FOOTBALL NETWORK ANALYSIS WITH GEPHI

TO DETERMINE A TEAMS STRATEGY.

GROUP 2: ROBERT FERRO, SEAN JAMES, YOGESH SHINDE,PRATIK DOSHI,MINGYANG CHEN AND MICHEAL ABAHO

FORMALITIES

• Explain basic concepts in a readable form:

• Vertex – a player• Edge – a relationship between 2 players.

• Objective: Analyse two opposing teams to see what tactics were used.• Arsenal Vs West Ham : 0-2

WHAT HAS BEEN UNDERTAKEN

• Chose between 3 ideas:• Football domain – most useful with real world application• Coffee & sandwich habits – JCR,Coffeeshop• Library habits

• Met at regular intervals/ social media /dropbox

• Initial research to find sites/resources of interest

WHY FOOTBALL AS A SUITABLE DATASET

• Other useful applications for this data

• How was data collected?

• Why is football domain a good choice? - Different tactics• Defensive play: more in than out.• Good team cohesion: triangles

HOW WE SPLIT THE TEAM

• Gephi construction team• Retrieve relevant data and format ready - .gml format.• 4-4-2.com• Gephi graph• How current is data(year of creation)

• Statistical analysis & visualisation team• Presentation creation

WHAT IS GEPHI?

• Other tools available:• Mathematica• MATLAB

• How is it used

• Visual demonstration

WHAT WAS ACHIEVED

• Our working methodology

• Statistical analysis techniques

• Effective visualisations

• Ability to infer strategies and relationships between data.

OUR METHODOLOGY

• Define objective• Research and collection of data• Gephi to draw graphs to visualize collected data• Then statistical reports,visualisation and analysis• Conclusions and evaluations

STATISTICAL ANALYSISArsenal Team West Ham Team

Total 110 edges Total 96 edges

Average Degree = 8.462 Average Degree = 13.714

Network Diameter = 2 Network Diameter: 3

Network radius = 1 Network Radius: 2

Number of Weakly Connected Components: 1

Number of Weakly Connected Components: 1

Number of Strongly Connected Components: 1

Number of Strongly Connected Components: 1

• Vertex degree chart

Vertex degree chart of Arsenal Team: Vertex degree chart of West Ham Team:

Players Degree In-degree

Out-degree

Theo Walcott 7 5 2Petr Cech 10 5 5

Alexis Sanchez 13 7 6 Olivier Giround 16 10 6

Laurent Koscienly

17 9 8

Mathieu Debuchy

17 8 9

Francis Coquelin 18 9 9Santiago Cazorla 19 8 11

Per Mertesacacker

19 9 10

Alex Oxlade 19 8 11Mesut Ozil 21 10 11

Nacho Monreal 21 11 10Aaron Ramsey 23 11 12

Players Degree In-degree Out-degreeModibo Maiga 4 3 1Matthew Jarvis 8 5 3 Kevin Nolan 8 4 4Diafra Sakho 10 7 3Mauro Zarate 13 7 6

Angelo Ogbonna 14 8 6Winston Reid 15 7 8

Aaron Creswell 15 8 7Reece Oxford 15 6 9

Cheikhou Kaouyate 15 6 9

Adrian 16 7 9James Tomkins 18 7 11Dimitri Payet 20 10 10Mark Noble 21 11 10

• Degree distribution chart for arsenal team

Degree distribution chart for west ham team

FEATURES OF THE GRAPHS

• Nature of the graph: directed

• In/out degrees

• Weighted

• Formation signified by the graph. 4-4-2 visually looks like a 4-4-2 formation

STATISTICAL ANALYSIS TECHNIQUES

• Vertex degree

• Number of nodes joined to that node (popularity)

• Vertex degree• Greater percentage for west ham because they passed more as a team.

• Isomorphic relationships

• Connectivity

Connectivity• In graph theory connectivity indicates whether all nodes in a

network can be reached from any other node.• If the graph is strongly connected(directed path) then it

represents high possession value of team.• If the graph is weakly connected(undirected path) then it

represents low possession value of team.• In our dataset by the graph analysis west ham team has slightly

greater possession value than arsenal team.

DIFFERENT TYPES OF CENTRALITY

• Betweeness centrality

• Closeness centrality

• Radius

• Diameter

• Pagerank algorithms

CENTRALITY- INDEPENDENT NODE ANALYSISID Label Eccentricity Closeness

CentralityBetweenness Centrality

Degree Centrality

9 Aaron Ramsey 1.0 1.0 11.56 238 Mesut Ozil 2.0 0.92 8.08 214 Nacho Monreal 2.0 0.86 6.86 21

6 Alex Oxlade - Chamerlain 2.0 0.92 4.06 19

2 Per Mertesacker 2.0 0.86 3.44 197 Santiago Cazorla 2.0 0.92 2.74 195 Francis Coquelin 2.0 0.8 1.64 183 Mathieu Debuchy 2.0 0.8 2.143 171 Laurent Koscienly 2.0 0.75 2.06 1710 Olivier Giroud 2.0 0.66 2.31 1612 Alexis Sanchez 2.0 0.66 0.64 130 Petr Cech 2.0 0.631 0.41 1011 Theo Walcott 2.0 0.54 0.0 7

CLOSENESS CENTRALITY

• More central player -> higher closeness centrality.• Easy access to any other player making them key to tactics

• Why is closeness good?

BETWEENESS CENTRALITY

• Number of times node acts as a bridge

• Central players ideally should have high betweeness centrality• E.g. Midfielders: Ramsey,Ozil,Cazorla

• Extreme ends of network

RADIUS / DIAMETER

• Radius: Least number of hops to traverse network

• Diameter: most hops to get around network

PAGERANK ALGORITHM

• Adjacency matrix for Arsenal.

• Convert probabilities using a ‘random surfer’ based model

• Summarises player importance:• Ramsey is central to

Arsenal.

PAGERANK CONT.

• Dead ends – pages with no out links• How do we address this?

• Spider traps – have out links but never link to other pages. • Player who only gets passed the ball and is then tackled.• Teleportation is a good compromise.

• A simplified pagerank formula• v= (1−β)n+βM v

VISUALISATION TECHNIQUES

• Formation based graph(show subs and players positions in a visual way)

• Large edge means important relationship.

• Communities (defensive, midfield and offensive)

• Considered other layout techniques but not that useful(11 nodes is quite easy to visualise)

VISUALISATION APPROACHES

TABLES AND FEATURES OF IN/OUT

• Explain in and out degrees

• Specific players as examples :• Monreal: good defender hence has more in than out(trusted also).

• Machine learning could facilitate this sort of analysis.

BENEFITS/LIMITATIONS

Benefits:1. Good analysis tool to visualize formation2. Infer what sort of position a player is playing3. Find out about certain players and their roles in the team4. Discover the football team style of play and its tactics5. Compared with two different network graphs for two teams to analyze

tactics differences between two teams 6. Also applicable to many other suitable domains

Limitations:1. Difficult to retrieve and extract relevant data2. The data from the graph is just theoretical3. Every match is different. Many uncontrollable factors can also affect the final result4. Players may change 5. ……..

ASSUMPTIONS• From graph analysis insufficient data exist substitute impossible 100% successful passes• From circumstances weather effects home and away• From players sports status• From coaches change tactics

LESSONS LEARNT AND CONCLUSIONS

• Retrieving data is difficult• Gephi is a powerful network tool• Visualisation is an important part of analysis• Graphs provide a very interesting way to visualise sports

team cohesion

TEAM CONTRIBUTION

• Rob – Design and implement gephi graphs and pagerank algorithms to draw useful conclusions from them

• Pratik-Statistical analysis & visualisation approach,maintaining dropbox

• Yogesh-Statistical analysis & visualisation approach, how to use gephi,presentation speaker.

• Sean- bring together presentation, review work, research key areas, provide insight into domain area and areas for future development.

• Chen- Key limitations and analysis, limitations (conclusion)

• Michael- Research into domain area, presentation speaker and detailed analysis of football games and domain.

OVERALL CONTRIBUTION