Penerapan Logistic Regression untuk Prediksi Kelas Karier Pembalap MotoGP Berdasarkan Data Historis
Article Sidebar
Main Article Content
Abstract
The career development of MotoGP riders is influenced by various factors from the early stages of their racing journey, including racing experience, technical performance, and team support. The complexity of these factors makes historical data analysis essential for understanding patterns in riders’ career progression. The use of Logistic Regression is relevant because it can model the probability of career classes based on measurable performance variables, allowing the classification process to be more objective and structured compared to conventional descriptive assessments. The dataset used in this study contains rider data from the beginning of their careers through their advancement into specific classes such as Moto3, Moto2, MotoGP, 125cc, and MotoE. Data processing was conducted using RapidMiner, involving data loading, model training using the Polynomial by Binomial Classification scheme, model application, and performance evaluation through a confusion matrix and accuracy measurement. The results show that Logistic Regression achieved a classification accuracy of 82.48%. Therefore, Logistic Regression can be utilized as an initial model for classifying MotoGP riders’ career stages, although further development is needed to improve performance for minority classes.
Article Details
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
References
J. M. Tshimula, E. G. Tajeuna, and K. Kyandoghere, “Challenges , and Future Directions,” 2021.
E. Science, Essentials of Performance Analysis in Sport.
Y. Jia et al., “A narrative review of deep learning applications in sports performance analysis : current practices , challenges , and future directions,” vol. 0, 2025.
R. Bunker, “The Application of Machine Learning Techniques for Predicting Match Results in Team Sport : A Review,” vol. 73, pp. 1285–1322, 2022.
“3rd-ed-alan_agresti_categorical_data_analysis.pdf.”
D. Berrar, “Performance Measures for Binary Classification 1,” pp. 1–31.
J. G. Hopker and E. Sciences, “Note . This article will be published in a forthcoming issue of the International Journal of Sports Physiology and Performance . The article appears here in its accepted , peer-reviewed form , as it was provided by the submitting author . It has not been copyedited , proofread , or formatted by the publisher .,” 2016.
A. Güllich, B. N. Macnamara, and D. Z. Hambrick, “What Makes a Champion ? Early Multidisciplinary Practice , Not Early Specialization , Predicts World-Class Performance,” 2021, doi: 10.1177/1745691620974772.
O. Höner, F. Bergmann, D. Leyhr, and O. Höner, ““ Don ’ t forget the number 1 in soccer talent research !” – Predictive validity of objectively tested motor attributes and subjectively rated position-specific skills in elite youth goalkeepers in elite youth goalkeepers,” J. Sports Sci., vol. 00, no. 00, pp. 1–15, 2025, doi: 10.1080/02640414.2025.2517974.
V. Der Graaff, D. Version, and V. Der Graaff, Predictive modelling for sports performance improvement and injury prevention. 2024. doi: 10.4233/uuid.
R. P. Bunker and F. Thabtah, “Applied Computing and Informatics A machine learning framework for sport result prediction,” Appl. Comput. Informatics, vol. 15, no. 1, pp. 27–33, 2019, doi: 10.1016/j.aci.2017.09.005.
C. Manitoba and M. Genetics, “Applications of Support Vector Machine ( SVM ) Learning in Cancer Genomics,” vol. 51, pp. 41–51, 2018, doi: 10.21873/cgp.20063.
P. R. Rathod, “A Research Review on Comparative Analysis of Data Mining Tools and Techniques,” vol. 16, no. 3, pp. 1–11, 2025.
A. A. Nababan, M. Zarlis, and E. B. Nababan, “Mathematical Modelling of Engineering Problems Multiclass Logistic Regression Classification with PCA for Imbalanced Medical Datasets,” vol. 11, no. 9, pp. 2377–2387, 2024.
Y. P. Hiola, M. Maulina, and A. Alamudi, “Performance Evaluation of Multinomial Logistic Regression , Random Forest , and XGBoost Methods in Data Classification,” vol. 8, no. 2, pp. 355–373, 2025.
J. Tanha, Y. Abdi, N. Samadi, N. Razzaghi, and M. Asadpour, “Boosting methods for multi ‑ class imbalanced data classification : an experimental review,” J. Big Data, 2020, doi: 10.1186/s40537-020-00349-y.
I. Systems, “Forecasting credit risk using multinomial logistic,” 2025.
S. Subhra, S. Datta, S. Gunesh, and S. Das, “Appropriateness of Performance Indices for Imbalanced Data”.
A. Nugroho, D. Maulana, I. Engineering, and U. P. Bangsa, “COMPARATIVE ANALYSIS OF CLASSIFICATION ALGORITHMS IN HANDLING IMBALANCED DATA WITH SMOTE OVERSAMPLING,” vol. 11, no. 2, pp. 487–495, 2025, doi: 10.33480/jitk.v11i2.6956.COMPARATIVE.
P. Wang, J. Liu, and B. Liao, “Prediction of Sports Performance and Analysis of Influencing Factors Based on Machine Learning and Big Data Statistics,” vol. 2022, 2022.
A. Fernández, S. García, M. Galar, R. C. Prati, and B. Krawczyk, “Learning from Imbalanced Data Sets”.