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 - http://www.scopus.com/inward/record.url?scp=85020500932&partnerID=8YFLogxK

M3 - Conference article in journal

VL - 1842

JO - CEUR Workshop Proceedings

JF - CEUR Workshop Proceedings

SN - 1613-0073

IS - 1842

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

Y2 - 19 September 2016

ER -