databallr

WNBA WOWY Lineups

databallr

2-Woman League Leaders • 2025 • Regular Season

Padded • All Leverage • Top 200

2-WOMAN LINEUPS
1
865
112.1+7.6
99.1-5.4
+13.0
2
682
113.4+8.9
100.7-3.8
+12.7
3
837
111.1+6.6
99.5-5.0
+11.6
4
1078
113.2+8.7
102.2-2.3
+11.0
5
492
110.6+6.1
99.7-4.8
+10.9
6
692
112.2+7.7
101.4-3.1
+10.9
7
1121
112.1+7.6
101.3-3.2
+10.8
8
884
112.3+7.8
101.5-3.0
+10.8
9
782
110.5+6.0
100.3-4.2
+10.2
10
399
111.9+7.4
101.7-2.8
+10.1
11
699
112.5+8.0
102.5-2.0
+10.0
12
544
108.6+4.1
98.7-5.8
+10.0
13
340
109.6+5.1
99.7-4.7
+9.8
14
885
110.4+5.9
100.6-3.9
+9.8
15
947
109.2+4.8
99.5-5.0
+9.8
16
553
109.8+5.3
100.1-4.4
+9.8
17
806
110.6+6.1
101.2-3.3
+9.4
18
1125
111.6+7.1
102.6-1.9
+9.0
19
629
109.4+4.9
100.4-4.1
+9.0
20
666
109.0+4.5
100.1-4.4
+8.9
21
948
108.9+4.4
100.0-4.5
+8.9
22
921
110.8+6.3
102.0-2.5
+8.9
23
901
109.5+5.0
100.7-3.8
+8.8
24
450
109.8+5.3
101.1-3.4
+8.8
25
419
108.4+3.9
99.6-4.8
+8.8
26
313
108.8+4.3
100.0-4.5
+8.7
27
879
111.6+7.1
102.9-1.6
+8.7
28
861
110.7+6.2
102.3-2.2
+8.4
29
343
110.5+6.0
102.3-2.2
+8.2
30
491
109.3+4.8
101.1-3.4
+8.2
31
429
110.4+5.9
102.3-2.2
+8.1
32
642
108.6+4.1
100.7-3.7
+7.8
33
483
108.6+4.1
100.9-3.6
+7.7
34
496
110.6+6.1
102.9-1.6
+7.7
35
736
109.5+5.0
101.8-2.7
+7.7
36
287
111.1+6.6
103.6-0.8
+7.5
37
454
109.3+4.9
101.9-2.6
+7.4
38
147
108.9+4.4
101.5-3.0
+7.4
39
121
108.1+3.6
100.7-3.7
+7.3
40
874
110.2+5.7
103.0-1.5
+7.2
41
153
108.6+4.1
101.6-2.9
+7.0
42
997
110.2+5.7
103.2-1.3
+7.0
43
323
108.8+4.3
101.8-2.7
+7.0
44
533
108.4+3.9
101.4-3.1
+7.0
45
441
109.6+5.1
102.6-1.8
+7.0
46
824
107.1+2.6
100.2-4.3
+6.9
47
241
107.3+2.8
100.4-4.1
+6.9
48
727
109.6+5.1
102.7-1.7
+6.8
49
945
110.0+5.5
103.2-1.3
+6.8
50
682
109.7+5.2
102.9-1.6
+6.8
51
512
110.0+5.5
103.2-1.3
+6.8
52
509
110.0+5.5
103.3-1.2
+6.7
53
333
108.2+3.7
101.5-3.0
+6.7
54
697
109.3+4.8
102.6-1.9
+6.7
55
253
107.3+2.9
100.8-3.7
+6.6
56
271
109.8+5.3
103.2-1.3
+6.5
57
654
107.0+2.5
100.6-3.9
+6.4
58
864
110.4+5.9
104.0-0.5
+6.4
59
630
109.8+5.3
103.4-1.1
+6.4
60
304
109.7+5.2
103.4-1.1
+6.3
61
333
107.9+3.4
101.6-2.9
+6.3
62
498
107.5+3.0
101.3-3.2
+6.3
63
784
107.9+3.5
101.7-2.8
+6.3
64
793
109.4+4.9
103.1-1.4
+6.2
65
325
109.9+5.4
103.7-0.8
+6.2
66
604
107.6+3.1
101.5-3.0
+6.1
67
516
106.4+1.9
100.4-4.1
+6.0
68
907
109.2+4.8
103.2-1.3
+6.0
69
1139
106.8+2.3
100.8-3.7
+6.0
70
360
109.4+4.9
103.3-1.2
+6.0
71
145
106.3+1.9
100.4-4.1
+6.0
72
1128
110.0+5.6
104.1-0.4
+6.0
73
1119
107.3+2.8
101.4-3.1
+5.9
74
217
108.1+3.6
102.2-2.3
+5.9
75
275
108.5+4.0
102.7-1.8
+5.8
76
632
108.7+4.2
103.0-1.5
+5.7
77
301
109.2+4.7
103.4-1.1
+5.7
78
67
107.2+2.7
101.4-3.0
+5.7
79
881
109.0+4.5
103.3-1.2
+5.7
80
390
107.7+3.2
102.0-2.5
+5.7
81
357
112.1+7.6
106.4+1.9
+5.6
82
407
107.1+2.7
101.6-2.9
+5.6
83
106
109.3+4.8
103.8-0.7
+5.5
84
364
107.2+2.8
101.7-2.8
+5.5
85
98
109.8+5.3
104.3-0.2
+5.5
86
524
110.1+5.6
104.6+0.1
+5.5
87
255
108.2+3.8
102.8-1.7
+5.4
88
318
107.1+2.7
101.8-2.7
+5.4
89
55
108.6+4.1
103.3-1.2
+5.4
90
836
107.4+2.9
102.1-2.4
+5.3
91
378
107.1+2.7
101.8-2.7
+5.3
92
210
106.4+2.0
101.3-3.2
+5.1
93
492
107.8+3.3
102.7-1.8
+5.1
94
397
107.2+2.7
102.1-2.4
+5.1
95
370
108.2+3.7
103.1-1.4
+5.1
96
253
109.4+4.9
104.3-0.1
+5.1
97
347
106.4+1.9
101.3-3.1
+5.1
98
246
106.0+1.5
100.9-3.6
+5.1
99
285
106.7+2.2
101.7-2.8
+5.0
100
502
106.6+2.1
101.6-2.9
+5.0
101
872
109.5+5.0
104.5+0.0
+5.0
102
495
106.4+1.9
101.4-3.0
+5.0
103
306
108.5+4.0
103.5-1.0
+5.0
104
405
108.1+3.7
103.2-1.3
+4.9
105
341
108.0+3.5
103.1-1.4
+4.9
106
992
109.8+5.3
105.0+0.5
+4.9
107
1155
107.4+2.9
102.6-1.9
+4.8
108
186
107.0+2.5
102.2-2.3
+4.8
109
735
106.9+2.5
102.2-2.3
+4.7
110
106
108.3+3.8
103.6-0.9
+4.7
111
153
107.2+2.7
102.5-2.0
+4.7
112
92
105.5+1.0
100.8-3.7
+4.7
113
37
107.5+3.0
102.8-1.6
+4.7
114
213
107.5+3.0
102.8-1.6
+4.7
115
155
108.1+3.6
103.6-0.9
+4.6
116
431
107.4+2.9
102.9-1.6
+4.6
117
180
104.3-0.2
99.8-4.7
+4.5
118
293
108.9+4.4
104.3-0.2
+4.5
119
114
108.2+3.7
103.7-0.8
+4.5
120
98
107.1+2.7
102.7-1.8
+4.4
121
371
108.5+4.0
104.2-0.3
+4.3
122
244
108.7+4.2
104.4-0.1
+4.3
123
78
107.1+2.6
102.8-1.7
+4.3
124
202
106.2+1.7
101.9-2.6
+4.3
125
194
107.0+2.5
102.8-1.7
+4.2
126
605
106.6+2.2
102.4-2.1
+4.2
127
54
105.3+0.8
101.1-3.4
+4.2
128
279
105.8+1.3
101.7-2.8
+4.2
129
105
106.5+2.0
102.4-2.1
+4.1
130
81
108.9+4.4
104.8+0.3
+4.1
131
307
105.5+1.0
101.4-3.1
+4.1
132
69
107.0+2.5
103.0-1.5
+4.0
133
115
105.2+0.8
101.3-3.2
+4.0
134
758
105.9+1.4
101.9-2.6
+4.0
135
700
111.7+7.2
107.7+3.2
+4.0
136
162
105.6+1.1
101.7-2.8
+4.0
137
97
109.6+5.2
105.7+1.2
+3.9
138
215
106.3+1.8
102.4-2.1
+3.9
139
292
104.5+0.1
100.6-3.8
+3.9
140
502
106.0+1.6
102.2-2.3
+3.9
141
541
107.6+3.1
103.7-0.8
+3.9
142
534
108.1+3.6
104.2-0.3
+3.9
143
413
104.1-0.4
100.3-4.2
+3.8
144
654
107.5+3.0
103.7-0.8
+3.8
145
943
105.5+1.1
101.8-2.7
+3.8
146
79
107.4+2.9
103.7-0.8
+3.7
147
298
105.7+1.2
102.0-2.5
+3.7
148
379
106.0+1.5
102.4-2.1
+3.7
149
402
108.5+4.0
104.9+0.4
+3.6
150
102
109.8+5.3
106.3+1.8
+3.5
151
237
106.2+1.7
102.7-1.8
+3.5
152
131
107.7+3.2
104.2-0.3
+3.5
153
138
105.0+0.5
101.6-2.9
+3.4
154
379
106.5+2.0
103.1-1.4
+3.4
155
132
105.6+1.2
102.3-2.2
+3.4
156
340
107.6+3.1
104.2-0.3
+3.4
157
991
105.6+1.1
102.2-2.3
+3.3
158
323
106.6+2.1
103.3-1.2
+3.3
159
336
106.3+1.8
103.0-1.5
+3.3
160
768
110.2+5.7
106.9+2.4
+3.3
161
71
104.6+0.1
101.3-3.2
+3.2
162
183
106.2+1.7
103.0-1.5
+3.2
163
185
105.0+0.5
101.8-2.7
+3.2
164
575
107.4+2.9
104.3-0.2
+3.1
165
116
105.1+0.7
102.0-2.5
+3.1
166
50
106.0+1.5
102.9-1.6
+3.1
167
23
106.0+1.5
102.9-1.6
+3.1
168
74
105.3+0.8
102.2-2.3
+3.1
169
125
106.1+1.6
103.0-1.4
+3.1
170
126
104.8+0.4
101.8-2.7
+3.0
171
62
110.0+5.5
107.0+2.5
+3.0
172
1059
104.8+0.3
101.7-2.8
+3.0
173
112
106.6+2.2
103.6-0.9
+3.0
174
122
107.3+2.9
104.3-0.2
+3.0
175
202
105.6+1.1
102.6-1.9
+3.0
176
58
107.2+2.7
104.1-0.3
+3.0
177
123
105.9+1.4
102.9-1.6
+3.0
178
24
107.5+3.0
104.5-0.0
+3.0
179
123
104.6+0.1
101.6-2.9
+3.0
180
181
105.8+1.4
102.9-1.6
+2.9
181
344
107.7+3.2
104.7+0.3
+2.9
182
246
105.9+1.4
103.0-1.5
+2.8
183
409
105.8+1.3
103.0-1.5
+2.8
184
394
108.0+3.5
105.2+0.7
+2.8
185
216
106.1+1.6
103.2-1.2
+2.8
186
396
105.5+1.0
102.7-1.8
+2.8
187
44
107.0+2.5
104.3-0.2
+2.7
188
165
102.1-2.3
99.4-5.1
+2.7
189
113
105.3+0.9
102.7-1.8
+2.7
190
539
107.1+2.6
104.4-0.1
+2.7
191
191
106.1+1.7
103.5-1.0
+2.7
192
496
107.5+3.0
104.8+0.3
+2.7
193
224
105.2+0.8
102.6-1.9
+2.6
194
59
106.1+1.6
103.4-1.1
+2.6
195
149
106.0+1.5
103.4-1.1
+2.6
196
103
107.0+2.5
104.4-0.1
+2.6
197
144
107.3+2.8
104.6+0.1
+2.6
198
432
106.0+1.5
103.4-1.1
+2.6
199
27
106.4+1.9
103.8-0.7
+2.6
200
204
105.1+0.6
102.5-2.0
+2.6
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?