Mitch McConnell

Mitch McConnell — CS2 Stats

76561198085730093[U:1:125464365]Steam profile ↗✓ No bans

613Tracked matches48%Win rate2021Tracked since
CSDB Rating3.8 LearningPositional Player
FaceitLevel 3Top 90.6% of ranked FACEIT players
Ladder ranks via Leetify

What changed since last observed

CSDB last observed this profile on 16 Sep 2026 (28 days ago). Ranks are recorded once per day this page is viewed.

+2Tracked matches · now 613
+3.7ppWin rate · 44.4% → 48.1%

Rating over time

44Days played since 2025-12-30

Premier is CSDB’s own observation — it builds from the day a profile is first viewed and cannot be backfilled.

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CSDB.GGMitch McConnellFACEITLevel 3STANDINGTop 90.6% of rankedcsdb.gg/stats

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Performance scores

Aim30
Positioning47
Utility36

0–100 skill scores via Leetify.

Recent form

STEADY44–43–13Last 10044%Win rateWWWWLLLWLW

Last 10 vs previous 10: +30pp win rate · +0.00 avg rating · +5.6pp headshot accuracy · −57ms reaction

Win rate up 30pp across the last 10 against the 10 before — more than a 10-match window's normal noise (±20pp).

Last 5 · 10 · 20 matches

Last 5

  • 4–1 · 80% win rate
  • Avg rating -0.02
  • Avg headshot accuracy 20%
  • Avg reaction 655ms

Last 10

  • 6–4 · 60% win rate
  • Avg rating -0.03
  • Avg headshot accuracy 19%
  • Avg reaction 631ms

Last 20

  • 9–11 · 45% win rate
  • Avg rating -0.03
  • Avg headshot accuracy 16%
  • Avg reaction 660ms

Newest first, from the last 100 tracked matches. Each block is its own sample — one result moves a 5-match win rate by 20 points.

Player DNA

Primary style: Positional Player — Positioning stands above the rest of this profile (+1.3 against its own average).

Aim3.0
Utility3.6
Positioning4.7
Opening Duels2.0

Limited utility dependence

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

donk

Plays most like donk 70% playstyle similarity

Most alike: utility contribution, positioning profile.

Where you differ: lower opening-duel success; 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

Areas to improve

Reaction time. 658ms 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

Aim3.0
Positioning4.7
Utility3.6
Mechanics5.2
Opening Duels1.8
Win Impact4.4

Composite 3.8/10 (Learning), a weighted mean of the bars with a small opposition adjustment (×1.00 for this rank band). Formula versioned (v1) and documented in code.

Trends

Match rating-0.03+0.00
first ⅓ avg -0.03 → last ⅓ avg -0.03
Reaction time658ms+6ms
first ⅓ avg 652ms → last ⅓ avg 658ms
Headshot accuracy15.3%+1.8%
first ⅓ avg 13.5% → last ⅓ avg 15.3%

Rolling 5-match average across the last 100 tracked matches, oldest to newest. The delta compares the first third of the window with the last.

Personal bests

0.07Best match rating · 13–5 · train, 2 Jan →
32%Best headshot accuracy · 4–13 · boulder, 16 Jul →
406msFastest reaction time · 12–12 · nuke, 7 Jan →
13–2Biggest win · vertigo, 3 Feb →

Across the last 100 tracked matches.

Highlights

4Longest win streak
W4Current streak
5–4In matches decided by ≤2 rounds
1Overtime games

Map breakdown

infernoBest map · 50% over 6vertigoWeakest map · 21% over 19
MapGradePlayedRecordWin rateAvg rating
nukeB3818–2047%-0.03
vertigoD194–1521%-0.04
trainC94–544%-0.01
infernoB63–350%-0.03
dust2—41–325%-0.07
palacio—43–175%0.01
cache—33–0100%-0.04
ancient—31–233%-0.05
warden—21–150%-0.01
office—21–150%-0.02
anubis—21–150%-0.01
shelter—11–0100%0.01
boulder—10–10%-0.05
stronghold—11–0100%0.03
alpine—10–10%-0.01
italy—11–0100%-0.02
agency—11–0100%-0.04
golden—10–10%-0.06
mirage—10–10%-0.01

Across the last 100 tracked matches.

Vertigo is currently your weakest sufficiently-sampled map (21% over 19). Start with the 6 essential Vertigo lineups, review the callouts, then spin up a practice server.

Skill profile

Aggregate performance across tracked matches — stats via Leetify. Percentile context against other CSDB-tracked players arrives as our own benchmark data accumulates.

15.5%Headshot accuracy
29.8%Accuracy (enemy spotted)
28.5%Spray accuracy
73.5%Counter-strafing
13.3°Preaim
658msReaction time
41.7%T opening success
32.4%CT opening success
0.42Enemies flashed / flash
4.5%Flash assists
12.79HE damage / grenade
4.79Flashes / match

Recommended for you

Chosen by comparing your tracked metrics against the thresholds we flag — the measurement behind each one is shown, so you can disagree with it.

  1. Advanced Mechanics

    Your crosshair sits further from where enemies appear than it needs to. Crosshair placement is the cheapest accuracy you can buy.

    Preaim 13.2838° — above the 12° mark we flag

    Aim Training →
  2. Grenades & Utility

    Most of your flashes are blinding nobody. A handful of reliable pop-flash lineups fixes this faster than anything else.

    Enemies flashed per flash 0.4197 — below the 0.5 mark we flag

    Grenade Lineups →
Spend your practice time on Vertigo

Map knowledge compounds faster than mechanics — lineups and callouts you learn once keep paying out every time the map comes up.

21% win rate across 19 tracked games — your weakest map with enough games to be worth reading into.

Vertigo callouts & strategy →Vertigo grenade lineups →

Recent matches

MapScoreRatingHS%Date
cache13–5-0.0316%18 Sept →
nuke13–80.0725%18 Sept →
dust213–8-0.0423%4 Sept →
nuke13–6-0.0215%4 Sept →
vertigo10–13-0.0619%29 Aug →
nuke3–13-0.0813%29 Aug →
nuke5–13-0.0413%29 Aug →
shelter13–30.0118%16 Jul →
boulder4–13-0.0532%16 Jul →
nuke13–5-0.0216%16 Jul →
nuke13–80.0222%3 Jun →
nuke6–13-0.0112%28 May →
nuke4–13-0.0723%28 May →
vertigo10–13-0.0513%28 May →
nuke13–7-0.0112%25 May →
vertigo13–90.0016%19 May →
nuke9–13-0.045%19 May →
nuke2–13-0.0913%16 May →
nuke3–13-0.0510%14 May →
nuke6–130.039%13 May →
nuke12–12-0.0419%13 May →
cache13–6-0.019%9 May →
cache13–11-0.0717%1 May →
ancient13–70.049%14 Mar →
warden13–8-0.0312%13 Mar →
train12–12-0.0210%13 Mar →
nuke12–12-0.0523%13 Mar →
nuke8–13-0.0815%12 Mar →
train3–13-0.0718%21 Feb →
nuke4–13-0.107%21 Feb →
vertigo3–13-0.0216%21 Feb →
office13–5-0.0412%21 Feb →
vertigo9–13-0.0613%18 Feb →
nuke12–12-0.0213%14 Feb →
vertigo0–13-0.059%14 Feb →
vertigo13–7-0.0117%12 Feb →
nuke13–30.019%12 Feb →
train2–13-0.0818%12 Feb →
nuke13–50.0413%11 Feb →
vertigo9–13-0.0211%9 Feb →
vertigo13–2-0.0810%3 Feb →
train11–13-0.0212%3 Feb →
nuke13–60.0010%3 Feb →
nuke4–13-0.0611%1 Feb →
inferno15–15-0.0611%1 Feb →
ancient5–13-0.124%1 Feb →
stronghold13–80.0311%31 Jan →
nuke13–2-0.063%31 Jan →
vertigo12–12-0.0210%30 Jan →
train8–20.0218%30 Jan →
nuke13–110.0214%30 Jan →
inferno13–30.0214%30 Jan →
vertigo11–13-0.0321%29 Jan →
anubis6–13-0.0811%29 Jan →
inferno13–90.0110%28 Jan →
train13–20.0714%28 Jan →
nuke13–10-0.048%28 Jan →
warden5–130.0114%27 Jan →
alpine5–13-0.0113%27 Jan →
office8–13-0.0111%21 Jan →
italy13–3-0.028%21 Jan →
palacio12–12-0.0217%21 Jan →
palacio13–80.059%15 Jan →
nuke3–13-0.130%13 Jan →
vertigo11–13-0.0515%12 Jan →
nuke10–13-0.0527%12 Jan →
dust26–13-0.0812%10 Jan →
vertigo6–13-0.0512%10 Jan →
nuke13–11-0.0314%10 Jan →
palacio13–80.0318%9 Jan →
vertigo5–13-0.0922%9 Jan →
nuke13–9-0.0615%9 Jan →
nuke12–70.026%8 Jan →
nuke12–12-0.0511%7 Jan →
inferno11–13-0.0512%6 Jan →
vertigo13–40.0216%6 Jan →
nuke13–10-0.045%6 Jan →
agency13–11-0.0415%5 Jan →
golden9–13-0.0617%5 Jan →
nuke13–80.0516%5 Jan →
train12–12-0.0214%4 Jan →
dust27–13-0.096%4 Jan →
nuke7–13-0.057%4 Jan →
vertigo1–13-0.1021%4 Jan →
nuke13–60.038%4 Jan →
ancient12–12-0.0820%3 Jan →
nuke7–13-0.0913%3 Jan →
train13–50.074%2 Jan →
palacio13–6-0.039%2 Jan →
dust28–13-0.0817%2 Jan →
nuke6–13-0.1013%2 Jan →
vertigo12–120.0118%2 Jan →
inferno2–13-0.1016%1 Jan →
mirage7–13-0.0116%1 Jan →
nuke13–11-0.0417%1 Jan →
vertigo12–12-0.0215%1 Jan →
anubis13–50.0611%30 Dec →
train13–6-0.0815%30 Dec →
inferno13–90.0115%30 Dec →
vertigo12–12-0.0511%30 Dec →

Match data via Leetify.

Recent teammates

Milestones

500 Matches
Compare this player with someone →Inventory value for this account →Where does this rating sit? Premier rank tiers →

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