https://github.com/raphaelsenn/playervectors-ai

Clustering and predicting PlayerVectors using Machine Learning and Deep Learning algorithms.
https://github.com/raphaelsenn/playervectors-ai

Last synced: about 1 year ago
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Clustering and predicting PlayerVectors using Machine Learning and Deep Learning algorithms.

• Host: GitHub
• URL: https://github.com/raphaelsenn/playervectors-ai
• Owner: raphaelsenn
• License: mit
• Created: 2025-02-12T12:35:31.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2025-02-25T13:40:35.000Z (over 1 year ago)
• Last Synced: 2025-02-25T14:39:56.074Z (over 1 year ago)
• Language: Jupyter Notebook
• Size: 2.25 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# playervectors-ai

Clustering and predicting PlayerVectors using Machine Learning and Deep Learning algorithms.

## Player Vectors

Player Vectors summarise the playing styles of individual football players.

#### Install Player Vectors

I implemented the entire concept of Player Vectors.

You can install my **`Player Vector`** package via pip:

```bash

pip install playervectors

```

## Cluster Player Vectors using t-SNE

![image](res/playervector_cluster_tsne.png)

## Predicting Player positions using Player Vectors

Used Classification Models:

* K Nearest Neighbour Classifier

* Suppor-Vector Classifier

* Decission Tree Classifier

* Random Forest Classifier

* Gradient Boosted Decission Tree Classifier

* Multilayer Perceptron

### Accurcay scores

![image](res/clf_accuracy.png)

### Confusion matrices of classifiers

![image](res/clf_confusion_matrix.png)

## Citations

```bibtex

@article{ecmlpkdd2019,

  title     = {Player Vectors: Characterizing Soccer Players’

Playing Style from Match Event Streams},

  author    = {Tom Decroos, Jesse Davis},

  journal   = {ecmlpkdd2019},

  year      = {2019},

}

```