Aim Trainer

Visuomotor speed · 30 targets · Average ms per click

Click 30 targets as quickly as possible. Your score is the mean time in milliseconds from target appearance to your click. Lower is faster.

Targets: 0 / 30

Avg: —

Time: 0.0s

What the Aim Trainer Measures

The Aim Trainer measures visuomotor target acquisition time — the complete chain from visual detection of a new target to successful motor execution (clicking). This composite score combines three sub-processes:

Visual Search

~20–80ms

Detecting the target's position in the field. Faster with high-contrast targets and peripheral attention training.

Motor Planning

~50–120ms

Computing the trajectory from current cursor position to target center. Affected by target size and distance (Fitts' Law).

Motor Execution

~80–200ms

Moving the cursor accurately to the target and registering a click. Affected by mouse hardware, surface, and fine motor skill.

Fitts' Law (1954)

The fundamental model governing target acquisition states that movement time is a function of target distance and target size:

MT = a + b · log₂(2D / W)

Where MT = movement time, D = distance to target, W = target width, and a/b are empirically determined constants. Human Benchmark uses a fixed target size (60px ≈ 0.85° at 70cm) and random placement, so your score reflects both target acquisition speed and cursor control precision.

How You Compare Globally

Benchmark data from 3.5M scored sessions. Hardware-agnostic — results include all device types.

Rank	Avg ms / target	Who scores here
Top 1%	<200ms	Pro-level esports players, trained aimers
Top 5%	200–250ms	Competitive FPS players, daily practice
Top 10%	250–280ms	Regular gamers with good hardware
Top 25%	280–340ms	Casual gamers, frequent PC users
Median	380ms	Global average across all users and devices
Bottom 25%	480–600ms	Infrequent PC use, touchscreen, older users
Bottom 10%	>600ms	Touchscreen, unfamiliar input, slow hardware

Percentile thresholds are approximate. Scores are not corrected for hardware latency. See device latency notes.

Factors That Affect Your Score

Estimated Hardware Impact on Aim Score

Hardware latency added to your measured score. Lower-latency hardware directly improves your result.

Gaming mouse (1000Hz wired)

+5ms

Standard wired mouse (125Hz)

+15ms

Wireless mouse (Bluetooth)

+30ms

Laptop trackpad

+60ms

Phone touchscreen

+80ms

Factor	Typical Effect	Notes
Mouse DPI / sensitivity	±30–80ms	Extreme DPI (very high or very low) reduces precision; 800–1600 DPI is optimal for most
Monitor refresh rate	±10–40ms	144Hz vs 60Hz saves ~6ms of display lag; also reduces motion blur
Mouse polling rate	±5–20ms	1000Hz polling vs 125Hz reduces input lag by ~7ms on average
Age	+2–5ms/yr	Visuomotor speed declines from ~25 onward; training partially offsets this
Sleep deprivation	+20–60ms	Even 1 night of poor sleep degrades motor execution significantly
Caffeine	−10–25ms	Peak effect at 45–60 min post-ingestion; less effective in habitual users
Practice volume	−20–80ms	Daily 20-min practice sessions yield measurable improvement in 2–4 weeks
Mouse pad surface	±5–15ms	Hard pads reduce friction variance; soft pads allow more glide speed

Age & Aim Performance

Visuomotor speed peaks in the early-to-mid 20s. Unlike simple reaction time (which plateaus after the 30s), aim performance — which requires both speed and spatial precision — declines earlier and more steeply in untrained populations.

Age Group	Mean Score	Top 10% Threshold	Notes
Under 16	320ms	240ms	Developing motor cortex; high variance
16–24 (peak)	310ms	225ms	Peak visuomotor performance window
25–34	330ms	245ms	Slight decline offset by strategy
35–44	360ms	270ms	Measurable decline without training
45–54	400ms	310ms	Hardware quality becomes more impactful
55–64	450ms	360ms	Reaction + fine motor both slowing
65+	530ms	430ms	Significant decline; high inter-individual variance

How to Improve Your Aim Score

Research on visuomotor training and gaming performance suggests these approaches have genuine, measurable effects:

Deliberate daily practice (20–30 min)

evidence

Consistent, focused repetition with immediate feedback drives neural adaptation in visuomotor circuits. Distributed practice (daily short sessions) outperforms massed practice (long infrequent sessions) by 30–40% in motor learning research.

Optimize your hardware setup

evidence

A wired mouse at 1000Hz polling with appropriate DPI (800–1600 for most users) removes unnecessary latency. A monitor with 1ms response time and ≥144Hz refresh rate reduces display lag. These hardware gains are permanent and immediate.

Warm up before testing

evidence

5 minutes of light aim practice before a test session reduces performance variance by ~15% and shifts your mean score lower (better). Motor systems require warm-up just like muscles.

Track progress consistently

evidence

Use the same hardware and environment each session. Log your score after each session to see trends. Improvement in aim is typically 15–25% over 4–6 weeks of daily practice.

Action video game training

evidence

Playing fast-paced action games (FPS, battle royale) develops target prediction, peripheral attention, and click timing skills that transfer measurably to aim trainer performance. Causally demonstrated in 20–50 hour training studies.

Test Methodology

The Aim Trainer measures the time from when a target renders on screen to when your click registers — using performance.now() for sub-millisecond precision. This is consistent with the paradigm from Fitts (1954) adapted for digital environments.

Target size

60px diameter (constant)

Targets per round

Scoring metric

Mean acquisition time (ms)

Timer precision

performance.now() (<1ms)

Target placement

Uniform random (padded)

Paradigm source

Fitts, 1954

For full methodology see the Science page. Device latency is not subtracted — see device latency table for hardware-specific adjustments.