In the early 1970s, Peter Hesbacher of the University of Pennsylvania published an article on Billboard’s data acquisition and chart creation methodology. He noted that there tended to be larger differences in the data between records near the top of the weekly list than the bottom. He proposed elongating the rank scoring scale by awarding 20 points for #1, 15 for #2, 12 for #3 and so on down to #30. No points were awarded for #31 to 100. There are other methodologies that approach the problem similarly.
In 1966, about 750 records entered the charts. By 1985, fewer than 400 did. While some of this surely derived from chart methodology, the business—selling albums vs singles and proliferation of new radio formats--was probably a larger change driver.
Since the same number of places—1-100—are awarded and scored each week, the score of an “average” record increased by nearly a factor of two over that period. Because the charts changed, direct score comparison doesn’t work. The only solution is to normalize scoring for the era in which the record charted, as only a few chartologists do.
A “best ever” list that spans a long period has to consider performance in time context. On an absolute basis, Barry Bonds hit the most home runs in a major league season: 73 in 2001 (Sammy Sosa finished second with 64). But was that the greatest performance ever? Consider Babe Ruth’s 1920 season wherein he hit only 54, but second place was 19. The most home runs in a non-Babe Ruth season in the modern era to that point was 24. His performance was transcendent not just beyond what had been done, but what had even been imagined.
And that’s our approach to normalizing for era: look for performances that are transcendent in their time. In this case, develop a raw score for every record, then divide it by the average score of all records entering the charts plus or minus 26 weeks. Thus, an “average” record is always 1, and the score of any record is multiples of the average. A record 10 times as popular as the average in one era should be comparable to a record 10 times the average in another era, and that ratio is certainly a better comparison than raw score.
About the Methodology
But this doesn’t totally solve the “best of all time” problem. Tastes, the record business and most importantly the charts changed over time—even over as short a time as five years. A record scoring 500 points in 1964 is not necessarily the equal of one scoring 500 points in 1968 or 1972.
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Most attempts to use the charts to rank records are incomplete. Some will cite a peak position: “It went to #1.” Some will note longevity: “Twenty-six weeks on the charts.” Both rank and position are important: but getting to “most popular in its time” requires deeper analysis, and in at least those two dimensions.
Most researchers who publish chart metrics start with the trajectory traced by a record as it rises and falls from rank to rank, week by week. These paths can be compared graphically—for example, Ticket To Ride, clearly the bigger hit, and its flip side, Yes It Is.
However, graphing the 700-odd songs that entered the charts each year in those days would be tedious, and mathematical metrics are much more convenient. Chartologists usually start the process by assigning points to ranks; the most common approach is #1 = 100 points, #2 = 99, and so on to #100 = 1. Add the points, high score wins. We call this the Reverse Rank method, and by this method, Ticket To Ride scores 939 points and Yes It Is scores 176.
Elongating the scoring scale--#1 = 1000, #2 = 824, #3 = 658… #99 = 2, #100 = 1--makes rank achievement more important while still valuing longevity. Applying this scale to the Ticket To Ride vs Yes It Is comparison shows how the new scale accentuates rank performance by enhancing area.
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Reverse Rank has flaws. The difference between #99 and #100 is one point, and so is the difference between #1 and #2, which doesn’t seem equitable. In Reverse Rank, three weeks at a mediocre #67 scores 102 points—more than one week at #1. That doesn’t seem to give enough credit for achieving high rank, and in fact, when all records are viewed using Reverse Rank, it’s clear the system favors longevity over achievement.
Ranking the '70s