Method

CPI Benchmarking Methodology

temppal.gg measures gaming mouse CPI fidelity by comparing motion-capture-reconstructed physical sensor motion against raw HID reports. The method is designed to test whether a mouse turns intended hand motion into predictable computer input across a broad motion range.

Reported quantities

CPI bias
Signed fitted base-CPI error. It is computed from c0 in the fitted CPI model, so zero means the fitted base CPI matches the nominal CPI.
CPI jitter
Residual variation between HID reports and motion-capture ground truth after model fitting. Lower means less report-level variation.
CPI drift
Signed effective-CPI shift with translational speed and angular speed. Closer to zero means CPI changes less with motion.

Research basis

Built on Peer-Reviewed Mouse and Pointing Research

temppal.gg is not a review blog. It is a measurement site from YESLAB researchers whose prior work covers mouse sensing, pointing models, gain functions, button mechanics, aim-and-shoot behavior, and esports input performance. These papers explain why CPI fidelity, jitter, drift, and player-side tests are scientific measurement problems.

  1. Journal of Electronic Gaming and Esports 2026Profiling Rhythm Game Performance Using Multi-Lane Moving-Target Acquisition Modelforthcoming
  2. CHI 2025Hardware-Embedded Pointing Transfer Function Capable of Canceling OS Gainsread
  3. International Journal of Human-Computer Studies 2025Modeling Visually-Guided Aim-and-Shoot Behavior in First-Person Shootersread
  4. CHI 2024User Performance in Consecutive Temporal Pointing: An Exploratory Studyread
  5. CHI 2024Quantifying Wrist-Aiming Habits with A Dual-Sensor Mouse: Implications for Player Performance and Workloadread
  6. Journal of Electronic Gaming and Esports 2024Operationalizing General Mechanical Skill in Time-Pressure Action Esportsread
  7. UIST 2024Effects of Computer Mouse Lift-off Distance Settings in Mouse Lifting Actionread
  8. CHI 2022Quantifying Proactive and Reactive Button Inputread
  9. UIST 2021Do We Need a Faster Mouse? Empirical Evaluation of Asynchronicity-Induced Jitterread
  10. CHI 2021A Simulation Model of Intermittently Controlled Point-and-Click Behaviourread
  11. CHI 2021Secrets of Gosu: Understanding Physical Combat Skills of Professional Players in First-Person Shootersread
  12. CHI 2020Button Simulation and Design via FDVV Modelsread
  13. CHI 2020AutoGain: Gain Function Adaptation with Submovement Efficiency Optimizationread
  14. CHI 2020Optimal Sensor Position for a Computer Mouseread
  15. CHI 2018Impact Activation Improves Rapid Button Pressingread
  16. CHI 2018Moving Target Selection: A Cue Integration Modelread
  17. CHI 2018Neuromechanics of a Button Pressread
  18. CHI 2016Modelling Error Rates in Temporal Pointingread
  19. Human-Computer Interaction 2015A Mouse With Two Optical Sensors That Eliminates Coordinate Disturbance During Skilled Strokesread
  20. Ergonomics 2013A Kinematic Analysis of Directional Effects on Mouse Controlread

See the full YESLAB publication list

Model used for public reporting

For a nominal CPI setting, temppal.gg summarizes effective CPI using the fitted model CPI = c0 + c1v + c2ω, where v is translational speed and ω is angular speed. Public records are tested over a broad range of approximately 0-1 m/s and 0-5 rad/s.

The public pages report the fitted quantities and benchmark summaries, while internal implementation details such as exact sampling and processing choices remain part of the measurement system.

How the aggregate score is combined

The aggregate score is a dataset-relative summary, not a separate measurement. Each condition row is standardized against the current public benchmark distribution. The score combines -z(|CPI bias|), -z(CPI jitter), and -z(|CPI drift|).

CPI bias has weight 0.5 because users can often compensate a small base-CPI offset after measurement by adjusting the selected CPI, including through temppal.gg's CPI Match tool. CPI jitter and CPI drift have weight 2 each because report inconsistency and motion-dependent drift cannot be corrected as cleanly with a simple CPI setting. Higher aggregate score is better. In benchmark tables, the Bias, Jitter, and Drift columns show the weighted contribution added by each term, so those three displayed values sum to the final Score.

Open CPI Match

Measurement pipeline

  1. Track the physical mouse body and reconstruct the optical sensor location.
  2. Record raw HID mouse reports under controlled CPI, pad, polling-rate, and motion-sync conditions.
  3. Align the physical motion trace and HID trace in time.
  4. Fit the CPI model and derive CPI bias, CPI jitter, and CPI drift.
  5. Publish a public snapshot with mouse-level and condition-level summaries.
© 2026 YESLAB Inc. (주)예스랩. All rights reserved. yeslab@temppal.gg Terms / Data License Privacy