If Early Advantages Snowball, Is Starting Late a Lost Cause?

At a junior hockey game in the mid-1980s, Paula Barnsley reportedly noticed that a lot of the players seemed to have been born in the first months of the year. Her husband Roger Barnsley and a colleague went and counted. That origin story is retold in later reviews of the relative age effect (I have not traced it to a primary source), and the count that followed is one of the cleaner demonstrations in social science of how a tiny head start gets amplified.

The short answer to the question in the title is no, not on the evidence. Cumulative-advantage research describes how selection, attention and credit flow toward whoever is slightly ahead early; it is a finding about allocation, and it has little to say about how much any one person can eventually learn. A late start is more expensive than an early one, and that cost can be located and attacked.

What did Merton actually claim in 1968?

Robert K. Merton’s “The Matthew Effect in Science” (Science, vol. 159, January 5, 1968) took its name from the line in Matthew 25:29: “For unto every one that hath shall be given.” His subject was credit, not talent. The same contribution, he argued, earns greater recognition when it comes from an established scientist, and less when it comes from someone who has not yet made a mark. The paper rested on Harriet Zuckerman’s interviews with American Nobel laureates, plus diaries, letters and biographies of other scientists.

Two details are easy to lose. First, the effect concerns visibility: whose work gets read, cited and remembered when several people could plausibly be credited. Second, Merton framed it as a feature of how science allocates attention, which, as later summaries of the paper put it, is rough on individual careers while arguably useful for filtering a flood of results. He did not claim that eminent scientists are undeserving. The mechanism he described works even if they are excellent, because excellence plus a spotlight produces more of both.

He also seems to have been using the phrase before it appeared in print. A researcher tracing the term found newspaper reports of Merton using “Matthew effect” at the AAAS meeting in Montreal in December 1964.

Why do January birthdays dominate hockey rosters?

Barnsley, Thompson and Barnsley’s 1985 article, “Hockey Success and Birthdate: The Relative Age Effect” (CAHPER Journal, 51(8), 23-28), is usually credited with coining the term. I could not read the 1985 article itself. The figures below come from Roger Barnsley’s own 2024 retrospective in Frontiers in Sports and Active Living, which reports that in 1983 rosters from two major junior leagues, 435 of 1,048 players (41.5 percent) were born in the first quarter of the year and 102 (9.7 percent) in the last. That is more than four to one.

The reason is a calendar rule. Canadian youth hockey groups children by birth year, so a boy born in January competes against boys born up to eleven months later. At age nine or ten, eleven months is a large share of a life. The older boy is bigger and more coordinated, gets picked for the better team, and then receives what Barnsley describes as differentiated experience: more practice time, more games, better coaching. In his account, those who are passed over tend to drop out. Four conditions have to hold together: age-cohort grouping, selection into skill-based tiers, unequal opportunity across tiers, and attrition among the disadvantaged. Remove any one and the snowball stalls.

Notice what carries the effect: not the habits of the boys, but a decision made by adults about who counts as good. Nobody in this story fails to practice.

The scale of the effect deserves some calibration. Cobley, Baker, Wattie and McKenna’s 2009 meta-analysis in Sports Medicine pooled 38 studies (253 samples, 14 sports, 16 countries) and found relative age effects to be consistent but small in size, strongest among adolescent males at regional and national representative levels in popular sports. I read that abstract, not the full paper. Elite hockey also shows complications. Several studies report that the over-representation is weaker, or reversed, among players who actually reach the NHL, a pattern debated in a back-and-forth between Nolan and Howell (2010) and Gibbs, Jarvis and Dufur (2012) in the International Review for the Sociology of Sport. One reading is that late-born players who survive the selection filter are unusually good. Another is that the filter simply gets stricter on those who started behind.

Malcolm Gladwell built the first chapter of Outliers (2008), titled “The Matthew Effect,” on this research, and Barnsley says the chapter was substantially influenced by the 1985 article. The reach of the book gave the finding a mass audience. It also gave critics something to push on: a selection process that shifts the odds is not the same as an explanation for who becomes a professional, and size, family circumstances and sheer chance remain in the picture. Gladwell himself, per Barnsley, looked at a 2020-21 junior roster in 2022 and said, “Not much has changed, has it?” The pattern persisted. Whether it is the main driver of any single career is a different question, and the data do not claim it is. The same book’s other famous claim gets a closer reading in what the Berlin violin study found.

Does the snowball show up outside sport?

Keith Stanovich borrowed Merton’s label for “Matthew Effects in Reading” (Reading Research Quarterly, 21(4), 1986, 360-407). Per the abstract, he proposed that the causal link between reading ability and the efficiency of cognitive processes runs in both directions, and that advantaged readers meet a non-random, richer environment. Children who decode fluently read more, which builds vocabulary and knowledge, which makes the next text easier. Children who struggle read less and fall further behind, a “rich-get-richer and poor-get-poorer” pattern. Stanovich presented these as hypotheses supported by a large synthesis rather than a single clean experiment, and I have only the abstract.

The cleanest modern test of Merton’s original domain is Bol, de Vaan and van de Rijt (2018, PNAS, 115(19), 4887-4890). They looked at Dutch early-career Veni grants and compared applicants ranked just above the funding line with those just below, people who by construction differ little in quality. Winners went on to collect about €180,000 more in other funding over eight years, and their chances of winning a mid-career grant rose from roughly 10 to 26 percent. Winners’ publication and citation records showed no jump at the line. Two forces seemed to drive the gap: reviewers rated prior winners more favorably, and near-miss applicants applied for later grants less often. The authors treat the evidence as suggestive, from one country’s scheme.

That second force matters for what comes next. Part of the gap was people leaving the competition. Everything else equal, a loser who keeps entering narrows the distance.

Is it too late, then?

Here the habits genre, and its opposite, both overreach. The “just show up daily” advice assumes everyone starts from the same line. Cumulative-advantage research says the line is sometimes drawn by someone else, and that effort spent in the wrong arena can be invisible. But the fatalistic reading fails too, because none of these studies measured a ceiling.

The most relevant counter-evidence comes from Sinatra, Wang, Deville, Song and Barabasi (2016, Science, 354(6312)). Analyzing scientific careers, they found a “random impact rule”: a scientist’s highest-impact paper is equally likely to appear anywhere in the sequence of their publications, first, middle or last. Their model separates productivity, an individual ability factor and luck. This does not say that age is irrelevant, since someone who starts later has fewer papers in total and so fewer draws. Science is one domain, measured through citations, and one should not stretch it to piano or running.

What the research does support is a more modest set of conclusions about where the cost of a late start sits. The table collects them. The fourth column is my inference, not a finding.

Domain	What accumulates	Mechanism in the research	Where a late start can still work
Youth hockey	Practice time, coaching, team tier	Birth-year grouping, then selection and differentiated experience (Barnsley)	Leagues without early cutoffs; adult and recreational play; sports where selection happens later
Reading	Vocabulary, knowledge, fluency	Reciprocal causation (Stanovich 1986)	Any text at the right difficulty; volume of reading can be raised at any age
Grant funding	Money, reviewer favor	Status effects and fewer re-applications (Bol et al. 2018)	Funders that score blind or reward re-applying
Scientific credit	Citations, visibility	Attention to the already eminent (Merton 1968)	Newer subfields with no incumbents; work whose credit is assigned by name order or by lab

Where can a late starter still get traction, and what does the first repetition cost?

Three practical readings follow, and I’d label all of them inference rather than established fact.

The first is to enter a domain where the accumulation hasn’t been locked in. The relative age effect operates where cohort grouping and tiered selection exist. A new subfield, a craft with no formal tiers, or a field that is simply too new to have eminent incumbents offers no early lead to fall behind. Where selection is decades old, the same effort buys less.

The second is to make the first deposit now, even a clumsy one. In the Bol study, the gap grew partly because near-miss applicants stopped entering competitions. A contest you don’t enter has a guaranteed result. Those who start late have fewer chances to be in the pool at all, so each unentered round costs proportionally more. A detective novelist who published his first book at 50 shows what late deposits look like when they come daily.

The third is to lower what the first few repetitions cost. Early advantage mostly shows up as cheaper repetition: the boy on the better team practices more because practice is available and rewarded. The late starter’s repetitions are expensive in motivation, awkwardness and the visible gap from people ahead, a cost Ira Glass framed as taste outrunning skill. Simple first versions help. Gall’s law, the old engineering observation that a working complex arrangement almost always grew out of a simple one that worked, reads here as advice to start with something small enough to run, as covered in this post on Gall’s law and the morning routine.

Outside stakes belong at exactly this point, because the first repetitions are where skipping is cheapest and the payoff furthest away. DontSnooze puts a price on the missed first move of a day: if the 30-second video after the alarm isn’t recorded, a random photo from your camera roll goes to friends. It does nothing for selection, funding or credit; it only makes the opening repetition harder to skip.

What the evidence leaves open

Three things are not established by anything above. The relative age effect is robust in aggregate, small in size, and weaker at the professional end, so a birthdate is not a verdict. Merton’s and Stanovich’s frameworks describe tendencies that vary by field, and the Veni study shows one funding scheme. And no study cited here follows adults who began a skill late and compares them against a matched early group, which is the experiment the question really needs.

Someone born in November and passed over at ten had a worse deal than someone born in January, for reasons unrelated to effort. That is a fact about the selection rule. What happens next depends on whether the person finds a game where the rule does not apply, enters it, and keeps entering.