SCORE: A convolutional approach for football event forecasting

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21240%2F25%3A00385171" target="_blank" >RIV/68407700:21240/25:00385171 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1016/j.ijforecast.2025.02.004" target="_blank" >https://doi.org/10.1016/j.ijforecast.2025.02.004</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.ijforecast.2025.02.004" target="_blank" >10.1016/j.ijforecast.2025.02.004</a>

Jazyk výsledku

angličtina

Název v původním jazyce

SCORE: A convolutional approach for football event forecasting

Popis výsledku v původním jazyce

Football (also known as soccer or association football) is the most popular sport in the world. It is a blend of skill and luck, making it highly unpredictable. To address this unpredictability, there has been a surge in popularity over the past decade in employing machine learning techniques for forecasting football-related features. This trend aligns with the growing professionalism in football analytics. Despite this progress, the existing body of work remains in its early stages, lacking the depth required to capture the intricate nuances of the sport. In this study, we introduce a convolutional approach designed to predict the occurrence of the next event in a football match, such as a goal or a corner kick, relying solely on easy-to-access past events for predictions. Our methodology adopts an online approach, meaning predictions can be computed during a live match. To validate our approach, we conduct a comprehensive evaluation against five baseline models, utilizing data from various elite European football leagues. Additionally, an ablation study is performed to understand the underlying mechanisms of our method. Finally, we present practical applications and interpretable aspects of our proposed approach.

Název v anglickém jazyce

SCORE: A convolutional approach for football event forecasting

Popis výsledku anglicky

Football (also known as soccer or association football) is the most popular sport in the world. It is a blend of skill and luck, making it highly unpredictable. To address this unpredictability, there has been a surge in popularity over the past decade in employing machine learning techniques for forecasting football-related features. This trend aligns with the growing professionalism in football analytics. Despite this progress, the existing body of work remains in its early stages, lacking the depth required to capture the intricate nuances of the sport. In this study, we introduce a convolutional approach designed to predict the occurrence of the next event in a football match, such as a goal or a corner kick, relying solely on easy-to-access past events for predictions. Our methodology adopts an online approach, meaning predictions can be computed during a live match. To validate our approach, we conduct a comprehensive evaluation against five baseline models, utilizing data from various elite European football leagues. Additionally, an ablation study is performed to understand the underlying mechanisms of our method. Finally, we present practical applications and interpretable aspects of our proposed approach.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

International Journal of Forecasting

ISSN

0169-2070

e-ISSN

1872-8200

Svazek periodika

41

Číslo periodika v rámci svazku

4

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

17

Strana od-do

1636-1652

Kód UT WoS článku

001583347600009

EID výsledku v databázi Scopus

2-s2.0-86000168423