databallr

WOWY Lineups

databallr

2-Man League Leaders • 2026 • Regular Season

Padded • All Leverage • Top 200

2-MAN LINEUPS
1
521
122.2+6.2
104.9-11.1
+17.3
2
560
122.4+6.4
106.2-9.8
+16.2
3
1003
124.5+8.5
108.6-7.4
+15.9
4
1173
124.5+8.5
108.8-7.2
+15.6
5
1231
123.8+7.8
108.2-7.8
+15.6
6
740
123.0+7.1
107.8-8.1
+15.2
7
587
121.5+5.6
106.8-9.1
+14.7
8
700
120.7+4.7
106.7-9.3
+14.0
9
694
122.0+6.0
108.0-7.9
+14.0
10
713
123.9+7.9
110.3-5.7
+13.6
11
1032
126.5+10.5
112.9-3.1
+13.6
12
1206
121.2+5.3
107.8-8.2
+13.5
13
650
127.8+11.8
114.4-1.6
+13.5
14
424
120.3+4.3
107.0-9.0
+13.3
15
1157
127.6+11.6
114.4-1.6
+13.2
16
397
120.2+4.3
107.3-8.7
+13.0
17
1213
122.7+6.7
109.9-6.1
+12.8
18
288
122.4+6.4
109.7-6.3
+12.7
19
1144
122.7+6.7
110.0-6.0
+12.7
20
620
124.7+8.7
112.4-3.6
+12.3
21
441
123.1+7.1
111.1-4.9
+12.0
22
1129
121.1+5.1
109.1-6.9
+12.0
23
1138
119.6+3.6
107.6-8.4
+12.0
24
505
122.2+6.2
110.3-5.6
+11.9
25
1582
122.1+6.1
110.3-5.7
+11.8
26
361
121.6+5.6
109.8-6.1
+11.7
27
779
125.2+9.2
113.6-2.4
+11.6
28
317
123.1+7.1
111.7-4.3
+11.4
29
1118
124.1+8.1
112.7-3.2
+11.4
30
994
122.0+6.0
110.7-5.3
+11.3
31
542
124.8+8.8
113.5-2.5
+11.3
32
1273
121.0+5.0
109.8-6.1
+11.2
33
425
118.4+2.4
107.2-8.8
+11.2
34
771
126.2+10.2
115.3-0.7
+10.9
35
1235
123.4+7.4
112.6-3.3
+10.8
36
222
123.0+7.0
112.3-3.7
+10.7
37
334
125.3+9.3
114.6-1.4
+10.7
38
490
121.9+5.9
111.2-4.8
+10.7
39
706
119.7+3.7
109.0-7.0
+10.7
40
858
124.3+8.3
113.6-2.4
+10.7
41
1706
128.7+12.7
118.0+2.1
+10.6
42
320
119.3+3.3
108.7-7.3
+10.6
43
319
121.1+5.1
110.6-5.4
+10.5
44
1248
124.8+8.8
114.2-1.8
+10.5
45
687
118.8+2.8
108.3-7.7
+10.5
46
1166
121.5+5.5
111.0-4.9
+10.5
47
672
123.9+7.9
113.5-2.5
+10.5
48
675
121.0+5.1
110.6-5.4
+10.4
49
396
122.7+6.7
112.4-3.6
+10.3
50
835
128.6+12.7
118.3+2.3
+10.3
51
1148
127.8+11.8
117.5+1.5
+10.3
52
320
123.9+7.9
113.5-2.4
+10.3
53
1254
126.5+10.5
116.2+0.2
+10.3
54
660
117.3+1.3
107.0-9.0
+10.3
55
691
119.7+3.7
109.4-6.5
+10.3
56
1249
121.7+5.7
111.5-4.5
+10.2
57
1239
123.4+7.4
113.3-2.7
+10.1
58
647
121.5+5.5
111.4-4.6
+10.1
59
1245
121.3+5.3
111.3-4.7
+10.1
60
109
125.2+9.2
115.1-0.8
+10.0
61
673
120.8+4.8
110.8-5.2
+10.0
62
625
120.3+4.3
110.3-5.7
+10.0
63
604
119.2+3.2
109.3-6.7
+9.9
64
1371
123.5+7.6
113.6-2.4
+9.9
65
821
120.0+4.0
110.1-5.9
+9.9
66
1528
120.8+4.8
110.9-5.1
+9.9
67
365
118.7+2.7
108.8-7.1
+9.8
68
1383
123.2+7.2
113.4-2.6
+9.8
69
1311
121.8+5.8
112.0-3.9
+9.8
70
232
119.1+3.1
109.4-6.6
+9.7
71
226
122.0+6.1
112.4-3.6
+9.7
72
1361
122.1+6.1
112.6-3.4
+9.6
73
1324
126.0+10.0
116.4+0.4
+9.6
74
694
118.3+2.3
108.8-7.2
+9.6
75
277
123.3+7.3
113.7-2.3
+9.5
76
1203
122.1+6.1
112.6-3.4
+9.5
77
1406
123.3+7.3
113.8-2.2
+9.5
78
1384
122.6+6.6
113.1-2.9
+9.5
79
479
119.7+3.7
110.2-5.8
+9.5
80
1222
125.0+9.1
115.6-0.4
+9.5
81
641
121.3+5.3
111.9-4.1
+9.5
82
521
122.8+6.8
113.4-2.6
+9.5
83
216
122.1+6.1
112.7-3.3
+9.4
84
1191
122.8+6.8
113.4-2.6
+9.4
85
895
123.1+7.1
113.7-2.2
+9.4
86
589
125.0+9.0
115.7-0.3
+9.3
87
156
119.5+3.6
110.2-5.8
+9.3
88
731
118.2+2.2
108.9-7.1
+9.3
89
1345
123.6+7.6
114.3-1.7
+9.3
90
542
119.1+3.1
109.8-6.2
+9.3
91
466
119.3+3.3
110.1-5.9
+9.2
92
1230
121.6+5.6
112.4-3.6
+9.2
93
414
126.5+10.5
117.4+1.4
+9.1
94
498
124.4+8.4
115.3-0.6
+9.0
95
1062
125.9+9.9
116.8+0.9
+9.0
96
901
120.9+4.9
111.9-4.1
+9.0
97
499
120.3+4.3
111.3-4.7
+9.0
98
393
124.5+8.5
115.6-0.4
+9.0
99
845
120.1+4.1
111.2-4.8
+8.9
100
915
122.6+6.6
113.6-2.3
+8.9
101
211
120.9+4.9
112.0-4.0
+8.9
102
278
121.4+5.4
112.5-3.5
+8.9
103
570
118.5+2.5
109.7-6.3
+8.8
104
365
119.5+3.5
110.8-5.2
+8.7
105
743
123.4+7.5
114.8-1.2
+8.7
106
437
121.7+5.7
113.0-2.9
+8.7
107
365
119.7+3.7
111.0-5.0
+8.7
108
953
118.7+2.7
110.0-5.9
+8.6
109
239
121.1+5.1
112.5-3.5
+8.6
110
384
116.7+0.7
108.1-7.9
+8.6
111
264
121.6+5.6
113.0-3.0
+8.6
112
1323
120.1+4.1
111.5-4.4
+8.6
113
789
125.0+9.0
116.4+0.5
+8.5
114
438
118.9+2.9
110.4-5.6
+8.5
115
184
121.7+5.7
113.2-2.8
+8.5
116
410
116.5+0.5
108.1-7.9
+8.5
117
547
122.6+6.6
114.2-1.8
+8.4
118
219
121.8+5.8
113.3-2.6
+8.4
119
167
118.1+2.1
109.6-6.3
+8.4
120
199
117.3+1.3
108.9-7.1
+8.4
121
576
118.2+2.2
109.8-6.2
+8.3
122
1093
121.9+5.9
113.6-2.4
+8.3
123
1149
125.0+9.0
116.8+0.8
+8.2
124
313
122.6+6.7
114.4-1.6
+8.2
125
1026
121.3+5.3
113.1-2.9
+8.2
126
878
123.0+7.0
114.8-1.2
+8.1
127
1256
123.5+7.5
115.4-0.6
+8.1
128
560
120.3+4.4
112.2-3.7
+8.1
129
367
120.6+4.7
112.6-3.4
+8.0
130
982
118.3+2.3
110.3-5.7
+8.0
131
359
120.4+4.4
112.4-3.6
+8.0
132
206
116.2+0.2
108.1-7.9
+8.0
133
615
119.2+3.2
111.2-4.8
+8.0
134
416
119.1+3.1
111.2-4.8
+8.0
135
354
120.8+4.8
112.9-3.1
+7.9
136
411
122.8+6.8
114.9-1.1
+7.9
137
548
122.2+6.3
114.3-1.7
+7.9
138
691
120.8+4.8
112.9-3.1
+7.9
139
729
119.6+3.6
111.7-4.3
+7.9
140
318
119.9+3.9
112.1-3.9
+7.8
141
120
122.3+6.3
114.5-1.5
+7.8
142
527
120.0+4.0
112.2-3.8
+7.8
143
166
122.0+6.0
114.3-1.7
+7.8
144
696
117.7+1.8
110.1-5.9
+7.7
145
330
119.0+3.1
111.4-4.6
+7.7
146
1425
125.3+9.3
117.6+1.6
+7.7
147
1084
121.1+5.1
113.5-2.5
+7.7
148
298
123.3+7.4
115.7-0.3
+7.7
149
309
117.2+1.2
109.6-6.4
+7.7
150
459
117.7+1.7
110.1-5.9
+7.6
151
329
122.4+6.4
114.7-1.3
+7.6
152
233
121.7+5.7
114.0-1.9
+7.6
153
655
123.2+7.2
115.6-0.4
+7.6
154
1081
120.5+4.5
112.9-3.1
+7.6
155
114
121.0+5.0
113.4-2.6
+7.6
156
1085
123.8+7.8
116.2+0.2
+7.6
157
242
120.7+4.8
113.2-2.8
+7.5
158
451
121.8+5.8
114.3-1.7
+7.5
159
1712
120.5+4.5
113.0-3.0
+7.5
160
561
119.0+3.0
111.5-4.5
+7.5
161
182
119.1+3.1
111.6-4.4
+7.5
162
583
117.9+1.9
110.4-5.6
+7.5
163
554
118.4+2.4
110.9-5.0
+7.4
164
1096
121.7+5.7
114.2-1.7
+7.4
165
460
119.2+3.3
111.8-4.2
+7.4
166
753
118.1+2.1
110.7-5.3
+7.4
167
153
119.8+3.8
112.4-3.6
+7.4
168
455
122.0+6.1
114.7-1.3
+7.4
169
574
123.6+7.7
116.3+0.3
+7.4
170
388
120.2+4.2
112.9-3.1
+7.3
171
64
118.3+2.3
111.0-5.0
+7.3
172
619
120.6+4.6
113.3-2.7
+7.3
173
376
122.1+6.1
114.8-1.2
+7.3
174
337
119.9+3.9
112.6-3.4
+7.3
175
212
120.6+4.6
113.4-2.6
+7.2
176
199
117.7+1.7
110.5-5.5
+7.2
177
66
121.1+5.2
114.0-2.0
+7.2
178
204
122.7+6.8
115.6-0.4
+7.2
179
539
118.1+2.1
110.9-5.1
+7.2
180
469
120.6+4.7
113.5-2.5
+7.2
181
311
120.5+4.6
113.4-2.6
+7.2
182
842
120.8+4.8
113.6-2.4
+7.1
183
531
122.6+6.6
115.5-0.5
+7.1
184
227
121.0+5.1
113.9-2.0
+7.1
185
271
118.2+2.2
111.1-4.9
+7.1
186
286
120.2+4.2
113.1-2.9
+7.1
187
287
122.5+6.5
115.5-0.5
+7.0
188
686
121.3+5.3
114.3-1.7
+7.0
189
1113
121.1+5.1
114.1-1.9
+7.0
190
525
117.9+1.9
110.9-5.1
+7.0
191
1040
118.5+2.5
111.5-4.4
+7.0
192
287
122.1+6.1
115.1-0.9
+7.0
193
192
122.2+6.2
115.2-0.8
+7.0
194
349
120.7+4.7
113.8-2.2
+6.9
195
487
122.4+6.4
115.4-0.6
+6.9
196
1022
120.3+4.3
113.4-2.6
+6.9
197
262
118.2+2.2
111.3-4.7
+6.9
198
622
119.7+3.7
112.8-3.2
+6.9
199
1124
123.4+7.4
116.5+0.5
+6.9
200
476
122.4+6.5
115.6-0.4
+6.8
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?