Fix the explanation of "coord" in the bool query.

110_Multi_Field_Search/05_Multiple_query_strings.asciidoc

@@ -61,12 +61,12 @@ Why did we put the translator clauses inside a separate `bool` query?  All four
 clauses at the same level as the title and author clauses?
 
 The answer lies in how the score is calculated.  The `bool` query runs each
-`match` query, adds their scores together then divides by the number of
-clauses. Each clause at the same level has the same weight. In the above
-query, the `bool` query containing the translator clauses counts for ⅓ of
-the total score. If we had put the translator clauses at the same level as
-title and author, then they would have reduced the contribution of the title
-and author clauses to ¼ each.
+`match` query, adds their scores together, then multiplies by the number of
+matching clauses, and divides by the total number of clauses. Each clause at
+the same level has the same weight. In the above query, the `bool` query
+containing the translator clauses counts for ⅓ of the total score. If we had
+put the translator clauses at the same level as title and author, then they
+would have reduced the contribution of the title and author clauses to ¼ each.
 
 ==== Prioritising clauses
 

110_Multi_Field_Search/15_Best_field.asciidoc

@@ -71,14 +71,15 @@ And we find that this query actually gives Document 1 the higher score:
 To understand why, think about how the `bool` query calculates its score:
 
 * First, it runs both of the queries in the `should` clause
-* then it adds their scores together
-* and divides the total by the number of clauses, ie two.
+* then it adds their scores together,
+* multiplies the total by the number of matching clauses,
+* and divides the result by the total number of clauses, ie two.
 
 Document 1 contains the word `"brown"` in both fields, so both `match` clauses
 are successful and have a score.  Document 2 contains both `"brown"` and
 `"fox"` in the `body` field but neither word in the `title` field. The high
 score from the `body` query is added to the zero score from the `title` query,
-and divided by two, resulting in a lower overall score than for Document 1.
+and multiplied by ½, resulting in a lower overall score than for Document 1.
 
 In this example, the `title` and `body` fields are competing with each other.
 We want to find the single *best-matching* field.