arXiv subjects

Gabriel Calvo

Publications and source records attributed to Gabriel Calvo.

10 recordsLinked to original sources

arXiv2026

Longitudinal Bayesian networks for assessing team performance in the National Basketball Association

Assessing the performance of a basketball team requires the consideration of multiple sources of information. In recent years, the volume and the quality of data generated in sport has increased considerably, particularly in basketball. In this work, we propose a Bayesian graphical modelling framework for the longitudinal analysis of basketball team performance. The framework is based on Bayesian networks that explicitly represent the nodes, that is, the random variables of interest, as longitudinal stochastic processes. We propose three baseline longitudinal models: a static Bayesian network, a dynamic Bayesian network with an autoregressive structure between successive games, and a dynamic Bayesian network based on a hidden Markov structure. We illustrate the proposed framework through a real-world sports analytics case study involving the Philadelphia 76ers of the National Basketball Association (NBA) during the 2005--06 season. The analysis includes player participation, minutes played, fouls drawn, and one-, two-, and three-point shots attempted and made.

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arXiv2026

Predicting subjective rage and facial expressions in human driving: A Bayesian network approach with beta-distributed nodes

A Bayesian network framework is proposed for modelling unit-bounded continuous variables using conditional beta-distributed nodes within a fully Bayesian inference setting. The model captures conditional dependencies and propagates uncertainty through the network, with inference performed via Markov Chain Monte Carlo methods implemented in WinBUGS. The framework is applied to an experimental study of emotional and facial responses, focusing on rage intensity and facial gestures. Results show that brow lowering is strongly associated with rage intensity and is more frequent in men, whereas upper lid raising decreases under provocation independently of rage or sex. The model also predicts rage severity from informative facial gestures.

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arXiv2026

Bayesian network approach to building an affective module for a driver behavioural model

This paper focuses on the affective component of a driver behavioural model (DBM). This component specifically models some drivers' mental states such as mental load and active fatigue, which may affect driving performance. We have used Bayesian networks (BNs) to explore the dependencies between various relevant random variables and assess the probability that a driver is in a particular mental state based on their physiological and demographic conditions. Through this approach, our goal is to improve our understanding of driver behaviour in dynamic environments, with potential applications in traffic safety and autonomous vehicle technologies.

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arXiv2025

Bayesian network approach to building an affective module for a driver behavioural model

This paper focuses on the affective component of a Driver Behavioural Model (DBM), specifically modelling some driver's mental states, such as mental load and active fatigue, which may affect driving performance. We used Bayesian networks (BNs) to explore the dependencies between various relevant variables and estimate the probability that a driver was in a particular mental state based on their physiological and demographic conditions. Through this approach, our goal is to improve our understanding of driver behaviour in dynamic environments, with potential applications in traffic safety and autonomous vehicle technologies.

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arXiv2023

Selecting the best compositions of a wheelchair basketball team: a data-driven approach

Wheelchair basketball, regulated by the International Wheelchair Basketball Federation, is a sport designed for individuals with physical disabilities. This paper presents a data-driven tool that effectively determines optimal team line-ups based on past performance data and metrics for player effectiveness. Our proposed methodology involves combining a Bayesian longitudinal model with an integer linear problem to optimise the line-up of a wheelchair basketball team. To illustrate our approach, we use real data from a team competing in the Rollstuhlbasketball Bundesliga, namely the Doneck Dolphins Trier. We consider three distinct performance metrics for each player and incorporate uncertainty from the posterior predictive distribution of the longitudinal model into the optimisation process. The results demonstrate the tool's ability to select the most suitable team compositions and calculate posterior probabilities of compatibility or incompatibility among players on the court.

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arXiv2023

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

Sports data analytics is a relevant topic in applied statistics that has been growing in importance in recent years. In basketball, a player or team has a hot hand when their performance during a match is better than expected or they are on a streak of making consecutive shots. This phenomenon has generated a great deal of controversy with detractors claiming its non-existence while other authors indicate its evidence. In this work, we present a Bayesian longitudinal hidden Markov model that analyses the hot hand phenomenon in consecutive basketball shots, each of which can be either missed or made. Two possible states (cold or hot) are assumed in the hidden Markov chains of events, and the probability of success for each throw is modelled by considering both the corresponding hidden state and the distance to the basket. This model is applied to a real data set, the Miami Heat team in the season 2005-2006 of the USA National Basketball Association. We show that this model is a powerful tool for assessing the overall performance of a team during a match or a season, and, in particular, for quantifying the magnitude of the team streaks in probabilistic terms.

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arXiv2022

Bayes factors for longitudinal model assessment via power posteriors

Bayes factor, defined as the ratio of the marginal likelihood functions of two competing models, is the natural Bayesian procedure for model selection. Marginal likelihoods are usually computationally demanding and complex. This scenario is particularly cumbersome in linear mixed models (LMMs) because marginal likelihood functions involve integrals of large dimensions determined by the number of parameters and the number of random effects, which in turn increase with the number of individuals in the sample. The power posterior is an attractive proposal in the context of the Markov chain Monte Carlo algorithms that allows expressing marginal likelihoods as one-dimensional integrals over the unit range. This paper explores the use of power posteriors in LMMs and discusses their behaviour through two simulation studies and a real data set on European sardine landings in the Mediterranean Sea.

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arXiv2021

Bayesian hierarchical nonlinear modelling of intra-abdominal volume during pneumoperitoneum for laparoscopic surgery

Laparoscopy is an operation carried out in the abdomen or pelvis through small incisions with external visual control by a camera. This technique needs the abdomen to be insufflated with carbon dioxide to obtain a working space for surgical instruments' manipulation. Identifying the critical point at which insufflation should be limited is crucial to maximizing surgical working space and minimizing injurious effects. Bayesian nonlinear growth mixed-effects models are applied to data coming from a repeated measures design. This study allows to assess the relationship between the insufflation pressure and the intra--abdominal volume.

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arXiv2020

Bayesian shared-parameter models for analysing sardine fishing in the Mediterranean Sea

European sardine is experiencing an overfishing around the world. The dynamics of the industrial and artisanal fishing in the Mediterranean Sea from 1970 to 2014 by country was assessed by means of Bayesian joint longitudinal modelling that uses the random effects to generate an association structure between both longitudinal measures. Model selection was based on Bayes factors approximated through the harmonic mean.

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arXiv2020

Bayesian longitudinal models for exploring European sardine fishing in the Mediterranean Sea

In the Mediterranean Sea, catches are dominated by small pelagic fish, representing nearly the 49\% of the total harvest. Among them, the European sardine (Sardina pilchardus) is one of the most commercially important species showing high over-exploitation rates in recent last years. In this study we analysed the European sardine landings in the Mediterranean Sea from 1970 to 2014. We made use of Bayesian longitudinal linear mixed models in order to assess differences in the temporal evolution of fishing between and within countries. Furthermore, we modelled the subsequent joint evolution of artisanal and industrial fisheries. Overall results confirmed that Mediterranean fishery time series were highly diverse along their dynamics and this heterogeneity was persistent throughout the time. In addition, results highlighted a positive relationship between the two types of fishing.

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