A Hybrid Prediction System for American NFL Results

Abstract

This research work investigates the use of machine learning algorithms (Linear Regression and K-Nearest Neighbour) for NFL games result prediction. Data mining techniques were employed on carefully created features with datasets from NFL games statistics using RapidMiner and Java programming language in the backend. High attribute weights of features were obtained from the Linear Regression Model (LR) which provides a basis for the K-Nearest Neighbour Model (KNN). The result is a hybridized model which shows that using relevant features will provide good prediction accuracy. Unique features used are: Bookmakers betting spread and players' performance metrics. The prediction accuracy of 80.65% obtained shows that the experiment is substantially better than many existing systems with accuracies of 59.4%, 60.7%, 65.05% and 67.08%. This can therefore be a reference point for future research in this area especially on employing machine learning in predictions.

March Madness prediction: Different machine learning approaches with non‐box score statistics

“…Given that the current study predicts the outcome of a match between any two teams during the March Madness tournament, multiple different learning algorithms are evaluated to find which would produce the best predictive model of college basketball. ANN (Candila & Palazzo, 2020; Thabtah et al, 2019), kNN (Uzoma & Nwachukwu, 2015), logistic regression (Lopez & Matthews, 2015; Wilkens, 2021), random forest (Shen et al, 2016; Wilkens, 2021), and SVM (Wilkens, 2021) were selected to form the base set of the models. All methods employed in this study were implemented using Orange, an open‐source machine learning software that uses common Python open‐source libraries (Demsar et al, 2013).…”