Statistical enhanced learning for modeling and prediction tennis matches at Grand Slam tournaments
Fuente:
arXiv
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909511556530176 |
|---|---|
| author | Buhamra, Nourah Groll, Andreas |
| author_facet | Buhamra, Nourah Groll, Andreas |
| contents | In this manuscript, we concentrate on a specific type of covariates, which we call statistically enhanced, for modeling tennis matches for men at Grand slam tournaments. Our goal is to assess whether these enhanced covariates have the potential to improve statistical learning approaches, in particular, with regard to the predictive performance. For this purpose, various proposed regression and machine learning model classes are compared with and without such features. To achieve this, we considered three slightly enhanced variables, namely elo rating along with two different player age variables. This concept has already been successfully applied in football, where additional team ability parameters, which were obtained from separate statistical models, were able to improve the predictive performance.
In addition, different interpretable machine learning (IML) tools are employed to gain insights into the factors influencing the outcomes of tennis matches predicted by complex machine learning models, such as the random forest. Specifically, partial dependence plots (PDP) and individual conditional expectation (ICE) plots are employed to provide better interpretability for the most promising ML model from this work. Furthermore, we conduct a comparison of different regression and machine learning approaches in terms of various predictive performance measures such as classification rate, predictive Bernoulli likelihood, and Brier score. This comparison is carried out on external test data using cross-validation, rolling window, and expanding window strategies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_01613 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Statistical enhanced learning for modeling and prediction tennis matches at Grand Slam tournaments Buhamra, Nourah Groll, Andreas Applications In this manuscript, we concentrate on a specific type of covariates, which we call statistically enhanced, for modeling tennis matches for men at Grand slam tournaments. Our goal is to assess whether these enhanced covariates have the potential to improve statistical learning approaches, in particular, with regard to the predictive performance. For this purpose, various proposed regression and machine learning model classes are compared with and without such features. To achieve this, we considered three slightly enhanced variables, namely elo rating along with two different player age variables. This concept has already been successfully applied in football, where additional team ability parameters, which were obtained from separate statistical models, were able to improve the predictive performance. In addition, different interpretable machine learning (IML) tools are employed to gain insights into the factors influencing the outcomes of tennis matches predicted by complex machine learning models, such as the random forest. Specifically, partial dependence plots (PDP) and individual conditional expectation (ICE) plots are employed to provide better interpretability for the most promising ML model from this work. Furthermore, we conduct a comparison of different regression and machine learning approaches in terms of various predictive performance measures such as classification rate, predictive Bernoulli likelihood, and Brier score. This comparison is carried out on external test data using cross-validation, rolling window, and expanding window strategies. |
| title | Statistical enhanced learning for modeling and prediction tennis matches at Grand Slam tournaments |
| topic | Applications |
| url | https://arxiv.org/abs/2502.01613 |