Analyzing pre-tournament data of all 32 teams of the FIFA World Cup 2014

1. July 15, 2014
2014
Justin M. King
Pre-Tournament Data Analysis of the World Cup

2. 1 Pre-Tournament Data Analysis of the World Cup
A look at the 32 participants in the 2014 FIFA World Cup by analyzing pre-tournament
data and seeing what insights can be gleaned.
This is the when the vuvuzelas hum like a swarm of annoying bees, and the drums beat
as if they were invented to celebrate with. Faces are intricately painted and costumes
are designed, bought, and adorned as fans travel from all over the globe to unite under
one sport. National pride swells immensely as 11 men are cheered and
glorified...nay…honored as gladiators…as they make a 120 x 60 yard field their arena.
But it is not bears or tigers they fight (Magdaleno 2014). This…is the FIFA World
Cup™.
Born into a football and baseball playing family, one can only guess the reasons I
chose to go completely against familial lineage and embark on this lifelong passion
called soccer. Maybe it was running an average of 6+ miles a game that called to me
(Harriman 2014), as I was genetically following my Father’s Track and Cross-Country
history. Maybe being the younger brother led me to a sport where I am always a part of
the game, unlike baseball, where you might not "play" at all for almost 3 full innings.
Either way, soccer has grabbed me with her passion and never let me go. I am a self-
proclaimed soccer fanatic, either when I shamelessly plug that I was the starting center
midfielder on my high school section champion team, or when I brag about attending
games and dancing with Brazil in Los Gatos, CA in 1994 when we staged the games
here ourselves (Peterson 2014).
So I decided to analyze pre-tournament data on every
team in the World Cup. This allows me to satiate my
desire for stats and numbers, as well as showing off my
analytical skills.
So without further ado, I present the World Cup
2014…in numbers.
THE DATA
When I first decided to take on this project, I was overwhelmed with choices
about what data to analyze. The number of spreadsheets one can find online is simply
mind-boggling, and the ease with which you can get data readily-available to analyze is
astounding. I was first going to analyze fast-food data. We all know it, we all love it
(secretly, of course), so it seemed to be the easiest topic with which to work with.
I started by going to several websites, the typical major fast-food chains –
McDonalds, Carl’s Jr., Burger King, Wendy’s, Taco Bell, Subway, Jack in the Box, etc.
Expecting the spreadsheets waiting for me, I came across online calorie-counting

3. 2 Pre-Tournament Data Analysis of the World Cup
generators. The data was there…but useless. I was able to download the information
in PDF format, however found the task of converting to Excel as I needed much more
daunting than expected. Not to mention each individual PDF that I did end up
converting had its own format, so I was faced with the struggle of converting, then
reformatting each spreadsheet so that they worked together and the data was
aggregatable.
So I continued on and flailed around online for a few hours, not making much
progress. I eventually turned to my online community and pleaded for help. I was
immediately responded to by a friend leading me to a website she uses to count
calories. Unfortunately this was no help either, as the data needed to be entered by
hand, and was simply too much work. Finally, with an errant thought that I happened to
catch, I modified a search term and was transported to the UC Berkeley library where
the spreadsheet I had been searching for in vain was waiting for me in all its completed
glory (LLC 2012).
I immediately began the work of analyzing data. My original goal was to show
which fast food restaurant and/or meal was the healthiest. After playing around with it a
little bit, I found this a little difficult to show...so I changed courses and started working
on getting the best “bang for your calorie buck,” if you will. I wanted to see which foods,
by weight, had the most (or least) caloric intake, hence rendering them the most (or
least) useful to consume solely as an energy source. But first…I needed to take a
break.
As I am wont to do from time-to-time, it is when I finally have a chance to turn my
mind off that the real magic happens. I hopped in the shower to wash the Super-Sized
data away, then laid down to get some much-needed rest. Unfortunately that rest
lasted all of 10 minutes as my mind zoned in on my eventual goal. Rest was no longer
necessary, as I sprang from my bed to get to work.
Two minutes was all it took to find the data I needed. Thanks to the folks across
the pond the data was simple to find and even easier to use (Arnett 2014). Now came
the fun part.
EXPLANATION OF THE DATA
The World Cup Qualifying process is a two-plus year ordeal between over 200
countries in FIFA’s (Fédération Internationale de Football Association) (contributors
2014) six qualifying federations. 820 individual games were played over this span, The
data was simple, complete, and useful. It contained information on every player on
each of the 32 countries’ rosters that had qualified for the World Cup. Each player was
issued a Player ID with their name, followed by the ensuing fields:

4. 3 Pre-Tournament Data Analysis of the World Cup
Position Number Club (Team
they play on
professionally)
Club
Country
(which
country
is their
club
team
in?)
DOB Age Country Caps (each
appearance
made for
their
national
team)
Int’l
Goals
Plays in
Home
Country? (a
TRUE/FALSE
value)
These are all relatively explainable to the soccer mind…we call our teams “Clubs,” it’s
not unusual for someone to play on a Club team in a different country, and Caps are “a
metaphorical term for a player's appearance (playing in a game, not simply dressing) on
a select team, such as a national team” (contributors, Cap (sport) 2014).
ANALYZING THE DATA
My first step in analysis was to get the easy things out of the way. So for
starters, let’s see how old these players are. So I ran the numbers for the Average Age
by Country.
The overall average age in the World Cup Tournament is 26.85 years old. The oldest
team in the tournament by average age is Argentina at 28.52 years old, and the
youngest is Ghana at 24.91 (USA comes in at 27.35, or #21). Not surprisingly, when
you look at the average age by position, your Goalie averages the oldest at 28.45,
followed by your defenders at 26.90. These are typical numbers because you usually
23
24
25
26
27
28
29
Ghana
Nigeria
Belgium
Switzerland
SouthKorea
Germany
Australia
Netherlands
England
Algeria
Cameroon
France
Bosnia&…
Croatia
Mexico
Japan
Colombia
Russia
CostaRica
Ecuador
USA
Italy
IvoryCoast
Chile
Spain
Brazil
Iran
Greece
Honduras
Uruguay
Portugal
Argentina
Average Age by Country
Total

5. 4 Pre-Tournament Data Analysis of the World Cup
want your goalie to be the experienced anchor in the back, along with wanting
experienced defenders.
This information is pretty standard and is a good bellwether on if we’re going to see
what I consider accurate data, based on experience. But what if we took a look at data
that might not have any correlation? Here’s a look at the average age by the number
they wear:
As you would expect, there is a correlation between the number 1, which is usually worn
by goalies, and having the highest average age, as evidenced in a previous chart.
Otherwise, the number I wore when I played, #22, interestingly happens to be the
second oldest player by average.
Another way we can get an indicator of age, or least of experience, is by caps (defined
above as appearances for your national team). Algeria is the least experienced team at
a total of 364 caps. Considering it’s a 23-man roster, that’s an average of 15.82
appearances per player. At the other end of the spectrum we see Spain at 1375 caps.
That brings the average number of appearances to 59.78 per player! That means on
24
26
28
30
Defender Forward Goalkeeper Midfielder
Average Age by Position
Total
23
24
25
26
27
28
29
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
Average Age by Number
Total

6. 5 Pre-Tournament Data Analysis of the World Cup
average, Spain’s players have played nearly 45 more games for their national team
each than Algeria’s players have.
All that being said, that doesn’t necessarily translate into the best team. Historically,
this Spanish team will go down as one of, if not the best ever team assembled to play
the game (Trecker 2014). They won Euro 2008, World Cup 2010, Euro 2012, and if
they would have won this World Cup they would undoubtedly cement themselves as the
greatest team to ever play the game. However their run was quickly and emphatically
silenced when they faced Holland, the same team they defeated in the finals in 2010.
Holland went on to trounce Spain 5-1, and solidify themselves as favorites.
But just when you think things are starting to get boring, you put the numbers together
like this, and come up with the total number of caps by position:
I thought that goalies were the oldest average age by position and number? Now we
get a staggering statistic like this that shows you that even though they are older and
wiser, they are usually the least “capped” player on the team!
0
500
1000
1500
Algeria
Australia
France
SouthKorea
Bosnia&…
Russia
England
Colombia
Netherlands
Nigeria
Brazil
Iran
Cameroon
Ghana
Argentina
Italy
Switzerland
Belgium
CostaRica
Ecuador
Chile
USA
Portugal
IvoryCoast
Greece
Croatia
Mexico
Japan
Germany
Honduras
Uruguay
Spain
Sum of Caps by Country
Total
0
5000
10000
Defender Forward Goalkeeper Midfielder
Sum of Caps by Position
Total

7. 6 Pre-Tournament Data Analysis of the World Cup
So let’s get into the fun stuff. Everybody likes the goals. Let’s take a look at the highest
scoring country in this World Cup:
Here again, we’re seeing the dominant Spanish side take the title of most international
goals in the tournament with 217, followed close behind by those crafty Germans with
212.
Another predictable stat is who is scoring these goals. If we look simply at the number
of the player scoring the goals, we can see your typical 9-10-11 scenario. For those
unaware, the #10 on the team is usually considered your “best” player. This is your
playmaker, your star, the guy who gets the big bucks. Your #9 and #11 usually follow
close behind, although do not have the same reputation nor expectation when it comes
to production.
0
50
100
150
200
250
Algeria
Argentina
Australia
Belgium
Bosnia&…
Brazil
Cameroon
Chile
Colombia
CostaRica
Croatia
Ecuador
England
France
Germany
Ghana
Greece
Honduras
Iran
Italy
IvoryCoast
Japan
Mexico
Netherlan…
Nigeria
Portugal
Russia
South…
Spain
Switzerland
Uruguay
USA
Goals by Country
Total
0
50
100
150
200
250
300
350
400
450
2 4 6 8 12 14 16 18 20 22 2 4 7 9 11 13 15 17 19 21 23 12 16 21 23 3 5 7 9 11 13 15 17 19 21 23
Defender Forward Goalkeeper Midfielder
Goals by Jersey Number/Position
Total

8. 7 Pre-Tournament Data Analysis of the World Cup
We can also see why the biggest clubs are the biggest clubs, and evidence that they
have some of the best players in the world.
I filtered out clubs that had less than 5 goals, as those comprised more than half the
results at 183 clubs, while only 116 remained that had more than 5 goals. You see your
usual giants of Manchester United (England), Barcelona (Spain), Bayern Munich
(Germany), Lazio (Italy), Galatasaray (Turkey), etc. I wonder what will happen if we
take a look at these club teams with the highest scorers, and find out where their goals
are coming from. For instance, are the majority of Manchester United’s international
goals coming from an Englishman or another nationality?
These are the top 10 highest scoring club teams for international goals. It’s not until we
get to #3 on the list in Italy do we see the majority of the international goals being
0
50
100
150
200
Manches…
Liverpool…
Cerezo…
VfL…
LilleOSC
Tottenha…
Villarreal…
Toronto…
Sporting…
FC…
Swansea…
Atalanta…
TSG…
Crystal…
SLBenfica
Trabzons…
Ulsan…
RSC…
Deportiv…
Kayseris…
Valereng…
NEC…
FSV…
Sunshine…
Fortuna…
Stadede…
GetafeCF
VfRAalen
Sanfrecc…
Club…
CF…
USMAlger
Sporting…
CD…
NumberofGoals
Club Team
Goals by Club Team
0
20
40
60
80
100
120
140
160
Algeria
Brazil
Ecuador
Greece
Mexico
Spain
Belgium
CostaRica
Iran
Switzerland
Bosnia&…
France
IvoryCoast
Spain
Bosnia&…
Croatia
Iran
Portugal
Bosnia&…
Nigeria
CostaRica
Ecuador
Brazil
Nigeria
Belgium
France
Nigeria
Brazil
England Spain Italy Germany TurkeyUSAMexicoRussia France Ukraine
Top 10 International Goals Per Club Team
Total

9. 8 Pre-Tournament Data Analysis of the World Cup
scored by another nationality, in this case, the majority scored by the German
international Miroslav Klose.
Finally let’s take a look at who actually plays in their home country.
A few of the glaring ones right out of the box are England , Italy , and Russia. These
are countries where the majority of their national team play within their own domestic
league. On the flipside you can see the African teams all play out of continent, and
surprisingly there’s a strong contingent of South Americans that play out of their
continent as well.
One final stat that needs to be recognized:
0
2
4
6
8
10
12
14
Defender - FALSE
Defender - TRUE
Forward - FALSE
Forward - TRUE
Goalkeeper - FALSE
Goalkeeper - TRUE
Midfielder - FALSE
Midfielder - TRUE
0
0.5
1
1.5
2
2.5
3
3.5
Total
Luis SUAREZ - Sum of Bites in the
World Cup
Luis SUAREZ - Sum of Bites Total

10. 9 Pre-Tournament Data Analysis of the World Cup
MICROSOFT EXCEL RAW DATA FILE
Please double click on this icon to open the data file used to compile these statistics
and associated Pivot Tables and Graphs.
SUMMARY
In summary, we’ve learned a few key details regarding the World Cup:
 Spain’s historic run through soccer legend has finally come to a halt, regardless
of the amount of experience and goal-scoring they have on their team.
 One bite is all it takes. Or, in this case, 3.
 Although my original pick to win it was Holland, considering their romp through
the tournament so far, doing this analysis has me wanting to change my pick to
Germany. Then again, whenever the World Cup is in South America or in
Europe, a country from that continent has won the tournament. I’m looking at
you, Columbia.
CONCLUSION
In conclusion, I would first like to extend a huge debt of gratitude. If you’ve made it this
far, you must be strong like a Taeguk Warrior (the nickname of the South Korean team).
This has been a tremendously enjoyable exercise to partake, and one in which I hope
will have positive ramifications all around.
All my best,
Justin M. King
408.832.9413
JustinMichaelKing@yahoo.com

11. 10 Pre-Tournament Data Analysis of the World Cup
REFERENCES
Arnett, George and Franklin, Will. The Guardian. June 6, 2014.
http://www.theguardian.com/football/datablog/2014/jun/06/world-cup-squads-rosters-
broken-down-club-age-height (accessed July 3, 2014).
contributors, Wikipedia. Cap (sport). July 1, 2014. http://en.wikipedia.org/wiki/Cap_(sport) (accessed
July 3, 2014).
—. FIFA. July 2, 2014. http://en.wikipedia.org/wiki/FIFA (accessed July 3, 2014).
Harriman, Dan. The Average Distance Run in a Soccer Game. December 18, 2013.
http://www.livestrong.com/article/400871-the-average-distance-run-in-a-soccer-game/
(accessed July 3, 2014).
LLC, UBM Medica. Healthier You. 2012. http://www.healthieryou.com/food2.html (accessed July 3,
2014).
Magdaleno, Alex. The Complete Guide to World Cup Nicknames. June 9, 2014.
http://mashable.com/2014/06/09/world-cup-nicknames/ (accessed July 3, 2014).
Trecker, Jamie. Spain Takes Mantle as Best Team Ever. May 21, 2014.
http://msn.foxsports.com/foxsoccer/eurocup/story/spain-4-0-italy-review-euro-2012-final-
greatest-team-of-all-time-070112 (accessed July 3, 2014).