Explore our new Volatility Insights dashboard: forecast next‑24‑hour volatility for the top ten Synth miners, compare prior forecasts to realized Bitcoin volatility, and visualize Bitcoin’s percentile ranges over both windows. Dive in and explore it!
This report analyzes miner performance within the Synth Subnet over April. We examine:
Leaderboard dynamics: consistency in the top ranks, week‑to‑week turnover, and the ability of miners to return to leading positions;
Performance drivers: how forecast distribution characteristics—volatility and tail fatness (kurtosis)—distinguish top miners from the rest.
Our aim is twofold: to identify whether dominant players exist and can maintain or regain leading status, and to highlight model attributes that correlate with success.
We analyze three aspects of Top 10 behavior:
Week‑to‑week flows into and out of the Top 10 based on average prompt scores.
Total time spent in the Top 10 (in minutes) during April, using smoothed leaderboard rewards.
Ranking trajectories of the longest‑standing Top 10 miners over the month.
Participant Overview. Over the course of April, a remarkable 798 distinct miners entered and exited the Synth Subnet leaderboard. Of these:
85 were active both before April 1 and after April 30,
245 began before April 1 but dropped out before month’s end,
317 joined after April 1 and remained past April 30,
151 joined and were deregistered within April.
This level of engagement underscores the broad interest in our forecasting contest—and against such a large field, securing a spot among the Top 10 is a true mark of excellence.
To gauge the depth of competition, we gathered every miner’s prompt scores from April 1 to April 30 and computed each miner’s weekly average (excluding the final four-day partial week). We then ordered miners by ascending average prompt score—an approach that isolates true shifts in ranking, independent of the smoothed leaderboard metrics. In total, 798 miners participated at various points in April (including 85 who were active throughout the entire month), so cracking—and holding—a spot in the Top 10 reflects an exceptionally strong forecasting engine.
Our week-to-week breakdown (Figures 1–4) reveals how a handful of standout miners consistently outpaced the field:
Week 1 → Week 2: Every Week 1 Top 10 remained within the Top 30, underscoring the solidity of their models. Notably, Miners 240, 91, and 196 preserved their Top 10 rankings in Week 2.
Week 2 → Week 3: Only Miner 208 managed to stay in the Top 10, while many of its peers slipped past the 100-rank mark—illustrating the high caliber required to stay at the forefront of the competition.
Week 3 → Week 4: While Week 3’s leaders faced challenges, with many dropping below the Top 200, their ability to recalibrate models paved the way for a strong recovery. Impressively, six of the Week 4 Top 10 had previously ranked in Week 1’s Top 10, underscoring that elite miners can swiftly reclaim leadership with refined strategies.
Key insight: The Synth Subnet’s Top 10 is a showcase of the truly best miners, where only the most sophisticated forecasting models thrive. While the leaderboard sees dynamic movement, reflecting the competitive vigor of nearly 800 miners, the ability of top performers to repeatedly secure Top 10 positions—sometimes across multiple weeks—signals their far superior models. This vibrant contest is successfully sourcing the highest-quality forecasters, driving innovation and excellence in the subnet.
We measured each miner’s total time in the Top 10 by ranking all participants according to their smoothed reward each time rewards were updated, then summing the minutes spent in the Top 10 over April. Figure 5 shows that Miners 33, 39, and 103 each exceeded a total of two weeks inTop 10 , while another cohort—including Miners 196, 91, 240, and 200—spent more than 10 days in the Top 10. These results highlight a group of consistently high‑quality forecasters who sustain leadership for extended periods.
Focusing on the 20 miners with the longest Top 10 tenures, Figure 6 charts their daily rankings through April. Once they break into the Top 10, many maintain positions for days or even weeks. Though temporary declines—driven by unexpected market conditions or model drift—do occur, miners frequently rebound with timely model adjustments.
Key insight: Sustained Top 10 presence is achievable, and setbacks are reversible through focused refinements.
To uncover what separates the most successful miners from the rest, we examined two fundamental features of each miner’s forecast distribution over April’s one hundred 24‑hour price paths: the way they estimate price volatility and the weight they assign to extreme events (captured by kurtosis).
Price volatility reflects the degree of market uncertainty—a higher volatility implies wider swings in Bitcoin’s returns, while lower volatility suggests more stable behavior. A model that adapts its volatility estimates to changing market conditions can better anticipate periods of calm and turbulence. We measured this adaptation in two complementary ways: first, by calculating the empirical standard deviation of each forecast’s 24‑hour returns and then assessing how those daily volatilities fluctuate across all submitted prompts; second, by reconstructing an intra‑day volatility profile for each forecast—estimating the volatility in hourly increments—and analyzing the dispersion of these profiles over time. The result is a clear view of whether a miner treats volatility as a static parameter or actively refines it to capture both day‑ahead and intra‑day market dynamics.
Kurtosis, on the other hand, quantifies the “fatness” of a distribution’s tails. A Gaussian (normal) distribution has a kurtosis of zero, implying that extreme deviations are exceedingly rare. Positive excess kurtosis indicates heavier tails, meaning the model anticipates and assigns higher probability to large, infrequent price moves. By averaging the excess kurtosis of returns across all forecasts for each miner, we gauge how much each forecaster prepares for tail risk—an essential trait when sudden market events can trigger outsized losses or gains.
Putting these two metrics together produces a nuanced picture of modeling sophistication. In Figures 7 and 8, boxplots of volatility variation reveal that the miners who ranked in the Top 20 by April rewards consistently display higher—and more tightly clustered—volatility adjustments than their lower‑ranked peers. Those top performers not only update their day‑ahead volatility but also intricately shape intra‑day patterns, suggesting a deep responsiveness to Bitcoin’s intraday rhythms.
For a deeper dive into how leading miners fine-tune their volatility forecasts across day vs. night and weekday vs. weekend periods, see our article “Bitcoin’s Volatility Hack: How Savvy Synth Models Crush the Market”, which walks through the exact intraday and weekend dynamics they’re capturing.
Figure 9 then plots each miner’s average kurtosis against their final ranking. The miners who secured the most rewards tend to cluster at much higher kurtosis values, demonstrating a deliberate emphasis on tail risk. Conversely, miners with near‑zero kurtosis mimic Gaussian assumptions and underplay the likelihood of extreme moves—an approach that correlates with lower leaderboard performance.
Together, these analyses illustrate that mastering both volatility adaptation and tail‑risk modeling is not optional but essential for success in the Synth Subnet. Models that treat volatility as dynamic and embrace heavy‑tailed forecasts consistently outperform those that do not.
April’s data reveals three core lessons for Synth miners:
First, the Top 10 leaderboard experiences substantial weekly turnover, reflecting the dynamic nature of Bitcoin markets and the competitive environment within the subnet. Yet within this churn, a resilient core of miners repeatedly reclaims leading positions by fine-tuning their approaches.
Second, dropping out of the Top 10 does not preclude a comeback. Whether due to model drift, temporary market shocks, or new entrants, miners that focus on targeted refinements—particularly in volatility calibration and tail-risk modeling—can regain top slots, even if they were previously unranked.
Third, the most consistent high performers distinguish themselves through distributional mastery. They treat volatility as a dynamic parameter, actively adjusting both day-ahead and intra-day estimates, and they build in heavy tails to anticipate extreme market moves. This combination proves essential for capturing rewards.
By emphasizing these three principles—adaptability, resilience, and statistical rigor—miners can sharpen their models for the days ahead. May these insights inspire your next breakthroughs on the Synth Subnet leaderboard.
Building on our findings that miners have mastered the Phase 1 forecasting challenge, we’re now elevating the contest’s demands and refining our incentive structure to drive ever-greater innovation:
Expanded Modeling Scope: Participants will be required in Phase 2 to forecast additional crypto assets alongside Bitcoin. By diversifying the asset set, we encourage miners to develop multi-asset forecasting frameworks that seek to perform across varied market regimes.
Sharper Reward Curve: With the subnet’s proven ability to identify top-tier forecasters, we’ll steepen the reward curve so that the very best miners capture a larger share of incentives, while minimizing rewards of average performers. This adjustment ensures that high-fidelity models receive proportional recognition and funding, further intensifying competition at the leading edge.
These enhancements are designed to push miners to continually raise the bar for forecasting excellence and to ensure that the Synth Subnet remains the premier environment for next-generation market models.