## similarity.find_similar_players()


Rank candidate players by similarity to `target`.


Usage


``` python
similarity.find_similar_players(
    target,
    candidates,
    n=10,
    use_position=True,
)
```


`target` is a dict of career totals (see [similarity_score](similarity.similarity_score.md#baseball_trajectory.similarity.similarity_score)). `candidates` is a Polars DataFrame with the same career-totals columns, plus a `playerID` and (optionally) `fullName`.

Returns the top `n` candidates sorted by descending similarity score, with an added `sim_score` column.