Year in Review: The 2025 NCAA Women’s Volleyball Season

Volleyball

Women's

Featured

Season Recap

A full fall season of NCAA women’s volleyball boxscore data, pulled through the self-hosted NCAA pipeline into BigQuery: Miami’s two-way offensive season, Utah Valley’s blocking wall, and how Texas A&M swept Kentucky for the national title.

Published

July 8, 2026

The women’s volleyball pipeline has now pulled a complete season: every Division I match from late August through the national championship in mid-December, backfilled game by game into BigQuery via the same self-hosted NCAA API proxy behind the men’s pipeline. This post is the first real analysis built on top of it.

One note on the data: a much smaller share of rows here (well under 1%) carry a blank player name, the same kind of NCAA-side gap described in the men’s post, not a pipeline bug.

Querying the season

from google.cloud import bigquery

client = bigquery.Client()

# NCAA's API returns team as its own code (e.g. "STFRPA"), not a school
# name. Tables and charts below keep the raw code as-is (matches how the
# hockey post's chart also uses raw team codes); this mapping is only
# used to spell out real school names in the prose. A code not in this
# dict falls back to showing the raw abbreviation rather than failing,
# in case a future re-render surfaces a school not covered yet.
TEAM_NAMES = {
    "MIAMI": "Miami",
    "PITT": "Pittsburgh",
    "PENNST": "Penn State",
    "JMU": "James Madison",
    "WISC": "Wisconsin",
    "STFRPA": "Saint Francis (PA)",
    "PURDUE": "Purdue",
    "SAC ST": "Sacramento State",
    "UK": "Kentucky",
    "TCU": "TCU",
    "UALR": "Little Rock",
    "UT VAL": "Utah Valley",
    "TX A&M": "Texas A&M",
}


def team_name(code):
    return TEAM_NAMES.get(code, code)


query = """
SELECT
  player_name,
  team,
  SUM(CAST(kills AS INT64)) AS kills,
  SUM(CAST(attack_attempts AS INT64)) AS attempts,
  SAFE_DIVIDE(
    SUM(CAST(kills AS INT64)) - SUM(CAST(attack_errors AS INT64)),
    SUM(CAST(attack_attempts AS INT64))
  ) AS hitting_pct,
  COUNT(DISTINCT game_id) AS games
FROM `maydaystats.ncaa_volleyball_women.boxscores`
WHERE player_name != ''
GROUP BY player_name, team
HAVING attempts >= 200
ORDER BY kills DESC
LIMIT 10
"""

leaders = client.query(query).to_dataframe()
leaders

	player_name	team	kills	attempts	hitting_pct	games
0	Flormarie Heredia Colon	MIAMI	687	1447	0.289565	28
1	Olivia Babcock	PITT	646	1383	0.334056	35
2	Kennedy Martin	PENNST	634	1423	0.319044	31
3	Kennedy Louisell	JMU	605	1436	0.300836	31
4	Mimi Colyer	WISC	598	1313	0.340442	33
5	Korrin Burns	STFRPA	588	1335	0.268165	29
6	Kenna Wollard	PURDUE	577	1383	0.261750	34
7	Victoria Marthaler	SAC ST	553	1440	0.213194	30
8	Eva Hudson	UK	546	1376	0.319041	33
9	Evan Hendrix	TCU	543	1440	0.241667	32

Flormarie Heredia Colon of Miami led the country with 687 kills across 28 matches. Miami shows up twice in the national leaderboards this season: the same team that produced the kills leader also produced the national leader in aces, a rare two-way offensive season for one program.

The top ten, by kills

import matplotlib.pyplot as plt

fig, ax = plt.subplots(figsize=(9, 5))
labels = leaders["player_name"] + " (" + leaders["team"] + ")"
ax.barh(labels[::-1], leaders["kills"][::-1], color="#2c3e50")
ax.set_xlabel("Kills")
ax.set_title("Top 10 Kills Leaders, 2025 Women's Season")
ax.spines[["top", "right"]].set_visible(False)
plt.tight_layout()
plt.show()

Figure 1: Top 10 kills leaders, 2025 NCAA D1 women’s season (min. 200 attempts)

Digs, blocks, and aces

digs_query = """
SELECT player_name, team, SUM(CAST(digs AS INT64)) AS total
FROM `maydaystats.ncaa_volleyball_women.boxscores`
WHERE player_name != ''
GROUP BY player_name, team
ORDER BY total DESC
LIMIT 1
"""
digs_leader = client.query(digs_query).to_dataframe()

blocks_query = """
SELECT player_name, team, SUM(CAST(total_blocks AS FLOAT64)) AS total
FROM `maydaystats.ncaa_volleyball_women.boxscores`
WHERE player_name != ''
GROUP BY player_name, team
ORDER BY total DESC
LIMIT 1
"""
blocks_leader = client.query(blocks_query).to_dataframe()

aces_query = """
SELECT player_name, team, SUM(CAST(service_aces AS INT64)) AS total
FROM `maydaystats.ncaa_volleyball_women.boxscores`
WHERE player_name != ''
GROUP BY player_name, team
ORDER BY total DESC
LIMIT 1
"""
aces_leader = client.query(aces_query).to_dataframe()

Andrea Roman (Little Rock) led all players in digs with 632. Bella Wooden of Utah Valley led the country in blocks with 188, part of a team effort that led the nation in team blocking as well. And Ariana Rodriguez (Miami) topped the ace leaderboard with 77, the same program that produced the season’s kills leader.

The championship: Texas A&M sweeps Kentucky

final_query = """
SELECT player_name, team, kills, attack_attempts, hitting_percentage, digs
FROM `maydaystats.ncaa_volleyball_women.boxscores`
WHERE game_id = '6500718'
  AND CAST(kills AS INT64) > 0
ORDER BY team, CAST(kills AS INT64) DESC
"""
final_box = client.query(final_query).to_dataframe()
final_box

	player_name	team	kills	attack_attempts	hitting_percentage	digs
0	Logan Lednicky	TX A&M	11	32	0.250	7
1	Kyndal Stowers	TX A&M	10	23	0.304	6
2	Ifenna Cos-Okpalla	TX A&M	8	17	0.235	0
3	Emily Hellmuth	TX A&M	6	18	0.167	3
4	Morgan Perkins	TX A&M	3	9	0.333	1
5	Maddie Waak	TX A&M	1	2	0.500	5
6	Eva Hudson	UK	13	45	0.200	4
7	Brooklyn DeLeye	UK	9	28	0.036	8
8	Asia Thigpen	UK	7	15	0.333	2
9	Lizzie Carr	UK	7	15	0.200	1
10	Kennedy Washington	UK	2	7	-0.286	1
11	Kassie O'Brien	UK	2	4	0.500	6

Texas A&M swept Kentucky 3-0 on December 21, 2025 to win the national championship. Logan Lednicky led the Aggies with 11 kills on a .250 hitting percentage, with Kyndal Stowers adding 10 more on .304 hitting, a balanced attack across multiple hitters rather than one player carrying the offense.

Kentucky’s Eva Hudson, who finished among the national top ten in kills for the season, led all players in the match with 13 kills, but needed 45 attempts to get there, a .200 hitting percentage. By far the highest attempt count on the floor, it meant Kentucky’s offense ran through one hitter without the efficiency to match the volume, the same pattern that showed up in the men’s final two days apart on the calendar year.

What’s next

This covers the season at a high level: the national leaderboards and the championship match itself. The same table supports much narrower questions too, like how a team’s blocking efficiency changed after a lineup shift, or a transfer’s production before and after switching programs. Those are posts for another day, now that a full season of clean data is sitting in BigQuery.

The pipeline code behind this post lives here.

Note

Like the baseball, hockey, and men’s volleyball posts, this one uses Quarto’s frozen execution (freeze: auto): the queries above ran once, locally, against BigQuery, and the deployed site reuses that committed output rather than re-querying on every build.