// team.jsx v10 — 团队/队伍仪表盘：运动员卡片矩阵 · Coach 模式 InsightFeed // 职责：渲染全队概览、排行榜、ACWR 风险提示；InsightFeed 在 Coach 模式下显示自动洞察 // 依赖：charts.jsx · components.jsx · data.js（ACWR / metricScore01） const { useState: tUseState, useMemo: tUseMemo, useEffect: tUseEffect, useRef: tUseRef } = React; const _algoLabel = window.algoLabel || ((a) => ({ range: 'Range mapping', zscore: 'Z-score', percentile: 'Percentile', swc: 'SWC', modzscore: 'Modified Z (individual baseline)', }[a] || a)); const ALGO_LIST = [ { id: 'range', name: 'Range mapping', short: 'Range', formula: '(v − worst) / (best − worst) · 100' }, { id: 'zscore', name: 'Z-score', short: 'Z-score', formula: 'clamp((z + 2) / 4) · 100' }, { id: 'percentile', name: 'Percentile', short: '%ile', formula: 'rank(v) / N · 100' }, ]; // ── helpers ──────────────────────────────────────────────────────────────── function pct(sortedArr, p) { if (!sortedArr.length) return 0; const idx = (p / 100) * (sortedArr.length - 1); const lo = Math.floor(idx), hi = Math.ceil(idx); return lo === hi ? sortedArr[lo] : sortedArr[lo] + (sortedArr[hi] - sortedArr[lo]) * (idx - lo); } // DATE-ROBUSTNESS guard (audit 2026-07-07). A malformed date string (e.g. the // season key "2025/7/5 9") parses to NaN via `+new Date`, poisoning // `Math.min/max(...dates)` so every point collapses to x=0 (data piles at the // left). Every time-axis chart drops unplottable-date points before scaling. // Mirror of charts.jsx trPlottablePoints; also guards ACWRChart below. const trPlottableDate = (x) => !isNaN(+new Date(x)); const trPlottablePoints = (points) => (points || []).filter(p => p && trPlottableDate(p.x)); // ────────────────────────────────────────────────────────────────────────── // Custom team trend SVG chart — multi-series with per-series ±SD bands // series: [{ label, color, values:[{x:iso,y}], bands:[{x:iso,upper,lower}] }] // ────────────────────────────────────────────────────────────────────────── function TeamTrendChart({ series, height = 240, trendConfig }) { // Drop unplottable-date points up front so bad dates can't NaN-poison the scale. series = (series || []).map(s => ({ ...s, values: trPlottablePoints(s.values), bands: s.bands ? trPlottablePoints(s.bands) : s.bands, })); const pad = { top: 28, right: 56, bottom: 38, left: 40 }; const [w, setW] = tUseState(640); const [hover, setHover] = tUseState(null); const ref = tUseRef(null); tUseEffect(() => { if (!ref.current) return; const ro = new ResizeObserver(entries => { for (const e of entries) { if (e.contentRect.width > 160) setW(e.contentRect.width); } }); ro.observe(ref.current); return () => ro.disconnect(); }, []); const allDates = series.flatMap(s => s.values.map(v => +new Date(v.x))); const tMin = allDates.length ? Math.min(...allDates) : 0; const tMax = allDates.length ? Math.max(...allDates) : 1; const tSpan = Math.max(1, tMax - tMin); const W = w, H = height; const iW = Math.max(40, W - pad.left - pad.right); const iH = H - pad.top - pad.bottom; const xAt = (iso) => pad.left + (iW * (+new Date(iso) - tMin)) / tSpan; const yAt = (y) => pad.top + iH * (1 - (Math.max(0, Math.min(100, y)) / 100)); // x-ticks: show up to 8 evenly-spaced dates const allIso = [...new Set(series.flatMap(s => s.values.map(v => v.x)))].sort(); const step = Math.max(1, Math.ceil(allIso.length / 8)); const xTicks = allIso.filter((_, i) => i % step === 0 || i === allIso.length - 1); const linePath = (values) => values.map((v, i) => `${i===0?'M':'L'}${xAt(v.x)},${yAt(v.y)}`).join(' '); const bandPath = (bands) => { if (!bands || bands.length < 2) return null; const top = bands.map(b => `${xAt(b.x)},${yAt(b.upper)}`).join(' L'); const bot = [...bands].reverse().map(b => `${xAt(b.x)},${yAt(b.lower)}`).join(' L'); return `M${top} L${bot} Z`; }; return (
{/* y-grid 0/25/50/75/100 */} {[0, 25, 50, 75, 100].map(t => ( {t} ))} {/* x-axis */} {xTicks.map(iso => { const x = xAt(iso); return ( {window.formatDate ? window.formatDate(iso, 'month') : iso.slice(0,7)} ); })} {/* SD bands */} {series.map(s => { const bp = bandPath(s.bands); return bp ? : null; })} {/* benchmark line (option 3) — dashed at score 50 */} {trendConfig?.showBenchmarkLine && ( Ref 50 )} {/* lines + dots */} {series.map((s, si) => { if (!s.values.length) return null; const last = s.values[s.values.length - 1]; return ( {s.values.map((v, vi) => { const isHov = hover && hover.si === si && hover.vi === vi; return ( setHover({ si, vi, v, s, prev: vi > 0 ? s.values[vi-1] : null })} onMouseLeave={() => setHover(null)}/> {/* data labels (option 1) — show value above each point, skip if hovered */} {trendConfig?.showDataLabels && !isHov && ( {Math.round(v.y)} )} ); })} {Math.round(last.y)} ); })} {hover && ( )} {hover && (() => { const tx = xAt(hover.v.x), ty = yAt(hover.v.y); const band = hover.s.bands?.find(b => b.x === hover.v.x); const sd = band ? Math.round((band.upper - band.lower) / 2) : null; const delta = hover.prev ? Math.round(hover.v.y - hover.prev.y) : null; return (
80 ? ty - 12 : ty + 14, transform: ty > 80 ? 'translate(-50%,-100%)' : 'translate(-50%,0)', background: 'var(--panel-2)', border: `1px solid ${hover.s.color}`, borderRadius: 6, padding: '8px 10px', fontSize: 11, pointerEvents: 'none', boxShadow: '0 8px 20px rgba(0,0,0,.5)', minWidth: 150, zIndex: 5, color: 'var(--text)', }}>
{hover.s.label} {window.formatDate ? window.formatDate(hover.v.x, 'medium') : hover.v.x}
{Math.round(hover.v.y)} {sd != null && ± {sd} SD}
{delta != null && (
0 ? 'var(--pos)' : delta < 0 ? 'var(--neg)' : 'var(--muted)' }}> {delta > 0 ? '▲' : delta < 0 ? '▼' : '–'} {Math.abs(delta)} vs prev
)}
); })()}
); } // ────────────────────────────────────────────────────────────────────────── // ACWR chart — acute/chronic workload ratio with risk zones // ────────────────────────────────────────────────────────────────────────── function ACWRChart({ athletes, groups, seasons, squadStatsAll, algo, height = 200, annotations = null, onPointClick = null }) { const D = window.DASHBOARD_DATA; // Drop unplottable season keys (malformed dates) so they can't NaN-poison the // time scale below (allTs/tMin/tMax) and pile every point at the left. const sorted = tUseMemo(() => [...seasons].filter(trPlottableDate).sort((a, b) => +new Date(a) - +new Date(b)), [seasons]); const [w, setW] = tUseState(640); const [hover, setHover] = tUseState(null); const ref = tUseRef(null); tUseEffect(() => { if (!ref.current) return; const ro = new ResizeObserver(es => { for (const e of es) if (e.contentRect.width > 160) setW(e.contentRect.width); }); ro.observe(ref.current); return () => ro.disconnect(); }, []); const acwrData = tUseMemo(() => { // Prefer the real per-athlete acwr field if every athlete has it populated // for at least half the season; otherwise fall back to the old // overall-score proxy. This lets the chart light up automatically once a // user starts logging sRPE + duration in Data Entry. const realLoads = sorted.map(s => { const loads = athletes .map(a => (a.seasons[s] || {}).session_load) .filter(v => v != null && isFinite(v)); return { s, n: loads.length, mean: loads.length ? loads.reduce((a,b) => a+b, 0) / loads.length : null }; }); const realFillRate = realLoads.filter(r => r.n > 0).length / Math.max(1, sorted.length); if (realFillRate >= 0.4) { // Session-count windows (4-session ATL / 12-session CTL). // After computing, deduplicate points within a 7-day window — keeps only // the most recent point per week. This prevents visual zigzag when an // athlete logs multiple sessions in quick succession while leaving // monthly-spaced data completely unchanged. const raw = realLoads.map((sv, i) => { const acuteWin = realLoads.slice(Math.max(0, i - 3), i + 1).filter(r => r.mean != null); const chronicWin = realLoads.slice(Math.max(0, i - 11), i + 1).filter(r => r.mean != null); if (!acuteWin.length || !chronicWin.length) return null; const atl = acuteWin.reduce((s, r) => s + r.mean, 0) / acuteWin.length; const ctl = chronicWin.reduce((s, r) => s + r.mean, 0) / chronicWin.length; return { x: sv.s, y: ctl > 0 ? Math.round((atl / ctl) * 1000) / 1000 : 1, real: true }; }).filter(Boolean); const MS_7D = 7 * 86400000; return raw.reduce((acc, pt) => { if (!acc.length) return [pt]; const gap = +new Date(pt.x) - +new Date(acc[acc.length - 1].x); if (gap < MS_7D) acc[acc.length - 1] = pt; // replace with more recent else acc.push(pt); return acc; }, []); } // Fallback: legacy score-based proxy const sessions = sorted.map(s => { const ss = squadStatsAll[s]; if (!ss || !athletes.length) return null; const total = athletes.reduce((sum, a) => sum + D.computeOverallScore(a.seasons[s] || {}, groups, ss, algo), 0); return { x: s, y: total / athletes.length }; }).filter(Boolean); return sessions.map((sv, i) => { const n4 = sessions.slice(Math.max(0, i - 3), i + 1); const n12 = sessions.slice(Math.max(0, i - 11), i + 1); const atl = n4.reduce((s,v) => s + v.y, 0) / n4.length; const ctl = n12.reduce((s,v) => s + v.y, 0) / n12.length; return { x: sv.x, y: ctl > 0 ? Math.round((atl / ctl) * 1000) / 1000 : 1, real: false }; }); }, [athletes, groups, sorted, squadStatsAll, algo]); // True if the current chart is sourced from real sRPE×duration (not the proxy) const isReal = acwrData[0]?.real === true; const pad = { top: 24, right: 56, bottom: 38, left: 48 }; const W = w, H = height; const iW = Math.max(40, W - pad.left - pad.right); const iH = H - pad.top - pad.bottom; const allTs = acwrData.map(v => +new Date(v.x)); const tMin = allTs.length ? Math.min(...allTs) : 0; const tMax = allTs.length ? Math.max(...allTs) : 1; const tSpan = Math.max(1, tMax - tMin); const yMin = 0.4, yMax = 2.0; const xAt = (iso) => pad.left + (iW * (+new Date(iso) - tMin)) / tSpan; const yAt = (y) => pad.top + iH * (1 - (y - yMin) / (yMax - yMin)); // Risk zone bands (y in ACWR units) const zones = [ { from: 0.4, to: 0.8, color: 'rgba(248,113,113,.12)', label: 'Under' }, { from: 0.8, to: 1.3, color: 'rgba(52,211,153,.1)', label: 'Optimal' }, { from: 1.3, to: 1.5, color: 'rgba(251,191,36,.1)', label: 'Caution' }, { from: 1.5, to: 2.0, color: 'rgba(248,113,113,.15)', label: 'High' }, ]; const step = Math.max(1, Math.ceil(acwrData.length / 8)); const xTicks = acwrData.filter((_, i) => i % step === 0 || i === acwrData.length - 1).map(v => v.x); const yTicks = [0.5, 0.8, 1.0, 1.3, 1.5, 1.8]; const linePath = acwrData.map((v, i) => `${i===0?'M':'L'}${xAt(v.x)},${yAt(v.y)}`).join(' '); return (
{/* annotations layer (events: injury, intervention, competition) */} {annotations && annotations.length > 0 && window.AnnotationFlags && (() => { const Flags = window.AnnotationFlags; return ( { const t = +new Date(dateStr); if (!isFinite(t) || t < tMin || t > tMax) return null; return xAt(dateStr); }} hostTop={pad.top} hostBottom={H - pad.bottom} /> ); })()} {/* risk zone fills */} {zones.map(z => { const y1 = yAt(Math.min(z.to, yMax)), y2 = yAt(Math.max(z.from, yMin)); if (y1 >= y2) return null; return ; })} {/* zone separator lines */} {[0.8, 1.3, 1.5].map(v => ( ))} {/* x axis */} {/* y ticks */} {yTicks.map(t => ( {t.toFixed(1)} ))} {/* zone labels on right */} {zones.map(z => { const mid = (z.from + z.to) / 2; if (mid < yMin || mid > yMax) return null; return ( {z.label} ); })} {/* x ticks */} {xTicks.map(iso => ( {window.formatDate ? window.formatDate(iso, 'month') : iso.slice(0,7)} ))} {/* line */} {acwrData.length > 1 && ( )} {/* dots */} {acwrData.map((v, vi) => { const zone = zones.find(z => v.y >= z.from && v.y < z.to) || zones[zones.length-1]; const dotColor = zone.label === 'Optimal' ? 'var(--pos)' : zone.label === 'Caution' ? 'var(--warn)' : 'var(--neg)'; const isHov = hover && hover.vi === vi; return ( setHover({ vi, v, prev: vi > 0 ? acwrData[vi-1] : null, color: dotColor })} onMouseLeave={() => setHover(null)} onClick={onPointClick ? () => onPointClick(v.x) : undefined}/> ); })} {hover && ( )} {hover && (() => { const tx = xAt(hover.v.x), ty = yAt(hover.v.y); const delta = hover.prev ? Math.round((hover.v.y - hover.prev.y) * 1000) / 1000 : null; const zone = zones.find(z => hover.v.y >= z.from && hover.v.y < z.to) || zones[zones.length-1]; return (
80 ? ty - 12 : ty + 14, transform: ty > 80 ? 'translate(-50%,-100%)' : 'translate(-50%,0)', background: 'var(--panel-2)', border: `1px solid ${hover.color}`, borderRadius: 6, padding: '8px 10px', fontSize: 11, pointerEvents: 'none', boxShadow: '0 8px 20px rgba(0,0,0,.5)', minWidth: 160, zIndex: 5, color: 'var(--text)', }}>
ACWR · {zone.label} {window.formatDate ? window.formatDate(hover.v.x, 'medium') : hover.v.x}
{hover.v.y.toFixed(2)} {delta != null && (
0 ? 'var(--warn)' : 'var(--pos)' }}> {delta > 0 ? '▲' : '▼'} {Math.abs(delta).toFixed(3)} vs prev
)}
); })()}
); } // ────────────────────────────────────────────────────────────────────────── // Season trend panel — Score mode (mean±SD) + ACWR mode // ────────────────────────────────────────────────────────────────────────── function SeasonTrendPanel({ athletes, groups, seasons, squadStatsAll, algo, trendConfig }) { const D = window.DASHBOARD_DATA; const sortedSeasons = tUseMemo(() => [...seasons].sort((a, b) => +new Date(a) - +new Date(b)), [seasons]); const [mode, setMode] = tUseState('score'); // 'score' | 'acwr' const [visibleIds, setVisibleIds] = tUseState(() => new Set(groups.map(g => g.id))); const allSelected = visibleIds.size === groups.length; const toggleAll = () => setVisibleIds(allSelected ? new Set([groups[0]?.id]) : new Set(groups.map(g => g.id))); const toggleGroup = (id) => setVisibleIds(prev => { const next = new Set(prev); if (next.has(id)) { if (next.size > 1) next.delete(id); } else next.add(id); return next; }); const series = tUseMemo(() => { return groups.filter(g => visibleIds.has(g.id)).map(g => { const pts = sortedSeasons.map(s => { const ss = squadStatsAll[s]; if (!ss || !athletes.length) return null; const scores = athletes.map(a => D.computeGroupScore(a.seasons[s] || {}, g, ss, algo)); const mean = scores.reduce((a,b) => a+b, 0) / scores.length; const sd = Math.sqrt(scores.reduce((sum, v) => sum + (v-mean)**2, 0) / scores.length); return { x: s, y: Math.round(mean), sd: Math.round(sd * 10) / 10 }; }).filter(Boolean); return { label: g.label, color: g.accent, values: pts.map(v => ({ x: v.x, y: v.y })), bands: pts.map(v => ({ x: v.x, upper: Math.min(100, v.y + v.sd), lower: Math.max(0, v.y - v.sd) })), }; }); }, [athletes, groups, sortedSeasons, squadStatsAll, algo, visibleIds]); return (
Team Fitness Trend
{mode === 'score' ? `Team avg score ± 1 SD per group · ${_algoLabel(algo)}` : 'ACWR · Acute (4-session) / Chronic (12-session) load ratio · Proxy via overall score'}
{/* mode toggle */}
{[['score','Score'], ['acwr','ACWR']].map(([id, label]) => ( ))}
{/* group toggles — only for Score mode */} {mode === 'score' && ( <> {groups.map(g => { const active = visibleIds.has(g.id); return ( ); })} )}
{mode === 'score' ? ( <>
{series.map(s => ( {s.label} ))} Shaded band = ±1 SD
) : ( <>
{[ { c:'rgba(248,113,113,.6)', l:'< 0.8 Undertraining' }, { c:'rgba(52,211,153,.7)', l:'0.8–1.3 Optimal' }, { c:'rgba(251,191,36,.7)', l:'1.3–1.5 Caution' }, { c:'rgba(248,113,113,.9)', l:'> 1.5 High risk' }, ].map(z => ( {z.l} ))}
)}
); } // ────────────────────────────────────────────────────────────────────────── // Box plot strip — replaces dot strip in DistributionPanel // ────────────────────────────────────────────────────────────────────────── function BoxStrip({ metric, athletes, season, squadStats, algo, accent, onSelectAthlete, prevSquadStats }) { const D = window.DASHBOARD_DATA; const [hover, setHover] = tUseState(null); const sv = squadStats[metric.id]?.values || []; const items = tUseMemo(() => athletes.map(a => { const raw = a.seasons[season]?.[metric.id]; if (raw == null) return null; const score = Math.round(D.metricScore01(metric, raw, sv, algo) * 100); return { id: a.id, name: a.name, jersey: a.jersey, position: a.position, raw, score }; }).filter(Boolean).sort((a,b) => a.score - b.score), [athletes, season, squadStats, algo]); if (metric.dir === 'neutral') { return (
{metric.label}
Neutral — scoring N/A
—
); } if (!items.length) { return (
{metric.label}
No data this session
); } // Box statistics const scores = items.map(it => it.score); const n = scores.length; const q1 = pct(scores, 25), q2 = pct(scores, 50), q3 = pct(scores, 75); const iqr = q3 - q1; const loFence = Math.max(scores[0], q1 - 1.5 * iqr); const hiFence = Math.min(scores[n-1], q3 + 1.5 * iqr); const mean = Math.round(scores.reduce((a,b)=>a+b,0) / n); // Risk zone from fixedRange const fr = metric.fixedRange; let riskScore = null, riskOnLeft = true; if (fr && sv.length > 0) { riskScore = Math.round(D.metricScore01(metric, fr.threshold, sv, algo) * 100); riskOnLeft = (metric.dir === 'higher' && fr.goodWhen === 'above') || (metric.dir === 'lower' && fr.goodWhen === 'below'); } // Previous period ghost IQR (scores based on prev-season squad distribution) let prevGhostQ1 = null, prevGhostQ3 = null; if (prevSquadStats && prevSquadStats[metric.id]?.values?.length >= 2) { const psv = prevSquadStats[metric.id].values; const prevScores = [...psv] .map(raw => Math.round(D.metricScore01(metric, raw, psv, algo) * 100)) .sort((a, b) => a - b); prevGhostQ1 = pct(prevScores, 25); prevGhostQ3 = pct(prevScores, 75); } const H = 46, boxTop = 12, boxBot = 34; // golden-ratio jitter within box height const jY = (idx) => boxTop + 3 + ((boxBot - boxTop - 6) * ((idx * 0.618034) % 1)); const hoverItem = hover ? items.find(it => it.id === hover) : null; const meanRaw = sv.length ? sv.reduce((a,b)=>a+b,0)/sv.length : null; return (
{metric.label} {metric.unit}
{/* track */}
{/* quartile guides */} {[25,50,75].map(p => (
))} {/* risk zone */} {riskScore != null && (
)} {/* whisker line */}
{/* whisker caps */} {[loFence, hiFence].map(w => (
))} {/* Previous period ghost IQR box */} {prevGhostQ1 != null && prevGhostQ3 != null && (
)} {/* IQR box */}
{/* median */}
{/* mean tick */}
{/* individual dots */} {items.map((it, idx) => { const isOut = it.score < loFence || it.score > hiFence; const isHov = hover === it.id; return (
onSelectAthlete?.(it.id)} onMouseEnter={() => setHover(it.id)} onMouseLeave={() => setHover(null)} style={{ position:'absolute', left:`${it.score}%`, top:jY(idx), transform:'translate(-50%,-50%)', width: isHov ? 11 : 7, height: isHov ? 11 : 7, borderRadius:99, background: isOut ? 'var(--neg)' : accent, border:'1.5px solid var(--panel)', cursor:'pointer', zIndex: isHov ? 10 : 1, transition:'width .1s, height .1s', boxShadow: isHov ? `0 0 0 3px ${accent}33` : 'none', }} /> ); })} {/* tooltip */} {hoverItem && (() => { const onRight = hoverItem.score > 65; const inRisk = riskScore != null && (riskOnLeft ? hoverItem.score < riskScore : hoverItem.score > riskScore); return (
{hoverItem.name} #{String(hoverItem.jersey).padStart(2,'0')}
Raw {formatValue(hoverItem.raw)} {metric.unit} Score {hoverItem.score} {meanRaw != null && ( <> vs μ meanRaw) ? 'var(--pos)' : 'var(--neg)', textAlign:'right', }}> {hoverItem.raw > meanRaw ? '+' : ''}{(hoverItem.raw - meanRaw).toFixed(Math.abs(hoverItem.raw-meanRaw)>=10?1:2)} )}
{riskScore != null && (
{inRisk ? '⚠ ' + window.t('Outside reference') : '✓ ' + window.t('Within reference')} (threshold {fr.goodWhen} {formatValue(fr.threshold)}{metric.unit})
)}
{hoverItem.position} · click to open report
); })()}
{Math.round(q1)}–{Math.round(q3)} IQR
); } // TEAM-4 (2026-07-09): legacy TeamDashboard page deleted per ruling // docs/decisions/2026-07-09-team-three-persona-direction.md §6③ — composite stones / // ability heat map / roster 综合分 column / computeRiskLevel risk grading were red-line // debt. Removed here: TeamRecentReviewedEvidence · TeamCoachDiscussionFrame · // TeamDashboard · TEAM_LEGACY_FLAG · TeamTopBar · TeamStone · TeamHero · RiskMatrix · // SquadHeatmap · ROSTER_* consts · rosterFmt · SquadLeaderboard · // computeLensMatrix. TeamWorkbenchView (below) is the sole team page. // ───────────────────────────────────────────────────────────────────────── // TM-R1 · Team Workbench (coach communication workbench) // 1:1 with mockups/proposals/team-workbench-mockup.html — fixed core (team head // + optional metric row + action bar) + bento module grid (Browse/Customize). // Assembly only: modules come from window.TeamModules (registry = extension // point); each module's data is a pure buildData over caller-prepared input. // Reuses the ST-1 athlete-story bento Customize interaction classes verbatim // (interaction-patterns baseline). Legacy TeamDashboard deleted (TEAM-4, 2026-07-09). // // CSS: mockup-specific card styles (.tw-tchip/.tw-qrow/.tw-arow/…) are injected // as a scoped
{/* 固定核心 · 队伍头 */}

{squadName}

{rangeLabel && {rangeLabel}}

真实训练负荷、审核结果与测试覆盖在同一个工作区内，并保留各自数据来源。

{/* TEAM-1 · 视角预设切换 */}
视角 {personas.map(p => { const active = p.id === persona; const showDot = active && deviated; return ( ); })} {deviated && ( )}
{/* 可选指标行 */}
{TEAM_STAT_METRICS.filter(m => selectedStats.includes(m.id)).slice(0, 4).map(m => { const v = statValues[m.id]; return (
{m.label}
{v == null ? '—' : v}{v != null && m.unit ? {m.unit} : null}
); })}
{/* Bento */}
{activeModules.map((mod, index) => { const meta = registry ? registry.getModule(mod.id) : null; const content = renderModuleContent(mod.id); if (!content) return null; const span = sizeSpan[mod.size] || 1; const rowSpan = mod.id === 'discussion-queue' && !strictScLayout ? 2 : 1; return (
{customizing && (
{meta ? meta.label : mod.id}
{sizeGrades.map(sz => ( ))}
)} {content}
); })}
{!customizing && (
当前{personas.find(p => p.id === persona)?.label || '角色'}视角：{activeModules.length} / {registry?.listModules()?.length || activeModules.length} 模块
)} {customizing && ( )}
{/* 可选库脚注（documented backlog） */}
可选库（后续切片）：数值排序表（中性化排行）· 热力图 · 对比矩阵 · InsightFeed（逐条中性化后）· 出勤板
{briefOpen && window.WeeklyBriefPreviewModal && ( setBriefOpen(false)} /> )} ); } function GapAnalysisPanel({ groups, athletes, season, seasons, squadStatsAll, algo }) { const D = window.DASHBOARD_DATA; const sortedSeasons = tUseMemo(() => [...seasons].sort((a, b) => +new Date(a) - +new Date(b)), [seasons]); const currIdx = sortedSeasons.indexOf(season); const prevSeason = currIdx > 0 ? sortedSeasons[currIdx-1] : null; const currSS = squadStatsAll[season] || {}; const prevSS = prevSeason ? (squadStatsAll[prevSeason] || null) : null; const groupData = tUseMemo(() => { if (!athletes.length) return []; return groups.map(g => { const curr = Math.round(athletes.reduce((s,a) => s + D.computeGroupScore(a.seasons[season]||{},g,currSS,algo),0)/athletes.length); const prev = prevSS ? Math.round(athletes.reduce((s,a) => s + D.computeGroupScore(a.seasons[prevSeason]||{},g,prevSS,algo),0)/athletes.length) : null; return { g, curr, delta: prev != null ? curr - prev : null }; }).sort((a,b) => a.curr - b.curr); }, [groups, athletes, season, prevSeason, squadStatsAll, algo]); const half = Math.ceil(groupData.length / 2); const focusAreas = groupData.slice(0, half); const strengths = [...groupData.slice(half)].reverse(); const GapRow = ({ item }) => { const { g, curr, delta } = item; return (
{g.label}
{curr} {delta != null && ( 0?'var(--pos)':delta<0?'var(--neg)':'var(--muted)', minWidth:32, textAlign:'right' }}> {delta>0?'+':''}{delta} )}
); }; return (
Gap Analysis
Team strengths vs focus areas · {_algoLabel(algo)} {prevSeason && · Δ vs {window.formatDate?window.formatDate(prevSeason,'short'):prevSeason}}
↓ {window.t('Focus Areas')}
{focusAreas.map(item => )}
↑ {window.t('Strengths')}
{strengths.map(item => )}
); } function DistributionPanel({ athletes, season, groups, group, squadStats, matrix, activeAlgo, onSelectAthlete, focusGroupId, onFocusGroup, prevSquadStats }) { if (!group) return null; return (
{group.label} · per-metric distribution
Box = Q1–Q3 · line = median · tick = mean · dots = each athlete · red zone = outside reference
{groups.map(g => ( ))}
{group.metrics.map(m => ( ))}
{/* legend */}
Q1–Q3 box {prevSquadStats && ( Prev session Q1–Q3 )} Median Mean Outlier / Outside reference
); } // ── P3-3: 全队预警矩阵 ──────────────────────────────────────────────────── // Rows = risk dimensions, Columns = athletes. Each cell is a traffic light block. // Gives coach a 30-second "who needs attention" overview. const RISK_CELL_COLORS = { gray: { bg: 'var(--panel-2)', text: 'var(--muted)', border: 'var(--border)' }, green: { bg: 'rgba(16,185,129,.10)', text: '#059669', border: 'rgba(16,185,129,.25)' }, amber: { bg: 'rgba(245,158,11,.13)', text: '#b45309', border: 'rgba(245,158,11,.35)' }, red: { bg: 'rgba(239,68,68,.13)', text: '#dc2626', border: 'rgba(239,68,68,.35)' }, }; // Kept in TEAM-4: consumed by GapAnalysisPanel above (not part of the deleted // legacy TeamDashboard tree). function scoreColor(s) { if (s >= 70) return '#34d399'; if (s >= 55) return '#60a5fa'; if (s >= 40) return '#fbbf24'; return '#f87171'; } // TEAM-4 (2026-07-09): TeamDashboard removed from the window exposure with the // legacy deletion; TeamWorkbench + ACWRChart remain live consumers' handles. Object.assign(window, { TeamWorkbench, ACWRChart });