1,721,061 research outputs found
Spatial gravity models for international trade: a panel analysis among OECD countries
Full text linkThe Gravity Model is the workhorse for empirical studies in International Economies and it is commonly used in explaining the trade flow between countries. Recently, several studies have showed the importance of taking into account the spatial effect. Spatial Econometric techniques meet this matter, proposing the specification of a set of models and estimators. We will make use of these Spatial Econometric techniques in order to estimate a Spatial Gravity of Trade for a 22-year-long panel of the OECD countries. The aim, therefore, is twofold: on one hand, we are going to use the newest Spatial Econometric techniques in a field where they aren't widely applicated. On the other hand, we provide an updated interpretation of the behaviour of the International Trade in an OECD context, taking into account potential spatial spillover effect due to the third country dependence, and the effects of the migratory phenomenon
Filtering procedures for sensor data in basketball
Full text linkBig Data Analytics help team sports’ managers in their decisions by processing a number of differ-
ent 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
Modelling the dynamic pattern of surface area in basketball and its effects on team performance
No full textDue to the advent of GPS techniques, a wide range of scientific literature on Sport Science is nowadays devoted to the analysis of players movement in relation to team performance in the context of big data analytics. A specific research question regards whether certain patterns of space among players affect team performance, from both an offensive and a defensive perspective. Using a time series of basketball players coordinates, we focus on the dynamics of the surface area of the five players on the court with a twofold purpose: (i) to give tools allowing a detailed description and analysis of a game with respect to surface areas dynamics and (ii) to investigate its influence on the points made by both the team and the opponent.
We propose a three-step procedure integrating different statistical modelling approaches. Specifically, we first employ a Markov Switching Model (MSM) to detect structural changes in the surface area. Then, we perform descriptive analyses in order to highlight associations between regimes and relevant game variables. Finally, we assess the relation between the regime probabilities and the scored points by means of Vector Auto
Regressive (VAR)models. We carry out the proposed procedure using real data and, in the analyzed case studies, we find that structural changes are strongly associated to offensive and defensive game phases and that there is some association between the surface area dynamics and the points scored by the team and the opponent
Air quality and traffic restriction in Milan: a counterfactual analysis based on a Matrix Completion Approach
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Spatio-Temporal Movements in Team Sports: A Visualization approach using Motion Charts
Full text linkA new approach to performance analysis in team sports consists in study-
ing movements and trajectories of players during the game. State of the
art tracking systems produce spatio-temporal traces of players that have fa-
cilitated a variety of research aimed to to extract insight from trajectories.
Several methods borrowed from machine learning, network and complex sys-
tems, geographic information system, computer vision and statistics have
been proposed. However, the use of an effective and easy-to-use visual tool
in support to these methods is of major importance. To this scope this pa-
per suggests the use of motion charts, built by means of the open-source
gvisMotionChart function in googleVis package in R, a user-friendly pro-
cedure that also allows to easily import data. A basketball case study is
presented. Data refers to a match played by an italian team militant in
C-gold league on March 22nd, 2016. Analyses show that motion charts give
insights on different spacing structures among offensive and defensive actions,
corroborating evidences from other supporting analyses
Modeling and forecasting traffic flows with mobile phone big data in flooding risk areas to support a data-driven decision making
Full text linkFloods are one of the natural disasters which cause the worst human, social and economic impacts to the detriment of both public and private sectors. Today, public decision-makers can take advantage of the availability of data-driven systems that allow to monitor hydrogeological risk areas and that can be used for predictive purposes to deal with future emergency situations. Flooding risk exposure maps traditionally assume amount of presences constant over time, although crowding is a highly dynamic process in metropolitan areas. Real-time monitoring and forecasting of people’s presences and mobility is thus a relevant aspect for metropolitan areas subjected to flooding risk. In this respect, mobile phone network data have been used with the aim of obtaining dynamic measure for the exposure risk in areas with hydrogeological criticality. In this work, we use mobile phone origin-destination signals on traffic flows by Telecom Italia Mobile (TIM) users with the aim of forecasting the exposure risk and thus to help decision-makers in warning to who is transiting through that area. To model the complex seasonality of traffic flows data, we adopt a novel methodological strategy based on introducing in a Vector AutoRegressive with eXogenous variable (VARX) model a Dynamic Harmonic Regression (DHR) component. We apply the method to the case study of the “Mandolossa”, an urbanized area subject to flooding located on the western outskirt of Brescia, using hourly-basis data from September 2020 to August 2021. A cross validation based on the hit-rate and the mean absolute percentage error measures show a good forecasting accurac
Measuring players' importance in basketball using the generalized Shapley value
Full text linkMeasuring players’ importance in team sports to help coaches and staff with the aim of winning the game is gaining relevance, mainly because of the advent of new data and advanced technologies. In this paper we evaluate each player’s importance - for the first time in basketball - as his/her average marginal contribution to the utility of an ordered subset of players, through a generalized version of the Shapley value, where the value assumed by the generalized characteristic function of the generalized coalitional game is expressed in terms of the probability a certain lineup has to win the game. In turn, such probability is estimated by applying a logistic regression model in which the response is represented by the game outcome and the Dean’s factors are used as explanatory features. Then, we estimate the generalized Shapley values of the players, with associated bootstrap confidence intervals. A novelty, allowed by explicitly considering single lineups, is represented by the possibility of forming best lineups based on players’ estimated generalized Shapley values conditional on
specific constraints, such as an injury or an “a-priori” coach’s decision. A comparison of our proposed approach with industry-standard counterparts shows a strong linear relation. We show the application of our proposed method to seventeen full NBA seasons (from 2004/2005 to 2020/21). We eventually estimate generalized Shapley values for Utah Jazz players and we show how our method is allowed to be used to form best lineups
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