The Value of Rocket Fuel: Is Jalen Green a Superstar?

Every NBA franchise starts the season optimistic about their plans for the season. At the bottom, there are rebuilding franchises trying to avoid another year in the NBA lottery, then you have the middle of the pack teams trying to fight their way into the playoffs or reach a higher seeding, finally you have the real contenders. None of these goals are achievable without some serious star power. Basketball is a team sport, but every team is on the lookout for the one guy who can shift the priorities of their franchise from rebuilding to championship aspirations. Enter Jalen Green, the Houston Rockets’ first round pick in 2021.

Green reaching the label of superstar could shift the fortunes of a franchise that has been floundering since the departure of James Harden, who oversaw the most prosperous time in Rockets history since their best player ever, Hakeem Olajuwon. The Rockets will look to see similar success under Jalen Green’s tenure. But the Rockets are a franchise worth over $3.2 billion dollars. So how do they know if they got a franchise cornerstone, or wasted their precious draft pick on a certified bust, and should look to cut their losses sooner rather than later?

Libraries Used:

library(dplyr)

library(ggplot2)

library(ggthemes)

library(hexbin) - to help draw the court plot.

library(RColorBrewer) - provides color palettes for the shot chart legend

library(plotly)

library(nbastatR) - provides real time NBA data scraped from the official NBA site daily.

library(cowplot) - for the shot chart

library(tidyverse)

library(jsonlite) - to help read in the NBA data.

library(httr) - to help read in NBA data.

library(scales) - to help calibrate the limits, breaks, and labels quickly on the shot charts.

library(ggimage) - for plotting the head shots of the players.

Well, since the genesis of the analytics era in the NBA, there has been a boomtown in player assessment. Millions have been spent on player tracking and data analysis to find trends that can eliminate the guesswork when making these franchise defining decisions at the top of the draft. The NBA provides a host of resources to let even the average fan try their hand at cracking the code. The NBAStarR library scrapes the NBA site and provides a lengthy dataset of every player that ever played in the league’s stats. This can get as fine as to the percentage they shoot from right or left sides of the court, and what kind of shots they take from those areas. I then cleaned that dataset to the first two years of Jalen Green and some players of similar archetypes. Lastly, I added a column for their headshots to replace the traditional plot points.

Below we have Jalen Green’s first two year average points per game compared against the first two year scoring totals of some players that had the same alpha-scorer archetype as undrafted prospects, most of whom went on to do very well in the league.

##    Season    Name  Tm  Lg Pos  G GS   MP  FG  FGA   FG. X3P X3PA  X3P. X2P X2PA
## 1       1    Beal WAS NBA  SG 56 46 31.2 5.0 12.3 0.410 1.6  4.2 0.386 3.4  8.1
## 2       2    Beal WAS NBA  SG 73 73 34.7 6.6 15.7 0.419 1.9  4.7 0.402 4.7 11.0
## 3       1  Booker PHO NBA  SG 76 51 27.7 4.8 11.4 0.423 1.3  3.8 0.343 3.5  7.6
## 4       2  Booker PHO NBA  SG 78 78 35.0 7.8 18.3 0.423 1.9  5.2 0.363 5.9 13.2
## 5       1 Oladipo ORL NBA  PG 80 44 31.1 4.9 11.7 0.419 0.9  2.8 0.327 4.0  8.9
## 6       2 Oladipo ORL NBA  SG 72 71 35.7 6.6 15.1 0.436 1.2  3.4 0.339 5.4 11.6
## 7       1   Green HOU NBA  SG 67 67 31.9 6.1 14.2 0.426 2.3  6.8 0.343 3.7  7.4
## 8       2   Green HOU NBA  SG 13 13 34.2 7.7 18.1 0.426 3.0  8.0 0.375 4.7 10.1
## 9       1   Kobe  LAL NBA  SF 71  6 15.5 2.5  5.9 0.417 0.7  1.9 0.375 1.8  4.0
## 10      2   Kobe  LAL NBA  SF 79  1 26.0 4.9 11.6 0.428 0.9  2.8 0.341 4.0  8.8
## 11      1  Murray DEN NBA  SG 82 10 21.5 3.6  8.9 0.404 1.4  4.2 0.334 2.2  4.7
## 12      2  Murray DEN NBA  PG 81 80 31.7 5.9 13.1 0.451 2.0  5.4 0.378 3.9  7.7
## 13      1  Turner PHI NBA  SG 78 14 23.0 2.9  6.8 0.425 0.2  0.6 0.318 2.7  6.2
## 14      2  Turner PHI NBA  SF 65 20 26.4 4.0  9.1 0.446 0.2  0.8 0.224 3.9  8.3
## 15      1 Waiters CLE NBA  SG 61 48 28.8 5.5 13.4 0.412 1.0  3.3 0.310 4.5 10.0
## 16      2 Waiters CLE NBA  SG 70 24 29.6 6.1 14.2 0.433 1.3  3.6 0.368 4.8 10.6
##     X2P.  eFG.  FT FTA   FT. ORB DRB TRB AST STL BLK TOV  PF  PTS
## 1  0.424 0.477 2.2 2.8 0.786 0.8 3.0 3.8 2.4 0.9 0.5 1.6 2.0 13.9
## 2  0.426 0.479 2.0 2.6 0.788 0.7 3.0 3.7 3.3 1.0 0.2 1.8 2.1 17.1
## 3  0.464 0.480 2.8 3.4 0.840 0.4 2.1 2.5 2.6 0.6 0.3 2.1 3.0 13.8
## 4  0.447 0.475 4.7 5.7 0.832 0.6 2.6 3.2 3.4 0.9 0.3 3.1 3.1 22.1
## 5  0.448 0.458 3.1 4.0 0.780 0.5 3.6 4.1 4.1 1.6 0.5 3.2 2.6 13.8
## 6  0.464 0.474 3.6 4.4 0.819 0.7 3.5 4.2 4.1 1.7 0.3 2.8 2.6 17.9
## 7  0.502 0.508 2.8 3.5 0.797 0.5 2.9 3.4 2.6 0.7 0.3 2.0 1.5 17.3
## 8  0.466 0.509 3.4 4.3 0.786 0.6 3.4 4.0 2.8 0.8 0.3 2.7 1.6 21.8
## 9  0.437 0.477 1.9 2.3 0.819 0.7 1.2 1.9 1.3 0.7 0.3 1.6 1.4  7.6
## 10 0.456 0.469 4.6 5.8 0.794 1.0 2.1 3.1 2.5 0.9 0.5 2.0 2.3 15.4
## 11 0.466 0.483 1.3 1.5 0.883 0.5 2.1 2.6 2.1 0.6 0.3 1.4 1.5  9.9
## 12 0.502 0.529 2.8 3.1 0.905 1.0 2.7 3.7 3.4 1.0 0.3 2.1 2.1 16.7
## 13 0.435 0.439 1.3 1.6 0.808 0.5 3.4 3.9 2.0 0.6 0.2 1.0 1.8  7.2
## 14 0.466 0.455 1.1 1.7 0.676 0.4 5.4 5.8 2.8 0.6 0.3 1.6 1.8  9.4
## 15 0.446 0.451 2.6 3.5 0.746 0.4 2.1 2.4 3.0 1.0 0.3 2.0 1.9 14.7
## 16 0.455 0.479 2.3 3.4 0.685 0.5 2.3 2.8 3.0 0.9 0.2 2.2 2.2 15.9

Unfortunately, there is a lot of noise around that graph. There are a lot of factors that play a part in the PPG stat. A high PPG doesn’t necessarily mean they’re scoring efficiency. Nor does it take into account the disparity of 3PT shots and 2PT shots. So for the alpha-scorer archetype what are the most important factors? Well for the purposes of this we’re going to look at 2PT efficiency. When the game is close and the defense is tight, you want your closer to be able to make tough shots when they’re run off the line. So let’s take a look at the number of attempts from within the arc, against the number of makes for the same group of players over the same amount of time.

## Assigning NBA player dictionary to df_dict_nba_players to your environment

However, this can also be deceiving as 2PT efficiency takes a large part of the court into account. An open lay-up and a contested jumper are weighed the same. Using a template I found created partly by Todd Schneider and a few tweaks by Owen Phillips, I was able to create a shot chart that allows you to see the distribution of shots on the court, and the efficiency of those shots. So let’s take a look at the shot charts to assess the validity of our work so far.

As you can see, Jalen is hovering around league average on most of the court except the middle mid-range. Obviously he could shoot better from deep, and at the rim, but for a 2nd year player these numbers are very good. Yes, there are cold areas, but they don’t encompass large parts of the graph that are highly valued. Mid-range shots are tough and teams have strayed away from them more and more as the data suggests 3’s and lay-ups are the most efficient shots in basketball. In those areas, Jalen Green is doing above average for his age and usage. To back this up we’ll take a look at some of his comps to get an idea of where he stacks up.

Well there you have it, yes, guys like Bradley Beal and Jamal Murray have hotter areas. But they also have very cold areas that span large parts of the court. Kobe’s chart shows a lot of potential, someone who can score all over the court, and Jalen’s chart isn’t too far off. Over time, he could go either way, but the numbers are very encouraging.

But why does any of this matter? This is America after all, if you aren’t making money, what are you doing? And if you can snag a legitimate superstar in the vein of Kobe Bryant it can mean BILLIONS of dollars of value for your franchise. From merchandise, to ad revenue and season tickets, a star player can inject liquidity into a franchise that is extremely rare. Let’s take a look at the value of the Warriors, Cavaliers, and Bucks over time. The Warriors were purchased for $450 million in 2010 and in October of 2022, after 4 championships, Forbes valued their franchise at $7 billion dollars. After Lebron left the Cavaliers, the team lost $121 million in value, only to rise by $400 million on his return. Similar trends occurred once the Bucks became contenders, and took a nice sized jump after their fist championships in years. Powered by one Giannis Antetokounmpo.

In conclusion, the value of a superstar can’t be understated. It is the single most important indicator to rebuild success, and multiple superstars can increase the value of a franchise by significant multiples. With the eye test, and his significant improvement, Jalen Green has cemented himself at the top of the young players to watch list going forward, and seems to be separating himself from that pack. The sky’s the limit for the young guard, and the Rockets couldn’t be happier with their pick. Now the question becomes, can they get another?