**College Basketball AOPR Betting System**

Long before college football used the BCS ratings and its now famous Strength of Schedule factor, sports bettors were doing the exact same thing. But instead of calling it Strength of Schedule, sports bettors referred to it as Average Opponent Power Rating, or AOPR.
AOPR is simply a way of looking at the opposition a team has played. Teams with higher AOPR figures have played tougher competition than teams with lower AOPR numbers.

A power rating is a numerical rating of a team, which in theory, is a way to compare that team to another team. If Duke has a power rating of 95 points and Wake Forrest has a power rating of 88 points, we would conclude that Duke is seven points better than Wake Forrest.

To find the AOPR for any team, merely add up the power rating of each opponent and divide by the number of games.

If Duke had played the following teams with the following power ratings:

Georgia Tech (83)

North Carolina (92)

Clemson (89)

Duke's AOPR would be 88, as (83+92+88=264 and 264/3 = 88).

As that involves a fair amount of work, many sports bettors turn to USA Today's Jeff Sagarin and his power ratings, which happen to include a listing called SCHEDL, which stands for Strength of Schedule, which is the exact same thing as AOPR. The one difference is that Sagarin's Strength of Schedule numbers are influenced by his power ratings, although his ratings are likely to be as good as any that you'll find elsewhere.

Several other sports betting publications will also list AOPR figures for those who lack the time to calculate their own power ratings and AOPR figures.

The other numbers that a bettor will need are the average points for and average points allowed by each team. These are available in several places, including www.thespread.com under college basketball match-ups, which will list the points scored and points allowed by each team.

Once we have our AOPR numbers and our points scored and points allowed, we're ready to begin.

**Using the Method**

For demonstration purposes, we'll use a game of Bradley at Drake. Bradley has an AOPR of 85 points and scored 68 points and allows 64. Drake has an AOPR of 82 points and scores 76 points and allows 74.
Our first step is to divide the higher AOPR by the lower AOPR. In this case, we divide Bradley's 85 by Drake's 82 and get a figure of 1.037. What this means is that Bradley has played a schedule that 3.7-percent more difficult than the teams Drake has played.

The second step is to take each team's offensive points for and divide that by the median points scored in college basketball (another method longtime readers will be familiar with), which is 71 points. Therefore, Bradley's 68 divided by 71 is .958, while Drake's 76 points divided by 71 points is 1.070.

Because Bradley has played the more difficult schedule, we will increase Bradley's offensive rating by the 3.7-percent from above, which gives us a new figure of .993.

We'll also decrease Drake's figure by the same 3.7-percent and get a new figure of 1.032.

Next, we'll take Bradley's figure of .993 and multiply that by Drake's points allowed, which is 74 and get a predicted score of 73.48 points, which rounded becomes 73 points.

Doing the same for Drake will show 1.032 multiplied by Bradley's points allowed of 64 points and will give us a predicted score of 66.04 points, which rounded down becomes 66.

Therefore our predicted score on the game is Bradley 73, Drake 66.

The final step is to add two points to the home team's predicted score and subtract two points from the road team's predicted score to allow for home court advantage.

**One More Example**

Let's use USC playing at UCLA for this example. The Trojans score 84 points and allow 80 and have an AOPR of 79. UCLA scores 63 points and allows 54 points and has an AOPR of 82 points.
Our first step is to divide the higher AOPR by the lower, which in this case is 82 by 79. This gives us a figure of 1.038, meaning UCLA has played a schedule that is 3.8-percent more difficult than USC.

Our next step is to divide each team's points scored by our median figure of 71. USC receives a figure of 1.183 (84/71) and UCLA receives a figure of .887 (63/71).

Because UCLA has played the more difficult schedule, we'll subtract 3.8-percent from USC's figure of 1.183 and get a new figure of 1.141. Next, we'll increase UCLA's figure of .887 by 3.8-percent and get an updated figure of .922.

We'll then multiply USC's updated figure of 1.141 by 54 (UCLA's points allowed) and get a predicted score of 61.61 points, which we'll round up to 62.

For UCLA, we'll multiply .922 by USC's points allowed (80) and get a predicted score of 73.76, which we'll round up to 74. On a neutral court, our prediction on the game would be UCLA 74, USC 62.

If UCLA was the home team, our prediction would be 76-60, while UCLA would be predicted to win by a score of 72-64 if the game was played at USC.

The system does need a minimum of seven or eight games to be played, as there can be large fluctuations in the earliest part of the season, but the system is one that is worth the time requirement.