TY - JOUR
T1 - On the Élö–Runyan–Poisson–Pearson Method to Forecast Football Matches
AU - López-Barrientos, José Daniel
AU - Zayat-Niño, Damián Alejandro
AU - Hernández-Prado, Eric Xavier
AU - Estudillo-Bravo, Yolanda
N1 - Publisher Copyright:
© 2022 by the authors.
PY - 2022/12/1
Y1 - 2022/12/1
N2 - This is a work about football. In it, we depart from two well-known approaches to forecast the outcome of a football match (or even a full tournament) and take advantage of their strengths to develop a new method of prediction. We illustrate the Élö–Runyan rating system and the Poisson technique in the English Premier League and we analyze their accuracies with respect to the actual results. We obtained an accuracy of 84.37% for the former, and 79.99% for the latter in this first exercise. Then, we present a criticism of these methods and use it to complement the aforementioned procedures, and hence, introduce the so-called Élö–Runyan–Poisson–Pearson method, which consists of adopting the distribution that best fits the historical distribution of goals to simulate the score of each match. Finally, we obtain a Monte Carlo-based forecast of the result. We test our mechanism to backcast the World Cup of Russia 2018, obtaining an accuracy of 87.09%; and forecast the results of the World Cup of Qatar 2022.
AB - This is a work about football. In it, we depart from two well-known approaches to forecast the outcome of a football match (or even a full tournament) and take advantage of their strengths to develop a new method of prediction. We illustrate the Élö–Runyan rating system and the Poisson technique in the English Premier League and we analyze their accuracies with respect to the actual results. We obtained an accuracy of 84.37% for the former, and 79.99% for the latter in this first exercise. Then, we present a criticism of these methods and use it to complement the aforementioned procedures, and hence, introduce the so-called Élö–Runyan–Poisson–Pearson method, which consists of adopting the distribution that best fits the historical distribution of goals to simulate the score of each match. Finally, we obtain a Monte Carlo-based forecast of the result. We test our mechanism to backcast the World Cup of Russia 2018, obtaining an accuracy of 87.09%; and forecast the results of the World Cup of Qatar 2022.
KW - English Premier League
KW - inverse transform method
KW - Poisson forecasting method
KW - Qatar 2022
KW - recursive distributions
KW - Russia 2018
KW - Élö–Runyan rating system
UR - http://www.scopus.com/inward/record.url?scp=85143620841&partnerID=8YFLogxK
U2 - 10.3390/math10234587
DO - 10.3390/math10234587
M3 - Artículo
AN - SCOPUS:85143620841
SN - 2227-7390
VL - 10
JO - Mathematics
JF - Mathematics
IS - 23
M1 - 4587
ER -