JFA Platform for Football Analysis

Fábio

Silva

Pedro

Passos

and

Octavian

Postolache

ISCTE-Instituto

Universitário

Lisboa, Lisboa, Portugal

CIPER, Faculdade de Motricidade Humana, Universidade de Lisboa,

Lisboa,

Portugal

Keywords: Football, Performance Analysis, Collective Behavior, Interpersonal Distances.

Abstract: The aim of the article was to present a Java Platform for Football Analysis, designed and implemented

for

football game analysis. The framework presents capability on game analysis based on players

coordinates

inputs and helping coaches and players to extract information from processed games. The

analysis tools to

achieve this aim used collective metrics based on player’s positioning on the three team

sectors: defensive;

middle and; offensive. This allows analyzed player’s collective behaviors before critical

situations (e.g.,

shot on goal). Data revealed that a decrease on interpersonal distances between the defensive

sector and the offensive sector affords an opportunity to score.

INTRODUCTION

Football has a large impact in our society. Starting

with the media to entrepreneurs, also going through

all the supporters who weekly fill football stadiums

around the world. This phenomenon leads to the in-

crease of frameworks linked to football. These frame-

works can range from the simple application, which

allows watching the games and results in real time,

to the more complex ones that collect data, process

data and calculate game statistics, such as team ball

possession, individual players distance traveled or in-

dividual player’s number of passes performed.

One issue that hasn’t been fully explored is the

player’s collective behavior during a match. The idea

of this kind of analysis is to find out, collective met-

rics which accurately describe the interactive behav-

ior of a set of players. Therefore, the aim of this

study was to create an user friendly platform where

performance analysts and coaches can easily analyze

player’s collective behaviors, usually associated to

tactical performance.

Recent studies have been create and developed

several coordinative variables aiming to analyze col-

lective performance in team sports (McGarry et al.,

2002) (Frencken et al., 2012) (Vilar et al., 2012). As

an exploratory work, for our platform we decided to

use players interpersonal distances as the coordinative

variable which describes player’s interactive behav-

ior (Duarte et al., 2012). This variable allows us to

collect data of intrateam and interteam collective be-

haviors. One main issue of our work was to relate the

behavior of this collective variable with player’s team

performance. Thus our platform provide the follow-

ing options: i) uploading data games files (from video

cameras or GPS); ii) analyze data directly on the plat-

form; iii) export data in a csv format; iv) select the

game time window. All this in just one click away

and totally user-friendly. Therefore, the main goal of

this study is to develop an analysis platform in web

browser environment capable of supporting sports an-

alysts and coaches on decision making and team per-

formance analysis.

RELATED WORK

This platform increases the ease of access of data

analysis. The main goal to achieve with this platform

is that team’s sports analysts or simple practition-

ers which have access to any data collection device

(e.g., GPS) may upload data and analyze team col-

lective performance. Moreover as previously stated

this platform provides data mainly focus on collective

behavior analysis, for instance the interpersonal dis-

tance between a team defensive ‘line’ and the opposi-

tion team offensive ‘line’. This goes beyond the most

common notational analysis mainly focus on the fre-

quency os passes performed or distances traveled by

the players.

Silva, F., Passos, P. and Postolache, O..

JFA Platform for Football Analysis.

In Proceedings of the 3rd International Congress on Sport Sciences Research and Technology Support (icSPORTS 2015), pages 345-351

ISBN: 978-989-758-159-5

345

2.1

Sports Platforms

Most of the platforms that are currently on the market

have its main focus on the notation of each individual

player technical skills and/or physical abilities.

Inmotio (Inmotio, 2015) is one of those platforms.

Its main modules support three main areas: i) phys-

ical analysis; ii) injury prevention; and iii) tactical

analysis. The first two modules are used by various

teams to improve physical conditioning, player’s in-

dividual performance and even to help in the player’s

injury recovery. In the tactical analysis, we see some

metrics like: i) Transition moments; ii) ball posses-

sion statistics of both teams; iii) the number of passes

when there was ball possession; iv) the duration of the

ball possession; v) pass options to detect and visualize

the pass options of a player during a game.

Data collection is done by technology LPM (Local

Position Measurement), which is based on a wireless

communication system. It’s a sort of technology is

already very used, but requires players to use a trans-

mitter to communicate their position at every moment

of the game to the base station, how we can see in

Figure 1. The position calculation is performed au-

tomatically and in real time (live) and therefore the

measured data can be analyzed even during the mea-

surements.

Figure 1: LPM technology (Adapted from (Inmotio,

2015)).

Viper Software (Technologies, 2015) is another

platform that uses this type of data collection. It has

a similar approach to Inmotio, however it has a mod-

ule of tactical analysis rather interesting for selecting

blocks of players and interpret their collective perfor-

mance in critical moments. One of drawback of this

platform is that it doesn’t allow us to visualize the

ball’s position at every moment.

With the players is easy to have their position

through the transmitter, but the ball position implies

the usage of transceiver attached to the ball. By video

observation the ball tracking is possible. Inmotion has

developed this kind of technology and it is, also, pos-

sible to get the position of several balls during train-

ing.

2.2

Technology

This game analysis platform has been developed us-

ing Bootstrap (Bootstrap, 2015), a powerful open-

source framework for creating web applications. It

contains HTML and CSS design templates for inter-

face and uses JavaScript to define and interact with

buttons, forms and other interfaces. It was

also used

another framework - Code Igniter based on

Model-

View-Controller development pattern. Used

for

building dynamic web sites with PHP. This frame-

work allows to create the connection to the database

and manages inserts, deletes and updates. Another

very important feature is the creation of controllers of

each web page that we have in the framework. The

controllers permit the passage of data to the database

by the POST Method, i. e., the values are sent by a

HTTP message and not written in the page URL (GET

method), because in this way, everyone could have ac-

cess to confidential data. The controllers also allow

redirect one page to another.

Figure 2: Database tables.

The database only has two tables. In the Figure 2

can see the attributes of users and he game tables.

Both are identify with id.

The password will be encrypted with SHA1 (Se-

cure Hash Algorithm) (Eastlake 3rd and Jones, 2001)

and stored in the database. This kind of encryption

only works in one way, i.e., we can always generate

the same encryption code using the password, but we

can’t get the password from encryption code. The

activation code is necessary to prevent register from

bots (piece of software designed to complete a minor

but repetitive task automatically) on our website. The

email field is used as username for the user login, it

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is a unique key. It is also asked for the name of each

user and their nationality, because it’s interesting to

know how many countries are reached by our frame-

work. Finally, the image is optional and is only for

user identification. If the login is done through API

Google with a Google account (one of the functions

provided to register), the user’s image is the image

that is associated with that account.

The game’s table contains a description of the

game and it can only be the name of the teams that are

competing. It contains the data file with the respective

coordinates, which will be explain in detail in subsec-

tion 3.1, Data Collection. The link field contains the

URL of the video, which is preloaded on a video shar-

ing platform. The availability on the game’s table, in-

dicates which games are available for all users or only

to the user who uploaded the game. If the availability

is public, all users have access to the game, if it’s pri-

vate, only the user who uploaded have access to the

game. That user is identified by user id that works as

a foreign key. Lastly, the status is a Boolean: if status

= false, game is not approved yet, status = true, game

is already approved.

SOFTWARE

IMPLEMENTATION

With the aim to be generic, the platform is im-

plemented as a web service. Using a computation

unit expressed by a PC or tablet with an installed

web browser and internet access the developed game

analysis is accessible without restrictions. Comparing

with windows based applications designed for game

analysis the web based platform may be used

through the browser independent by the operation

system.

3.1

Data Collection

The used data to test the developed platform have

been video recorded at 25 fps (frames-per-second).

Due to data collection technological constraints this

video data are coordinates x and y

for the 22 players

in the game (home team and away

team), and

coordinates x, y and z for the ball. The file

containing this data is in .csv (comma-separated

values) format. Our platform affords to load another

files

for further analysis. This file has to comply

with the

format and specific rules to be correctly

interpreted in

the virtual simulator.

These data were provided by SportVU (SportVU,

2015) for testing. These data are from a match of

the Dutch League of the 2011/2012 season, which

ended with the victory of the away team by 0-2.

3.2

Web Interface

As we can see in the Figure 3 below, the GUI is

ex-

pressed by a control panel with some options to

interact with the applet, we have a video that has

be previously loaded from a video-sharing

website

like Youtube, lastly we have charts that

show metrics

about the game.

Figure 3: Panel options in analysis mode.

To access the interface showed in Figure 3 is re-

quired to make the registration in the website. After

registration, any registered user may upload the video

and coordinates to our platform. (If the file isn’t

available in this moment, it’s because we need to

validate

him). The file must comply with the

formatting rules,

which

they are explained on the

screen where the upload is

done. Data integrity must

be maintained. It is difficult to ensure that files are

always sent in the

correct format. Due to this, it is

necessary to make a

manual validation to the file

after the upload is done,

so is only available for

analysis after that validation

has been successfully

performed.

Some of the analysis mode functionalities are

graphical expressed in Figure 4. Three groups of

options were considered:

•

General option – includes information related to

both teams;

•

Home option – includes information for the

home

team;

•

Away option – include information for the away

team;

JFA Platform for Football Analysis

347

3.2.1

General Options

In this tab we have the standard options, like: ’Start’,

’Pause/Resume’ and ’Reset’. These options make

the interaction with the virtual representation and the

video. Another implemented functionality is ’Export

Data’. This

one makes it possible to export data in

csv format that

is collected throughout the game. We

have the possibility to choose the analyzed period of

time and the metrics that are considered for each

team.

Figure 4: Platform control panel in analysis mode: (a) gen-

eral options, (b) options for each team (home in this case).

Options such as:

forward in time, analysis period

setup, change the correlation window, change the

field size and change the

game speed are presented

in Figure 4. a..

3.2.2

Home/Away Options

In Figure 4.a. we only can see ’Home’ tab because

the options are the same, regardless of team. In the

beginning we have all the players identified by num-

ber, along with a checkbox. This checkbox allows

the player to appear or not in the field (virtual repre-

sentation). We can change the colors of equipment

and numbers of the players. It’s possible to select

the

tactics of each team. This option is very impor-

tant

because it’s based on a tactic where the lines are

calculated: defensive, midfielder and offensive. You

can see them in the virtual representation with their

respective colors. You can choose the line color us-

ing the checkbox and the color picker. Another op-

tion, which is much easier to see without resorting to

graphic, is ’Show Lines Distances’, which will show

the distance, in meters, between the lines. Finally, the

last option allows the thickness increase of the line,

only for visual effects.

3.2.3

Analysis Mode/Compare Mode

The framework has two modules: Analysis mode and

Compare Mode. Both of this modes, contains: i) vir-

tual representation of the game; ii) video of the game;

iii) timer; iv) charts. In the first one, we have an anal-

ysis of one game, with the options mentioned in 3.2

Web Interface. The charts show the metrics we ana-

lyze in the framework, it’s explained on the subsection

metrics 3.4. We have two charts, one for each team,

and each chart presents the distance between lines and

their correlation. We use two axes to present the met-

rics in the same chart.

The second one, Compare Mode, has as main goal

compare two different parts of the game. For exam-

ple, we want to see the behavior of the team, in the

first 10 minutes, and compare it with the last 10m in

the first half. In this mode, this kind of approach is

possible. The time is totally customizable, i.e., we can

choose the analysis interval we want. In this window

is showed two virtual representations, and four charts,

two for each part we are comparing. The panel con-

trol is almost the same, having only one more option

that allows us to select the time each part of the game

that we will compare.

3.3

Game Virtual Representation

The g a m e virtual representation was implemented

in Java and exposed on browser as an applet (Farrell,

2011).

As it can be observed in the Figure 5, a

simplified representation of a football game is

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provided the included elements were: 22

players and

1 ball. The representation is updated at 25 frames

per second. The implemented applet

performs file

reading and shows the information contained on the

pitch. Many of the functions available

on the

interface web are interpreted by the applet, like

export data, fast forward or step in the time during the

game.

Figure 5: Example of virtual representation with applet.

Java Technology was chosen due to the facts:

•

Cross-platform solution web (Windows, OS X

and

Linux);

•

Totally scalable with the number of users / clients;

•

Java applets are fast - similar performance to na-

tive installed software;

•

Easy to develop with java language and debug the

applet.

3.4

Metrics

The perception of the space is a crucial issue to de-

cide and act during a football match. The space be-

tween lines (e.g., the distance between the midfielder

line and the defensive line) may provide the players

with different opportunities for action (e.g., shot on

goal; or pass to a team mate). Passos et al., (2011)

and Duarte et al., (2012) used the player’s interper-

sonal distances as coordinative variable to describe

player’s interactive behavior during the game. Thus

to measure the space that is available we used the dis-

tance between imaginary horizontal lines, parallel to

the field end line. Accordingly the distance to the goal

the lines under analysis were i) the defensive line; ii)

the midfielder line; and iii) the offensive line.

3.4.1

Distance between Lines

Figure 6 pr ese n ts the lines madden by the

three-

team sectors. The computation of these lines,

which

defines each sector, depends by the current

tactic

chosen for the team. In presented case, the team was

chosen 4-4-2 as tactical system. Accordingly with

the team

tactical system it is possible to change the

computed lines, through the GUI, as it can be

observed in Figure 4.b.

The defensive line is

computed based on the position of

the four players

closest to the pitch bottom line (i.e.,

the pitch end

line; the end line closest its own goal).

Figure 6: Representation of the lines during the game: (a)

defensive line, (b) midfielder line, (c) offensive line. The

values on left side show the distance between lines in me-

ters.

The midfielder line is composed by the next four play-

ers, and the offensive line by the two players farthest

the pitch end line. Note that the goalkeeper positions

it wasn’t used to calculate these lines.

The computation of these lines takes into account

the dynamic of player’s position, which means that

when a defending player collaborate in a team offen-

sive phase, he contributes to the computation of the

midfielder or offensive line. The same is true when a

midfielder backs on the pitch to collaborate with de-

fending functions. That player will contribute to the

computation of the defensive line. With these options,

the platform ensure that the team tactical system is re-

spected, although the players, being defenders or at-

tackers, contribute to different lines over time.

JFA Platform for Football Analysis

349

3.4.2

Correlation between Lines

Correlation is a statistical linear technique to measure

how well two sets of data are related. In this case, it

was used, the Pearson Coefficient of Correlation. The

values of this correlation will be between -1 and 1.

When the correlation value approaches to zero, there

is a greater variability of the set of data around the line

of best fit (Benesty et al., 2009). Correlation values

can be classified as such:

•

Strong correlation: 0.5 to 1.0 or -0.5 to -1.0

•

Moderate correlation: 0.3 to 0.5 or -0.3 to 0.5

•

Weak correlation: 0.1 to 0.3 or -0.1 to -0.3

,2 2 2 2

()()

[()]

·,

)]·[ (

Nxy x y

Nx x Ny y

 − 

 − − 

(1)

In this case, the Pearson coefficient of correlation

was used to relate the distances between lines (e.g.,

defensive line with the midfielder line; midfielder line

with the offensive line). Examining how this relation-

ship between lines varies throughout the game, may

provide information regarding the players collective

behavior before critical moments (e.g., shots on goal).

RESULTS AND DISCUSSIONS

To test the utility of the platform and implemented

metrics we will analyze a critical situation more

specifically the moments before the goal in a foot-

ball match of the Dutch League that occurred after

the minute 39:40. The move that preceded the goal

of the away team (i.e., white team) begins with loss

of ball possession of the home team (i.e., red team) at

minute 39:31. Bellow it was provided exemplar data

of the movements of the lines of both teams analyzed

for an intrateam perspective and for an interteam per-

spective, in the moments before goal scoring.

4.1

Intra Team Analysis

Figure 7 and Figure 8 display the distance between

lines of each team. The lost of ball possession occurs

at time 39:31, and the away team goal was scored at

minute 39:40.

For the home team the distance between the

defensive line and the midfielder line (blue line on

Figure 7) increases being characterized by values

included in 20m to 25m interval until the opposition

team scored its goal. Despite a moderate correlation

between these two team sectors this data captures a

possible less efficient relationship between sectors.

Figure 7: Home chart metrics.

We may suggest that it was this gap size between

midfield and defensive lines on the home team,

affords the away team an

opportunity to score a goal.

Figure 8: Away Chart.

4.2

Inter Team Analysis

As it can be observed in Figure 9, the distances

between the home team defensive line and the away

team offensive line achieved

the shortest values in

the time interval defined by the following moments:

ball possession moment (39:31) and goal scored

m o m e n t (39:40). So, we may suggest that this

distance between lines has a relevant influence on

goal

scoring of the away team.

Figure 9: The evolution of distance and correlation during

the critical moment.

The calculated correlation value reinforces our

previous suggestion regarding the influence of the

distance between lines on goal scoring. It is worth

noting that soon after

regain

ball possession (away

team) a decrease on the correlation a c h i e v e d

PerSoccer 2015 - Special Session/Symposium on Performance Analysis in Soccer: How does Technology Challenge Current Practices? -

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350

values

close to 0 (zero) (between minute 39:32 and

39:36).

This might suggest that for brief moments

the defensive line of the home team and the

offensive line of

the away team were unrelated (i.e.,

not moving in the

same direction or at the same

speed) which provides

the offensive line of the away

team getting closer the

defensive line of the home

team, which seems to be

a suitable position to score

a goal...

CONCLUSIONS

In this article, we propose a football analysis plat-

form designed and implemented to support coaches

and match analyzers decision making.

Functionalities such as virtual representation of the

uploaded data using an applet, and

game analysis as

part of extended game analysis were

implemented.

The game analysis functionalities are

associated

with appropriate GUI. Additionally data

management, data storage and data exporting func-

tionalities were implemented and tested. The data

provided by this platform allow an intrateam and an

interteam game analysis of the dynamics of the

different sectors of each team for moments

previously identified as critical, for instance loss of

ball possessions and goals scored.

FUTURE WORK

For further analysis it will be possible to add new met-

rics to this platform. The main idea is to develop this

version to a fully customize platform where coaches

and game analysts can choose the metrics which more

accurately describe team and individual performance.

Other important point would be to use data from other

sources, i.e., from other devices. This platform was

built to accept football player’s positional data, up-

load on any part of the world. Now we just need to

test it www.footalysis.eu.

ACKNOWLEDGEMENTS

We would like to thank Stats-SportVU for, kindly,

provided the data used on this study.

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