Feature Extraction and Aggregation for Predicting the Euro 2016

Maryam Tavakol; Hamid Zafartavanaelmi; Ulf Brefeld

Feature Extraction and Aggregation for Predicting the Euro 2016

Maryam Tavakol
, Hamid Zafartavanaelmi
, Ulf Brefeld

Professorship of Information Systems, in particular Machine Learning

Technical University of Darmstadt

Research output: Journal contributions › Conference article in journal › Research › peer-review

Abstract

This paper is addressing the challenge of predicting Euro 2016 outcomes. A set of processed features alongside with a new proposed feature are used to train a linear model to compute scores of 24 participating countries. The obtained scores form fwin, lose, drawg probabilities for all possible fixtures. The empirical evaluation until the semi-finals shows that the conceptually simple approach proves accurate for countries with historical data.

Original language	English
Journal	CEUR Workshop Proceedings
Volume	1842
Issue number	1842
Number of pages	7
ISSN	1613-0073
Publication status	Published - 09.2016
Event	Machine Learning and Data Mining for Sports Analytics - MLSA 2016 : ECML/PKDD 2016 workshop - Riva del Garda, Italy Duration: 19.09.2016 → … Conference number: 3 https://dtai.cs.kuleuven.be/events/MLSA16/

Bibliographical note

Session 1. urn:nbn:de:0074-1842-7

Research areas and keywords

Business informatics
Feature extraction
ridge regression
ranking

ASJC Scopus Subject Areas

Computer Science(all)

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Cite this

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title = "Feature Extraction and Aggregation for Predicting the Euro 2016",

abstract = "This paper is addressing the challenge of predicting Euro 2016 outcomes. A set of processed features alongside with a new proposed feature are used to train a linear model to compute scores of 24 participating countries. The obtained scores form fwin, lose, drawg probabilities for all possible fixtures. The empirical evaluation until the semi-finals shows that the conceptually simple approach proves accurate for countries with historical data.",

keywords = "Business informatics, Feature extraction, ridge regression , ranking",

author = "Maryam Tavakol and Hamid Zafartavanaelmi and Ulf Brefeld",

note = "Session 1. urn:nbn:de:0074-1842-7; Machine Learning and Data Mining for Sports Analytics - MLSA 2016 : ECML/PKDD 2016 workshop, MLSA 2016 ; Conference date: 19-09-2016",

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month = sep,

language = "English",

volume = "1842",

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TY - JOUR

T1 - Feature Extraction and Aggregation for Predicting the Euro 2016

AU - Tavakol, Maryam

AU - Zafartavanaelmi, Hamid

AU - Brefeld, Ulf

N1 - Conference code: 3

PY - 2016/9

Y1 - 2016/9

N2 - This paper is addressing the challenge of predicting Euro 2016 outcomes. A set of processed features alongside with a new proposed feature are used to train a linear model to compute scores of 24 participating countries. The obtained scores form fwin, lose, drawg probabilities for all possible fixtures. The empirical evaluation until the semi-finals shows that the conceptually simple approach proves accurate for countries with historical data.

AB - This paper is addressing the challenge of predicting Euro 2016 outcomes. A set of processed features alongside with a new proposed feature are used to train a linear model to compute scores of 24 participating countries. The obtained scores form fwin, lose, drawg probabilities for all possible fixtures. The empirical evaluation until the semi-finals shows that the conceptually simple approach proves accurate for countries with historical data.

KW - Business informatics

KW - Feature extraction

KW - ridge regression

KW - ranking

UR - http://ceur-ws.org/Vol-1842/

UR - https://www.scopus.com/pages/publications/85020500932

M3 - Conference article in journal

SN - 1613-0073

VL - 1842

JO - CEUR Workshop Proceedings

JF - CEUR Workshop Proceedings

IS - 1842

T2 - Machine Learning and Data Mining for Sports Analytics - MLSA 2016

Y2 - 19 September 2016

ER -

Feature Extraction and Aggregation for Predicting the Euro 2016

Abstract

Bibliographical note

Research areas and keywords

ASJC Scopus Subject Areas

Access to Document

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Cite this