ANGOL PREZI_kész

MULTIVARIATE STATISTICAL MODELS TO FORECAST THE RESULTS OF EURO2016 QUALIFIERS

Bence Jámbor – Dávid Szabó – Máté JámborBudapest Business School - Consultant: Dr. Tamás Kovács university docent

SUBSTANCE

I. IntroductionII. Overview of the literature III. Development of the teams

performance forecasting model

IV. Results

INTRODUCTION Hungarian football: nadir (WC1986; EC: 1972)

Present and the recent past: failures(2006: Malta 1-2; 2013: Netherlands 8-1)

Successes in the past: 1938, 1954 (Aranycsapat/The Gold team) Puskás; WC: II.place 1964: EC: III. place

(Mészöly-Bene-Albert) 1966: WC-quarterfinal Olympic Champions:

1952,1964,1968

LITERATUREHungarian and international scientific articles forecast of a football match outcome FIFA World Ranking (MAREK KAMINSKI –

INCONSISTENCIES IN THE FIFA RANKINGS) InStat (PÉTER KAKAS- JUST CAN NOT

PLAY FOOTBALL INSTEAD OF THEM ALL-SEEING SOFTWARE)

Home field advantage(HENRIK HEGEDŰS: AWAY THE DRAW IS GOOD RESULT TOO)

BASIC CONCEPT OF THE FORECASTING MODEL

Final outcome of a national match

1. Performance form of the players in their club teams

2. Basic qualities of the players

3. Other factors

Defending(%):- Succesful passes- Challenges won- Aerial challenges won - Succesful tackles

Attacking(%):- Succesful passes- Challenges won- Aerial challenges won - Succesful keypasses - Shots on goal- Succesful dribblings

- Fifa world ranking

- Last 5 comptetitivegames

- Home field

10.000 data

Balázs’s performance on the club match before the examined national match

Keypass% (here it’s missing)

We used this page to examine all the players of the 58 matches.

An example of the 58 examined matches

Defense skills

Attack skills

Create one defense- and attack value in each teamsCreating defense factors SPSS, factor analysis, 3 factors from 5 variablesVariance-proportion

method the variables with the closest relation one factor

1.factor: save-, tackles %

2. factor: challenges won-, aerial challenges won %

3. factor: passes %

3 factors 1 defense value

Creating attack factors

3 factors from 6 variables

1. factor: challenges won-, aerial challanges won-, dribbling %

2. factor: keypasses-, shots on goal %

3. factor: aerial challenges won-, dribblings-, passes %

3 factors 1 attack value

MULTIVARIATE STATISTICAL MODELSMultiple linear regression

difference between current form of attacker players and current form of opponent defender players (both team)

difference between current InStat index of attacker players and current InStat index of opponent defender players (both team)

difference between FIFA index of attacker players and opponent defender players

number of scored goals of attacker players in their previous club game

number of assists of attacker players in their previous club game

difference between the trends of their previous five matches of the two national teams

difference between the Fifa world rank score of the the two national teams

dummy variable of home field

HOME FIELD ADVANTAGE

In 2012 English Premier League decided in the last fixture Manchester City is the champion

REGRESSION MODEL #1Model

Coefficientst p

B Std. Error

Own attacker– opponent defender ,030 ,007 4,097 ,000

Own attacker– opponent defender InStat ,005 ,003 2,028 ,045

Own attacker– opponent defender Fifa ,092 ,032 2,836 ,005

Own FIFA World Rank– Opponent FIFA World Rank -,001 ,000 -1,680 ,096

Own Homefield Advantage ,675 ,221 3,052 ,003

REGRESSION MODEL #2

ModelCoefficients

t pB Std.

Error

Attacker– Defender (based on Fifa

form)

,033 ,010 3,337 ,001

Own attacker– Own defender (InStat),011 ,002 4,653 ,000

Own homefield advantage,850 ,227 3,754 ,000

THE EXPLANATORY POWER OF THE REGRESSION MODELSR - multiple correlation coefficientR2 - multiple determination coefficient

Model #1

R R Square Adjusted R

Square

,717 ,515 ,493

Model 2#

R R Square Adjusted R

Square

,678 ,459 ,440

TESTING THE RESULTS OF THE MODELSn=58 matchesPrediction of the outcomes

Model #1 56,9% (33/58) Model #2 62,1% (36/58)

Preditction of the number of scored goals Model #1 33,6% (39/116)Model #2 31,0% (36/116)

HITSMatch Result Estimated Result

Greece– Bosnia 0-0 0-0

Bosnia – Slovakia 0-1 0-1

Ukraine– England 0-0 0-0

Greece– Slovakia 1-0 1-0

France– Spain 0-1 0-1

Portugal– Sweden 1-0 1-0

Croatia– Island 2-0 2-0

Switzerland– Slovenia 1-0 1-0

SOME OUTCOME HITTED MATCHES

Match Result Estimated ResultBosnia-Greece 3-1 1-0

Romania-Greece 1-1 0-0

Austria-Germany 1-2 0-1

Sweden-Austria 2-1 1-0

Island-Slovenia 2-4 0-1

Italy-Denmark 3-1 2-0

Denmark-Italy 2-2 0-0

COULD HUNGARY… QUALIFIY TO EC2016 ?

sequence   match w d l score

goals receiv

edpoin

ts

1. ROM 10 7 2 1 10 2 23

2. NIR 10 5 3 2 9 7 18

3. HUN 10 4 4 2 8 6 16

4. GRE 10 3 3 4 4 6 12

5. FIN 10 3 2 5 7 9 11

Already played matchesEstimated result

Qualify to EC2016Remount-qualifier

  HUN ROM GRE NIR FIN

HUN - 1-0 0-0 1-2 1-0

ROM 1-1 - 1-0 2-0 1-0

GRE 1-0 0-1 - 0-2 1-0

NIR 1-1 0-0 0-0 - 2-1

FIN 1-1 0-2 1-1 2-0 -

THANK YOU FOR ATTENTION!

Jámbor Bence – Szabó Dávid – Jámbor Máté