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Network structure of UEFA Champions League teams: association with classical notational variables and variance between different levels of success

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A comprehensive review of plus-minus ratings for evaluating individual players in team sports

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SeaGlass: Enabling City-Wide IMSI-Catcher Detection

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Ordinal versus nominal regression models and the problem of correctly predicting draws in soccer

. (2001). The Economics of Football. Cambridge University Press, Cambridge. Dobson, S. & Goddard, J. (2003). Persistence in sequences of football match results: A Monte Carlo analysis. European Journal of Operational Research , 148 , 247–256. Dobson, S. & Goddard, J. (2008). Forecasting scores and results and testing the efficiency of the fixed-odds betting market in scottish league football. In: Albert, J. & Koning, R., eds., Statistical Thinking in Sports, Boca Raton, Florida, USA: Chapman & Hall, 91–110. Forrest, D., Goddard, J., & Simmons, R

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I never signed up for this! Privacy implications of email tracking

traffic. In Proceedings of the 14th Annual International Conference on Mobile Systems, Applications, and Services , pages 361–374. ACM, 2016. [35] Franziska Roesner, Tadayoshi Kohno, and David Wetherall. Detecting and defending against third-party tracking on the web. In Proceedings of the 9th USENIX conference on Networked Systems Design and Implementation , pages 12–12. USENIX Association, 2012. [36] scikit-learn. Jaccard Similarity Score. http://scikit-learn.org/stable/modules/generated/sklearn.metrics.jaccard_similarity_score.html . Online; accessed

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A comparison of competitive profiles across the Spanish football leagues

(4), 42-50. Krustrup, P., Mohr, M., Ellingsgaard, H., & Bangsbo, J. (2005). Physical demands during an elite female soccer game: importance o raining status. Medicine & Science in Sports & Exercise, 37 (7), 1242-1248. Lago-Peñas, C., & Lago-Ballesteros, J. (2011). Game location and team quality effects on performance profiles on professional soccer. Journal of Sports Science and Medicine, 10 , 465-471. Lago-Peñas, C., Gómez-Ruano, M., Megías-Navarro, D., & Pollard, R. (2016). Home advantage in football: Examining the effect of scoring first on

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Modelling Match Outcome in Australian Football: Improved accuracy with large databases

. (2013). The Elements of Statistical Learning: Data Mining, Inference, and Prediction : Springer New York. Higham, D. G., Hopkins, W. G., Pyne, D. B., & Anson, J. M. (2014). Performance indicators related to points scoring and winning in international rugby sevens. Journal of Sports Science & Medicine, 13 (2), p 358. Jacklin, P. B. (2005). Temporal changes in home advantage in English football since the Second World War: What explains improved away performance? Journal of Sports Sciences, 23 (7), pp. 669-679. Retrieved from http

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Bayesian Analysis of Skills Importance in World Champions Men’s Volleyball across Ages

. (2015). Analysis of game variables to predict scoring and performance levels in elite men's volleyball. International Journal of Performance Analysis in Sport (15), pp. 816-829. Zetou, E., Moustakidis, A., Tsigilis, N., & Komninakidou, A. (2007). Does effectiveness of skill Complex 1 predict win in Men's Olympic Volleyball Games? Journal of Quantitative Analysis in Sports , 3 (4). Zetou, E., Tsigilis, N., Moustakidis, A., & Komninakidou, A. (2006, 6). Playing characteristics of men's Olympic Volleyball teams in complex II. International Journal of

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Automated Experiments on Ad Privacy Settings
A Tale of Opacity, Choice, and Discrimination

near parity - for now: Despite gains, many see roadblocks ahead,” 2013. [32] T. Z. Zarsky, “Understanding discrimination in the scored society,” Washington Law Review, vol. 89, pp. 1375-1412, 2014. [33] R. S. Zemel, Y. Wu, K. Swersky, T. Pitassi, and C. Dwork, “Learning fair representations,” in Proceedings of the 30th International Conference on Machine Learning, ser. JMLR: W&CP, vol. 28. JMLR.org, 2013, pp. 325-333. [34] Adgooroo, “Adwords cost per click rises 26% between 2012 and 2014,” http

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A Webgis Framework for Disseminating Processed Remotely Sensed on Land Cover Transformations

on multivariate statistical modeling using remote sensing data. Environmental Modeling & Assessment, 18(5), 547-558. Coscarelli, R., Caloiero, T., Minervino, I., & Sorriso-Valvo, M. (2015). Sensitivity to desertification of a high productivity area in Southern Italy. Journal of Maps, 1-9. Figorito, B., Mancini, F., Novelli, A., & Tarantino, E. (2014). Monitoring land cover changes at watershed scale using LANDSAT imagery. Score@Poliba. Han, L., Zhang, Z., Zhang, Q., & Wan, X. (2015). Desertification assessments in the

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