Applying Data Analytics as an Alternative to Subjective Rankings of Players in Fantasy Basketball

This paper demonstrates the ranking of players for fantasy basketball using one of the platforms of Multi Criteria Decision Making (MCDM), the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method. Specially, it compares results of TOPSIS generated fantasy rankings from the 2016-2017 NBA Season against industry fantasy experts’ 2017-2018 NBA pre-season rankings. Fantasy experts combine various techniques to create their rankings. Frequently blending quantitative and qualitative factors in order to project bottom-up rankings, they incongruently mix subjective and objective criterion. Conversely, TOPSIS is a mathematical way of doing literally what its name describes, ranking by a predetermined preference. The best ranking should be closest to the positive ideal solution and be the furthest away from the least ideal, or negative, solution. This model allows a user to subjectively or objectively select a weighting criteria, determined by scarcity of statistics, and find the solutions that are most positively aligned to the ideal solution, or how the ideal player should perform. As a result, TOPSIS ranks players based on “super-player” attributes and selects them to identify the players with qualities that will most help and least hurt their fantasy basketball team. Notably, the comparison reveals TOPSIS as a better forecast of individual players’ statistics and rankings for the 2017-2018 NBA season and a superior option beyond the Top-100 players. The analysis and results demonstrate how the TOPSIS method can be incorporated in different fantasy basketball leagues and settings