2019, Vol. 4, Issue 1
Forecasting the winner of pro kabaddi league matches
Author(s): Amritashish Bagchi, Shiny Raizada, Aniket Mhatre and Anthony Augustine
The purpose of the study was to develop a prediction model to forecast the outcome of pro kabaddi league matches. These probabilities can assist a coach, team captain or manager in considering a certain tactics for the other half. The data was collected from 2017 season of Pro Kabaddi League (PKL). A total data of 272 matches were recorded, out of which 32 matches were draw and therefore not included in the study. The dependent variable selected for this study was Match Outcome (Win/Loss). Raid Points, Tackle points, All Out Points and Extra Points were selected as the predictor variables. For the purpose of this study only the first half data was used and in statistical technique Binary Logistic regression was used to predict the outcome of a match (Win/Loss). The result indicates that the developed Logistic regression Model was significant. According to the statistical significance of the predictor variables, they were numerically weighted and can be used to predict the match outcome. All the predictor variables except Extra Points were included in the prediction model with coefficient of determination (R2) of.203 (Cox & Snell) and .271 (Nagelkerke). The classification matrix shows that 68.8% of match results were correctly classified by the model.
Pages: 383-386 | 217 Views 4 Downloads
How to cite this article:
Amritashish Bagchi, Shiny Raizada, Aniket Mhatre, Anthony Augustine. Forecasting the winner of pro kabaddi league matches. Int J Physiol Nutr Phys Educ 2019;4(1):383-386.