Recently, we have been working on the DBpedia / Wikipedia Page Link dataset. We have considered the English and the German language versions for this project. In the current DBpedia 3.9 page links English and German datasets 18 million and 6 million entities are represented respectively. But the original DBpedia only contains about 4 million and 1 million distinct entities for English and German versions.
This significant difference is mainly due to the current DBpedia pagelinks dataset include redirect pages and pagelinks with resources that are not considered as entites (as e.g. thumbnails and other images). So we considered cleaning up DBpedia pagelinks dataset for the computation of statistical parameters (a.g. pagerank or HITS). For the Cleanup we have removed all unnecessary and redundant RDF-Triples from the pagelinks dataset, i.e all removing the redirect pages (Redirection pages are just URIs that automatically forward a user to another Wikipedia page, but do not represent entities) as well as RDF-Triples representing resources that do not have an own rdfs:label (as per DBpedia documentation every entity has an rdfs:label reference).
One of the benefits of the cleaned up pagelink dataset is the faster computation of statistical graph measures (while not influencing the overall statistics, i.e. redirect pages usually don't have incoming links and theother removed resources (as e.g. images) don't have outgoing links). Based on this dataset we have computed PageRank, Hub and Authorities (HITS), PageInlink Counts and PageOutLink Counts. Please find the details of the datasets here on our research group's webpage [1].
For Computation of the DBpedia graph statistics we have used JUNG — the Java Universal Network/Graph Framework. Please find the source code for PageRank and HITS computation here via GitHub [2].
References and further Reading:
[1] New PageRank Computations for DBpedia 3.9 (English/German) at SemanticMultimedia
[2] Source code for DBpedia Graph Statistics
...just a few words about life, the universe, and research on topics related to the semantic web
Showing posts with label statistics. Show all posts
Showing posts with label statistics. Show all posts
Wednesday, July 16, 2014
Monday, July 14, 2014
Harald's Original Miscellany - More Truth about Football - Part 4
Finally, Germany has won the Soccer Worldcup 2014. Therefore, also our little statistics on soccer will come to an end with the post today. You might ask yourself what kind of data is left for soccer players in Wikipedia and DBpedia. Well, unfortunately only a little. But, we will try to make something out of it. Last time, we've ask for the number of team changes and the correlation to popularity vs. scored goals for soccer players. What is left, if we look at the available data?
We have the data about the years in which the football players were active or have played in their national soccer team. Let's start with the national team years [1]:
Well, it was obvious that the most players have only 1 or two years in the national team. But, there are exceptional players who achieved even 8 years. But, who are these long term players? [2]:
We have the data about the years in which the football players were active or have played in their national soccer team. Let's start with the national team years [1]:
| nationalyears | NumPlayers |
|---|---|
| 8 | 3 |
| 7 | 20 |
| 6 | 85 |
| 5 | 371 |
| 4 | 1070 |
| 3 | 2479 |
| 2 | 6116 |
| 1 | 28211 |
Well, it was obvious that the most players have only 1 or two years in the national team. But, there are exceptional players who achieved even 8 years. But, who are these long term players? [2]:
| nationalyears | Player | Team |
|---|---|---|
| 8 | Wojciech Łobodziński | "Poland"@en |
| 8 | Wojciech Łobodziński | "Poland Under 16"@en |
| 8 | Wojciech Łobodziński | "Poland Under 17"@en |
| 8 | Wojciech Łobodziński | "Poland Under 21"@en |
| 8 | Wojciech Łobodziński | "Poland Under 18"@en |
| 8 | Santiago Cañizares | "Spain Under-17"@en |
| 8 | Santiago Cañizares | "Spain Under-16"@en |
| 8 | Santiago Cañizares | "Spain Under-21"@en |
| 8 | Santiago Cañizares | "Spain Under-18"@en |
| 8 | Santiago Cañizares | "Spain Under-23"@en |
| 8 | Santiago Cañizares | "Spain Under-19"@en |
| 8 | Santiago Cañizares | "Spain Under-20"@en |
| 7 | Aydın Yılmaz | "Turkey Under-21"@en |
| 7 | Aydın Yılmaz | "Turkey"@en |
| 7 | Aydın Yılmaz | "Turkey"@en |
| 7 | Aydın Yılmaz | "Turkey Under-19s"@en |
| 7 | Aydın Yılmaz | "Turkey Under-17"@en |
| 7 | Aydın Yılmaz | "Turkey A2"@en |
| 7 | Ismael Urzaiz | "Spain Under-17"@en |
| 7 | Ismael Urzaiz | "Spain Under-16"@en |
Possibly you will never have heard of Poland's Wojciech Łobodziński or Spain's Santiago Cañizares. Here another flaw in the data becomes visible. There is no such thing as the unique national team. We have "under 16", "under 17", "under 18", and so on... So you start your career already with 15 and after 8 years you would be 23 and possibly be in the "real" national team.
Possibly this is because of people not only writing single year's into the Wikipedia infoboxes, but time spans and other things. In the list above are only the Top 20. Just remove the LIMIT from the SPARQL query and further down you will find more valid data.
OK, let's come to our last problem related to football. Is there a correlation between the height of a player (simply because we have that data) and the number of achieved goals [4]?
It is rather difficult to recognize something in this data. You see heights and the number of goals that player of that height have scored. Fascinating that there seem to be a significant number of players that are taller than 2 meters. I guess that the list leader with 2.43 meters is just incorrect data. Now. this is so many data that we have to visualize it to recognize something....
In the diagram to the left you see the soccer players height (x-axis) vs. the number of scored goals (y-axis). Interesting thing to notice is that the heights approximately show a Gaussian distribution, i.e most players have a "middle" height, on the extremes there are only a few. Well, there seems to be an exception. on the outer left you will notice a large fraction of players with a height of 1.52m with goal scores ranging among 0 to 300. This is extraordinary, because I have no idea what kind of group this is or if there is simply an error in the data again. What I noticed is that among this group there seems to be a larger fraction of female Asian soccer players. Maybe they are responsible for that large number of outliers, but this requires further investigation.
Alas, I want to add one last table. For each player there is the information on which position she or he is playing. Of course Wikipedia authors are far from any sort of agreement how to name the player's position. Thus, there is a rather huge variety. Nevertheless, I will leave you with the table to make any sense from it. Enjoy [5]:
Of course I limited the table to 30 rows. Interestingly, the average height of goalkeepers is larger than for midfielders or strikers. As expected, strikers on the average score more goals than defenders or goalkeepers.
Let's have a look at the active years of players. Unfortunately, here the data is rather messy [3]:
Possibly this is because of people not only writing single year's into the Wikipedia infoboxes, but time spans and other things. In the list above are only the Top 20. Just remove the LIMIT from the SPARQL query and further down you will find more valid data.
OK, let's come to our last problem related to football. Is there a correlation between the height of a player (simply because we have that data) and the number of achieved goals [4]?
| Height | sumgoals |
|---|---|
| 243.84 | 1 |
| 23.0 | 59 |
| 215.9 | 1 |
| 206.0 | 0 |
| 204.0 | 0 |
| 203.2 | 24 |
| 203.2 | 47 |
| 203.0 | 0 |
| 203.0 | 0 |
| 203.0 | 51 |
| 202.0 | 19 |
| 202.0 | 0 |
| 202.0 | 15 |
| 201.0 | 20 |
| 201.0 | 0 |
| 201.0 | 0 |
| 200.66 | 11 |
| 200.66 | 48 |
| 200.66 | 0 |
| 200.66 | 0 |
| 200.66 | 0 |
| 200.66 | 124 |
| 200.66 | 146 |
| 200.66 | 0 |
| 200.66 | 1 |
| 200.66 | 16 |
| 200.66 | 0 |
| 200.66 | 102 |
| 200.66 | 6 |
| 200.66 | 0 |
| 200.0 | 0 |
| 200.0 | 20 |
| 199.0 | 0 |
| 199.0 | 0 |
| 199.0 | 78 |
| 199.0 | 47 |
| 199.0 | 0 |
| 199.0 | 62 |
| 199.0 | 32 |
| 199.0 | 84 |
It is rather difficult to recognize something in this data. You see heights and the number of goals that player of that height have scored. Fascinating that there seem to be a significant number of players that are taller than 2 meters. I guess that the list leader with 2.43 meters is just incorrect data. Now. this is so many data that we have to visualize it to recognize something....
![]() |
| Correlation between height of soccer players and scored goals |
Alas, I want to add one last table. For each player there is the information on which position she or he is playing. Of course Wikipedia authors are far from any sort of agreement how to name the player's position. Thus, there is a rather huge variety. Nevertheless, I will leave you with the table to make any sense from it. Enjoy [5]:
| position | number | avheight | avgoals |
|---|---|---|---|
| midfielder | 4450 | 176.822828644929299 | 18.077078651685393 |
| defender | 3429 | 181.37523034450506 | 8.058326042578011 |
| striker | 2643 | 180.169761559433703 | 65.213015512674991 |
| goalkeeper | 2086 | 186.186064557569709 | 0.174496644295302 |
| forward | 1787 | 178.865346269495465 | 46.388919977616116 |
| centre back | 786 | 185.7391350821381 | 10.651399491094148 |
| winger | 648 | 174.69354896725695 | 27.583333333333333 |
| attacking midfielder | 490 | 175.897877222177931 | 38.006122448979592 |
| left back | 461 | 177.661735253323698 | 6.995661605206074 |
| defensive midfielder | 427 | 179.741452037869347 | 11.978922716627635 |
| right back | 405 | 177.829456545982828 | 7.301234567901235 |
| central defender | 196 | 185.005408462213008 | 8.897959183673469 |
| central midfielder | 161 | 178.223105294363834 | 20.099378881987578 |
| full back | 158 | 174.659366173080242 | 6.759493670886076 |
| centre forward | 140 | 179.09685701642717 | 80.792857142857143 |
| left winger | 113 | 174.790176753449224 | 28.398230088495575 |
| defender, midfielder | 102 | 179.789412255380659 | 10.607843137254902 |
| inside forward | 96 | 172.094789346059145 | 64.083333333333333 |
| defender/midfielder | 82 | 179.178292995545911 | 11.463414634146341 |
| defender / midfielder | 79 | 180.188100887250282 | 15.050632911392405 |
| left-back | 65 | 175.642922504131603 | 7.692307692307692 |
| right winger | 62 | 175.247418803553424 | 30.096774193548387 |
| centre-back | 56 | 521.327500479561941 | 9.375 |
| right-back | 54 | 176.101110952871815 | 6.111111111111111 |
| midfielder/forward | 52 | 172.013075924836657 | 22.557692307692308 |
| midfield | 52 | 176.982691251314588 | 30.673076923076923 |
| defender (retired) | 50 | 181.37344055175781 | 14.48 |
| second striker | 48 | 174.673332850138346 | 63.854166666666667 |
| full-back | 44 | 175.908408771861666 | 2.681818181818182 |
| striker / winger | 41 | 178.235609845417295 | 49.219512195121951 |
Of course I limited the table to 30 rows. Interestingly, the average height of goalkeepers is larger than for midfielders or strikers. As expected, strikers on the average score more goals than defenders or goalkeepers.
SPARQL Queries and original data:
[2] player name and team name of national team players with the longest playing terms
[3] players ordered by number of active years
[4] is there a correlation between soccer player height and scored goals?
[5] is there any correlation between player position, height, and scored goals?
[3] players ordered by number of active years
[4] is there a correlation between soccer player height and scored goals?
[5] is there any correlation between player position, height, and scored goals?
Labels:
DBpedia,
soccer,
soccer worldcup 2014,
SPARQL,
statistics
Thursday, July 03, 2014
Harald's Original Miscellany - More Truth about Football - Part 3
![]() |
| To change the team means to earn more money...what about the football millionaires? How often do they change the team? |
Have you ever wondered about this kind of slave trade in professional football? Well, I wouldn't exactly call the transfer of a millionaire to a higher paying job a 'slave trade'. But, have you ever thought about the following question: Do the real good (and well paid) players more often change the team - or is it vice versa, that teams try to get rid of players that have a bad season or are on the decline? Who knows? Let's have a look on the data:
| TeamChanges | NumPlayers |
|---|---|
| 16 | 2 |
| 15 | 26 |
| 14 | 84 |
| 13 | 287 |
| 12 | 792 |
| 11 | 2247 |
| 10 | 3848 |
| 9 | 5109 |
| 8 | 6464 |
| 7 | 8110 |
| 6 | 9790 |
| 5 | 11264 |
| 4 | 11837 |
| 3 | 11448 |
| 2 | 10961 |
| 1 | 6515 |
| person | TeamChanges | popularity |
|---|---|---|
| http://dbpedia.org/resource/Cristiano_Ronaldo | 8 | 1794 |
| http://dbpedia.org/resource/David_Beckham | 11 | 1572 |
| http://dbpedia.org/resource/Thierry_Henry | 9 | 1414 |
| http://dbpedia.org/resource/Lionel_Messi | 7 | 1404 |
| http://dbpedia.org/resource/Wayne_Rooney | 5 | 1343 |
| http://dbpedia.org/resource/Frank_Lampard | 5 | 1188 |
| http://dbpedia.org/resource/Pel%C3%A9 | 4 | 1111 |
| http://dbpedia.org/resource/Didier_Drogba | 8 | 1047 |
| http://dbpedia.org/resource/Ronaldo | 10 | 1037 |
| http://dbpedia.org/resource/Michael_Owen | 6 | 1011 |
But, we get a better overview, if we look at the average popularity of each switching group in the table [3]:
| TeamChanges | NumPlayers | avgindegree |
|---|---|---|
| 16 | 2 | 107.5 |
| 15 | 26 | 76.769230769230769 |
| 14 | 84 | 47.297619047619048 |
| 13 | 287 | 60.885017421602787 |
| 12 | 788 | 45.073604060913706 |
| 11 | 2235 | 37.206263982102908 |
| 10 | 3800 | 32.577631578947368 |
| 9 | 5023 | 30.939080230937687 |
| 8 | 6332 | 29.685881238155401 |
| 7 | 7935 | 25.499054820415879 |
| 6 | 9525 | 23.188346456692913 |
| 5 | 10886 | 18.682895462061363 |
| 4 | 11423 | 14.937844699290904 |
| 3 | 10842 | 11.131802250507286 |
| 2 | 10089 | 7.704628803647537 |
| 1 | 5534 | 5.19588001445609 |
| TeamChanges | NumPlayers | AvgGoals |
|---|---|---|
| 16 | 2 | 22.5 |
| 15 | 26 | 28.807692307692308 |
| 14 | 83 | 37.855421686746988 |
| 13 | 287 | 38.062717770034843 |
| 12 | 781 | 39.939820742637644 |
| 11 | 2205 | 38.625850340136054 |
| 10 | 3784 | 35.646141649048626 |
| 9 | 4980 | 32.826907630522088 |
| 8 | 6296 | 29.489517153748412 |
| 7 | 7826 | 24.455788397648863 |
| 6 | 9314 | 21.937835516426884 |
| 5 | 10471 | 18.655142775284118 |
| 4 | 10627 | 16.317869577491296 |
| 3 | 9612 | 12.756450270495214 |
| 2 | 7193 | 9.911858751564021 |
| 1 | 4624 | 4.444204152249135 |
Looks interesting. Top goal scorer have 9 to 14 team switches. This is way above the average. Thus, the more goals you score, the more often you will have the chance of being transferred (and thus earn more money). Players that don't score goals will obviously not be transferred (that often).
References:
[1] How many players have how many team changes overall?
[2] The Top10 popular soccer players and their number of team changes
[3] The average popularity of football players regarding the number of team switches
[4] Number of average goals per soccer player with respect to the number of team changes
[2] The Top10 popular soccer players and their number of team changes
[3] The average popularity of football players regarding the number of team switches
[4] Number of average goals per soccer player with respect to the number of team changes
Labels:
data mining,
DBpedia,
football,
soccer,
SPARQL,
statistics
Wednesday, June 25, 2014
Harald's Original Miscellany - The Truth about Football - Part 2
![]() |
| John Terry Celebration Meme, read on and you will understand... |
Of course you always wanted to know, who is the best football player of all times. Sure this might be a question about which real football afficionados might argue forever. Also Wikipedia will not be able to give you the definite answer. But, we can play around with the available data and maybe we find out something interesting about football players again ...
But, first at all, I want to say thank you to Kingsley Idehen, who gave me the hint for my SPARQL query links to use the parameter "qtxt=" instead of "query=", which enables others to see the original query and to use it for further data explorations. Thus, all SPARQL query links will be given in this form.
So let's start with the most simple query: Select all football players and their popularity (indegree) in descending order starting with the most popular player. We must be a little bit careful, because the class SoccerPlayer does not only contain "real persons" but also popular roles of football players such as e.g. "Captain". Therefore, we filter the results for entities that have a name (via foaf:name). Here are the Top50 football players according to wikipedia. For the entire list, please refer to the references [1].
| Name | Popularity |
|---|---|
| Cristiano Ronaldo | 1794 |
| David Beckham | 1572 |
| Thierry Henry | 1414 |
| Lionel Messi | 1404 |
| Wayne Rooney | 1343 |
| Frank Lampard | 1188 |
| Pelé | 1111 |
| Didier Drogba | 1047 |
| Ronaldo | 1037 |
| Michael Owen | 1011 |
| Steven Gerrard | 1002 |
| Zlatan Ibrahimović | 964 |
| Alessandro Del Piero | 926 |
| Ronaldinho | 914 |
| Raúl (footballer) | 903 |
| Ryan Giggs | 894 |
| Fernando Torres | 889 |
| Zinedine Zidane | 867 |
| Ruud van Nistelrooy | 861 |
| Robbie Keane | 861 |
| Samuel Eto'o | 859 |
| Landon Donovan | 835 |
| Andriy Shevchenko | 823 |
| Kaká | 804 |
| Francesco Totti | 730 |
| Robin van Persie | 720 |
| Paul Scholes | 692 |
| Hernán Crespo | 680 |
| David Villa | 669 |
| John Terry | 669 |
| Cesc Fàbregas | 669 |
| George Best | 667 |
| Carlos Tévez | 666 |
| Robinho | 643 |
| Gary Lineker | 641 |
| Teddy Sheringham | 633 |
| Andrew Cole | 620 |
| Dwayne De Rosario | 617 |
| Xavi | 616 |
| Jermain Defoe | 613 |
| Craig Bellamy | 609 |
| Dimitar Berbatov | 587 |
| David Trezeguet | 587 |
| Luis Suárez | 581 |
| Peter Crouch | 577 |
| Michael Ballack | 572 |
| Miroslav Klose | 568 |
| Luís Figo | 567 |
| Lee Dong-Gook | 558 |
| Filippo Inzaghi | 557 |
Yes, it was obvious for everybody that names such as Ronaldo, Beckham, Thierry, Pelé occur among the top popular players. Unfortunately, I'm not a football expert to comment further on that. Let's have a look, whether popularity corresponds with the number of achieved goals. However, this information is not easy to extract. For some of the football players, there's a property dbprop:totalGoals, while most of them has dbprop:goals. But the later sometimes exists multiple times for single years or periods. Thus, we have to sum up all dbprop:goals, while keeping in mind not to count any number more often than once (because an entry might be reproduced in our result list for several reasons).
| Name | Goals | Popularity |
|---|---|---|
| David Schofield (footballer) | 76543210 | 9 |
| Alcindo Sartori | 5019110 | 160 |
| Oh Seung-Bum | 1842256 | 32 |
| Marei Al Ramly | 6037 | 11 |
| Darío Espínola | 1715 | 6 |
| Kim Andersson | 1537 | 23 |
| Stefan Lövgren | 1328 | 18 |
| Nikola Karabatić | 1318 | 84 |
| Elias Ribeiro de Oliveira | 1187 | 26 |
| Mohd Amar Rohidan | 1020 | 38 |
| Slaviša Žungul | 856 | 113 |
| John Bartley (footballer) | 762 | 1 |
| Zoran Karić | 759 | 11 |
| Jimmy Greaves | 748 | 342 |
| Ernest Spiteri Gonzi | 704 | 11 |
| Pierre van Hooijdonk | 670 | 238 |
| Reg Date | 664 | 3 |
| Trevor Phillips (footballer) | 655 | 4 |
| Joan Linares | 645 | 12 |
| Domenic Mobilio | 625 | 58 |
| Pelé | 620 | 1111 |
| Harry Johnson (footballer born 1899) | 610 | 25 |
| Ernst Stojaspal | 602 | 23 |
| Max Morlock | 588 | 66 |
| Ernie Hine | 574 | 72 |
| Branko Šegota | 561 | 38 |
| Serhiy Koridze | 557 | 4 |
| Salvinu Schembri | 538 | 6 |
| Konstantin Yeryomenko | 537 | 13 |
| Tony Brown (English footballer) | 498 | 47 |
| Waldo Machado | 497 | 36 |
| Nguyen Minh Phuong | 496 | 50 |
| Tony Cascarino | 496 | 141 |
| Leônidas da Silva | 484 | 98 |
| Zeki Rıza Sporel | 470 | 75 |
| Ángeles Parejo | 469 | 9 |
| Tommy Dickson | 457 | 13 |
| Elisabetta Vignotto | 454 | 22 |
| Alberto Spencer | 445 | 99 |
| Stefan Schwoch | 435 | 7 |
| Peter Kitchen | 429 | 16 |
| Edgar Kail | 427 | 7 |
| Eusébio | 423 | 433 |
| Giorgos Sideris | 415 | 51 |
| Tommy Browell | 414 | 88 |
| Patricio Margetic | 412 | 14 |
| Arsénio Trindade Duarte | 409 | 19 |
| Uwe Seeler | 406 | 153 |
| Hughie Gallacher | 406 | 131 |
| Dragan Džajić | 401 | 150 |
Again we see, that DBpedia data (resp. Wikipedia data) is somehow 'noisy'. The first 3 ranks are obviously wrong concerning the number of goals. Simply because if David Schofield really would have achieved 76,543,210 goals, it would mean that he had won about 5 goals per minute of all the 32 years of his entire life so far. This must be kind of an extraction error. If we look at the players with more than 1000 goals, then a closer inspection reveals some handballers that either are also footballers or are wrongly declared to be footballers. In handball it is easier to achieve a higher number of goals compared to football. Trevor Phillips and John Bartley really achieved more than 600 goals, but their popularity score signals that they did achieve this not necessarely in the major league. The first top ranked prominent football player in this list definitely is Pelé with 620 goals. The only other two in this Top50 list I have already heard of are Eusébio and Uwe Seeler, but don't take me as a reference :)
Lets order the list again the other way around according to the most popular players to investigate their goal score:
| Name | Goals | Popularity |
|---|---|---|
| Cristiano Ronaldo | 227 | 1794 |
| David Beckham | 95 | 1572 |
| Thierry Henry | 265 | 1414 |
| Lionel Messi | 223 | 1404 |
| Wayne Rooney | 156 | 1343 |
| Frank Lampard | 163 | 1188 |
| Pelé | 620 | 1111 |
| Didier Drogba | 160 | 1047 |
| Ronaldo | 217 | 1037 |
| Michael Owen | 163 | 1011 |
| Steven Gerrard | 98 | 1002 |
| Zlatan Ibrahimović | 198 | 964 |
| Alessandro Del Piero | 223 | 926 |
| Ronaldinho | 157 | 914 |
| Raúl (footballer) | 280 | 903 |
| Ryan Giggs | 114 | 894 |
| Fernando Torres | 161 | 889 |
| Zinedine Zidane | 95 | 867 |
| Ruud van Nistelrooy | 249 | 861 |
| Robbie Keane | 179 | 861 |
| Samuel Eto'o | 219 | 859 |
| Landon Donovan | 135 | 835 |
| Andriy Shevchenko | 219 | 823 |
| Kaká | 114 | 804 |
| Francesco Totti | 226 | 730 |
| Robin van Persie | 130 | 720 |
| Paul Scholes | 107 | 692 |
| Hernán Crespo | 198 | 680 |
| John Terry | 30 | 669 |
| David Villa | 234 | 669 |
| Cesc Fàbregas | 50 | 669 |
| George Best | 238 | 667 |
| Carlos Tévez | 135 | 666 |
| Robinho | 122 | 643 |
| Gary Lineker | 243 | 641 |
| Teddy Sheringham | 289 | 633 |
| Andrew Cole | 226 | 620 |
| Dwayne De Rosario | 94 | 617 |
| Xavi | 57 | 616 |
| Jermain Defoe | 151 | 613 |
| Craig Bellamy | 113 | 609 |
| Dimitar Berbatov | 189 | 587 |
| David Trezeguet | 218 | 587 |
| Luis Suárez | 128 | 581 |
| Peter Crouch | 102 | 577 |
| Michael Ballack | 117 | 572 |
| Miroslav Klose | 181 | 568 |
| Luís Figo | 91 | 567 |
| Filippo Inzaghi | 184 | 557 |
| Patrick Vieira | 45 | 551 |
As we would expect, most of the popular football players are also good goal scorers. Well, there are a few exceptions. Take John Terry with a popularity score of 669 and only 30 goals. Why might he be so popular then? Taking a closer look at Wikipedia reveals that Terry plays at centre back position and is the captain of Chelsea in the Premier League. Well, that's already something for popularity. But, if you look even closer, you will find more: under the topic 'Controversies' you will find charges for assault and affray, a £60 fine for parking his Bentley in a disabled bay, extramarital affair allegations as well as racial abuse allegations. But, neither of these is directly responsible for Terry's popularity. In fact it's an internet meme (cf. introductory picture of this article).
John Terry was suspended for the UEFA Final and had to watch his team in a suit and tie on the sidelines. He did look quite miserable as he sat there, watching his team defend for their lives and then miraculously pull out the victory. However, as soon as Chelsea made the victory, it was party time for Terry! He immediately threw off his suit like Superman and revealed his full Chelsea kit underneath his suit. The internet community enjoyed his dedication to his club and soccer so much that immediately a popular internet meme lampooning his behaviour appeared on the web, becoming one of the most popular online jokes in 2012. Terry has been pictured taking part in great moments in history and fiction. These included the fall of the Berlin Wall, the freeing of Nelson Mandela, the triumph of Rocky Balboa, as well as the first landing on the Moon [3]. Well, this should be reason for some popularity :)
Labels:
data mining,
DBpedia,
football,
Kingsley Idehen,
SPARQL,
spccer,
statistics,
web science
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