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This project analyzes how turnovers, field goal percentage, rebounds, and Luka Doncic's minutes impact the points scored by the Dallas Mavericks in the 2023-2024 NBA season. Using data from 30 games, the analysis employs both Apache Spark (Scala) and SAS.

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Dallas Mavericks Points Prediction: 2023-2024 Season Analysis

This repository contains a project investigating the factors influencing the number of points scored by the Dallas Mavericks in games during the 2023-2024 NBA season. The primary objective is to explore the linear relationships between points scored and the following key variables:

  • Turnovers: High turnover rates can lead to more possessions for the opposing team, impacting scoring opportunities.
  • Field Goal Percentage: This metric reflects the efficiency of the team’s shooting; a higher percentage indicates a better chance of scoring.
  • Total Rebounds: More rebounds increase the chances of maintaining possession and scoring.
  • Luka Doncic's Minutes Played: As a star player, Doncic's playing time is expected to correlate positively with the Mavericks' scoring.

Data Collection

The data for this analysis was collected from ESPN, consisting of 30 randomly selected games from the 2023-2024 season. Each observation reflects the team's cumulative statistics from individual games, focusing solely on the Mavericks without accounting for the opponent's stats.

Components

  • Apache Spark (Scala): Implements data processing and regression analysis to identify relationships among the variables.
  • SAS: Conducts additional regression analysis and visualizations, providing a comparative perspective on the findings.

Usage

  • Run the Spark Scala script for data processing and analysis.
  • Execute the SAS script for further exploration and visualization of the regression results.

About

This project analyzes how turnovers, field goal percentage, rebounds, and Luka Doncic's minutes impact the points scored by the Dallas Mavericks in the 2023-2024 NBA season. Using data from 30 games, the analysis employs both Apache Spark (Scala) and SAS.

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