A statistical model to predict the results of Novak Djokovic's matches in the Australian open tennis event using the binary logistic regression

The purpose of the research was to construct a model that could forecast probability winning in case Novak Djokovic men’s singles grand slam event Australian Open and determine relative relevance match data contribute victory. A total number 147 matches were recorded for all nine years i.e., from 2013 2021, first round exit over years. One few assumptions logistic regression is dependent variable must be binary nature. Therefore, selected this study Match Outcome (Win/Loss). Ace, (DF) Double Fault, (FS) First Serve, (FSPW) serve point win, (SSPW) second (BPC) Breakpoint converted, (TPW) Total win as predictor variables. All collected ATP world tour.com. In order accomplish goals research, only competed during Grand Slam AO (Australian Open), analyzed. prediction likelihood Mr. or losing open by fitting model. According statistical significance variables, they numerically weighted can used predict outcome. Out seven Converted included with coefficient determination (R2) of.424 (Cox & Snell) .588 (Nagelkerke). adds independent variables one played 2021. given result it verifies conclusive evidence fits quite well classifies an 88.9% probability.