NBA MVP Race Analysis
In this project, using data from the NBA League Leader Stats section of the NBA website, I will showcase data manipulation and formatting techniques using Google Sheets and create a Tableau dashboard focused on the NBA MVP race.
How I Got Game: Identifying and Collecting the Dataset for NBA MVP Race Analysis
After analyzing the NBA season up to its 66th game, I have determined that there are 3-4 players who have emerged as front runners for the MVP award. In order to be considered, a player must demonstrate reliability by participating in at least 70-80% of the season and exhibit outstanding statistical performance. With these criteria in mind, I collected the necessary data by exploring various methods and ultimately manually copied and pasted the top 50 league leaders from the NBA website into a Google Sheet, ensuring compliance with regulations related to open-public data use.
Get the Brooms Out: Sweeping Up NBA Data for MVP Analysis
After pasting the data into Google Sheets, I noticed a couple of issues that needed to be addressed. Firstly, the header was offset by one column from the data below it. To fix this, I removed the number rankings from the first column (Player) by highlighting the numbers and clicking "delete cells and shift left". Additionally, I renamed the column labeled "+/-" to "PlusMinus" to improve clarity. Lastly, I encountered a formatting issue with the column labeled "3PM", which was automatically formatted to display as "3:00 PM". To resolve this, I renamed the column to "3_PM".
Basketball Excellence: Filtering Out the Best NBA MVP Candidates
In order to narrow down the list of players to the true contenders for the MVP race, I utilized a filter on the column titles for the top 50 players. To be the best, a player must truly be the cream of the crop. With this in mind, I set the filter to include only those who had played more than 45 games out of the 66 games played this season so far. Additionally, a key aspect of MVP candidacy is team success, as players whose teams have poor records are unlikely to be considered. To account for this, I further filtered the list to include only players whose teams had won at least 33 games out of the 66 played or were at least at a .500 win percentage. By applying these filters, I ensured that only the most deserving players remained in the running for the MVP award.
I performed the identical procedure on the dataset that was founded on 'Per One Possession'.
Lace 'Em Up: Create Data Viz in Tableau
After cleaning and filtering the data, I used Tableau to create visualizations to analyze the NBA MVP race. To begin, I connected the data source to Tableau and imported two CSV files containing statistics such as points per game, assists per game, and points per possession.
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Next, I organized the dashboards and worksheets by creating five dashboards, with four pages for each player on the MVP race and a front page showcasing the top four players. I also created background templates for each dashboard page using Canva.
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To showcase why each player should be chosen as the MVP, I created a worksheet for each data visualization. For example, for Jayson Tatum, I created a pie chart for 3-point makes to 3-point attempts, a bar chart on turnovers per possession, and stacked area charts showcasing games played, average minutes, wins, and losses.
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Similarly, for Joel Embiid, I created a bar chart on points, assists, rebounds, blocks, and steals, a text table on average points per game and possession, and a stacked bar chart on defensive stats per possession. For Nikola Jokic, I used a scatter plot chart to show percentile rank on field goal percentage and wins, and a side-by-side bar chart to show the number of triple-doubles and double-doubles each candidate has this season.
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For Giannis Antetokounpo, I created a text table showing average minutes per game, a lollipop chart on field goal makes to attempts per possession, and another lollipop chart on points to rebounds per possession.
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Once I had created each visualization, I arranged them into the respective player's page and added captions to some of the charts. Finally, I added navigation buttons to each page, edited tool tips and fonts, and made other finishing touches to the dashboard. By following these steps, I was able to create a comprehensive and engaging data visualization dashboard to analyze the NBA MVP race.
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4th Quarter: Analysis
In analyzing the four players in the NBA MVP race, several key insights emerge. Tatum's commitment to winning the championship this season is evident in his high number of games played and minutes logged. His shooting ability is also a standout, as evidenced by his high number of 3-pointers per game. Notably, Tatum has also maintained the lowest turnovers per possession despite his high usage rate. Giannis' dominance on the court is equally impressive, with his ability to achieve high points and rebounds per possession while playing fewer minutes than his competitors. Embiid's well-rounded performance is reflected in his impressive stats, with his high PTS+AST+REB+BLK+STL score at 50.2 and his defensive stats per possession making him a strong contender for both the MVP and DPOY awards. Finally, Jokic's consistently high FG percentage to wins and +/- to wins make him a formidable candidate, as does his league-leading number of triple-doubles and double-doubles. Overall, these insights provide valuable context for understanding the strengths and weaknesses of each player in the MVP race.