# Synth Subnet - Miner Performance Review #16

By [Synth](https://paragraph.com/@synthdata) · 2025-07-03

---

**Data From 2025-06-23 To 2025-06-29**

**BTC and ETH Volatility**
--------------------------

Figure 1 presents the hourly log returns and volatility observed throughout the week, highlighting the relationship between BTC and ETH. Volatility generally declined as the week progressed, reaching its lowest levels on Sunday, June 29, before a final spike at the end of the day.

![](https://storage.googleapis.com/papyrus_images/52e7b9dae5686d7b049e131abb0e3cb3e5a334e82b03084c64835225d7376604.png)

**Miners Performance**
----------------------

This week, we analyzed six miners: Miners 100, 236, and 238—who started the week at the top of the leaderboard—and Miners 32, 200, and 221, who ended the week in the leading positions.

As usual, performance was 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.

In the early part of the week, miners submitting “equivalent paths” performed best, benefiting from the high-volatility environment. However, as volatility declined, Miners 32, 200, and 221 made a strong comeback, consistently outperforming Miners 100, 236, and 238—especially over the weekend.

![](https://storage.googleapis.com/papyrus_images/98620f78076d76abfbff8825665fcb0c9b7c131b9a789353576a0c33e740d471.png)

**Leaderboard Scores**. These are exponentially weighted averages of past CRPS values over a 10-day window. Lower scores indicate better performance.

The leaderboard trends clearly illustrate the decline in performance for Miners 100, 236, and 238. Their use of equivalent paths and lack of adaptability to changes in the volatility regime negatively impacted their results. Conversely, miners able to adapt to changing conditions quickly regained top positions—even after temporarily falling behind.

![](https://storage.googleapis.com/papyrus_images/96deb3590af51dad24fbd8df169331de2a34cdf06e937976edb7d89c64f153c3.png)

**Miners’ Strategies**
----------------------

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.

![](https://storage.googleapis.com/papyrus_images/1a14df80ce205d0ddce3c37f8f7267d7f082328d0ce51f821f0dfb9530edc933.png)

The results confirm that Miners 32, 200, and 221 employed a more dynamic approach to modeling volatility, with their estimates varying significantly more than those of Miners 100, 236, and 238. They also produced more symmetrical return distributions, while the others tended to show more negative skewness. In terms of kurtosis, the top-performing miners modeled extreme events more cautiously, resulting in lower kurtosis values overall.

**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.

![](https://storage.googleapis.com/papyrus_images/ff1d191454bc3649512eb60ef4557989489b057502362e93dae5e377b44f9bf7.png)

During the high-volatility early part of the week, miners that consistently forecasted high volatility—without adjusting—captured most of the rewards. Their lack of adaptation also resulted in low variability in kurtosis estimates.

**Analysis by Axons**
---------------------

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.

![](https://storage.googleapis.com/papyrus_images/9006f2d7698fedaf7cd8e68000a70351922413f4b1ad70c4524266019accaeed.png)

As in the previous week, axon **116.202.53.142** had the highest number of registered miners.

Figure 7 shows the evolution of average (per miner) and total daily rewards for the top 10 axons by average reward.

![](https://storage.googleapis.com/papyrus_images/c52082231e5dda63b36339943648b0d72bbc197ab5c728b04efc97acaae1453d.png)

As previously noted, miners generating equivalent high-volatility paths received most of the rewards until the weekend. These miners mainly belonged to axon **186.233.184.223**. However, as the volatility regime shifted, their performance declined, and they lost ground to axons with miners who better adapted to changing conditions.

Figure 8 compares the top 10 axons (by average rewards) based on key BTC modeling characteristics: average intraday volatility, volatility variability, and average intraday kurtosis.

Axons **186.233.184.223** and **160.202.130.77** (which likely belong to the same group and overlap visually in the chart) maintained stable volatility estimates throughout the week, showing nearly zero variation. In contrast, miners from other axons adjusted their volatility estimates downward, following the trends observed in BTC and ETH. Regarding kurtosis, axon **95.111.205.93** stood out for its extremely high values—indicating a modeling approach that overestimated the likelihood of extreme events.

**Takeaways**
-------------

This week once again demonstrated that miners who fail to adapt their forecasts to evolving market and volatility conditions are unlikely to remain on top for long. While static, high-volatility forecasts may perform well in turbulent conditions, they lose effectiveness as volatility moderates. In contrast, adaptive miners—those who adjust their forecasts to reflect changing market realities—are better positioned to sustain high performance in the longer term.

---

*Originally published on [Synth](https://paragraph.com/@synthdata/synth-subnet-miner-performance-review-16)*