Archive for Data Visualization

Putting into perspective the spending of Manchester City

If you are not from the blue half of Manchester, any discussion that involves Manchester City quickly boils down to buying titles.

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:

Club Overall Spending 1999-2011 (€ mil) Overall Spending 1999-2007 (€ mil)
Chelsea 1399.46 1196.35
Manchester City 679.70 (2nd) 195.17 (5th)
Spurs 556.63 501.81
Liverpool 486.75 431.9
Manchester United 426.07 431.61
Arsenal 63.3 93
  • 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)

City United
Overall net spend 1999-2011 € 679 mil € 426 mil
Excluding Ronaldo & Beckham € 679 mil € 647 mil

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.

Season Player Bought Actual price TPI adjusted CPI Adjusted
(€ mil)
2001-02 Juan Veron 42.6 71.1 58.4
Van Nistelrooy 28.5 47.6 39
2002-03 Rio Ferdinand 46 95.7 62.1
2003-04 Cristiano Ronaldo 17.5 43.2 22.7
Louis Saha 17.5 43.2 22.7
2004-05 Rooney 37 84 47
2006-07 Carrick 27.2 59 32.4
2007-08 Anderson 31.5 45 36.2
Nani 25.5 36.5 29.3
Hargreaves 25 35.7 28.7

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

Season Player Sold Actual price TPI adjusted CPI Adjusted
(€ mil)
2001-02 Jaap Stam 25.7 43 35.3
2003-04 Beckham 37.5 93.7 48.7
Veron 22.5 56.2 29.2
2009-10 Ronaldo 94 117.5 104.4
  • 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.

Player Bought Actual price TPI adjusted CPI Adjusted
(€ mil)
2002-03 Nicolas Anelka 19.8 41.2 26.7
2008-09 Robinho 43 42.1 47.3
2009-10 Carlos Tevez 29 36.2 32.2
E. Adebayor 29 36.2 32.2
J. Lescott 27.5 34.4 30.5
2010-11 Edin Dzeko 37 38.5 38.8
Yaya Toure 30 31.2 31.5
Mario Balotelli 29.5 30.7 312
David Silva 28.75 29.9 30.2
2011-12 Kun Aguero 45 45 45
Season Player Sold Actual price TPI adjusted CPI Adjusted
(€ m
2005-06 S. Wright-Phillips 31.5 71.5 38.7

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
Year Revenue Matchday Broadcasting Commercial Ranking
2001 54 NA NA NA NR
2002 43 NA NA NA NR
2003 71 NA NA NA NR
2004 94 NA NA NA 16
2005 90 22.3 38.7 29.1 17
2006 89.4 22.7 35 31.7 17
2007 85 NA NA NA NR
Post-takeover by Abu Dhabi United Group
2008 104 23.4 54.6 26 NR
2009 102.2 24.4 56.7 21.1 19
2010 152.8 29.8 66 57 11
2011 169.6 29.5 76.1 64 12

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 Visual Display of Qualitative Information

The astute reader will recognize the title of this post as a play on Edward Tufte’s book of a similar name.  While Tufte’s work focuses on turning quantitative data into an easily consumable format that has a clear message, it’s also important to do so with qualitative data.  Qualitative data can often be the “how” or “why” to go along with the “what” provided by quantitative data.

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.

Visualizing Completed Passes by Position

I’m always on the lookout for new ways to visualize data in the hopes that it might lead to a better understanding of the data.  In the first leg of the tie between Real Salt Lake and Seattle Sounders FC, the Sounders midfield was completely MIA for large portions of the game while RSL enjoyed large periods of maintaining possession.  I wanted to come up with a generic way to visualize similar situations.  I decided to use a stacked time series, broken down by position.  In the examples below I looked at completed passes by position.  Any metric could be used and you could also use different variables to slice the data.  Another thing to look at could be which third of the pitch the event occurs in.  I like the idea of the stacked time series because it allows you to look at the team total as well as some finer detail at the same time.
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Football Factories: Where does talent come from?

Going global: Birthplaces of Premier League players (excluding UK).

Another summer is on its way out with Arsenal barely making a splash in the transfer market. Once again it looks like Arsenal will be relying on youth this season. It got me thinking — are Arsenal really good at producing players from their youth academy who are capable of playing in the Premier League?

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Comparing the Top 100 Salaries from MLS and Europe

I recently added an interactive graph of MLS salary info to the site but I haven’t done much with the data since then.  With the summer transfer window opening and fans debating whether or not it is worth it for their MLS club to import expensive Designated Players, I decided to take a look at how the top earners in MLS compare to their European counterparts.  Top 100 European and MLS Salaries

  • David Beckham, Thierry Henry and Rafael Marquez still earn a salary that would be competitive with the biggest stars in Europe.
  • Only 7 players in MLS earn more than $1million a season, while the majority of the top 100 earn $250,000 or less.
  • Cristiano Ronaldo earns roughly $340,000 a week, which is more than all but 21 MLS players earn in an entire season.
  • David Beckham earns $250,000 in two weeks, more than all but 38 MLS players earn in an entire season,

Not surprisingly, European players earn considerably more than their MLS counterparts (excluding Beckham, Henry and Marquez).  When you compare the weekly wage of a European player with the annual wage of an MLS player, parity starts to emerge.

Top European Wages Per Week vs Annual MLS Salaries

The top 100 European salaries can be found at Futebol Finance and the top MLS Salaries can be found at MLS Players Union.

Do you think MLS clubs are smart to shell out that sort of cash for veterans like Beckham and Henry or do you think the money is better spent elsewhere? Does wage disparity cause problems within the club?

#CityAtWembley during the FA Cup Semifinal

Last week Manchester City announced a special hash-tag for the FA Cup Semifinal against Manchester United.  So what did it look like during the match?

#CityAtWembley Tweets

Volume of Tweets tagged with #CityAtWembley during FA Cup Semifinal

You can clearly see the major events of match based on tweet volume, although people didn’t seem too concerned with Scholes’ red card (did seem like it was coming, didn’t it?).  Can’t wait to see what it looks like during the final.

If you want a really great example of visualizing tweets, check out what the Guardian produced for the World Cup.

Castrol Performance Index Contributions for the EPL

Previously I posted the number of games played based on nationality for the top 4 clubs in the EPL. Since the Castrol Performance Index was updated today, I thought it would be interesting to see how the players from each country contribute to a team’s performance.

Castrol Performance Index for the Top 4 Read more

Who’s Playing For the Top Four in England?

Michael Moritz wrote a piece for the Wall Street Journal (subscription required) earlier this week drawing a parallel between open immigration in the English Premier League and how it could be beneficial to the American tech start-up scene.  What Moritz failed to point out (but other readers noticed) is that the EPL isn’t completely open.  Non-EU players require work permits and driven by concerns that foreign players are hurting the development of English players (and subsequently affecting the success of the national team), rules about “home grown” players were put into place this season.

I decided to take a look at the nationalities of players for the top four teams in the EPL.  The width of the edges is the number of games played by someone of that nationality for that club. Manchester City relies most heavily on English players and Arsenal the least.  Arsenal, instead, plays the highest number of French players.  Chelsea has the highest number of African players.

Nationalities of Players for the Top 4 teams in the EPLClearly, England is contributing the most players, but they only account for 23.88% of games played. Non-British European players account for almost 50% of games played, with the remaining 25% split mostly between African and South American players.

Region Percentage of Games Played
UEFA 49.19%
Britain 25.94%
CAF 12.26%
CONMEBOL 10.35%
CONCACAF 1.43%
AFC 0.83%

Sports Chart Porn: CricInfo vs BBC

One of the reoccurring themes in the soccer analytics world is that you have to present your findings in a manner that is easily understandable. Done well, data visualization is an excellent way to present a lot of information in a way that is easily consumable. I’m always on the lookout for new ideas about how to present data, especially sports data, so I was stoked when AupaSubmarino pointed me towards CricInfo.  I know almost nothing about cricket so when someone says “India need 147 in 24 overs” I have no idea if that is good or bad.

Enter the worm chart.

Worm Chart from BBC

Worm Chart from BBC. Notice the axes aren't labeled and there is no information about wickets.

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Data Visualization and Sport: Passing Wheel

One of the main themes of the Sloan Sports Analytics Conference was the need to present your ideas to the rest of the organization in a manner that is easily understandable.  Rambling on to a club’s manager about a stat varying from the mean by several standard deviations won’t go over so well.  It got me thinking about the field of data visualization and it’s potential application to sport.  Used properly, it can easily tell the story behind the numbers.

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