VincentAuriau / Tennis-Prediction

Prediction of the winner of a tennis matches with machine learning

Tennis-Prediction Repository

The goal of this project is to predict the outcome of a tennis match using the data of both players. The data used comes from Jeff Sackmann's repository.

Installation

To clone the repository, with the data you need to also clone the submodules:

git clone --recurse-submodules https://github.com/VincentAuriau/Tennis-Prediction.git

Usage

You can find examples in /examples:

Loading players statistics at match time + match outcome

from data.data_loader import matches_data_loader
data_df = matches_data_loader(path_to_data="submodules/tennis_atp")

data_df contains let you access information about players (statistics prior to the match) along statistics of the match. A basic example statistic: the victory percentage of the best ranked player in a match, depending on players rankings.

Number of ATP main matches depending on players rank	Victory % of best ranked player

It can be easily used to also compute players statistics over their carreer, and/or at match time. Here is a simple example with Stan Wawrinka:

Stan's Victory % in main ATP matches	Stan's career aces % diff with adversary

Train/Testing on matches outcome:

from sklearn.ensemble import RandomForestClassifier
from evaluation.train_test import train_test_evaluation

test_score = train_test_evaluation(
    train_years=[2020, 2021],
    test_years=[2022, 2023],
    model_class=RandomForestClassifier,
    model_params={"n_estimators": 2000, "max_depth": None},
    match_features=[],
    player_features=["Ranking"],
    encoding_params={},
    additional_features=[],
    save_path="./results",
    save_all_results=False
)

print("Test Score", test_score)

Models and hyperparamters can easily be compared with the file results.csv saved in save_path.

Different models performances

If the argument save_all_results is set to True, the whole csv of test data is saved. It helps to get more in-depth analysis of results

Model precision compared with best ranked player wins strategy	Model precision depending of players ranks

Encoding match

In order to represent history of a player, one can use MatchEncoders:

from history_modeling.encoding_model import PCAMatchEncoder

model = PCAMatchEncoder(num_pca_features=2)
model.fit(data_df, transform_data=True)
X_r, match_info = model.predict(data_df, transform_data=True)

2D representation of match outcome: