
PvpSense
AI Player Position Prediction for Mace PVP using neural networks
Screenshots



About this Mod
This Mods uses Neural Networks to predict the future position of the Players.
!!! It is not trained when you install it you have to train it yourself !!!
⚠️ Important:
It only predicts your OWN position 40 ticks into the future therefore it is no cheat.
If you run into problems, read all instructions carefully and check your log files.
Stats:
Uses movement data from:
Input Data Used
Velocitys
Yaw & pitch
Jumping
On ground
Shield usage
Eating
Holding a Wind Charge
Holding an Ender Pearl
Gliding
Right-click action in that tick
x,y and z distance to the nearest Player or Zombie
The neural network analyzes how your position changes over the next 40 ticks based on this data and trains itself accordingly.
Nothing happens automatically when you join a world.
How to Use:
Start Collecting data-blocked: /trainSense toggleGathering
Enable the information HUD: /trainSense toggleHud
The HUD shows how many data ticks have been collected.
All of this data is stored temporarily in RAM.
If you stop moving or acting for 1 second, data collection stops automatically.
In newer Versions its /pvpSense
Training the Neural Network:
Train Using Saved Data
/trainSense useSaved addToNet ,Loads saved training data and adds it to the existing network
/trainSense useSaved overwriteNet ,Creates a new network using only the saved training data
Earlystop training if it doesnt advande by pressing "Y"
Saving Training Data
You can permanently store gathered data to combine multiple gathering sessions:
/trainSense saveData addToOld ,Saves RAM data into new file
Once a neural network has been trained and saved:
Press the default keybind Z
The mod will render your predicted position 40 ticks into the future
Read all instructions carefully again and check your logs if you find to have problems
also its recomendet to use the build in information hud during network creation
All recordet data is saved in: config/pvpSense
If you change versions clear all files.
Problems: The mod isn't acurate for far Enderpearling, or Movement speed over 20 Blocks/second
!!!!Further versions may change the struckture of the net so don't spend a tremendous amount of time training with the first verion!!!!!
Its also recommendet to completely clear your pvpSense config folder if you experience problems after swiching versions.
This project uses Encog™ (Apache License 2.0)
If you were a coder just could change the lines marked with .?.?.? but dont do that.
This project will recive major updates very often so be sure to allways get the latest version
Get help and talk about the mod: discord.gg/MX4y9Nsk
Available Versions
How to Install PvpSense on Your Server
Order Server
Order a Minecraft Java server with at least 3 GB RAM (4 GB recommended).
Set fabric Loader
In the panel under "Egg", select the fabric loader and matching Minecraft version (1.21.11).
Install Mod
Open the mod browser in the dashboard and search for "PvpSense". Click "Install" – done! Alternatively, upload the .jar via SFTP to the /mods folder.
Compatibility
Mod Loaders
Minecraft Versions
1.21.11
Server-side
✗ UnsupportedRecommended RAM
4 GB(min. 3 GB)Frequently Asked Questions
PvpSense server crashes on startup – what to do?
Most common cause: wrong fabric version or insufficient RAM. Check the server log (latest.log) for "OutOfMemoryError" or "Mixin" errors. With Mado Hosting: ensure at least 3 GB RAM is allocated and the loader matches the mod version (1.21.11). You can switch loaders with one click in the panel.
Is PvpSense compatible with fabric?
PvpSense officially supports fabric for Minecraft 1.21.11. The Mado dashboard automatically detects incompatible loader combinations.
Server lagging with PvpSense – how to optimize performance?
Recommended RAM: 4 GB (per 8 players). Use /spark profiler to check if PvpSense consumes the most tick time. Common fixes: reduce server view-distance to 8-10, install "performant" or "starlight" as supplementary mods on Forge. With Mado Hosting, your server runs on NVMe SSDs with dedicated CPU cores for minimal latency.
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