We are obsessed with outliers. We explain them by effort (the 10,000 hour rule), by genetics, even by luck. Yet our analysis is wrong because we analyze the wrong sample.
Gladwell explains outliers through birthdates and effort. For professional hockey, “a hugely disproportionate number of professional hockey and soccer players are born in January, February and March.” The reason behind this odd correlation is the eligibility cutoff for age-class hockey January first. If you are born in the beginning of the year, you enter the junior leagues with up to eleven months of time advantage over your peers born later in the year.
Think about that – at the ages of eight to ten, when even two months might imply physical differences, you have eleven months! These advantages at an early age then multiply as junior players perform better, thereby getting more playing time, more attention, and eventually, more skill.
Gladwell then describes the 10,000 hour rule around the amount of effort and practice to achieve “expert” level. Gladwell exemplifies this rule with the software billionaires of the eighties, including Bill Gates and Bill Joy, who were fortunate to live in locales close to actual computers, which at the time numbered less than ten. They programmed early, programmed often (putting in their 10,000 hours), and gaining a skill advantage over those who did not hack until starting in their twenties.
The Sports Gene explains outliers via genetic advantage. More blood cells advantage Kenyan runners, elongated Achilles heel muscles advantage high jumpers. The fortunes of winning a genetic lottery.
But aren’t real outliers those who succeed despite lacking such advantages? Shouldn’t we discount these innate advantages wrought by being born in a lucky month, a lucky city, a lucky time, a lucky family, even a lucky race?
Because in sports, in software, in everything, there are people who perform exceptionally despite the lack of these advantages. The hockey players born in December. The software entrepreneurs who learned to code in their twenties after only saving enough money from driving limo’s. The basketball player dismissed from the top colleges and NBA draft who proved himself to be an NBA Starter.
The difficulty is because real outliers do not show up in the data AS outliers. Real outliers hide in the data as ordinary or above average performers. They generally do not come out first because they lack the advantages of their luckier peers. They may be second, third, or worse – and for most writers and analysts, writing about second place is writing about last place. Yet their success is far more impressive for what they’ve overcome versus what they’ve achieved.
The other reason we should analyze real outliers is because we can actually learn from them. Studies are beginning to prove this observation where exceptional performers are actually less skilled than the second tier and succeed generally based on luck.
The apparent outliers are actually noise, and the true outliers perform without exception.