
If G(g) = constant for all games for all teams, then the result is a pure combination of winning percentage, opponents' winning percentage, opponents' opponents' winning percentage, and so on. This algorithm is Boyd Nation's original Iterative Strength Rating.
We can account for the game location by reducing the constant for a home win and increasing it for a home loss, with corresponding increases for road wins and decreases for road losses. Empirical results for division one baseball suggest a 10 percent adjustment. My implementation for basketball uses 16 percent based upon a much smaller sample size than Boyd had available for baseball.
The ISR is an improvement over static formulas such as the RPI or PI2, because it dynamically "discovers" an appropriate weight for OWP, OOWP, etc. based upon how a team's schedule fits within the overall games graph. It does not explicitly use scoring data.
To incorporate scoring information, instead of a constant we can assign a value to each game based upon the percentage of total points scored by the winner. One measure that combines the ability to score and ability to prevent scores was developed to characterize NFL games, and is called Strength of Victory:
SOV_{game} =  (winning score  losing score) 
( winning score + losing score ) 
When we set
G(g) =  (average points per game per team)  ×  SOV_{game} 
(average SOV for all games) 
Accounting for the Home Advantage in the ISOV is more complicated than in the ISR. The HA must be expressed in units of points, not percentage of wins, and there just aren't enough sameteam differentvenue data points within a season to support an ideal derivation. To provide at least some distinction, my implementation uses 1/2 the difference between average visitors' scores and average home teams' scores (ignoring neutralsite games) and reduces the home teams' scores by that amount in the calculation of game SOVs.
We can use the average SOV and average total points scored to define a standard game that characterizes a sport. This is similar to just taking the average winner's and loser's scores, but because the SOV maps every game into the [0,1] interval, basketball, baseball, and football scores are put into the same reference frame. Within a sport, we can characterize games by how they relate to the reference "standard game." Through games of 15 December, for Division 1 basketball the reference is 76.15  61.52 and that corresponds to a Division 1A football score of 32.33  14.62, and a baseball score of 8.403.41.
What the ISOV does is give more credit for wins better than a standard win and less credit for wins not as good as a standard win in the process of comparing every team to every other team using the results of all games.