whats the best sensitivity according to this paper: Mouse Sensitivity in First-person Targeting Tasks
Ben Boudaoud
NVIDIA
Durham, USA
<PRESIDIO_ANONYMIZED_EMAIL_ADDRESS>
Josef Spjut
NVIDIA
Durham, USA
<PRESIDIO_ANONYMIZED_EMAIL_ADDRESS>
Joohwan Kim
NVIDIA
Santa Clara, USA
<PRESIDIO_ANONYMIZED_EMAIL_ADDRESS>
Abstract—Despite billions of hours of play and copious discussion online, mouse sensitivity recommendations for first-person
targeting tasks vary by a factor of 10x or more and remain
an active topic of debate in both competitive and recreational
gaming communities. Inspired by previous academic literature in
pointer-based gain optimization, we conduct the first user study
of mouse sensitivity in first person targeting tasks, reporting
a statistically significant range of optimal values in both task
completion time and throughput. Due to inherent incompatibility
(i.e., lack of convert-ability) between sensitivity metrics adopted
for prior pointer-based gain literature and those describing firstperson targeting, we provide the first analytically demonstrated,
statistically significant optimal sensitivity range useful for firstperson camera controls. Furthermore, we demonstrate that this
optimal sensitivity range arises (at least in part) from a speedprecision trade-off impacted by spatial task difficulty, similar to
results reported in pointer-based sensitivity literature previously.
Index Terms—pointing devices, mouse, mouse sensitivity, firstperson targeting, first-person games
I. INTRODUCTION
Mouse sensitivity is the relationship between physical motion of a mouse and the virtual motion this induces in a
computer application. This virtual motion could be translational (e.g., pointer-based tasks), rotational (e.g., first-person
targeting), or even categorical (e.g., directional selection). In
modern computer systems mouse sensitivity is the product of
several factors, including the mouse sensor resolution (up to
the hardware limitations of the mouse) as well as the operating
system’s and application’s sensitivity settings.
The competitive gaming community considers mouse sensitivity a significant factor of player performance in targeting
tasks [1]. Yet, there is wide variation in individual player
settings. To demonstrate this, we collected and analyzed
sensitivity settings data from prosettings.com for professional
esports athletes across multiple First Person Shooter (FPS)
gaming titles. The chart in Fig. 1 shows that the sensitivity
range employed varies across professional gamers by up to 8x
within a single title and by up to 12x across titles. Furthermore,
the default sensitivities for some of these games (scaled by
common mouse DPI) lie beyond even the 12x range commonly
used by professionals.
Academic research on mouse sensitivity has focused predominantly on productivity-oriented, pointer-based tasks, leaving a gap in research around first-person targeting. Unlike
pointer-based user interfaces, designed for easing selection of
static targets, FPS games present dynamic targets of various
sizes at different virtual distances, deliberately challenging the
player in order to reward skill. Thus, competitive FPS games
eschew strict adherence to traditional user interface design
patterns, as skilled gamers attempt to find the right mouse
sensitivity to balance aiming speed and precision. Though
FPS targeting tasks have been demonstrated to conform to
Fitts’ law [2], this does not imply that they share a common
set of optimal sensitivities with pointer-based targeting tasks.
Or, in fact, that the mouse sensitivities used to characterize
pointer-based targeting sensitivity are convertible to firstperson targeting sensitivities. This paper presents the first
study of mouse sensitivity’s impacts on FPS targeting task
performance, providing data useful for connection of pointerbased and first-person targeting mouse sensitivity in the future.
II. BACKGROUND
A. Sensitivity and Fitts’ Law
Fitts’ law [3] is a widely accepted model that explains
spatial targeting and selection task difficulty as a function
of target distance and size. Nearly all formulations of Fitts’
law and its variants [3]–[5] normalize the distance to a target
(D) by the target width (W) in common units, producing a
unitless difficulty ratio. The Index of Difficulty (ID), a measure
of spatial task difficulty, is a base-2 logarithm of the D/W
ratio (measured in bits), and is commonly linearly fit to task
completion time. For ID computation in this work we use the
Shannon formulation of Fitts’ law as proposed by MacKenzie
et al. [6] as provided in Eq. 1.
ID = log2

D
W
+ 1
bits (1)
Task throughput [7], also referred to as index of performance [4], is the ratio of ID to task movement time (Tmove),
wherein changes in movement time resulting from human
actuation are accounted for (see Eq. 2).
T hroughput =
ID
Tmove
(2)
As Fig. 2 demonstrates, changing mouse sensitivity either
scales both D and W in physical mouse movement space
or neither in in-game rotation space, and therefore does not
modify the ID. Instead, trends in optimal mouse sensitivity
0.1 0.2 0.5 0.9 1.8 3.6 7.2
0%
5%
10%
15%
20% N = 115
Valorant
0.1 0.2 0.5 0.9 1.8 3.6 7.2
0%
2%
5%
8%
10%
12%
15% N = 125
Fortnite
0.1 0.2 0.5 0.9 1.8 3.6 7.2
0%
5%
10%
15%
20% N = 397
CS:GO
0.1 0.2 0.5 0.9 1.8 3.6 7.2
0%
5%
10%
15%
20%
25% N = 54
Overwatch
0.1 0.2 0.5 0.9 1.8 3.6 7.2
0%
5%
10%
15%
20%
25%
30% N = 22
Apex Legends
Mouse Sensitivity (°/mm)
% of Gamers
Fig. 1. Professional player mouse sensitivities by first-person game played. Note the logarithmic X-axes. Data sourced from prosettings.com in Sept. 2020.
In-Game Target
Width [°]
In-Game Target
Distance [°]
Mouse Target
Width [mm]
Mouse Target
Distance [mm]
Hard
Target
ID = 2.32
Easy
Target
ID = 0.58
(Easier T
Lower ID
asks)
Increase Sensitivity
Reduce Sensitivity
Mouse
Mapping
Fig. 2. Mapping an in-game (virtual) rotational task to (physical) mouse
motion shows the orthogonality of sensitivity and task difficulty (ID).
show up in task throughput. Mouse sensitivity has been
demonstrated to impact movement time, with previous work
showing significant interaction with target size and task ID
[8], [9]. We explain the effect of mouse sensitivity on task
performance using kinematic characteristics observed in our
data in Section IV-D.
B. Mouse Sensitivity in Pointer-based Targeting
Most prior work in optimal mouse sensitivity has focused
on pointer-based, Fitts’ law targeting tasks. In these tasks
users move the mouse, translating an on-screen virtual proxy
(cursor), towards a target, then click to indicate a selection.
The control-display (CD) gain, or ratio between the travel
distance of a controller (e.g., mouse) and the corresponding
cursor movement on a display, has been the conventional
choice of sensitivity metric in this work. Reported optimal
CD gains vary from 2 to 16 (an 8x range) [8]–[12] with some
studies reporting higher optimal CD gain for tracking tasks
[13].
Older studies [10], [11] suggest lower CD gain, concluding
mouse sensitivity does not significantly impact task completion
time, with quantization error limiting the range of sensitivity
interesting for study. Later work [8], [9], [14], [15] implies
that movement time does depend on mouse sensitivity, and
highlights interactions with target size. Most recently, these
results have been extended by monitoring limb usage range
and rate of motion to show users do in fact scale their
kinematic interaction with CD gain [12]. Additional studies
indicate that tracking task completion time benefits from
higher sensitivities or more direct methods of interaction
like touch interfaces [13]. Broadly speaking all surveys tend
towards globally optimal CD gains in the range of 2-8, which
aligns well with the default gains applied by operating systems
and pointer-based user interfaces [16].
C. Mouse Sensitivity in First Person Targeting
First person targeting interfaces, such as those used in
FPS games, provide an aim point indicator (often called the
crosshair or reticle) fixed at the center of the screen. A user
rotates the view direction in azimuth and/or elevation by translating the mouse horizontally and/or vertically respectively,
aligning a target with the reticle. Since the view direction
rotates, the entire virtual world, including the target, moves in
the direction opposite the mouse motion (e.g., mouse moves
left, target moves right). Similarly to pointer-based interfaces,
the user clicks to perform selection. Despite obvious differences from pointer-based targeting, both approaches share
similar spatial task difficulty characteristics explained well
by Fitts’ law [2]. The difference between pointer-based CD
gain and first-person rotational mouse sensitivity is further
demonstrated in Fig. 3.
In the context of first-person aiming, many games provide
settings sliders with the label ”mouse sensitivity”. These
sliders are typically unit-less, with higher values corresponding
to faster view rotation per unit of physical displacement. These
sliders’ values (when reported) do not generalize across games
and require a conversion factor to provide common units for
gamers to communicate. FPS players use a variety of metrics
to talk about mouse sensitivity, the most universal of which
is a displacement-to-rotation ratio, commonly measured in
cm/360◦
(cm per full turn). The primary motivation for this
metric is ease of measurement in game by moving the mouse
in one direction until a full rotation has been completed then
measuring the distance the mouse traveled to complete that
turn. We use an inverse proportional metric, ◦/mm (degrees
per millimeter) to match the intuition that higher sensitivity
means the aim moves more quickly per unit displacement.
For those who may be more familiar with the cm/360◦ metric,
Table I maps the two for the settings used in this study.
TABLE I
CONVERSION BETWEEN CM/360◦, CM/
◦, AND ◦/MM SENSITIVITIES
SELECTED FOR OUR STUDY. THE RELATIONSHIP IS GIVEN BY y = 36/x.
cm / 360◦ 160 80 40 20 10 5
cm / ◦ 0.444 0.222 0.111 0.056 0.028 0.014
◦ / mm 0.225 0.45 0.9 1.8 3.6 7.2
User
DDisplay
View
Position
Top Down View
ΘView
Side View
Control-Display Gain First-person Sensitivity
FoV
GCD = DDisplay / DCont [unitless] S = ΘView / DCont
 [°/mm]
DCont
DCont
Fig. 3. Comparison of CD gain and first-person sensitivity. CD gain uses
common units of displacement/velocity for a unit-less ratio of mouse to pointer
movement. First-person sensitivity converts mouse movement into rotation of
the view in an application’s world space, a ratio that must have units.
a) CD Gain and First Person Targeting: In addition to
the problem of CD gain scaling with display size, there is
no direct connection between a CD gain and the cm/360◦ or
◦/mm sensitivity settings commonly used in FPS targeting
applications. We could attempt to convert common historical
CD gains into angular sensitivities by converting display space
pointer motion (at an assumed distance) into angular motion
instead. However, if we were to convert a CD gain to an
assumed ◦
/mm angular ratio, we would confound degrees
of user focal point translation when tracking a pointer with
degrees of camera rotation in mouse controlled cameras. This
challenge is further complicated by common FPS practices,
such as projecting a large rendered field of view (often
≥ 100◦
) onto a display that subtends a much smaller physical
field of view (typically < 50◦
) and the user fixating at the
center of the display instead of tracking a mouse cursor as it
moves within the display area.
In an attempt to validate our results based on prior art measuring optimal CD gain in pointer-based tasks, we compare
our experimental results to reported CD gains only using selfreferential multiplicative ranges (i.e. the max to min ratio of
4x between a reported CD gain of 2 and 8 can be compared
to that of a first-person sensitivity range of 0.5 and 2 ◦/mm).
We avoid comparing reported CD gains between studies as
different display size or viewing conditions may change the
reported optimal values.
b) Non-linear Gain (Acceleration) in First Person Targeting: Though relatively common in pointer-based interfaces
[12], [16], [17], velocity-dependent sensitivity, often referred
to as cursor/pointer acceleration or nonlinear CD gain, has
been historically avoided in competitive FPS game play [18]–
[20]. When applying nonlinear gain, mouse sensitivity is no
longer a constant, but rather a (user-specified) function of the
velocity at which the mouse is currently moving.
We suggest that competitive FPS gamers’ aversion to nonlinear sensitivity is sourced largely from the added challenge
it presents in actuation space, where skilled users often operate using an open loop targeting model to minimize task
completion time. In this actuation model, players learn the
spatial relationship between screen and cursor space, moving
the mouse to align with a target and clicking quickly, without
performing additional visual verification. This skill is often
referred to as the flick shot technique or simply flicking.
When flicking, controlling the speed of view rotation change
is more difficult as a movement takes place over a very short
time period, and repeat-ability of the action becomes extremely
important, as an additional perception-action cycle effectively
negates the value of the technique over more traditional tracking approaches. For this reason, we suggest players prefer to
learn to target their flick shots in the displacement space of the
mouse pad, explicitly avoiding the more precise speed control
required to get benefit from nonlinear mouse sensitivity.
It is worth noting that some subsets of the gaming community have, and continue to, attempt to optimize various nonlinear sensitivity curves for various games of their choosing [21],
[22]. However, the nonlinear sensitivity curves arrived at for
these applications do not always conform to those suggested
in prior academic literature or modern operating systems as
they are deliberately intended to be easier to learn and adapt to
quickly for open loop actions (i.e., 2-tier sensitivity structures).
III. EXPERIMENTAL DESIGN
We designed an experiment to better understand the interaction of target width and distance with mouse sensitivity in
a publicly available FPS experimentation platform [23].
A. Questions and Hypotheses
The following questions motivate our experiment design.
a) Does a single optimal sensitivity (region) exist among
competitive FPS gamers?: We hypothesize that the optimal
sensitivity regions observed in prior art result from humans’
innate nonlinear kinematic characteristics, particularly those
around excessively small targets and/or long travel distances.
If this is true, we should observe similar trends among
competitive FPS gamers and more conventional (i.e. pointing)
interface users despite years of aim training experience. To
test this hypothesis, we included 6 fixed sensitivity conditions
exponentially spaced over the set of sensitivities reported from
our survey of esports athletes. We also widely varied target
distance and size, into the ranges at which nonlinear motion
characteristics have been observed in prior art.
b) Does an individual competitive gamers’ preferred sensitivity setting approximate their generally optimal setting(s)
for FPS targeting tasks?: We hypothesize individual gamers’
preferred sensitivity settings do tend to lie within regions of
near optimal performance given sufficient time to adapt to ingame conditions. To validate this hypothesis, we allocated a
preferred sensitivity condition as the first session for all the
participants. We re-used subjects’ preferred sensitivity settings,
selected through practice with a similar aiming task where the
sensitivity was set by the subjects themselves. Subjects were
not made aware that their preferred sensitivities were among
the tested conditions. We compare subjects’ performance at
their preferred sensitivity to the surrounding sensitivities from
our pre-selected set to evaluate this claim.
c) Does long(er) term muscle memory play a crucial
role in aiming performance?: Competitive gamers commonly
assert that muscle memory can be “hard-wired” and their
performance suffers when changing sensitivity too frequently.
Fig. 4. (Left) A screenshot from our application. The gray square at right
was used to monitor system latency during the experiment. (Right) Flow of a
single trial for our FPS task. (1) a reference target (white) is used to center the
user’s view direction, with a test target (gray) drawn to familiarize the subject
with it’s position. (2) after destroying the reference target the user moves
their aim to the (now green) task target and click as quickly as possible. If
the target is hit (3) the trial ends successfully (5), otherwise on a miss (4) the
trial ends in failure (6).
We hypothesize that changing sensitivity (at least for the
duration of this experiment) does not have a significant impact
on overall performance. To test whether this is true, we allocate
a preferred sensitivity condition as the final session of data
collection, comparing these results to those from the first
session. By the time they start the last session, participants
have experienced 6 widely varied sensitivity conditions (over a
32x range). This should considerably confuse muscle memory
and, if it plays a crucial role, performance in the last session
should be significantly worse than the first.
B. Subjects
15 experienced gamers (age 11-34, 1 female and 14 males)
voluntarily participated in our experiment. All subjects were
regular FPS players with minimum play times of 10 hours
per week. Subjects were not made aware of the experimental
hypothesis. All subjects gave informed consent (for the two
under-aged subjects, age 11 and 14 respectively, their parent
gave informed consent). The experiment was conducted in
accordance with the Declaration of Helsinki and each subject
household was provided with their own dedicated system
to minimize the need to clean/sanitize hardware resources
between users.
One subject’s test system experienced a degradation in
performance (frame rate) during data collection, as a result
we excluded this user from our data set. Two other subjects
skipped a significant portion of (difficult) trials due to fatigue.
Of these two subjects, one subject voluntarily repeated the
experiment without skipping trials. We chose to include this rerun subject data (shown in orange in all per-user plots) as much
of our analysis was within subjects and, as the subject was
already familiar with the experimentation platform and FPS
gaming in general, we did not observe evidence of significant
long-term training effects. We excluded the data from the other
subject who skipped trials. In total, 13 out of 15 participating
subjects’ data are included in our analysis (age 11-33, 1 female
and 12 males).
C. Task and Stimulus
Subjects performed a first-person perspective aiming task
with a stationary target (as shown in Fig. 4). We avoid
rendering a weapon or complex background scene to minimize
target view occlusion or possible distractions while subjects
performed the task.
A trial began with presenting a stationary, white reference target 0.57◦
in diameter (8 pixels on the screen) and
a stationary, but inactive, grey test target preview. Subjects
aimed by translating the mouse to align their reticle with
the reference target. The task timer started as the subject
cleared the reference target by pressing the left shift key on
the keyboard, activating the test target (indicated visually by
changing its color to green). We chose to use left shift to
begin the trial as this avoided effects from multi-purposing
the left mouse button for both starting and ending the trial,
including accidentally skipping trials by clicking twice. The
subject then quickly changed their aim by moving the mouse
toward the test target. The task timer stopped when the subject
clicked the left mouse button, with only a single click allowed
per trial. If the reticle was aligned with the target when this
click occurred the task is completed successfully, otherwise
the task ends in failure. Because the test target was visible
before the timer started, the measured time consisted only of
motor response time (visual search time and initial reaction
time were excluded).
Subjects were instructed to clear the test targets as quickly
and accurately as possible, and provided with a score indicative of this metric that was updated per trial. If the reticle was
within the target bounds at the time of click, then subjects
were scored by the amount of remaining task completion time
in seconds (faster completion implies higher score). In the
case of a failure, or the reticle being outside of the target
at the time of click, the maximum completion time (1.5 s)
was assumed for scoring purposes, resulting in a score of
0. The score penalty of the maximum completion time was
substantially larger than that of the average task completion
time (typically 0.4 - 0.8 s). This scoring system is intended
to motivate the subjects to maintain relatively high accuracy
levels throughout the experiment (average hit rate of >85%).
D. Conditions
Each subject completed 8 sessions of 500 trials, with one
mouse sensitivity level assigned to each session. The first and
last sessions used the subject’s preferred sensitivity setting. We
varied target distance and size within each session to provide
targets with a wide range of index of difficulties.
1) Sensitivities: Sensitivity was the main variable of interest in our study. We selected 7 sensitivity levels for exploration: 0.225, 0.45, 0.9, 1.8, 3.6, 7.2 ◦/mm selected for their
round number in the cm/360◦
scale (Table I), as well as the
preferred sensitivity of each subject. We controlled sensitivity
by changing the scaling multiplier between the movement of
the mouse and the rotation of the rendering camera in the
experimentation platform.
2) Target Width and Eccentricity: In each condition we
provided targets that varied both in width and eccentricity (i.e.,
distance from the central view direction, where the reference
target was located). The targets were all icosahedra with
diameters of: 0.57◦
, 1.15◦
, 2.29◦
, 4.59◦
, and 9.15◦
, expressed
in perspective angle from the player view camera, which used
a 103◦
rendered field of view. When the targets are projected
near the center of the screen (25 inch or 635 mm, 1920x1080
monitor), these targets were 8, 15, 31, 61, and 122 pixels wide.
The target distance from the central view direction randomly
varied between 7-25◦ horizontally and 0-2.8◦ vertically, with
this distance range logarithmically divided into five subregions for a more uniform distribution of index of difficulty.
Each trial’s target distance was drawn uniformly from the subregion designated for that trial. The total distance variation
corresponded to 93-339 pixels from the central view direction.
The combination of selected target distance and width resulted
in a range of ID of 0.83-5.50 bits.
3) Equipment: All subjects used their own copy of an
identical PC setup (Intel Core i7-9700k @ 3.6 GHz, 32 GB of
RAM, RTX 2080 Ti). Subjects were seated about 24 inches
(610 mm) from a 240 Hz, 1920 x 1080 25 inch (635 mm),
LCD monitor. All subjects completed the experiment with the
same Logitech G203 mouse at a 3,200 DPI sensor resolution,
which was sufficiently high to provide pixel-level rotations
in all tested sensitivity conditions, preventing any impact of
quantization error.
E. Procedure
All subjects had experience with the experimentation platform prior to this study, acquainting them to the visual
environment, controls, and high-level targeting task. No further
orientation was needed beyond clarification on the scoring
system used in this experiment. To encourage all subjects to
put in their best effort, we issued a $25 gift card to the subject
with the highest score after completing all 8 sessions.
Each session consisted of 500 trials (4000 trials/subject
total), which were further divided into 10 blocks of 50 trials.
Subjects were encouraged to take breaks between blocks to
refresh themselves as needed. Subjects could complete as
many sessions as they liked in a single sitting, but were
strongly encouraged to stop if they felt any mental or physical
fatigue. Each session took about 10 minutes to complete, with
the entire experiment lasting about 80 minutes per subject. All
participants completed the experiment within a single week.
IV. RESULTS AND ANALYSIS
We analyze our results from a mean and variation-based
perspective, as all of these metrics impact player performance
in the competitive gaming context. We consider hit rate, task
completion time, and task throughput (using a Fitts’ based ID
formulation).
We discard the first 50% of trials from each session as an
adaptation period for the new sensitivity condition, leaving
250 trials per session (see Section V and Fig. 9 for more
information on average adaptation rate), or 2,000 trials per
user, for analysis. Not all trials were successfully completed,
in some cases the user clicked with the reticle outside of the
target bounds. We choose to analyze unsuccessful trials along
with successful ones based on the assumption these trials were
still valid aiming attempts. Accordingly, we discard trials in
0.225 0.450 0.900 1.800 3.600 7.200
Sensitivity (°/mm)
65
70
75
80
85
90
95
100
Hit rate (%)
Fig. 5. Subject trial hit (success) rate vs sensitivity per user (colors) and
overall (thicker black line). Sensitivity preferences are displayed as diamonds.
Note the X-axis is logarithmic.
which the aim error at the time of click was greater than 50%
of the initial distance to the target. These are trials where the
user translated the view less than halfway to the target before
clicking. 45 total trials were removed based on this criteria,
accounting for <0.2% of all data and <0.5% (10/2000) of any
given user’s trials.
A. Hit Rate
We analyze our data for hits as a percentage of total trials,
where each trial is either a hit or miss. Only one shot was
allowed per trial. As expected, most of our subjects maintain
a hit rate above 85% for nearly all sensitivity conditions. At the
highest sensitivity setting nearly all users’ hit rate decreased.
Unlike other analyzed metrics, hit rate does not show a strong
optimal point overall or per user in the range tested. Generally
speaking hit rate was highest for the lowest sensitivities, with
minimal change across all but the highest sensitivity tested.
Though ANOVA reports a significant main effect (F(5, 72) =
6.12, p < 0.0001) of sensitivity on hit rate for the data in
Fig. 5, pairwise t-testing reveals differences of the means to be
significant only when comparing the highest sensitivity setting
(7.2◦/mm or 5 cm/360◦
) to the remainder of sensitivities (p =
0.0146).
B. Task Completion Time and Throughput
Within and between subjects we observed strong evidence
of effects of mouse sensitivity on task completion time and
throughput. Mean task completion time and throughput versus
sensitivity are plotted per user (one per color) and overall
(thicker black line) in Fig. 6. Each user’s preferred sensitivity
condition is indicated by a diamond marker, with many of
the users selecting an overlapping setting of 1.2◦/mm (30
cm/360◦
).
0.225 0.450 0.900 1.800 3.600 7.200
Sensitivity (°/mm)
0.4
0.5
0.6
0.7
0.8
0.9
Mean Completion Time (s)
0.225 0.450 0.900 1.800 3.600 7.200
Sensitivity (°/mm)
4
5
6
7
Mean Throughput (bit/s)
Fig. 6. Mean task completion time (left) and throughput (right) per user
(colors) and overall (thicker black line) versus sensitivity. Per-user preferred
sensitivity conditions are indicated by the diamond markers. Error bars
represent the standard error metric. The X-axis is logarithmic and the yellow
shaded region represents the zone of optimal performance.
0.225 0.450 0.900 1.800 3.600 7.200
Sensitivity (°/mm)
0.15
0.20
0.25
0.30
0.35
0.40
0.45
0.50
 of Completion Time (s)
0.00 0.25 0.50 0.75 1.00 1.25 1.50 1.75
Completion Time (s)
0%
1%
2%
3%
4%
5%
6%
7%
Portion of Times
7.2°/mm
3.6°/mm
1.8°/mm
0.9°/mm
0.45°/mm
0.225°/mm
Overall
Fig. 7. (Left) standard deviation of task completion time by user (one per
color) and overall (thicker black line). Diamond markers indicate preferred
sensitivity conditions. The X-axis is logarithmic and the yellow shaded region
represents the zone of optimal performance. (Right) per sensitivity and overall
task completion time distributions.
ANOVA indicates a significant main effect of sensitivity on
task completion time (F(5, 19955) = 111.72, p < 0.0001),
with pairwise t-testing validating these differences (p < 0.001)
for all adjacent sensitivity pairings except those in the 0.45-1.8
◦/mm (20-80 cm/360◦
) range. The range of this optimal zone
mirrors the factor of 4 in optimal mouse sensitivities reported
for pointer-based tasks in prior art.
In addition to completion time, we analyze mean task
throughput (see Eq. 2) per individual and over all subjects
based on our experiment data. This metric decorrelates the
spatial difficulty of a particular task from the time measured
for completion of that task by taking the ratio of the index of
difficulty (in bits) to the completion time. ANOVA confirms
a significant main effect of mouse sensitivity on throughput
(F(5, 19955) = 275.59, p < 0.0001). Additional pair-wise
t-testing between adjacent sensitivities confirms significant
differences of mean (p < 0.001) for all tested sensitivities.
Interestingly, many subjects performed better at a preselected sensitivity than at their preferred sensitivity setting,
both in completion time and task throughput, though generally
speaking this preferred sensitivity was within their range of
optimal performance. All users performed optimally in mean
performance in the range of 0.45-1.8◦
/mm (20-80 cm/360◦
).
C. Variance of Task Completion Time
In addition to mean task completion time and throughput,
esports athletes seek to minimize variation of task completion
time. In many cases increasing repeatability (reducing the
spread) of a performance metric may in fact be more vital
to overall outcomes than producing a small change in the
mean of the metric, as it makes individual performance more
predictable and team strategy more robust.
Figure 7 shows the standard deviation of task completion
time by subject versus mouse sensitivity, color coded the same
as the mean time and throughput in Fig. 6. We also present
the distributional shape of completion time across subjects for
each sensitivity condition. Again, we see optimal (minimum)
variance for mouse sensitivities in the range of 0.45-1.8◦
/mm
(20-80 cm/360◦
), agreeing with the optimal range from our
mean completion time and throughput results. Note that though
we use standard deviation as a metric to analyze the spread
of this data it is not distributed according to a symmetric
Gaussian distribution (see Fig. 7).
To validate whether the differences in standard deviation/variance reported above were significantly different between sensitivities we perform a Levene test for equality of
variances. Overall results indicate a significant difference in
task completion time variance between sensitivities (W =
44.12, p < 0.0001). Additional pairwise Levene testing
between adjacent sensitivities yields significant differences
(p < 0.005) for all sensitivities outside of the 0.45-1.8◦
/mm
(20-80 cm/360◦
) range.
D. Submovement Analysis
The previous analysis in this section, specifically that of task
completion time and throughput, begs an interesting kinematic
question. What was the effect of sensitivity on physical user
motion? Did users exploit higher sensitivities to turn faster
in-game at the same real-world motion velocity, and if so,
why does performance often degrade more at the highest
sensitivities than the lowest ones? To answer these questions
we turn to submovement analysis.
Submovements are short, (pseudo)ballistic motion trajectories that arise from the human motor response planning process
[24]–[27]. We employ a submovement segmentation technique
inspired by that of Meyer [24]. We extend this work by
considering periods of initialization, pauses, and verification
[25] and online regulation of movements [26], [27].
A 5th order, 7 Hz low pass Butterworth filter is used
to smooth the azimuth/elevation data prior to submovement
processing. Initialization, pauses, and verification periods are
distinguished from periods of motion using a start velocity
threshold of 8 ◦/s. Multiple submovements detected within a
single psychological refractory period of 80 ms are considered one submovement. A submovement ends whenever the
minimum duration of a submovement has elapsed and the
combined angular velocity falls below an end threshold of 4
◦/s.
By partitioning player motion into submovements, and only
analyzing the first submovement of the series (typically the
largest motion) we can better separate slower performance
due to long initialization or verification windows from slower
average player motion. This analysis demonstrates that for
the lower, and in some cases the highest, tested sensitivities,
players rotated their view more slowly in game. However, the
degradation of movement speed at high sensitivities is far less
than that of the high sensitivities in Fig. 6. Instead, the average
number of submovements monotonically increases with sensitivity (Fig. 8 center plot). This indicates that targeting motion
precision degrades with mouse sensitivity, requiring more
corrective submovements before successfully completing a
targeting action. This increase in submovement count drives an
increase in mean task completion time in turn, as demonstrated
in the right plot of Fig. 8. Therefore, the optimal region in task
completion time, throughput, and average view rotation speed
in the plots in Fig. 6 are not solely the result of increased
player motion speed, but rather the trade-off of movement
speed impacts at low mouse sensitivity, and increased time
for small, corrective movements at high sensitivities.
0.225 0.450 0.900 1.800 3.600 7.200
Sensitivity (°/mm)
0
10
20
30
40
50
60
70
80
1st Submovement Speed (°/s)
0.225 0.450 0.900 1.800 3.600 7.200
Sensitivity (°/mm)
1.0
1.2
1.4
1.6
1.8
Mean # of Submovements
1 2 3 4 5 6
# of Submovements
0.5
1.0
1.5
2.0
2.5
3.0
Mean Completion Time (s)
Fig. 8. (Left) average velocity of 1st submovement vs mouse sensitivity. (Center) positive correlation between sensitivity and submovement count. (Right)
positive correlation between submovement count and average task completion time. Note that since subjects were less likely to make higher numbers of
submovements (standard error) error bars grow substantially for large submovement counts.
1 2 3 4 5 6 7 8 9 10 11 12 13
User
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
Mean Task Time (s)
1 2 3 4 5 6 7 8 9 10
Block
0.50
0.55
0.60
0.65
0.70
Mean Task Time (s)
Preferred 1
7.2°/mm
3.6°/mm
1.8°/mm
0.9°/mm
0.45°/mm
0.225°/mm
Preferred 2
Overall
Fig. 9. (Top) comparison of average task completion time between the first
(left bar) and final (right bar) session, both at the preferred sensitivity, by user.
Error bars are standard error. (Bottom) per-session average completion time
over blocks of 50 trials. The dashed vertical red line represents the threshold
for discarding trials due to short-term training effects.
V. DISCUSSION
A. Experimental Questions
Our experiment design asked three fundamental questions
about competitive gamers’ mouse sensitivity settings:
• Does an optimal sensitivity range exist for FPS aiming?
• Are FPS gamers’ preferred sensitivities (near) optimal?
• Does (long term) muscle memory play a crucial role in
aiming performance?
Based on our measured and modeled results we answer
these questions as follows.
a) Optimal Sensitivity Range: Our competitive FPS
gamers did demonstrate a preference for an overall optimal
range of mouse sensitivity, a 4x range from 0.45-1.8◦
/mm
or 20-80 cm/360◦
. This 4x range of near optimal sensitivity
mirrors that of previous work in CD gain based pointer-based
literature, suggesting a possible common basis for this range
of near optimal performance.
b) Individually Optimal Sensitivity: The gamers we studied did not demonstrate significantly better choice of preferred
sensitivity than generally optimal ones; however, all but two
did use preferred sensitivities within the globally optimal
region. The two users who selected sensitivities outside of
(higher than) the optimal range were among the poorest performers, but still demonstrated less degradation in performance
near these choices than other users.
c) Long-term Effects of Sensitivity Changes: While
short-term muscle memory was demonstrated by a strong
within-session training effect as demonstrated in Fig. 9 (controlled for by only analyzing the last 50% of trials from
0.225 0.450 0.900 1.800 3.600 7.200
Sensitivity (°/mm)
0.2
0.4
0.6
0.8
1.0
Mean Comp. Time (s)
0.225 0.450 0.900 1.800 3.600 7.200
Sensitivity (°/mm)
4.0
4.5
5.0
5.5
6.0
6.5
Mean Throughput (bit/s)
ID Range
5-6 bits
4-5 bits
3-4 bits
2-3 bits
1-2 bits
0-1 bits
Fig. 10. Mean task completion time (left) and throughput (right) plotted by
ID range (colors). X axes are logarithmic, mean completion time is plotted
with log Y axis. Error bars represent standard error.
any given condition), longer-term (i.e., session-to-session)
training effects were not as pronounced in our data. Overall
our subjects did not demonstrate a significant degradation
of performance between their first (preferred) sensitivity session, and their final sensitivity session. Individually, about
half of subjects demonstrate degradation of performance, an
increase in mean task time, from the first trial to the last,
but nearly as many showed improvement between these same
sessions. Generally, this degradation/improvement was small
in magnitude (<40% of the standard deviation of completion
time, and <15% of average completion time) and statistically
insignificant.
B. Suggestions for Mouse Sensitivity Selection
Per traditional user interface recommendations, a player
interacting with low ID (nearby) targets should select a higher
mouse sensitivity, allowing them to rotate the view more
quickly towards these targets, and make rapid changes in
view direction to track nearby targets. This player’s risk of
under/overshooting is greatly reduced by the increased target
size they experience. A player experiencing higher ID (more
distant) targets stands to benefit from lower mouse sensitivity,
as since the need to make sudden, dramatic changes in view
direction is lower this player benefits more from the increased
precision of lower sensitivity. We demonstrate this claim by
plotting our task completion time and throughput data broken
out by range of IDs in Fig. 10. The results demonstrate subjects
performed better at lower sensitivities for high ID tasks, and
higher sensitivities for low ID tasks, in accordance with our
suggestion and prior art.
C. Suggestions for Future Work
The range of target widths and distances studied in this
work represents a reasonable sampling of ID, but a subset
of targeting tasks presented in real FPS games. Studying the
impacts of sensitivity in large angular rotations (i.e. 180◦
turns in game) may present an added benefit to lower mouse
sensitivities not represented in this work. This may be more
relevant to some game conditions than others. In addition,
many games implement separate sensitivities for “scoped” or
“zoomed” modes that modify aiming behavior, particularly
when trying to hit small targets at reasonable distances. Our
study did not include this ability, but a future study could
include this dynamic and study how the ability to select between two different target sizes/difficulties changes the optimal
behavior, at the cost of field of view while zoomed in.
Typically gamers learn the limits of their mouse pad or
surface and tend to lift (or clutch) the mouse prior to these
limits, creating additional non-linearity in task completion time
data. The mouse motion studied in this work occurs over
a range of 100 mm (the maximum target distance at the
lowest sensitivity corresponds to 111.8 mm of mouse travel)
to discourage subjects from lifting their mouse during trial
data collection. However, gaming mouse pads can be as large
as 400-500 mm [28]. Future work exploring larger target
distances (i.e. user motion >500 mm) would encourage more
lifting actions, improving understanding of how their speed
and duration is related to target task difficulty and sensitivity.
VI. CONCLUSIONS
We conduct the first performance-oriented study of mouse
sensitivity in FPS targeting tasks and report a range of generally optimal sensitivities from 0.45-1.8◦/mm. This agrees
well with the 4x range of CD gain recommended by prior
art in pointer-based mouse sensitivity literature, as well as the
preferred sensitivities selected by our experienced FPS gamer
subjects. We show that low sensitivities reduce in-game view
rotation speed while high sensitivities result in reduced motion
precision (increased corrective motions), both increasing overall task completion time. In addition, we demonstrate that low
ID tasks benefit from higher sensitivities, while more spatially
difficult (high ID) tasks benefit from lower sensitivities, again
conforming to traditional pointer-based results. Finally we did
not observe long-term “hard wired” muscle memory of mouse
sensitivity for all players, so we encourage FPS gamers to
experiment with changing mouse sensitivity to continue to
optimize their settings to their role and individual play style.
REFERENCES
[1] /u/StruthGaming. (2019, Feb) Your aim isn’t terrible, your
mouse sensitivity is (pc). Reddit. [Online]. Available:
https://www.reddit.com/r/apexlegends/comments/apbo8z/your aim
isnt terrible your mouse sensitivity is/
[2] J. Looser, A. Cockburn, and J. Savage, “On the validity of using firstperson shooters for fitts’ law studies,” People and Computers XIX, vol. 2,
pp. 33–36, 2005.
[3] P. M. Fitts, “The information capacity of the human motor system
in controlling the amplitude of movement.” Journal of experimental
psychology, vol. 47, no. 6, p. 381, 1954.
[4] P. M. Fitts and J. R. Peterson, “Information capacity of discrete motor
responses.” Journal of experimental psychology, vol. 67, no. 2, p. 103,
1964.
[5] I. S. MacKenzie and W. Buxton, “Extending fitts’ law to twodimensional tasks,” in Proceedings of the SIGCHI Conference on
Human Factors in Computing Systems, ser. CHI ’92. New York,
NY, USA: Association for Computing Machinery, 1992, p. 219–226.
[Online]. Available: https://doi.org/10.1145/142750.142794
[6] I. S. MacKenzie, “Fitts’ law as a research and design tool in humancomputer interaction,” Human-computer interaction, vol. 7, no. 1, pp.
91–139, 1992.
[7] I. S. MacKenzie and P. Isokoski, “Fitts’ throughput and the speedaccuracy tradeoff,” in Proceedings of the SIGCHI Conference on Human
Factors in Computing Systems, 2008, pp. 1633–1636.
[8] M. Bohan, S. Thompson, D. Scarlett, and A. Chaparro, “Gain and target
size effects on cursor-positioning time with a mouse,” in Proceedings of
the Human Factors and Ergonomics Society Annual Meeting, vol. 47,
no. 4, SAGE Publications Sage CA: Los Angeles, CA. Los Angeles,
CA: SAGE Publications, 2003, pp. 737–740.
[9] J. Sandfeld and B. R. Jensen, “Effect of computer mouse gain and
visual demand on mouse clicking performance and muscle activation
in a young and elderly group of experienced computer users,” Applied
Ergonomics, vol. 36, no. 5, pp. 547–555, 2005.
[10] H. D. Jellinek and S. K. Card, “Powermice and user performance,”
in Proceedings of the SIGCHI Conference on Human Factors in
Computing Systems, ser. CHI ’90. New York, NY, USA: Association
for Computing Machinery, 1990, p. 213–220. [Online]. Available:
https://doi.org/10.1145/97243.97276
[11] U. Trankle and D. Deutschmann, “Factors influencing speed and preci- ¨
sion of cursor positioning using a mouse,” Ergonomics, vol. 34, no. 2,
pp. 161–174, 1991.
[12] G. Casiez, D. Vogel, R. Balakrishnan, and A. Cockburn, “The impact
of control-display gain on user performance in pointing tasks,” Human–
computer interaction, vol. 23, no. 3, pp. 215–250, 2008.
[13] R. Senanayake and R. S. Goonetilleke, “Pointing device performance in
steering tasks,” Perceptual and motor skills, vol. 122, no. 3, pp. 886–910,
2016.
[14] M. L. Lin, R. G. Radwin, and G. C. Vanderheiden, “Gain effects on performance using a head-controlled computer input device,” Ergonomics,
vol. 35, no. 2, pp. 159–175, 1992.
[15] Y. H. Pang, E. R. Hoffmann, and R. S. Goonetilleke, “Effects of gain
and index of difficulty on mouse movement time and fitts’ law,” IEEE
Transactions on Human-Machine Systems, vol. 49, no. 6, pp. 684–691,
2019.
[16] G. Casiez and N. Roussel, “No more bricolage! methods and tools
to characterize, replicate and compare pointing transfer functions,” in
Proceedings of the 24th annual ACM symposium on User interface
software and technology, 2011, pp. 603–614.
[17] R. Balakrishnan, ““beating” fitts’ law: virtual enhancements for pointing
facilitation,” International Journal of Human-Computer Studies, vol. 61,
no. 6, pp. 857–874, 2004.
[18] Max, “What is mouse acceleration?” Nov 2019. [Online]. Available:
https://prosettings.net/library/what-is-mouse-acceleration/
[19] “What is mouse acceleration?” Sep 2019. [Online]. Available:
https://www.bestsettings.com/library/what-is-mouse-acceleration/
[20] F. Beaufort, “Disable mouse acceleration to provide a better
fps gaming experience,” Nov 2020. [Online]. Available: https:
//web.dev/disable-mouse-acceleration/
[21] M. Mamerow, “Is mouse acceleration good for gaming? [aiming myth
uncovered],” Jun 2020. [Online]. Available: https://raiseyourskillz.com/
is-mouse-acceleration-good-or-bad-for-first-person-shooters/
[22] N. Johnson, “This is why you might want to use mouse acceleration
in csgo,” Mar 2021. [Online]. Available: https://win.gg/news/7539/
this-is-why-you-might-want-to-use-mouse-acceleration-in-csgo
[23] J. Spjut, B. Boudaoud, K. Binaee, A. Majercik, M. McGuire, and J. Kim,
“Firstpersonscience: Quantifying psychophysics for first person shooter
tasks,” in UCI Esports Conference, 2019.
[24] D. E. Meyer, R. A. Abrams, S. Kornblum, C. E. Wright, and
J. Keith Smith, “Optimality in human motor performance: ideal control
of rapid aimed movements.” Psychological review, vol. 95, no. 3, p. 340,
1988.
[25] Y. Chen, E. R. Hoffmann, and R. S. Goonetilleke, “Structure of
hand/mouse movements,” IEEE Transactions on Human-Machine Systems, vol. 45, no. 6, pp. 790–798, 2015.
[26] R. Chua and D. Elliott, “Visual regulation of manual aiming,” Human
Movement Science, vol. 12, no. 4, pp. 365–401, 1993.
[27] T.-Y. Hsieh, Y.-T. Liu, and K. M. Newell, “Submovement control
processes in discrete aiming as a function of space-time constraints,”
Plos one, vol. 12, no. 12, p. e0189328, 2017.
[28] prosettings.net. (2021) Best gaming mousepad - the ultimate guide.
Accessed: 2021-02-11. [Online]. Available: https://prosettings.net/
best-gaming-mousepad/