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 the outcomes of sporting events makes it possible to undertake significant research concerning the forecasts of those events.
As a professional sports bettor, my goal is to find and take advantage of edges to earn a compounding return. Winning 55% of games is significant, and with modest bet sizing, you can grow returns quickly. Putting $10,000 into the stock market for a year and earning a 10% return is considered a great year, but your return by winning a modest 54% of your sports bets would thrash your 10% stock market return.
The SportsTrackerBot™ models make sports bets daily by casting a wide net. It "automagically" grades ALL games for every in-season sport, plus calculates the proper amount of units to risk (+EV). So each day you get a high volume of consistent, filtered selections.
My 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. My predictive models have the ability to do this daily by processing tens of thousands of data points down to single numbers for 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, and predict the game scores and differentials.
SportsTrackerBot™ is a group of analytics-based predictive models made by award-winning, industry-leading, and elite level algorithms. It evaluates and rates ALL teams against daily point spreads for the NFL, NBA, NCAAF, NCAAB, and Soccer or moneylines for MLB, and the NHL. My sports forecasting provides you with automated investing solutions. My performance data provides instant analysis by estimating the likelihood (probability) of an event and ranking teams according to the day's schedule. My proprietary version of a sport beta.
My daily sports betting picks are made by these proprietary, predictive models developed by me, Larry King. All predictive models employ aspects of machine learning (all daily data is trained, gradient descent optimization, 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. Also, all league-specific models are diligently monitored daily by several benchmarks, one being the log loss of each model.
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 I 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.