databallr

WOWY Lineups

databallr

2-Man League Leaders • 2026 • Regular Season

Padded • All Leverage • Top 200

2-MAN LINEUPS
1
488
121.9+6.3
104.6-11.0
+17.3
2
329
121.5+5.9
105.8-9.8
+15.7
3
764
122.6+7.0
107.5-8.1
+15.1
4
941
123.3+7.7
108.1-7.5
+15.1
5
423
120.4+4.8
105.8-9.8
+14.6
6
552
123.4+7.8
109.6-6.0
+13.9
7
912
122.8+7.2
109.3-6.3
+13.5
8
827
127.2+11.6
113.9-1.7
+13.3
9
582
122.0+6.4
108.9-6.7
+13.1
10
435
121.3+5.7
108.4-7.2
+12.8
11
686
123.8+8.2
111.2-4.3
+12.5
12
356
124.7+9.2
112.3-3.3
+12.4
13
716
124.9+9.3
112.5-3.0
+12.4
14
442
122.9+7.4
110.6-5.0
+12.4
15
203
120.3+4.7
108.3-7.3
+12.0
16
556
121.0+5.4
109.1-6.5
+11.9
17
991
118.8+3.2
107.2-8.4
+11.6
18
1279
121.0+5.4
109.4-6.2
+11.5
19
840
123.0+7.4
111.5-4.1
+11.5
20
861
120.7+5.1
109.5-6.1
+11.2
21
984
120.7+5.1
109.4-6.1
+11.2
22
983
119.4+3.9
108.5-7.1
+11.0
23
233
122.8+7.2
111.9-3.7
+10.9
24
517
119.6+4.0
108.9-6.6
+10.6
25
244
124.0+8.4
113.4-2.2
+10.6
26
502
123.6+8.0
113.1-2.5
+10.5
27
366
117.9+2.3
107.7-7.9
+10.2
28
1048
121.4+5.8
111.2-4.3
+10.2
29
290
120.4+4.8
110.3-5.3
+10.1
30
772
128.0+12.4
117.9+2.3
+10.1
31
1284
127.4+11.8
117.3+1.7
+10.1
32
712
120.7+5.1
110.7-4.9
+10.0
33
543
125.3+9.7
115.3-0.3
+10.0
34
898
121.5+5.9
111.6-4.0
+9.9
35
244
119.2+3.6
109.3-6.3
+9.9
36
941
119.3+3.7
109.3-6.2
+9.9
37
1110
122.1+6.5
112.2-3.4
+9.9
38
935
122.8+7.2
112.9-2.6
+9.9
39
941
121.6+6.0
111.9-3.7
+9.8
40
322
118.4+2.9
108.7-6.9
+9.8
41
528
117.8+2.2
108.0-7.6
+9.7
42
303
121.3+5.7
111.6-4.0
+9.7
43
1046
119.0+3.4
109.3-6.3
+9.7
44
257
120.7+5.1
111.1-4.5
+9.6
45
642
118.9+3.4
109.4-6.2
+9.6
46
895
125.7+10.1
116.2+0.6
+9.6
47
287
117.9+2.3
108.4-7.2
+9.6
48
972
119.7+4.1
110.2-5.4
+9.5
49
306
123.5+7.9
114.0-1.6
+9.5
50
475
122.3+6.7
112.8-2.8
+9.5
51
584
122.2+6.6
112.7-2.8
+9.4
52
404
119.0+3.4
109.6-6.0
+9.4
53
186
119.8+4.2
110.5-5.1
+9.3
54
1061
122.0+6.4
112.7-2.9
+9.3
55
901
122.1+6.5
112.9-2.7
+9.2
56
683
119.4+3.8
110.2-5.3
+9.2
57
1291
119.6+4.0
110.4-5.2
+9.2
58
509
123.1+7.5
113.9-1.7
+9.2
59
403
122.3+6.7
113.2-2.4
+9.1
60
1205
120.1+4.5
111.0-4.6
+9.1
61
383
126.7+11.1
117.7+2.1
+9.0
62
774
126.2+10.6
117.3+1.7
+8.9
63
155
121.2+5.6
112.3-3.3
+8.9
64
586
117.9+2.3
109.0-6.6
+8.9
65
876
119.6+4.0
110.7-4.9
+8.8
66
412
119.8+4.2
111.0-4.6
+8.8
67
286
120.5+4.9
111.8-3.8
+8.7
68
548
122.1+6.5
113.4-2.2
+8.7
69
229
122.8+7.2
114.2-1.4
+8.6
70
216
122.6+7.1
114.0-1.6
+8.6
71
304
118.2+2.6
109.6-6.0
+8.6
72
838
122.7+7.1
114.1-1.5
+8.5
73
293
120.9+5.3
112.4-3.2
+8.5
74
95
121.9+6.3
113.3-2.2
+8.5
75
316
116.7+1.2
108.2-7.4
+8.5
76
1018
122.1+6.5
113.6-2.0
+8.5
77
1460
118.7+3.1
110.3-5.3
+8.4
78
562
117.1+1.5
108.7-6.9
+8.4
79
576
117.9+2.3
109.6-6.0
+8.3
80
183
119.2+3.6
110.9-4.7
+8.3
81
515
120.9+5.3
112.6-3.0
+8.3
82
944
124.5+8.9
116.2+0.7
+8.2
83
416
119.0+3.4
110.8-4.8
+8.2
84
1211
121.5+5.9
113.3-2.3
+8.2
85
219
121.3+5.7
113.1-2.5
+8.2
86
288
121.2+5.6
113.0-2.6
+8.2
87
853
117.8+2.2
109.7-5.9
+8.2
88
187
120.5+4.9
112.4-3.2
+8.2
89
643
122.8+7.2
114.7-0.9
+8.1
90
441
121.5+5.9
113.4-2.2
+8.1
91
778
122.5+6.9
114.4-1.2
+8.1
92
351
119.8+4.2
111.7-3.9
+8.1
93
1015
120.2+4.6
112.2-3.4
+8.0
94
216
120.1+4.5
112.1-3.5
+8.0
95
217
121.0+5.4
113.0-2.6
+8.0
96
284
119.9+4.3
111.9-3.7
+8.0
97
738
120.6+5.0
112.6-3.0
+8.0
98
521
119.0+3.4
111.1-4.5
+8.0
99
515
117.3+1.7
109.3-6.2
+8.0
100
399
116.9+1.4
109.0-6.6
+7.9
101
191
119.0+3.4
111.1-4.5
+7.9
102
338
121.9+6.3
114.0-1.6
+7.9
103
572
119.0+3.4
111.1-4.5
+7.9
104
598
121.6+6.0
113.8-1.8
+7.8
105
505
115.0-0.6
107.1-8.5
+7.8
106
444
117.9+2.3
110.1-5.5
+7.8
107
289
122.5+6.9
114.7-0.9
+7.8
108
75
119.0+3.4
111.3-4.3
+7.8
109
665
123.0+7.4
115.3-0.3
+7.8
110
1268
121.2+5.6
113.5-2.1
+7.7
111
729
121.2+5.6
113.5-2.1
+7.7
112
797
118.1+2.5
110.4-5.2
+7.7
113
526
120.2+4.6
112.5-3.1
+7.7
114
904
120.3+4.7
112.6-3.0
+7.7
115
258
119.0+3.4
111.4-4.2
+7.7
116
1084
121.4+5.9
113.8-1.8
+7.7
117
708
117.8+2.2
110.1-5.4
+7.6
118
121
119.6+4.0
112.0-3.5
+7.6
119
1085
121.4+5.9
113.9-1.7
+7.6
120
777
125.0+9.4
117.4+1.9
+7.6
121
68
118.8+3.2
111.2-4.4
+7.5
122
142
120.4+4.8
112.8-2.7
+7.5
123
190
119.9+4.3
112.4-3.2
+7.5
124
797
120.2+4.6
112.7-2.9
+7.5
125
651
119.4+3.8
111.9-3.7
+7.5
126
502
117.3+1.7
109.8-5.8
+7.5
127
1255
119.5+3.9
112.0-3.5
+7.5
128
434
121.1+5.5
113.6-2.0
+7.5
129
543
125.3+9.8
117.9+2.3
+7.4
130
329
121.9+6.3
114.5-1.1
+7.4
131
1193
120.4+4.8
113.0-2.6
+7.4
132
116
121.5+6.0
114.1-1.4
+7.4
133
512
120.1+4.6
112.8-2.8
+7.4
134
469
120.5+4.9
113.1-2.5
+7.4
135
353
119.9+4.3
112.5-3.1
+7.4
136
212
120.4+4.8
113.0-2.6
+7.4
137
424
117.4+1.8
110.0-5.6
+7.4
138
412
120.1+4.5
112.7-2.9
+7.3
139
316
120.7+5.1
113.4-2.2
+7.3
140
385
117.7+2.1
110.4-5.2
+7.3
141
681
123.0+7.4
115.7+0.1
+7.3
142
99
118.8+3.2
111.5-4.1
+7.3
143
140
120.1+4.5
112.9-2.7
+7.2
144
974
120.1+4.5
113.0-2.6
+7.1
145
73
121.1+5.5
113.9-1.7
+7.1
146
1453
120.7+5.1
113.6-2.0
+7.1
147
230
117.8+2.2
110.7-4.9
+7.1
148
96
119.2+3.6
112.1-3.5
+7.1
149
464
120.7+5.1
113.6-2.0
+7.1
150
525
117.8+2.2
110.7-4.9
+7.1
151
546
121.4+5.8
114.3-1.2
+7.1
152
345
120.4+4.8
113.4-2.2
+7.0
153
484
120.8+5.2
113.8-1.8
+7.0
154
722
119.4+3.8
112.5-3.1
+7.0
155
163
119.6+4.0
112.7-2.9
+6.9
156
454
121.7+6.1
114.8-0.8
+6.9
157
872
117.8+2.2
110.9-4.6
+6.9
158
397
119.0+3.4
112.1-3.5
+6.8
159
400
117.7+2.1
110.9-4.7
+6.8
160
845
123.2+7.6
116.4+0.8
+6.8
161
1580
120.9+5.3
114.1-1.5
+6.8
162
1039
119.1+3.5
112.3-3.3
+6.8
163
1085
123.8+8.3
117.1+1.5
+6.7
164
583
119.4+3.8
112.7-2.9
+6.7
165
347
117.1+1.5
110.4-5.2
+6.7
166
289
117.7+2.1
111.1-4.5
+6.7
167
110
119.5+3.9
112.8-2.8
+6.7
168
1213
120.8+5.2
114.2-1.4
+6.7
169
118
118.9+3.3
112.3-3.3
+6.7
170
215
117.6+2.0
111.0-4.6
+6.7
171
299
115.2-0.4
108.6-7.0
+6.6
172
1403
119.7+4.1
113.1-2.5
+6.6
173
185
120.9+5.3
114.3-1.3
+6.6
174
291
119.0+3.4
112.3-3.2
+6.6
175
136
120.0+4.4
113.4-2.2
+6.6
176
421
117.5+1.9
110.9-4.7
+6.6
177
528
119.2+3.6
112.6-3.0
+6.6
178
260
118.6+3.0
112.0-3.6
+6.6
179
192
118.7+3.1
112.2-3.4
+6.6
180
914
120.6+5.0
114.1-1.5
+6.5
181
1498
121.2+5.6
114.7-0.9
+6.5
182
422
116.7+1.1
110.1-5.5
+6.5
183
282
119.6+4.0
113.1-2.5
+6.5
184
194
118.5+2.9
112.0-3.6
+6.5
185
372
120.6+5.1
114.2-1.4
+6.5
186
507
119.4+3.8
113.0-2.6
+6.5
187
153
119.5+3.9
113.0-2.6
+6.4
188
84
118.3+2.7
111.9-3.7
+6.4
189
174
119.4+3.8
113.0-2.6
+6.4
190
248
119.9+4.3
113.5-2.1
+6.4
191
1129
126.3+10.8
120.0+4.4
+6.4
192
1209
120.0+4.4
113.7-1.9
+6.4
193
840
119.4+3.8
113.0-2.6
+6.4
194
701
121.9+6.4
115.6+0.0
+6.3
195
349
116.3+0.8
110.0-5.6
+6.3
196
365
122.0+6.4
115.7+0.1
+6.3
197
1018
118.2+2.6
111.9-3.7
+6.3
198
278
119.7+4.1
113.4-2.2
+6.3
199
847
121.9+6.3
115.6-0.0
+6.3
200
457
116.8+1.3
110.6-5.0
+6.2
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?