A new method for comparing rankings through complex networks: Model and analysis of competitiveness of major European soccer leagues

In this paper, we show a new technique to analyze families of rankings. In particular, we focus on sports rankings and, more precisely, on soccer leagues. We consider that two teams compete when they change their relative positions in consecutive rankings. This allows to define a graph by linking teams that compete. We show how to use some structural properties of this competitivity graph to measure to what extend the teams in a league compete. These structural properties are the mean degree, the mean strength, and the clustering coefficient. We give a generalization of the Kendall's correlation coefficient to more than two rankings. We also show how to make a dynamic analysis of a league and how to compare different leagues. We apply this technique to analyze the four major European soccer leagues: Bundesliga, Italian Lega, Spanish Liga, and Premier League. We compare our results with the classical analysis of sport ranking based on measures of competitive balance.This paper was partially supported by Spanish MICINN Funds and FEDER Funds MTM2009-13848, MTM2010-16153 and MTM2010-18674, and Junta de Andalucia Funds FQM-264.Criado Herrero, R.; García González, E.; Pedroche Sánchez, F.; Romance, M. (2013). A new method for comparing rankings through complex networks: Model and analysis of competitiveness of major European soccer leagues. Chaos. 23(4):1-10. https://doi.org/10.1063/1.4826446S110234Dobson, S., & Goddard, J. (2009). The Economics of Football. doi:10.1017/cbo9780511973864Kendall, M. G., & Smith, B. B. (1939). The Problem of $m$ Rankings. The Annals of Mathematical Statistics, 10(3), 275-287. doi:10.1214/aoms/1177732186KENDALL, M. G. (1938). A NEW MEASURE OF RANK CORRELATION. Biometrika, 30(1-2), 81-93. doi:10.1093/biomet/30.1-2.81Fagin, R., Kumar, R., Mahdian, M., Sivakumar, D., & Vee, E. (2006). Comparing Partial Rankings. SIAM Journal on Discrete Mathematics, 20(3), 628-648. doi:10.1137/05063088xLegendre, P. (2005). Species associations: the Kendall coefficient of concordance revisited. Journal of Agricultural, Biological, and Environmental Statistics, 10(2), 226-245. doi:10.1198/108571105x46642Emond, E. J., & Mason, D. W. (2002). A new rank correlation coefficient with application to the consensus ranking problem. Journal of Multi-Criteria Decision Analysis, 11(1), 17-28. doi:10.1002/mcda.313Blumm, N., Ghoshal, G., Forró, Z., Schich, M., Bianconi, G., Bouchaud, J.-P., & Barabási, A.-L. (2012). Dynamics of Ranking Processes in Complex Systems. Physical Review Letters, 109(12). doi:10.1103/physrevlett.109.128701Radicchi, F. (2011). Who Is the Best Player Ever? A Complex Network Analysis of the History of Professional Tennis. PLoS ONE, 6(2), e17249. doi:10.1371/journal.pone.0017249Chartier, T. P., Kreutzer, E., Langville, A. N., & Pedings, K. E. (2011). Sensitivity and Stability of Ranking Vectors. SIAM Journal on Scientific Computing, 33(3), 1077-1102. doi:10.1137/090772745Park, J., & Newman, M. E. J. (2005). A network-based ranking system for US college football. Journal of Statistical Mechanics: Theory and Experiment, 2005(10), P10014-P10014. doi:10.1088/1742-5468/2005/10/p10014Callaghan, T., Mucha, P. J., & Porter, M. A. (2007). Random Walker Ranking for NCAA Division I-A Football. The American Mathematical Monthly, 114(9), 761-777. doi:10.1080/00029890.2007.11920469Motegi, S., & Masuda, N. (2012). A network-based dynamical ranking system for competitive sports. Scientific Reports, 2(1). doi:10.1038/srep00904Pawlowski, T., Breuer, C., & Hovemann, A. (2010). Top Clubs’ Performance and the Competitive Situation in European Domestic Football Competitions. Journal of Sports Economics, 11(2), 186-202. doi:10.1177/1527002510363100A. Feddersen and W. Maennig, “ Trends in competitive balance: Is there evidence for growing imbalance in professional sport leagues?” Hamburg contemporary economic discussions No. 01/2005, University of Hamburg, 2005.Pedroche Sánchez, F. (2010). Competitivity groups on social network sites. Mathematical and Computer Modelling, 52(7-8), 1052-1057. doi:10.1016/j.mcm.2010.02.031PEDROCHE, F. (2012). A MODEL TO CLASSIFY USERS OF SOCIAL NETWORKS BASED ON PAGERANK. International Journal of Bifurcation and Chaos, 22(07), 1250162. doi:10.1142/s0218127412501623Pedroche, F., Moreno, F., González, A., & Valencia, A. (2013). Leadership groups on Social Network Sites based on Personalized PageRank. Mathematical and Computer Modelling, 57(7-8), 1891-1896. doi:10.1016/j.mcm.2011.12.026García, E., Pedroche, F., & Romance, M. (2013). On the localization of the personalized PageRank of complex networks. Linear Algebra and its Applications, 439(3), 640-652. doi:10.1016/j.laa.2012.10.051BOCCALETTI, S., LATORA, V., MORENO, Y., CHAVEZ, M., & HWANG, D. (2006). Complex networks: Structure and dynamics. Physics Reports, 424(4-5), 175-308. doi:10.1016/j.physrep.2005.10.009Humphreys, B. R. (2002). Alternative Measures of Competitive Balance in Sports Leagues. Journal of Sports Economics, 3(2), 133-148. doi:10.1177/152700250200300203M. Kringstad, “ Competitive balance in complex professional sports leagues,” Doctoral thesis (The University of Leeds. Leeds University Business School, 2008).Owen, P. D., Ryan, M., & Weatherston, C. R. (2007). Measuring Competitive Balance in Professional Team Sports Using the Herfindahl-Hirschman Index. Review of Industrial Organization, 31(4), 289-302. doi:10.1007/s11151-008-9157-

Project of a group of multi-purpose mobile robots using advanced technologies

Niniejszy artykuł prezentuje wyniki prac dotyczących grupy wielozadaniowych robotów mobilnych wykorzystujących zaawansowane technologie. Opracowane roboty pozwalają wspomagać człowieka w realizacji zadań w środowisku mogącym stwarzać zagrożenie. Grupa składa się ze zdalnie sterowanych robotów: robota transportowego, robota eksploracyjnego oraz małych robotów monitorujących. Grupa robotów umożliwia m.in. monitorowanie oraz dokonywanie pomiarów wybranych wielkości fizycznych na terenie dowolnego obiektu, a następnie zdalne przesyłanie danych do użytkownika.The paper presents results of the research concerning a group of multitasking mobile robots that use advanced technologies. The developed robots allow aiding humans in accomplishing tasks in an environment that may be dangerous. The group consists of remote controlled robots: a transporting robot, an exploring robot, and small monitoring robots. The group of robots is capable of monitoring and carrying out measurements of selected physical quantities, that can occur within the territory of any object, and then remote transmission of data to the user

Hospitals with and without neurosurgery: a comparative study evaluating the outcome of patients with traumatic brain injury

Background: We leveraged the data of the international CREACTIVE consortium to investigate whether the outcome of traumatic brain injury (TBI) patients admitted to intensive care units (ICU) in hospitals without on-site neurosurgical capabilities (no-NSH) would differ had the same patients been admitted to ICUs in hospitals with neurosurgical capabilities (NSH). Methods: The CREACTIVE observational study enrolled more than 8000 patients from 83 ICUs. Adult TBI patients admitted to no-NSH ICUs within 48 h of trauma were propensity-score matched 1:3 with patients admitted to NSH ICUs. The primary outcome was the 6-month extended Glasgow Outcome Scale (GOS-E), while secondary outcomes were ICU and hospital mortality. Results: A total of 232 patients, less than 5% of the eligible cohort, were admitted to no-NSH ICUs. Each of them was matched to 3 NSH patients, leading to a study sample of 928 TBI patients where the no-NSH and NSH groups were well-balanced with respect to all of the variables included into the propensity score. Patients admitted to no-NSH ICUs experienced significantly higher ICU and in-hospital mortality. Compared to the matched NSH ICU admissions, their 6-month GOS-E scores showed a significantly higher prevalence of upper good recovery for cases with mild TBI and low expected mortality risk at admission, along with a progressively higher incidence of poor outcomes with increased TBI severity and mortality risk. Conclusions: In our study, centralization of TBI patients significantly impacted short- and long-term outcomes. For TBI patients admitted to no-NSH centers, our results suggest that the least critically ill can effectively be managed in centers without neurosurgical capabilities. Conversely, the most complex patients would benefit from being treated in high-volume, neuro-oriented ICUs