1. sports
2. football

Detection of real-time patterns
in sports interactions in football
Gudberg K. Jonsson, Sigridur H. Bjarkadottir, Baldvin Gislason,
Andrew Borrie et Magnus S. Magnusson
Introduction
Football is a challenging research domain. Each match involves 22 players
dem onstrating collaborati ve behavi our that requires specifi c roles in an
adversarial, uncertain, and dynamic environment. The behaviour of players
and the decision making processes can range from the most simple reactive
behaviours, such as running towards the ball, to complex reasoning that take
into account the behaviour and perceived strategies of team-mates and
opponents (Jonsson, 1998).
In the pursuit of generating quantitative information on performance sport
researchers have traditionally used frequency of event occurrence as their
index of performance e.g. the analyst has recorded how many passes have
been made from particular playing zones or how many times possession has
been lost (Jonsson et al., 2000). In essence the analyst has been answering
the question "how many times did ’x’ occur?". However frequency of event
occurrence has been shown to be an inadequate index of performance that
cannot di ff erenti at e bet ween effect ive perform ances (Borrie and Jones,
1998). I f one accepts the argument that sport performance consists of a
complex series of interrelationships between a vast array of performance
variabl es then simpl e frequency data can only ever provi de a rel ativel y
superficial view of performance.
If performance analysis is to continue to advance understanding of sports
performance then it must find better methods of collecting and analyzing
match analysis data. The purpose of this paper is to introduce and explain a
new data analysis method that has the potential to make a significant contribution to analyses of sports performance. Data from preliminary studies of
football performance are also presented to show the potential outcome from
the analysis process.
T-pattern detection and analysis
The a nalysis approach present ed i s based on a p rocess known as Tpattern detection which allows the detection of the temporal and sequential
structure of a data set. The method has been developed, outside of sport, on
the assumption that complex streams of human behaviour have a temporal/
sequential structure than cannot be fully detected through unaided observa t i o n o r w i t h t h e h el p of s t an d ar d st a t i s t i c al a nd be h av i o ur a n al y si s
methods. Given that observational records of human behaviour, including
sport performance analysis, have both a temporal and sequential structure
an analysis tool that can describe this structure will enhance understanding
of the behaviour (s) being studied. A generic observational software package
called Theme has been specifically developed to operationalise T-pattern
detection as an analysis process (Magnusson, 1996, 2000).
37
Figure 1:
Schematic representation
of a T-pattern viewed
within a normal data string
and as it appears
in isolation.
Présentation schématique
d’un T-pattern dans une
séquence normale
et lorsqu’il est isolé.
L’axe représente le temps.
a, b, c, d, k et w sont
des évènements-types.
A schemat ic representat ion of a T-patt ern i s shown i n fi gure 1. If one
assumes that the letters in line 1 correspond to specific performance events
(e.g. pass, tackle and shot in football) that appear on the line in proportion to
the time of their occurrence t hen line 1 i s a visual representat ion of t he
temporal structure of a sports performance.
W it hin t he upper l ine t here are four events (a, b, c, d) that occur in a
regul ar t em poral pat t ern howe ver t he pat te rn has been m asked by th e
surrounding, more random, occurrence of the events w and k. If a performance analyst or coach were simply visually inspecting the data string it is
unlikely that the pattern would have been detected. The T-pattern analysis
w o ul d ha v e i d e nt i f i e d t h e pa t t e r n be c a u se o f i ts c o n si st e nt t e m p o ra l
structure. The T-pattern detection algorithms allow an analyst to separate out
randomly occurring events from temporal patterns even when the random
events occur in between elements of the pattern.
A T-pattern is essentially a combination of events where the events occur
in the same order with the consecutive time distances between consecutive
pattern components remaining relatively invariant with respect to an expectation assuming, as a null hypothesis, that each component is independently
and randomly distributed over time. As stated by Magnusson ’that is, if A is
an earlier and B a later component of the same recurring T-pattern then after
an occurrence of A at t, there is an interval that tends to contain at least one
occurrence of B more often than would be expected by chance’ (Magnusson,
2000, p. 94). The t emporal relationship between A and B is def ined as a
critical interval and this concept lies at the centre of the pattern detection
algorithms.
The pattern detection algorithms can analyze both ordinal and temporal
data however, for the algorithms to generate the most meaningful analyses
the raw data must be time coded i.e. an event must be coded according to
time of occurrence as well as event type. The coding of many event-types
and corresponding times results in the type of data set shown in figure 2.
This figure displays a behaviour record from the second half of a club football
match and consists of 250 series of occurrence times (one for each coded
event type) ordered according to their first occurrence time.
38
Gudberg K. Jonsson, Sigridur H. Bjarkadottir, Baldvin Gislason, Andrew Borrie et Magnus S. Magnusson
Figure 2:
A time series behaviour
record from the second half
of a football match from
the European Champions
League 1997.
The match was coded from
a digitized video recording
of approximately
45 minutes duration
(time is in seconds).
Exemples de séries
temporelles des
comportements enregistrés
lors de la seconde
mi-temps d’un match de
football. Le temps est
exprimé en secondes et
la séquence totale est de
45 minutes environ.
Only limited aspects of T-pattern detection has been presented here to give
some insight in to the theoretical base of the process. A complete explanation of the theoretical roots of the pattern-detection algorithms together
wi t h an o ve rvi e w of t h e w i de r u se o f t he p roc es s ha s be en p re sen t ed
elsewhere (Magnusson, 1996, 2000).
Method
A team sport, football, has been analyzed with the intention of identifying
whether T-pattern detection has relevance as an analytical method within
performance analysis. The research utilized multiple game analysis with
each game being treated as a single case. Twenty football matches, thirteen
club and seven international matches, were coded using a combination of the
f o o t b a l l m a t c h a na l y si s sy s t e m d e ve l o pe d a t L i v e rp o o l J o h n M o o r e s
University and ThemeCoder, enabling detailed coding of digitized video files
(25 frames per sec.). Coding included data on pi tch posit ion, pl ayer and
match events. Pitch position was classified according to the pitch divisions
shown in figure 3. The primary event categories for data collection were:
pass; tackle; header; run; dri bbl e; clearance; shot ; cross; set -pl ay; lost
control; foul. Additional qualifying statements could be tagged to each event
category. All data was analyzed using the Theme software package.
Figure 3:
A schematic
representation of the zones
identified for analysis
of ball movement
within football.
Présentation schématique
des zones pour l’analyse
des déplacements
du ballon.
Direction of play
Detection of real-time patterns in sports interactions in football
39
Results and discussion
The data show that a high number of temporal patterns exist in football.
The number, frequency and complexity of the detected patterns, indicates
that sport behaviour is very structured. This synchrony was found to exist on
different levels, with highly complex time structures that extended over considerable t ime spans wi thin perform ances wi th pat terns occurring in bot h
cyclical and acyclical fashion.
The data taken from football show three discrete examples that identify
within-team patterns (e.g. figure 4, ball movement) and interactive patterns
involving both teams (figures 5 and 6, goal scoring).
Figures 4a et 4b:
A temporal pattern relating
to attacking movement
of the ball through
the centre of the pitch.
Exemple d’un T-pattern
relatif à un mouvement
d’attaque depuis le centre
du terrain.
a. Data output from Theme
analysis software showing
temporal and hierarchical
representation of
a T-pattern. The three
boxes in this figure involve
the same observation
period and the T-pattern
relates to ball movement in
the centre of the pitch
within in a football match.
a. Données obtenues avec le logiciel Theme montrant une représentation temporelle et
hiérarchique d’un T-pattern. Les 3 cadres de la figure concernent la même période
d’observation et le T-pattern correspond au déplacement
du ballon sur le terrain.
b. Schematic
representation
of the same data.
1. Player A receives
the ball in Zone 8, passes
the ball to a team mate
and runs forward.
2. Player A receives
the ball in Zone 11, passes
the ball to a team mate
and runs forward.
3. Player A receives
the ball in Zone 14, passes
the ball to a team mate in
Zone 15 (4).
b. Représentation schématique des mêmes données. 1. Le joueur A reçoit le ballon en zone
8, passe le ballon à un équipier et court. 2. Le joueur A reçoit le ballon en zone 11,
passe le ballon à un équipier et court. 3. Le joueur A reçoit le ballon en zone 14,
passe le ballon à un équipier dans la zone 15 (4).
40
Gudberg K. Jonsson, Sigridur H. Bjarkadottir, Baldvin Gislason, Andrew Borrie et Magnus S. Magnusson
A typical within-team event pattern from the football analysis is shown in
figures 4a and 4b. This figure displays a detected T-pattern that occurred
three times during the first half of a European Championship qualifying match
(1998). The three boxes in figure 4a show the same observation period. The
upper-left box shows the hierarchical construction of the pattern. The tree
structure identifies the simple patterns on it’s right hand edge and, as the
tree builds towards the left edge of the box, shows how the simple patterns
are linked together to form the more complex pattern. The upper-right box
displays the time point of each event-type in the pattern and their pattern
connection based on the critical interval relationship between their occurrence series. The bottom box shows the pattern, as a hierarchical structure,
expressed in relation to the observation period i.e. when it occurred during
the match (only complete patterns are shown in this box).
The pattern describes how player A moves the ball towards the opponents
goal by receiving the ball in, and then passing it out of, pitch zones 8, 11 and
then 14 consecutively. Player A then completes the sequence by passing it
on to player B who receives it in zone 15. The pattern describes an attacking
movement through the middle of the pitch, which opponents would clearly
wi sh t o preven t . Tradi t i ona l f requ ency an al ysi s o f passi ng w oul d have
identified the ball reception and subsequent pass from each zone as discrete
events but would not have linked the consecutive actions in the four zones.
The movement from zone 11 to 14 also occurred on another five occasions
during the first half (figure 4a, upper right box) further suggesting that player
A was worki ng eff ect ivel y through the cent ral channel of the pit ch. Thi s
integrated form of analysis would potentially enhance the information given
to the coach.
Figure 5:
1. BCL - Rivaldo
passes the ball;
2. PSV - Faber
intercepts the ball;
3. BCL - Sergi
passes the ball;
4. PSV - Jonk
makes a bad pass;
5. BCL - Dugarry
successful individual act;
6. BCL - Dugarry
attempts a shoot, saved;
7. BCL - Sergi
intercepts the ball;
8. PSV - Jonk
takes a free-kick;
9. BCL - Luis Enrique
scores a goal
(two occurrences, p<.005).
1. BCL - Rinaldo passe le ballon ; 2. PSV - Faber intercepte le ballon ;
3. BCL - Sergi passe le ballon ; 4. PSV - Jonk fait une mauvaise passe ;
5. BCL - Dugarry réussit individuellement ; 6 - BCL - Dugarry tente un tir, sauve ;
7. BCL - Sergi intercepte le ballon. 8. - PSV - Jonk tire un coup franc ;
9. BCL - Luis Enrique marque un but (2 occurrences, p < 0,005).
Figures 5 and 6 show examples of detected T-patterns from European Cup
match between PSV Eindhoven and Barcelona, which finished with a 2-2
draw. An analysis of the match revealed patterns including all four goals that
were scored, Barcelona goals in figure 5 and PSV goals in figure 6. The data
shows the capacity of the analysis process to identify longer, more complex
Detection of real-time patterns in sports interactions in football
41
patterns that involve extensive interactions between opposing teams. There
is a cl ear consistent t emporal pat tern preceding al l goals scored in t hi s
match. The patterns cover an extended period of time within which the two
teams exchange possession on at least two occasions. The length of the
time periods between the events f orming the pattern was such that other
match events will have occurred between pattern events. The length and
nature of the patterns is such that one could question whether, given the
extended time period of the patterns and the changes of possession, the
pa tt erns e vents were ca usal ly rel at ed. H oweve r th e consi st ency in th e
temporal pattern preceding each goal is quite clear. In this case the inform at i o n p ro vi d ed by t he T-pa t t e rn a na l ys i s ra i se s q ue st i o n s ab ou t t h e
relationship between events that are spread over an extended time period.
This doesn’t provide a coach with clear and simple answers about the nature
of the goals scored but without the analysis the potential significance of the
relationships between events would not have been considered. This analysis
may therefore prompt a coach to revisit the video footage of passages of play
to identify causally linked elements of performance that might have been
missed had the temporal pattern not been identified.
Figure 6:
Pattern 3:
1. PSV - Petrovic
passes the ball;
2. BCL - Hesp,
keeper throws in the ball;
3. PSV - Vampeta
passes the ball;
4. BCL - Luis Enrique
makes a bad pass;
5. BCL - Dugarry
attempts a shoot, saved;
6. PSV - scores a goal
(two occurrences, p<.005).
1. PSV - Petrovic passe le ballon ; 2. BCL - Hesp, le gardien, lance la balle ;
3. PSV - Vampeta passe le ballon. 4. BCL - Luis Enrique fait une mauvaise passe.
5. BCL - Dugarry tente un tir et sauve ; 6. PSV marque un but (2 occurrences, p < 0,005).
In addition to immediate analysis of individual matches the data were also
used to look at two additional issues relating to structure within team performance. The first issue investigated related to the potential interrelationship
between performance rating by coaches and the degree of structure in team
p er f o rm a n ce . T h re e e xp e ri e n ce d f o o t b al l c oa c he s a nd f i v e a m at e ur s
observed several club and international matches and were asked to rate the
performance of every player (on both teams) on a simple ten point Likert type
scale. For each coach the player ratings for a specific team were averaged to
give a team performance rating. Team performance ratings were then correlated (Pearson product-moment correlation) against the number of patterns
exhibited by each team. The data (cf. figure 7) show that the coaches’ ratings
of team performance were significantly correlated to the number of patterns
ide nti f ied for each te am (r=0. 81, p<0. 05). Lower correlat i on was foun d
bet wee n t he ama te urs rat in gs of t ea m perfo rm ance and t he n umbe r of
patterns identified for each team (r=0.53, p<0.05).
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Gudberg K. Jonsson, Sigridur H. Bjarkadottir, Baldvin Gislason, Andrew Borrie et Magnus S. Magnusson
The link between performance rating and pattern participation suggests
that coaches were recognizing, albeit at a potentially subconscious level, the
structure within a team’s play. However the traditional rationale for performance anal ysis i s that coaches cannot observe and remember discrete
events within critical event sequences (Franks and Miller, 1986). Yet, in this
sample, the fact that coach performance ratings were correlated with pattern
participation suggests that coaches were perceiving information about the
interrelationships between events. This finding also warrants further investigation since it relates to such a fundamental foundation in the performance
analysis literature.
Figure 7:
Correlation between team
pattern participation and
coaches and novice
subjective ratings (p>0.05).
Correlation with Pattern
Participation
1
Corrélation entre
les patrons de participation
et la valeur subjective
donnée par des
entraîneurs et des novices.
0,8
0,6
0,4
0,2
0
Coaches
Novice
Ratings
Th e secon d i ss ue cons id ered wa s t he co mpara ti ve l eve l of t e mp oral
structure within club and international football matches. I n a simple data
manipulation three randomly selected club and three international matches
were compared in t erms of th e mean num ber of pat terns and t he mean
number of patt ern occurrences ident if i ed in each ma tch t ype. The data
(cf. figure 8) show that international football has a more defined temporal
st ructure t han club football . This finding may be due to the presence of
higher technical abilities in international footballers which help create a more
structured game or, alternatively, contextual differences between club and
international football e.g. club football is played at a higher pace throughout
mitigating against the development of structure within the game. Whatever
the reason the clear difference in temporal structure between club and international football merits further investigation.
Figure 8:
Nombre d’évènements,
nombre de types de
patrons différents et
nombre d’occurrences de
patrons dans des matchs
de club et nationaux
(p > 0,001).
6000
5000
4000
Number
Number of events,
different pattern types and
pattern occurrences
between national team
matches and club matches.
All differences are
significant at p>0.001.
National Matches
3000
Club Matches
2000
1000
0
Events
N. dif. Pat.
Pattern occ.
Detection of real-time patterns in sports interactions in football
43
Discussion
The number, frequency and complexity of detected patterns indicates that
the behaviour of football players is more synchronized than the human eye
can detect. This synchrony was found to exist on different levels, with highly
complex time structures that extended over considerable time spans, often in
a cyclical fashion, as well as less complex patterns with a shorter time span.
Synchrony of this kind was found to correlate hi ghly wi th assessment of
performance. A stronger correlation was also discovered between performance assessments of professional coaches and team pattern participation
than between assessments of amateurs and team participation in patterns.
National matches were found to be more structured than club matches.
The results show that pattern analysis can be used to track elements in the
game such as timing of events, the passing of the ball, team structure etc. in
a novel way, indicating that pattern analysis is useful in enhancing existing
methods used in football analysis. New kinds of profiles, for both individuals
and teams, can be discovered using the detected behavioural patterns in
combination with elementary statistics. Moreover, some answers are already
suggested to questions, such as: Are there certain patterns that are related
to doing well or bad? What responses seem to be evoked by certain actions
or sequences of actions? Coaches could use this kind of structural inf ormation when selecting players or when searching for the opponent’s "weak
spots".
The preliminary data highlights the potential for T-pattern analysis to make
a significant contribution to sport performance analysis. Current analytical
methods that focus on simple frequency analysis cannot identify the temporal
patterns within a sports performance. Consequentl y wit hout this form of
analysis meaningful information is not being made available to the coach. If
this information is not available then it possible that performance is not being
optimized.
The data also point t owards the need t o invest igat e the potent ial li nk
between temporal structure in sport performance and the understanding of
performance being generated by coach observations. The data suggest that
whilst coaches may not be able to accurately recall discrete events they do
perceive inter-relationships between events. This analysis approach can
assist in generating a greater understanding of coach knowledge
construction.
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