Sun, May 3, 2026

Propagation anomalies - 2026-05-03

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 >= '2026-05-03' AND slot_start_date_time < '2026-05-03'::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 >= '2026-05-03' AND slot_start_date_time < '2026-05-03'::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 >= '2026-05-03' AND slot_start_date_time < '2026-05-03'::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 >= '2026-05-03' AND slot_start_date_time < '2026-05-03'::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 >= '2026-05-03' AND slot_start_date_time < '2026-05-03'::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 >= '2026-05-03' AND slot_start_date_time < '2026-05-03'::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 >= '2026-05-03' AND slot_start_date_time < '2026-05-03'::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 >= '2026-05-03' AND slot_start_date_time < '2026-05-03'::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: 7,178
MEV blocks: 6,760 (94.2%)
Local blocks: 418 (5.8%)

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 = 1677.5 + 18.19 × blob_count (R² = 0.009)
Residual σ = 593.4ms
Anomalies (>2σ slow): 617 (8.6%)
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
14249216 5 3882 1768 +2114 ether.fi Local Local
14250240 0 3729 1677 +2052 luno 0x8527d16c... Ultra Sound
14245280 5 3780 1768 +2012 whale_0xdc8d 0xb26f9666... Titan Relay
14248000 0 3641 1677 +1964 upbit Local Local
14252224 1 3619 1696 +1923 luno 0xac23f8cc... BloXroute Max Profit
14252064 0 3595 1677 +1918 blockdaemon_lido 0x8527d16c... Ultra Sound
14245871 1 3521 1696 +1825 blockdaemon 0x8a850621... Titan Relay
14251924 2 3520 1714 +1806 blockdaemon_lido 0x88a53ec4... BloXroute Regulated
14249239 8 3611 1823 +1788 solo_stakers 0x8527d16c... Ultra Sound
14246809 2 3474 1714 +1760 kiln 0x8db2a99d... Ultra Sound
14247648 0 3431 1677 +1754 blockdaemon_lido 0x851b00b1... BloXroute Max Profit
14245725 5 3516 1768 +1748 blockdaemon 0x88a53ec4... BloXroute Regulated
14252296 10 3604 1859 +1745 blockdaemon_lido 0x857b0038... BloXroute Max Profit
14251143 1 3432 1696 +1736 blockdaemon 0x8a850621... Titan Relay
14245499 0 3410 1677 +1733 blockdaemon_lido 0xb26f9666... Titan Relay
14250885 0 3406 1677 +1729 blockdaemon 0xb67eaa5e... BloXroute Regulated
14248055 5 3490 1768 +1722 blockdaemon 0x88a53ec4... BloXroute Regulated
14245263 3 3451 1732 +1719 nethermind_lido 0x856b0004... BloXroute Max Profit
14245568 0 3395 1677 +1718 coinbase 0x8a850621... BloXroute Max Profit
14250400 0 3389 1677 +1712 revolut 0x853b0078... BloXroute Max Profit
14245952 1 3406 1696 +1710 0xb72cae2f... Ultra Sound
14247346 0 3370 1677 +1693 nethermind_lido 0x9129eeb4... Agnostic Gnosis
14250596 0 3366 1677 +1689 blockdaemon 0xb72cae2f... Ultra Sound
14251178 5 3453 1768 +1685 blockdaemon 0xb67eaa5e... BloXroute Max Profit
14246030 5 3452 1768 +1684 blockdaemon 0x88a53ec4... BloXroute Regulated
14252104 6 3462 1787 +1675 blockdaemon 0x8527d16c... Ultra Sound
14249192 10 3534 1859 +1675 blockdaemon_lido 0x88857150... Ultra Sound
14247283 1 3369 1696 +1673 nethermind_lido 0x823e0146... BloXroute Max Profit
14249360 0 3350 1677 +1673 blockdaemon 0xb67eaa5e... Ultra Sound
14246793 0 3347 1677 +1670 blockdaemon 0x88857150... Ultra Sound
14245890 0 3344 1677 +1667 nethermind_lido 0x85fb0503... Aestus
14248931 2 3377 1714 +1663 blockdaemon 0xb26f9666... Titan Relay
14248560 1 3358 1696 +1662 ether.fi 0x823e0146... Flashbots
14248200 0 3336 1677 +1659 blockdaemon 0x88a53ec4... BloXroute Max Profit
14250648 2 3363 1714 +1649 blockdaemon_lido 0xb26f9666... Titan Relay
14250025 0 3326 1677 +1649 blockdaemon 0xb72cae2f... Ultra Sound
14245234 1 3344 1696 +1648 blockdaemon 0x8db2a99d... BloXroute Max Profit
14249870 1 3344 1696 +1648 blockdaemon_lido 0xb26f9666... Titan Relay
14250880 1 3343 1696 +1647 blockdaemon 0x8db2a99d... BloXroute Max Profit
14246357 2 3361 1714 +1647 blockdaemon_lido 0x8527d16c... Ultra Sound
14251611 1 3342 1696 +1646 blockdaemon 0x9129eeb4... Ultra Sound
14247483 0 3321 1677 +1644 blockdaemon_lido 0xb67eaa5e... BloXroute Regulated
14248566 0 3317 1677 +1640 nethermind_lido 0x823e0146... BloXroute Max Profit
14252115 2 3353 1714 +1639 blockdaemon_lido 0xa965c911... Ultra Sound
14249211 6 3424 1787 +1637 blockdaemon 0x8a850621... Titan Relay
14246414 5 3405 1768 +1637 blockdaemon_lido 0x8527d16c... Ultra Sound
14250485 0 3312 1677 +1635 blockdaemon 0xb26f9666... Titan Relay
14248961 1 3327 1696 +1631 blockdaemon_lido 0xb26f9666... Titan Relay
14247931 8 3452 1823 +1629 0xb67eaa5e... BloXroute Max Profit
14245799 6 3415 1787 +1628 blockdaemon_lido 0x88857150... Ultra Sound
14246189 0 3301 1677 +1624 blockdaemon 0xb26f9666... Titan Relay
14245328 5 3391 1768 +1623 blockdaemon 0x88a53ec4... BloXroute Max Profit
14246849 5 3390 1768 +1622 nethermind_lido 0x853b0078... BloXroute Max Profit
14246326 0 3299 1677 +1622 whale_0xdc8d 0x8db2a99d... BloXroute Max Profit
14246145 0 3298 1677 +1621 0xb26f9666... Titan Relay
14246946 5 3383 1768 +1615 blockdaemon 0x8a850621... Titan Relay
14250269 5 3382 1768 +1614 ether.fi 0x8527d16c... Ultra Sound
14250727 1 3309 1696 +1613 ether.fi 0x853b0078... BloXroute Max Profit
14247923 1 3309 1696 +1613 luno 0x823e0146... BloXroute Max Profit
14249571 5 3381 1768 +1613 whale_0xdc8d 0xb26f9666... Titan Relay
14249577 6 3399 1787 +1612 blockdaemon 0x8a850621... Titan Relay
14246583 0 3288 1677 +1611 luno 0xb26f9666... Titan Relay
14247825 1 3304 1696 +1608 blockdaemon 0x8a850621... Titan Relay
14250372 2 3322 1714 +1608 blockdaemon_lido 0xb67eaa5e... Ultra Sound
14250069 0 3284 1677 +1607 blockdaemon 0xb26f9666... Titan Relay
14251248 5 3374 1768 +1606 coinbase 0xb67eaa5e... BloXroute Regulated
14251843 2 3318 1714 +1604 blockdaemon 0x88857150... Ultra Sound
14248223 0 3280 1677 +1603 blockdaemon_lido 0xb26f9666... Titan Relay
14247433 5 3368 1768 +1600 blockdaemon 0xb26f9666... Titan Relay
14246087 0 3275 1677 +1598 blockdaemon_lido 0xb67eaa5e... BloXroute Max Profit
14249936 0 3274 1677 +1597 blockdaemon 0x8527d16c... Ultra Sound
14246102 1 3292 1696 +1596 blockdaemon_lido 0xb67eaa5e... BloXroute Regulated
14251470 1 3292 1696 +1596 blockdaemon 0x8db2a99d... Ultra Sound
14248381 1 3288 1696 +1592 luno 0x9129eeb4... Ultra Sound
14250980 12 3482 1896 +1586 blockdaemon 0xb67eaa5e... BloXroute Regulated
14246835 5 3354 1768 +1586 ether.fi 0x88a53ec4... BloXroute Regulated
14250301 1 3280 1696 +1584 whale_0xfd67 0x88a53ec4... BloXroute Max Profit
14248588 6 3370 1787 +1583 blockdaemon 0x823e0146... BloXroute Max Profit
14248458 0 3260 1677 +1583 luno 0x8527d16c... Ultra Sound
14251964 5 3350 1768 +1582 revolut 0xb26f9666... Titan Relay
14247031 0 3258 1677 +1581 rocklogicgmbh_lido 0x8db2a99d... Titan Relay
14245769 7 3384 1805 +1579 blockdaemon 0x8a850621... Titan Relay
14247044 0 3253 1677 +1576 luno 0x8db2a99d... BloXroute Max Profit
14246568 0 3249 1677 +1572 blockdaemon 0xb26f9666... Titan Relay
14246545 0 3248 1677 +1571 blockdaemon 0x8527d16c... Ultra Sound
14245692 0 3247 1677 +1570 blockdaemon_lido 0x88857150... Ultra Sound
14245356 0 3247 1677 +1570 blockdaemon 0xb26f9666... Ultra Sound
14245628 0 3246 1677 +1569 blockdaemon 0x8527d16c... Ultra Sound
14252198 3 3300 1732 +1568 blockdaemon 0x8527d16c... Ultra Sound
14245518 0 3243 1677 +1566 luno 0xb26f9666... Ultra Sound
14249008 0 3243 1677 +1566 blockdaemon 0x88857150... Ultra Sound
14248313 2 3278 1714 +1564 blockdaemon_lido 0x88a53ec4... BloXroute Regulated
14248952 9 3403 1841 +1562 0xb26f9666... Titan Relay
14248167 1 3257 1696 +1561 whale_0x8914 0xb7c5e609... Titan Relay
14251242 0 3237 1677 +1560 0xba003e46... BloXroute Max Profit
14247940 12 3455 1896 +1559 revolut 0xb7c5e609... BloXroute Max Profit
14250338 6 3345 1787 +1558 gateway.fmas_lido 0x850b00e0... BloXroute Max Profit
14247879 10 3417 1859 +1558 blockdaemon_lido 0x88857150... Ultra Sound
14250850 5 3326 1768 +1558 blockdaemon 0x88857150... Ultra Sound
14251897 1 3252 1696 +1556 whale_0xdc8d 0x9129eeb4... Ultra Sound
14248953 0 3230 1677 +1553 coinbase 0x8527d16c... Ultra Sound
14248274 5 3320 1768 +1552 blockdaemon 0xb26f9666... Titan Relay
14252144 0 3228 1677 +1551 blockdaemon 0xb26f9666... Titan Relay
14245818 0 3227 1677 +1550 0x88a53ec4... BloXroute Regulated
14245773 1 3242 1696 +1546 coinbase 0xb26f9666... Titan Relay
14247777 10 3402 1859 +1543 blockdaemon_lido 0xb67eaa5e... BloXroute Regulated
14249700 15 3491 1950 +1541 blockdaemon_lido 0x850b00e0... BloXroute Max Profit
14251605 0 3214 1677 +1537 whale_0x4b5e 0x8527d16c... Ultra Sound
14250672 1 3231 1696 +1535 blockdaemon_lido 0x8db2a99d... BloXroute Max Profit
14246726 5 3303 1768 +1535 blockdaemon_lido 0x8527d16c... Ultra Sound
14249112 7 3336 1805 +1531 blockdaemon_lido 0x8527d16c... Ultra Sound
14248141 0 3208 1677 +1531 coinbase 0x857b0038... BloXroute Max Profit
14248287 0 3208 1677 +1531 blockdaemon_lido 0x88a53ec4... BloXroute Max Profit
14245361 4 3279 1750 +1529 blockdaemon_lido 0xb67eaa5e... BloXroute Regulated
14247784 2 3241 1714 +1527 kiln 0x857b0038... BloXroute Max Profit
14246952 6 3309 1787 +1522 gateway.fmas_lido 0xb67eaa5e... BloXroute Max Profit
14245585 5 3289 1768 +1521 0xb26f9666... BloXroute Regulated
14247042 5 3289 1768 +1521 blockdaemon_lido 0x88a53ec4... BloXroute Max Profit
14248914 1 3216 1696 +1520 whale_0x8ebd 0xb26f9666... BloXroute Regulated
14246888 2 3231 1714 +1517 whale_0x8ebd 0xb5a65d00... Flashbots