Numerai Submission Scores: From Day 1 to Resolution
Daily Numerai submission scores are noisy early and converge late. We measure how predictive day-one MMC is, and when rankings actually stabilize.
Day-one MMC correlates with final MMC at just 0.21 — barely above zero. By day seven that climbs to 0.55, day fourteen to 0.80, and only at day twenty does it lock at 0.97. Numerai scores evolve over a multi-week window, and the gap between "early read" and "settled" is wider than most participants act on. The practical question is when the signal is stable enough to react to.
This post tracks daily MMC evolution using submission-level data you can also explore on any round detail page. For background on the metrics, see How Numerai Works and MMC vs Correlation.
How Scoring Works
Each round's predictions are scored against realized market returns over a multi-week window, and updated daily as new return data lands. Early scores reflect short-term moves; later scores incorporate longer-term patterns. The final score feeds the payout formula alongside stake size and the tournament-level payout factor.
Day one reflects a tiny slice of the information that will eventually set payout — informative, but unreliable on its own.
The Score Journey
How does the average daily score evolve from round open to resolution?

Each thin line is one round's average daily MMC across all models; the bold line is the mean across rounds. In the first week, individual rounds swing between roughly -0.04 and +0.025 MMC before the cross-round average settles near 0.003. Two rounds with nearly identical final MMC can take wildly different paths to get there.
Do Early Scores Matter?
The cross-model correlation between day-one and final MMC tells you how much early information is worth.

The 10-round average sits near 0.2, with individual rounds swinging from about -0.4 to +0.6. Day-one scores carry some signal, but the relationship is far from deterministic. Plenty of strong day-one models regress by resolution, and plenty of weak starters recover.
The information curve compounds fast. Across 600+ rounds of submission data, mean correlation to final MMC is 0.21 at day one, 0.46 at day five, 0.55 at day seven, 0.80 at day fourteen, and 0.97 by day twenty — most of the signal arrives in the second week, not the first. A model sitting in the top 20% on day three has a 39% chance of finishing in the top 20%: nearly 2x random, but still a 61% chance it falls out.
Volatility Across Rounds
Rounds differ sharply in how much daily scores bounce around.

Across rounds 1119 to 1214, median daily-score standard deviation climbs from about 0.014 in the 1120s to peaks above 0.033, topping out near 0.040 at round 1184. High-volatility rounds line up with turbulent market conditions, visible in the broader trends view. Patience matters most in exactly these rounds, when daily swings are largest and least informative.
When Rankings Stabilize
How quickly do model rankings converge to their final order?

Spearman correlation between daily and final rankings shows clear convergence. Rankings start noisy — some rounds dip below 0 in the first few days — then climb steadily into the 0.6–0.9 range by day 14. By resolution they all pin near 1.0.
Absolute scores stay unreliable early, but your relative position on the leaderboard stabilizes well before the round closes.
Takeaways
Day fourteen is the inflection point. Day-one MMC correlates 0.21 with final MMC; day-seven correlates 0.55; by day fourteen it hits 0.80. Wait for the second week before treating a round's signal as informative.
Volatility is a round property, not a model property. Market conditions drive most of the daily swing, and recent rounds have been unusually volatile.
Rankings converge faster than scores. Focus on rank rather than raw score for mid-round assessment — the round detail timeline makes this convergence visible.
Patience reduces avoidable churn. Participants who restake based on daily fluctuations risk reacting before the signal stabilizes. For a broader view of how payouts actually land, see Round Economics.