Aging Curves in Career Sims: What Happens After the Peak

Almost everything written about career modes is about getting better. Draft position, training plans, breaking into a lineup, earning a role. Almost nothing is written about the second half, which is longer than people expect and is where most of the interesting decisions live.

This is what the research actually says about when athletes peak and how they decline, and how to use that as a mental model for the back half of a simulated career.

Peak age is not one number

The first thing to abandon is the idea of a single age at which athletes are best.

An editorial by Tanaka and Toussaint in Frontiers in Physiology, reviewing work on growth, peaking and aging in competitive athletes, describes the position plainly: "the age of peak athletic performance varies widely across different sports and between sexes/genders, typically ranging from 20 to 30 years old." A ten-year band is not a rounding error. It reflects genuinely different demands.

How wide the variation gets inside a single sport is visible in a large dataset. Ganse and colleagues analysed 27,088 competitive results from track and field athletes aged 11 to 89 across twelve disciplines and reported that "on average women peak approximately 2 years later than men (21.6 years vs. 19.7 years)," with the 400 metres showing the highest peak age in both and the shorter sprints and jumps peaking earlier.

Two things follow. Peak age tracks what the event asks for, and any model that uses one number for every role is a simplification you should expect to notice.

Decline is gradual, and it starts earlier than people assume

Faulkner and colleagues, reviewing aging in elite male athletes in the Clinical Journal of Sport Medicine, note that "most elite athletes begin to show some decline in performance by their early thirties," while some exceptionally gifted athletes hold an elite level into their forties.

The shape matters as much as the timing. The Ganse analysis found performance declining in a broadly linear way until around age 70, after which the decline in the running events accelerated — while the throwing and jumping disciplines did not accelerate in the same way. Decades of gentle, predictable loss, then a change of regime much later.

For a career mode, that argues against a cliff. A player who is excellent at 30 and unplayable at 32 is not modelling an aging curve; they are modelling a switch.

Different capacities age differently

This is the part that changes how you play.

Because peak age depends on the demands of the event, it follows that the different capacities inside one athlete are not on the same schedule. Explosive output — the thing sprinting and jumping test — peaks earliest in the track and field data. Events with a larger pacing and judgement component peak later. The editorial's ten-year band across sports is largely a reflection of that mix.

Translated to a baseball career: a player built entirely on speed and raw power is betting on the capacities that come off first. A player who has also accumulated plate vision, pitch selection and situational judgement has assets that hold their value longer, because they are not the same kind of asset.

The practical version of this is a diversification argument. A narrow build produces a spectacular peak and a short one. A broader build peaks lower and stays useful for more seasons, and over a full career the second often ends with the better totals.