Maybe we should try this:

Let's say we have these data scores

```      
     8     5     6  
1 | 0.60  0.00  0.72
3 | 0.65  0.00  0.72
4 | 0.74  0.63  0.96 
```

We can interpret these as probabilities that a row and column will match.

If we do that we can say the probability of the first row not matching is
```
(1-0.60)*(1-0.00)*(1-0.72)=0.122
```

and the probability of the first column not matching is
```
(1-0.60)*(1-0.65)*(1-0.74)=0.04
```

so we pad out the matrix

```      
     8     5     6     x     x
1 | 0.60  0.00  0.72  0.12  0.12
3 | 0.65  0.00  0.72  ?     ?
4 | 0.74  0.63  0.96  ?     ?
x | 0.04  ?     ?     ?     ?
x | 0.04  ?     ?     ?     ?
```