Section 4: Handicapping Theory 3/3 (Combined Analysis)
The key to the research on my methods, first done in 2004 and updated every year, was finding a way to combine my situational analysis, fundamental indicators and my math model to give me an overall chance of a team covering at any given number.
As an example, consider a hypothetical game between USC and Notre Dame. USC applies to a 140-60-5 ATS situation that uses 6 parameters, but Notre Dame applies to a statistical profile indicator with a record of 86-28-4 ATS. My math model favors USC by 13.9 points when they are only a 7 point favorite in reality. As discussed above, a situation with a record of 140-60-5 and 6 parameters has a 55.1% chance of winning if the line is fair. The fundamental indicator favoring the Notre Dame has a 55.6% chance of winning given a fair line, and my math model would give the Trojans a 56.9% chance of covering at a line of -7 points. The trick is assigning a point value to the situation and the fundamental indicator based on their chance of covering at a fair line. I simply put everything in terms of points based on the relationship between point differentials and the chance of covering of my math model. Each point difference in my math model is worth about 1.0% in chance of covering, so each percentage point away from 50% is worth 1.0 points (1/1.0). In this case, the situation favoring USC is worth 5.1 points while the fundamental indicator favoring Notre Dame is worth 5.6 points. My math model favors USC by 13.9 points, so adding the value of the situation and the indicator would result in an overall prediction of the Trojans by 13.4 points (+5.1 – 5.6 + 13.9 = 13.4), which would give them a 56.4% chance of covering at the line of -7 points if each point differential from the line is worth 1.0%.
That illustration is a simplified version of what I do to assign an overall chance of covering to a team, as any such analysis obviously has to take into account key numbers (i.e. 3, 7, 10, etc). Things also can become a lot more complicated when there are multiple situations and indicators applying to a particular game – which is most often the case, but my years of studying probability theory have given me the tools to sort through it all and come up with an accurate measure of the overall affect of the situations and indicators.
A lot of handicappers use situational analysis and math models in their handicapping but few, if any, of them have studied the predictability of their methods, as I have, or found a realistic way of combining their methods for an overall measure of predicted success on every game.