Using machine learning algorithms to predict whether an NBA team will cover the spread
Application : https://william-li.shinyapps.io/nbamoneylineprediction/
Report : https://rpubs.com/itswill273/706346
This exercise in machine learning will build models to predict whether or not a team beats its pregame betting spread. Point spreads are a method used by bookkeepers to handicap games. Bettors can bet the over or under on the spread. For example if a game between the Raptors and the Lakers is set at -6.5, that means the Raptors are expected to beat the Lakers by 6.5 points.
If the Raptors beat the Lakers by 6 points or less or the Raptors lose, the bettors who bet under the spread win their bets. If the Raptors win by 7 points or more the Raptors have “beat the spead” and the bettors who bet on the over will win their bets. Losing bets lose 100% of their money and winning bets win a certain number of cents to their dollar usually around $0.90 per dollar bet.
Data will be scraped from the web and the “Caret” package will be used to build machine learning models. Other complementary packages will be used to produce visuals and implement machine learning models.
This exercise in machine learning will build models to predict whether or not a team beats its pregame betting spread. Point spreads are a method used by bookkeepers to handicap games. Bettors can bet the over or under on the spread. For example if a game between the Raptors and the Lakers is set at -6.5, that means the Raptors are expected to beat the Lakers by 6.5 points.