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However, the often used ordinal models have an assumption of proportional odds: the influence of an independent variable on the log odds is the same for each outcome. This paper illustrates how ordinal regression models therefore fail to fully utilize independent variables that contain information about the likelihood of matches ending in a draw. However, in practice, this flaw does not seem to have a substantial effect on the predictive accuracy of an ordered logit regression model when compared to a multinomial logistic regression model.<\/jats:p>","DOI":"10.1515\/ijcss-2017-0004","type":"journal-article","created":{"date-parts":[[2017,7,24]],"date-time":"2017-07-24T10:01:00Z","timestamp":1500890460000},"page":"50-64","source":"Crossref","is-referenced-by-count":3,"title":["Ordinal versus nominal regression models and the problem of correctly predicting draws in soccer"],"prefix":"10.1515","volume":"16","author":[{"given":"L. 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