Analysis of NBA Players and Shot Prediction Using Random Forest and XGBoost Models

Maram Shikh Oughali; Mariah Bahloul; Sahar A. El Rahman

doi:10.1109/ICCISci.2019.8716412

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Analysis of NBA Players and Shot Prediction Using Random Forest and XGBoost Models

Maram Shikh Oughali, Mariah Bahloul and Sahar A. El Rahman

2019 International Conference on Computer and Information Sciences (ICCIS), pp.1-5

04/2019

DOI: https://doi.org/10.1109/ICCISci.2019.8716412

Abstract

defenses

NBA

prediction

Random Forest

shooting

XGBoost

This paper proposes a comparative study using machine learning algorithms to predict the shooting success by basketball players in the National Basketball Association (NBA). This work is focusing on analyzing NBA's regular session dataset, which will help NBA teams to prepare their play plan for future games based on the other team players' performance. For instance, how good is each player usually in shooting from different distance and what defense strategies they often use. In this work, Random Forest and XGBoost models are used for shooting prediction.

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Details

Title: Analysis of NBA Players and Shot Prediction Using Random Forest and XGBoost Models
Creators - without role: Maram Shikh Oughali - Princess Nourah bint Abdulrahman University
Mariah Bahloul - Princess Nourah bint Abdulrahman University
Sahar A. El Rahman - Princess Nourah bint Abdulrahman University
Publication Details: 2019 International Conference on Computer and Information Sciences (ICCIS), pp.1-5
Publisher: IEEE
Identifiers: 9927434108331
Academic Unit: Princess Nourah bint Abdulrahman University
Language: English
Resource Type: Conference proceeding