Sun, Apr 19, 2026

Propagation anomalies - 2026-04-19

Detection of blocks that propagated slower than expected, attempting to find correlations with blob count.

Show code
display_sql("block_production_timeline", target_date)
View query
WITH
-- Base slots using proposer duty as the source of truth
slots AS (
    SELECT DISTINCT
        slot,
        slot_start_date_time,
        proposer_validator_index
    FROM canonical_beacon_proposer_duty
    WHERE meta_network_name = 'mainnet'
      AND slot_start_date_time >= '2026-04-19' AND slot_start_date_time < '2026-04-19'::date + INTERVAL 1 DAY
),

-- Proposer entity mapping
proposer_entity AS (
    SELECT
        index,
        entity
    FROM ethseer_validator_entity
    WHERE meta_network_name = 'mainnet'
),

-- Blob count per slot
blob_count AS (
    SELECT
        slot,
        uniq(blob_index) AS blob_count
    FROM canonical_beacon_blob_sidecar
    WHERE meta_network_name = 'mainnet'
      AND slot_start_date_time >= '2026-04-19' AND slot_start_date_time < '2026-04-19'::date + INTERVAL 1 DAY
    GROUP BY slot
),

-- Canonical block hash (to verify MEV payload was actually used)
canonical_block AS (
    SELECT DISTINCT
        slot,
        execution_payload_block_hash
    FROM canonical_beacon_block
    WHERE meta_network_name = 'mainnet'
      AND slot_start_date_time >= '2026-04-19' AND slot_start_date_time < '2026-04-19'::date + INTERVAL 1 DAY
),

-- MEV bid timing using timestamp_ms
mev_bids AS (
    SELECT
        slot,
        slot_start_date_time,
        min(timestamp_ms) AS first_bid_timestamp_ms,
        max(timestamp_ms) AS last_bid_timestamp_ms
    FROM mev_relay_bid_trace
    WHERE meta_network_name = 'mainnet'
      AND slot_start_date_time >= '2026-04-19' AND slot_start_date_time < '2026-04-19'::date + INTERVAL 1 DAY
    GROUP BY slot, slot_start_date_time
),

-- MEV payload delivery - join canonical block with delivered payloads
-- Note: Use is_mev flag because ClickHouse LEFT JOIN returns 0 (not NULL) for non-matching rows
-- Get value from proposer_payload_delivered (not bid_trace, which may not have the winning block)
mev_payload AS (
    SELECT
        cb.slot,
        cb.execution_payload_block_hash AS winning_block_hash,
        1 AS is_mev,
        max(pd.value) AS winning_bid_value,
        groupArray(DISTINCT pd.relay_name) AS relay_names,
        any(pd.builder_pubkey) AS winning_builder
    FROM canonical_block cb
    GLOBAL INNER JOIN mev_relay_proposer_payload_delivered pd
        ON cb.slot = pd.slot AND cb.execution_payload_block_hash = pd.block_hash
    WHERE pd.meta_network_name = 'mainnet'
      AND slot_start_date_time >= '2026-04-19' AND slot_start_date_time < '2026-04-19'::date + INTERVAL 1 DAY
    GROUP BY cb.slot, cb.execution_payload_block_hash
),

-- Winning bid timing from bid_trace (may not exist for all MEV blocks)
winning_bid AS (
    SELECT
        bt.slot,
        bt.slot_start_date_time,
        argMin(bt.timestamp_ms, bt.event_date_time) AS winning_bid_timestamp_ms
    FROM mev_relay_bid_trace bt
    GLOBAL INNER JOIN mev_payload mp ON bt.slot = mp.slot AND bt.block_hash = mp.winning_block_hash
    WHERE bt.meta_network_name = 'mainnet'
      AND slot_start_date_time >= '2026-04-19' AND slot_start_date_time < '2026-04-19'::date + INTERVAL 1 DAY
    GROUP BY bt.slot, bt.slot_start_date_time
),

-- Block gossip timing with spread
block_gossip AS (
    SELECT
        slot,
        min(event_date_time) AS block_first_seen,
        max(event_date_time) AS block_last_seen
    FROM libp2p_gossipsub_beacon_block
    WHERE meta_network_name = 'mainnet'
      AND slot_start_date_time >= '2026-04-19' AND slot_start_date_time < '2026-04-19'::date + INTERVAL 1 DAY
    GROUP BY slot
),

-- Column arrival timing: first arrival per column, then min/max of those
column_gossip AS (
    SELECT
        slot,
        min(first_seen) AS first_column_first_seen,
        max(first_seen) AS last_column_first_seen
    FROM (
        SELECT
            slot,
            column_index,
            min(event_date_time) AS first_seen
        FROM libp2p_gossipsub_data_column_sidecar
        WHERE meta_network_name = 'mainnet'
          AND slot_start_date_time >= '2026-04-19' AND slot_start_date_time < '2026-04-19'::date + INTERVAL 1 DAY
          AND event_date_time > '1970-01-01 00:00:01'
        GROUP BY slot, column_index
    )
    GROUP BY slot
)

SELECT
    s.slot AS slot,
    s.slot_start_date_time AS slot_start_date_time,
    pe.entity AS proposer_entity,

    -- Blob count
    coalesce(bc.blob_count, 0) AS blob_count,

    -- MEV bid timing (absolute and relative to slot start)
    fromUnixTimestamp64Milli(mb.first_bid_timestamp_ms) AS first_bid_at,
    mb.first_bid_timestamp_ms - toInt64(toUnixTimestamp(mb.slot_start_date_time)) * 1000 AS first_bid_ms,
    fromUnixTimestamp64Milli(mb.last_bid_timestamp_ms) AS last_bid_at,
    mb.last_bid_timestamp_ms - toInt64(toUnixTimestamp(mb.slot_start_date_time)) * 1000 AS last_bid_ms,

    -- Winning bid timing (from bid_trace, may be NULL if block hash not in bid_trace)
    if(wb.slot != 0, fromUnixTimestamp64Milli(wb.winning_bid_timestamp_ms), NULL) AS winning_bid_at,
    if(wb.slot != 0, wb.winning_bid_timestamp_ms - toInt64(toUnixTimestamp(s.slot_start_date_time)) * 1000, NULL) AS winning_bid_ms,

    -- MEV payload info (from proposer_payload_delivered, always present for MEV blocks)
    if(mp.is_mev = 1, mp.winning_bid_value, NULL) AS winning_bid_value,
    if(mp.is_mev = 1, mp.relay_names, []) AS winning_relays,
    if(mp.is_mev = 1, mp.winning_builder, NULL) AS winning_builder,

    -- Block gossip timing with spread
    bg.block_first_seen,
    dateDiff('millisecond', s.slot_start_date_time, bg.block_first_seen) AS block_first_seen_ms,
    bg.block_last_seen,
    dateDiff('millisecond', s.slot_start_date_time, bg.block_last_seen) AS block_last_seen_ms,
    dateDiff('millisecond', bg.block_first_seen, bg.block_last_seen) AS block_spread_ms,

    -- Column arrival timing (NULL when no blobs)
    if(coalesce(bc.blob_count, 0) = 0, NULL, cg.first_column_first_seen) AS first_column_first_seen,
    if(coalesce(bc.blob_count, 0) = 0, NULL, dateDiff('millisecond', s.slot_start_date_time, cg.first_column_first_seen)) AS first_column_first_seen_ms,
    if(coalesce(bc.blob_count, 0) = 0, NULL, cg.last_column_first_seen) AS last_column_first_seen,
    if(coalesce(bc.blob_count, 0) = 0, NULL, dateDiff('millisecond', s.slot_start_date_time, cg.last_column_first_seen)) AS last_column_first_seen_ms,
    if(coalesce(bc.blob_count, 0) = 0, NULL, dateDiff('millisecond', cg.first_column_first_seen, cg.last_column_first_seen)) AS column_spread_ms

FROM slots s
GLOBAL LEFT JOIN proposer_entity pe ON s.proposer_validator_index = pe.index
GLOBAL LEFT JOIN blob_count bc ON s.slot = bc.slot
GLOBAL LEFT JOIN mev_bids mb ON s.slot = mb.slot
GLOBAL LEFT JOIN mev_payload mp ON s.slot = mp.slot
GLOBAL LEFT JOIN winning_bid wb ON s.slot = wb.slot
GLOBAL LEFT JOIN block_gossip bg ON s.slot = bg.slot
GLOBAL LEFT JOIN column_gossip cg ON s.slot = cg.slot

ORDER BY s.slot DESC
Show code
df = load_parquet("block_production_timeline", target_date)

# Filter to valid blocks (exclude missed slots)
df = df[df["block_first_seen_ms"].notna()]
df = df[(df["block_first_seen_ms"] >= 0) & (df["block_first_seen_ms"] < 60000)]

# Flag MEV vs local blocks
df["has_mev"] = df["winning_bid_value"].notna()
df["block_type"] = df["has_mev"].map({True: "MEV", False: "Local"})

# Get max blob count for charts
max_blobs = df["blob_count"].max()

print(f"Total valid blocks: {len(df):,}")
print(f"MEV blocks: {df['has_mev'].sum():,} ({df['has_mev'].mean()*100:.1f}%)")
print(f"Local blocks: {(~df['has_mev']).sum():,} ({(~df['has_mev']).mean()*100:.1f}%)")
Total valid blocks: 7,183
MEV blocks: 6,889 (95.9%)
Local blocks: 294 (4.1%)

Anomaly detection method

The method:

  1. Fit linear regression: block_first_seen_ms ~ blob_count
  2. Calculate residuals (actual - expected)
  3. Flag blocks with residuals > 2σ as anomalies

Points above the ±2σ band propagated slower than expected given their blob count.

Show code
# Conditional outliers: blocks slow relative to their blob count
df_anomaly = df.copy()

# Fit regression: block_first_seen_ms ~ blob_count
slope, intercept, r_value, p_value, std_err = stats.linregress(
    df_anomaly["blob_count"].astype(float), df_anomaly["block_first_seen_ms"]
)

# Calculate expected value and residual
df_anomaly["expected_ms"] = intercept + slope * df_anomaly["blob_count"].astype(float)
df_anomaly["residual_ms"] = df_anomaly["block_first_seen_ms"] - df_anomaly["expected_ms"]

# Calculate residual standard deviation
residual_std = df_anomaly["residual_ms"].std()

# Flag anomalies: residual > 2σ (unexpectedly slow)
df_anomaly["is_anomaly"] = df_anomaly["residual_ms"] > 2 * residual_std

n_anomalies = df_anomaly["is_anomaly"].sum()
pct_anomalies = n_anomalies / len(df_anomaly) * 100

# Prepare outliers dataframe
df_outliers = df_anomaly[df_anomaly["is_anomaly"]].copy()
df_outliers["relay"] = df_outliers["winning_relays"].apply(lambda x: x[0] if len(x) > 0 else "Local")
df_outliers["proposer"] = df_outliers["proposer_entity"].fillna("Unknown")
df_outliers["builder"] = df_outliers["winning_builder"].apply(
    lambda x: f"{x[:10]}..." if pd.notna(x) and x else "Local"
)

print(f"Regression: block_ms = {intercept:.1f} + {slope:.2f} × blob_count (R² = {r_value**2:.3f})")
print(f"Residual σ = {residual_std:.1f}ms")
print(f"Anomalies (>2σ slow): {n_anomalies:,} ({pct_anomalies:.1f}%)")
Regression: block_ms = 1678.6 + 13.54 × blob_count (R² = 0.009)
Residual σ = 583.1ms
Anomalies (>2σ slow): 631 (8.8%)
Show code
# Create scatter plot with regression band
x_range = np.array([0, int(max_blobs)])
y_pred = intercept + slope * x_range
y_upper = y_pred + 2 * residual_std
y_lower = y_pred - 2 * residual_std

fig = go.Figure()

# Add ±2σ band
fig.add_trace(go.Scatter(
    x=np.concatenate([x_range, x_range[::-1]]),
    y=np.concatenate([y_upper, y_lower[::-1]]),
    fill="toself",
    fillcolor="rgba(100,100,100,0.2)",
    line=dict(width=0),
    name="±2σ band",
    hoverinfo="skip",
))

# Add regression line
fig.add_trace(go.Scatter(
    x=x_range,
    y=y_pred,
    mode="lines",
    line=dict(color="white", width=2, dash="dash"),
    name="Expected",
))

# Normal points (sample to avoid overplotting)
df_normal = df_anomaly[~df_anomaly["is_anomaly"]]
if len(df_normal) > 2000:
    df_normal = df_normal.sample(2000, random_state=42)

fig.add_trace(go.Scatter(
    x=df_normal["blob_count"],
    y=df_normal["block_first_seen_ms"],
    mode="markers",
    marker=dict(size=4, color="rgba(100,150,200,0.4)"),
    name=f"Normal ({len(df_anomaly) - n_anomalies:,})",
    hoverinfo="skip",
))

# Anomaly points
fig.add_trace(go.Scatter(
    x=df_outliers["blob_count"],
    y=df_outliers["block_first_seen_ms"],
    mode="markers",
    marker=dict(
        size=7,
        color="#e74c3c",
        line=dict(width=1, color="white"),
    ),
    name=f"Anomalies ({n_anomalies:,})",
    customdata=np.column_stack([
        df_outliers["slot"],
        df_outliers["residual_ms"].round(0),
        df_outliers["relay"],
    ]),
    hovertemplate="<b>Slot %{customdata[0]}</b><br>Blobs: %{x}<br>Actual: %{y:.0f}ms<br>+%{customdata[1]}ms vs expected<br>Relay: %{customdata[2]}<extra></extra>",
))

fig.update_layout(
    margin=dict(l=60, r=30, t=30, b=60),
    xaxis=dict(title="Blob count", range=[-0.5, int(max_blobs) + 0.5]),
    yaxis=dict(title="Block first seen (ms from slot start)"),
    legend=dict(orientation="h", yanchor="bottom", y=1.02, xanchor="right", x=1),
    height=500,
)
fig.show(config={"responsive": True})

All propagation anomalies

Blocks that propagated much slower than expected given their blob count, sorted by residual (worst first).

Show code
# All anomalies table with selectable text and Lab links
if n_anomalies > 0:
    df_table = df_outliers.sort_values("residual_ms", ascending=False)[
        ["slot", "blob_count", "block_first_seen_ms", "expected_ms", "residual_ms", "proposer", "builder", "relay"]
    ].copy()
    df_table["block_first_seen_ms"] = df_table["block_first_seen_ms"].round(0).astype(int)
    df_table["expected_ms"] = df_table["expected_ms"].round(0).astype(int)
    df_table["residual_ms"] = df_table["residual_ms"].round(0).astype(int)
    
    # Build HTML table
    html = '''
    <style>
    .anomaly-table { border-collapse: collapse; width: 100%; font-family: monospace; font-size: 13px; }
    .anomaly-table th { background: #2c3e50; color: white; padding: 8px 12px; text-align: left; position: sticky; top: 0; }
    .anomaly-table td { padding: 6px 12px; border-bottom: 1px solid #eee; }
    .anomaly-table tr:hover { background: #f5f5f5; }
    .anomaly-table .num { text-align: right; }
    .anomaly-table .delta { background: #ffebee; color: #c62828; font-weight: bold; }
    .anomaly-table a { color: #1976d2; text-decoration: none; }
    .anomaly-table a:hover { text-decoration: underline; }
    .table-container { max-height: 600px; overflow-y: auto; }
    </style>
    <div class="table-container">
    <table class="anomaly-table">
    <thead>
    <tr><th>Slot</th><th class="num">Blobs</th><th class="num">Actual (ms)</th><th class="num">Expected (ms)</th><th class="num">Δ (ms)</th><th>Proposer</th><th>Builder</th><th>Relay</th></tr>
    </thead>
    <tbody>
    '''
    
    for _, row in df_table.iterrows():
        slot_link = f'<a href="https://lab.ethpandaops.io/ethereum/slots/{row["slot"]}" target="_blank">{row["slot"]}</a>'
        html += f'''<tr>
            <td>{slot_link}</td>
            <td class="num">{row["blob_count"]}</td>
            <td class="num">{row["block_first_seen_ms"]}</td>
            <td class="num">{row["expected_ms"]}</td>
            <td class="num delta">+{row["residual_ms"]}</td>
            <td>{row["proposer"]}</td>
            <td>{row["builder"]}</td>
            <td>{row["relay"]}</td>
        </tr>'''
    
    html += '</tbody></table></div>'
    display(HTML(html))
    print(f"\nTotal anomalies: {len(df_table):,}")
else:
    print("No anomalies detected.")
SlotBlobsActual (ms)Expected (ms)Δ (ms)ProposerBuilderRelay
14149952 0 6106 1679 +4427 upbit Local Local
14145888 0 4988 1679 +3309 upbit Local Local
14150112 0 4562 1679 +2883 upbit Local Local
14150912 0 4436 1679 +2757 upbit Local Local
14150982 0 4136 1679 +2457 lido Local Local
14149596 0 4005 1679 +2326 whale_0x8ebd Local Local
14148075 2 3840 1706 +2134 bloxstaking 0xb67eaa5e... BloXroute Regulated
14150656 3 3799 1719 +2080 abyss_finance Local Local
14144439 8 3821 1787 +2034 ether.fi Local Local
14147312 0 3690 1679 +2011 staked.us 0xb26f9666... Titan Relay
14151584 0 3677 1679 +1998 blockdaemon_lido Local Local
14146336 6 3677 1760 +1917 csm_operator98_lido Local Local
14149306 0 3586 1679 +1907 whale_0x8ebd 0x8a850621... Titan Relay
14147491 1 3592 1692 +1900 whale_0x8ebd 0x8527d16c... Ultra Sound
14149241 19 3832 1936 +1896 kraken 0xb26f9666... EthGas
14150924 0 3549 1679 +1870 coinbase 0x88a53ec4... Aestus
14149571 0 3515 1679 +1836 blockdaemon_lido 0xb26f9666... Ultra Sound
14148327 1 3510 1692 +1818 whale_0xdc8d 0xb67eaa5e... BloXroute Regulated
14144848 5 3556 1746 +1810 nethermind_lido 0xb26f9666... Aestus
14146407 1 3493 1692 +1801 blockdaemon_lido 0xb67eaa5e... Titan Relay
14150702 0 3441 1679 +1762 blockdaemon 0x8a850621... Titan Relay
14149314 0 3432 1679 +1753 ether.fi 0xb26f9666... Titan Relay
14150581 0 3423 1679 +1744 luno 0x88a53ec4... BloXroute Max Profit
14146104 14 3580 1868 +1712 solo_stakers 0x850b00e0... Aestus
14147380 0 3389 1679 +1710 nethermind_lido 0xb26f9666... Aestus
14151201 7 3480 1773 +1707 blockdaemon 0x857b0038... Ultra Sound
14147032 6 3466 1760 +1706 bloxstaking 0xb26f9666... Titan Relay
14151224 5 3450 1746 +1704 whale_0xdc8d 0x856b0004... BloXroute Max Profit
14147062 1 3393 1692 +1701 blockdaemon 0x8527d16c... Ultra Sound
14144921 6 3455 1760 +1695 nethermind_lido 0x8527d16c... Ultra Sound
14150382 2 3398 1706 +1692 whale_0xdc8d 0xb67eaa5e... BloXroute Regulated
14149208 0 3369 1679 +1690 blockdaemon_lido 0x805e28e6... BloXroute Max Profit
14149082 6 3447 1760 +1687 ether.fi 0xb26f9666... Titan Relay
14148403 1 3373 1692 +1681 blockdaemon_lido 0xb26f9666... Titan Relay
14150589 1 3372 1692 +1680 coinbase 0x88a53ec4... Aestus
14147094 0 3357 1679 +1678 blockdaemon 0x857b0038... Ultra Sound
14144737 0 3355 1679 +1676 blockdaemon 0x8a850621... Titan Relay
14147902 17 3582 1909 +1673 whale_0xdc8d 0xb67eaa5e... BloXroute Regulated
14150069 5 3416 1746 +1670 blockdaemon_lido 0xb67eaa5e... Titan Relay
14147222 0 3347 1679 +1668 blockdaemon 0x8a850621... Titan Relay
14148485 21 3627 1963 +1664 blockdaemon 0x88857150... Ultra Sound
14149912 1 3350 1692 +1658 blockdaemon 0x857b0038... Ultra Sound
14146931 0 3336 1679 +1657 blockdaemon 0xb26f9666... Titan Relay
14145223 0 3334 1679 +1655 ether.fi 0x850b00e0... BloXroute Max Profit
14147877 1 3347 1692 +1655 blockdaemon 0x8a850621... Titan Relay
14147214 1 3346 1692 +1654 blockdaemon 0xb67eaa5e... BloXroute Regulated
14150011 2 3357 1706 +1651 blockdaemon_lido 0xb67eaa5e... BloXroute Max Profit
14147301 10 3465 1814 +1651 blockdaemon 0x8a850621... Titan Relay
14149448 8 3437 1787 +1650 blockdaemon 0x8a850621... Titan Relay
14150613 1 3342 1692 +1650 whale_0xdc8d 0xb67eaa5e... BloXroute Regulated
14146558 0 3327 1679 +1648 0xb67eaa5e... BloXroute Regulated
14144838 0 3317 1679 +1638 solo_stakers 0xb67eaa5e... BloXroute Max Profit
14150498 5 3379 1746 +1633 whale_0xdc8d 0xb26f9666... Titan Relay
14150684 0 3308 1679 +1629 blockdaemon 0x88a53ec4... BloXroute Regulated
14145947 0 3308 1679 +1629 blockdaemon_lido 0x850b00e0... BloXroute Regulated
14150360 4 3362 1733 +1629 blockdaemon 0x8a850621... Titan Relay
14150486 5 3375 1746 +1629 blockdaemon 0xb67eaa5e... BloXroute Regulated
14146351 1 3318 1692 +1626 whale_0x8ebd 0x8a850621... Titan Relay
14147563 0 3304 1679 +1625 nethermind_lido 0x8db2a99d... BloXroute Max Profit
14148341 0 3304 1679 +1625 blockdaemon 0x8a850621... Titan Relay
14145639 1 3315 1692 +1623 blockdaemon 0xb26f9666... Titan Relay
14146190 0 3298 1679 +1619 blockdaemon_lido 0xb67eaa5e... BloXroute Regulated
14150620 1 3310 1692 +1618 blockdaemon 0x88a53ec4... BloXroute Regulated
14150387 1 3309 1692 +1617 blockdaemon 0x8db2a99d... BloXroute Max Profit
14149829 0 3294 1679 +1615 blockdaemon 0x88857150... Ultra Sound
14148810 0 3290 1679 +1611 blockdaemon 0x83d6a6ab... BloXroute Max Profit
14144961 6 3369 1760 +1609 blockdaemon 0xb26f9666... Titan Relay
14149294 10 3420 1814 +1606 blockdaemon 0xac23f8cc... BloXroute Max Profit
14149526 20 3553 1949 +1604 blockdaemon 0xb67eaa5e... BloXroute Regulated
14149074 0 3282 1679 +1603 blockdaemon_lido 0x99cba505... BloXroute Max Profit
14145383 0 3280 1679 +1601 blockdaemon 0x88857150... Ultra Sound
14148256 0 3279 1679 +1600 whale_0xdc8d 0x88857150... Ultra Sound
14146296 6 3360 1760 +1600 blockdaemon_lido 0xb67eaa5e... BloXroute Max Profit
14150395 5 3346 1746 +1600 blockdaemon 0x857b0038... Ultra Sound
14148693 3 3318 1719 +1599 whale_0xdc8d 0x850b00e0... BloXroute Regulated
14144830 4 3331 1733 +1598 whale_0xdc8d 0xb67eaa5e... BloXroute Regulated
14149056 12 3437 1841 +1596 gateway.fmas_lido 0x8527d16c... Ultra Sound
14144578 0 3273 1679 +1594 0x857b0038... BloXroute Regulated
14150949 1 3284 1692 +1592 everstake 0x857b0038... BloXroute Regulated
14151193 8 3377 1787 +1590 blockdaemon_lido 0x850b00e0... Ultra Sound
14151198 1 3280 1692 +1588 blockdaemon_lido 0x88a53ec4... BloXroute Regulated
14146348 5 3334 1746 +1588 blockdaemon 0x8527d16c... Ultra Sound
14145004 11 3415 1828 +1587 0xb26f9666... Titan Relay
14144715 0 3266 1679 +1587 blockdaemon_lido 0xb67eaa5e... BloXroute Regulated
14151118 0 3260 1679 +1581 blockdaemon 0x8527d16c... Ultra Sound
14146995 6 3338 1760 +1578 0xb67eaa5e... BloXroute Regulated
14146849 6 3337 1760 +1577 blockdaemon_lido 0xb26f9666... Titan Relay
14146654 5 3323 1746 +1577 0xb26f9666... Titan Relay
14151510 5 3320 1746 +1574 blockdaemon 0x88857150... Ultra Sound
14145283 5 3320 1746 +1574 blockdaemon_lido 0x8527d16c... Ultra Sound
14144683 0 3252 1679 +1573 whale_0xdc8d 0xb67eaa5e... BloXroute Regulated
14151418 0 3238 1679 +1559 whale_0xdc8d 0xb67eaa5e... BloXroute Max Profit
14145380 0 3235 1679 +1556 blockdaemon_lido 0x851b00b1... BloXroute Max Profit
14149670 6 3312 1760 +1552 0x8527d16c... Ultra Sound
14150651 0 3230 1679 +1551 whale_0xdc8d 0x8db2a99d... BloXroute Max Profit
14148675 1 3237 1692 +1545 luno 0x856b0004... Ultra Sound
14149812 5 3290 1746 +1544 revolut 0xb26f9666... Titan Relay
14146834 0 3220 1679 +1541 solo_stakers 0x9129eeb4... Agnostic Gnosis
14151186 0 3217 1679 +1538 revolut 0xb26f9666... Titan Relay
14148982 0 3215 1679 +1536 blockdaemon 0x8527d16c... Ultra Sound
14148275 0 3215 1679 +1536 whale_0x8ebd 0xb4ce6162... Ultra Sound
14148511 4 3269 1733 +1536 p2porg 0x8db2a99d... BloXroute Max Profit
14150815 5 3280 1746 +1534 blockdaemon_lido 0x88510a78... Ultra Sound
14147295 2 3239 1706 +1533 gateway.fmas_lido 0xb67eaa5e... BloXroute Max Profit
14144453 4 3266 1733 +1533 blockdaemon_lido 0xb67eaa5e... BloXroute Max Profit
14147511 2 3238 1706 +1532 blockdaemon_lido 0xb67eaa5e... BloXroute Regulated
14148560 1 3221 1692 +1529 revolut 0x8527d16c... Ultra Sound
14145221 1 3220 1692 +1528 whale_0x8914 0xb67eaa5e... Titan Relay
14149272 0 3206 1679 +1527 whale_0xdc8d 0xac23f8cc... Ultra Sound
14151563 1 3219 1692 +1527 p2porg 0x88510a78... Flashbots
14146520 5 3271 1746 +1525 gateway.fmas_lido 0xb67eaa5e... BloXroute Max Profit
14147059 2 3230 1706 +1524 revolut 0xb26f9666... Titan Relay
14149170 1 3216 1692 +1524 blockdaemon_lido 0xb67eaa5e... BloXroute Regulated
14150152 0 3201 1679 +1522 blockdaemon 0xb67eaa5e... BloXroute Max Profit
14150159 1 3208 1692 +1516 gateway.fmas_lido 0xb67eaa5e... BloXroute Regulated
14151411 2 3220 1706 +1514 gateway.fmas_lido 0x88a53ec4... BloXroute Regulated
14147593 0 3191 1679 +1512 whale_0x8ebd 0x8527d16c... Ultra Sound
14145120 8 3299 1787 +1512 whale_0x1435 0xb67eaa5e... BloXroute Regulated
14150406 4 3244