@techreport{TR-IC-PFG-18-08,
   number = {IC-PFG-18-08},
   author = {Renato Toshiaki {Shibata} and Hélio {Pedrini}},
   title  =  {{Data  Mining  Approach  to Prediction of Nominee Soccer
                   Players for Ballon d’Or Award}},
   month = {July},
   year = {2018},
   institution = {Institute of Computing, University of Campinas},
   note = {In English, 38 pages.
    \par\selectlanguage{english}\textbf{Abstract}
       This  is  a dissertation of Computer Engineering Bachelor Final
       Thesis,  course  whose initials are MC030, offered by Institute
       of  Computing  UNICAMP  in  the first semester of 2018. In this
       paper,  a data set of professional football players’ historical
       in-game statistics from recent years were built through Feature
       Engineering   methodology  and  then  it  was  applied  Machine 
       Learning  algorithms  in order to characterize players who were
       nominated  to  the  annually award called Ballon d’Or. That was
       approached  as  a  classification  problem, where the predictor
       should classify correctly whether the current player, according
       to his annual performance, will be nominated to the Ballon d’Or
       award  at  the  end of the year or not. We applied some Machine
       Learning   and   Data  Mining  algorithms  like:  ZeroR,  OneR, 
       Iterative Dichotomiser 3 (ID3), Logistic Regression and Support
       Vector  Machine  (SVM). The obtained results by each method are
       discussed  and  compared  between them in order to evaluate how
       accurate they are according to a football expert’s opinion.
  }
}