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NBA Deviation for Totals


NBA Deviation for Totals

When it comes to sports handicapping writers, Bob McCune was one of the best idea men there was and his books remain must reading for every serious sports bettor.

The problem was that Bob fancied himself a budding wordsmith and writing wasn't really his strong suit, so his books can read awkwardly at some times and at others can be a bit painstaking to sift through. That's why you'll typically see his books given low reviews on sites such as Amazon.

But there is no denying that each book contained some solid information, which often times didn't jump out at you, but would be found in unexpected spots. The books, which were collection of articles, didn't always come right out and get to the point. Sometimes you would read an article that appeared to have little, if anything, to do with sports betting, but Bob was always trying to relay some sort of information to the reader.

Some people may remember McCune from his days as a professional bodybuilder in the 1940s and 1950s or as a professional wrestler in the 50s, but his true calling was as a sports bettor. McCune, who passed away in 2002, was an adamant believer in making his own line and then comparing it to the Las Vegas line and looking for value.

One of his best ideas was what he called the NBA Deviation Factor.

NBA Deviation Factor

The premise behind was the Deviation Factor was that teams will exceed their average difference from the league norm. In this case, it was related to NBA teams and totals.

The first step McCune used was to determine the league average of points scored and allowed. Because the NBA is essentially a closed system, meaning that all games are played against other NBA teams, these numbers will be the same. (That isn't the case in college sports, as Division I teams often play Division I-AA or Division II teams in preseason games, etc., which is why the average points for is typically greater than points allowed for college football or basketball.)

For ease of demonstration purposes, let's assume the average points scored and points allowed in the NBA is 90. A game played between two teams averaging and allowing 90 points per game should have a total of 180. That should make sense.

How about a game played between two teams that average and allow 100 points? While the simple answer would be that the game should have a total of 200 that's not entirely correct because it's not considering the fact that both teams exceed the league average. If teams are scoring 100 points against a league that allows 90, they should score more than 100 points playing against a team that allows 100, or 10 points more than the league average.

What McCune did was to assign a points rating for each point difference from the league average. A team that had a two-point difference from the league average received a 3, a team with a three-point difference received a 4.8, etc.

McCune would end up with four numbers, one for points scored for both teams and one for points allowed by both teams. He would then total them up and add or subtract from the league average to get his predicted total. It was a good method, but was time consuming and could be a bit confusing, so what we'll do is use a modified version with a base deviation factor of 1.5.

Modified Deviation Factor

Using our earlier example of teams averaging 90 points per game, we have a base of 180 total points for an NBA game. So a game involving two average teams would see a predicted total of 180 points.

Now, let's use our example of two teams both scoring and allowing 100 points per game. The first step we will do is to add all four numbers (Road team points scored, road team points allowed, home team points scored and home team points allowed) together and divide by two. In this case it's simple, as 100+100+100+100=400 and 400 divided by two is 200.

We now have a prediction total of 200, which is 20 points greater than our base league average of 180. Our next step is to take our 20 point difference and multiply that by our base deviation factor of 1.5, which gives us a total of 30. Our predicted total is now 30 points greater than the league average of 180 which is 210. So our predicted total for the game is 210 points.

Rather than try to explain the process again, we'll give a few more examples.

If we have a game involving two teams that average and allow 87 points, our first step is to add 87+87+87+87 to get 348, which we divide by two to get 174. Since 174 is six less than our base of 180, we have a figure of -6, which multiplied by 1.5 becomes -9. If you add -9 to 180 you get a predicted total of 171 points in the game.

One last example, this time involving a team that scores 98 and allows 102 playing a team that scores 85 and allows 81. Adding together all four numbers gives us 366, which divided by two gives a figure of 183. Subtracting 180 from 183 gives a total of 3, which multiplied by 1.5 becomes 4.5. Our predicted total for this game becomes 184.5 points.


It's unlikely this method will ever duplicate its success it enjoyed in its heyday, which was right around 61 percent, as it is now factored into the line released by the sportsbooks, as are many of our methods. But it does help give the bettor an additional advantage over others and a bettor can never have too many edges.

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