Data From 2025-06-16 To 2025-06-22
Figure 1 presents the hourly log returns and volatility observed throughout the week, highlighting the relationship between BTC and ETH under the current market regime.
This week, we analyze the performance of six miners: three that reached the top of the leaderboard (Miners 46, 109, and 236), and three that lost their leading positions (Miners 170, 200, and 165).
As usual, performance is evaluated using two key metrics:
CRPS (Continuous Ranked Probability Score) for BTC and ETH forecasts
Leaderboard Scores
Continuous Ranked Probability Score (CRPS) Lower CRPS values indicate better predictive accuracy.
Similar to previous weeks, the competition for BTC and ETH forecasts was tight through the beginning of the week, with relatively stable volatility. Toward the end of the week—particularly between Saturday the 22nd and Sunday the 23rd—a clear performance gap emerged between Miners 46, 109, and 236, and the others. This shift coincided with a spike in unexpected weekend volatility. Figure 2 shows that these three miners belonged to the same group and submitted identical forecast paths throughout the week, resulting in identical scores.
Leaderboard Scores. These are exponentially weighted averages of past CRPS values over a 10-day window. Lower scores indicate better performance.
These scores confirm the weekend struggles of Miners 165, 170, and 200, who lost their lead and saw a significant decline in performance. Conversely, the group of miners submitting identical paths capitalized on the change in volatility during the weekend.
This section analyzes summary statistics—volatility, kurtosis, and skewness—to detect potential strategic differences among top miners for both BTC and ETH.
Summary Statistic Definitions:
Volatility: Variability in forecasted returns
Kurtosis: Measures the "tailedness" of the distribution—how likely extreme events are
Skewness: Measures the asymmetry of return distributions (0 = symmetric)
Figure 4 presents these statistics for the four miners across the week.
The results show that the miners who ended the week at the top of the leaderboard benefited from distributions with higher kurtosis and lower skewness. In terms of volatility, their BTC forecasts were on par with (or slightly less volatile than) other miners but were more consistent. In contrast, Miners 46, 109, and 236 displayed more variable volatility, suggesting a strategy that relies on consistently large volatility, which does not adapt well to changing conditions but pays off during high-volatility periods. This pattern is also evident in ETH forecasts, where the leading miners clearly projected higher volatilities than their peers.
Miners’ Ranking vs. Summary Statistics: All miners who submitted forecasts were ranked by total weekly rewards. Figure 5 shows how summary statistics vary across ranking tiers.
As in previous weeks, top-ranked miners (across the full week, not just the weekend) demonstrated better volatility adaptation than lower-ranked miners. However, some lower-ranked miners appear to be experimenting with modeling kurtosis for both BTC and ETH. While this has not yet translated into higher rewards, this kind of innovation is essential for long-term success in the Synth Subnet.
To assess performance at the group level, we analyzed:
Axon size (number of miners)
Average and total rewards per axon
Axon-level summary statistics (BTC forecasts)
Figure 6 shows the number of miners per axon at the end of the week.
As in the previous week, axons 116.202.53.142 had the largest memberships with 49 miners. Axons 95.216.99.113, on the other hand, saw a reduction in the number of registered miners, from 33 to 25.
Figure 7 shows the evolution of average (per miner) and total daily rewards for the top 10 axons by average rewards.
Axons 186.233.184.223 and 160.202.130.77 benefited the most from weekend volatility. The top-performing miners during the weekend belonged to Axon 186.233.184.223.
Figure 8 compares the top 10 axons (by average rewards) based on BTC modeling characteristics: intraday average volatility, volatility variability, and average intraday kurtosis.
The data confirms that the top axons employed more stable volatility strategies, which proved advantageous over the weekend. Moreover, the modeling outputs of Axons 186.233.184.223 and 160.202.130.77 were nearly identical, suggesting they likely belong to the same underlying group.
The unexpected weekend volatility disrupted the leaderboard, displacing previous frontrunners and elevating miners who used strategies built on consistently large volatility. While this shift temporarily favored miners submitting identical paths, we expect upcoming regime changes and better model adaptability to return more sophisticated models and miners to the top.
As noted in previous weeks, miners are encouraged to focus on developing high-quality models rather than registering multiple miners. Larger groups do not show a clear advantage in terms of average or total rewards.
Data From 2025-06-16 To 2025-06-22
Figure 1 presents the hourly log returns and volatility observed throughout the week, highlighting the relationship between BTC and ETH under the current market regime.
This week, we analyze the performance of six miners: three that reached the top of the leaderboard (Miners 46, 109, and 236), and three that lost their leading positions (Miners 170, 200, and 165).
As usual, performance is evaluated using two key metrics:
CRPS (Continuous Ranked Probability Score) for BTC and ETH forecasts
Leaderboard Scores
Continuous Ranked Probability Score (CRPS) Lower CRPS values indicate better predictive accuracy.
Similar to previous weeks, the competition for BTC and ETH forecasts was tight through the beginning of the week, with relatively stable volatility. Toward the end of the week—particularly between Saturday the 22nd and Sunday the 23rd—a clear performance gap emerged between Miners 46, 109, and 236, and the others. This shift coincided with a spike in unexpected weekend volatility. Figure 2 shows that these three miners belonged to the same group and submitted identical forecast paths throughout the week, resulting in identical scores.
Leaderboard Scores. These are exponentially weighted averages of past CRPS values over a 10-day window. Lower scores indicate better performance.
These scores confirm the weekend struggles of Miners 165, 170, and 200, who lost their lead and saw a significant decline in performance. Conversely, the group of miners submitting identical paths capitalized on the change in volatility during the weekend.
This section analyzes summary statistics—volatility, kurtosis, and skewness—to detect potential strategic differences among top miners for both BTC and ETH.
Summary Statistic Definitions:
Volatility: Variability in forecasted returns
Kurtosis: Measures the "tailedness" of the distribution—how likely extreme events are
Skewness: Measures the asymmetry of return distributions (0 = symmetric)
Figure 4 presents these statistics for the four miners across the week.
The results show that the miners who ended the week at the top of the leaderboard benefited from distributions with higher kurtosis and lower skewness. In terms of volatility, their BTC forecasts were on par with (or slightly less volatile than) other miners but were more consistent. In contrast, Miners 46, 109, and 236 displayed more variable volatility, suggesting a strategy that relies on consistently large volatility, which does not adapt well to changing conditions but pays off during high-volatility periods. This pattern is also evident in ETH forecasts, where the leading miners clearly projected higher volatilities than their peers.
Miners’ Ranking vs. Summary Statistics: All miners who submitted forecasts were ranked by total weekly rewards. Figure 5 shows how summary statistics vary across ranking tiers.
As in previous weeks, top-ranked miners (across the full week, not just the weekend) demonstrated better volatility adaptation than lower-ranked miners. However, some lower-ranked miners appear to be experimenting with modeling kurtosis for both BTC and ETH. While this has not yet translated into higher rewards, this kind of innovation is essential for long-term success in the Synth Subnet.
To assess performance at the group level, we analyzed:
Axon size (number of miners)
Average and total rewards per axon
Axon-level summary statistics (BTC forecasts)
Figure 6 shows the number of miners per axon at the end of the week.
As in the previous week, axons 116.202.53.142 had the largest memberships with 49 miners. Axons 95.216.99.113, on the other hand, saw a reduction in the number of registered miners, from 33 to 25.
Figure 7 shows the evolution of average (per miner) and total daily rewards for the top 10 axons by average rewards.
Axons 186.233.184.223 and 160.202.130.77 benefited the most from weekend volatility. The top-performing miners during the weekend belonged to Axon 186.233.184.223.
Figure 8 compares the top 10 axons (by average rewards) based on BTC modeling characteristics: intraday average volatility, volatility variability, and average intraday kurtosis.
The data confirms that the top axons employed more stable volatility strategies, which proved advantageous over the weekend. Moreover, the modeling outputs of Axons 186.233.184.223 and 160.202.130.77 were nearly identical, suggesting they likely belong to the same underlying group.
The unexpected weekend volatility disrupted the leaderboard, displacing previous frontrunners and elevating miners who used strategies built on consistently large volatility. While this shift temporarily favored miners submitting identical paths, we expect upcoming regime changes and better model adaptability to return more sophisticated models and miners to the top.
As noted in previous weeks, miners are encouraged to focus on developing high-quality models rather than registering multiple miners. Larger groups do not show a clear advantage in terms of average or total rewards.
SynthMode Synth (SN50) Subnet Whitepaper and How to Guide
The creation and use of synthetic price data has traditionally been dominated by opaque, well-funded entities such as large financial institutions, centralized technology firms, and regulatory agencies, including JPMorgan Chase, Goldman Sachs, the Federal Reserve, and PayPal. The closed-source nature of these datasets stems from the self-serving priorities of these organizations, which often lack the incentive to democratize data access. This approach exacerbates disparities, creating a wider...
SynthSynth Subnet - Inside Synth’s Accuracy Surge
IntroductionThis report provides a focused assessment of recent progress in the Synth subnet, where miners forecast BTC’s return distribution every day. Over the past month, the subnet has seen a major shift: miners prioritizing volatility accuracy have rapidly climbed the leaderboards, outperforming more complex models that previously held top positions. We analyze the performance of these top miners across multiple metrics, volatility, intraday variation, and kurtosis, using data from June ...
SynthSynth content transitioned to Synth X articles
You can stay up to date with Synth’s ongoing research, performance analysis, and ecosystem updates through our regularly published X articles. All new releases are posted directly on our X account here: https://x.com/SynthdataCo/articles.
SynthMode Synth (SN50) Subnet Whitepaper and How to Guide
The creation and use of synthetic price data has traditionally been dominated by opaque, well-funded entities such as large financial institutions, centralized technology firms, and regulatory agencies, including JPMorgan Chase, Goldman Sachs, the Federal Reserve, and PayPal. The closed-source nature of these datasets stems from the self-serving priorities of these organizations, which often lack the incentive to democratize data access. This approach exacerbates disparities, creating a wider...
SynthSynth Subnet - Inside Synth’s Accuracy Surge
IntroductionThis report provides a focused assessment of recent progress in the Synth subnet, where miners forecast BTC’s return distribution every day. Over the past month, the subnet has seen a major shift: miners prioritizing volatility accuracy have rapidly climbed the leaderboards, outperforming more complex models that previously held top positions. We analyze the performance of these top miners across multiple metrics, volatility, intraday variation, and kurtosis, using data from June ...
SynthSynth content transitioned to Synth X articles
You can stay up to date with Synth’s ongoing research, performance analysis, and ecosystem updates through our regularly published X articles. All new releases are posted directly on our X account here: https://x.com/SynthdataCo/articles.
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