NBA Top Players Production and Value

By: Aditya Fuldeore

In this project, I compare top players from each NBA team, primarily through their scoring and offensive production, games played, team wins, and salary. I completed this project with R programming to filter and plot data, using the data to analyze if the player chosen is an ideal star player for their team, and if that player is a good fit to help the team win games. I analyzed Salary vs Scoring Production, Team Wins vs Games Played, Star Player Age vs Salary vs Team Success, and Assists vs Rebounds vs Points. I made two plots split by teams with 31 or more wins and teams with 30 or less wins, to separate by successful and unsuccessful teams throughout the project for each analysis.

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Statistics Used

*Salary in millions, rounded to nearest hundred thousands

*Statistics and age from Basketball-Reference.com website. Data used is from the 2019-2020 NBA season through the March 11, 2020 COVID–19 stoppage

Salary vs Scoring Production

Theory: Points Per Minute and Salary (in Millions) have a linear relation (best-fit line).

- Best for the team and player if players who score more points per minute make more money.

- Plots: To the right of the best-fit line, a player’s contract is usually more advantageous for the player since they would make more money despite scoring less points per minute. To the left, it is more advantageous for the team since the player would make less money despite scoring more points per minute.

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Salary vs Scoring Production

*The line added into the plot relates every 0.1 points per minute to 10 million dollars, starting at 0.5 points per minute. This is an ideal relation and line of best fit found when comparing the data. Scoring one whole point per minute would equate to 50 million dollars a year, as per the line, while scoring 0.6 points per minute would equate to 10 million dollars a year. For example: If a player scores 25 points in 25 minutes, the ideal contract for both the team and player would be 50 million dollars per year. There are exceptions: if a player scores 5 points in only 5 minutes, they would not be deserving of a 50 million dollar contract, as there is not a large enough sample size for this player. The line starts at 0.5 points per minute, as someone scoring less than half a point per minute would not be scoring enough to be considered a star player for their team, since the majority of top players score more than 0.5 points per minute, and only top players are being compared here.

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Teams with 30 or less wins(16 teams):

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Points Per Minute vs Salary Analysis: Plot 1

• In these bottom 16 teams, there are more players with contracts better for the team, since they are left of the line. In cases of contracts to the right of the line, these are primarily players whose teams are close to making the playoffs or teams who have had recent success.
• For example, Stephen Curry won 3 championships with the Warriors and signed a large contract, but in the 2019-2020 season, the Warriors and Curry have not had as much success. This is likely due to an injury to Curry.
• RJ Barrett is the only player to the right of the line whose team is not close to the making the playoffs or has not had recent success. He is a rookie, and still has room to improve and develop, so this may grant an exception for the Knicks.
• Devonte Graham, Karl-Anthony Towns, Kyrie Irving, and Damian Lillard are making an ideal amount of money for the rate that they are scoring.

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Teams with more than 30 wins(14 teams):

Points Per Minute vs Salary Analysis: Plot 2

• Successful teams have the same amount of players on the best-fit line as the unsuccessful teams (four). However, they also have more outliers further from the best-fit line, which could show that more successful teams tend to have more overpaid players on the right of the line, and more team - friendly contract players on the left side of the line.
• 3 out of the 4 players on the right of the line are age 30 or older: Jimmy Butler, Lebron James, and Chris Paul. They are likely on the right of the line because they have been in the NBA for a while and have a pedigree that has given them a large salary. They show that there could be a relationship between age and team success.

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Salary vs Scoring Production Summary

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These plots only account for points scored, but there are other variables to consider in order to explain these plots. For instance, injuries may have impacted a few of the players. Additionally, the age of a player could show veteran experience and prior success, which could have led to a higher salary. Lastly, a few players to the right of the line in the plots could be better at rebounding, getting assists, or defense rather than scoring, which these plots analyzed. For example, Chris Paul scores less points per minute than most players, but he has a reputation for being one of the best passers in the NBA, as he has consistently been among league leaders in assists throughout his career.

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Team Wins vs Games Played

Theory: Players further to the top right or bottom left of the plot are more essential to the team’s success.

- Players that play in more games and get less wins may not be capable of being stars for their team. Similarly, if a team has more wins while the player has not played in many games, that player may not be necessary for the team to win. The most ideal relationship would be 1 Game Played = 1 Team Win.

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Teams with 30 or less wins(16 teams):

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Wins vs Games Played Analysis: Plot 1

• More players are clumped to the top for the bottom teams. This is because these players are on unsucessful teams, so they may play in most games, but their team still did not win many games. This plot only shows the bottom 16 teams in the league, so it can be used to evaluate which players are potential stars and which aren’t. Collin Sexton might be less of a star than Brandon Ingram, because Sexton’s team wins less despite the fact that he has played in most games, whereas Ingram has played in most games, and his team has more wins amongst this set of teams.
• Stephen Curry did not play in many games due to injury, but his team also did not win many games, showing he could be essential for his team’s success. Alternatively, Kyrie Irving’s team has won 30 games, while he has only played in 20 games. This could show that his team may be well-rounded and does not always need him to win games. It could also show that his team may have won many games that he played in, but has not been successful when he has not played.

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Teams with more than 30 wins(14 teams):

*This plot is more zoomed in than the previous plot since all players played more than 40 games

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Wins vs Games Played Analysis: Plot 2

• For the top 14 teams, players are more spread out around the middle and top of the plot. This shows that more successful teams may not need players that play in every game to win. They may have a more rounded team that carries some of the burden of the star player so the star player does not have to do all the work to win.
• Giannis Antetokounmpo and Lebron James are further to the right and top, which shows a more direct relationship between games played and team wins than other players. They are widely considered two of the top players in the league, which may explain their positioning on the plot.

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Team Wins vs Games Played Summary

It would be most beneficial for any team to have their star player on the right side of the plot, as it would show that the team gets wins, with or without their star player. On the other hand, a player would want to be on the bottom left or top right, to show their team that they are key to the team’s success and ask for a higher-paying contract. The players on teams with more wins tend to show less of a direct relationship between wins and games played, likely because they have other players to carry the burden, explaining their success. On the other hand, the unsuccessful teams have more of a direct win to games played relationship, which may mean they have to acquire supporting players for their stars.

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Star Player Age, Salary, Team Success

Theory: A younger player is likely to have a smaller salary and be on a team with less wins.

*Salary = size of the dot.

- It is better for the team if the player is closer to the top left of the plot and has a smaller dot size (smaller salary).

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*Pelicans and Kings stars, Brandon Ingram and De’Aaron Fox, have the same age(22), the same amount of wins(28), and they have close salaries, Fox makes 6.4 million, Ingram makes 7.3 million

Teams with 30 or less wins(16 teams):

Age, Salary, Wins Analysis: Plot 1

• From the bottom 16 teams, the teams with the most wins (Nets, Magic, Trailblazers) all have stars over the age of 25 and pay them more than 20 million dollars. Most of the teams with less wins have younger stars (Knicks, Hawks, Cavaliers, Timberwolves) or are stars paid less than 10 million dollars (Pistons). The exception to this is the Warriors. Their star, Stephen Curry, is over 30 years old, and makes more than 40 million dollars. However, his team has the least wins in the league. This can be explained by the fact that Curry has been injured most of the season (Team Wins vs Games Played analysis), and therefore his team has not been as successful.

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Teams with more than 30 wins(14 teams):

Age, Salary, Wins Analysis: Plot 2

• In the top 14 teams, the data is clearer. Teams with older players have larger dots, showing that older players make more money. On the other hand, the younger players make less, and a reason for that could be that younger players sign rookie contracts after they are drafted, and those contracts usually last 4 years, so many players under 25 are on their rookie contracts.
• The Grizzlies’ dot is larger than the Jazz’ dot, which means the Grizzlies pay their star, Ja Morant, more than the Jazz pay their star, Donovan Mitchell. Since both are young players likely on their rookie contracts, we can infer that Ja Morant was drafted at a higher pick than Donovan Mitchell, since rookies make more when they are drafted at a higher pick. Using this, we can also infer that the Raptors’ star, Pascal Siakam, was drafted lower than any of the other players because the Raptors dot is the smallest on the plot, meaning Siakam is paid the least.

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Star Player Age, Salary, Team Success Summary

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These plots show that younger players are paid less, which is likely because they are on rookie contracts. They also show that older stars are usually the ones that make the most money, which can be attributed to their past success. Lastly, they also show that the teams with older stars likely have more wins, which is more evident amongst the plot with the 16 unsuccessful teams. The outliers to these insights could be the result of injuries, or cases where the teams made a bad decision and “overpaid” a player.

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Assists vs Rebounds vs Points

Theory: The ideal star for a team would be further to the right, top, and have a larger dot, meaning they would have more rebounds, more assists, and more points scored per game.

*Points Per Game = size of the dot.

- Players further to the top could be rebounding specialists, players further to the right could be passing specialists, and players with larger dots could be scoring specialists.

- It is more likely to get a well-balanced player who is a high scorer, and is in the middle for assists and rebounds per game.

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Teams with 30 or less wins(16 teams):

RPG = Rebounds Per Game

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APG = Assists Per Game

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PPG = Points Per Game

APG vs RPG vs PPG Analysis: Plot 1

• In this plot of the bottom 16 teams in the league, the best-fit curve rises at first, then dips and evens out. The players at the rise of the curve have more rebounds and less assists (Nikola Vucevic, Lamarcus Aldridge, Karl-Anthony Towns), which shows they are rebounding specialists. So, we can infer that they are taller players that play the Center or Power Forward positions (which require taller rebounding players).
• The second half of the curve, which evens out into an almost straight line, consists of players with more assists than rebounds. These players are likely Point Guards or Shooting Guards who are smaller and specialize more in passing rather than rebounding.

APG vs RPG vs PPG Analysis: Plot 1 (cont.)

• Trae Young is one of the more ideal star players on this plot. He has a large dot, showing that he scores many points, and he is the furthest player to the right, showing his high assist numbers. He is only in the middle for his rebounding numbers, but it is rare to get a player that specializes in all 3 aspects of the plot.
• RJ Barrett, Derrick Rose, and Collin Sexton are the least well-rounded players. Rose is a passing specialist, because he does not get many rebounds or score as many points as Kyrie Irving or Stephen Curry, who are above him in the plot. Sexton does not get many assists or rebounds. He is a scorer, but he does not score as many points as Zach Lavine, the closest player to him on the plot. Barrett scores the least and gets very few assists, while he is in the middle for rebounds, showing that he is the furthest from ideal star on this plot.

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Teams with more than 30 wins(14 teams):

APG vs RPG vs PPG Analysis: Plot 2

• In the plot of the top 14 teams, the best-fit curve dips, rises, dips, then slowly rises again. It is slightly similar to the plot of the 16 unsuccessful teams, but the players are also more well-balanced than the unsuccessful teams’ players.
• The plot is more populated in the center, showing that the stars for the more successful teams are usually well-balanced. Players like Ja Morant, Chris Paul, and Domantas Sabonis, who don’t score as many points as other stars, are closer to the middle of the plot than the least well-balanced players from the unsuccessful teams plot.
• The most ideal player in this plot is Lebron James. He gets high assist numbers, high rebound numbers, and scores many points as well. A case can also be made for Giannis Antetokounmpo and Luka Doncic because both players get more rebounds, but less assists than Lebron, while scoring about the same amount of points. There are more players that could be considered ideal star players on this plot than the unsuccessful teams plot.

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Assists vs Rebounds vs Points Summary

In general, the more successful teams have more well-balanced stars, while less successful teams have more players that specialize in an aspect of the game. An ideal star excels in all 3 categories: points, assists, and rebounds, but this is not always the case. The players that are not clumped in the center of the plots either specialize in one aspect of the game (Nikola Vucevic, Joel Embiid, Ja Morant, Derrick Rose), or they are ideal stars (Trae Young, Lebron James).

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Bonus

Same graph, but colors sorted by position: Centers and Power Forwards are further up, showing they specialize in rebounds, while Point Guards and Shooting Guards are further to the right, showing they specialize in assists. Small Forwards are the most balanced position.

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*More specialists

*Well-balanced players at all positions

Final Analysis

Through these plots, it is apparent some of these players may not be ideal star players for their teams. Stephen Curry is one such player, but he is an exception due to his injury. Lamarcus Aldridge and RJ Barrett are two players that may not be ideal stars for their team. Demar DeRozan may be a better star for the San Antonio Spurs than Aldridge, while Julius Randle may be a better star for the New York Knicks over RJ Barrett. We can compare DeRozan to Aldridge and Randle to Barrett through the Points Per Minute vs Salary plot analyzed first to see if they would be better stars in at least the scoring and salary aspect.

Final Analysis

• In the plot, we can see that Randle makes more money than Barrett, but is also closer to the line of best fit used in the first pair of plots when comparing salary vs scoring production. He scores more points per minute and is closer to being an ideal star than Barrett. So, Randle may be a better star player for the Knicks.
• Derozan is paid slightly more than Aldridge, but scores more points per minute. He is also closer to the line of best fit in the plot, showing he is closer to being an ideal star than Aldridge. So, Derozan may be a better star player for the Spurs.

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Overall Summary of Insights

• Salary vs Scoring Production Both successful/unsuccessful teams have a similar relationship in Points Per Minute vs Salary, but successful teams have more outliers. They have more successful players, but also more overpaid players.
• Team Wins vs Games Played Unsuccessful teams have more players that “carry the burden”: more players with a direct relation between games played and team wins. Successful teams’ players have less of a direct relation between games played and team wins, which could show successful teams are more well-rounded and have more players that can carry some of the burden of the top player.

Overall Summary of Insights (cont.)

• Star Player Age, Salary and Team Success Both successful/unsuccessful teams pay older players more, likely because younger players are on rookie contracts. Among the unsuccessful teams, older players are more likely to be stars for teams with more wins. Among the successful teams, it is a mix of older and younger players who star for teams with more wins.
• Assists vs Rebounds vs Points Successful teams are more likely to have stars that have a balanced game in terms of assists, rebounds, and points. Both successful/unsuccessful teams have stars that specialize in an aspect of the game, either rebounding, passing or scoring. However, specialists for successful teams are often better at their aspect than specialists for unsuccessful teams.

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Conclusion

In general, a seemingly obvious insight can be derived: successful teams have more ideal stars than unsuccessful teams. Diving deeper, the plots can be used to decide which players are worth getting paid, which players can be considered stars, and which teams are in the best position to succeed with their star. However, there were caveats to this analysis. Offensive statistics were used in this analysis, not defensive, so it is possible some players that did not come out favorable here are valued better defensively. Additionally, player stats could be hurt by injuries, and overall team wins could also be hurt by the fact that multiple top players on that team were injured. The Golden State Warriors are a good example of that, as they won the least games in the league because star Stephen Curry was injured the majority of the season, and their next best player, Klay Thompson, was also out the entire season. This was shown by the fact that the Warriors and Curry were outliers on multiple plots. Overall, injuries and the fact that primarily offensive stats were used could have skewed some of the insight, but the data is still an effective way of determining player value from their offensive production.

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