Table tennis analytics

We propose new metrics to further analyze table tennis matches. Our approach involves adapting existing metrics by incorporating additional attributes provided by the detailed data, such as player zones and shot angles. Furthermore, we present a methodology for visualizing all metrics simultaneously during a single set, enabling a comprehensive assessment of their significance.

This repository is linked to a publication presented at the 10th Workshop on Machine Learning and Data Mining for Sports Analytics MLSA 2023. You can read the pdf version of the publication here. And reproduce the figures of the publication by executing :

# Clone the repository
$ git clone https://github.com/centralelyon/table-tennis-analytics.git
$ cd Metrics/Lebrun_Zhendong/
# Run the file MatchAnalysis.py
$ python3 MatchAnalysis.py

How to cite

If you find this work useful, please consider using the follwing citations:

Gabin Calmet, Aymeric Erades, Romain Vuillemot. Exploring Table Tennis Analytics: Domination, Expected Score and Shot Diversity. Machine Learning and Data Mining for Sports Analytics, Sep 2023, Turin, Italy.

@inproceedings{calmet:hal-04240982,
    TITLE = {{Exploring Table Tennis Analytics: Domination, Expected Score and Shot Diversity}},
    AUTHOR = {Calmet, Gabin and Erades, Aymeric and Vuillemot, Romain},
    URL = {https://hal.science/hal-04240982},
    BOOKTITLE = {{Machine Learning and Data Mining for Sports Analytics}},
    ADDRESS = {Turin, Italy},
    EDITOR = {Springer Link},
    SERIES = {Communications in Computer and Information Science},
    YEAR = {2023},
    MONTH = Sep
}

Domination

We defined the domination as a situation in which a player (or a team) consistently outperforms their opponents and maintains a significant advantage. This function is close to 1 when the first player (or team) dominates, and it is close to -1 if it is the second.

Note : we computed the domination as a function of three parameters : the score advantage, the physical domination and the mental domination.

Expected Score

The expected score is a statistical metric to estimate the probability of winning a point based on various factors such as player skill, shot quality, and opponent performance.

Note : To compute this metric, we used a Playing Pattern Tree in order to explore all the possible patterns of a point. We used Simulated Data to extend the database and to increase the precision.

Creativity (or Shots Diversity)

The creativity is defined as the variety of shots and techniques employed by a player during a match, including variations in racket side, placement, and shot selection.

Note : To compute the distance between two openings, we look at the depth of their common ancestor in the Playing Pattern Tree.