Unexpected Match query scoring on a FirstMiddleLast field - elasticsearch

Question

I am using a match query to search a fullName field which contains names in (first [middle] last) format. I have two documents, one with "Brady Holt" as the fullName and the other as "Brad von Holdt". When I search for "brady holt", the document with "Brad von Holdt" is scored higher than the document with "Brady Holt" even though it is an exact match. I would expect the document with "Brady Holt" to have the highest score. I am guessing it has something to do with the 'von' middle name causing the score to be higher?
These are my documents:
[
{
"id": 509631,
"fullName": "Brad von Holdt"
},
{
"id": 55425,
"fullName": "Brady Holt"
}
]
This is my query:
{
"query": {
"match": {
"fullName": {
"query": "brady holt",
"fuzziness": 1.0,
"prefix_length": 3,
"operator": "and"
}
}
}
}
This is the query result:
"hits": [
{
"_index": "demo",
"_type": "person",
"_id": "509631",
"_score": 2.4942014,
"_source": {
"id": 509631,
"fullName": "Brad von Holdt"
}
},
{
"_index": "demo",
"_type": "person",
"_id": "55425",
"_score": 2.1395948,
"_source": {
"id": 55425,
"fullName": "Brady Holt"
}
}
]

Answer 1

A good read on how Elasticsearch does scoring, and how to manipulate relevancy, can be found in the Elasticsearch Guide: What is Relevance?. In particular, you may want to experiment with the explain functionality of a search query.
The shortest answer for you here is that the score of a hit is the product of its best-matching term according to a TF/IDF calculation. The number of matching terms will affect which documents are matched, but it's the "best" term that determine's a document's score. Your query doesn't have an "exact" match, per se: it has multiple matching terms, the scores of which are calculated independently.
Tuning relevancy can be a bit of a subtle art, and depends a lot on how the fields are being analyzed, the overall frequency distributions of various terms, the queries you're running, and even how you're sharding and distributing the index within a cluster (different shards will have different term frequencies).
(It may also be relevant, so to speak, that your example has two spellings of "Holt" and "Holdt".)
In any case, getting familiar with explain functionality and the underlying scoring mechanics is a helpful next step for you here.
Also, if you want an exact phrase match, you should read the ES guide on Phrase Matching.