Simulators have been used as effective tools in nearly every business sector of the global economy. They are used in hospitals in help predict the staging and prognosis for cancer patients and they are used by credit card companies to anticipate fraudulent activity before it occurs.
Simulators have been quite useful in predicting the future of economic activity and the subsequent price movements of stock, commodities, and currencies. The ability to predict the future accurately and be financially rewarded accordingly has grown exponentially with the mammoth advances in technology over the past two decades.
The ability to organize and structure enormous databases of information has become the foundation of nearly every business enterprise. The goal is to simply learn from the past mistakes and successes and then apply that accumulated knowledge to the current environment. Ultimately, our goal here is to learn when teams have the greatest probabilities to win or lose game.
The statistical method called backpropagation is the foundation for many numerical problems. For example, currency prices for a specific country can be forecast based on current levels of economic activity and through the use of historical patterns. History does not always repeat itself in the financial markets, but there are times when historical outcomes are very useful to predicting a future movement of a stock market.
There are similarities between sports betting and the stock market. In both cases, the linesmaker (market maker) does not have the priority of wanting a specific outcome. Their primary focus is to determine what the market will bear and establish a price level where there are equal buyers and sellers. The profits of both types of business are essentially made the same way. For the financial markets, commissions, and market pricing make up the large part of profits. In sports gambling, the betting odds maker charges a 10% ‘fee’ for the opportunity to bet a game.
Backpropagation was developed by Paul Werbos in the early 1970’s and grew in popularity and acceptance with the work done by Rumelhart and McClelland in 1986. This work was centered on proving why the human brain is more powerful than a computer in being able to learn from experience. They used several case examples, one of which was how children learn the past tense of a verb. I will not bore you any longer with the details of this work, but it is worth noting as it is landmark research in the evolution of artificial intelligence modeling.
Backpropagation and Sports Outcomes
With high powered computers now available on a laptop, large statistical databases on sports stats can be saved and manipulated to form new information. The BCS standings in College Football are a perfect example of the direct output from a large pool of data. Of course, too, it is not a perfect science, and the computer rankings associated with the BCS poll have been largely scrutinized and their validity debated endlessly. However, I have found that using backpropagation can determine the outcome of sporting events generating enough winners to make it a viable investment option. Sports betting has tremendous risk and in my opinion, must be done in a highly disciplined and controlled manner. We all know how fast money can be lost simply betting on a ‘gut feel’ or ‘with the heart’. So, I have always emphasized the need to recognize that none of us truly knows what the exact final score will be for a game and no matter how strong the play appears to be, it can lose against the spread. Wagering the same amount per * unit and not allowing any emotion to enter into the decision making process is essentially the only way to make money in sports wagering.
The simplest example of a model would be to use two inputs to produce one output. For example, we could use offensive and defensive scoring stats for two teams to predict which team will win the game and by how much. However, this single-layered approach has obvious shortcomings; these start with the location of the game, time of year, what part of the season, common opponents and the like.
What the high powered computers allow us to do that our brains cannot is to process large mountains of data. So, in the case of baseball, 15 years of box scores and all the games stats can be built into a large database. A computer programmed with a backpropagation algorithm can quickly determine and match historical situations that match the current game’s matchup. So, the model can alert us to a possible money-making opportunity.
In any given neural network simulator, there are hundreds of input variables. Adjusting these weights is always necessary to get the most reliable and meaningful results. For example, 30 years ago, starting pitchers in baseball completed a large number of their games. In the modern game, pitching has evolved and now we have stats named quality starts, and the relief pitching has become a very important part of the game. So, the weightings for bullpens is far greater now than it was in decades past.
This is just a brief start to artificial intelligence modeling and neural network simulators. In the weeks ahead, I will delve into more specific definitions and provide enough information so that you can try to build one on your own to augment your current handicapping methods.