arXiv subjects

Rodolfo Metulini

Publications and source records attributed to Rodolfo Metulini.

15 recordsLinked to original sources

arXiv2026

Eigenvector Spatial Filters Nuclear Norm Matrix Completion with Application to Air Quality Data

Reliable reconstruction of missing observations in environmental panel datasets is essential for accurate exposure assessment and policy analysis. Traditional nuclear norm matrix completion methods effectively impute missing entries in low-rank matrices, yet often overlook the spatial dependence inherent to air quality processes. This paper introduces the Eigenvector Spatial Filters Nuclear Norm Matrix Completion (ESFNNMC) method, an extension of nuclear norm fixed-effects matrix completion that replaces unit-specific intercepts with a set of Moran-type eigenvectors capturing the dominant spatial dependence patterns implied by a spatial weights matrix. To estimate the model, we propose a block-coordinate descent algorithm, combined with soft-thresholded singular value decomposition and cross-validated regularization. Through comprehensive simulations varying missingness patterns, the level of spatial and temporal autocorrelation, and dimension, shape, and rank structure of the matrices, we demonstrate that ESFNNMC improves imputation accuracy when unit heterogeneity exhibits spatial structure, while remaining competitive under mild departures from this assumption. Furthermore, it keeps the computational cost approximately unchanged. The method is applied to impute missing entries in daily PM10 measurements in 64 monitoring stations in Lombardy, Italy, during the year 2021.

stat.AP↗

arXiv2026

A model-based restricted shapley value to measure the players' contribution to shot actions in football

This paper proposes a novel framework to assess individual player contributions in football, explicitly accounting for the cooperative nature of shot-ending offensive actions. By incorporating team interaction into player evaluation, it also supports economically sustainable decision-making, with practical implications for performance analysis and player scouting. Extending the expected Goals (xG) paradigm, we propose the expected Goal Action (xGA), an action-based metric designed to assess the quality of actions through passing networks. Furthermore, we adapt cooperative game theory and introduce the Player's Restricted Shapley Value (PRSV) statistic, a contribution metric based on restricted coalition structures derived from observed passing interactions, where xGA is adopted to compute the cohesion function. Unlike traditional Shapley approaches, the PRSV one restricts coalitions to tactically admissible player subsets, offering action-specific, interpretable measures of marginal contribution in a cooperative context. We apply the framework to 8,421 shot-actions from the Italian League Serie A season 2022/23, and the case studies of AC Milan and SSC Napoli reveal heterogeneity in contributions within teams. Combining the PRSV statistic with an individual performance metric highlights the discrepancies between a player's cooperative engagement and goal-conversion ability.

stat.AP↗

arXiv2022

Hierarchical Clustering and Matrix Completion for the Reconstruction of World Input-Output Tables

World Input-Output (I/O) matrices provide the networks of within- and cross-country economic relations. In the context of I/O analysis, the methodology adopted by national statistical offices in data collection raises the issue of obtaining reliable data in a timely fashion and it makes the reconstruction of (part of) the I/O matrices of particular interest. In this work, we propose a method combining hierarchical clustering and Matrix Completion (MC) with a LASSO-like nuclear norm penalty, to impute missing entries of a partially unknown I/O matrix. Through simulations based on synthetic matrices we study the effectiveness of the proposed method to predict missing values from both previous years data and current data related to countries similar to the one for which current data are obscured. To show the usefulness of our method, an application based on World Input-Output Database (WIOD) tables - which are an example of industry-by-industry I/O tables - is provided. Strong similarities in structure between WIOD and other I/O tables are also found, which make the proposed approach easily generalizable to them.

stat.ML↗

arXiv2019

Modelling basketball players' performance and interactions between teammates with a regime switching approach

Basketball players' performance measurement is of critical importance for a broad spectrum of decisions related to training and game strategy. Despite this recognized central role, the main part of the studies on this topic focus on performance level measurement, neglecting other important characteristics, such as variability. In this paper, shooting performance variability is modeled with a Markov Switching dynamic, assuming the existence of two alternating performance regimes. Then, the relationships between each player's variability and the lineup composition is modeled as an ARIMA process with covariates and described with network analysis tools, in order to extrapolate positive and negative interactions between teammates

stat.AP↗

arXiv2019

The HOG-FDA Approach with Mobile Phone Data to Modeling the Dynamic of People's Presences in the City

In the context of Smart City, the dynamic of the presence of people can be analysed using high-dimensional spatio-temporal mobile phone data. In order to find regularities and detect anomalies in the daily profiles, we propose an approach that considers the spatial structure by means of Histogram of Oriented Gradients (HOG) method and the temporal evolution using a Model-Based Clustering Functional Data Analysis (FDA). An application to the case study of the Municipality of Brescia is provided. Similarities among days, that follow a seasonal or a days of the week trend, exist. The number of users in the city, depending on the season, the day of the week and the time of the day, varies from 30 to 60 thousands of people

stat.AP↗

arXiv2019

Detecting and classifying moments in basketball matches using sensor tracked data

Data analytics in sports is crucial to evaluate the performance of single players and the whole team. The literature proposes a number of tools for both offence and defence scenarios. Data coming from tracking location of players, in this respect, may be used to enrich the amount of useful information. In basketball, however, actions are interleaved with inactive periods. This paper describes a methodological approach to automatically identify active periods during a game and to classify them as offensive or defensive. The method is based on the application of thresholds to players kinematic parameters, whose values undergo a tuning strategy similar to Receiver Operating Characteristic curves, using a ground truth extracted from the video of the games.

stat.AP↗

arXiv2019

A strategy for the matching of mobile phone signals with census data

Administrative data allows us to count for the number of residents. The geo-localization of people by mobile phone, by quantifying the number of people at a given moment in time, enriches the amount of useful information for "smart" (cities) evaluations. However, using Telecom Italia Mobile (TIM) data, we are able to characterize the spatio-temporal dynamic of the presences in the city of just TIM users. A strategy to estimate total presences is needed. In this paper we propose a strategy to extrapolate the number of total people by using TIM data only. To do so, we apply a spatial record linkage of mobile phone data with administrative archives using the number of residents at the level of sezione di censimento.

stat.AP↗

arXiv2018

Filtering Procedures for Sensor Data in Basketball

Big Data Analytics help team sports' managers in their decisions by processing a number of different kind of data. With the advent of Information Technologies, collecting, processing and storing big amounts of sport data in different form became possible. A problem that often arises when using sport data regards the need for automatic data cleaning procedures. In this paper we develop a data cleaning procedure for basketball which is based on players' trajectories. Starting from a data matrix that tracks the movements of the players on the court at different moments in the game, we propose an algorithm to automatically drop inactive moments making use of available sensor data. The algorithm also divides the game into sorted actions and labels them as offensive or defensive. The algorithm's parameters are validated using proper robustness checks.

stat.AP↗

arXiv2018

Players Movements and Team Shooting Performance: a Data Mining approach for Basketball

In the domain of Sport Analytics, Global Positioning Systems devices are intensively used as they permit to retrieve players' movements. Team sports' managers and coaches are interested on the relation between players' patterns of movements and team performance, in order to better manage their team. In this paper we propose a Cluster Analysis and Multidimensional Scaling approach to find and describe separate patterns of players movements. Using real data of multiple professional basketball teams, we find, consistently over different case studies, that in the defensive clusters players are close one to another while the transition cluster are characterized by a large space among them. Moreover, we find the pattern of players' positioning that produce the best shooting performance.

stat.AP↗

arXiv2017

Space-Time Analysis of Movements in Basketball using Sensor Data

Global Positioning Systems (GPS) are nowadays intensively used in Sport Science as they permit to capture the space-time trajectories of players, with the aim to infer useful information to coaches in addition to traditional statistics. In our application to basketball, we used Cluster Analysis in order to split the match in a number of separate time-periods, each identifying homogeneous spatial relations among players in the court. Results allowed us to identify differences in spacing among players, distinguish defensive or offensive actions, analyze transition probabilities from a certain group to another one.

stat.AP↗

arXiv2017

Sensor Analytics in Basketball

A new approach in team sports analysis consists in studying positioning and movements of players during the game in relation to team performance. State of the art tracking systems produce spatio-temporal traces of players that have facilitated a variety of research aimed to extract insights from trajectories. Several methods borrowed from machine learning, network and complex systems, geographic information system, computer vision and statistics have been proposed. After having reviewed the state of the art in those niches of literature aiming to extract useful information to analysts and experts in terms of relation between players' trajectories and team performance, this paper presents preliminary results from analysing trajectories data and sheds light on potential future research in this field of study. In particular, using convex hulls, we find interesting regularities in players' movement patterns.

stat.AP↗

arXiv2017

The Indirect Effects of FDI on Trade: A Network Perspective

The relationship between international trade and foreign direct investment (FDI) is one of the main features of globalization. In this paper we investigate the effects of FDI on trade from a network perspective, since FDI takes not only direct but also indirect channels from origin to destination countries because of firms' incentive to reduce tax burden, to minimize coordination costs, and to break barriers to market entry. We use a unique data set of international corporate control as a measure of stock FDI to construct a corporate control network (CCN) where the nodes are the countries and the edges are the corporate control relationships. Based on the CCN, the network measures, i.e., the shortest path length and the communicability, are computed to capture the indirect channel of FDI. Empirically we find that corporate control has a positive effect on trade both directly and indirectly. The result is robust with different specifications and estimation strategies. Hence, our paper provides strong empirical evidence of the indirect effects of FDI on trade. Moreover, we identify a number of interplaying factors such as regional trade agreements and the region of Asia. We also find that the indirect effects are more pronounced for manufacturing sectors than for primary sectors such as oil extraction and agriculture.

q-fin.GN↗

arXiv2016

Spatio-Temporal Movements in Team Sports: A Visualization approach using Motion Charts

To analyze the movements and to study the trajectories of players is a crucial need for a team when it looks to improve its chances of winning a match or to understand its performances. State of the art tracking systems now produce spatio-temporal traces of player trajectories with high definition and frequency that has facilitated a variety of research efforts to extract insight from the trajectories. Despite many methods borrowed from different disciplines (machine learning, network and complex systems, GIS, computer vision, statistics) has been proposed to answer to the needs of teams, a friendly and easy-to-use approach to visualize spatio-temporal movements is still missing. This paper suggests the use of gvisMotionChart function in GoogleVis R package. I present and discuss results of a basketball case study. Data refers to a match played by an italian team militant in "C-gold" league on March 22nd, 2016. With this case study I show that such a visualization approach could be useful in supporting researcher on preliminar stages of their analysis on sports' movements, and to facilitate the interpretation of their results.

stat.AP↗

arXiv2013

A structural analysis on Gravity of Trade: on removing distance from the model

The Gravity Model is the workhorse for empirical studies in International Economies for its empirical power and it is commonly used in explaining the trade flow between countries; it relies on a function that relates the trade with the masses of the two countries and the distance (as a proxy of the trasport costs) between them. However, the notion that using of distance functions in conventional interaction models effectively captures spatial dependence in international flows has long been challenged. It has been recently fully recognized that a spatial interaction effect exists essentially due to the spatial spillover and the third country effect. This motivates the introduction of the spatial autoregressive components in the so-called spatial gravity model of trade. A so-called weight matrix is used in order to define the set of the spatial neighbors and it is traditionally based on the inverse of the distance. Two issues follow from this standard procedure: the first regards the biasness of the distance if it is used as a proxy of the transport costs in a panel data, the second is related to the collinearity emerging if we use distance twice. So, several attempt were made in the recent literature having the scope of remove the distance. We propose a theoretically consistent procedure based on Anderson, Van Wincoop derivation model, and some ad-hoc tests, relating to this attempt. The empirical results based on a 22-years panel of OECD countries are conforting, and they allow us to estimate the model without the distance, if properly replaced by a set of fixed effects. This article, in addition, fits in the dispute about how to estimate the multilateral resistance terms.

stat.AP↗

arXiv2013

The Relation Between Global Migration and Trade Networks

In this paper we develop a methodology to analyze and compare multiple global networks. We focus our analysis on the relation between human migration and trade. First, we identify the subset of products for which the presence of a community of migrants significantly increases trade intensity. To assure comparability across networks, we apply a hypergeometric filter to identify links for which migration and trade intensity are both significantly higher than expected. Next we develop an econometric methodology, inspired by spatial econometrics, to measure the effect of migration on international trade while controlling for network interdependencies. Overall, we find that migration significantly boosts trade across sectors and we are able to identify product categories for which this effect is particularly strong.

physics.soc-ph↗