How can I predict rank of a team based on list of team members and past placement?

I just started with ML, so this could potentially be a pretty stupid question, plz forgive me.

Here's the gist of the problem:

I have a list in json format like this:

[
  {
    // Final rank of the team after all the matches are done
    rank: 7,
    // List of team members
    characters: [
      { char_id: my_char_1, level: 2, item_ids: [1, 2, 3] }
      ...
    ]
  }
]

I want to predict the rank for a given team of characters without looking at the competitors. Basically, I want to go from this:

[
  { char_id: my_char_1, level: 2, item_ids: [1, 2, 3]  }
  ...
]

to a single output neuron with a value from 1 to 100.

I parsed the source data into the first json structure, but I'm currently stuck at trying to give Tensorflow pairs of data (team + past rank), ideally in the form of two arrays where elements at a given index belong together.

TLDR:

Assuming this is possible at all (and not a lack of information or something), how do I feed Tensorflow this example data (list of team members - past rank) properly to predict the future rank of a completely new team?

I've found this question which is somewhat similar: Predicting outcome of MOBA team games

Based on this answer, I'm assuming I'll have to normalize the data, so instead of an array I would need a map like this:

{
  my_char_1: null,
  my_char_2: {
    level: 2,
    item_ids: {
      1: true,
      2: false,
      3: true
      ...
    }
  },
  ...
}

However, I struggle with the act of passing the data itself, not so much how to prepare the json structure. Basically, take whatever json structure would be optimal and assume it already exists.