Why I’d Rather Have Bradley Beal On The Celtics Than Damian Lillard

With the Boston Celtics being eliminated in the first round by the Brooklyn Nets this postseason, it’s clear that changes must be made in Beantown. Damian Lillard and Bradley Beal are two names swirling around the NBA parts of the Twitterverse as potential targets for the Celtics. Those two players would be acquired very differently and would lead to very different situations in Boston. This article will delve into my reasoning as to why I’d rather have Beal on the Celtics than Lillard. So, without further ado, let’s get into it!

Reason 1: Jayson Tatum and Bradley Beal are friends

Bradley Beal and Jayson Tatum have been friends for over a decade. They met when a 6 (ish) year-old Beal was babysitting a toddler-aged Tatum. Beal’s mother and Tatum’s mother were great friends, which opened up a friendship for the two sons. Growing up in St. Louis together, Tatum and Beal attended high school together and played basketball together growing up. Their old trainer, Drew Hanlen, speaks very fondly of the relationship between Tatum and Beal since they were teenagers. Throughout each of their tenure’s in the NBA, they’ve maintained a close comradery despite always being on different teams. When asked, both Beal and Tatum have raved about the idea of them being able to team up. This past All-Star Break, they finally got that chance. Sort of. They were able to play together on team Kevin Durant, but I suspect that they are interested in a longer-term union. They’ll be playing with each other this summer in the Olympics for Team USA, which is known for creating relationships. DeAndre Jordan, Kevin Durant, and Kyrie Irving plotted their team-up in Brooklyn during their stint on Team USA together, so it makes sense that something like that can happen (if it hasn’t been in the works already).

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Reason 2: Bradley Beal is easier to acquire than Damian Lillard

While Damian Lillard is rumored to be unhappy in Portland and could be available by trade, the asking price for him would be immense. Lillard is a better player than Beal, and therefore more difficult to acquire via trade. (I’ll go through my mock trade ideas later in the article) Mock trades in which the Celtics trade for Lillard would include the Celtics parting with a combination of valuable assets including (some, not all of) Jaylen Brown, Marcus Smart, Robert Williams III, Payton Pritchard, and Aaron Nesmith, along with a massive slew of draft picks. Mock trades in which the Celtics trade for Beal involve the Celtics sending out a vast amount of different packages, but none of them require Boston to completely blow up their core in order to complete the trade. Beal also becomes an unrestricted free agent in the 2022 offseason, the same offseason in which the Celtics have a max slot available for someone just like Beal. If the Celtics wait a season, they could get Beal without having to sacrifice any assets at all! Some people point out that waiting to sign Beal is not as smart as trading for Lillard immediately, but I think keeping their core together and retaining depth while waiting a season for Beal is the better choice. I’d rather have Jayson Tatum, Jaylen Brown, Bradley Beal, Marcus Smart, and Robert Williams III than Jayson Tatum and Damian Lillard.

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Reason 3: Damian Lillard’s contract

Right now, Damian Lillard’s contract is pretty fair given his insanely high level of production. But, his contract on the Celtics doesn’t make sense given Boston’s timeline. Tim Bontemps on Twitter, the year that Jayson Tatum decides his future with the Celtics after his current contract extension runs out is the same year that Damian Lillard will be 34 years old and making $50 million a year. That’s not a good financial situation to be in. Tatum needs to be at the forefront of the team, and Lillard’s contract will most likely be widely regarded as one of the worst in the league. Lillard right now is an excellent player, but for the future of this Celtics team, Bradley Beal makes more financial sense.

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Reason 4: It’s supposed to be a big 3, not a big 2!

Jayson Tatum is obviously the cornerstone of this Celtics franchise, as he’s nearly a top 10 player in the NBA. Being paired with Jaylen Brown elevates not only Tatum’s performance but Brown’s too, so breaking them up to acquire Damian Lillard or Bradley Beal wouldn’t make as much sense as just waiting until the 2022 offseason to sign Beal with the max slot that Boston will have. The point of signing Bradley Beal is to form a big 3 in Boston, but trading Brown for Beal (or Lillard) would defeat the purpose of acquiring a 3rd star. My ideal scenario would be if Boston signed Beal next offseason, retaining Brown and Tatum.

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My ideal move for the Celtics

With many possible routes that lead to Beal in Boston, I have a couple of my best-case scenarios to get Beal on the Celtics.

Move #1 (my first choice): Celtics sign Bradley Beal in the 2022 NBA Offseason.

This path is really straightforward. Boston will have a max slot to sign a high-caliber free agent in the 2022 offseason if they choose to do so. If they do intend on exercising that slot (which they most definitely will), one of the best candidates available (that would realistically sign with the Celtics) at that time will be Beal. With players like Kevin Durant, Kawhi Leonard, Stephen Curry, Kyrie Irving, James Harden, and Jimmy Butler potentially hitting the open market, Beal is a guy who can fly under team’s radars and be signed by the Celtics.

Move #2 (my second choice): Celtics trade for Bradley Beal.

If I were Boston’s President of Basketball Operations Brad Stevens, my trade package for Beal would look something like this:

Washington Wizards receive: Al Horford, Romeo Langford, Moses Brown, 2023 first-round pick, 2025 first-round pick, 2027 first-round pick, 2022 second-round pick (all FRP’s via Boston), 2022 second-round pick (via Charlotte).

Boston Celtics receive: Bradley Beal.

This is a mock trade that I’ve workshopped for some time now. The Wizards aren’t going to be a contending team anytime soon, so they are looking for young players and lots of draft picks. This trade incorporates both of those things. Romeo Langford and Moses Brown are two promising young pieces to aid Washington in their rebuild. But the real gold mine in this deal is the 3 first-round draft picks over the next 6 seasons that Washington receives, including two second-round selections in 2022, all unprotected. And for Al Horford, he’s thrown in the deal for salary matching purposes.

For Boston, this deal is a home run. They form their big 3 and solidify their core. And the best part is, they don’t have to part with Jaylen Brown.

Move #3 (my third choice): Celtics trade for Damian Lillard.

Portland Trail Blazers receive: Jaylen Brown, Robert Williams III, Romeo Langford, Aaron Nesmith, 2023 first-round pick, 2025 first-round pick, 2027 first-round pick (all FRP’s via Boston), 2022 second-round pick (via Charlotte), 2023 second-round pick (via Oklahoma City).

Boston Celtics receive: Damian Lillard.

In this trade, the Trail Blazers fully commit to a rebuild and give Damian Lillard a better chance to win a championship. They receive tons of young talent, with all-star Jaylen Brown and rising force Robert Williams III headlining Portland’s haul. They also receive a massive slew of first-round picks, and a couple of second-rounders as the metaphorical cherry on top. With this trade, Portland would probably deal C.J. McCollum as well since one doesn't really make sense without the other, but that’s a discussion for another post.

While this is not my ideal scenario for the Celtics, it’s not like it’s a bad one. Lillard is a marvelous talent, and fills a big hole at point guard for Boston. He’s a superstar in this league, but the problem here for Boston is that they part with Jaylen Brown and Robert Williams III. Boston would lose this trade, seeing as they’d decrease their depth and sacrifice a wonderful fit between Brown and Jayson Tatum.

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All in all, neither of these situations would be particularly bad for the Celtics. I might be arguing for what would be considered the unpopular opinion in this scenario, but I feel as though Bradley Beal would be a better fit for Boston, while also being easier to acquire.

Using Play-by-Play Data to Examine Volume and Shot Difficulty in the NBA

There are two main aspects to shooting and scoring in the NBA: volume and efficiency. The balance of the two is an interesting topic for debate, and it's hard to evaluate a scorer without both. But is volume really necessary? Once you cross a qualifying mark and eliminate the majority of sample size issues, can efficiency be the sole determiner? One of the reasons the answer is a relatively firm "no" is that the two should be inversely correlated. Scorers who have a larger portion of a team's offensive load should have to take more difficult shots more often, dragging down their overall efficiency. So, what happens if we put it to the test using play-by-play data?

Introduction

Unfortunately, there are a few issues that make testing this philosophy difficult. The first is that better shooters are likely given more volume by their coaches. This presents a survivorship bias of sorts. The second is that player-tracking data about defender distance that would be very valuable to answer this question isn't publicly available. The second problem doesn't have a real solution, so instead, I'll be using shot distance. Because of large differences in playstyle that would confuddle the data, I'll only be using three-point stats. As a Blazers fan, I've seen Damian Lillard launch many a thirty-footer because that's where the open shots are for him. But does this hold as a statistical trend?

The first problem results in a cleaner but more complicated solution. Essentially, the plan is to find the difficulty of a shot at each distance by looking at how players shoot there compare to their average shot. Then, I'd take this data and use it to determine the average difficulty of each NBA's players shots. Finally, I'd graph it with volume to see if a trend emerged. It's a bit complicated, so I'll explain with a simple example using two players from two shot distances. Capital M will represent a make, and lowercase m a miss.

Player A (7 shots): 25M, 25m, 25m, 25m, 25m, 25m, 35m

Player B (11 shots): 25M, 25M, 25M, 25m, 25m, 25m, 35M, 35M, 35m, 35m, 35m

As I mentioned above, we can't simply take the percentage from each distance. This is a problem because of something known as Simpson's paradox. It deals with how groups of data can interact with the data as a whole. In this example, taking the raw 3P% would show that shooting from 25 feet (4/12) and 35 feet (2/6) is equally efficient. Both player A and player B, though, individually shoot better from the shorter distance. How is this possible? Well, because the more efficient shooter (B) is shooting the longer shots at a higher rate, it skews the data. To solve this, we need to compare each shots' percentage (always either 100% or 0%) to the expected rate (the player's overall 3P%). 

Player A (7 shots, 14.3%): 25 (+85.7), 25 (-14.3), 25 (-14.3), 25 (-14.3), 25 (-14.3), 25 (-14.3), 35 (-14.3)

Player B (11 shots, 45.5%): 25 (+54.5), 25 (+54.5), 25 (+54.5), 25 (-44.5), 25 (-44.5), 25 (-44.5), 35 (+54.5), 35 (+54.5), 35 (-44.5), 35 (-44.5), 35 (-44.5)

Then, we need to take the average conversion rate over expected from each distance:

25 feet: +3.683%

35 feet: -6.467%

After that, we can find the average difficulty of each player's shots:

Player A: +2.233%

Player B: -0.930%

We can then stick these points on a graph, and we have our answer! For this example, there is a perfect negative correlation between volume and shot difficulty.

Obviously, it was going to be much more difficult on a larger scale. The most efficient path would likely require some coding. However, because I don't know any coding languages, I decided to work in Excel and learn as I went along if I didn't know the particular formula I needed. This is the end result of a lot of trial and error. It wasn't as easy as it sounds, nor was my process as clean as it is written. If you aren't interested in the specifics, there will be a TL;DR at the end of the section. 

The Process

The first step was to download the play-by-play data. This would not have been possible without coders writing open-source scripts that they use to "scrape" data from various sites. I used play-by-play data released by the user schmadamco on Kaggle. The data is directly from Basketball-Reference, and I used the latest complete season (2019-20). Of course, it wasn't in the exact format I needed. There were no functions that directly showed whether or not a shot was a three-pointer, and the function for makes/misses was still in a text format, which isn't very good to work with. The solution to both issues was simple, as I could simply write a function asking Excel to check a cell and see if its contents were "3-pt jump shot," for the first and "make" for the second. If so, it would return the number one and, if not, zero.

Next, I sorted the sheet based on whether or not the value for a three-pointer was one. I copied out all of the three-pointers and the values for who the shooter was and whether the shot was made. At this point, I would need to know the three-point percentage of each player. Instead of having to manually plug this data in another way, though, I could thankfully use a feature of Excel known as a pivot table to get the work done for me. Pivot tables allow you to organize data based on other data. For this, I needed for it to take the average of the "make" value for each player. Because all makes are shown as a one and all misses a zero, this formulates their 3-point percentage.

Even with the three-point percentages, though, it would still be a lot of work to manually plug in the value to every shot. Thankfully, there is an Excel command that takes a cell, references a table, and finds an identical cell in the left-most column of that table. Then, it takes the value from the same row of a column that you select. Doing this, I could add a three-point percentage value for every shot based on the player who took it. Here is a small slice of the data I now had:

Next, like the example, I compared the make value to the 3P% of that player. This would form 3-point Percentage Over Expected or 3POE. As in the example, it will either be one or zero minus the player's 3P% so it looks weird, but it averages out nicely in the long run. After finding the 3POE for every shot, I used another pivot chart to take the averages by distance (I also adjusted every distance above 47 feet or half-court down to 47 to help with sample size issues). Then, I smoothed it by taking the mean of the scores for each distance and the two distances directly bordering it. Visualized in this chart are both the raw and smoothed values:

It definitely makes intuitive sense, which is great. Shots get progressively more difficult as players move away from the basket until a steep drop-off when players go from true shots to what is likely primarily heaves. Because these shots prioritize luck to skill, the percentages plateau. While this range is noisy even in the smoothed version, I decided to leave it as-is because these shots shouldn't be statistically significant in moving a player's degree of difficulty, apart from super small sample sizes.

After that, I had to link the smoothed difficulty to the distances on a shot-by-shot level. To do this, I repeated the same process I used to link the 3P% to each player. I used the exact same formula in the exact same way, and it ended up giving the difficulty for every distance, which would be used in the next step to determine the overall difficulty of a particular players' shots. Here is a slice of what that looked like:

I was now just one pivot table away from being able to graph the final result. All I did was chart the average difficulty by the player along with their volume of shots. It was now time to graph the final result. Because I am more comfortable with the interface and slightly prefer how the graphs look, especially scatter plots with a bunch of points, I copied the data over to Google Sheets. First I graphed every player and their average shot difficulty, before eliminating players who took less than 72 threes (one per game for the median team) so that the data could be more easily understood. Here are both charts:

TL:DR: I downloaded play-by-play data then formatted it so it was usable. Then I got rid of all the data points that weren't attempted threes and used a feature of Excel to calculate every player's 3P%. After that, I added a 3P% for every shot based on the player who took it and compared the actually shooting percentage (either 100% or 0%) to that. I took the average of what I just calculated for each distance to decide the difficulty of a shot from there. Then, I calculated the average shot difficulty by player and used it to make the charts you can see above.

Results

I was really happy with how this turned out. You can see that there is an imperfect but clear trend towards more difficult shots for high-volume shooters, proving my hypothesis. 59.5% of players who shoot less than 200 threes have an average shot difficulty higher than zero (easier than average); this number drops to 53.3% for those shooting 201-400, and all the way to 42.6 for players who shot more than 400 threes in 2020. Overall, more than 55% of players shot easier than average. This may not make sense, but it's similar to the difference between medians and means: the averages are different when averaging players than averaging shots because much of the shooting load is handled by a select few. 

The data also supports the consensus about certain players around the league. For example, the two qualifiers shooting the most difficult shots were Trae Young (-2.37%) and Damian Lillard (-2.00%). They both love to launch deep threes and highlight a general trend visible in the data. Players that are the focal points of their offenses are likely to have a higher difficulty rating, regardless of how many shots they take (at least among those who already have a high shot volume). By manually searching the USG% in 2020 of players who took at least 500 threes that year, we can build this chart:

Even without using defender distance, which would probably show the trend even better, we can see that there is an obvious connection between load and shot difficulty. The more of an offensive load a player handles, the harder, on average, their shots are. This is unlikely to hold for players that don't take a ton of threes, as their gravity would focus almost entirely on defender distance. Intuitively, this trend makes sense for high-volume shooters, who often fall into two categories. There are the Damian Lillards of the NBA who have offenses revolving around getting them open anywhere downtown. Then, there are players like Duncan Robinson who shoot a ton of efficient shots because they benefit from the gravity of the true stars in their offense.

There are two lower-volume outliers that stand out because of how far they are from everyone else. The first is Montrezl Harrell. Of the twelve players that took more difficult threes on average than Trae Young, eleven of them took ten shots or fewer. Harrell, though, took twenty-three and still finished fifth among those twelve. Of Harrell's shots, though, ten (43.5%) were from 40+ feet. Damian Lillard, who is not only known for launching deep threes but also took more than thirty times as many shots as Harrell, took just five. You have to respect players who are willing to let it fly from deep when their team would benefit from it.

On the exact opposite end of the spectrum is P.J. Tucker. This isn't exactly surprising, as Tucker is known to be a corner-three specialist. But just how much he stands out is still kind of crazy. Because of how it's constructed, the maximum possible 3POE is 3.1%. By going one foot farther from the basket that changes to 2.6%. If you are just two feet behind the line in the corner (equivalent to being right behind the line anywhere else), you would end up with an average shot difficulty of +1.89. Tucker's is +1.92. That takes serious scheming and is a great example of the extremities of the Morey-era Rockets.

One final thing I wanted to do was adjust the 3P% of all of these players based on their 3POE. What 3POE essentially measures is how much above or below average any given player might shoot if they took the same shots. It isn't perfect, but by subtracting a player's 3POE from their actual 3P%, we can roughly adjust for difficulty and get an idea of what a player might shoot with a perfectly average range of shots. Subtraction is used because negative scores mean harder shots and therefore those players should be given a boost. Of course, this is far from an objective ranking of the best three-point shooters, but I think it might be a slightly better indicator than normal 3P%.

All things considered, it's a relatively minor adjustment. There is little deviation from the real 3P% to the adjusted 3P%, and this is once again primarily because I'm only considering shot distance. Players shooting this many threes are unlikely to deviate too far from an average distance. However, players on the extreme ends see some relatively major re-calculations. Trae Young goes from a below-average shooter (40th percentile) to a good one (66th). Damian Lillard goes from great (83rd) to elite (94th). P.J. Tucker sinks to a flat-out bad shooter (18th) when the unadjusted version thinks he's alright (38th). In total, 54 of 167 players see their percentile adjusted by at least five points. The rest stay about in the same place.

None of this is definitive, and it didn't make me wildly reconsider how the NBA works. However, I think it was an interesting look at both answering my original question and applying raw data to elegantly solve a problem. There was a clear trend in the direction I thought there would be, which is always nice, and it supported other basic intuitive ideas that I and others have about the NBA. It was definitely cool to go from a long incomprehensible list of plays to interesting charts that let me visualize certain trends. Overall, this was a great experience and something I may try again with a different problem.