It is well known that data & analytics have changed sports in a big way.
This is quite apparent in basketball – where the game has changed dramatically over the last thirty years – with players focusing on taking three-pointer shots as opposed to (“easier”) two-pointers, because in the long run the (points) value outweighs the risk.
Three-pointers offer more positive expected value (EV) in basketball than two pointers – so in the modern NBA the play strategy has changed, and on average, teams that take more three-point shots ultimately score more points in any given game.
MACHINE LEARNING AND AI SPORTS BETTING PICKS
Professional data scientists and sports handicappers have adapted to the new data-rich world in which we live, leveraging powerful analysis, visualization tools, access to reliable sports datasets and APIs, and neural networks and AI in order to get an edge over other bettors, and bookmakers.
UFC Fight prediction with AI
Prediction models can be trained and tested on thousands of historical fights and AI and deep-learning can be used to predict fight outcomes, for example using Keras to build a neural network model with an input layer, 2 hidden layers and 1 output layer – as follows:
model = Sequential()
model.compile(loss=’binary_crossentropy’, optimizer=’Adam’, metrics=[‘accuracy’])
After training on historical odds, such a model can achieve 70%+ accuracy on the training set and 74% on the test set.
“It’s possible to build accurate prediction models for sports-betting – but profit is not guaranteed” explains Thomas Nielsen, a data scientist at sports odds website Vegas-odds.com.
“Due to how sportsbooks odds and lines move (with bookmakers changing the odds on players to keep the “book balanced” and make a profit) – being able to pick winners 75% of the time does not mean you are guaranteed to make profit!”
Once your prediction model is complete, it’s possible to backtest your predictions with historical betting odds and sports statistics for the fights or sport in question to work out how profitable your neural network would be if you had bet all its selections for specific time intervals.