MMC vs CORJ60: What Numerai Actually Pays For
Across every Numerai round, MMC flips the sign of expected payout while CORJ60 drives the extreme rounds. Here is what the data shows about where stake should go.
Crossing from the bottom MMC decile into the middle of the field flips a model's average per-round payout from roughly -0.3 NMR to roughly +0.1 NMR — bigger than any swing CORJ60 produces on its own. That sign-flip is the most important fact about Numerai's payout formula: 0.5 × CORJ60 + 2.0 × MMC is not a tweak, it is a different game from raw correlation. Optimize for only one half and you may leave NMR on the table — or hand a 25% burn to the protocol.
Which metric actually pays better? The answer depends on whether you care about expected payout sign or tail outcomes.
What the Metrics Measure
CORJ60 is Numerai's 60-day correlation score, measuring how well your predictions align with actual market outcomes over that window. High CORJ60 means your model predicts returns well. The distribution across staked models clusters near zero with a slight positive skew.
MMC (Meta-Model Contribution) is subtler. It asks whether your model still adds value after subtracting the crowd's combined prediction (the meta-model). A high-MMC model knows something the rest of the tournament does not. You can post strong correlation and near-zero MMC if your predictions are redundant with everyone else's.
CORJ60 rewards being right. MMC rewards being right in a way nobody else is. Both metrics are charted over time on the trends page, with per-round distributions on any round detail view.
The Payout Formula
Numerai's payout formula makes its priorities explicit:
The MMC coefficient is four times the CORJ60 coefficient, though realized impact also depends on each metric's distribution. Numerai is a hedge fund, and redundant predictions do not help it trade. The meta-model needs diverse, uncorrelated signals, so the payout formula pays a steep premium for originality.
A model with mediocre correlation but strong MMC can outperform a high-correlation model that tracks the meta-model closely. Does that play out in the data?
MMC vs CORJ60: The Scatter View
The scatter plots each model-round observation with CORJ60 on the x-axis (about -0.10 to 0.13), MMC on the y-axis (about -0.08 to 0.07), and color encoding NMR payout. CORJ60 has a wider spread than MMC — roughly 0.23 across versus 0.15. Raw correlation varies more model-to-model than meta-model contribution does.
The two metrics are only weakly correlated. Plenty of models sit at high CORJ60 with near-zero MMC (accurate but unoriginal), and a smaller group lives at modest CORJ60 with positive MMC (contributing something new). The highest-payout observations (greenest points) cluster in the upper right, where both MMC and CORJ60 are positive. Pure contrarianism in the upper-left — high MMC, negative CORJ60 — is thinly populated. Models that contribute uniquely to the meta-model tend to also clear a baseline level of accuracy.
Do Big Stakers Optimize for MMC?
This chart compares stake-weighted averages against simple medians for both MMC and CORJ60 across rounds 200–1200. The same comparison is available interactively via metric distribution on the models page. The gap between the two lines shows how capital is allocated relative to skill.
For both metrics, stake-weighted values sit meaningfully above the median and swing more violently round-to-round. The median MMC and median CORJ60 both hover close to zero the entire time, while stake-weighted CORJ60 spikes as high as ~0.06 in several windows and stake-weighted MMC regularly runs 0.01–0.04 above its median. NMR is disproportionately concentrated in models that are both more accurate and more original than the typical submission; the capital-weighted field scores better than the median field.
As stake flows toward high-MMC models, the meta-model absorbs more of their signal, raising the originality bar over time.
Payout Distribution: Top-10% MMC vs Top-10% CORJ60
To make the comparison concrete, we isolated the top 10% of models by MMC and the top 10% by CORJ60 in each round, then plotted their NMR payout distributions.
The top-CORJ60 cohort has the wider spread and a fatter right tail, with individual observations reaching above 100 NMR in a single round. The top-CORJ60 cohort contains the largest single-round payouts, likely reflecting both score extremes and stake concentration.
The top-MMC cohort is tight around zero. Originality delivers a steadier profile: most observations land within a few NMR of break-even. High MMC rarely produces monster single-round paydays on its own, but it also avoids the deep negative tail.
Correlation is the boom-and-bust axis; MMC is the steadier one. A large stake on a high-CORJ60 model captures the biggest upside rounds but wears the biggest drawdowns. Blending both is how you capture the 2x MMC multiplier without giving up CORJ60's ceiling.
Is the MMC-Payout Relationship Linear?
Bucketing models into MMC deciles and charting average payout per decile makes the MMC-to-pay relationship direct. The bottom two deciles (0–20%) average negative payouts, bottoming out near -0.3 NMR. The 20–30% bucket is roughly flat, and from the 30–40% decile onward the climb is steady and roughly linear, reaching about 0.55 NMR in the top decile.
Crossing from a bottom-quintile to a mid-range MMC flips the sign of your expected payout — the single biggest jump in the chart. After that, every additional decile adds roughly 0.05–0.10 NMR in average payout. The largest payout improvement is moving out of the bottom MMC quintile; gains after that are more incremental.
Strategy Implications
A few takeaways from the data:
- MMC flips the sign of your expected payout. The bottom two MMC deciles lose money on average and the 20–30% bucket is roughly flat; everything above that earns. Clearing the bottom quintile is the single most important line to cross.
- CORJ60 is where the extreme rounds come from. The fat right tail of single-round payouts belongs to high-correlation models. Correlation alone is not enough to avoid the matching left tail.
- Stake is already concentrated in skilled models. Stake-weighted MMC and stake-weighted CORJ60 both run well above the field median. You are not competing against the median submission, you are competing against the capital-weighted one.
- Originality compounds. As stake flows toward high-MMC models, the meta-model absorbs their signal, raising the bar for newcomers. Early movers on novel features and architectures have a structural advantage, and the benchmark models show how far a well-tuned public baseline can go.
The tournament's incentive structure is unusually transparent: the formula is public, the data is available on every round page, and the outcomes are observable. Models with weak MMC tend to transfer payout opportunity to models with differentiated positive signal.