Searching for a Unique Style in Soccer

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Our methodol- Motif: ABAC ogy starts with the extraction of the passing sequences, i.e., 7.5 the order of players whom the ball traversed. Afterwards, we determine computationally the significance of the different k-pass-long motifs in the passing style of the teams. Our flow 5.0 motif profile focuses on how ball traverses within a team. We not only count the number of passes, but also check which 2.5 players are involved in, and how they organize the flow of Z score passes. Based on these computed flow motif profiles, we finally cluster the teams. To the best of our knowledge, our 0.0 study is the first of its kind that investigates motifs in soccer passing sequences. Our contribution is twofold: 1. we propose a method to quantify the motif characteristics of soccer teams based on their pass networks, Sevilla Getafe Málaga

and Levante Mallorca Valencia Osasuna Espanyol Barcelona Real Betis Real Madrid Granada CF Granada Celta de Vigo Athletic Bilbao Real Sociedad Real Zaragoza Real Valladolid Rayo Vallecano Rayo Atlético de Madrid

2. we identify similarities and disparities between teams Deportivo de La Coruña and leagues using the teams’ motif ﬁngerprints.

In the recent decade, several data-provider companies and Figure 1: The prevalence of the ABAC motif in websites have arisen to annotate soccer matches and to pub- case of the teams of the Spanish first division (me- lish soccer datasets. For example, such initiatives include dian, quartiles) with respect to their z-scores. FC Prozone [9], OptaPro [7], Instat Football [3], and Squawka [10], Barcelona applies the ABAC motif much more fre- among others. The prevalence of data-providers enables us quently than any other team in the league. to take a data-driven, quantitative approach to identify the styles of the soccer teams. We focus on the 2012/13 seasons of major European soccer leagues and analyze the passing where Tmax denotes the time threshold between two passes. strategies of the teams throughout the whole season. These constraints assure that the passes are consecutive (i.e., a player receives the ball and then passes it forward) and 2. METHODOLOGY not having major breaks. Throughout our study, we use Tmax = 5sec to determine if two passes are belonging to the The “flow motifs” of a pass network, in which players are same ball possession. linked via executed passes, consist of a given number of con- Third, we extract all the three-pass long sub-possessions secutive passes, namely, an ordered list of players who were from the ball possessions (e.g., a ball possession having n involved in the particular passes. Throughout this paper we passes contains n − 2 motifs) and convert the player identi- focus on motifs consisting of three consecutive passes, how- fiers into the appropriate A, B, C, and D labels to assemble ever, it is straightforward to generalize our methodology to the motifs. For example, a ball possession where the ball investigate motifs with fewer/more passes. Our methodol- moves between players as 2 → 4 → 5 → 6 → 4 → 6 trans- ogy relaxes the identity of the involved players, i.e., it does lates into three motifs, namely, ABCD, ABCA, and ABCB: not differentiate motifs based on the names of the players, rather focuses on the certain structure of the passes. There z ABCA}| { are five distinct motifs when we analyze three-pass long mo- 2 → 4 → 5 → 6 → 4 → 6 | {z } tifs: ABAB, ABAC, ABCA, ABCB, and ABCD. For exam- ABCD ple, the motif ABAB denotes the following pass sequence: After having the motifs that are present in the pass net- first, player 1 passes to player 2; second, player 2 passes work, we quantify the prevalence of the motifs by comparing the ball back to player 1; and finally, player 1 passes again the pass network of the team to random pass networks hav- to player 2. If a similar pass sequence happens between ing identical properties (in particular, the number of vertices player 3 and player 4 the identified motif is ABAB again and their degree distribution). Specifically, we perturb the (i.e., the crucial characteristic is what happened and not labels of the motifs prevalent in the original pass network between whom). randomly and such we create pseudo motif-distributions. In Our methodology quantifies the prevalence of the flow our data analyses, we generate 1000 random pass networks motifs in the pass networks compared to random networks for each original pass network. Finally, we compute the z- whose degree distribution is the same. To achieve this, we scores (a.k.a. standard scores) of the motifs by comparing start with a list of passes that a team made during a match. the original and the constructed random pass networks. As The format of a pass record is a result, we have a characteristic of the (passing) style of a pn =< playeri(n), playerj (n),t(n) > team for every match—in terms of the z-scores of the motifs. where playeri(n) passed the ball to playerj (n) in the t(n) time instance. Second, we derive all the ball possessions 3. DATA ANALYSIS AND RESULTS that a team had. A ball possession < p1,p2,,pn > consists We use publicly accessible information on the pass net- of such passes that fulfill two constraints: works of soccer teams. In particular, the dataset contains information from the 2012/13 seasons of the Spanish, Ital- playerj (m) = playeri(m + 1), ∀m ∈ {1,...,n − 1} ian, English, French, and German first division. For exam- t(m + 1) − t(m) ≤ Tmax, ∀m ∈ {1,...,n − 1} ple, the part of the dataset that contains information on the Motif: ABAB Motif: ABCA Motif: ABCB 7.5 4

10 2 5.0

0 2.5

Z score 5 Z score Z score

−2 0.0

0 Sevilla Sevilla Sevilla Getafe Getafe Getafe Málaga Málaga Málaga Levante Levante Levante Mallorca Mallorca Mallorca Valencia Valencia Valencia Osasuna Osasuna Osasuna Espanyol Espanyol Espanyol Barcelona Barcelona Barcelona Real Betis Real Betis Real Betis Real Madrid Real Madrid Real Madrid Granada CF Granada CF Granada CF Granada Celta de Vigo Celta de Vigo Celta de Vigo Athletic Bilbao Athletic Bilbao Athletic Bilbao Real Sociedad Real Sociedad Real Sociedad Real Zaragoza Real Zaragoza Real Zaragoza Real Valladolid Real Valladolid Real Valladolid Rayo Vallecano Rayo Vallecano Rayo Vallecano Rayo Atlético de Madrid Atlético de Madrid Atlético de Madrid Deportivo de La Coruña Deportivo de La Coruña Deportivo de La Coruña

Figure 3: Z-scores of the ABAB, ABCA, and ABCB motif in the Spanish league.

Motif: ABCD Atlético de Madrid

AthleticCelta Bilbao de Vigo 1.0 0.0

Málaga Espanyol −2.5 0.5 Mallorca

Z score −5.0 Real Madrid Deportivo de La Coruña Osasuna

PC2 0.0 Getafe −7.5 Valencia Real Betis Real Sociedad Sevilla Granada CF −10.0 −0.5 Levante Real Zaragoza Rayo Vallecano

−1.0 Barcelona Sevilla Getafe Málaga Levante Mallorca Valencia Osasuna Espanyol Barcelona Real Betis Real Madrid Granada CF Granada Real Valladolid Celta de Vigo Athletic Bilbao Real Sociedad Real Zaragoza Real Valladolid Rayo Vallecano Rayo Atlético de Madrid

Deportivo de La Coruña −7.5 −5.0 −2.5 0.0 2.5 PC1

Figure 2: FC Barcelona uses the ABCD motif less Figure 4: K-means clustering of the teams in the often than the other teams. Spanish league. One of the four clusters contains only a single team, namely, FC Barcelona that has an unique style based on its passing motifs. Spanish league spreads 20 teams, 380 matches, and more than 250 thousands of passes. We quantify the motif characteristics of the teams using the aforementioned dataset. team’s usage of motifs. We use the mean of the z-scores We first present results on the passing styles of teams in the of the five distinct motifs as the features (by averaging the Spanish first division and later on we compare our finding z-scores over 38 matches a team had in the season). After- with the other European leagues and teams. wards, we cluster the teams based on their five-motif long We compare the Spanish teams with respect to their ABAC feature vectors. We use two methods for cluster analysis: motifs in Figure 1. Most of the teams have similar z-scores, k-means and hierarchical clustering. We illustrate the result i.e., apply the ABAC pass motif to comparable extent. How- of the k-means clustering in Figure 4 (the clusters are color- ever, FC Barcelona has a quite distinct strategy: applies coded), where the ratio of the within the cluster and the ABAC motifs significantly more often than the other teams total sum of squares is 90.3%. For example, the cluster that (the difference is at least 2.5 standard deviation). The trend contains Atletico Madrid and Athletic Bilbao, among oth- is similar in case of the ABCD motif; the only difference ers, is characterized by extensive usage of ABAB and ABCA is that the majority of the teams have notably larger z- motifs. While most of the teams are clustered in three ma- scores than FC Barcelona (Figure 2). This means that FC jor groups, FC Barcelona is separated from the other teams. Barcelona applies this motif significantly less frequently than FC Barcelona is the only team in its cluster; hence, it has a the other teams. In general, FC Barcelona uses structured distinctive motif characteristics. motifs (i.e., motifs with more back and forth passes such as The Ward hierarchical clustering algorithm reveals simi- ABAB, ABAC, and ABCB) more often than simpler ones lar trend as shown in Figure 5. Again, FC Barcelona has a compared to other teams. We present the results of the solitary style while the other teams are having resembling remaining motifs in Figure 3. features. The implications of the two clustering schemes are consistent: FC Barcelona had a unique, significantly differ- We next analyze the similarities and the differences of the ent passing style than any other team in the Spanish league. teams’ motif characteristics via cluster analysis. First, for Finally, we take a broader point of view and investigate each team, we construct a feature vector representing the whether the style of FC Barcelona remains unique if we con- admpse u ahrhsapeie nl constructed finely precise, a has uncountabl shed of rather quantita- structure. consist results but Barcelona not Our passes FC does random tiki-taka of years. famous philosophy recent the unique the tively: in the success- seen on quite have light viable—and we also as is com- a ful, surprisingly, soccer to style, of us homogenous strategy apply allows unique to Althoug networks tend teams. pass teams different of most the styles the in anal- differentiate and motifs The pare the structures. passing of their ysis through teams soccer of CONCLUSIONS 5. moti and different motifs, the the in involvement of of players’ prevalence motifs impact the the the pass explore on study (iii) the games (ii) we away matches, condition work, the and (i) of home future results finer- areas: As the ways on reveal three based players. several to address and are motifs to teams There pass plan of of details investigation teams. grained the soccer extend of to motifs flow the WORK FUTURE of 4. usage frequent less of involves feature motif. it distinctive ABCA that The the is in leagues. strategy vast teams Italian Torino’s Juventus—dominant the and and from French diverse with Milan, the properties style Lille, shares a like and has teams teams relegated considered season, I the nearly the of team of majority style. Italian end distinct an the rare, Torino, FCat its that characteristics, maintain surprising pass to is their able in still variation Barcelona teams more more prin- using analyze have motifs we that their Although on analysis. We based component 6 leagues. cipal Figure soccer in teams European the additional show four of teams sider teams. the of groups not does major Barcelona any FC to soccer league. belong the Spanish of the clustering in hierarchical teams Ward 5: Figure 0 5 10 15 epooe uniaiemto oeaut h styles the evaluate to method quantitative a proposed We analyzin of potential the illustrate results presented The

Rayo Vallecano Granada CF Real Betis Mallorca Deportivo de La Coruña Getafe Real Valladolid Osasuna Levante Real Zaragoza Barcelona Málaga Real Madrid Athletic Bilbao Celta de Vigo Real Sociedad Sevilla Valencia Atlético de Madrid

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