positional data in football

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Universidade Trs-os-Montes e Alto Douro

Departamento de Cincias do Deasporto, Exercicio e SaudeMestrado em Cincias do Desporto, Ramo de Jogos

Desportivos Coletivos

POSITIONAL DATA IN

FOOTBALL PERFORMANCE

ANALYSIS

Vila Real, 2015

Hugo Ferreira, n51190


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Positional data in football performance analysis

2

INDEX

Positional Data in Football- A Short Review ....................................................... 3

Football as a complex dynamical system ........................................................... 4

Technological Advances .................................................................................... 7

Measuring Tactical Performance ........................................................................ 9

Physical Measuring through Positional Data .................................................... 18

Technical measuring ........................................................................................ 20

Conclusion........................................................................................................ 22

REFERENCES ................................................................................................. 22


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their analysis in other variables like the teams tactical behaviour, using

positional data.

Football as a complex dynamical system

A few years ago, sport scientists introduced a new approach that

contemplates and analyses sports competition based on principles of a

dynamical system. This point of view has been studied in the context of

individual sports (squash and tennis) and recently on team sports, more

specifically in basketball or football.

In a study of 2002, McGarry et al considered that complex spatial-

temporal patterns characterize a sports contest as a dynamical system instead

of the previous assumption of an equal weighting within a general system

description of sports performance analysis.

Starting with the presupposition that in team sports, each player on the

same team seeks to coordinate with his or her team members in the pursuit of a

common competitive goal, the author defended that sports competition is

characterized in a game rhythm that takes one of two forms where the ball

possession alternate equally (e.g. tennis, badminton, squash) or unequally (e.g.

hockey, basketball, soccer, rugby football). Hereupon, McGarry et al suggested

that the mathematical language of dynamical systems could provide the

understanding of these patterned behaviours.

McGarry et al described yet the perturbation as factor which can lead to

instability inside a dynamical system. Hughes (1998), described a perturbation

in football as an incident that changes the rhythmic flow of attacking and


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defending, leading to a shooting opportunity. For example, in a football match a

perturbation can be identified by a changing of pace, a penetration pass or a

dribble that allows to the attacking player a shooting opportunity. Finally,

McGarry et al launched the concept of intra and inter-coupling among the team

sports. They suggested that the varied and mixed patterns seen in a team

sports game are the result of self-organization among the players, presumed in

this case as the many coupled oscillators of the system. Intra-coupling refers to

the connection between two players from the same team and inter-coupling to

the connection between two players from opposing teams. Nevertheless,

individual sports incorporate a single inter-coupling between two opponents,

whereas team sports offer the possibility of multiple dyads comprising both

intra- and inter couplings. (McGarry cit. in J. Bourbousson et al., 2010).

There are already some combined variables which allow capturing and

analysing complex group and collective patterns of performance in sports. One

way of categorize such variable is by: team dispersion (stretch index, team

spread, surface area, team length per width ratio), team centre (centroids and

weighted centroids), team synchrony (relative phase, cluster phase), labor

division (dominant regions, heat maps, major ranges, player-to-locus distance),

and team communication networks (social networks), (Duarte Arajo et al.

2014).

Team centre

The stretch index is calculated by computing the average radial distance of all

players to their teams centroid. It can also be calculated according to the axis

expansion, providing distinct measures of dispersion in longitudinal and lateral


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directions. There are available some studies using this index. For example Yue,

Broich, Seifriz, and Mester (2008) analysed the dynamics of attacking and

defending in football by representing the intermittent expansion and contraction

patterns of competing teams. Team spread measures have been reported by

Moura, Martins, Anido, Barros, and Cunha (2012), who observed a counter-

phase relation between expansion in attack and contraction in defence, with

greater dispersion values when teams had ball possession. Clemente et al.

determined a weighted stretch index that accounted for the dispersion of

players in relation to the game centre containing the ball. They observed a

negative relationship between both teams stretch index values and lower

values of this variable without possession of the ball, compared to being in

possession of the ball, in seven-a-side, under-13 (years of age) football. It

seems that the expansion and contraction properties of a team are constrained

by proximity of players to the ball.

The effective playing space (or surface area) is defined by the smallest

polygonal area delimited by the peripheral players, containing all players in the

game. It can also provide information about the surface that is being effectively

covered by opposing teams, and informs how the occupation of space unfolds

throughout performance and how stretched both teams are on the field.

Team Synchrony

Several tools have been used to assess coordination between two

oscillatory units (e.g., the coupling of two centroids, or the phase relations of

two players in a dyad). For instance, the phase synchronization of two signals

has been previously studied in team sports through relative phase analysis


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(Bourbousson et al., 2010a, 2010b) and running correlations (Duarte et al.,

2012b; Frencken et al., 2013).

Technological Advances

Since the last decade of 20th century and the beginning of this century, the

world is assisting constantly to changes and developments in the technological

area. Sports in general and particularly football, as a global business have

taken advantage of these devices who allow capturing and processing data

from the games and training sessions, in real time. Position data of players and

athletes are widely used in sports performance analysis for measuring the

amounts of physical activities as well as for tactical assessments in game

sports. These technological advances are mostly based in GPS units, radio

frequency systems or semi-automated video tracking systems.

GPS system permits measurement of player position, velocity, and

movement patterns. The receivers worn by players during training and

competition draw on signals sent from at least four of the earth orbiting satellites

used in the GPS to locate their position. Also, provides scope for better

understanding of the specific and positional physiological demands of team

sport and can be used to design training programs that adequately prepare

athletes for competition with the aim of optimizing on-field performance

(Cummins et. al, 2013). A study of Cummins and colleagues, conducted a

systematic review of the depth and scope of reported GPS and micro

technology measures used within individual sports in order to present the

contemporary and emerging themes of GPS application within team sports.


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Researchers concluded that that GPS technology has been used more often

across a range of football codes than across other team sports. Work rate

pattern activities are most often reported, whilst impact data, which require the

use of micro technology sensors such as accelerometers, are least reported.

The main benefit of these measurement systems is movability and low-cost

price, when compared to other systems. However, the system operates only

outdoors and requires the attachment of portable devices, which are still not

allowed in official football competitions (Folgado et. al, 2014).

On the other hand, there are the radio frequency systems. One example

is the Local Positioning System. This system is based on the frequency-

modulated continuous wave principle, measuring the distance between fixed

base stations and mobile tags placed on the players and have been established

as an accurate and valid tool to record positions of players in outdoor and

indoor fields, providing accurate data in static and dynamic conditions at various

speeds (Leser, Baca, & Ogris, 2011). However, there some disadvantages in

using this technology, like the weakness of the radio signal and the number of

players' tracked (Mandeljc, Kovacic, Kristan, & Pers, 2013). Also this not

portable and players cant use it during official competitions.

Finally there is the semi-automated video tracking systems. This

technology uses multiple video cameras to provide players' tracking information

(Mandeljc et al., 2013). The computer vision cameras capture video and,

afterwards, several combined algorithms extract the positioning data from all

objects on the field. Then, the obtained data are converted into performance

variables. (Sampaio et. al, 2014). The players do not need to carry any device,

which allows using the technology during formal competitions. Nevertheless,


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there are difficulties in maintaining automatic tracking over longer periods, since

the players move quickly, unexpectedly change direction and collide with other

players (Needham & Boyle, 2001). Also, these systems are not easily portable

and have major costs associated. One example of a video tracking system is

Prozone which is a new computerised video system that allows the tracking of

many individuals performing a sporting activity. A study directed by Valter et. al

proved that represents a valid motion analysis system for analysing movement

patterns of footballers on a football pitch.

Measuring Tactical Performance

Tactical performance is perceived as the individual and collective

behaviours, emerging from the opposing sides interactions, while attempting to

gain advantage over the adversary, both attacking and defending (McGarry,

Anderson, Wallace, Hughes, & Franks, 2002) and its measure implies analyzing

individual players positions, taking into account their time and context. As

shown before, its possible now to measure the tactical performance of a team

and its players interactions during the game, using their positioning data.

Recent studies have focused their analyses in the: intra/inter team/player

interactions and to the density of relations established. This density can be

between two players (dyad), a small group of players (group, normally inside of

a team sector) or the relations of the whole team. Thus, we can analyse and

describe players and team performance from different levels of interactions

(micro, meso and macro).


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Micro Level Interactions

There are considerable studies analysing the interactions between

players when in 1x1 situations. Usually, the analysts focus their analyses in

factors like the distances between opponents players (attacker and defender),

as the relative and individual velocities between them and the distance to the

goal. For example R. Duarte et al. (2010), investigated the informational

constraints that influence the dynamics of 1v1 sub-phases in football, through

the analysis of interpersonal distance and relative velocity between the players,

based on the ecological dynamics approach. Although dyadic system

behaviours appear in an ecological exploratory process and cant be explained

entirely by only one control parameter, the results showed that attackers are

more successful on passing the defender when a higher difference of relative

velocity is achieved. In other study also performed by R. Duarte et al. (2012),

was made a research in order to examine whether interpersonal coordination

tendencies emerging between opposing players influenced the performance

outcomes of 1-vs-1 sub-phases of soccer, using relative phase calculations to

measure the phase relations of the minimum distance of each player to the end

line over the entire duration of each trial. The authors showed that while

successful outcomes for attackers were related to a high level of spatiotemporal

synchronisation between players, the success of the defenders was distinctly

associated with their ability to lead the relationship (i.e., the to-and-fro

movement displacements of defenders preceded the moves of the attacking

player). Thus, results suggest that trials in which performance was controlled by


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the defending players were associated with larger time delays in the phase

relations controlled by these players. Thus, it seems that the success of the

attacking players in destabilizing dyads was based on creating a tight coupling

with the defender. It implies that the dyadic interpersonal coordination

tendencies emerging in relation to the attacking players successful outcomes

were characterized by a higher level of irregularity (less periodicity). This higher

level of unpredictability seemed to be a key feature related to successful

attacking performance in the 1-vs-1 sub-phases of play. In contrast, the success

of the defenders seemed to be associated with higher levels of regularity and

predictability in the interpersonal coordination tendencies that emerged.

In other study developed this time in futsal games, L. Vilar et al. (2014) explored

the coordination patterns of attackers and defenders respecting key task

constraints on performance (e.g. locations of the goal and the ball), that enable

the creation/prevention of opportunities to score goals during team sports.

Distinctive patterns of movement coordination between a shooter, a closest

defender and the location of the ball were identified that managed to the

creation/prevention of opportunities to score goals. These required relationships

of an attacker with the defender and the goal were also shown to emerge before

an assisting player received the ball to create a shooting opportunity. Moreover,

results suggested that, even when the defender was not able to intercept the

balls trajectory, he might have constrained an attacker to shoot earlier than he

needed to, providing the goalkeeper with possible conditions to intercept the

shot at goal.


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Meso level interactions

When we refer to a meso level analysis, we consider the interaction between

groups of players, mostly in a small sided game situation. On this specific

situation, the research is focused on the teams centroid and its distance when

compared with opponent teams, teams' areas and/or teams' length and width.

For instance, a study of W. Frencken et al. (2011), studied centroid and surface

area measures to capture the collective behaviours of teams in 4 vs 4 small-

sided football games. They confirmed that measurement of team centroids

accurately captured the synchronized tendencies between opposing teams.

These investigators reported that the variable occupied surface area did not

seem to adequately describe the interaction between opposing teams during

competition. However, in some performance contexts there may be some intra-

team coordination trends for surface area in these sub-group relations over

time. In other words, observed variations in surface area may express intra

team coordination processes as a consequence of cooperative goal-directed

behaviours (e.g., a number of teammates coordinating together to create a

goal-scoring opportunity). Moreover, using a variable with only one dimension,

based on the forward-backward oscillations, presents a higher correlation

coefficient between teams centroid, and it is assumed that this is the most

dominant direction of play. Frias (2011) meant to analyze the influence of the

variation of the defensive play method on the players collective behaviour in a

six-a-side game (GK+5 v. 5+GK). The small sided games were performed in


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two experimental conditions (zone and man-to-man defense). The teams

behaviour was captured by four compound positional variables: surface area,

stretch index, lpwratio and teams centres distance. Concerning zone and man-

to-man defensive playing methods results showed that zone defense appeared

to be a more organized and less willing to opposing team initiatives. Also, lower

variability gives zone defense an economic character that can result important

in practice. The results clearly confirmed hypothesis that the defensive method

influenced teams collective behaviour. Sampaio and Mas, (2012) used

dynamic positional data of players to assess tactical behaviour by measuring

movement patterns and inter-player coordination. A pre and post-test design

was used to assess the effects of a 13-week constructivist and cognitivist

training program by measuring behaviour in a 5-a-side game. They used GPS

devices (SPI Pro, GPSports, Canberra, Australia) and analyzed with non-linear

signal processing methods. Approximate entropy values were lower in post-test

situations suggesting that these time series became more regular with

increasing expertise in football. Folgado et al. (2012) investigated how collective

behaviour varies with age (under 9, under 11 and under 13 years old) in

different small sided games formats (3vs3 and 4vs4). The collective behaviour

was measured by players field position ratio (lpwratio) to study team behaviour

variability and teams centroid distance to study their interaction within the game.

The results exhibited that team variable values were influenced by the age of

the players, as younger teams tend to present a higher value of lpwratio in their

dispersion on the pitch. The variability of this variable also showed a decrease

for teams with older players, indicating a more consistent application of the

width and concentration principles of play and reflecting a higher level of


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collective tactical behaviour. Match variable showed a larger centroid distance

for the older age groups in comparison with the younger players in the

GK+3x3+GK, while all age groups demonstrated similar large centroid

distances in the GK+4x4+GK game format. These results suggest that length

and width ratio and centroid distance are useful measures of tactical

performance in small-sided games in youth football- Furthermore, , Duarte,

Arajo, Freire, Folgado, Fernandes & Davids (2012), investigated how collective

behaviours emerge in 3 vs 3 football near the scoring zone by identifying

coordination tendencies for the centroid and surface area of each and

comparing team these group-motion variables in three key moments of play, to

understand their temporal evolution and clarify the intra- and inter-group

coordination tendencies developed by the two sub-groups. The results showed

that emerging coordination tendencies displayed a mainly symmetric pattern

between the centroid of the teams in all trials. Despite the fluctuations in

centroid displacement time-series, results showed that the average position of

both teams approached and moved away from a defensive line in a highly

coupled fashion as demonstrated by high positive correlation values. Equally,

analysis of the surface area of each team did not reveal a clear coordination

pattern between subgroups. But the difference in the occupied area between

the attacking and defending sub-groups significantly increased over time.

Silva et.al (2013) aimed to analyse the influence of field dimension and players

skill level on collective tactical behaviours during small sided and conditioned

games. Positioning and displacement data were collected using global

positioning system (15 Hz) during small sided and conditioned games (Gk+4 v.

4+Gk) played by two groups of participants. Team tactical performance was


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assessed through established dynamic team variables (effective playing space,

playing length per width ratio and team separateness) and nonlinear signal

processing techniques (sample entropy of distances to nearest opponents and

the teams centroids mutual information). They concluded that the effective

playing space and team separateness increased significantly with pitch size

regardless of participant skill level. Furthermore, small sided and conditioned

games played on fields of different dimensions clearly constrained different

interpersonal interactive behaviours in players of distinct skill levels. Increases

in field dimensions promoted similar larger playing areas and similar larger

distances between direct opponents in both groups. In fact, the more skilled

players presented higher unpredictable values of distances to immediate

opponents, which was interpreted as a strategy for creating space and avoid

close marking. Sampaio et al. (2013) studied the heart rate, time-motion

characteristics and players tactical behaviour according to game status, team

unbalance (winning and losing when in superiority and inferiority) and the pace

of the game (slow, normal or fast) in football 5-a-side small sided games. They

used a GPS system to measure positioning data. The results showed that when

inferiority and winning, teams revealed more distance covered, more % heart

rate (majority in > 90% HRmax) and more distance to the team centroid.

However, when in superiority and losing team showed more distance covered,

more % heart rate (majority between 75-84% HRmax). When winning and in

superiority, team only showed more distance to team centroid. This research

brought new findings and insights to be applied on the training sessions like For

instance, positioning variables such as distance and randomness to centroid


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were more accurate in discriminating the constraints and need to be considered

when planning and monitoring performance.

More recently, another research was developed in order to better

understand football tactical behaviour in a meso level. Travassos et. al (2014),

investigated how the change on targets information modifies teams tactical

behaviour during football small-sided games. To measure that information, they

played two 5 vs 5 games where one of them had 2 official targets with

goalkeepers and the other, 6 small targets. A GPS system was used do capture

all positioning data like the distance between the centres of gravity (CG) of

teams, the stretch index and the relative stretch index. Results showed a

moderate increase on the distance between the centre gravity of each team and

a small decrease on the stretch index and on the relative stretch index from 2

targets to the 6 targets games. It was also identified that pitch location affected

the interaction between teams. Also, an increase on the time that teams

displayed on lateral corridors and defensive sectors were observed on the 6

scoring targets in comparison with the 2 scoring targets small sided game.

Macro level interactions

There are some studies made in the macro level, where the interactions

between player-team and team-team are described, concerning new insights in

football tactical behaviour.

For instance, Duarte and colleagues (2012) investigated changes in the

complexity (magnitude and structure of variability) of the collective behaviours

of association football teams during competitive performance. By using a


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prozone tracking system, researchers measured five compound positioning

variables such as the surface area, stretch index, team length, team width and

geometrical centre during an English premier league football match.

With the results it was possible to take some conclusions possible to see

that while the home team tended to show patterns of collective behaviour

characterised by high levels of depth, the visiting team exhibited patterns of

behaviour in which the lateral spread (width) was predominant in the first two

time periods of the match. By this researchers can achieve that visiting team

could have exploited the lateral spaces of the field more greatly, probably

pointing for a predominant lateral circulation of the ball. On the contrary, the

home team could have exploited more the spaces created by the increase in

depth, and possibly, their passing sequences might were short and related with

a direct playing style. The surface area and stretch index measures showed

alike patterns in their variations, indicating that both compound variables share

a similar nature at the 11vs11 level of analysis. However, there were key

events (goals) changing the tactical behaviours during the game, which appear

to be essential constraining the appearance of collective patterns of

performance.

Following the same researching line, Frencken et. al (2012) investigated

how inter-team distance dynamics correspond to match events through

continuous analysis of variability. As a method, researchers collected position

data from the Amisco system and determined periods of high variability in the

distance between the teams centroid positions longitudinally and laterally in an

international-standard soccer match and evaluated corresponding match

events. Investigators hypothesized that periods of high variability in inter-team


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distance would indicate critical match periods like goal-scoring opportunities or

goals, but contrary to their predictions, results showed that periods of highly

variable inter-team distance were associated with collective defensive actions

and team reorganisation in dead-ball moments rather than goals or goal

attempts.

Physical Measuring through Positional Data

As referred before, studies try to describe physical profiles of football

players.

As an example, Gonalves et al. (2013), identify differences in time

motion, modified training impulse, body load and movement behaviour between

defenders, midfielders and forwards, during an 11-a-side simulated football

game. During two periods of 25 minutes of a simulated football match, the

investigators used a GPS system to measure distance covered by the players

heart rate values and position variables. The results showed that the total

distance covered during the game was similar for all players positions.

However, forwards spent less time in 93%HR comparing to defenders and

midfielders. Also, forwards exhibited lower body load values. About the

positional data, results indicated that all players (defenders, midfielders and

forwards) were nearer and more coordinated with their own position- specific

centroid. Moreover, all players dynamical positioning showed more irregularity


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when related to the forwards centroid, as a consequence of their need to be

less predictable when playing.

Recently Folgado, Duarte, Fernandes & Sampaio J (2014), aimed to

quantify the time-motion demands and intra-team movement synchronization

tendencies during the pre-season of a professional soccer team, according to

the opponent levels. Positional data from 20 players were captured during the

first half of six pre-season matches of a Portuguese first league team. Time

motion demands were measured by the total distance covered and distance

covered at different speed categories. Intra-team coordination was measured by

calculating the relative phase of all pairs of outfield players. Outcomes showed

that there were no differences in total distance covered per opposition levels,

while matches opposing teams of superior level revealed more distance

covered at very high intensity. Playing against superior level teams implied

more time in synchronized behaviour for the overall displacements and

displacements at higher intensities. Also, the results suggest that reducing the

opponent level tends to lower the requested movement synchronization.

Therefore playing against higher-level opponents (1st league teams) may

increase time motion demands at high intensities in tandem with intra-team

movement synchronization tendencies. Thus, teams should be aware that

playing against opponents of lower levels might not present sufficient stimulus

for tactical and physical development and may be prejudicial during the

competing season.

Recent studies tend to study physical demands during an overloaded

period of a football season. Folgado et. al developed a study in order to

examine the physical and tactical performances of a professional football team


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under congested and on congested fixture periods. Six home matches of an

English professional football team were analysed during competitive season (3

matches distancing three days from the previous fixture and 3 matches

distancing six or more days from the previous fixture). Players physical

performances were measured by the total distance covered and distance

covered at different speed categories. Tactical performances were measured by

the percentage of time of players movement synchronization of lateral and

longitudinal displacements. Results showed that that there was no difference in

physical performance between congested and non-congested periods, although

players did spend more time synchronized during the non-congested fixtures.

Given that players cover the same amount of distance at similar intensities in

both fixture distributions, the reduction in synchronization during congested

periods could be associated with adaptations due to the perception of fatigue.

Technical measuring

There are also some studies from researchers, trying to describe and

analyse the players technical behaviour, during elite football matches. For

example, Rampinini et. al (2007), examined the changes in technical and

physical performance between the first and second half during official matches

of Italian Serie A league and compared the technical and physical performance

of the players of the more successful teams (ranked in the first 5 positions) with

the players of the less successful teams (ranked in the last 5 positions) from the

same league. By measuring the total distance covered, high intensity running

distance, very high-intensity running distance, total distance with the ball, high-


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intensity running distance with the ball, very high-intensity running distance with

the ball and the number of skill involvements, they showed that the players from

the more successful teams covered greater total distance with the ball and high-

intensity running distance with the ball and also had more involvements with the

ball, completed more short passes, successful short passes, tackles, dribbling,

shots and shots on target compared to the less successful teams between the

first and second half was found for both physical performance and some

technical scores (involvements with the ball, short passes and successful short

passes). This study showed a decline in technical and physical performance

between the first and second half, and that both physical performance and

technical skills were different between players from more successful and less

successful teams.

Also, Bradley and colleagues (2014), studied the influence of situational

variables on ball possession in elite soccer and quantified the variables that

discriminate between high or low percentage ball possession teams across

different playing positions. Results showed that Playing against weak opposition

was associated with an increase in time spent in possession while playing away

decreased the time spent in possession was increased when losing than

winning or drawing. Finally, the better the ranking of a team, the higher the time

spent in possession. The results also demonstrates that high percentage ball

possession teams and low percentage ball possession teams developed

different possession strategies during matches and that selected variables such

as successful passes were identified to explain these data trends across

various playing positions.


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Conclusion

As we could see, positional data appears to be a new important and relevant

tool to understand teamstactical behaviour. Coaches and football players have

now one more way to improve their training method, by monitoring it in real

time. Also, positional data can be captured during official competitions, which

allow to coach staffs analyse their game performance.

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