All of The United Sports of America's predictive models employ aspects of machine learning (all daily data is trained, gradient descent optimization, estimating expected values, etc.). Machine learning is a method of data analysis that automates analytical model building and is based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.
Like a Wall St. firm employing quants and algorithms to improve their trading ability,
sports predictive modeling involves data management, predictive models, and information systems to predict specific sport-related outcomes. Predicting the outcome of sporting events most definitely falls within the purview of the field of forecasting. Plus, the massive amount of sport data regarding game outcomes makes it possible to undertake significant research concerning the forecasts of those events.
Our predictive models use data-mining to collect, clean, process, analyze, and gain useful insight from a large amount of external data. Data-mining starts when methods to collect data is employed and ends when the results and recommendations for a specific system are given through analysis of that collected data. The most important part is the transformation of massive data into a standardized format one can comprehend more easily. Our predictive models have the ability to do this daily by processing millions of data points down to single numbers for low-effort, superior decision-making.
When constructing a predictive system of individuals or teams, some issues need to be addressed. Any predictive system should order all teams, compare teams, adjust for the quality of the opponents, predict outcomes of games, predict the game scores, and the various win probabilities.
TheUSA uses a group of analytics-based predictive models that evaluate and rate ALL teams against daily point spreads for the NFL, NBA, NCAAF, and NCAAB, or moneylines for MLB and the NHL. Our performance data provides instant analysis by estimating the likelihood (probability) of an event and ranking teams according to the day's schedule. Our company's unique version of a sport beta.
Information Systems are the networks of hardware and software that people and organizations use to collect, filter, process, create and also distribute data. Time for a quick word association game: If we say, “college football analytics,” what do you think of? Something involving Moneyball or when to go for it on fourth down, right? A standard definition for sports analytics is gathering information and applying it in a way that derives a competitive advantage. Translation: doing what football coaches do, only with more help from computers. Analytics are a path toward a winning edge. They see every game. They separate emotion from reality. They separate what you can control from what you cannot.
Mathletics: How Gamblers,
Managers, and Sports
Mathematics in Baseball,
Basketball, and Football
by Wayne Winston
Princeton University Press
How Do Bettors
Make Money Gambling?
Let p = probability that a gambler wins a point spread bet. If 10p-11(1-p) = 0, our expected profit on a bet equals 0. We find that p = 11/21 = .524 makes our expected profit per bet equal to 0. Therefore, if we can beat the spread or totals more than 52.4% of the time we can make money. Suppose we are really good at picking games and can win 57% of our bets. What would be our expected profit per dollar invested? Our expected return per dollar invested is (.57(10)+.43(-11))/11 = 8.8%. Thus if we can pick winners 57% of the time we can make a pretty good living betting.