Fps Sensitivity Converter
Convert mouse sensitivity settings between different FPS games maintaining the same cm/360. Enter values for instant results with step-by-step formulas.
Calculator
Adjust values & calculateAll Game Conversions (25.98 cm/360)
Formula
Each game has a unique yaw value (degrees per mouse count at sensitivity 1). By matching the degrees-per-count across games, cm/360 remains identical. cm/360 = (360 x 2.54) / (DPI x Yaw x Sensitivity).
Last reviewed: December 2025
Worked Examples
Example 1: CS2 to Valorant Conversion
Example 2: Overwatch to Apex Legends Conversion
Background & Theory
The Fps Sensitivity Converter applies the following established principles and formulas. Statistics and probability provide the mathematical framework for drawing conclusions from data under uncertainty. The measures of central tendency describe where data cluster. The mean is the arithmetic average, computed as the sum of all values divided by the count. The median is the middle value of an ordered dataset, robust to extreme outliers. The mode is the most frequent value. Spread is quantified by variance, the average squared deviation from the mean, and by its square root, the standard deviation. For a sample, variance uses n minus one in the denominator to correct for bias in estimation. The normal distribution, defined by its mean and standard deviation, is the cornerstone of parametric statistics. Its bell-shaped probability density follows the formula f(x) = (1 / (sigma * sqrt(2*pi))) * exp(-0.5 * ((x - mu) / sigma)^2). The empirical rule states that approximately 68 percent of observations fall within one standard deviation of the mean, 95 percent within two, and 99.7 percent within three. A z-score standardizes a data point by subtracting the mean and dividing by the standard deviation, expressing how many standard deviations an observation lies from the mean. In hypothesis testing, the p-value is the probability of observing a result at least as extreme as the one obtained, assuming the null hypothesis is true. Confidence intervals express the range within which the true population parameter falls with a specified probability, typically 95 percent. Correlation measures linear association between two variables, with Pearson's r ranging from negative one to positive one. Correlation does not imply causation. Linear regression fits a line of the form y = a + bx to minimize the sum of squared residuals. Bayes' theorem relates conditional probabilities: P(A|B) = P(B|A) * P(A) / P(B), allowing prior beliefs to be updated on new evidence. The law of large numbers guarantees that the sample mean converges to the population mean as sample size grows. The central limit theorem states that the distribution of sample means approaches normality regardless of the population distribution, provided the sample size is sufficiently large, typically 30 or more.
History
The history behind the Fps Sensitivity Converter traces back through the following developments. The mathematical study of probability emerged in the 17th century from correspondence between Blaise Pascal and Pierre de Fermat in 1654. Their exchange, prompted by a gambling problem posed by the Chevalier de Mere, established the foundations of probability theory by calculating expected outcomes through systematic enumeration of cases. Jacob Bernoulli formalized the law of large numbers in his posthumously published Ars Conjectandi of 1713, proving rigorously that empirical frequencies converge to theoretical probabilities with increasing observations. His work laid the groundwork for inferential statistics by connecting mathematical probability to observed data. Carl Friedrich Gauss developed the method of least squares around 1795 while adjusting astronomical observations, and he recognized the bell-shaped error distribution that now bears his name. Pierre-Simon Laplace independently worked on the normal distribution and proved an early version of the central limit theorem around 1810, demonstrating why errors in measurement tend toward normality. The late 19th century saw statistics emerge as a distinct scientific discipline. Francis Galton introduced regression and correlation in the 1880s while studying heredity. Karl Pearson formalized these concepts, developed the chi-squared test, and founded the journal Biometrika in 1901, establishing statistics as a rigorous academic field. Ronald Fisher transformed statistical practice in the early 20th century. His 1925 book Statistical Methods for Research Workers introduced significance testing, analysis of variance, and the concept of the p-value as a decision threshold, establishing the framework still used in scientific research. Fisher and Jerzy Neyman engaged in a prolonged methodological dispute over the interpretation of hypothesis tests. The Bayesian approach, rooted in the 18th century work of Thomas Bayes and Laplace, was largely eclipsed by frequentist methods through much of the 20th century but experienced a revival after World War II and accelerated with computational advances. The late 20th and early 21st centuries brought statistics into every domain through big data, machine learning, and the routine availability of software capable of processing millions of observations.
Frequently Asked Questions
Formula
Target Sens = (Source Yaw x Source Sens) / Target Yaw
Each game has a unique yaw value (degrees per mouse count at sensitivity 1). By matching the degrees-per-count across games, cm/360 remains identical. cm/360 = (360 x 2.54) / (DPI x Yaw x Sensitivity).
Worked Examples
Example 1: CS2 to Valorant Conversion
Problem: Convert CS2 sensitivity of 2.0 at 800 DPI to Valorant.
Solution: CS2 yaw = 0.022, Valorant yaw = 0.07\nTarget sensitivity = (0.022 x 2.0) / 0.07 = 0.6286\ncm/360 = (360 x 2.54) / (800 x 0.022 x 2.0) = 914.4 / 35.2 = 25.98 cm\nVerification: Valorant cm/360 = (360 x 2.54) / (800 x 0.07 x 0.6286) = 914.4 / 35.2 = 25.98 cm\nBoth games produce identical cm/360, confirming correct conversion
Result: CS2 sens 2.0 at 800 DPI = Valorant sens 0.6286 at 800 DPI (25.98 cm/360)
Example 2: Overwatch to Apex Legends Conversion
Problem: Convert Overwatch 2 sensitivity of 5.0 at 1600 DPI to Apex Legends.
Solution: Overwatch yaw = 0.0066, Apex yaw = 0.022\nTarget sensitivity = (0.0066 x 5.0) / 0.022 = 1.5\ncm/360 = (360 x 2.54) / (1600 x 0.0066 x 5.0) = 914.4 / 52.8 = 17.32 cm\nThis is a relatively high sensitivity (wrist aimer range)\nVerification: Apex cm/360 = 914.4 / (1600 x 0.022 x 1.5) = 914.4 / 52.8 = 17.32 cm
Result: Overwatch sens 5.0 at 1600 DPI = Apex Legends sens 1.5 at 1600 DPI (17.32 cm/360)
Frequently Asked Questions
How do I convert sensitivity between different FPS games?
Converting sensitivity between FPS games requires understanding that each game uses a different yaw value, which determines how many degrees the camera rotates per mouse count at a given sensitivity. The conversion formula is: target sensitivity equals source yaw times source sensitivity divided by target yaw. For example, CS2 uses a yaw of 0.022 while Valorant uses 0.07, so a CS2 sensitivity of 2.0 converts to Valorant sensitivity of 2.0 times 0.022 divided by 0.07 which equals approximately 0.629. The DPI remains the same because it is a hardware setting independent of the game. Fps Sensitivity Converter automatically handles these conversions for all major FPS games.
What DPI should I use for FPS gaming?
Most professional FPS players use a DPI between 400 and 1600, with 800 being the most popular choice. Higher DPI does not inherently mean better performance. The key is the combination of DPI and in-game sensitivity, which determines your effective sensitivity or cm/360. Using 400 DPI with 2.0 sensitivity gives the same result as 800 DPI with 1.0 sensitivity. However, higher DPI values (800-1600) can provide smoother cursor movement and reduce pixel skipping at low in-game sensitivities. Some players prefer 400 DPI for its predictable behavior in older games and desktop navigation, while others choose 800 or 1600 for improved sensor tracking precision.
What sensitivity do professional FPS players use?
Professional FPS player sensitivities vary by game and play style, but most cluster within a specific range. In CS2, the average pro sensitivity at 400 DPI is around 2.0 to 2.5 (eDPI 800-1000), corresponding to roughly 35-50 cm/360. Valorant pros average about 0.25-0.45 at 800 DPI. Overwatch professionals tend to use slightly higher sensitivities of 3-8 at 800 DPI because the game requires more frequent large angle movements. Apex Legends pros typically use 1.5-2.5 at 800 DPI. The general trend is that tactical shooters with precise aim requirements (CS2, Valorant) favor lower sensitivities, while faster-paced games with more movement favor slightly higher sensitivities.
Should I use the same sensitivity across all FPS games?
Using the same cm/360 across all FPS games is generally recommended for building consistent muscle memory, but there are valid reasons to adjust. Matching your cm/360 means the same physical mouse movement produces the same angular rotation in every game, which helps maintain aim consistency. However, different games have different movement speeds, engagement distances, and mechanical requirements that might warrant small adjustments. A game with very fast movement like Overwatch might benefit from slightly higher sensitivity than a tactical shooter like Valorant. Many players use their primary game sensitivity as a baseline and adjust within 10-20 percent for other games based on the gameplay demands.
How does mousepad size affect sensitivity choice?
Mousepad size directly limits the minimum practical sensitivity you can use comfortably. A standard small mousepad around 25 by 21 centimeters restricts you to higher sensitivities because you lack the physical space for large sweeping arm movements. A large mousepad of 45 by 40 centimeters or larger accommodates the low sensitivities of 40-55 cm/360 that most professional players prefer. Extended desk mats measuring 90 by 40 centimeters provide maximum freedom for even the lowest sensitivities. If you find yourself frequently lifting and repositioning your mouse during gameplay, either your sensitivity is too low for your mousepad or your mousepad is too small for your desired sensitivity. Most competitive players use large or extended mousepads.
What is pixel skipping and how does it relate to sensitivity?
Pixel skipping occurs when the smallest possible mouse movement results in the in-game crosshair jumping over one or more pixels rather than moving smoothly to the adjacent pixel. This happens when the combination of DPI and in-game sensitivity does not provide enough granularity to address every single pixel on the screen. At very low DPI with high in-game sensitivity, the jumps between addressable angles become large enough to skip pixels, reducing aiming precision. The formula to check for pixel skipping involves comparing the minimum mouse movement angle to the angular size of a pixel on screen. Using 800 DPI or higher effectively eliminates pixel skipping at common resolutions, which is another reason competitive players favor 800-1600 DPI over 400.
References
Reviewed by Daniel Agrici, Founder & Lead Developer · Editorial policy