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Rocket League Skillshots Data Set Data Set
Download: Data Folder, Data Set Description

Abstract: This dataset contains data of players of the game Rocket League, performing different skillshots.

Data Set Characteristics:  

Multivariate, Time-Series

Number of Instances:




Attribute Characteristics:


Number of Attributes:


Date Donated


Associated Tasks:


Missing Values?


Number of Web Hits:



Romain Mathonat, romain.mathonat '@', Université de Lyon, CNRS, INSA Lyon, LIRIS, UMR5205, F-69621, France

Data Set Information:

Each skillshot performed is characterized by 18 features, composed of players inputs and in-game metrics, collected at different time, creating a multivariate time serie.
You can see what skillshots look like in the github of the project:
[Web Link]

There are seven classes, -1 representing noise (composed of failed figures and random moves)
Note that lengthes of those multivariate timeseries varie, and that sample is not collected at regular time iterval (see paper for more details).

Using our pattern mining approach, we obtained an accuracy of 87.6% on a 5-fold stratified cross-validation setup.

Classes meaning:
-1: noise
1: ceiling shot
2: power shot
3: waving dash
5: air dribble
6: front flick
7: musty flick

Attribute Information:

The first line contains the name of each of the 18 features.
The format is the following:

BallAcceleration_1, Time_1, ..., jump_1
BallAcceleration_2, Time_2, ..., jump_2
BallAcceleration_n, Time_n, ..., jump_n

Relevant Papers:

R Mathonat, JF Boulicaut, M Kaytoue, 'A Behavioral Pattern Mining Approach to Model Player Skills in Rocket League', IEEE Conference on Games 2020.

Citation Request:

author={R. {Mathonat} and D. {Nurbakova} and J. {Boulicaut} and M. {Kaytoue}},
booktitle = {{IEEE} Conference on Games, CoG 2020, Online},
title={A Behavioral Pattern Mining Approach to Model Player Skills in Rocket League,

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