Oscars viewership over time tells a different story from a headline about one year’s increase or decrease. The chart below follows U.S. viewers from 1991 through 2026. It shows why a “four-year high” can coexist with a much smaller audience…
tl;dr: Oscars ratings keep hitting “all-time lows,” but those single-year drops don’t mean much without context. A Process Behavior Chart shows the real story: a long-term downward shift in viewership, with recent years signaling another significant change. The useful question…
tl;dr: Year-to-year changes in Oscars TV ratings are usually just noise, not meaningful signals. Blaming or praising the host (or lack of one) based on small fluctuations is a mistake. Looking at longer-term trends with tools like Process Behavior Charts…
As I blogged about yesterday, things went well at the Oscars... or, at least, no errors were made in the announcements. But that thing that didn't go well was the TV ratings.
Two Data Points Are Not a Trend
The headlines I saw had a lot of two-data-point comparisons. Headlines sometimes gave the percentage decrease in viewers or how many million fewer viewers there were. Many talked about "record low" but if you're tracking a metric "record low" or "all-time high" doesn't mean there's a "special cause." That "record low" could still be noise in the system.
Tomorrow, my post will be about headlines that scream about ratings for The Oscars being "down from last year" or "the lowest in X years." As I've blogged about before, I'm always skeptical of such simplistic comparisons that might mask the real underlying trend.
But first, could the Academy avoid last year's embarrassing mixup?
Today's Post in <50 words: Lean thinkers don't blame individuals who are in a bad system, whether that's a presenter at Oscars, a kicker in a football game, or a healthcare professional in a hospital.
