The Poisson Regression and Quasi-Poisson Regression Analysis on FIFA World Cup Games
The FIFA World Cup's surging popularity has attracted a diverse fan base, including passionate enthusiasts in Malaysia. This widespread interest has motivated researchers to delve into the details of the tournament, using it as a crucial platform for predictions, team performance evalua...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2023
|
| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/12485/1/P17880_012cee6c36971a5dc5ed3405996a084d.pdf http://eprints.uthm.edu.my/12485/ https://smpu.uthm.edu.my/uploads/P17880_012cee6c36971a5dc5ed3405996a084d.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | The FIFA World Cup's surging popularity has attracted a diverse fan
base, including passionate enthusiasts in Malaysia. This widespread
interest has motivated researchers to delve into the details of the
tournament, using it as a crucial platform for predictions, team
performance evaluations, and an exploration of international football
competition at its top. This study aims to predict number of goals in
World Cup matches, employing Poisson and quasi-Poisson regression
models. The optimal model is determined through a comprehensive
assessment, considering AIC, BIC, standard error, and p-values. Utilizing
a dataset from Kaggle, originally sourced from the official FIFA website,
the findings consistently associate variables such as goal inside the
penalty area, goal outside the penalty area, left inside channel, right
channel, attempted defensive line breaks, completed defensive line
breaks, yellow cards, passes, and own goals with a raised probability of
number of goals. Significantly, the quasi-Poisson model exhibits a
superior fit, as evidenced by its lower AIC value of -50.0853 and a
reduced deviance value of 38.7935. Consequently, the quasi-Poisson
regression model emerges as a more suitable choice than the Poisson
regression model, particularly for addressing the overdispersion
inherent in the data. |
|---|