In recent years, trading of sports cards has created a great attraction among collectors and investors. Although sports cards investment is considered for many a high risk, this risk could be reduced creating a long-term portfolio. For example, buying rookie cards (RC) from professional sports players in the early years of his/her career and holding them expecting until they are considered for the Hall of Fame. However, it is difficult to predict whether a player will likely be nominated for the Hall of Fame since their career depends on many factors on and off the field. Therefore, the purpose of this research is to create a model that helps collectors and investors to predict if a professional sports player will be inducted into a Hall of Fame (HOF). To achieve this aim, the authors focused their work on the Baseball game, used the SCRUM methodology, and analyzed current Baseball Hall of Famer players performance statistics. The final logistic regression model consisted of seven independent variables and predict the induction of current baseball players in the HOF with an accuracy of 98 percent.
A Logistic Regression Model to Predict the Induction of Professional Baseball Players into the Hall of Fame
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