Six RingsNBAWNBA

WNBA WOWY Lineups

databallr

2-Woman League Leaders • 2026 • Regular Season

Padded • All Leverage • Top 200

2-WOMAN LINEUPS
1
715
117.2+7.7
104.6-4.9
+12.6
2
825
118.5+9.0
106.7-2.7
+11.8
3
499
118.5+9.0
107.7-1.8
+10.8
4
135
115.9+6.4
105.1-4.4
+10.8
5
848
115.7+6.2
105.3-4.2
+10.4
6
796
116.2+6.7
106.1-3.4
+10.1
7
557
116.2+6.7
106.4-3.1
+9.8
8
139
114.9+5.4
105.6-3.9
+9.4
9
779
117.7+8.2
108.4-1.1
+9.3
10
316
114.3+4.8
105.0-4.5
+9.3
11
810
116.7+7.2
107.5-2.0
+9.2
12
213
118.1+8.6
109.1-0.4
+9.0
13
367
119.5+10.0
110.5+1.1
+8.9
14
791
114.9+5.4
106.1-3.4
+8.9
15
189
113.7+4.2
104.8-4.7
+8.9
16
363
116.1+6.6
107.4-2.1
+8.7
17
323
117.8+8.4
109.3-0.2
+8.5
18
588
114.0+4.5
105.7-3.8
+8.3
19
622
114.5+5.0
106.3-3.2
+8.2
20
684
115.5+6.0
107.4-2.1
+8.1
21
156
116.2+6.7
108.2-1.3
+8.0
22
746
111.4+1.9
103.8-5.7
+7.6
23
801
113.3+3.8
105.9-3.6
+7.4
24
180
116.4+6.9
109.1-0.4
+7.3
25
240
114.0+4.5
106.8-2.7
+7.2
26
573
115.8+6.3
108.6-0.8
+7.2
27
280
113.8+4.3
106.7-2.8
+7.1
28
861
114.2+4.7
107.1-2.4
+7.1
29
683
113.5+4.0
106.6-2.9
+6.9
30
581
110.6+1.1
103.7-5.8
+6.9
31
89
115.2+5.7
108.2-1.3
+6.9
32
551
115.8+6.3
108.8-0.7
+6.9
33
89
113.2+3.7
106.3-3.2
+6.9
34
717
113.9+4.4
107.1-2.4
+6.9
35
159
115.3+5.8
108.5-1.0
+6.9
36
93
116.0+6.5
109.1-0.4
+6.8
37
237
115.7+6.2
108.9-0.6
+6.8
38
823
113.1+3.6
106.3-3.2
+6.7
39
112
115.0+5.5
108.4-1.1
+6.7
40
513
116.2+6.7
109.6+0.1
+6.6
41
775
111.5+2.1
104.9-4.6
+6.6
42
119
114.7+5.3
108.2-1.2
+6.5
43
297
114.1+4.6
107.7-1.8
+6.4
44
255
114.3+4.9
108.0-1.5
+6.3
45
239
112.7+3.2
106.4-3.1
+6.3
46
640
112.8+3.3
106.5-3.0
+6.3
47
146
113.6+4.2
107.4-2.1
+6.2
48
165
112.8+3.3
106.6-2.9
+6.2
49
161
113.0+3.5
106.7-2.8
+6.2
50
112
115.4+5.9
109.2-0.3
+6.2
51
254
115.7+6.2
109.5-0.0
+6.2
52
820
112.1+2.6
105.9-3.6
+6.2
53
149
115.1+5.6
109.0-0.5
+6.1
54
459
112.8+3.3
106.8-2.7
+6.1
55
639
115.6+6.2
109.6+0.1
+6.0
56
694
113.3+3.8
107.3-2.2
+6.0
57
604
114.5+5.0
108.5-1.0
+5.9
58
255
113.8+4.3
107.9-1.6
+5.9
59
136
112.4+2.9
106.5-3.0
+5.9
60
321
111.5+2.0
105.6-3.9
+5.9
61
639
114.3+4.8
108.4-1.1
+5.9
62
191
111.9+2.4
106.1-3.4
+5.7
63
532
115.8+6.3
110.1+0.6
+5.7
64
171
114.0+4.5
108.4-1.1
+5.6
65
283
114.8+5.3
109.2-0.3
+5.6
66
192
114.6+5.1
109.0-0.5
+5.6
67
618
111.5+2.0
106.0-3.5
+5.5
68
366
114.7+5.2
109.2-0.2
+5.5
69
193
114.4+4.9
109.0-0.5
+5.4
70
174
111.4+1.9
105.9-3.6
+5.4
71
459
116.0+6.5
110.6+1.1
+5.4
72
41
111.9+2.4
106.5-3.0
+5.4
73
585
110.6+1.1
105.2-4.3
+5.4
74
119
114.0+4.5
108.7-0.8
+5.3
75
157
113.8+4.4
108.6-0.9
+5.3
76
372
114.1+4.6
108.8-0.7
+5.3
77
722
112.6+3.2
107.4-2.1
+5.2
78
522
115.5+6.0
110.4+0.9
+5.1
79
410
115.2+5.7
110.1+0.6
+5.1
80
308
111.6+2.1
106.5-3.0
+5.1
81
160
112.1+2.6
107.1-2.4
+5.1
82
90
112.1+2.6
107.1-2.3
+5.0
83
761
114.2+4.7
109.2-0.3
+5.0
84
163
112.4+2.9
107.5-1.9
+4.9
85
110
115.6+6.1
110.7+1.2
+4.9
86
86
112.0+2.6
107.2-2.3
+4.8
87
218
110.4+0.9
105.6-3.9
+4.8
88
127
113.4+3.9
108.6-0.9
+4.8
89
235
112.0+2.5
107.2-2.2
+4.7
90
778
110.6+1.1
105.9-3.6
+4.7
91
117
113.2+3.7
108.5-1.0
+4.7
92
357
113.3+3.9
108.6-0.9
+4.7
93
151
114.5+5.0
109.8+0.3
+4.7
94
160
111.9+2.4
107.3-2.2
+4.6
95
145
111.2+1.7
106.6-2.9
+4.6
96
129
115.6+6.1
111.0+1.5
+4.6
97
96
112.4+2.9
107.9-1.6
+4.5
98
344
113.5+4.0
109.0-0.5
+4.5
99
194
114.7+5.2
110.2+0.7
+4.5
100
417
113.7+4.2
109.3-0.2
+4.4
101
79
112.5+3.0
108.1-1.4
+4.4
102
503
109.7+0.2
105.3-4.2
+4.4
103
210
110.6+1.1
106.3-3.2
+4.4
104
255
114.0+4.5
109.7+0.2
+4.3
105
51
113.5+4.0
109.2-0.3
+4.3
106
218
112.9+3.4
108.6-0.9
+4.3
107
316
111.3+1.9
107.1-2.4
+4.3
108
191
111.6+2.1
107.4-2.1
+4.2
109
198
111.8+2.3
107.6-1.8
+4.2
110
199
112.4+2.9
108.2-1.3
+4.2
111
140
113.7+4.2
109.5+0.0
+4.1
112
818
112.7+3.2
108.5-1.0
+4.1
113
172
112.5+3.0
108.4-1.1
+4.1
114
107
111.8+2.3
107.7-1.8
+4.1
115
570
114.1+4.6
109.9+0.5
+4.1
116
83
112.3+2.8
108.2-1.2
+4.1
117
240
113.1+3.6
109.1-0.4
+4.1
118
166
111.3+1.8
107.3-2.2
+4.0
119
253
110.7+1.2
106.7-2.7
+4.0
120
611
115.1+5.6
111.1+1.6
+4.0
121
423
111.1+1.6
107.2-2.3
+4.0
122
168
115.0+5.5
111.1+1.6
+3.9
123
486
110.8+1.3
107.0-2.5
+3.8
124
110
112.9+3.4
109.1-0.4
+3.8
125
88
112.1+2.6
108.2-1.2
+3.8
126
228
110.2+0.7
106.4-3.1
+3.8
127
39
112.5+3.0
108.7-0.8
+3.8
128
263
112.1+2.6
108.3-1.2
+3.8
129
153
112.6+3.1
108.9-0.6
+3.7
130
58
112.8+3.3
109.1-0.4
+3.7
131
44
112.6+3.1
108.9-0.6
+3.7
132
180
110.6+1.1
107.0-2.5
+3.6
133
29
111.2+1.7
107.6-1.9
+3.6
134
118
111.7+2.2
108.2-1.3
+3.5
135
160
111.0+1.5
107.5-2.0
+3.5
136
124
108.9-0.6
105.4-4.0
+3.5
137
351
113.7+4.2
110.2+0.8
+3.5
138
360
110.6+1.1
107.2-2.3
+3.5
139
703
111.5+2.0
108.1-1.4
+3.4
140
68
110.2+0.7
106.8-2.7
+3.4
141
240
107.9-1.6
104.5-5.0
+3.4
142
141
114.9+5.4
111.6+2.1
+3.3
143
50
110.8+1.3
107.5-2.0
+3.3
144
628
114.8+5.3
111.5+2.0
+3.3
145
208
110.3+0.8
107.0-2.5
+3.3
146
125
112.7+3.2
109.5-0.0
+3.3
147
53
110.1+0.6
106.8-2.7
+3.3
148
224
114.0+4.5
110.7+1.2
+3.3
149
76
110.8+1.3
107.5-2.0
+3.3
150
73
110.7+1.2
107.5-2.0
+3.2
151
864
112.8+3.3
109.5+0.0
+3.2
152
41
111.4+2.0
108.2-1.3
+3.2
153
310
115.1+5.6
111.9+2.4
+3.2
154
277
110.6+1.1
107.5-2.0
+3.2
155
390
111.4+2.0
108.3-1.2
+3.1
156
91
112.5+3.0
109.4-0.1
+3.1
157
44
111.1+1.7
108.1-1.4
+3.1
158
742
112.1+2.6
109.0-0.5
+3.0
159
257
113.9+4.4
110.9+1.4
+3.0
160
94
112.6+3.1
109.6+0.1
+3.0
161
356
112.8+3.3
109.8+0.3
+3.0
162
258
111.1+1.6
108.1-1.4
+3.0
163
87
112.2+2.7
109.3-0.2
+2.9
164
202
109.8+0.3
106.9-2.6
+2.9
165
38
111.4+1.9
108.5-1.0
+2.9
166
25
112.2+2.7
109.4-0.1
+2.8
167
66
111.2+1.7
108.4-1.1
+2.8
168
60
112.8+3.4
110.1+0.6
+2.8
169
176
112.5+3.0
109.7+0.2
+2.7
170
44
111.3+1.8
108.6-0.9
+2.7
171
165
111.9+2.4
109.2-0.3
+2.7
172
60
111.8+2.3
109.1-0.3
+2.7
173
259
111.2+1.7
108.5-1.0
+2.7
174
86
109.0-0.5
106.3-3.1
+2.7
175
8
111.4+1.9
108.7-0.7
+2.6
176
166
112.4+2.9
109.8+0.3
+2.6
177
30
110.6+1.1
108.0-1.5
+2.6
178
50
110.7+1.2
108.1-1.3
+2.6
179
39
112.6+3.1
110.0+0.5
+2.6
180
97
113.0+3.5
110.4+0.9
+2.6
181
116
113.8+4.3
111.3+1.8
+2.6
182
95
111.0+1.5
108.4-1.1
+2.5
183
62
111.6+2.1
109.1-0.4
+2.5
184
258
111.7+2.2
109.1-0.4
+2.5
185
325
110.5+1.0
107.9-1.6
+2.5
186
236
111.4+1.9
108.9-0.6
+2.5
187
204
111.3+1.8
108.8-0.7
+2.5
188
38
109.3-0.2
106.8-2.7
+2.5
189
26
112.3+2.8
109.8+0.3
+2.5
190
262
113.8+4.3
111.3+1.8
+2.5
191
9
111.0+1.5
108.5-1.0
+2.5
192
9
111.0+1.5
108.5-1.0
+2.5
193
173
110.1+0.6
107.6-1.9
+2.5
194
157
112.4+2.9
109.9+0.4
+2.5
195
150
109.9+0.4
107.4-2.1
+2.4
196
38
110.6+1.1
108.2-1.3
+2.4
197
83
110.0+0.5
107.6-1.9
+2.4
198
45
111.6+2.1
109.2-0.3
+2.4
199
379
110.7+1.2
108.3-1.2
+2.4
200
199
110.8+1.3
108.4-1.1
+2.4
Click a team icon to filter. Click a lineup to open the lineup card. Click a stat header to sort.

Why Padding Exists

Consider a simple question.

Early in the season you see two lineups:

  • Lineup A: 300 minutes, +15 Net Rating
  • Lineup B: 100 minutes, +20 Net Rating

Which lineup do you think is better?

More importantly:

Which one would you bet on to finish the season with the higher net rating?

Most people choose Lineup A.

Not because +15 is larger than +20.

Because 300 minutes is stronger evidence than 100 minutes.

Small samples are volatile. A few hot shooting stretches, a few opponent misses, and a lineup's rating can spike. As the sample grows, those swings begin to average out.

The Leaderboard Problem

This creates a problem when ranking lineups.

If we simply sort by Net Rating, the leaderboard will be dominated by tiny samples. A lineup that played 10 minutes and went +40 would appear at the top.

That clearly isn't what anyone means by “the best lineup.”

So the usual solution is to introduce a minutes cutoff.

But that only partially solves the issue.

A lineup that barely clears the threshold still has a much easier path to an extreme rating than one that has played hundreds of minutes. Smaller samples fluctuate more, which means they are naturally overrepresented at the extremes of the leaderboard.

The Lineup-of-the-Year Question

Imagine we wanted to crown the best lineup performance of the season.

How should we do it?

Net Rating alone? A tiny sample wins.

Net Rating with a minutes cutoff? Now the winner is often the lineup that happened to run hot just above the threshold.

Raise the cutoff further? Now we begin excluding genuinely dominant lineups that simply did not accumulate enough minutes.

Each approach forces an uncomfortable tradeoff between performance and sample size.

Padding

Padding resolves this tradeoff.

Instead of discarding small samples, we simply temper them according to how much evidence exists behind them.

On Databallr, every lineup begins with the equivalent of:

  • ~266 minutes of league-average offense
  • ~410 minutes of league-average defense

Since the table is shown in minutes, the cleanest way to think about the prior is exactly that: about 266 offensive minutes and 410 defensive minutes of league-average play.

Under the hood, that corresponds to 550 offensive possessions and 850 defensive possessions.

What This Means in Practice

A cleaner way to feel the math is to ask how much of an observed edge survives the prior.

Real minutes
266
Raw offense edge
+10.0
Raw defense edge
+10.0
OFF
50.0%
kept
Raw
+10.0
Padded
+5.0
50.0%50.0%
266sample266prior
DEF
39.3%
kept
Raw
+10.0
Padded
+3.9
39.3%60.7%
266sample410prior
Offense: +10 x 266 / (266 + 266) = +5.0
Defense: +10 x 266 / (266 + 410) = +3.9
Raw net edge
+20.0
Padded net edge
+8.9
Net shaved off
+11.1

So at 266 real minutes, a +10 offensive edge gets cut to +5.0. The same +10 defensive edge only keeps about 39.3% of itself, landing at +3.9, because defense carries the larger prior.

That is the core idea: offense and defense do not stabilize at the same speed, so the same real-minute sample gets trusted differently on each side of the ball.

Padding Sandbox

Minutes
266min
Raw
OFF
+10.0
DEF
+10.0
NET
+20.0
OFF
50.0%
kept
Raw
+10.0
Padded
+5.0
50.0%50.0%
266sample266prior
DEF
39.3%
kept
Raw
+10.0
Padded
+3.9
39.3%60.7%
266sample410prior
Net
+8.9
+20.0 -> +8.9
Dropped
+11.1
Kept
44.7%

Why This Works

Small samples are volatile. Large samples are stable.

Padding allows every lineup to appear on the leaderboard while ensuring that extreme results backed by very little evidence do not dominate the rankings.

Only once the sample gets much larger does the neutral prior fade into the background and the lineup's observed play start to dominate the estimate.

The result is a leaderboard that better answers the real question: which lineups have actually been the most impressive this season?

What is WNBA WOWY analysis?

WNBA WOWY, short for With or Without You, compares how a team performs with specific players or lineups on and off the court. databallr lets you study minutes, offensive rating, defensive rating, net rating, shooting, turnovers, and rebounding across selected seasons, leverage filters, and lineup sizes.

What does on-off tell you?

On-off results describe how team performance changed across the selected minutes. They are useful context, not proof that one player caused the full difference; teammates, opponents, roles, and sample size still matter.

How do lineup modes differ?

WOWY mode compares with-and-without combinations for selected players. The 2-man through 5-man modes rank exact lineup sizes, so you can move from player-pair context to full five-player units.

Why are league leaders padded?

League Leaders adds league-average performance to 550 offensive and 850 defensive possessions. This reduces extreme small-sample ratings while keeping larger-minute lineups closer to their observed results.

Need a metric definition? Use the databallr Stats Glossary.