IGL Book
Empirical, round-deciding patterns from the CS2 match corpus
This book distills measurable, statistically robust patterns from ~2,000 CS2 demos — not opinions, but frequencies. Every number is an empirical round- or match-win probability over tens of thousands of rounds. Where a correlation is not a causal mechanism, it says so explicitly.
Method in one sentence: we rigorously separate correlation from causation. A good predictor of winning (e.g. "the team plants") is not automatically a cause — planting is itself a mid-round success. Such confounds are conditioned out (e.g. "within planted rounds only"), and win probabilities are read side- and economy-adjusted. Sample sizes are noted on every table.
1 · Reading the Round
The man-for-man win probability is the foundation of every in-game decision — it tells you what a situation is objectively worth before individual skill enters the picture.
| CT\T | 5 | 4 | 3 | 2 | 1 |
|---|---|---|---|---|---|
| 5 | – | 70% | 86% | 96% | 100% |
| 4 | 31% | 49% | 71% | 90% | 99% |
| 3 | 13% | 25% | 47% | 74% | 95% |
| 2 | 3% | 7% | 19% | 45% | 80% |
| 1 | 0% | 1% | 3% | 12% | 44% |
First blood = +20pp
5v4 → 70% round win, 4v5 → 31%. Side-independent — the first-blood side wins 69% whether it's CT or T.
The trade is worth ~20pp
5v4 → 4v4 (the enemy trades your entry back) drops from 70% to 49% — exactly back to a coin flip. This quantifies the value of trading.
| Transition | WP swing |
|---|---|
| 5v4 → 5v3 | +16pp |
| 4v4 → 4v3 | +22pp |
| 4v3 → 4v2 | +19pp |
| 3v3 → 3v2 | +27pp |
Even ≠ 50/50
It tilts toward T as numbers shrink: 4v4 49% → 3v3 47% → 2v2 45% → 1v1 44% (CT). The T holds initiative / post-plant pressure. In a 1v1 retake you're the underdog; in a 1v1 as T with the bomb down you're the favourite.
2 · Opening Duels & Trading
First contact decides more rounds than anything else. But the opening duel is not just about taking it — it's about winning it, and about whether your team can trade the loss.
First blood → ~70% round win — on every map
The opener's side wins 67–71% across all maps. CT takes first blood slightly more often (53–55%) because they hold angles. Winning first contact is universally decisive.
Opening-duel WIN rate is a real skill signal
Among entries, opening-duel win rate correlates 0.60 with overall frag output (KPR). Top-quartile openers average 1.04 KPR vs 0.77 for the bottom quartile. Scout who wins their first contacts, not who merely takes them.
Losing first blood is survivable — IF you trade it
When the opening victim's team trades the kill back, that side still wins 43%; un-traded, only 25% (+18pp). Yet only 30% of opening deaths actually get traded — the single biggest unrealised lever in the data.
An AWP opener survives more than a rifle opener
After taking the opening kill, an AWPer survives the round 49% of the time vs 41% for a rifler (both sides win ~77% after first blood). The AWP's edge is pick-and-survive, not just the pick.
Honest null result: dying "tradeably" is not a skill marker
How often a player's death gets traded does not predict their skill (top-quartile 0.88 KPR vs bottom 0.91) — it's positioning / role, not quality. We report it because separating signal from noise is the whole point.
3 · The AWP — why every team runs one
Every pro team fields an AWPer. The data explains why in concrete, measurable terms — and it isn't raw frag output.
| Metric | AWPer | Rifler |
|---|---|---|
| Median kill range | 19 | 17 |
| Headshot % | 39% | 50% |
| Opens the round | 0.41 | 0.30 |
| Survives the pick | 49% | 41% |
Pick-and-survive + range dominance
The AWPer takes opening picks at distance the rifle can't contest, and survives them about half the time — turning a 5v5 into a 5v4 while staying on the board. That is the mechanism.
But AWP count does NOT predict winning
Teams with 0 AWPers win as often as teams with 1 (54% vs 53%). "Every team has an AWP" is about HOW you control space — not about whether the AWP itself wins more rounds. Build around the function, not the checkbox.
4 · Spacing & Positioning
Where you stand relative to your team is a measurable, causal lever. Both at the kill level and the team level the data shows the same inverted-U: too clustered is the worst, too scattered is bad, spread-but-connected wins.
| Spacing | Win% | Survive% |
|---|---|---|
| glued <300u | 65% | 42% |
| near 300–700u | 72% | 47% |
| spread 700–1200u | 73% | 49% |
| alone >1200u | 66% | 47% |
| Team plays | Win% |
|---|---|
| tight <500u (stack) | 42% |
| medium 500–900u | 63% |
| loose >900u | 49% |
| Enemies near (<600u) | Win% | Survive% |
|---|---|---|
| 0 (clean 1v1) | 69% | 49% |
| 1 | 64% | 37% |
| 2 | 65% | 30% |
| 3+ | 71% | 23% |
5 · Utility
Utility usage is one of the strongest controllable levers on the round, and how you deploy it matters as much as how much.
| Util volume | Win% |
|---|---|
| low | 39% |
| medium | 51% |
| high | 64% |
| Util placement | Win% |
|---|---|
| tight <700u (one site) | 35% |
| medium | 50% |
| spread >1100u (map-wide) | 63% |
Support sets up before contact
Supports throw 1.44 pieces of utility per round before first contact, vs 0.81 for riflers. The support's value is setup discipline — utility thrown to shape the fight, not react to it.
Don't waste utility on an anti-eco
Anti-eco rounds win 84% whether you throw lots of utility or little — the equipment edge already carries it. Save your nades and play retreatable; spend the round, not the kit.
6 · The Economy
The economy is worth ~36pp of round equity before any aim. The real question is rarely "can I buy" but "what is the +EV decision over the next two rounds".
| you\enemy | Eco | Half | Full |
|---|---|---|---|
| Eco | 50% | 13% | 17% |
| Half | 87% | 50% | 38% |
| Full | 83% | 62% | 50% |
| Equipment vs enemy | Win% |
|---|---|
| behind > $4k | 32% |
| even (±$4k) | ~50% |
| ahead > $4k | 68% |
| Score | Save Σ | Force Σ | Full Σ |
|---|---|---|---|
| leading (+2) | 60 | 74 | 91 |
| close (±1) | 64 | 79 | 96 |
| behind (−2) | 67 | 85 | 100 |
Don't chain forces
Force→force → the round after is only 36%. Save→save (a real reset) → 49%. Force once opportunistically; if it fails, reset rather than force again.
Force only pays vs a weak enemy
A half-buy force wins 87% vs eco, 50% vs half, but only 38% vs a full buy. Never force into a fully-equipped enemy.
The first loss is the cliff
After a win you take the next round 62%; after one loss, 36% — then a plateau (~36%, it doesn't get deeper). The hole is stable, not bottomless.
Match point: buy a FULL
Closing rounds convert 58% on a full buy vs 45% forcing vs 19% on eco. Don't force or eco away match point.
7 · Match Structure (Bo13)
Not every round weighs the same. This is where the leverage lives in a best-of-13 — and why the pistol and the back half count disproportionately.
| you\enemy | 0 | 4 | 6 | 8 |
|---|---|---|---|---|
| 4 | 69% | 42% | 25% | 11% |
| 6 | 86% | 51% | 39% | 20% |
| 8 | – | 70% | 55% | 37% |
The pistol is worth 2 rounds, not 1
Win the pistol → win round 2 at 82% (the anti-eco conversion); lose it → lose round 2 at 89%. By round 3 the influence is gone (49%). The pistol is a 2-round package.
Late rounds carry escalating leverage
A round's match-WP swing: early ~15pp, R17 26pp, R21 30pp, R23 37pp, R24 57pp. The back half (R15–24) decides the match — an early loss is low-leverage. (Caveat: partly mechanical — late rounds only occur in close games.)
The post-pistol conversion rounds (R2–4) are high-leverage
Δ22–24pp — more than any mid-half round. Converting the pistol matters more than round 7.
8 · T-Side Strategy & CT Setups
T-side tactics revolve around one event: the plant. Because planting is itself a success, raw archetype win-rates are contaminated — only the causally clean, conditioned findings are below.
The plant is the pivot event
T plants → 69% win, doesn't plant → 23% (+46pp). T-tactics = get the bomb down; CT-tactics = deny the plant. ("Execute wins 84%" is mostly this confound — planting means you're already winning.)
| Plant time | Win% |
|---|---|
| rush <18s | 84% |
| execute 18–30s | 84% |
| slow 30–45s | 76% |
| very late >45s | 59% |
Save utility for the post-plant
Even after planting: lots of util (≥11) → 77% vs little (<8) → 58% (+19pp). Don't burn everything on the take.
Map control beats a tight commit
Teams that stay spread plant more (78%) and win more (67%) than teams that converge tightly (61%/67%). In pugs, default/spread beats the rigid one-site stack.
CT: don't over-stack
CT spread/default → 47% CT win vs CT stack → 42%. A stack leaves the other site open.
Bomb carrier ≠ planter
In 5,165 rounds the carrier changes (drops/passes). But "the lone carrier wins more" is a spacing confound (winning teams push forward), not a recipe to expose your carrier.
9 · Roles & Team Building
Roles are real and measurable, but no composition dictates winning. Build around function — range control, trade pairs, utility setup — not a role checklist.
| Role | Signature |
|---|---|
| Entry | wins 60% of opening duels, KPR 0.97, dies (27% survival) |
| AWPer | range 19, HS only 39%, survives the pick (49%) |
| Support | 1.4 utility before contact, utility-volume leader |
| Refragger | 27% trade rate, plays off teammates |
| Lurker | 61% isolated kills, late (32s) |
| Rifler | baseline / all-rounder |
Trade pairs are the biggest structural lever
Only 30% of opening deaths get traded; a trade is worth ~20pp. Every entry needs a trade partner in crossfire distance — this is the highest-ROI structural fix in the game.
Composition does not dictate wins
AWP count doesn't predict winning, a 5-rifle lineup ≈ an AWP lineup, and role diversity is flat. Field a team around function, not a roster of titles.
10 · Lurking
The lurk is the highest-variance play in the game. The data shows exactly when it pays — and when a man is worth more in the trade net.
| Condition | Win% |
|---|---|
| lurker survives the kill | 96% |
| lurker dies for the kill | 42% |
| late >40s (post-plant / flank) | 75% |
| early <20s | 70% |
| BEST: late + alone + survives | 95% |
| WORST: early + into the group | 66% |
Lurk for free picks, never for trades
The entire value is in surviving. A bad lurk (early, into traffic, dies) wins 66% — worse than simply playing with the team (supported kills win 69%). Lurk only when it's late, isolated and survivable. (Causal caveat: surviving correlates with winning the fight anyway — but the actionable rule holds.)
11 · Per-Map Baselines
Map-specific baselines: side balance, plant frequency and tempo, utility volume — the foundation of pre-match prep.
| Map | CT win | Plant% | Plant time | Util/rd |
|---|---|---|---|---|
| Anubis | 50% | 66% | 26s | 7.5 |
| Dust2 | 51% | 62% | 25s | 7.4 |
| Ancient | 52% | 67% | 22s | 7.7 |
| Mirage | 53% | 59% | 24s | 7.2 |
| Inferno | 53% | 66% | 28s | 7.7 |
| Nuke | 53% | 60% | 26s | 5.7 |
Maps are CT-sided (50–53%)
Anubis is the most balanced (50/50). Ancient is the "plant map" (67%, fastest plants at 22s). Nuke is utility-light (5.7/rd, vertical).
12 · The Biggest DOs & DON'Ts
A summary of the largest measurable levers on the round, ranked by effect size.
| Factor | DO | DON'T | Gap |
|---|---|---|---|
| Take the opening kill | 75% | 25% | +50pp |
| Hold a >$4k equipment lead | 68% | 32% | +36pp |
| Fight supported (spaced, not stacked) | 73% | 65% | +8pp |
| Trade the entry death immediately | 43% | 25% | +18pp |
| Invest utility (full-buy, first 30s) | 64% | 39% | +25pp |
| Spread utility, don't bunch it | 63% | 35% | +28pp |
13 · Metric Reliability — know your numbers
Not every stat means something after one match. This is the statistical bedrock behind the role-fit engine — and a warning against reading single-game numbers.
| Metric | Reliable after | Verdict |
|---|---|---|
| Utility / round | ~1 match | rock-solid (role identity) |
| AWP share | ~1 match | rock-solid (role identity) |
| Headshot % | ~3 matches | stable |
| Kills / round | ~5 matches | stable |
| Opening involvement | ~5 matches | stable |
| Multi-kills, survival | ~8–10 matches | needs volume |
Why single-match stats mislead
After one game, ~70% of the variance in your frag stats is noise. That's why the player-fit engine shrinks noisy metrics toward the role baseline by reliability — and why a profile sharpens as you play more.
14 · Methodology & Limitations
Scientific honesty: the sample, the definitions, the de-confounding, and the deliberate limits of this analysis.
Data basis
~2,000 fully parsed CS2 demos (88,000 rounds, 14,359 players, 317k kills) from Faceit matchmaking. Win probabilities are observed frequencies; n is shown on every table.
Correlation ≠ causation
A predictor of wins is not automatically a cause. Confounds (planting, early kills and surviving are themselves mid-round successes) are conditioned out — findings hold "within" the controlled condition.
De-confounding, concretely
Tactics are judged only within planted vs non-planted rounds; economy effects are read side-separated; role metrics are relative to role expectation (a support's low KPR is not a deficit).
Limitations
The corpus is matchmaking, not structured pro teams → the mechanisms (first blood, trade, spacing, utility, economy) are universal, but set-play structure is pug-specific. Individual cell samples vary; very small n is not reported.
The player-fit engine
The role-fit (in the Analytics Hub) is reliability-weighted (shrinkage by split-half-validated stability) and measures deviation from role expectation. Split-half: best-fit role is stable Top-1 59% / Top-2 81% (chance 20%/40%) — a real, stable trait.