Bob McCune stands out in the annals of sports handicapping literature. His innovative ideas, encapsulated in his books, are essential reads for dedicated sports bettors. However, McCune’s enthusiasm for writing occasionally led to prose that wasn’t as polished as his insights deserved. As a result, some of his works receive less-than-stellar reviews on platforms like Amazon.
Yet, sifting through his books reveals gems of wisdom, often nestled in unexpected sections. While the content wasn’t always direct, McCune always aimed to convey critical information to his readers.
Many remember McCune from his days as a professional bodybuilder during the 1940s and 1950s and as a wrestler in the 1950s. But his true passion was sports betting. A firm advocate of crafting his own betting line, McCune compared it to the Las Vegas line to spot potential value.
One of his most noteworthy concepts was the “NBA Deviation Factor.”
The NBA Deviation Factor Explained
McCune’s Deviation Factor is built on the notion that teams will surpass their average difference from the league’s norm. This approach was especially applicable to NBA teams’ game totals.
McCune began by identifying the league’s average points scored and allowed. Given that NBA games pit NBA teams against each other, these averages mirror one another. (This isn’t the case in college sports, where Division I teams might play against lower-tier teams, leading to skewed averages.)
To illustrate: if the NBA average for points scored and allowed is 90, a match between two such average teams should result in a total of 180 points.
McCune’s genius lay in his methodology. He assigned ratings for each team’s deviation from the league average. For example, a two-point deviation would receive a score of 3, a three-point deviation a score of 4.8, and so on. By aggregating these ratings for both teams, McCune could adjust the expected game total to predict outcomes more accurately.
However, this method was intricate and time-consuming. Thus, we present a streamlined version utilizing a base deviation factor of 1.5.
Simplified Deviation Factor
Using the hypothetical average of 90 points per team, an NBA game involving two such teams would predict a total of 180 points.
For teams both scoring and allowing 100 points, their aggregated score is 400. Dividing this by two yields 200, a deviation of 20 points from the base 180. Multiplying this by our 1.5 factor results in 30. Thus, the adjusted game total prediction is 210 points.
To further clarify, consider two teams averaging 87 points. Their combined total is 348, which when halved is 174. This is 6 points less than the base, giving a deviation of -6. Multiplying by 1.5 results in -9. Hence, the predicted game total is 171 points.
Finally, a matchup between teams scoring 98/allowing 102 and scoring 85/allowing 81 gives an aggregated score of 366. Halving this provides 183, a 3-point deviation from the base. Multiplying by 1.5, we get 4.5. The predicted game total is 184.5 points.
Conclusion
While the Deviation Factor’s golden days—where it boasted an impressive 61% success rate—are past, it remains a valuable tool. Modern sportsbooks factor this method into their lines, but it still offers bettors a competitive edge. After all, in betting, every advantage counts.