What does FiveThirtyEight’s CARMELO projection system think of the Utah Jazz? And can the Wins Above Replacement projection it produces tell us anything about how productive individual Jazz players will be in 2018-19?
Because the system doesn’t spit out predictions for raw numbers like points, rebounds and assists, I took a different path to try to find those things. Under the WAR projections for any player you search in the system, you’ll find that player’s “10 most comparable players,” which FiveThirtyEight explained in June:
The basics of the system are largely similar to previous years, with the backbone of CARMELO remaining an algorithm that compares current players to past ones who had statistically similar profile through the same age. For instance, Utah Jazz phenom Donovan Mitchell is similar to players such as Gilbert Arenas, Ray Allen, Stephen Curry, Ben Gordon, Victor Oladipo and O.J. Mayo through this early point in their respective careers. Some of those players (Allen, Curry) became superstars, while others (Gordon, Mayo) didn’t really pan out. The combination of those good and not-so-good outcomes gives us a probabilistic forecast for the rest of Mitchell’s career.
To project raw production, then, I took the following steps:
- Looked up the season after the one shown in the system for all 10 comparable players;
- Totaled points, rebounds, assists, steals, blocks and minutes for each season;
- Multiplied each total by FiveThirtyEight’s similarity scores;
- Calculated per-36-minute averages for the five major categories, to give us a nice, level playing field.
Following are the results for Utah’s projected reserves (I published the starters last week), along with some comments on which numbers I think may be way off.
2017-18 Numbers: 7.2 PTS, 6.7 REB, 3.3 BLK, 2.4 AST, 1.9 STL per 36 minutes, 50 FG%, 75 FT%
2018-19 Projection: 10.4 PTS, 9.3 REB, 2.2 BLK, 1.2 AST, 1 STL per 36 minutes, 51.3 FG%, 65.9 FT%
Ekpe Udoh has the lowest similarity scores of anyone I’ve seen on the team, so it should come as no surprise that his projection is fairly different from what he did last season.
The system does do a solid job of forecasting his biggest strength again, though. It’s all about defense with Udoh. And his ability on that end makes him one of the very best third-string centers in the NBA.
2017-18 Numbers: 13.9 PTS, 7.2 REB, 1.5 AST, 2.3 STL, 0.5 BLK per 36 minutes, 49.2 FG%, 38.1 3P%, 81.5 FT%
2018-19 Projection: 10.6 PTS, 6.2 REB, 2.4 AST, 1.4 STL, 0.6 BLK per 36 minutes, 40.4 FG%, 32.5 3P%, 76.1 FT%
Thabo Sefolosha is getting up there in age. He’s already 34. So, a little bit of a dip from last season’s solid production wouldn’t be surprising. This much of a dip would be, though.
I tend to think Sefolosha’s late-career breakout with Utah was more a function of fit than luck. And while he’ll likely lose some minutes per game if the team stays healthy, I still imagine him at least being close to last season’s levels of efficiency.
2017-18 Numbers: 10.7 PTS, 7.4 REB, 3 AST, 1.1 STL, 0.5 BLK per 36 minutes, 42.3 FG%, 35.6 3P%, 80.3 FT%
2018-19 Projection: 13.4 PTS, 5.7 REB, 2.5 AST, 1.4 STL, 0.6 BLK per 36 minutes, 43.9 FG%, 37.3 3P%, 77 FT%
This one actually feels fairly accurate. Royce O’Neale was one of last season’s biggest surprises, and although he’s already 25, this was his first NBA offseason.
With a full summer with the Jazz and its player development program, upticks in scoring and efficiency wouldn’t be a shock for O’Neale.
2017-18 Numbers: 16.8 PTS, 6.5 REB, 2.3 AST, 1.3 STL, 0.3 BLK per 36 minutes, 41.1 FG%, 33.1 3P%, 86.3 FT%
2018-19 Projection: 16.6 PTS, 5.2 REB, 2.4 AST, 1.1 STL, 0.4 BLK per 36 minutes, 44.3 FG%, 32.2 3P%, 77.6 FT%
Not much change projected for Alec Burks. He still figures to be on the fringe of the rotation, especially if his efficiency numbers don’t trend upward next season.
It will be interesting to see how the guard/wing rotation shakes out with O’Neale developing, Dante Exum returning from injury and Burks entering a contract year.
2017-18 Numbers (Utah Only): 15.4 PTS, 4.9 REB, 2 AST, 1.1 STL, 0.4 BLK per 36 minutes, 38.6 FG%, 31.6 3P%, 76.8 FT%
2018-19 Projection: 13.2 PTS, 4.9 REB, 2.2 AST, 1.1 STL, 0.3 BLK per 36 minutes, 43.9 FG%, 36.8 3P%, 80 FT%
This much of a boost in Jae Crowder’s shooting numbers would do wonders for a Jazz squad that was already lights out when he was on the floor.
Among lineups that logged at least as many minutes (194), Ricky Rubio, Donovan Mitchell, Joe Ingles, Jae Crowder and Rudy Gobert had the best Net Rating (net points per 100 possessions with those five players on the floor) in the NBA.
If you add above-average shooting from Crowder to that already ridiculous bunch, that lineup will be devastating next season.
2017-18 Numbers: 13.2 PTS, 5.4 AST, 3.5 REB, 0.9 STL, 0.3 BLK per 36 minutes, 45.7 FG%, 40.4 3P%, 74.3 FT%
2018-19 Projection: 12.4 PTS, 5 AST, 3.8 REB, 1.4 STL, 0.2 BLK per 36 minutes, 44.9 FG%, 31.2 3P%, 77.2 FT%
Raul Neto shot 32.3 percent from three in 2016-17, so this kind of dropoff from the outside wouldn’t be unprecedented, but I doubt it happens.
Neto figures to be his same solid self as Utah’s third-string point guard. He always seems to provide just what the Jazz need from him.
2017-18 Numbers: 17.5 PTS, 6.6 AST, 4 REB, 1.2 STL, 0.5 BLK per 36 minutes, 48.3 FG%, 27.8 3P%, 80.6 FT%
2018-19 Projection: 13.9 PTS, 3.7 AST, 4.6 REB, 1.1 STL, 0.5 BLK per 36 minutes, 42.9 FG%, 30.3 3P%, 78.8 FT%
I’ll have to forgive the projection system on this one. With the amount of time Exum’s missed due to injuries, it’s probably difficult to get a handle on great comps.
I’d expect his 2018-19 numbers to look much closer to last season’s than this projection.
2017-18 Numbers (Duke): 15.6 PTS, 4.7 AST, 3.4 REB, 1.7 STL, 0.05 BLK per 36 minutes, 41.8 FG%, 37 3P%, 85 FT%
2018-19 Projection: 11.7 PTS, 3.8 AST, 4.1 REB, 1.3 STL, 0.4 BLK per 36 minutes, 40.7 FG%, 33.7 3P%, 76.7 FT%
I had to adjust the formula a bit with this one. Since everyone compared to Grayson Allen was understandably a rookie, I went ahead and used those rookie years. It wouldn’t have made sense to bake a second-year jump into Allen’s rookie season.
And while these numbers may not look all that impressive, I wouldn’t be surprised if this is around where he ends up.
Utah’s loaded. For the third season in a row, it’s one of the deepest teams in the NBA. If he’s able to find any sort of significant role this season, the team either had huge injury problems or Allen was much better than most non-lottery rookies.