Tollie — CS2 Stats
GG76561198974509360[U:1:1014243632]Steam profile ↗✓ No bans
What changed since last observed
CSDB last observed this profile on 18 Sep 2026 (yesterday). Ranks are recorded once per day this page is viewed.
CSDB reads this profile's Premier rating from its tracked match history, and records Faceit ELO once on every day the page is viewed. 4 days observed since 3 Sep 2026. Come back after the next session and the change shows above.
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Rating over time
Premier CS Rating
- At peak — 21,224
- Next: Red band at 25,000 — 3,776 to go
- Reached: Pink band · Purple band · Blue band · Light Blue band
- Pink band first seen 2026-09-03
- Purple band first seen 2026-09-03
Premier is CSDB’s own observation — it builds from the day a profile is first viewed and cannot be backfilled.
Compare periods
Last 7 days vs previous 7
| Metric | Last 7 | Previous 7 | Change |
|---|---|---|---|
| Matches | 35 | 59 | −24 |
| Win rate | 54% | 47% | +7pp |
| K/D | 1.20 | 1.16 | +0.03 |
| Headshot % | 55% | 56% | −1pp |
Measured 13 Sep 2026 → 19 Sep 2026; previous 3 Sep 2026 → 13 Sep 2026.
Differences between Valve's lifetime totals on the days CSDB observed this profile — every mode Valve counts, not only ranked. A window appears only when an observation sits within a few days of each end.
How this compares with the same rank
Median values for Pink band among CSDB-tracked players (n=36,630), from Valve's own lifetime stats. Aim, positioning and utility scores are deliberately not benchmarked here — those are a third-party provider's derived metrics, which CSDB does not store.
| Metric | This player | Pink band median | Red band median | vs Red band |
|---|---|---|---|---|
| Headshot rate | 45.5% | 46.9% | 49.2% | 3.7% short |
| Shot accuracy | 11.7% | 13.0% | 13.3% | 1.6% short |
| Kill/death ratio | 1.13 | 1.08 | 1.12 | meets |
| Match win rate | 48.0% | 46.7% | 48.5% | meets |
This profile matches the typical Red band player on 2 of 4 comparable metrics.
Widest gap: Shot accuracy. That is the metric furthest from the Red band median in relative terms — not necessarily the one holding a rank back, which no statistic here can establish.
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Performance scores
0–100 skill scores via Leetify.
Player DNA
Primary style: Support — Utility contribution stands above the rest of this profile (+0.9 against its own average).
Style profile from tracked-match aggregates — how this player plays, not how good they are. Classification rules are deterministic and documented in code.
What this cannot see yet: which weapons you use — so CSDB cannot identify an AWPer, and no style here implies a rifle or a sniper. It also cannot see how often you take opening duels, only how often you win them, nor where you hold, so roles that depend on those (entry, lurk, anchor) are deliberately absent rather than guessed. All of it needs round-by-round demo data, which is the next thing being built.
Your pro match
Plays most like jL 85% playstyle similarity
Most alike: utility contribution, opening-duel success.
Where you differ: lower aim profile.
Similarity of playstyle shape across shared dimensions — it says how you play, not that you play at their level. Full comparison →
Strengths & areas to improve
Strengths
T openings. 63% T opening-duel success — entries that actually open the round.
Areas to improve
Reaction time. 593ms from enemy-visible to first shot leaves fights decided before they start — warmup routines move this number more than anything else.
Generated by fixed rules over this profile's own numbers — no model, no guessing; silent when the sample is too small to support a claim.
CSDB Rating breakdown
Composite 7.4/10 (Strong), a weighted mean of the bars with a small opposition adjustment (×1.02 for this rank band). Formula versioned (v1) and documented in code.
Lifetime stats
Most-used weapons
Lifetime map wins
Lifetime totals via Steam — visible because this profile's game details are public. Spans CS:GO and CS2.
Skill profile
Aggregate performance across tracked matches — stats via Leetify. Percentile context against other CSDB-tracked players arrives as our own benchmark data accumulates.