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Basketball predictions in the NCAAB and NBA: Similarities and differences
Author(s) -
Zimmermann Albrecht
Publication year - 2016
Publication title -
statistical analysis and data mining: the asa data science journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.381
H-Index - 33
eISSN - 1932-1872
pISSN - 1932-1864
DOI - 10.1002/sam.11319
Subject(s) - basketball , league , contrast (vision) , outcome (game theory) , psychology , association (psychology) , statistics , applied psychology , computer science , artificial intelligence , mathematics , geography , mathematical economics , physics , archaeology , astronomy , psychotherapist
Most work on predicting the outcome of basketball matches so far has focused on National College Athletics Association Basketball (NCAAB) games. Since NCAAB and professional (National Basketball Association, NBA) basketball have a number of differences, it is not clear to what degree these results can be transferred. We explore a number of different representations, training settings, and classifiers, and contrast their results on NCAAB and NBA data. We find that adjusted efficiencies work well for the NBA, the NCAAB regular season is not ideal for training to predict its post‐season, the two leagues require classifiers with different bias, and Naïve Bayes predicts the outcome of NBA playoff series well. © 2016 Wiley Periodicals, Inc. Statistical Analysis and Data Mining: The ASA Data Science Journal, 2016