Washington Mystics

Season: 2026 2025 2024 2023 2022 2021 2020 2019 2018 2017

Also known as: Washington Mystics

Program History

2016 Team Stats (34 games)

80.7

PPG

82.2

Opp

-1.5

Margin

42.3%

FG%

35.8%

3P%

80.0%

FT%

32.7

RPG

17.4

APG

11.8

79.6

Est. poss./game

79.4

Adj PPG

80.7

Adj Opp PPG

Scoring Rank

Model Outputs

2016

Catalog

Output is shown as model rating with league rank in parentheses when available.

Model	Output	Notes
Elo Elo Streaming paired-comparison rating with recency baked into sequential updates. More →	899 (#10)	-
Bradley-Terry Bradley-Terry Static logistic paired-comparison model with one team strength parameter. More →	926 (#10)	-
Bradley-Terry Recency Bradley-Terry Recency Static Bradley-Terry with exponential recency weights on newer games. HCA +51 elo More →	942 (#10)	HCA +51 elo
Margin Margin Linear team-strength model fit on point differential instead of binary wins. HCA +2.5 More →	-1.3 (#9)	HCA +2.5
Margin Recency Margin Recency Margin regression with exponential recency weights on newer games. HCA +3.7 More →	+0.6 (#6)	HCA +3.7
Pythagorean Pythagorean Pythagorean win expectation from raw points scored and allowed. More →	0.448 (#9)	-
Adjusted Efficiency Adjusted Efficiency Opponent-adjusted efficiency model with separate offensive and defensive components. AdjNet -1.7 More →	0.451 (#9)	AdjNet -1.7
Log Adjusted Log Adjusted Log-scale adjusted efficiency model that downweights blowout leverage. AdjNet -1.7 More →	0.451 (#9)	AdjNet -1.7
Points Off/Def Points Off/Def Raw points regression with separate offensive and defensive team parameters. AdjO 79.4 \| AdjD 80.7 More →	0.470 (#9)	AdjO 79.4 \| AdjD 80.7
Points Off/Def Recency Points Off/Def Recency Off/def points regression with exponential recency weights. AdjO 79.8 \| AdjD 79.3 More →	0.512 (#6)	AdjO 79.8 \| AdjD 79.3
Core Ensemble Core Ensemble Equal-logit blend of Elo, recency BT, recency margin, log-adjusted pyth, and points off/def. Blend of Elo, BT, Margin, PythLog, PtsOD More →	0.438 (#10)	Blend of Elo, BT, Margin, PythLog, PtsOD
Recency Ensemble Recency Ensemble Equal-logit blend of Elo, recency BT, recency margin, log-adjusted pyth, and recency points off/def. Blend of Elo, BT, Margin, PythLog, PtsOD More →	0.447 (#9)	Blend of Elo, BT, Margin, PythLog, PtsOD
Dynamic Bradley-Terry Dynamic Bradley-Terry Time-evolving paired-comparison model with latent team strength drift. RD 165 \| GP 34 More →	915 (#10)	RD 165 \| GP 34

2016 Schedule & Results

Date	Vs/At	Opponent	Result	Score
2016-05-14	vs	New York Liberty	L	76 - 87
2016-05-18	vs	Dallas Wings	L	77 - 87
2016-05-20	vs	Los Angeles Sparks	L	67 - 97
2016-05-21	@	Connecticut Sun	W	84 - 76
2016-05-26	@	Seattle Storm	W	84 - 82
2016-05-29	@	Phoenix Mercury	L	77 - 93
2016-06-01	vs	Chicago Sky	L	78 - 86
2016-06-03	@	Chicago Sky	L	72 - 98
2016-06-05	@	Atlanta Dream	W	86 - 79
2016-06-08	@	Dallas Wings	W	87 - 79
2016-06-11	vs	Minnesota Lynx	L	76 - 83
2016-06-14	@	Connecticut Sun	W	109 - 106
2016-06-18	vs	Atlanta Dream	W	95 - 65
2016-06-22	vs	Indiana Fever	W	76 - 62
2016-06-24	vs	Phoenix Mercury	L	79 - 91
2016-06-26	vs	Minnesota Lynx	W	87 - 63
2016-06-29	vs	San Antonio Stars	W	84 - 67
2016-07-01	@	Chicago Sky	L	84 - 86
2016-07-06	@	San Antonio Stars	L	70 - 77
2016-07-10	@	Los Angeles Sparks	L	82 - 93
2016-07-13	@	Phoenix Mercury	L	74 - 78
2016-07-15	@	Seattle Storm	L	51 - 80
2016-07-20	vs	New York Liberty	L	81 - 88
2016-07-22	vs	Los Angeles Sparks	L	75 - 95
2016-08-27	@	Indiana Fever	W	92 - 69
2016-08-28	vs	San Antonio Stars	L	74 - 85
2016-09-02	@	Minnesota Lynx	L	69 - 75
2016-09-04	@	Dallas Wings	W	88 - 83
2016-09-07	vs	Chicago Sky	W	118 - 81
2016-09-09	vs	Seattle Storm	L	76 - 81
2016-09-11	vs	Indiana Fever	L	73 - 80
2016-09-13	@	New York Liberty	W	75 - 62
2016-09-15	@	Atlanta Dream	L	91 - 94
2016-09-18	vs	Connecticut Sun	L	78 - 87

2016 Roster

Show Alumni

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 GP	PM/G	PM/40	FG%	3P%	FT%	RAPM	TS%	eFG%
T. Hill	G	33	29.1	15.5	2.6	2.9	1.2	0.1	1.2	12.5	8.5	-	-	-	-	35.6	35.2	82.9	-	51.6	43.1
Emma Meesseman	C	34	29.2	15.2	5.6	2.3	1.2	0.7	1.3	11.6	12.2	-	-	-	-	53.3	44.8	80.0	-	60.0	57.1
S. Dolson	C	36	22.4	9.1	4.7	1.1	0.4	0.9	1.2	7.1	7.9	-	-	-	-	48.8	29.0	83.3	-	56.2	50.6
I. Latta	G	22	20.7	8.3	1.7	1.9	0.4	0.0	1.4	7.5	3.3	-	-	-	-	33.1	30.5	90.9	-	49.1	42.8
T. Ruffin-Pratt	G	34	24.2	7.4	4.3	2.7	1.0	0.3	1.6	6.3	7.8	-	-	-	-	37.4	30.2	73.3	-	48.4	41.1
Jeff Hagen	-	4	17.5	7.2	3.0	0.2	0.8	2.5	0.8	4.5	8.5	-	-	-	-	50.0	0	84.6	-	61.1	50.0
Kahleah Copper	G	32	16.2	6.8	3.2	0.8	0.5	0.1	1.1	5.3	4.9	-	-	-	-	43.2	46.7	72.2	-	53.2	47.3
B. Hartley	G	24	16.9	6.5	2.0	2.2	0.4	0.0	1.5	5.5	4.2	-	-	-	-	37.6	36.2	80.0	-	51.4	45.5
Kia Vaughn	C	36	19.6	6.1	4.3	0.7	0.6	0.5	0.9	5.5	5.8	-	-	-	-	50.3	0	73.3	-	52.3	50.3
Leilani Mitchell	Forward	10	16.1	5.9	1.7	1.9	0.1	0.0	1.3	4.6	3.7	-	-	-	-	43.5	35.5	80.0	-	58.5	55.4
Natasha Cloud	G	33	24.1	5.8	3.0	3.7	0.8	0.2	1.5	5.2	6.8	-	-	-	-	35.3	33.8	75.4	-	48.3	42.4
Tianna Hawkins	F	26	11.0	5.0	2.5	0.4	0.3	0.2	1.0	3.7	3.7	-	-	-	-	51.0	50.0	87.0	-	60.8	56.8
Z. Dimitrakou	F	4	13.2	4.2	1.8	1.0	0.5	0.2	1.2	4.8	1.8	-	-	-	-	26.3	33.3	100.0	-	39.3	28.9
L. Galdeira	G	2	14.5	4.0	2.5	1.5	0.5	0.0	7.0	4.0	-2.5	-	-	-	-	25.0	0.0	57.1	-	36.1	25.0
D. Grant	G	2	6.0	3.0	0.0	0.5	0.0	0.0	1.0	2.5	0.0	-	-	-	-	40.0	0	100.0	-	51.0	40.0
A. Malott	F	25	8.5	2.6	1.3	0.5	0.3	0.1	0.7	2.4	1.7	-	-	-	-	35.0	36.8	76.9	-	50.2	46.7
J. Weisner	G	7	3.7	1.7	0.4	0.3	0.0	0.0	0.4	1.3	0.7	-	-	-	-	44.4	50.0	100.0	-	60.7	55.6
C. Lee	F	2	10.0	0.5	3.0	0.5	0.5	0.0	0.5	0.5	3.5	-	-	-	-	0.0	0	50.0	-	26.6	0.0
J. Payne	G	1	14.0	0.0	1.0	0.0	0.0	1.0	0.0	4.0	-2.0	-	-	-	-	0.0	0.0	0	-	0.0	0.0

Numbers/Game vs RAPM

Not enough players with both Numbers/Game and RAPM to plot.

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

2016 Shot Zones

2369 located field-goal attempts in 36 games (ESPN). Each zone is coloured by the player with the largest share of the team's made-shot points there (left) and of its misses there (right).

Points: who the team's points here most likely come from

Misses: who misses most here

Emma MeessemanT. HillS. DolsonNatasha CloudI. LattaA. Malott

Zone	FGM-FGA	FG%	Pts	Top scorer	Runner-up	Most misses	Runner-up
Restricted area	360-648	55.6	720	Emma Meesseman 22% (156 pts)	T. Hill 15% (106 pts)	T. Hill 20% (59 misses)	Emma Meesseman 15% (44 misses)
Paint (non-RA)	131-353	37.1	262	Emma Meesseman 32% (84 pts)	Kia Vaughn 19% (50 pts)	Emma Meesseman 21% (46 misses)	T. Hill 17% (37 misses)
Mid-range left	105-272	38.6	210	Emma Meesseman 32% (68 pts)	T. Hill 14% (30 pts)	Emma Meesseman 25% (42 misses)	T. Hill 13% (22 misses)
Mid-range center	58-118	49.2	116	S. Dolson 34% (40 pts)	Kahleah Copper 14% (16 pts)	S. Dolson 22% (13 misses)	T. Hill 15% (9 misses)
Mid-range right	119-301	39.5	238	S. Dolson 24% (56 pts)	Emma Meesseman 16% (38 pts)	S. Dolson 16% (30 misses)	T. Hill 14% (25 misses)
Left corner 3	6-19	31.6	18	A. Malott 33% (6 pts)	B. Hartley 17% (3 pts)	Natasha Cloud 23% (3 misses)	B. Hartley 15% (2 misses)
Right corner 3	7-15	46.7	21	I. Latta 29% (6 pts)	Natasha Cloud 29% (6 pts)	T. Hill 38% (3 misses)	I. Latta 25% (2 misses)
Above-break 3 left	98-232	42.2	294	T. Hill 29% (84 pts)	Emma Meesseman 14% (42 pts)	T. Hill 32% (43 misses)	I. Latta 15% (20 misses)
Above-break 3 center	40-136	29.4	120	Emma Meesseman 28% (33 pts)	Natasha Cloud 15% (18 pts)	T. Hill 21% (20 misses)	I. Latta 18% (17 misses)
Above-break 3 right	86-275	31.3	258	T. Hill 28% (72 pts)	I. Latta 14% (36 pts)	T. Hill 25% (47 misses)	I. Latta 17% (32 misses)