ABSTRACT This study provides an empirical analysis on the main univariate and multivariate stylized facts iin return series of the two of the largest cryptocurrencies, namely Ethereum and Bitcoin. A Markov-Switching Vector AutoRegression model is considered to further explore the dynamic relationships between cryptocurrencies and other financial assets. We estimate the presence of volatility clustering, a rapid decay of the autocorrelation function, an excess of kurtosis and multivariate little cross-correlation across the series, except for contemporaneous returns. The analysis covers the pandemic period and sheds lights on the behaviour of cryptocurrencies under unexpected extreme events.

On stylized facts of cryptocurrencies returns and their relationship with other assets, with a focus on the impact of COVID-19

Football, as one of the most popular sports, can provide exciting examples to motivate students learning statistics. In this paper, we analyzed the number of goals scored in the UEFA EURO 2020 final phase as well as the waiting times between goals, considering censored times. Such a dataset allows us to consider some aspects of count data taught at an introductory level (such as the Poisson distribution), as well as more advanced topics (such as survival analysis taking into account the presence of censored times). Employing data from the final phase of UEFA EURO 2020, depending on the course level, the student will acquire knowledge and understanding of a range of key topics and analytical techniques in statistics, develop knowledge of the theoretical assumption underlying them and learn the skills needed to model count data.

https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/test.12315

UEFA EURO 2020: An exciting match between football and probability

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.

/pdf/on-the-elo-runyan-poisson-pearson-method-to-forecast-2lau7m1p.pdf

On the Élö–Runyan–Poisson–Pearson Method to Forecast Football Matches

Abstract Sports data analytics has been gaining importance over recent years as an essential topic in applied statistics. Specifically, basketball has emerged as one of the iconic sports where the use and immediate collection of data have become widespread. Within this domain, the hot hand phenomenon has sparked a significant scientific controversy, with sceptics claiming its non-existence while other authors provide evidence for it. We propose a Bayesian longitudinal hidden Markov model that examines the hot hand phenomenon in consecutive shots of a basketball team, each of which can be either missed or made. We assume two states (cold or hot) in the hidden Markov chains associated with each math and model the probability of success for each shot with regard the hidden state, the random effects related the match, and the covariates. This model is applied to real data sets of three teams from the USA National Basketball Association: the Miami Heat team and the Toronto Raptors team in the 2005–2006 season, and the Chicago Bulls in the 2022–2023 season. We show that this model is a powerful tool for assessing the overall performance of a team during a game and, in particular, for quantifying the magnitude of team streaks in probabilistic terms. 

Can the hot hand phenomenon be modelled? A Bayesian hidden Markov approach

Machine learning has become a common approach to predicting the outcomes of soccer matches, and the body of literature in this domain has grown substantially in the past decade and a half. This chapter discusses available datasets, the types of models and features, and ways of evaluating model performance in this application domain. The aim of this chapter is to give a broad overview of the current state and potential future developments in machine learning for soccer match results prediction, as a resource for those interested in conducting future studies in the area. Our main findings are that while gradient-boosted tree models such as CatBoost, applied to soccer-specific ratings such as pi-ratings, are currently the best-performing models on datasets containing only goals as the match features, there needs to be a more thorough comparison of the performance of deep learning models and Random Forest on a range of datasets with different types of features. Furthermore, new rating systems using both player- and team-level information and incorporating additional information from, e.g., spatiotemporal tracking and event data, could be investigated further. Finally, the interpretability of match result prediction models needs to be enhanced for them to be more useful for team management.

Machine Learning for Soccer Match Result Prediction

We propose a penalized likelihood approach in hidden Markov models (HMMs) to perform automated variable selection. To account for a potential large number of covariates, which also may be substanti...

A regularized hidden Markov model for analyzing the ‘hot shoe’ in football:

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A regularized hidden Markov model for analyzing the ‘hot shoe’ in football: