Stake-Weighted Age: Is Numerai's Meta-Model Getting Older?

Stake-weighted model age has climbed past 1,700 days. Here is what that means for newcomers, veterans, and the quality of Numerai's meta-model.

In the current Classic round, stake-weighted model age is 1,735 days — over 4.7 years — while the equal-weight average is just 994 days. The average dollar of stake comes from a model 741 days older than the average submission. Models more than two years old account for 78% of all stake but only 41% of submissions; sub-90-day models are 18% of the field and 2.7% of stake. Veterans steer the meta-model, but the cohort-level scores below show that's incumbency of capital, not skill.

For background on how the meta-model is built, see The Meta-Model. For the live numbers behind the charts below, see the Models page.

How Old Is the Meta-Model?

Two averages tell different stories. The equal-weight version treats every model the same. The stake-weighted version gives more influence to models with more NMR at stake, so it reflects who actually shapes the meta-model.

Line chart comparing stake-weighted and equal-weight average model age in days, by round. Stake-weighted age climbs from roughly 500 days to over 1,700, consistently above the equal-weight line near 1,000.
Line chart comparing stake-weighted and equal-weight average model age in days, by round. Stake-weighted age climbs from roughly 500 days to over 1,700, consistently above the equal-weight line near 1,000.

Stake-weighted age now sits roughly 740 days above equal-weight age, and that gap has widened from around 500 days four years ago. Older models carry larger stakes because veterans have had more rounds to compound NMR, survive burns, and build confidence in their approach.

Both lines trend up, but stake-weighted age is rising faster. Some of that is mechanical: surviving models simply get older. The rest is selection: models that last tend to be models participants are more willing to keep funded.

Does Age Equal Skill?

If older models score better, their dominance is justified. If not, the tournament has an incumbency bias problem.

Rolling 10-round average MMC broken out by model age cohort: under 90 days, 90–365 days, 1–2 years, and 2+ years. All four cohorts oscillate near zero within roughly ±0.006 MMC.
Rolling 10-round average MMC broken out by model age cohort: under 90 days, 90–365 days, 1–2 years, and 2+ years. All four cohorts oscillate near zero within roughly ±0.006 MMC.

The cohort picture is less dramatic than the stake gap suggests. All four buckets oscillate around zero MMC (Meta-Model Contribution) within roughly ±0.006, and no cohort sits persistently above the others. The under-90-day cohort is the most volatile, swinging widest on almost every wiggle — new models include both promising ideas and a lot of noise.

There is no clean ranking by age. The 2+ year cohort is steadier but not systematically higher. Experience in Numerai is associated with lower volatility in this view, not a guaranteed scoring premium.

Can Newcomers Compete?

Every prospective participant asks this. If the leaderboard is locked up by entrenched veterans, there is little reason to join.

Rolling 10-round median MMC for newcomers in their first 10 rounds versus veterans with 50+ rounds. The two lines track each other closely, trading leads from round to round.
Rolling 10-round median MMC for newcomers in their first 10 rounds versus veterans with 50+ rounds. The two lines track each other closely, trading leads from round to round.

The score gap is small and not consistently in the veterans' favor. The newcomer line (orange) trades leads with the veteran line (teal) across the full history, tracking it closely through most of the tournament. Newcomers are noisier because their cohort is smaller in any given round, but they are not systematically worse scorers.

The gap has not widened over time either. Numerai is not becoming impenetrable to new entrants on score. Both groups are competing in a more mature field than they were 800 rounds ago, but the newcomer line still gets chances to lead.

Who Holds the Stake?

Scoring parity only matters if it translates into stake. The meta-model is shaped by stake weight, not raw score, so control of NMR is what drives influence.

Stacked area chart showing share of total stake by model age cohort over time. The 2+ year cohort (red) dominates, filling roughly 70–90% of recent rounds, with 1–2 year models second and sub-90-day models a thin sliver at the bottom.
Stacked area chart showing share of total stake by model age cohort over time. The 2+ year cohort (red) dominates, filling roughly 70–90% of recent rounds, with 1–2 year models second and sub-90-day models a thin sliver at the bottom.

The 2+ year cohort holds roughly 70–90% of total stake in recent rounds. The 1–2 year group is a distant second, and sub-90-day models are a thin sliver at the bottom. The red band has expanded steadily since around round 600.

Stake concentration is not automatically bad. If older models are more proven, weighting them more heavily can improve aggregate predictions. The risk is the feedback loop: new entrants need stake to influence the meta-model, but stake accrues slowest to models that have not yet proven themselves.

Takeaways

The meta-model is getting older. Stake-weighted average model age has climbed past 1,700 days and keeps rising faster than equal-weight age.

Age buys consistency, not scoring edge. All four age cohorts oscillate around zero MMC within similar bands. Older models are steadier, not systematically higher.

Newcomers score competitively. Median MMC for first-10-round models tracks 50+ round veterans closely, with no widening gap over time.

Stake concentration is the real barrier. The 2+ year cohort holds the large majority of stake, so newcomer influence on the meta-model grows much more slowly than newcomer skill. Compounding stake takes time, which is why surviving long enough matters as much as any single round's score.