Handicapping Methodology

Soft Line and Situational Analysis

Bettors who handicap pro-sports that utilize a “point spread” for wagering, such as football and basketball, often do not realize Vegas oddsmakers are only interested in keeping bets evenly split on both teams. This way, sports betting bookmakers have a guaranteed profit from the commissions they charge on winning bets, regardless of which team actually covers the spread.

In reality, oddsmakers do not determine if team A is exactly 7.5 pts better than Team B. Oddsmakers measure whether public opinion believes Team A is exactly 7.5 points better than Team B. This causes money to be wagered fairly evenly on both teams

Yet, public opinion is often wrong when assessing team strength. So, it is not difficult to expose “weakness” or “softness” in the majority of point spreads the oddsmakers post weekly. That’s assuming we know what statistics to look at—this is where I come in. Here is an example NFL game to analyze:

My first step in analyzing the point spread Vegas posts for each NFL game is to use key offensive and defensive statistics to calculate a Team Efficiency Rating—or TER—for each team. TER is a complicated measurement that is difficult to explain fully in a single paragraph, and because it is the basis for my system, I am not at liberty to discuss exactly how it is calculated.

To give you just a brief description: TER’s are a more up-to-date version of the old fashioned “Power Rating” and I calculate them by measuring the overall ability of each team to move the ball down the field when on offense—stop their opponent from moving the ball on defense—and how these statistics are translated into points on the scoreboard. Hence the term: Efficiency Rating. TER’s can fall anywhere between -20 and +20.

After I have calculated a TER for both teams, I combine these 2 ratings with the point spread (SPR). Finally, I factor in a calculation of home field advantage. This combination of TER, SPR and home field advantage allows me to measure the accuracy of the Vegas line for any match up.

The team with the higher total has what I call a soft line advantage. Teams with the soft line advantage are usually good bets. But in some situations, I will side with the other team. To understand why, you must understand the 2nd section of my analysis. Consider the following table:

This table details different won/loss records for various situations within a certain soft line advantage “range”. Remember that in the first section you saw a calculation showing San Francisco with an advantage of 5.82 – that is 4.93 minus -0.89. This 5.82 falls into a range of 5.00 to 6.25.

This table is part of a larger grid that contains 16 distinct ranges that break down into the 4 same situations for each range for a total of 64 different “categories”. The different situations are fairly self explanatory:

1) Both teams coming off a win.
2) The team with the soft line advantage (San Francisco) off a win / their opponent
(St Louis) off a loss.
3) The team with the soft line advantage (San Francisco) off a loss / their opponent
(St Louis) off a win.
4) Both teams coming off a loss.

Situations with their record highlighted in orange are negative. Situations highlighted in green are positive. The situation that applies here is shown in bold: San Francisco off a loss; St Louis off a loss. This table tells us that teams with a soft line advantage of between 5.00 and 6.25, coming off a loss, and now facing an opponent off a loss, are 35-11 ATS since 2005.

You might notice when both teams are coming off a win, the record of a team with a soft line advantage between 5.00 and 6.25 is actually under .500 (19-23). If this were the case, I would not take San Francisco; I would pick St Louis instead.

What does this mean? Well, from week to week, every team in the league will experience the normal roller coaster of ups and downs in their level of play, and the quality of the personnel that they put on the field. These changes in momentum can sometimes be predicted by comparing the result of each team’s most recent game with a longer range statistical measure—soft line analysis, in this case.

By combining these 2 methods, I enhance the success of my mathematical formula by isolating profitable situations in which a team is exhibiting signs that its momentum is about to reverse direction, pick up speed, or stay on its current course perhaps.

This combination of statistical and situational handicapping methods allows me to stay ahead of the curve. I can take into account effects of recent team personnel changes—injuries, substitutions, or changes in coaching strategy—often before the oddsmakers get around to factoring them into the line. At the same time, I can measure the subtle emotional factors left over from a team’s most recent game that may have a small effect on their upcoming game.

I should make it clear that the type of situational analysis I employ still depends largely on my proven mathematical system. It in no way compares to the “voodoo” logic of trend handicapping.