We are down to 4 teams in the playoffs, which means 26 teams and their fan bases are anxiously looking forward to the draft. As most know, I’m a huge Oklahoma City Thunder fan. As I look at the many possible places for improvement, one that immediately pops out is our perimeter shooting. The Thunder had the sixth worst 3-pt shooting percentage in the league at 33.1%. However, drafting a player that is a pure sharpshooter isn’t enough; in today’s league, a wing player needs to be sound in both shooting and perimeter defense—a role often termed as a “3 and D” player. In this blog post, I have created two separate regression models to forecast a player’s catch-and-shoot ability and defensive rating. This first blog post will examine a player’s catch-and-shoot ability.
Data Collection:
In both regression models, the sample size chosen was completely random. I picked 49 players from the 2013 draft and onwards, looking at both their college and draft combine measurement stats. The table below shows the players that were studied:
Kentavious Caldwell-Pope | Allen Crabbe | Michael Carter-Williams | Tim Hardaway Jr | Victor Oladipo | CJ McCollum | Trey Burke |
Otto Porter | Reggie Bullock | Andre Roberson | Tony Snell | Robert Covington | Zach Lavine | Jordan Clarkson |
Aaron Gordon | Marcus Smart | Elfrid Payton | Doug McDermott | T.J. Warren | Joe Harris | Rodney Hood |
Shabazz Napier | Devin Booker | Rondae Hollis-Jefferson | Pat Connaughton | Terry Rozier | Cameron Payne | TJ McConnell |
Kelly Oubre | Delon Wright | Denzel Valentine | Ron Baker | Malcolm Brogdon | Tyler Ulis | Taurean Prince |
Patrick McCaw | Josh Hart | Justin Jackson | Kyle Kuzma | Derrick White | Frank Jackson | Dillon Brooks |
Donovan Mitchell | Jalen Brunson | Kevin Huerter | Aaron Holiday | Allonzo Trier | Josh Okogie | Landry Shamet |
The combine and college stats (sources are NBA.com/stats & sports-reference.com/cbb respectively) that I factored in as independent variables were as such:
- Body Fat (%)
- Hand Length (inches)
- Hand Width (inches)
- Standing Reach (inches)
- Weight (lbs)
- Lane Agility (seconds)
- Shuttle run (seconds)
- Height (inches)
- Wingspan (inches)
- Years in College
- College Average Career 3PT%
- College Average Career FT%
- College Average Career 3Par
- College Average Career TS%
- College Average Career eFG%
Finally, the dependent variable—the variable of interest—was a player’s average career catch-and-shoot 3PT% in the NBA, provided by NBA.com/stats.
Creating a Regression Model
I ran the regression for a first iteration. I set my alpha level for my model at 10%. What does this mean? It means any independent variable that returns a p-value less than 10% are deemed as significant variables for the model. It essentially means that if a variable (i.e. College Average Career FT%) returns a p-value of 5% then that variable is deemed as an important piece of the puzzle for forecasting a player’s catch-and-shoot ability in the NBA. If a variable exhibits an alpha level at or slightly above 10%, then that variable is subject for further examination rather than being immediately thrown out. If it makes logical sense to include that variable, then that variable is still included. For example, pretend that I’m forecasting Coachella’s attendance numbers and a variable (like weather) returns a p-value of 15%. Although it’s not below 10%, I may still try to include it because it makes intuitive sense that if the weather is bad, then attendance numbers may be dismal—conversely if the weather is great, then attendance numbers may be great.
OK! Enough nerd talk. After the first iteration, these variables were deemed as outside my alpha level of 10%:
- Body Fat (%)
- Hand Length (inches)
- Hand Width (inches)
- Standing Reach (inches)
- Weight (lb)
- Years in College
- College Career 3Par
The next step is to re-do the regression taking the above variables out. Once again, we determine if our variables in our second iteration meet the alpha level of 10%. These variables were ruled out after the second variable:
- Shuttle Run
- College Career TS%
- College Career eFG%
I then repeat the practice for a third iteration with the remaining variables. Here’s where it gets interesting. The variables height; wingspan; college career 3PT%; and college career FT% all meet the alpha level of 10%. The variable lane agility exhibits an alpha level of 15%. This model gives me a R-squared value of 41%. [A brief aside for those that are unfamiliar with “r-squared”: to put it simply, r-squared is essentially how much of the puzzle has the model figured out. An r-squared of 41% means that the model has figured out 41% of the puzzle, while 59% is still waiting to be solved. An r-squared value of 41% isn’t great, but nonetheless, these variables are still significant pieces to the puzzle.]
Back to the lane agility variable. In one sense, it could make sense to include lane agility as lane agility is a combination of how a player changes direction; how fast that player changes directions; along with how fast the player is. This could indicate how a player moves without the ball as the player comes off screens. However, here I choose to not include it (shoot me an email if you want to understand my rationale for not including it).
A final regression iteration is run without include the lane agility variable. All of my variables now exhibit less than the 10% alpha level threshold and a r-squared value of 38.2%. Once again, further improvements to the model could be made in the future to raise the r-squared value. Nonetheless, we have our final model:
where:
Interpreting the Results:
Along with making sure that the variables meet the alpha level threshold, it’s good practice to rationalize why each variable may influence our dependent variable: catch-and-shoot 3PT%. Let’s run through each variable:
- Height: The sign in front of our height variable is positive—this means that a player is a better catch-and-shoot shooter if that player is taller. This makes logical sense. The taller a player is, the likelier that player is to see over the defender (and his hand) when shooting the ball.
- Wingspan: The sign is negative in front of our wingspan variable. This also makes sense. Every shooting coach mentions that the more compact the shooter’s form, the better. Longer wingspans make that much harder. Furthermore—and this is just pure subjective conjecture—the longer a player’s wingspan, the likelier it is for a player’s shooting arc to be flat.
- College Career 3PT% & College Career FT%: These obviously make sense to be positive.
Perhaps more interesting is that height and wingspan actually play a larger role than a player’s college shooting résumé. To examine, let’s create an “average” shooting guard: 6’6” with a 6’9” wingspan who shot an average of 35% from the 3’s and 70% from the free-throw line in college. Plugging in these variables, we obtain these results.
The forecast shows the player to have a catch-and-shoot 3PT% of 34%, but more importantly, observe the impact of a player’s physicals. The total magnitude of height and wingspan are 58.828 and -37.017, respectively, while the college shooting percentages are much lower than that in total magnitude.
Forecasting 2019 Shooter’s 3PT Catch-and-Shoot Ability:
Now, the fun part. The NBA combine results are slowly being shared with the public online. Using the above regression model, I was able to forecast some of the best 3PT catch-and-shoot shooters in this draft [note: combine results are incomplete, so an update to this post will be available later. Furthermore, some of these results are using unofficial height & wingspan data]. These are picks that are within the range for the Thunder’s first round selection. A full table of all available players will be in the subsequent update.
Player |
College | Forecasted NBA
Catch-and-Shoot 3PT % |
1. Tyler Herro |
University of Kentucky |
42.29% |
2. Cameron Johnson |
University of North Carolina | 40.64% |
3. Ty Jerome |
University of Virginia
[Go Hoos!] |
39.99% |
4. Louis King |
University of Oregon |
37.84% |
5. Coby White |
University of North Carolina |
37.74% |
6. Admiral Schofield |
University of Tennessee |
36.92% |
7. Nickeil Alexander-Walker |
Virginia Tech |
36.61% |
8. Chuma Okeke |
Auburn | 36.35% |
9. Keldon Johnson |
University of Kentucky |
35.77% |
10. Matisse Thybulle |
University of Washington |
34.54% |
Before jumping into any conclusions about drafting Tyler Herro, be sure to check out the second part of the blog which will forecast each player’s defensive rating!
Updated table of this year’s wing players based on combine results:
Player |
College | Forecasted NBA Catch-and-Shoot 3PT% |
Tyler Herro | University of Kentucky |
42.3% |
Cameron Johnson |
University of North Carolina | 40.6% |
Ty Jerome |
University of Virginia | 40% |
Kyle Guy |
University of Virginia | 38.9% |
Jordan Poole | University of Michigan |
38.8% |
Dylan Windler | Belmont |
38.7% |
Ignas Brazdeikis | University of Michigan |
38.6% |
Zach Norvell Jr |
Gonzaga | 38.5% |
Louis King |
University of Oregon | 37.8% |
Coby White |
University of North Carolina | 37.7% |
Jaylen Nowell | University of Washington |
37.7% |
Jordan Bone | University of Tennessee |
37.4% |
Admiral Schofield | University of Tennessee |
36.9% |
Nickeil Alexander-Walker |
Virginia Tech | 36.6% |
Devon Dotson |
Kansas | 36.4% |
Chuma Okeke |
Auburn | 36.4% |
Jaylen Hands | UCLA |
36.1% |
Quinndary Weatherspoon | Mississippi State |
35.9% |
Keldon Johnson | University of Kentucky |
35.8% |
Miye Oni |
Yale | 35.5% |
Cam Reddish |
Duke | 35.5% |
Ky Bowman |
Boston College | 35.4% |
Daquan Jeffries | Oral Roberts/University of Tulsa |
35.4% |
Jalen McDaniels | San Diego State |
35.3% |
Shamorie Ponds | St Johns |
35.1% |
Mariol Shayok |
Iowa State | 35.0% |
Eric Paschall |
Fordham/Villanova | 35.0% |
Carsen Edwards |
Purdue | 34.8% |
Matisse Thybulle | University of Washington |
34.5% |
Grant Williams | University of Tennessee |
34.5% |
Jarrett Culver | Texas Tech |
34.4% |
Kris Wilkes |
UCLA | 34.2% |
Terance Mann |
Florida State | 34.1% |
Tremont Waters |
LSU | 34.1% |
Oshea Brissett | Syracuse |
34.0% |
KZ Okpala | Stanford |
33.9% |
Rui Hachimura | Gonzaga |
33.6% |
Terance Davis |
Ole Miss |
33.6% |
Reggie Perry |
Mississippi State |
33.3% |
Jared Harper |
Auburn |
33.2% |
Cody Martin |
NC State/University of Nevada |
32.5% |
PJ Washington |
University of Kentucky |
32.5% |
Kevin Porter Jr |
USC |
32.4% |
Quentin Grimes |
Kansas |
32.2% |
Luguentz Dort |
Arizona State |
31.9% |
Romeo Langford |
University of Indiana |
31.3% |
Brandon Clarke |
Gonzaga |
31.2% |
Nassir Little |
University of North Carolina |
31.1% |
Charles Matthews |
University of Kentucky/Michigan |
30.4% |
Talen Horton-Tucker |
Iowa State |
28.2% |
As always, I appreciate all of my readers and feel free to email me with any questions:
2 thoughts on “Forecasting Ideal 3-and-D Players: Part One”