FlareWatch 為每個 FTSO 數據提供者在 12 個計分維度上分配 0–100 的綜合評分。計算方法是確定性的，輸入資料為公開的鏈上及 Flare 生態系統數據[Flaremetrics] [FSE] [Flare Explorer]，相同的算法適用於每個提供者——包括 FlareWatch 自己的 FTSO 提供者，它由這個確切的函數評分，無特殊待遇。此頁面記錄了每個維度和閾值，因此委託者和運營商可以準確看到分數是如何計算的以及選擇每個值的原因。這裡的每個聲稱都鏈接回其主要上游來源——見來源和參考 在底部。
委託模式 中顯示的內容（將 WFLR 委託給 FTSO 數據提供者以獲得 FTSO 通脹份額）。在 質押模式 中顯示的 驗證者分數（將 FLR 委託給 P-Chain 驗證者以獲得 VRM + MIRROR 獎勵）使用單獨的 9 維演算法，專注於驗證者運營 — 正常運行時間、手續費、可靠性等。它們是不同的鏈上角色，擁有不同的獎勵，分開計分。請參閱 驗證者分數方法論 了解質押方面的詳細資訊。| 90+ | 頂級層 — 前約 10–20% 的 FTSO 提供者。典型資料：高於中位數的獎勵率、高準確性、完全的 V2 協議參與（FTSO Scaling + Fast Updates + FDC）、低/零手續費、大型委託人基礎、支付 MIRROR 的驗證者節點、知名品牌。不需要單一維度 — 提供者通過在大多數類別中堆疊優勢來達到頂級層。 |
| 80–89 | 強大 — 符合大多數關鍵基準；缺少頂級層的一或兩個維度。 |
| 70–79 | 良好 — 符合所有基線條件；無重大差距。 |
| 60–69 | 可接受 — 可用但沒有差異化。 |
| <60 | 低於中位數 — 一個或多個維度存在重大差距。數學事實，不是品質判斷。 |
if (rate <= 0 || medianRate <= 0): score = 0 else: ratio = rate / medianRate score = min(25, round(ratio * 12.5 * 10) / 10) // 1 decimal place // Anomaly detection: providers > 3× the median are capped at // median × 3 for scoring purposes (prevents data outliers from // distorting the linear curve).
if (fseAccuracySecondary > 0): pct = fseAccuracySecondary / 100 if (pct >= 97) score = 25 elif (pct >= 95) score = 18 + (pct - 95) * 3.5 // 95 → 18, 97 → 25 elif (pct >= 93) score = 16 + (pct - 93) * 1 // 93 → 16, 95 → 18 elif (pct >= 90) score = 13 + (pct - 90) * 1 // 90 → 13, 93 → 16 elif (pct >= 85) score = 10 + (pct - 85) * 0.6 // 85 → 10, 90 → 13 elif (pct >= 80) score = 6 + (pct - 80) * 0.8 // 80 → 6, 85 → 10 else score = max(0, 2 + (pct - 70) * 0.4) elif (fseAccuracyPrimary > 0): // Primary fallback: similar piecewise linear curve. else: score = WEIGHT_ACCURACY / 2 // Neutral — no FSE data
earned = last 12 epochs where rewardRate > 0, keyed by epochId pairs = [earned[i-1], earned[i]] where epochId[i] - epochId[i-1] == 1 if (pairs.length < 3) return 10 // Neutral — too few consecutive pairs // DOWNSIDE volatility only: rises never hurt, so a stable or RISING // rate scores near full and a recovery lifts the score. Only drops // between CONSECUTIVE epochs count, in proportion to depth (RMS), // weighted toward recent pairs so an active recovery pulls the score up. drops[i] = max(0, (prev - cur) / prev) // rise -> 0 w[i] = 0.8 ^ (age of pair) // newest pair = highest cv = sqrt( sum(w[i] * drops[i]^2) / sum(w[i]) ) score = max(0, round((1 - min(1, cv * 6)) * 20 * 10) / 10)
if (!isActive) score = 0 // see "Active" below
else:
score = 3 // active baseline
if (hasSubmitAddress AND hasSigningPolicyAddress AND voterRegistered):
score += 4 // V1 registered
if (fseFtsoScaling) score += 8/3 // ~2.67 each
if (fseFastUpdates) score += 8/3
if (fseFdc) score += 8/3
score = min(15, round(score * 10) / 10)anchor = max(lowest active fee observed, protocol fee floor) // FIP-16 sets a 20% minimum entity fee. All 98 providers charge // exactly 20%, so the anchor is 20% and nobody is docked for // charging the only fee the protocol permits. Same curve and // same anchor the validator page's Fee dimension uses. if (!isActive) score = 0 d = fee - anchor // distance ABOVE the best real offer if (d <= 0) score = 15 // at or below the anchor → full marks elif (d <= 5) score = 15 - d * 0.43 elif (d <= 10) score = 12.9 - (d - 5) * 0.64 elif (d <= 15) score = 9.6 - (d - 10) * 0.86 elif (d <= 20) score = 5.4 - (d - 15) * 1.07 else: score = 0 // extractive
if (no fseNodeIDs) score = 0 if (mirrorStatsMap empty) score = 12 / 2 = 6 // Neutral seed before data lands activeCount = nodes_with_status_active fraction = activeCount / fseNodeIDs.length base = 12 * fraction // Overperformance bonus per node, applied only when ALL gates pass: // - >= 30 days observed since first reading (firstObservedAtMs) // - >= 3 paid stake observations // - finite medianOverpaymentRatio // Bayesian shrinkage with prior k=5 toward 1.0 (neutral): // shrunken = (observedRatio * N + 1.0 * 5) / (N + 5) // Bucketed bonus from shrunken: // < 1.05 → 0 < 1.15 → 1 < 1.30 → 2 >= 1.30 → 3 avgBonus = sum_per_node(bonus) / fseNodeIDs.length score = round((base + avgBonus) * 10) / 10
if (count <= 5) score = 0 elif (count >= 500) score = 12 else: ratio = log(count / 5) / log(100) // maps [5, 500] → [0, 1] score = round(min(12, max(0, ratio * 12)) * 10) / 10
if (fseActive) score = 10 elif (rewardRate > 0) score = 7 else score = 0
change = abs(votePowerDailyChangePct * 100) if (change < 1) score = 10 elif (change < 3) score = 10 - (change - 1) * 1.5 // 1 → 10, 3 → 7 elif (change < 5) score = 7 - (change - 3) * 1.5 // 3 → 7, 5 → 4 elif (change < 10) score = 4 - (change - 5) * 0.4 // 5 → 4, 10 → 2 else score = max(0, 2 - (change - 10) * 0.1)
if (no fspData OR totalEpochs <= 0) score = 5 // active window = epochs from the provider's first paid epoch to now missed = (active-window epochs) - (epochs the provider was paid) score = max(0, 10 - missed * 3)
if (no name) score = 0 elif (name starts with 0x or matches hex regex) score = 0 elif (name.length < 4) score = 4 else score = 8
ownBond = operator's own P-Chain node bond (FLR) total = ownBond + delegated WFLR vote power alignment = piecewise-linear ratio curve (0% → 0 … ≥10% → 7) absolute = min(7, ownBond / 5,000,000 * 7) // saturates at 5M FLR score = max(alignment, absolute)
fspRewardRate、delegationFeePercentage、wNatWeight 和 votePowerDailyChangePct。providersuccessrate.secondary 獲取精準度，加上 entityminimalconditionslatest.ftso_scaling / ftso_fast_updates / fdc 旗標獲取 V2 狀態。RewardClaimed 事件，其中 claimType=3 引用你的 nodeID。若任何節點最近沒有此類事件，你會在分數上顯示為 MIRROR-非活躍。// Step 1 — Raw composite (sum of 12 dimensions, max 172)
raw = rewardRate + accuracy + consistency + v2 + fee + mirror
+ delegators + participation + stability + compliance
+ identity + selfBond
// An UNMEASURED reward rate is not a FAILED one. When a provider is
// demonstrably distributing but we hold no rate figure for it, the
// Reward Rate weight leaves the DENOMINATOR rather than scoring 0
// against it — we score what we measured, over what we could measure.
maxRaw = (rewardRate figure missing AND isActive) ? 172 - 25 : 172
normalized = round((raw / maxRaw) * 100)
// (The separate vote-power dilution penalty was REMOVED in v4.9. It
// double-counted: an over-cap provider already earns a below-median
// realized reward rate, so the median-anchored Reward Rate dimension
// docks it for dilution once. Cap proximity is now a neutral DISPLAY
// signal on every provider, not a second deduction in the score.)
// Step 2 — Dynamic weight redistribution
// Across the active provider set, dimensions where everyone clusters
// (stddev < 1.0) become non-discriminating. Their weight gets
// redistributed evenly across dimensions where the spread is wider
// (stddev >= 1.0). The redistribution recomputes the score:
//
// for each dim d:
// multiplier[d] = 1.0 if stddev[d] < 1.0
// = (weight[d] + bonus) / weight[d] otherwise
// bonus = sum(weights of low-stddev dims) / count(high-stddev dims)
//
// adjusted_raw = sum(breakdown[d] * multiplier[d])
// adjusted_max = sum(weight[d] * multiplier[d])
// final = round((adjusted_raw / adjusted_max) * 100)
// Step 3 — Final score (capped at 100)
score = min(100, final)services/ftso/scoring.ts 中。想要直接檢查實現代碼（而不是閱讀上方文字和公式）或想要為自己的用途修改它的運營者或研究人員，可以發送郵件至 [email protected] 以請求訪問權限。如果有實際需求，我們將把該文件發佈為獨立的開源軟體包。/api/cron/refresh-validators 的 cron 任務內執行，每 5 分鐘 重新計算每個活躍提供者的評分（與 P-Chain 驗證者重新評分的同一批次執行）。每次執行都會重新獲取輸入資料（Flaremetrics、FSE、FSP 獎勵、V2 RewardManager 事件）。