Washington Mystics
2022 Team Stats (30 games)
80.4
PPG
77.8
Opp
+2.6
Margin
43.8%
FG%
34.1%
3P%
77.7%
FT%
34.7
RPG
20.1
APG
12.6
TO
87.5
Pace
Model Outputs
2022
Output is shown as model rating with league rank in parentheses when available.
| Model | Output | Notes |
|---|---|---|
| Bradley-Terry Bradley-Terry Static logistic paired-comparison model with one team strength parameter. More → | 896 (#13) | - |
| Margin Margin Linear team-strength model fit on point differential instead of binary wins. More → | +4.8 (#11) | HCA +2.0 |
| Pythagorean Pythagorean Pythagorean win expectation from raw points scored and allowed. More → | 0.338 (#12) | - |
2022 Schedule & Results
2022 Roster
Minutes by Position
The surface stays filled across the five on-court roles. Use the labels or legend to isolate how each player absorbs guard-to-big minutes.
| Player | Pos | GP | MIN | PTS | REB | AST | STL | BLK | TO | FGA | Numbers | PM | PM/G | PM/40 | FG% | 3P% | FT% | RAPM | TS% | eFG% |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
E. Delle Donne
|
F | 21 | 27.8 | 17.2 | 5.9 | 2.6 | 0.4 | 1.0 | 1.1 | 13.1 | 12.8 | - | - | - | 48.2 | 33.3 | 93.3 | -0.59 | 58.4 | 52.7 |
Ariel Atkins
|
G | 30 | 30.2 | 14.5 | 3.1 | 2.4 | 1.2 | 0.3 | 1.4 | 12.6 | 7.4 | - | - | - | 40.6 | 35.3 | 86.1 | 0.17 | 52.6 | 48.4 |
Natasha Cloud
|
G | 30 | 31.8 | 11.0 | 3.7 | 6.8 | 1.0 | 0.4 | 2.9 | 9.2 | 10.8 | - | - | - | 41.3 | 33.9 | 81.8 | 0.14 | 53.4 | 48.6 |
Myisha Hines-Allen
|
F | 28 | 19.1 | 9.4 | 5.2 | 1.8 | 0.8 | 0.2 | 1.6 | 8.4 | 7.5 | - | - | - | 41.5 | 36.2 | 76.8 | -0.09 | 50.6 | 46.8 |
S. Austin
|
F | 30 | 22.0 | 8.3 | 6.6 | 0.9 | 0.7 | 0.7 | 1.6 | 6.1 | 9.5 | - | - | - | 51.9 | 0.0 | 59.8 | - | 54.9 | 51.9 |
Alysha Clark
|
F | 27 | 27.2 | 8.1 | 4.5 | 2.1 | 1.1 | 0.3 | 1.2 | 6.7 | 8.3 | - | - | - | 46.2 | 32.6 | 90.9 | - | 57.1 | 54.7 |
Shatori Walker-Kimbrough
|
G | 29 | 19.1 | 6.8 | 1.5 | 1.4 | 0.9 | 0.3 | 1.0 | 5.0 | 5.0 | - | - | - | 45.1 | 40.6 | 87.2 | 0.58 | 59.8 | 54.2 |
Elizabeth Williams
|
C | 29 | 14.4 | 5.3 | 3.8 | 0.3 | 0.6 | 0.6 | 0.6 | 4.6 | 5.5 | - | - | - | 49.2 | 0 | 54.5 | 0.28 | 50.9 | 49.2 |
T. Hawkins
|
F | 18 | 12.1 | 4.9 | 2.3 | 0.5 | 0.3 | 0.1 | 0.9 | 4.7 | 2.4 | - | - | - | 35.7 | 31.0 | 93.8 | 0.64 | 48.3 | 43.5 |
Kennedy Burke
|
F | 12 | 12.8 | 4.2 | 2.0 | 0.3 | 0.8 | 0.2 | 0.7 | 4.2 | 2.8 | - | - | - | 38.0 | 33.3 | 50.0 | - | 46.9 | 46.0 |
Evina Westbrook
|
G | 6 | 5.3 | 3.3 | 0.0 | 0.5 | 0.3 | 0.2 | 0.5 | 1.5 | 2.3 | - | - | - | 66.7 | 50.0 | 83.3 | - | 85.9 | 83.3 |
R. Machida
|
G | 30 | 10.9 | 1.5 | 0.9 | 2.3 | 0.4 | 0.1 | 0.8 | 1.9 | 2.5 | - | - | - | 29.3 | 18.5 | 66.7 | - | 36.3 | 33.6 |
J. Jones
|
G | 1 | 3.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | - | - | - | 0.0 | 0.0 | 0 | - | 0.0 | 0.0 |
Numbers/Game vs RAPM
X-axis = Numbers/Game (PTS+REB+AST+STL+BLK-TO-FGA), Y-axis = RAPM.
Advanced: Numbers = PTS+REB+AST+STL+BLK-TO-FGA, PM = total +/-, PM/G = per game, PM/40 = per 40 minutes, RAPM = Regularized Adj Plus-Minus, TS% = True Shooting, eFG% = Effective FG