FIFA World Cup 2010: A Network Analysis of the Champion Team Play

C. Cotta1, A.M. Mora2, Cecilia Merelo-Molina and J.J. Merelo2

1Department of Languages and Computer Science, University of Malaga (Spain)2Department of Architecture and Computer Technology, University of Granada (Spain)

ccottap@lcc.uma.es, {amorag,jmerelo}@geneura.ugr.es

Abstract

We analyze the pass network among the players of the Span-ish team (the world champion in the FIFA World Cup 2010),with the objective of explaining the results obtained from thebehavior at the complex network level. The team is consid-ered a network with players as nodes and passes as (directed)edges, and a temporal analysis of the resulting passes networkis done, looking at the number of passes, length of the chainof passes, and the centrality of players in the turf. Results ofthe last three matches indicate that the clustering coefficientof the pass network increases with time, and stays high, indi-cating possession by Spanish players, which eventually leadsto victory, even as the density of the pass network decreaseswith time.

IntroductionThe hypothesis that a complex network analysis can helpunderstand soccer matches has been present for a longtime. Several teams, formal or informally, have performedanalysis of soccer matches from the point of view of thenetwork of passes formed along the match. One start-ing point happened in 2004, when a competition to pre-dict the four best-classified teams in the EuroCup thatwas celebrated that year was done in the Redes (Span-ish for “network”) social-network mailing list (in Span-ish). The results, which completely failed to predict theoutcome, were published in http://revista-redes.rediris.es/webredes/eurocopa2004.htm.

The main problem with these predictions, besides the out-come, which was completely wrong (not even the two final-ists, Portugal and Greece, were included in any of them) wasthat they were looking at the static picture of the team as itemerged from a previous match. It is quite clear that socceris a game of two players, whose networks clash. While theremust be some quantity that is kept from a game to the next,and that quantity is related to the team structure (midfielders,forwards, defense), the other team will do its best to preventthat network to move information (that is, the football) fromone part to other, resulting in a quite unpredictable result.

On the other hand, the network does not have any kind ofspatiotemporal information. A network might show a per-fect structure, well formed, with short distances from goalie

to forward players, but if it plays out of place or simply in itsown field it will not be able to obtain a good result. On theother hand, if the network develops quite slowly with a lownumber of passes and low precision (low transitivity), theresult will not be good either. In both cases, the static struc-ture, while meaningful and a good qualitative description ofthe overall game, is unable to reflect them.

In this paper, spurred by the victory of the Spanish selec-tion in the 2010 world cup, we have performed a spatiotem-poral analysis of the essential games that led to victory. Inthis analysis, we have looked at the temporal evolution ofthe number of passes, the centrality of the player and pitchzones networks, and also at the length of the chains of passesand its transitivity (taking thus into account the effect of theopponent of simple ball losses). In this way, we take intoaccount both the complex network structure (reflected in thepower-law structure of the length of the number of passesand overall network structure) and the spatiotemporal na-ture of the game. In that sense, this paper is the first to makethat kind of analysis, which can latter be complemented withother kind of static micro, macro, and meso measurementsof the same type.

The rest of the paper is structured as follows: next, weexamine the state of the art in analysis of the outcome ofsoccer matches. Then, the methodology used to extract datafrom the match is presented. An overall examination of thematches played by the Spanish team is subsequently per-formed. We close the paper with some conclusions andguidelines for future work.

State of the ArtDespite the huge cultural and popular interest that soccerarises, being arguably the most popular sport (or maybespectacle) in the world, there have not been many scientificapproaches to sport performance and prediction. This wastrue when Onody and de Castro (2004) wrote their often-referenced paper in 2004, and it is still true today.

However, since then, several papers try to apply complexnetwork analysis to the soccer world. The above-mentionedpaper itself was seminal in its thoroughness: it analyzed the

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network of all Brazilian soccer players, and linked them ifthey had shared a team, and found that several metrics (num-ber of teams by player, number of goals by player, numberof games played) follow truncated power laws or exponen-tial distributions. However, there was no attempt to relateany of those quantities to performance. Could the numberof teams a player has participated be related to the numberof goals?

In general, the prediction of performance has concen-trated on the hypothesis that a team has some kind of intrin-sic fitness –see Heuer and Rubner (2009)– whose differencebetween teams affect the probability (not the certainty) ofone beating the other. These studies have mainly concen-trated on time series analysis (Heuer et al., 2010) but notintra-game dynamics.

Since the pass data for several world-class events (EuroCup and World Cup) were made available, one of the authorshas been doing informal analysis on the team’s networks anddeducting from them some kind of qualitative prediction onthe result of a match. However, this analysis was not formalat all, and even as differences between the network qualitiesof different teams were appreciated, it was difficult to re-late them either to the team fitness or to the match outcome.The ARSfutbol team, based in Argentina, has done exten-sive analysis of world-class events as well as local low-levelsoccer teams (Bundio and Conde, 2009), concluding that theperformance of a team is mainly related to the existence ofa well-coordinated core of players (such as the players com-ing from FC Barcelona in the Spanish national team or theset of players from Porto FC in the Portuguese selection).Even those post-hoc observations cannot be easily used forpredicting performance.

In this paper, we will look at the micro-dynamics of a soc-cer team, the Spanish selection during the latest World Cupin which they emerged as champions, bringing happiness tomillions of Spaniards all over the world. We will try to findout which quantities made the Spanish team excellent by fo-cusing on quantitative analysis of its game-play. This willbe used as a first step for a second leg of analysis which willbe focus on prediction.

Data extractionIn order to collect the data for the analysis, an important is-sue was the fact that we intended to analyze the game playfrom a spatiotemporal perspective. The spatial dimensiontries to capture the fact that a player can act in different partsof the pitch and his role (and therefore the way he performsand interacts with the teammates) may be different in eachof these zones. For example, a certain midfielder can havea defensive role when the opposing team is attacking, andhave the teammates in the last defensive line as preferredtarget for a pass whenever he regains control of the ball. Onthe other hand, this very same player can adhere to a muchmore combinative play when in offensive positions, interact-

Figure 1: Model of the football field for data extraction.Playing zones.

ing more with wingers and other creative midfielders. To thisend, the pitch has been divided into nine zones as depicted inFigure 1: four zones correspond to defensive positions (ownbox, wingback lanes, and own midfield), other four zonescorrespond to offensive positions (opponent’s box and mid-field and wingers’ lanes), and the the very central zone ofthe pitch which plays an important role for both initiatingstatic attacks and pressing the opponent in defense. For thepurpose of this analysis, each player is figuratively dividedinto 9 different players depending of the pitch zone in whichthey touch the ball (of course, most players only visit a lim-ited number of zones throughout a given game).

As to the temporal perspective, our goal is to capture thefact that a football game can go through different phases inwhich a team can change its way of playing (e.g., the dom-inant team can become dominated even if just sporadicallyor the trainer may introduce tactical changes) or at least canchange its effectiveness (e.g., a player that was nullified byan effective defense can resurface later on, when tirednessprevents the defenders for keeping on tight marks). To ac-count for this, we also keep track of the minute in whicheach pass was done.

Once the kind of data we needed was defined, data extrac-tion was done by ourselves, reviewing the knock-out gamesplayed by Spain (contrarily to regular group games in whicha team can speculate with a draw or even with a somehowminimal loss in order to qualify for the next phase, knock-out games imply a win-loss situation and therefore the play-ing style and tactics is effectively directed at winning thegame without depending on external factors1). These corre-

1Winning the game must be interpreted here as progressing on

spond to the games against Portugal (1/8th) (not analyzed inthis paper), Paraguay (1/4th), Germany (semifinals) and theNetherlands (final). The raw data obtained from this visualinspection consists of a list of passes with the format

<half> <minute> <player> <zone> <player’> <zone’>

indicating which player passed to which player, in whichminute, and in which pitch zones were each of them. Asmentioned before, each pair (player, zone) can be interpretedas a virtual player for the purposes of network construction.More precisely we consider a moving window of 15 min-utes (which we believe is long enough to capture the stateof the game at any given instant) and build a series of di-rected graphs Gi(V,Ei) where V = {(p, z) | p ∈ T, z ∈{1, · · · , 9}}, T is the set of actual players in the nationalteam, and (u → v) ∈ Ei if, and only if, virtual player upassed to virtual player v within the i-th time window.

This data was extracted by visualizing the matches by theauthors; data collection took approximately 4-5 times theduration of matches2.

The Spanish Team AnalysisThe Spanish national team attended the World Cup 2010 asone of the favorites to win the trophy. While to some ex-tent this was not a completely new situation from the localpoint of view (and even the scientific point of view: Spainhosted the world cup in 1982, and New Scientist publishedan article proving why it should win due to home advan-tage (Dowie, 1982)), in this case this consideration was quitereasonable a priori, since Spain was the current incumbentin Europe after winning the Euro Cup in 2008 for the 2ndtime in history (the 1st time dating back to 1964 in the EuroCup hosted precisely in Spain). This win at the Europeanlevel (and even more precisely the win over Italy in penaltyshootouts in the 1/4th game) was seen by many as a turningpoint from the psychological point of view, finally break-ing the “curse of the 1/4ths” that had been unescapable forseveral other –very talented– Spanish teams. On reflection,the current generation of players was probably the most ad-equately suited for this purpose, since they had been surf-ing on a winning wave in the national team since juniorstages (Iker Casillas, Xavi Hernandez and Carlos Marchenawere members of the team than won the U-20 World cup in1999) and were also part of the last dominant cycles of Real

to the next knock-out stage rather than, e.g., scoring more goalsthan the opponent. For example, a team may decide to play verydefensively in order to reach the penalty shootout if they considerthey will be outplayed otherwise; be it as it may, this reflects adefinite and valid style, and is anyway not applicable to the gamesconsidered in which Spain went effectively to win the game beforepenalty shootouts.

2This data is available to the scientific community, with the pro-vision that this paper is referenced should any publication arisefrom the study.

Madrid CF and FC Barcelona at club level (holding numer-ous trophies a European and International club level).

The lack of this “choking culture” that had haunted pre-ceding generations, and the final discovery of a playing stylethat players felt their own (incidentally a style that departedby long from the traditional “Spanish fury” that had been thenational team’s trademark until them, and arguably a find-ing that was in part possible due to the unfortunate series ofinjuries of several players) led to the current success cycleof the the team. While this style –informally termed “tiki-taka” (an otherwise meaningless phrase that can be trans-lated as “touchy-touch”) by the late Andres Montes, a fa-mous TV commentator– is now considered a “dogma offaith” by many in Spain, and is commonly assimilated tothat used by FC Barcelona (a claim based on the number ofplayers of this club in the national team), a deeper inspectionindicates that there are definite stylistic differences not onlybetween the national team and other clubs in Spain, but alsobetween the Euro 2008 team and the World Cup 2010 team.The former had players such as Fernando Torres (then atLiverpool FC) or David Silva and David Villa (then at Valen-cia CF) at their highest performance level and played a muchmore direct, faster combinative football. The latter team hadseveral of these players at a lower performance level, somekey players in midfield or defense were obviously two yearsolder, and other players more suited to passing play were in-corporated. The result was a slower playing pace in whichthe ball and players move rapidly, but in which the net ad-vance is slow (yet steady). In the following we will analyzesome of the features of this style using the network informa-tion collected as indicated in Section .

Quarter Final Match: Paraguay-SpainSpain reached quarterfinals after winning 1-0 (a result thatwould repeat itself in all remaining games) against Por-tugal. While the result of that last game was tight (andwhether Villa, the scorer of the sole goal was in off side isopen to some question), from a global point of view Spainwas widely regarded as having dominated the game and de-servedly progressing to quarterfinals, the historically unsur-mountable barrier for the national team. The game was ap-proached by Paraguay (a strong opponent, as demonstratedby their being runner-ups in the Copa America 2011) from atactical premise: to disrupt the flow of the ball among Span-ish players. They succeeded at it for most of the game, withSpain hardly achieving more than 3-4 consecutive passes(see Figure 2) and only attaining a moderate pace of about6 passes per minute (see Figure 3). Although Spain even-tually settled to its usual combinative game (see clusteringcoefficients in Figure 4), the first half was very irregular forthe team, with no single player emerging as the most cen-tral (in terms of in-/out-degree), and with the game takingplace mostly in Spain’s defensive zone (see Figure 5). Thisdoes not mean Spain was necessarily under attack, but that

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Figure 2: Paraguay-Spain. Average number of consecutivepasses. In this figure and all subsequent ones, the white lineis the mean and the shadowed area covers one standard errorof the mean above and below the former.

Paraguay’s pressing avoided Spain’s settling the ball in of-fensive zones. The game’s turning point took place with twopenalty shoots failed successively by Paraguay and Spain(low valley shown in Figure 3–right), after which Paraguaylowered their defensive strength due to tiredness, and Spaincould finally enter into longer sequences of passes, withXavi Hernandez and Xabi Alonso emerging as dominantplayers in the midfield.

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Figure 4: Paraguay-Spain. Clustering rate.

Semi Final Match: Germany-Spain

The victory over Paraguay in the quarterfinals meant Spainadvanced for the first time in history to semifinals3, and ageneral consensus that the team had fulfilled the minimumexpectations of the supporters. This meant that the semifi-nal was approached as a game in which there was nothingto loose (although it must be noted that the same held forGermany, who had presented a very young team and a new,less physical style, and were in a transitional year). Maybedue to this lack of pressure, Spain put forward their best per-formance in all the tournament.

The game was thoroughly dominated by Spain, who man-aged to engage in a fast game (well above 6 passes perminute, the ceiling reached for the most part of the gameagainst Paraguay, cf. Figures 3 and 7) and more elabo-rated sequences of passes (roughly twice as much as againstParaguay, cf. Figures 2 and 6). It is interesting to note thatthe clustering coefficient was rather stable (the drop nearthe end of the 2nd half corresponds to the final push ex-erted by Germany once they were 1 goal down) and sim-ilar to that reached against Paraguay, which indicates theunderlying passing pattern was maintained in both games(but was more effective –i.e., resulted in longer sequencesof passes– against Germany). Xabi Alonso and speciallyXavi Hernandez during the 2nd half were the clear hubs ofthe game (Paraguay had partly succeeded in disabling thesetwo players, hence the more disconnected game). Actuallythe 2nd half saw the ball parked deeply inside the Germanfield (Figure 9–bottom, right), indicating a clear dominationof the game: sequences of up to 24 passes took place be-

3The previous best performance of Spain had been a 4th placein Brazil’s World Cup 1950, an edition in which there was a smallleague among the top-four teams.

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Figure 5: Paraguay-Spain. Zone-based player centrality.

fore Spain’s goal (which paradoxically was a header after acorner kick).

Final Match: Netherlands-SpainThe great final took place against the Netherlands, a verytalented team that featured one of the best players of the sea-son in the midfield (Wesley Sneijder, surprisingly left out ofthe Ballon d’Or contest) and possibly the fastest and finestwinger in the whole tournament (Arjen Robben). Indeed, theconnection among these two players could have very welltilted the balance in favor of Netherlands had not Iker Casil-las provided an outstanding performance. Despite the flairavailable on the pitch, the Dutch team engaged however ina very rough playing, forever iconized in Nigel de Jong’sominous tackle on Xabi Alonso’s chest4. To some extent thetactical objective was similar to that of Paraguay’s –disruptthe smooth flow of the ball in the Spanish team— yet the ex-ecution was much more questionable (and so was the conde-scending attitude of the referee towards it). The game settled

4http://l-g.me/deJong

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Figure 6: Germany-Spain. Average number of consecutivepasses.

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Figure 9: Germany-Spain. Zone-based player centrality.

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Figure 7: Germany-Spain. Passes per minute (right).

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Figure 8: Germany-Spain. Clustering rate.

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Figure 10: Netherlands-Spain. Average number of consecu-tive passes.

for the most part in a very slow pace due to the number of in-terruptions (Figure 12), and sequences of passes were gener-ally short, analogously to the game against Paraguay. Unlikethe latter game, the Dutch team succeeded in disabling XabiAlonso but the balance of the game followed a similar pat-tern in that the physical style of the Netherlands became lessconsistent as time passed. The midfield was eventually con-trolled by Xavi Hernandez and –despite two clear fast breaksby Robben mentioned before– the ball advanced away fromSpain’s defensive zone. The average number of consecu-tive passes gained momentum at the end of the 2nd half andwas rather stable during the 1st part of the extra time. Notsurprisingly, the pace of the game decreased steadily dur-ing the extra time (Figure 13), due to both the exhaustion ofthe players and the team strategy shifting to a safer mode ofholding possession in order to avoid conceding a late goaland trying to exploit a more direct approach when possible.Indeed, the clustering coefficient during the 2nd part of theextra time is much lower than in remaining games (Figure15), which is consistent with a less structured developmentof the game and larger distances between team lines. Thewhole extra time witnessed the emergence of Cesc Fabregas(Figure 17), coming from the bench to dominate the mid-field, providing eventually the assist that resulted in AndresIniesta’s decisive goal.

Conclusions and Future WorkThis work has attempted to use some simple graph and net-work metrics to analyze the performance and playing styleof the Spanish national football team in the World Cup 2010.When analyzed from a temporal perspective, global mea-sures such as the number of consecutive passes or the num-ber of passes per minute provide a measure of the success

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Figure 11: Netherlands-Spain. Average number of consecu-tive passes in the extra time.

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Figure 12: Netherlands-Spain. Passes per minute.

of the team in imposing its style (or alternatively the successof the opposing team in disrupting the style of the Spanishteam). A deeper insight is obtained by observing the clus-tering coefficient with captures the combinative nature of the“tiki-taka” style. While prone to a kind of baroqueness in thesense that many passes are often done in a short distance andmight be expendable, it is evident that it has prime value asa defensive strategy, by depriving the opponent of ball pos-session, one of the factors that is determinant in the gameoutcome (Lago and Martın, 2007; Bate, 1988). Even in thegames in which the performance of the Spanish team hasbeen deemed worse by analysts and the general public, thiscombinativeness imprint globally remains, with marked val-

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Figure 13: Netherlands-Spain. Passes per minute in the extratime.

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Figure 14: Netherlands-Spain. Clustering rate.

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Figure 17: Netherlands-Spain. Zone-based player centrality in the extra time. Please note that player number 6, is AndresIniesta, the one that scored the goal against the Netherlands (and which eventually led to victory).

leys in the aftermath of scoring a goal (and the subsequentpush of the trailing team) and sporadically during the final,a special game in which emotions and passion often led tolower precision and less elaborated game-play.

There is of course much work to be done. From the puremethodological point of view, it is of crucial importance tokeep analyzing other network measures such as node andedge betweenness, which naturally capture the hubs and es-sential associations in the distribution of the ball (let us noteen passant here as an example that the role of Sergio Bus-quets was initially much criticized by the press and a largepart of the public due to his lower creativeness; however itis clear that he was instrumental in providing balance to themidfield; uncovering his role in the circuit of distribution ofthe ball by means of an objective network metric would be offoremost interest). Other measures such as eigenvector cen-trality can be very valuable for this purpose. This can havenot just an explanatory value, but also a predictive value interms of identifying the weak points of the circuit that maybe targeted by the opponent. In addition to these method-

ological issues, it will be very interesting to deploy this kindof analysis on further data both at club level (e.g., to iden-tify similarities and dissimilarities between team an playerperformance in their clubs and in the national team), and atthe national team level such as in the upcoming Euro 2012in Poland and Ukraine or –looking beyond– the World Cup2014 of Brazil.

Acknowledgements

This work has been supported in part by the CEI BioTICGENIL (CEB09-0010) MICINN CEI Program (PYR-2010-13) project, the Andalusian Regional Government P08-TIC-03903, P08-TIC-03928, and TIC-6083 projects, andMICINN TIN2008-05941. We would like to acknowledgethe support of recently awarded project TIN2011-28627-C04-02 and TIN2011-28627-C04-01.

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12 half extra time

clu

ste

ring c

oeffic

ient

minute

Figure 15: Netherlands-Spain. Clustering rate in the extratime.

Appendix. Spain’s Squad NumbersReferences

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Dowie, J. (1982). Why Spain should win the world cup. NewScientist, 94(10):693–695.

Heuer, A., Mueller, C., and Rubner, O. (2010). Soccer: Is scoringgoals a predictable poissonian process? EPL (EurophysicsLetters), 89:38007.

Heuer, A. and Rubner, O. (2009). Fitness, chance, and myths:an objective view on soccer results. The European Phys-ical Journal B - Condensed Matter and Complex Systems,67:445–458. 10.1140/epjb/e2009-00024-8.

Lago, C. and Martın, R. (2007). Determinants of possession of theball in soccer. Journal of Sports Sciences, 25(9):969–974.

Onody, R. and de Castro, P. (2004). Complex network study ofbrazilian soccer players. Physical Review E, 70(3):037103.

# Name Position1 Iker Casillas Goalkeeper2 Raul Albiol Defender3 Gerard Pique Defender4 Carlos Marchena Defender5 Carles Puyol Defender6 Andres Iniesta Midfielder7 David Villa Forward8 Xavi Hernandez Midfielder9 Fernando Torres Forward10 Cesc Fabregas Midfielder11 Joan Capdevila Defender12 Vıctor Valdes Goalkeeper13 Juan Mata Forward14 Xabi Alonso Midfielder15 Sergio Ramos Defender16 Sergio Busquets Midfielder17 Alvaro Arbeloa Defender18 Pedro Rodrıguez Forward19 Fernando Llorente Forward20 Javi Martınez Midfielder21 David Silva Midfielder22 Jesus Navas Forward23 Jose Manuel Reina Goalkeeper