I recently read the fascinating ‘Thinking, Fast and Slow’ by Nobel Laureate Daniel Kahneman. The novel is a detailed look at how the human mind functions, makes decisions and the inherent biases that we all have when it comes to solving simple and complex problems.
As I was reading it, I came across a concept that Kahneman calls Regression to the Mean. He describes it using two equations that he once entered into an Edge magazine competition for ‘favorite equation’:
Success = talent + luck
Great success = a little more talent + a lot of luck
For most people, the idea of luck seems strange. Surely luck doesn't really play a role in success, does it? But when you think about it, you realize that – of course – luck plays a role in all outcomes.
Kahneman describes these equations using a simple golf example (apologies for the quote length but you won’t regret reading it):
“The unsurprising idea that luck often contributes to success has surprising consequences when we apply it to the first two days of a high-level golf tournament. To keep things simple, assume that on both days the average score of the competitors was at par 72. We focus on a player who did very well on the first day, closing with a score of 66. What can we learn from that excellent score? An immediate inference is that the golfer is more talented than the average participate in the tournament. The formula for success suggests that another inference is equally justified: the golfer who did so well on day 1 probably enjoyed better-than-average luck on that day. If you accept that talent and luck both contribute to success, the conclusion that the successful golfer was lucky is as warranted as the conclusion that he is talented.
By the same token, if you focus on a player who scored 5 over par on that day, you have reason to infer both that he is rather weak and had a bad day. Of course, you know that neither of these inferences is certain. It is entirely possible that the player who scored 77 is actually very talented but had an exceptionally dreadful day. Uncertain though they are, the following inferences from the score on day 1 are plausible and will be correct more often than they are wrong.
Above-average score on day 1 = above-average talent + lucky on day 1
Below-average score on day 1 = below-average talent + unlucky on day 1
Now, suppose you know the golfer’s score on day 1 and are asked to predict his score on day 2. You expect the golfer to retain the same level of talent on the second day, so your best guesses will be “above average” for the first player and “below average” for the second player. Luck, of course, is a different matter. Since you have no way of predicting the golfer’s luck on the second (or any) day, your best guess must be that it will be average, neither good nor bad. This means that in the absence of any other information, your best guess about the players’ score on day 2 should not be a repeat of their performance on day 1. This is the most you can say:
· The golfer who did well on day 1 is likely to be successful on day 2 as well, but less than on the first, because the unusual luck he probably enjoyed on day 1 is unlikely to hold.
· The golfer who did poorly on day 1 will probably be below average on day 2, but will improve, because his probably streak of bad luck is not likely to continue.
We also expect the difference between the two golfers to shrink on the second day, although our best guess is that the first player will still do better than the second.”
Think about Tiger Woods. For years, he was the most talented player in golf - winning 70+ tournaments, including 14 major championships. He was unstoppable and couldn't miss. His putts fell when they needed to and he got it done. But what phase was more likely? Continuing his above-average pace or coming back to the average? Suddenly, 2 years goes by and he hasn't won a tournament - was it because he no longer was as talented? That other players had caught up? That his personal issues had forever impacted his game? Or that his luck changed? In truth, it's a combination of everything, but the point is that he had above-average luck and that had to go back towards the average eventually.
Regression to the Mean resonates with me, especially as someone who works at an agency. All agencies are full of talented people – some more talented, some less – but most who are equally driven to create great work, build value for their brands and clients and do the best that they can to create world-class experiences.
When you look at the history of agencies, from when Saatchi & Saatchi got hot in the 80’s to BBH in the 90’s and CP+ B / GSP in the last decade, you can see that these runs are a combination of the right people who have created great ideas and won business – but shops that have had luck on their side as well. This isn’t meant to discredit them in any way – it’s just a fact that these runs have a bit of luck mixed in with the skill, passion and talent of the people working on them.
And, as Kahneman writes, those with above-average luck for a period are more likely to experience average luck at a later point. Goodby, Silverstein and Partners is a prime example of this – an strong agency that had a meteoric rise in the last decade only to have it’s worst week in history last February after losing Spring and HP in a matter of days.
Regression to the Mean.
Luck, especially in our creative industry, plays a role in almost everything – in winning business, coming up with the right idea, executing it well and getting noticed by the target. So how do agencies ‘plan’ for luck?
The truth is, we can’t. We can only keep improving what we can – helping our people get better, pushing for stronger ideas, working harder, not settling and doing everything we can to help our brands succeed. Get half of the equation (the most important half) as strong as we can and hope that we land on the right side of chance. The key, I think, is recogizing that luck is there and that even when an outcome might appear to be only talent related, there are always bad breaks and - of course - lucky outcomes.