01670nas a2200181   4500008004100000245006900041210006500110260001600175300001300191490000700204520109000211100002101301700001901322700002101341700001801362700003001380856007801410       2021                        eng d00aExplaining the difference between men’s and women’s football0 aExplaining the difference between men s and women s football  cApr-08-2021  ae02554070 v163 aWomen’s football is gaining supporters and practitioners worldwide, raising questions about what the differences are with men’s football. While the two sports are often compared based on the players’ physical attributes, we analyze the spatio-temporal events during matches in the last World Cups to compare male and female teams based on their technical performance. We train an artificial intelligence model to recognize if a team is male or female based on variables that describe a match’s playing intensity, accuracy, and performance quality. Our model accurately distinguishes between men’s and women’s football, revealing crucial technical differences, which we investigate through the extraction of explanations from the classifier’s decisions. The differences between men’s and women’s football are rooted in play accuracy, the recovery time of ball possession, and the players’ performance quality. Our methodology may help journalists and fans understand what makes women’s football a distinct sport and coaches design tactics tailored to female teams.1 aPappalardo, Luca1 aRossi, Alessio1 aNatilli, Michela1 aCintia, Paolo1 aConstantinou, Anthony, C.  uhttps://journals.plos.org/plosone/article?id=10.1371/journal.pone.025540702283nas a2200265   4500008004100000245013900041210006900180520142700249100001901676700001801695700002201713700001901735700002101754700002501775700001901800700001701819700002101836700002001857700002201877700001801899700002101917700002301938700001901961856003701980       2020                        eng d00aMobile phone data analytics against the COVID-19 epidemics in Italy: flow diversity and local job markets during the national lockdown0 aMobile phone data analytics against the COVID19 epidemics in Ita3 aUnderstanding collective mobility patterns is crucial to plan the restart of production and economic activities, which are currently put in stand-by to fight the diffusion of the epidemics. In this report, we use mobile phone data to infer the movements of people between Italian provinces and municipalities, and we analyze the incoming, outcoming and internal mobility flows before and during the national lockdown (March 9th, 2020) and after the closure of non-necessary productive and economic activities (March 23th, 2020). The population flow across provinces and municipalities enable for the modelling of a risk index tailored for the mobility of each municipality or province. Such an index would be a useful indicator to drive counter-measures in reaction to a sudden reactivation of the epidemics. Mobile phone data, even when aggregated to preserve the privacy of individuals, are a useful data source to track the evolution in time of human mobility, hence allowing for monitoring the effectiveness of control measures such as physical distancing. We address the following analytical questions: How does the mobility structure of a territory change? Do incoming and outcoming flows become more predictable during the lockdown, and what are the differences between weekdays and weekends? Can we detect proper local job markets based on human mobility flows, to eventually shape the borders of a local outbreak?1 aBonato, Pietro1 aCintia, Paolo1 aFabbri, Francesco1 aFadda, Daniele1 aGiannotti, Fosca1 aLopalco, Pier, Luigi1 aMazzilli, Sara1 aNanni, Mirco1 aPappalardo, Luca1 aPedreschi, Dino1 aPenone, Francesco1 aRinzivillo, S1 aRossetti, Giulio1 aSavarese, Marcello1 aTavoschi, Lara  uhttps://arxiv.org/abs/2004.1127801574nas a2200193   4500008004100000020001400041245005500055210005400110260001600164300001100180490000800191520100500199100002301204700002301227700001801250700001901268700002101287856007201308       2020                        eng d  a0169-023X00aPRIMULE: Privacy risk mitigation for user profiles0 aPRIMULE Privacy risk mitigation for user profiles  c2020/01/01/  a1017860 v1253 aThe availability of mobile phone data has encouraged the development of different data-driven tools, supporting social science studies and providing new data sources to the standard official statistics. However, this particular kind of data are subject to privacy concerns because they can enable the inference of personal and private information. In this paper, we address the privacy issues related to the sharing of user profiles, derived from mobile phone data, by proposing PRIMULE, a privacy risk mitigation strategy. Such a method relies on PRUDEnce (Pratesi et al., 2018), a privacy risk assessment framework that provides a methodology for systematically identifying risky-users in a set of data. An extensive experimentation on real-world data shows the effectiveness of PRIMULE strategy in terms of both quality of mobile user profiles and utility of these profiles for analytical services such as the Sociometer (Furletti et al., 2013), a data mining tool for city users classification.1 aPratesi, Francesca1 aGabrielli, Lorenzo1 aCintia, Paolo1 aMonreale, Anna1 aGiannotti, Fosca  uhttps://www.sciencedirect.com/science/article/pii/S0169023X1830534203169nas a2200469   4500008004100000245011000041210006900151520175800220100001801978700001901996700002102015700002102036700002102057700002002078700001802098700001902116700002202135700002202157700002302179700002102202700002702223700001802250700002202268700002102290700002402311700001902335700002202354700001902376700002202395700002302417700002102440700002202461700001902483700002902502700002702531700002302558700002002581700002302601700001802624700002002642856003702662       2020                        eng d00aThe relationship between human mobility and viral transmissibility during the COVID-19 epidemics in Italy0 arelationship between human mobility and viral transmissibility d3 aWe describe in this report our studies to understand the relationship between human mobility and the spreading of COVID-19, as an aid to manage the restart of the social and economic activities after the lockdown and monitor the epidemics in the coming weeks and months. We compare the evolution (from January to May 2020) of the daily mobility flows in Italy, measured by means of nation-wide mobile phone data, and the evolution of transmissibility, measured by the net reproduction number, i.e., the mean number of secondary infections generated by one primary infector in the presence of control interventions and human behavioural adaptations. We find a striking relationship between the negative variation of mobility flows and the net reproduction number, in all Italian regions, between March 11th and March 18th, when the country entered the lockdown. This observation allows us to quantify the time needed to "switch off" the country mobility (one week) and the time required to bring the net reproduction number below 1 (one week). A reasonably simple regression model provides evidence that the net reproduction number is correlated with a region's incoming, outgoing and internal mobility. We also find a strong relationship between the number of days above the epidemic threshold before the mobility flows reduce significantly as an effect of lockdowns, and the total number of confirmed SARS-CoV-2 infections per 100k inhabitants, thus indirectly showing the effectiveness of the lockdown and the other non-pharmaceutical interventions in the containment of the contagion. Our study demonstrates the value of "big" mobility data to the monitoring of key epidemic indicators to inform choices as the epidemics unfolds in the coming months.1 aCintia, Paolo1 aFadda, Daniele1 aGiannotti, Fosca1 aPappalardo, Luca1 aRossetti, Giulio1 aPedreschi, Dino1 aRinzivillo, S1 aBonato, Pietro1 aFabbri, Francesco1 aPenone, Francesco1 aSavarese, Marcello1 aChecchi, Daniele1 aChiaromonte, Francesca1 aVineis, Paolo1 aGuzzetta, Giorgio1 aRiccardo, Flavia1 aMarziano, Valentina1 aPoletti, Piero1 aTrentini, Filippo1 aBella, Antonio1 aAndrianou, Xanthi1 aDel Manso, Martina1 aFabiani, Massimo1 aBellino, Stefania1 aBoros, Stefano1 aUrdiales, Alberto, Mateo1 aVescio, Maria, Fenicia1 aBrusaferro, Silvio1 aRezza, Giovanni1 aPezzotti, Patrizio1 aAjelli, Marco1 aMerler, Stefano  uhttps://arxiv.org/abs/2006.0314102108nas a2200217   4500008004100000245005300041210005100094520145500145100002301600700002301623700002101646700002201667700001801689700002001707700002101727700002101748700002201769700002301791700001101814856006501825       2020                        eng d00a(So) Big Data and the transformation of the city0 aSo Big Data and the transformation of the city3 aThe exponential increase in the availability of large-scale mobility data has fueled the vision of smart cities that will transform our lives. The truth is that we have just scratched the surface of the research challenges that should be tackled in order to make this vision a reality. Consequently, there is an increasing interest among different research communities (ranging from civil engineering to computer science) and industrial stakeholders in building knowledge discovery pipelines over such data sources. At the same time, this widespread data availability also raises privacy issues that must be considered by both industrial and academic stakeholders. In this paper, we provide a wide perspective on the role that big data have in reshaping cities. The paper covers the main aspects of urban data analytics, focusing on privacy issues, algorithms, applications and services, and georeferenced data from social media. In discussing these aspects, we leverage, as concrete examples and case studies of urban data science tools, the results obtained in the “City of Citizens” thematic area of the Horizon 2020 SoBigData initiative, which includes a virtual research environment with mobility datasets and urban analytics methods developed by several institutions around Europe. We conclude the paper outlining the main research challenges that urban data science has yet to address in order to help make the smart city vision a reality.1 aAndrienko, Gennady1 aAndrienko, Natalia1 aBoldrini, Chiara1 aCaldarelli, Guido1 aCintia, Paolo1 aCresci, Stefano1 aFacchini, Angelo1 aGiannotti, Fosca1 aGionis, Aristides1 aGuidotti, Riccardo1 aothers  uhttps://link.springer.com/article/10.1007/s41060-020-00207-301999nas a2200181   4500008004100000245011100041210006900152300001100221490000700232520140700239100002101646700001801667700002101685700002301706700002001729700002101749856004701770       2019                        eng d00aPlayeRank: data-driven performance evaluation and player ranking in soccer via a machine learning approach0 aPlayeRank datadriven performance evaluation and player ranking i  a1–270 v103 aThe problem of evaluating the performance of soccer players is attracting the interest of many companies and the scientific community, thanks to the availability of massive data capturing all the events generated during a match (e.g., tackles, passes, shots, etc.). Unfortunately, there is no consolidated and widely accepted metric for measuring performance quality in all of its facets. In this article, we design and implement PlayeRank, a data-driven framework that offers a principled multi-dimensional and role-aware evaluation of the performance of soccer players. We build our framework by deploying a massive dataset of soccer-logs and consisting of millions of match events pertaining to four seasons of 18 prominent soccer competitions. By comparing PlayeRank to known algorithms for performance evaluation in soccer, and by exploiting a dataset of players’ evaluations made by professional soccer scouts, we show that PlayeRank significantly outperforms the competitors. We also explore the ratings produced by PlayeRank and discover interesting patterns about the nature of excellent performances and what distinguishes the top players from the others. At the end, we explore some applications of PlayeRank—i.e. searching players and player versatility—showing its flexibility and efficiency, which makes it worth to be used in the design of a scalable platform for soccer analytics.1 aPappalardo, Luca1 aCintia, Paolo1 aFerragina, Paolo1 aMassucco, Emanuele1 aPedreschi, Dino1 aGiannotti, Fosca  uhttps://dl.acm.org/doi/abs/10.1145/334317201689nas a2200193   4500008004100000245007700041210006900118300001100187490000600198520109400204100002101298700001801319700001901337700002301356700002101379700002001400700002101420856005401441       2019                        eng d00aA public data set of spatio-temporal match events in soccer competitions0 apublic data set of spatiotemporal match events in soccer competi  a1–150 v63 aSoccer analytics is attracting increasing interest in academia and industry, thanks to the availability of sensing technologies that provide high-fidelity data streams for every match. Unfortunately, these detailed data are owned by specialized companies and hence are rarely publicly available for scientific research. To fill this gap, this paper describes the largest open collection of soccer-logs ever released, containing all the spatio-temporal events (passes, shots, fouls, etc.) that occured during each match for an entire season of seven prominent soccer competitions. Each match event contains information about its position, time, outcome, player and characteristics. The nature of team sports like soccer, halfway between the abstraction of a game and the reality of complex social systems, combined with the unique size and composition of this dataset, provide an ideal ground for tackling a wide range of data science problems, including the measurement and evaluation of performance, both at individual and at collective level, and the determinants of success and failure.1 aPappalardo, Luca1 aCintia, Paolo1 aRossi, Alessio1 aMassucco, Emanuele1 aFerragina, Paolo1 aPedreschi, Dino1 aGiannotti, Fosca  uhttps://www.nature.com/articles/s41597-019-0247-701847nas a2200169   4500008004100000245014600041210006900187300000900256490000600265520126200271100001901533700001801552700002101570700001801591700001901609856004901628       2019                        eng d00aRelationship between External and Internal Workloads in Elite Soccer Players: Comparison between Rate of Perceived Exertion and Training Load0 aRelationship between External and Internal Workloads in Elite So  a51740 v93 aThe use of machine learning (ML) in soccer allows for the management of a large amount of data deriving from the monitoring of sessions and matches. Although the rate of perceived exertion (RPE), training load (S-RPE), and global position system (GPS) are standard methodologies used in team sports to assess the internal and external workload; how the external workload affects RPE and S-RPE remains still unclear. This study explores the relationship between both RPE and S-RPE and the training workload through ML. Data were recorded from 22 elite soccer players, in 160 training sessions and 35 matches during the 2015/2016 season, by using GPS tracking technology. A feature selection process was applied to understand which workload features influence RPE and S-RPE the most. Our results show that the training workloads performed in the previous week have a strong effect on perceived exertion and training load. On the other hand, the analysis of our predictions shows higher accuracy for medium RPE and S-RPE values compared with the extremes. These results provide further evidence of the usefulness of ML as a support to athletic trainers and coaches in understanding the relationship between training load and individual-response in team sports.1 aRossi, Alessio1 aPerri, Enrico1 aPappalardo, Luca1 aCintia, Paolo1 aIaia, Marcello  uhttps://www.mdpi.com/2076-3417/9/23/5174/htm01671nas a2200181   4500008004100000245008700041210006900128300001300197490000700210520107500217100001901292700002101311700001801332700001901350700002301369700001901392856007801411       2018                        eng d00aEffective injury forecasting in soccer with GPS training data and machine learning0 aEffective injury forecasting in soccer with GPS training data an  ae02012640 v133 aInjuries have a great impact on professional soccer, due to their large influence on team performance and the considerable costs of rehabilitation for players. Existing studies in the literature provide just a preliminary understanding of which factors mostly affect injury risk, while an evaluation of the potential of statistical models in forecasting injuries is still missing. In this paper, we propose a multi-dimensional approach to injury forecasting in professional soccer that is based on GPS measurements and machine learning. By using GPS tracking technology, we collect data describing the training workload of players in a professional soccer club during a season. We then construct an injury forecaster and show that it is both accurate and interpretable by providing a set of case studies of interest to soccer practitioners. Our approach opens a novel perspective on injury prevention, providing a set of simple and practical rules for evaluating and interpreting the complex relations between injury risk and training performance in professional soccer.1 aRossi, Alessio1 aPappalardo, Luca1 aCintia, Paolo1 aIaia, Marcello1 aFernàndez, Javier1 aMedina, Daniel  uhttps://journals.plos.org/plosone/article?id=10.1371/journal.pone.020126401594nas a2200169   4500008004100000245007800041210006900119260001200188300000700200490000600207520108600213100002201299700002201321700001801343700002301361856004001384       2017                        eng d00aDiscovering and Understanding City Events with Big Data: The Case of Rome0 aDiscovering and Understanding City Events with Big Data The Case  c06/2017  a740 v83 aThe increasing availability of large amounts of data and digital footprints has given rise
to ambitious research challenges in many fields, which spans from medical research, financial and
commercial world, to people and environmental monitoring. Whereas traditional data sources and
census fail in capturing actual and up-to-date behaviors, Big Data integrate the missing knowledge
providing useful and hidden information to analysts and decision makers. With this paper, we focus
on the identification of city events by analyzing mobile phone data (Call Detail Record), and we study
and evaluate the impact of these events over the typical city dynamics. We present an analytical
process able to discover, understand and characterize city events from Call Detail Record, designing
a distributed computation to implement Sociometer, that is a profiling tool to categorize phone users.
The methodology provides an useful tool for city mobility manager to manage the events and taking
future decisions on specific classes of users, i.e., residents, commuters and tourists.1 aFurletti, Barbara1 aTrasarti, Roberto1 aCintia, Paolo1 aGabrielli, Lorenzo  uhttps://doi.org/10.3390/info803007401507nas a2200121   4500008004100000245007100041210006900112300001200181520108400193100002101277700001801298856006901316       2017                        eng d00aQuantifying the relation between performance and success in soccer0 aQuantifying the relation between performance and success in socc  a17500143 aThe availability of massive data about sports activities offers nowadays the opportunity to quantify the relation between performance and success. In this study, we analyze more than 6000 games and 10 million events in six European leagues and investigate this relation in soccer competitions. We discover that a team’s position in a competition’s final ranking is significantly related to its typical performance, as described by a set of technical features extracted from the soccer data. Moreover, we find that, while victory and defeats can be explained by the team’s performance during a game, it is difficult to detect draws by using a machine learning approach. We then simulate the outcomes of an entire season of each league only relying on technical data and exploiting a machine learning model trained on data from past seasons. The simulation produces a team ranking which is similar to the actual ranking, suggesting that a complex systems’ view on soccer has the potential of revealing hidden patterns regarding the relation between performance and success.1 aPappalardo, Luca1 aCintia, Paolo  uhttp://www.worldscientific.com/doi/abs/10.1142/S021952591750014X02029nas a2200145   4500008004100000245008700041210006900128520139000197100001801587700002101605700002001626700002101646700002001667856019601687       2015                        eng d00aThe harsh rule of the goals: data-driven performance indicators for football teams0 aharsh rule of the goals datadriven performance indicators for fo3 a—Sports analytics in general, and football (soccer in
USA) analytics in particular, have evolved in recent years in an
amazing way, thanks to automated or semi-automated sensing
technologies that provide high-fidelity data streams extracted
from every game. In this paper we propose a data-driven
approach and show that there is a large potential to boost the
understanding of football team performance. From observational
data of football games we extract a set of pass-based performance
indicators and summarize them in the H indicator. We observe a
strong correlation among the proposed indicator and the success
of a team, and therefore perform a simulation on the four major
European championships (78 teams, almost 1500 games). The
outcome of each game in the championship was replaced by a
synthetic outcome (win, loss or draw) based on the performance
indicators computed for each team. We found that the final
rankings in the simulated championships are very close to the
actual rankings in the real championships, and show that teams
with high ranking error show extreme values of a defense/attack
efficiency measure, the Pezzali score. Our results are surprising
given the simplicity of the proposed indicators, suggesting that
a complex systems’ view on football data has the potential of
revealing hidden patterns and behavior of superior quality.1 aCintia, Paolo1 aPappalardo, Luca1 aPedreschi, Dino1 aGiannotti, Fosca1 aMalvaldi, Marco  uhttps://www.researchgate.net/profile/Luca_Pappalardo/publication/281318318_The_harsh_rule_of_the_goals_data-driven_performance_indicators_for_football_teams/links/561668e308ae37cfe4090a5d.pdf00491nas a2200121   4500008004100000245006900041210006900110260003100179300001400210100002300224700001800247856010400265       2015                        eng d00aTowards a Boosted Route Planner Using Individual Mobility Models0 aTowards a Boosted Route Planner Using Individual Mobility Models  bSpringer Berlin Heidelberg  a108–1231 aGuidotti, Riccardo1 aCintia, Paolo  uhttps://kdd.isti.cnr.it/publications/towards-boosted-route-planner-using-individual-mobility-models00447nas a2200109   4500008004100000245006800041210006700109100001800176700002100194700002000215856010200235       2014                        eng d00aMining efficient training patterns of non-professional cyclists0 aMining efficient training patterns of nonprofessional cyclists1 aCintia, Paolo1 aPappalardo, Luca1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/publications/mining-efficient-training-patterns-non-professional-cyclists01818nas a2200205   4500008003900000245002300039210002300062260001500085300000900100520128200109100001701391700002201408700001801430700002201448700001801470700002301488700001801511700002101529856006201550       2014                          d00aMobility Profiling0 aMobility Profiling  bIGI Global  a1-293 aThe ability to understand the dynamics of human mobility is crucial for tasks like urban planning and transportation management. The recent rapidly growing availability of large spatio-temporal datasets gives us the possibility to develop sophisticated and accurate analysis methods and algorithms that can enable us to explore several relevant mobility phenomena: the distinct access paths to a territory, the groups of persons that move together in space and time, the regions of a territory that contains a high density of traffic demand, etc. All these paradigmatic perspectives focus on a collective view of the mobility where the interesting phenomenon is the result of the contribution of several moving objects. In this chapter, the authors explore a different approach to the topic and focus on the analysis and understanding of relevant individual mobility habits in order to assign a profile to an individual on the basis of his/her mobility. This process adds a semantic level to the raw mobility data, enabling further analyses that require a deeper understanding of the data itself. The studies described in this chapter are based on two large datasets of spatio-temporal data, originated, respectively, from GPS-equipped devices and from a mobile phone network. 1 aNanni, Mirco1 aTrasarti, Roberto1 aCintia, Paolo1 aFurletti, Barbara1 aRenso, Chiara1 aGabrielli, Lorenzo1 aRinzivillo, S1 aGiannotti, Fosca  uhttps://kdd.isti.cnr.it/publications/mobility-profiling-000410nas a2200109   4500008004100000245008700041210006900128100001800197700002100215700002000236856004400256       2013                        eng d00a"Engine Matters": {A} First Large Scale Data Driven Study on Cyclists' Performance0 aEngine Matters A First Large Scale Data Driven Study on Cyclists1 aCintia, Paolo1 aPappalardo, Luca1 aPedreschi, Dino  uhttp://dx.doi.org/10.1109/ICDMW.2013.4100445nas a2200133   4500008004100000245005900041210005700100100001800157700002200175700003400197700001800231700002000249856004200269       2013                        eng d00aA Gravity Model for Speed Estimation over Road Network0 aGravity Model for Speed Estimation over Road Network1 aCintia, Paolo1 aTrasarti, Roberto1 ade Macêdo, José, Antônio F1 aAlmada, Livia1 aFereira, Camila  uhttp://dx.doi.org/10.1109/MDM.2013.8300432nas a2200121   4500008003900000245005500039210005500094260001600149100001800165700002200183700001800205856008700223       2013                          d00aInferring human activities from GPS tracks UrbComp0 aInferring human activities from GPS tracks UrbComp  aChicago USA1 aCintia, Paolo1 aFurletti, Barbara1 aRenso, Chiara  uhttps://kdd.isti.cnr.it/publications/inferring-human-activities-gps-tracks-urbcomp