compare teams.  We know FC Dallas and Sounders players have logged a lot of minutes.  What we want to find out, is how they are being rotated.  Above is a box plot of minutes played for all the teams that qualified for the playoffs, with CCL teams in red.  Teams that have done a good job at distributing minutes will have a low median value and a low interquartile range (IQR).  I’ve chosen to use these metrics instead of mean and standard deviation because median and IQR are less affected by outliers.  Keepers or a players that made only one or two sub appearances are examples of outliers.

LA, Seattle and RSL look to have done the best job of rotating their squads.  RSL did not participate in CCL this year.  However, because of the way the schedule is set up, last year’s tournament was still running during the first part of the MLS season, so they had to deal with a few extra matches and some fixture congestion because of that.  FC Dallas and Colorado are the other two teams that were involved in CCL and their squad management was the other end of the spectrum from Seattle, LA and RSL.  DAL/COL had the two highest median minutes played and only NYRB had a higher IQR than these two.

It will be interesting to see how these 5 teams progress through the playoffs. If I were a betting woman, I’d put money on either Seattle or LA to win it all.  They’ve been the most consistent teams all season, especially towards the end of the season when others were struggling.  Sure, they’ve played more matches than most teams, but the data indicates they’ve dealt with that in a manner different from FC Dallas and Colorado and hopefully they’ve been successful in limiting the fatigue in their players.  LA and Seattle look most likely to “break” the curse of the CONCACAF Champions League.  Yes, there is a negative correlation with number of matches played and the probability of winning a playoff series, but also keep in mind that this season the roster sizes were expanded.  Perhaps historically teams haven’t had enough healthy, experienced players to rotate successfully?

All matches for the 2011/2012 and 2010/2011 season.

The color of each team’s line is scaled to the number of points (red is the lowest, blue highest).  In the 2010/2011 season, the best teams had a considerable advantage in both Shot Differential and Conversion Rate Differential.  For the 2011/2012 season, a similar pattern is emerging although some successful teams have a negative shot differential.  Manchester United has a negative shot differential however they have an incredible advantage when it comes to conversion rate.  Not only are they taking advantage of the opportunities they create, but somehow they are also preventing their opponents from finishing their chances.  The quality of the shot opportunity isn’t taken into consideration here so it’s possible Manchester United isn’t allowing their opponents to get in to good positions.  Arsenal, who have gotten off to a slow start, have a much lower conversion rate differential than last year.

Wins for the 2011/2012 and 2010/2011 season

Looking at just wins, last year most of the poorly performing teams had a negative shot differential, while the more successful teams all had a positive one.  All teams had a positive conversion rate differential for victories which is not surprising.

Ties for the 2011/2012 and 2010/2011 season

Looking at ties, in the previous season, most good teams had a positive shot differential, but a negative conversion rate difference.  The opposite was true for most of the weaker teams.

Losses for the 2011/2012 and 2010/2011 season

And finally, looking at losses, a similar patterns to wins is evident, with more successful teams retaining their positive shot differential and less successful teams with a negative shot differential.

The good news for Arsenal is that even though the season has gotten off to a great start, the metrics look like they are doing alright and hopefully it is just a matter of time until they turn it around.