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654 lines (612 loc) · 41.9 KB
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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1, viewport-fit=cover">
<link rel="manifest" href="/manifest.json">
<title>Lab — Vector Hoops</title>
<meta name="description" content="Inside the model: how 12,966 player-seasons become an embedding, how it is trained and gated, and how it is evaluated on held-out seasons."/>
<link rel="stylesheet" href="assets/shell.css">
<link rel="stylesheet" href="assets/responsive.css">
<link rel="stylesheet" href="assets/final-qa.css">
<link rel="stylesheet" href="assets/unified.css">
<link rel="stylesheet" href="assets/motion.css">
<style>
:root{
--paper:#FFFEF7;--ink:#1A150F;--orange:#D55E00;--blue:#0072B2;--yellow:#F0E442;
--mono:ui-monospace,monospace;--sans:ui-sans-serif,system-ui;
--page-gutter:clamp(12px,3.2vw,20px);
}
html,body{margin:0;background:var(--paper);color:var(--ink);font-family:var(--sans);font-size:18px;line-height:1.65;overflow-x:clip;-webkit-text-size-adjust:100%}
*,*::before,*::after{box-sizing:border-box}
img,svg,video,canvas{max-width:100%;height:auto;display:block}
a{color:inherit}
.site-nav{position:sticky;top:0;z-index:100}
/* main shell mobile-first */
.research-main.network-main{
width:min(1180px,100%);
max-width:1180px;
margin:0 auto;
padding:12px var(--page-gutter) calc(96px + env(safe-area-inset-bottom));
display:flex;flex-direction:column;gap:16px;
}
.research-head.network-head{display:flex;flex-direction:column;gap:12px}
.research-head h1{
margin:0;
font-family:var(--mono);
font-size:clamp(28px,6vw,48px);
line-height:.95;
letter-spacing:-.02em;
}
.network-hero{
margin:0;max-width:68ch;
font-size:clamp(15px,3.6vw,18px);
line-height:1.55;color:var(--fg-2);
}
.network-hero b{font-weight:900;color:var(--ink)}
/* scrollable chip strips — thumb-friendly */
.stats-strip,.cqs-strip{
display:flex;gap:8px;overflow-x:auto;overflow-y:hidden;
-webkit-overflow-scrolling:touch;scrollbar-width:none;
padding:2px 0 8px;margin:0 -2px;
scroll-snap-type:x proximity;
flex-wrap:nowrap;
}
.stats-strip::-webkit-scrollbar,.cqs-strip::-webkit-scrollbar{display:none}
.stats-chip,.cqs-badge{
flex:0 0 auto;white-space:nowrap;
border:1px solid var(--line-2);border-radius:999px;
padding:6px 12px;background:var(--surface);
font-family:var(--mono);font-size:11px;font-weight:800;
box-shadow:none;
scroll-snap-align:start;
min-height:32px;display:inline-flex;align-items:center;
}
.cqs-badge b{font-size:14px;margin-right:6px}
.cqs-badge.is-primary{background:var(--accent);color:var(--on-accent)}
.cqs-badge.is-primary b{color:var(--yellow)}
/* anchor nav as scrollable pills */
.anchor-nav{
display:flex;gap:8px;overflow-x:auto;flex-wrap:nowrap;
padding:4px 0 6px;-webkit-overflow-scrolling:touch;scrollbar-width:none;
position:sticky;top:calc(48px + env(safe-area-inset-top,0));
z-index:5;background:var(--paper);
margin:0 calc(var(--page-gutter) * -1);padding-inline:var(--page-gutter);
border-bottom:1px solid var(--line);
}
.anchor-nav::-webkit-scrollbar{display:none}
.anchor-nav a{
flex:0 0 auto;white-space:nowrap;text-decoration:none;
border:1px solid var(--line-2);border-radius:999px;
padding:8px 14px;background:var(--surface);
font-family:var(--mono);font-size:11px;font-weight:900;
text-transform:uppercase;letter-spacing:.04em;
box-shadow:none;
min-height:40px;display:inline-flex;align-items:center;
}
.anchor-nav a:active{transform:translate(1px,1px);box-shadow:none}
/* cards */
.drift-card{
background:var(--surface);border:1px solid var(--line-2);
border-radius:16px;box-shadow:none;
overflow:hidden;display:flex;flex-direction:column;
}
.drift-card__head{padding:14px 16px 10px;border-bottom:1px solid var(--line)}
.card-kicker{
font-family:var(--mono);font-size:10px;font-weight:800;
text-transform:uppercase;letter-spacing:.08em;color:var(--fg-2);
display:flex;gap:6px;align-items:center;
}
.card-kicker::before{content:'';width:6px;height:6px;border-radius:50%;background:var(--orange);display:inline-block}
.drift-card__head h2{
margin:6px 0 0;
font-family:var(--mono);font-size:clamp(16px,4vw,22px);
line-height:1.15;letter-spacing:-.01em;
}
/* cockpit */
.cockpit-grid{
display:grid;grid-template-columns:1fr;gap:0;
border-top:1px solid #f0ede5;
}
@media(min-width:900px){.cockpit-grid{grid-template-columns:1fr 1fr}}
.cockpit-panel{padding:14px 16px;min-width:0}
.cockpit-panel + .cockpit-panel{border-top:1px solid var(--line)}
@media(min-width:900px){
.cockpit-panel + .cockpit-panel{border-top:0;border-left:1px solid var(--line)}
}
.cockpit-panel h3{
margin:0 0 8px;font-family:var(--mono);
font-size:12px;text-transform:uppercase;letter-spacing:.04em;
}
.code-block{
font-family:var(--mono);font-size:12px;line-height:1.5;
background:#0f0f0f;color:#e8e6e1;
border-radius:10px;padding:12px;
overflow-x:auto;white-space:pre-wrap;word-break:break-word;
border:1px solid var(--line-2);
-webkit-overflow-scrolling:touch;
}
.pipe{
display:flex;gap:8px;overflow-x:auto;flex-wrap:nowrap;
padding:6px 0 8px;-webkit-overflow-scrolling:touch;scrollbar-width:none;
}
.pipe::-webkit-scrollbar{display:none}
.pipe-step{
flex:0 0 auto;min-width:84px;
border:1px solid var(--line-2);border-radius:12px;
padding:8px 10px;background:var(--surface);box-shadow:none;
display:flex;flex-direction:column;gap:2px;
}
.pipe-step b{font-size:12px}
.pipe-step i{font-style:normal;font-family:var(--mono);font-size:10px;color:var(--fg-2)}
.step-num{
font-family:var(--mono);font-size:10px;font-weight:800;
background:var(--accent);color:var(--on-accent);border-radius:999px;
width:18px;height:18px;display:inline-flex;align-items:center;justify-content:center;
}
.pipe-arrow{flex:0 0 auto;display:flex;align-items:center;font-weight:900}
/* weights */
.weights-grid{display:flex;flex-wrap:wrap;gap:6px;margin-top:10px}
.weight-chip{
border:1px solid var(--line-2);border-radius:999px;
padding:4px 8px;background:var(--surface);
font-family:var(--mono);font-size:10px;font-weight:800;
display:inline-flex;gap:6px;align-items:center;
}
.weight-chip b{opacity:.6}
.weight-chip span{font-weight:900}
/* manim videos */
.manim-grid{
display:grid;grid-template-columns:1fr;gap:12px;
padding:12px;
}
@media(min-width:700px){.manim-grid{grid-template-columns:1fr 1fr}}
.manim-card{
border:1px solid var(--line-2);border-radius:14px;
overflow:hidden;background:#000;box-shadow:none;
display:flex;flex-direction:column;
}
.manim-card video{width:100%;aspect-ratio:16/9;object-fit:cover;background:#000}
.manim-caption{
padding:10px 12px;background:var(--surface);
font-size:13px;line-height:1.5;color:var(--fg-2);
border-top:1px solid var(--line-2)
}
.manim-caption b{font-weight:900}
/* network flow */
.network-section__head{padding:12px 14px;display:flex;flex-direction:column;gap:8px}
.viz-panel__label{
font-family:var(--mono);font-size:11px;font-weight:900;
text-transform:uppercase;letter-spacing:.06em;
}
.network-flow-subhead{font-size:13px;line-height:1.5;color:var(--fg-2);margin:4px 0 0;max-width:70ch}
.network-flow-tools{display:flex;gap:8px;align-items:center;flex-wrap:wrap}
.network-trace-status{font-family:var(--mono);font-size:11px;color:var(--fg-2);background:var(--surface);border:1px solid var(--line);border-radius:999px;padding:5px 10px}
.network-trace-clear{
appearance:none;border:1px solid var(--line-2);border-radius:999px;
padding:6px 12px;background:var(--surface);font-family:var(--mono);font-size:11px;font-weight:800;
box-shadow:none;min-height:36px;cursor:pointer
}
.network-flow-layout{display:grid;grid-template-columns:1fr;gap:0}
@media(min-width:980px){.network-flow-layout{grid-template-columns:1.2fr .8fr}}
.network-flow-host{
min-height:280px;background:var(--surface);border-top:1px solid var(--line);
border-bottom:1px solid var(--line);overflow:hidden;
position:relative;
}
.network-flow-host svg{width:100%;height:auto;display:block}
.network-insights{padding:12px 14px;font-size:13px;line-height:1.5}
.network-node-inspector{border-top:1px solid var(--line);background:var(--surface)}
@media(min-width:980px){.network-node-inspector{border-top:0;border-left:1px solid var(--line)}}
.network-flow-outputs{border-top:1px solid var(--line-2)}
.network-flow-outputs__head{padding:12px 14px}
.network-flow-outputs__grid{
display:grid;grid-template-columns:1fr;gap:10px;padding:0 12px 12px;
}
@media(min-width:700px){.network-flow-outputs__grid{grid-template-columns:1fr 1fr}}
.viz-panel{background:var(--surface);border:1px solid var(--line-2);border-radius:12px;padding:10px;box-shadow:none}
.network-arch-out,.network-skill-out{display:flex;flex-direction:column;gap:6px;margin-top:6px}
/* explorer */
.network-controls{display:flex;flex-direction:column;gap:12px}
.search-row{display:flex;gap:8px;align-items:center;flex-wrap:wrap}
.wiki-search.network-search{
flex:1 1 220px;min-width:0;
border:1px solid var(--line-2);border-radius:12px;
padding:12px 14px;font-size:16px;min-height:48px;
background:var(--surface);box-shadow:none;
}
.network-player-tag{
font-family:var(--mono);font-size:11px;background:var(--surface);
border:1px solid var(--line-2);border-radius:999px;
padding:5px 10px;box-shadow:none}
.network-step-nav{
display:flex;gap:6px;overflow-x:auto;flex-wrap:nowrap;
padding:2px 0 6px;-webkit-overflow-scrolling:touch;scrollbar-width:none
}
.network-step-nav::-webkit-scrollbar{display:none}
.network-step-btn{
flex:0 0 auto;white-space:nowrap;
border:1px solid var(--line-2);border-radius:999px;
padding:8px 12px;background:var(--surface);
font-family:var(--mono);font-size:11px;font-weight:800;
box-shadow:none;min-height:40px;cursor:pointer
}
.network-step-btn.is-active{background:var(--accent);color:var(--on-accent)}
.network-play-btn{
appearance:none;border:1px solid var(--line-2);border-radius:12px;
padding:12px 16px;background:var(--accent);color:var(--on-accent);
font-family:var(--mono);font-size:12px;font-weight:900;
text-transform:uppercase;letter-spacing:.04em;
box-shadow:none;min-height:48px;cursor:pointer;
width:100%;justify-content:center;display:inline-flex;align-items:center;gap:8px;
}
@media(min-width:500px){.network-play-btn{width:auto}}
.network-timebar{margin-top:8px;padding:10px 12px;background:var(--surface);border:1px solid var(--line-2);border-radius:12px}
.network-timebar__head{display:flex;justify-content:space-between;gap:8px;flex-wrap:wrap}
.network-timebar__label,.network-timebar__current{font-family:var(--mono);font-size:11px;font-weight:800}
#network-time-scrubber{width:100%;accent-color:var(--orange);height:36px}
.network-step-caption{font-size:13px;line-height:1.5;color:var(--fg-2);margin:8px 0 0}
.network-story{margin-top:10px;padding:12px;background:var(--surface);border:1px dashed var(--line-2);border-radius:12px;font-size:14px;line-height:1.6}
.attr-grid{display:grid;grid-template-columns:1fr;gap:10px;padding:12px}
@media(min-width:800px){.attr-grid{grid-template-columns:1fr 1fr 1fr}}
.attr-panel{min-height:120px}
/* map */
.network-map-layout{display:grid;grid-template-columns:1fr;gap:0}
@media(min-width:900px){.network-map-layout{grid-template-columns:1fr 360px}}
.network-map-stage{padding:12px;background:#0f0f0f}
.network-map-canvas{width:100%;aspect-ratio:1/1;max-height:560px;background:#111;display:block;border-radius:12px;border:2px solid #222}
.network-map-legend{display:flex;gap:6px;flex-wrap:wrap;padding:10px 0 0;list-style:none;margin:0}
/* next */
.site-next{
display:flex;gap:8px;flex-wrap:wrap;align-items:center;
padding:8px var(--page-gutter) 0;
font-family:var(--mono);font-size:12px;font-weight:800;
}
.site-next__label{color:var(--fg-2)}
.site-next a{
border:1px solid var(--line-2);border-radius:999px;
padding:6px 12px;background:var(--surface);text-decoration:none;
box-shadow:none}
.network-arch-pre{
font-family:var(--mono);font-size:11px;line-height:1.5;
background:#111;color:#ddd;border-radius:10px;
padding:12px;overflow:auto;max-height:60vh;
border:1px solid var(--line-2);white-space:pre-wrap;word-break:break-word;
}
.drift-loading{color:var(--fg-3);font-size:13px;font-style:italic}
</style>
<meta name="color-scheme" content="dark light"/>
<meta name="theme-color" content="#07090C" media="(prefers-color-scheme: dark)"/>
<meta name="theme-color" content="#F3F1EC" media="(prefers-color-scheme: light)"/>
<link rel="icon" href="/assets/favicon.svg" type="image/svg+xml"/>
<link rel="apple-touch-icon" href="/assets/apple-touch-icon.png"/>
<link rel="canonical" href="https://hoops.dumbmodel.com/model"/>
<meta property="og:type" content="website"/>
<meta property="og:site_name" content="Vector Hoops"/>
<meta property="og:url" content="https://hoops.dumbmodel.com/model"/>
<meta property="og:title" content="Lab — Vector Hoops"/>
<meta property="og:description" content="Inside the model: how 12,966 player-seasons become an embedding, how it is trained and gated, and how it is evaluated on held-out seasons."/>
<meta property="og:image" content="https://hoops.dumbmodel.com/assets/og-1200x630.png"/>
<meta property="og:image:width" content="1200"/><meta property="og:image:height" content="630"/>
<meta name="twitter:card" content="summary_large_image"/>
<meta name="twitter:title" content="Lab — Vector Hoops"/>
<meta name="twitter:description" content="Inside the model: how 12,966 player-seasons become an embedding, how it is trained and gated, and how it is evaluated on held-out seasons."/>
<meta name="twitter:image" content="https://hoops.dumbmodel.com/assets/og-1200x630.png"/>
<link rel="preconnect" href="https://fonts.googleapis.com"/><link rel="preconnect" href="https://fonts.gstatic.com" crossorigin/>
<link rel="stylesheet" href="https://fonts.googleapis.com/css2?family=Archivo:wdth,wght@62..125,400..800&family=Inter:opsz,wght@14..32,400..700&display=swap"/>
<link rel="stylesheet" href="/assets/atlas.css?v=2"/>
<style>
.research-main.network-main{ width:100%; max-width:calc(var(--content) + var(--gutter)*2); margin:0 auto; padding:0 var(--gutter) var(--s-9); }
.research-main h1, .research-main h2, .research-main h3, .research-main h4{ font-family:var(--font-display) !important; letter-spacing:-.01em; text-transform:none; }
.research-main .mono, .research-main p.mono{ font-family:var(--font-text) !important; }
.research-head{ display:flex; flex-direction:column; gap:16px; padding-bottom:var(--s-6); border-bottom:1px solid var(--line); margin-bottom:var(--s-6); }
.research-head > p.mono{ max-width:84ch; margin-left:auto !important; margin-right:auto !important; text-align:center; font-size:13px !important; line-height:1.7 !important; color:var(--fg-3) !important; }
.research-head .cqs-strip{ width:100%; margin:0; background:var(--surface); }
.cqs-badge{ align-items:center; text-align:center; padding:18px 12px; }
.cqs-badge span{ font-family:var(--font-display); font-size:9.5px; font-weight:600; font-stretch:112%; letter-spacing:.14em; text-transform:uppercase; color:var(--fg-3); }
.cqs-badge b{ font-size:clamp(22px,2.4vw,30px) !important; font-variant-numeric:tabular-nums; }
.cqs-badge.is-primary{ background:transparent !important; color:var(--fg-3) !important; box-shadow:inset 0 -2px 0 var(--accent); }
.cqs-badge.is-primary b{ color:var(--accent) !important; }
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<noscript><div role="alert" style="padding:16px;text-align:center">Enable JS — Vector Hoops needs JavaScript for Daily Court + Map</div></noscript>
<nav class="site-nav" data-active="/model" aria-label="Vector Hoops site"></nav>
<main id="main" class="research-main network-main">
<div class="research-head network-head">
<header class="title-card">
<div class="title-card__eyebrow reveal">The Lab</div>
<h1 class="title-card__title reveal reveal-1">How the vector <em>knows</em></h1>
<p class="title-card__lede reveal reveal-2">Every player-season becomes a fingerprint: 130 inputs in 17 masked feature families, each passed through its own residual tower, then fused into a unit vector where cosine is similarity. This is how that model is trained, gated and evaluated — with the numbers it has to beat.</p>
<div class="title-card__meta reveal reveal-3"><span><b>12,966</b>Seasons</span><span><b>17</b>Towers</span><span><b>130</b>Inputs</span><span><b>8 / 5 / 14 / 18</b>Heads</span></div>
</header>
<p class="mono" style="font-size:12px; color:var(--fg-2); margin:6px 0 2px;">Promote-gate baseline, re-anchored 2026-07-24. The old baseline (CQS 85.87, recall@10 1.0, purity 0.8726) was measured on the pre-protocol loop that trained on held-out positives — that 1.0 was memorization, so the gate demanded ≥0.98 and no honest model could ever pass it. These numbers are leak-free, averaged over 4 seeds.</p>
<div class="cqs-strip" aria-label="Promote gate baseline (leak-free, 4 seeds)">
<div class="cqs-badge is-primary"><b>75.62</b><span>CQS baseline (leak-free)</span></div>
<div class="cqs-badge"><b>0.742</b><span>test recall@10 (sd 0.128)</span></div>
<div class="cqs-badge"><b>0.7822</b><span>purity@20 archetype</span></div>
<div class="cqs-badge"><b>0.119</b><span>continuity spread (guard)</span></div>
<div class="cqs-badge"><b>6</b><span>seeds averaged</span></div>
</div>
<p class="mono" style="font-size:12px; color:var(--fg-2); margin:8px 0 2px;">Promoted 2026-07-25: <code>mtnn_v5_concat_b2_h160_t32_d64_mlp128_fus256</code>, replacing the 2026-07-14 build. On the identical held-out protocol it more than doubles top-5 retrieval on never-trained 2024+ pairs — <b>0.363 → 0.757</b> (top-1 0.163 → 0.438). The previous model predated the position-label, career-feature and durability-leak fixes.</p>
<div class="cqs-strip" id="eval-scoreboard-strip" aria-label="Held-out retrieval scoreboard">
<div class="cqs-badge is-primary"><b id="evsb-top5">…</b><span>held-out top-5 retrieval</span></div>
<div class="cqs-badge"><b id="evsb-top1">…</b><span>held-out top-1</span></div>
<div class="cqs-badge"><b id="evsb-base5">…</b><span>14-d baseline top-5</span></div>
<div class="cqs-badge"><b id="evsb-pairs">…</b><span>eligible pairs</span></div>
</div>
<p id="evsb-caption" class="mono" style="font-size:12px; line-height:1.6; color:var(--fg-2); margin:6px 0 0;">Loading assets/eval_scoreboard.json…</p>
<nav class="anchor-nav" aria-label="Page sections">
<a href="#training-cockpit">Training Cockpit</a>
<a href="#manim-mtnn">Architecture</a>
<a href="#network-flow">Flow</a>
<a href="#explorer">Explorer</a>
<a href="#pipeline">Pipeline</a>
<a href="#arch-spec">Spec</a>
</nav>
</div>
<!-- 1. TRAINING COCKPIT -->
<section class="drift-card" id="training-cockpit">
<div class="drift-card__head"><span class="card-kicker">Live Training Cockpit • MTNN v4 → v6</span><h2>Training cockpit — what ships now and what trains next</h2></div>
<div class="cockpit-grid">
<div class="cockpit-panel">
<h3>Current deployed — mtnn_v5_concat_b2_h160_t32_d64_mlp128_fus256</h3>
<div id="arch-current-meta" class="mono" style="font-size:12px; line-height:1.6;">Loading mtnn_arch.json…</div>
<div class="weights-grid" id="weights-grid"></div>
<p style="font-size:13px; line-height:1.6; margin-top:12px; color:var(--fg-2);">CQS weights from <code>pipeline/composite_score.py</code>: recall 0.18, purity 0.16, margin_14d 0.08, archetype 0.08, position 0.05, skills_r2 0.14, skill_nn 0.05, next_r2 0.12, next_mae 0.06, aux_r2 0.08. <b>Gate re-anchored 2026-07-25</b> to a leak-free baseline (CQS 75.62 / recall 0.742 / purity 0.7822, 6 seeds, 120-feature matrix). Thresholds are now 2× the standard error of the seed mean rather than a flat 0.02 — measured seed sd is 0.128 on test recall alone, so the old slack was adjudicating sampling noise. A candidate must also hold same-player continuity spread under its bar, which is what catches a model that memorizes the training window instead of generalizing.</p>
</div>
<div class="cockpit-panel">
<h3>Hill-climb 01→05 + loss weights</h3>
<div class="pipe">
<div class="pipe-step is-done"><span class="step-num">01</span><b>Gather</b><i>stats.nba 12,452</i></div><div class="pipe-arrow">→</div>
<div class="pipe-step is-done"><span class="step-num">02</span><b>Vectors</b><i>per-season z ±4 + mask</i></div><div class="pipe-arrow">→</div>
<div class="pipe-step is-done"><span class="step-num">03</span><b>Towers</b><i>17×160→32×3 1088</i></div><div class="pipe-arrow">→</div>
<div class="pipe-step is-done"><span class="step-num">04</span><b>Fusion</b><i>556→128→64 L2 7.8M</i></div><div class="pipe-arrow">→</div>
<div class="pipe-step is-done"><span class="step-num">05</span><b>Deploy</b><i>ONNX + WASM + Procrustes</i></div>
</div>
<p style="font-size:13px; margin-top:12px; line-height:1.6;">v6 champion is not shallow: 17 towers × 3 residual blocks 256 hidden → 64 out LN GELU + skip =1088, +17 mask dims =1105 concat → 512 fusion → 128 L2 unit sphere. ~224K / 233K sklearn fallback. Shared 128-d for 8 archetype +5 position +14 next +18 skill heads. Capacity holds 12,966 player-seasons, 1901 players 2+ seasons, MIN≥800 filter, Procrustes-chained drift.json maps any season into 1996-97 root frame.</p>
<p style="font-size:13px; margin-top:8px; line-height:1.6;"><b>v6 champion deployed:</b> 120 feats → robust median/IQR clip[-3,3] → 17 families cat([x·m,m]) 1105-d → towers b3 h256 t64 d128 mlp256 fus512 → 17×64 tokens =1088 → CLS + season 12→128 + 17 tokens =19 tokens → concat fusion 556→128→64 L2. ~224K, 233K sklearn fallback. CQS gate, purity, continuity guard, position + skills + next + aux. <b>Production serves ONE champion only — MTNN v6 is champion. Forecast Transformer v2 is research-only.</b></p>
</div>
</div>
<div style="padding:0 16px 16px;">
<div class="cockpit-panel" style="margin-top:4px;border:1px solid var(--line-2);border-radius:12px;background:var(--surface);">
<h3>Exact v6 training command — run from vector-hoops/</h3>
<pre class="code-block" id="train-command">python pipeline/train_mtnn.py --epochs 150 --dim 64 --tower-width 40 --tower-hidden 192 --tower-blocks 3 --mlp-heads --d-head-hidden 128 --fusion transformer --d-model 128 --n-fusion-layers 4 --n-attn-heads 4 --fusion-hidden 512 --lr 0.001 --lr-schedule onecycle --warmup-pct 0.1 --anneal-strategy linear --weight-decay 0.0002 --nce-loss hybrid --nce-player-weight 0.65 --nce-arch-weight 0.35 --hard-neg-boost 0.4 --drop-p 0.15 --checkpoint-metric cqs --val-every 5 --era-align procrustes --robust-scaling --phase auto</pre>
<p style="font-size:11px; color:var(--fg-3); margin-top:8px;" class="mono">Preprocessing: Procrustes chains drift.json back to 1996-97 root RᵀR=I, robust median/IQR clip[-3,3]. Loss: NCE hybrid player 0.65 arch 0.35 + archetype/position/profile/next/skills/salary/team_fit/roster_lift/form_recon/career_slope/competition/pedigree/playoff/honors. Split: player leakfree. Optim: AdamW no decay bias/LN. Scheduler: OneCycle warmup 10% linear.</p>
</div>
<div class="cockpit-panel" style="margin-top:12px;border:1px dashed var(--line-2);border-radius:12px;background:var(--surface);">
<h3>Forecast — same family, one season forward <span style="font-family:var(--mono);font-size:10px;background:var(--accent-wash);color:var(--accent);border:1px solid var(--line-2);border-radius:999px;padding:2px 8px;margin-left:6px">Where you're headed</span></h3>
<p style="font-size:13px;line-height:1.6;margin:8px 0">Same 12,966×64 MTNN v6 champion embedding that powers the map. <b>3-season context → next-season embedding</b> (first 8 of 128 dims). Forecast Transformer v2 0.4.0-incremental-full64-2plus, 10551 windows chronological train 8921/val 804/test 826, val MAE 0.0403 vs naive-last 0.0354 and seasonal-avg 0.0383 — loses to baselines so <b>research-only per policy</b>, not served as map. Uses same tokens: stone dashed = uncertainty, terracotta dashed = headed direction. Shared contract <code>/data/timesfm_forecasts.json</code> (10551 windows) used by Map, Trends, Players for ghost rings. ONE champion served.</p>
<div style="display:flex;gap:6px;flex-wrap:wrap;margin:8px 0"><span class="stats-chip">8 of 128 dims</span><span class="stats-chip">10551 windows</span><span class="stats-chip">503 honest if missing</span><span class="stats-chip">Past → Modern → Headed</span></div>
<canvas id="forecast-lab-canvas" width="800" height="180" style="width:100%;border:1px solid var(--line-2);border-radius:10px;background:var(--surface)"></canvas>
<div id="forecast-lab-legend" style="font-family:var(--mono);font-size:11px;margin-top:6px;color:var(--fg-2)">Loading forecast…</div>
<div id="forecast-lab-slot" style="margin-top:10px"></div>
</div>
</div>
</section>
<!-- 2. ARCHITECTURE — 4 VIDEOS -->
<section class="drift-card" id="manim-mtnn">
<div class="drift-card__head"><span class="card-kicker">Architecture — 4 animations • code-generated</span><h2>How MTNN actually works</h2></div>
<div class="manim-grid">
<div class="manim-card">
<video autoplay loop muted playsinline controls preload="metadata" src="assets/manim/MTNNFlow.mp4" aria-label="MTNNFlow"></video>
<div class="manim-caption"><b>MTNNFlow — glass box</b><br>17 towers, 120 feats cat([x·m,m]) → 17× 160→32 LN+GELU×3 → 1088 +17 mask → 556→128→64 L2 → heads 8/5/14/18. Every box is real dimension. <em style="display:block;margin-top:6px;background:var(--surface);border:1px dashed var(--line-2);border-radius:8px;padding:6px 8px">Video render is conceptual v4/v5 glass-box (48-d). Current deployed is MTNN v6 champion: same 17 families, 3 blocks 256→64, 1105→512→128, ~224K. 120 feats, 131 with readout families (injury is durability read-out, not input tower).</em></div>
</div>
<div class="manim-card">
<video autoplay loop muted playsinline controls preload="metadata" src="assets/manim/ChimeraEquation.mp4" aria-label="ChimeraEquation"></video>
<div class="manim-caption"><b>ChimeraEquation — how Chimera scores</b><br>Donor A + Donor B → fuse in 128-d → nearest real among 12,452 by cosine. The argmin behind daily puzzle. <span style="font-family:var(--mono);font-size:10px;background:var(--accent-wash);color:var(--accent);border:1px solid var(--line-2);border-radius:999px;padding:1px 6px">MTNN v6 128-d champion</span></div>
</div>
<div class="manim-card">
<video autoplay loop muted playsinline controls preload="metadata" src="assets/manim/InputFamilies.mp4" aria-label="InputFamilies"></video>
<div class="manim-caption"><b>InputFamilies — 120 feats → 17 families → cat([x·m,m])</b><br>volume·play·reb·def·eff·shotmix·bio·tracking*·form*·market·roster·career·comp·team·pedig·playoffs·honors =120. Missing tracking pre-2013 masked to zero.</div>
</div>
<div class="manim-card">
<video autoplay loop muted playsinline controls preload="metadata" src="assets/manim/EmbeddingL2.mp4" aria-label="EmbeddingL2"></video>
<div class="manim-caption"><b>EmbeddingL2 — 128-d → L2 → unit sphere</b><br>v̂ = v/||v||₂, ||v̂||=1, cos = v̂·ŵ. 12,966 points on sphere powers Chimera + Era Twin.</div>
</div>
</div>
</section>
<!-- 3. FLOW -->
<section class="drift-card" id="network-flow" aria-label="Network data flow">
<div class="network-section__head">
<div><p class="viz-panel__label" style="margin:0;">Data flow — truthful boxes measured</p><p class="network-flow-subhead">Input mask m∈{0,1} • cat([x·m,m]) 1105-d → 17 towers b3 h256 → t64 ×3 residual LN GELU + skip → 1088 +17 mask =1105 concat → 512 fusion → L2 embed 128-d → MLP heads 8/5/14/18. Tap any node. MTNN v6 champion 12,966×64.</p></div>
<div class="network-flow-tools"><span id="network-trace-status" class="network-trace-status">Tap a node to see what fed it.</span><button type="button" id="network-trace-clear" class="network-trace-clear" hidden>✕ Clear</button></div>
</div>
<div class="network-flow-layout">
<div class="network-flow-main">
<div id="network-flow-svg" class="network-flow-host" role="img" aria-label="MTNN layer diagram"></div>
<div id="network-flow-insights" class="network-insights"><p class="drift-loading">Loading tower activations, family share per head…</p></div>
</div>
<div id="network-node-inspector" class="network-insights network-node-inspector"><p class="drift-loading">Tap an input, tower, or output to see exact numbers.</p></div>
</div>
<div class="network-flow-outputs" id="network-output-card">
<div class="network-flow-outputs__head"><p class="viz-panel__label">Guess & grade — heads decode 128-d MTNN v6</p><p style="font-size:12px; color:var(--fg-3); margin-top:4px;">Same MLP heads as rightmost column. Tap a row to lock trace back through network.</p></div>
<div class="viz-duo-grid network-flow-outputs__grid">
<div class="viz-panel"><p class="viz-panel__label">Archetype — 8</p><div id="network-arch-out" class="network-arch-out"></div></div>
<div class="viz-panel"><p class="viz-panel__label">Position — 5</p><div id="network-pos-out" class="network-arch-out"></div></div>
<div class="viz-panel"><p class="viz-panel__label">Skill grades — 18×(48→16→1)</p><div id="network-skill-out" class="network-skill-out"></div></div>
<div class="viz-panel"><p class="viz-panel__label">Next-season forecast — per 100</p><div id="network-next-out" class="network-skill-out"></div></div>
</div>
</div>
</section>
<!-- 4. EXPLORER -->
<section class="drift-card network-controls-card" id="explorer">
<div class="drift-card__head"><span class="card-kicker">Explorer • Glass box debugger</span><h2>Probe any player-season</h2></div>
<div class="network-controls" style="padding:14px 16px;">
<div class="search-row">
<label class="viz-panel__label" for="network-search">Player</label>
<input type="search" id="network-search" class="wiki-search network-search" placeholder="Search 12,452 — e.g. LeBron 2013, Curry 2016… MTNN v5 champion" autocomplete="off" autocorrect="off" spellcheck="false">
<ul class="network-suggest" id="network-suggest" hidden></ul>
<span id="network-player-tag" class="network-player-tag">Loading index…</span>
</div>
<div class="search-row">
<label class="network-compare-toggle" for="network-compare-toggle"><input type="checkbox" id="network-compare-toggle"> <span style="font-family:var(--mono);font-size:12px;font-weight:800">Compare</span></label>
<input type="search" id="network-compare-search" class="wiki-search network-search" placeholder="2nd player-season" autocomplete="off" autocorrect="off" spellcheck="false" disabled style="min-width:180px;">
<ul class="network-suggest" id="network-compare-suggest" hidden></ul>
<span id="network-compare-tag" class="network-player-tag">No compare</span>
</div>
<div class="network-step-nav" id="network-step-nav" role="tablist">
<button type="button" class="network-step-btn is-active" data-step="0" role="tab" aria-selected="true">◉ Input</button>
<button type="button" class="network-step-btn" data-step="1" role="tab" aria-selected="false">⬢ Towers</button>
<button type="button" class="network-step-btn" data-step="2" role="tab" aria-selected="false">⬣ Fusion</button>
<button type="button" class="network-step-btn" data-step="3" role="tab" aria-selected="false">⬔ Embed</button>
<button type="button" class="network-step-btn" data-step="4" role="tab" aria-selected="false">⬕ Heads</button>
</div>
<button type="button" id="network-play" class="network-play-btn">▶ Play flow — trace signal</button>
</div>
<div style="padding:0 16px 16px;">
<div class="network-timebar" id="network-timebar" hidden>
<div class="network-timebar__head"><span class="network-timebar__label">Career timeline — scrub season</span><span id="network-timebar-current" class="network-timebar__current"></span></div>
<input type="range" id="network-time-scrubber" min="0" max="0" step="1" value="0" aria-label="Career timeline">
<div id="network-timebar-range" class="network-timebar__range"></div>
</div>
<p id="network-step-caption" class="network-step-caption">Loading architecture… MTNN v6: 17×160→32 LN GELU ×3, 12-d season emb, 1088+17=556→128→64 L2 ~224K / 233K sklearn fallback. Era-honest sigma per-season, masked, Procrustes-chained drift.json root 1996-97 Q^T Q=I.</p>
<div id="network-story" class="network-story"><p class="drift-loading">Loading story…</p></div>
<div id="network-compare-summary" class="network-insights" style="margin-top:12px;"><p class="drift-loading">Compare off — toggle to diff two seasons.</p></div>
</div>
</section>
<section class="drift-card network-attr-card" id="network-attr-card" aria-label="What drove this prediction" hidden>
<div class="network-section__head" style="flex-direction:column; align-items:stretch;"><div style="display:flex; flex-wrap:wrap; justify-content:space-between; gap:10px; align-items:center;"><p class="viz-panel__label" style="margin:0;">What drove this prediction</p><div style="display:flex; flex-wrap:wrap; gap:6px;"><div id="attr-target-tabs" role="tablist"></div><div id="attr-scope" role="group"></div><button type="button" id="attr-table-toggle" class="network-trace-clear" aria-pressed="false">Table view</button></div></div><p class="attr-subject" id="attr-subject" style="margin:8px 0 0; font-size:13px;"></p></div>
<div class="attr-grid"><div class="viz-panel attr-panel" id="attr-tile"></div><div class="viz-panel attr-panel" id="attr-towers"></div><div class="viz-panel attr-panel" id="attr-features"></div></div>
<div id="attr-population" hidden></div>
</section>
<section class="drift-card network-map-card" aria-label="128-d MTNN v6 embedding map">
<div class="network-section__head"><div><p class="viz-panel__label" style="margin:0;">Embedding map — 128-d MTNN v6 rendered in 3D</p><p style="font-size:12px; color:var(--fg-3); margin-top:6px;">Slow auto-rotate • drag to orbit • scroll to zoom • orange=selected — 12,966 points L2-normalized.</p></div></div>
<div class="network-map-layout">
<div class="network-map-stage">
<canvas id="network-map-canvas" class="network-map-canvas" aria-label="3D MTNN embedding map"></canvas>
<ul id="network-map-legend" class="network-map-legend"></ul>
</div>
<div id="network-map-insights" class="network-insights"><p class="drift-loading">Loading nearby players…</p></div>
</div>
</section>
<!-- 5. PIPELINE -->
<section class="drift-card" id="pipeline">
<div class="drift-card__head"><span class="card-kicker">Lab pipeline 01→05</span><h2>From box score to fingerprint</h2></div>
<div class="cockpit-grid">
<div class="cockpit-panel">
<h3>01 Gather → 02 Vectors</h3>
<p style="font-size:14px; line-height:1.6;">Pull every per-100 split from stats.nba.com 1996-2026. 12,966 player-seasons after MIN≥800 filter. Per-season z-score so 1999 and 2026 are both 0 = average of their era. Mask m creates cat([x·m,m]) so model knows never-measured vs zero.</p>
<ul style="font-size:12px; line-height:1.6; margin:8px 0 0 18px;">
<li>volume, playmaking, rebounding, defense, efficiency, shotmix</li>
<li>bio age/height/weight/draft, tracking 2013+ masked, form 2015+ masked</li>
<li>market salary, roster complement, career lag cosine, competition SOS, team, pedigree, playoffs, honors</li>
</ul>
</div>
<div class="cockpit-panel">
<h3>03 Train → 04 Export → 05 Deploy</h3>
<p style="font-size:14px; line-height:1.6;">Residual towers 17×160→32 ×3 LN GELU + skip =1088, +17 mask =1105 concat fusion 556→128→64 L2 ~224K / 233K sklearn fallback. 17 families, cat([x·m,m]) masked, Procrustes chained RᵀR=I root 1996-97 so Era Twin compares directly. Heads MLP decode 8/5/14/18. Forecast v2 research-only. ONNX WASM 2MB, ExecuTorch mobile. Drift uses Procrustes chained RᵀR=I root frame 1996-97 so Era Twin compares directly.</p>
</div>
</div>
</section>
<!-- 6. ARCH SPEC -->
<section class="drift-card" id="arch-spec">
<div class="drift-card__head"><span class="card-kicker">Shipped spec reproducible</span><h2>Arch spec — mtnn_arch.json</h2></div>
<div style="padding:14px;">
<pre id="arch-layers-pre" class="network-arch-pre">Loading mtnn_arch.json…</pre>
</div>
</section>
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<script src="assets/network-viz.js" defer></script>
<script>
(() => {
async function loadArch(){
try{
const r = await fetch('assets/mtnn_arch.json?v='+Date.now());
const j = await r.json();
const el = document.getElementById('arch-current-meta');
const pre = document.getElementById('arch-layers-pre');
if(el){
el.innerHTML = `
<div><b>model</b> ${j.model||'mtnn_v4'} • <b>dTower</b> ${j.dTower||32} • <b>dEmb</b> ${j.dEmb||48} • <b>blocks</b> ${j.towerBlocks||2} • <b>fusion</b> ${j.fusion||'concat'}</div>
<div style="margin-top:6px;"><b>families</b> ${(j.towerFamilies||[]).join(', ')} (${(j.towerFamilies||[]).length})</div>
<div style="margin-top:6px;"><b>checkpoint</b> mtime ${j.checkpoint?.mtime||'—'} • ${(j.checkpoint?.bytes/1e6||0).toFixed(2)}MB • ${j.nArchetypes||8} archetypes • ${j.skillKeys?.length||18} skills</div>
`;
}
if(pre) pre.textContent = JSON.stringify(j, null, 2).slice(0, 12000);
}catch(e){
const el=document.getElementById('arch-current-meta');
if(el) el.textContent='Failed to load assets/mtnn_arch.json — ' + e;
}
}
function loadWeights(){
const WEIGHTS = {recall:0.18,purity:0.16,margin_14d:0.08,archetype:0.08,position:0.05,skills_r2:0.14,skill_nn:0.05,next_r2:0.12,next_mae:0.06,aux_r2:0.08};
const grid=document.getElementById('weights-grid');
if(!grid) return;
grid.innerHTML = Object.entries(WEIGHTS).map(([k,v])=>`<div class="weight-chip"><b>${k}</b><span>${v}</span></div>`).join('');
}
async function loadScoreboard(){
const cap = document.getElementById('evsb-caption');
try{
const r = await fetch('assets/eval_scoreboard.json?v='+Date.now());
const j = await r.json();
const pct = x => (x*100).toFixed(1) + '%';
const test = j.results.mtnn.by_split.test;
const base = j.results.baseline_transparent_14d.by_split.test;
const rnd = j.results.baseline_random;
document.getElementById('evsb-top5').textContent = pct(test.top5);
document.getElementById('evsb-top1').textContent = pct(test.top1);
document.getElementById('evsb-base5').textContent = pct(base.top5);
document.getElementById('evsb-pairs').textContent = j.eligible_pairs.toLocaleString('en-US');
if(cap) cap.textContent =
'Held-out retrieval: query the shipped 48-d space with a player’s season N — does that same player’s season N+1 rank in the top-5 nearest neighbors among all ' +
j.embedding_asset.rows.toLocaleString('en-US') + ' player-seasons? Scored on ' + test.n.toLocaleString('en-US') +
' pairs whose target season (2024+) was never a training positive; ' + j.eligible_pairs.toLocaleString('en-US') +
' adjacent-season pairs total, keyed by stable PLAYER_ID. Baselines: transparent 14-d era-z profile (' + pct(base.top5) +
') and random rank (' + (rnd.top5*100).toFixed(2) + '%). Recomputed + gated every rebuild — pipeline/build_eval_scoreboard.py.';
}catch(e){
if(cap) cap.textContent = 'Failed to load assets/eval_scoreboard.json — ' + e;
}
}
function loadForecastLab(){
if(!window.HoopsForecast) return;
window.HoopsForecast.renderForecastCanvas('forecast-lab-canvas', {n: 64, legendId: 'forecast-lab-legend'});
window.HoopsForecast.load().then(d=>{
const slot=document.getElementById('forecast-lab-slot');
if(slot && d.forecasts && d.forecasts[0]){
const f=d.forecasts[0];
slot.innerHTML = window.HoopsForecast.forecastCardHTML(f.name, f);
}
});
}
window.addEventListener('DOMContentLoaded', ()=>{ loadArch(); loadWeights(); loadScoreboard(); setTimeout(loadForecastLab, 600); });
})();
</script>
<script>if('serviceWorker' in navigator){window.addEventListener('load',()=>{navigator.serviceWorker.register('/sw.js').catch(()=>{})})}</script>
<script>
/* Empty state: the forecast module is research-only and does not publish a
browser API, so replace the endless "Loading" with a plain statement. */
window.addEventListener('load',function(){setTimeout(function(){
if(window.HoopsForecast) return;
[['forecast-lab-canvas','forecast-lab-legend']].forEach(function(p){
var c=document.getElementById(p[0]), l=document.getElementById(p[1]);
if(c) c.hidden=true;
if(l){ l.textContent='The forecast is research-only and isn’t drawn on the site yet. Its method and limits are on the Methods page.'; l.classList.add('empty-note'); }
});
},1500);});
</script>
</body>
</html>