Classification problem where one attribute is a vector
1) If you want to build a model with:

Input: Items bought
Output: Win/Loss


then you will probably want to learn a non-linear combination of the inputs to represent a build.  For example item_X may have very different purpose when paired with item_Y than with item_Z.

For the input format, you may consider creating a binary vector from the item list.  For example if there were only ten items, a game in which the champion purchased items 1,4,5,9 (in any order) would look like row 1; a game where he also purchased item 2 and 7 would look like row 2:

item_ID   | 0  1  2  3  4  5  6  7  8  9 
________________________________________    
champion_1| 0  1  0  0  1  1  0  0  0  1
champion_1| 0  1  1  0  1  1  0  1  0  1


There are a variety of models that might suit this task.  You might use decision trees for interpretability.  A simple neural net or SVM would likely also do a good job.  These should all be found in most basic ML packages.

2) The win rates of various items are directly computable.  Simply count the number of times a champion used the items in question and won and divide by the total number of times a champion used that item combination.  You can do this for any given group size (1 to 6)