Year in Review: The 2025-26 NCAA Men’s Volleyball Season

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Season Recap

A full spring season of NCAA men’s volleyball boxscore data, pulled through the self-hosted NCAA pipeline into BigQuery: the kills leaders, and how Hawaii beat UC Irvine for the national title.

Published

July 8, 2026

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

One note on the data: about 6% of rows carry a blank player name despite having real stat values, a gap on NCAA’s own side for a small number of games rather than a pipeline bug (see the pipeline’s DEPLOY.md for the full explanation). Everything below filters those rows out.

Querying the season

from google.cloud import bigquery

client = bigquery.Client()

# NCAA's API returns team as its own 6-character code (e.g. "PEPPER"), 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 = {
    "PEPPER": "Pepperdine",
    "UC IRV": "UC Irvine",
    "BALLST": "Ball State",
    "CATAWB": "Catawba",
    "PENNST": "Penn State",
    "MCKEND": "McKendree",
    "EMMAN": "Emmanuel (Ga.)",
    "LEWIS": "Lewis",
    "HAWAII": "Hawaii",
    "UCLA": "UCLA",
    "USC": "USC",
}


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_men.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	Cole Hartke	PEPPER	452	937	0.352188	29
1	Andrej Jokanovic	UC IRV	419	998	0.278557	30
2	Patrick Rogers	BALLST	414	786	0.399491	31
3	Justin Arrowood	CATAWB	397	938	0.210021	27
4	Sean Kelly	UCLA	396	753	0.398406	31
5	Sean Harvey	PENNST	388	806	0.328784	29
6	Zach Rama	UCLA	386	803	0.332503	30
7	Dillon Klein	USC	385	770	0.340260	27
8	Bryce Wetjen	MCKEND	375	813	0.258303	29
9	Aidan Feeney	EMMAN	374	880	0.244318	26

Cole Hartke of Pepperdine led the country with 452 kills, on a 0.352 hitting percentage across 29 matches. UC Irvine’s Andrej Jokanovic finished a close second in raw kills, but needed nearly 60 more swings to get there, a volume-over-efficiency trade that shows up again later in his team’s championship match.

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, 2025-26 Men's Season")
ax.spines[["top", "right"]].set_visible(False)
plt.tight_layout()
plt.show()

Figure 1: Top 10 kills leaders, 2025-26 NCAA D1 men’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_men.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_men.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_men.boxscores`
WHERE player_name != ''
GROUP BY player_name, team
ORDER BY total DESC
LIMIT 1
"""
aces_leader = client.query(aces_query).to_dataframe()

Nico Paula (Lewis) led all players in digs with 264. Owen Rose (Penn State) topped the block chart with 156, well clear of the field. And Andrew Rowan (UCLA) led in aces with 57.

The championship: Hawaii over UC Irvine

final_query = """
SELECT player_name, team, kills, attack_attempts, hitting_percentage, total_blocks
FROM `maydaystats.ncaa_volleyball_men.boxscores`
WHERE game_id = '6598557'
  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	total_blocks
0	Kristian Titriyski	HAWAII	16	31	0.387	1
1	Adrien Roure	HAWAII	15	31	0.323	0
2	Louis Sakanoko	HAWAII	12	20	0.500	3
3	Justin Todd	HAWAII	7	10	0.500	1
4	Trevell Jordan	HAWAII	5	7	0.714	3
5	Tread Rosenthal	HAWAII	3	6	0.167	3
6	Andrej Jokanovic	UC IRV	13	35	0.229	3
7	William D'arcy	UC IRV	9	24	0.292	0
8	Andreas Brinck	UC IRV	6	18	0.111	1
9	Trevor Clark	UC IRV	6	12	0.333	6
10	Micah Goss	UC IRV	3	4	0.750	6
11	Cameron Kosty	UC IRV	1	2	.000	5

Hawaii beat UC Irvine 3-1 on May 11, 2026 to win the national championship, the program’s first title since 2021. Kristian Titriyski led the way with 16 kills on a .387 hitting percentage, with Adrien Roure adding 15 more. Louis Sakanoko was the most efficient hitter on the floor, going 12-for-20 (.500) with 3 blocks off the bench.

UC Irvine’s offense leaned almost entirely on Andrej Jokanovic, the same volume hitter who finished second nationally in kills for the season. He had a team-high 13 kills in the final, but needed 35 attempts to get there, a .229 hitting percentage that never let UC Irvine pull away the way Hawaii’s more balanced, more efficient attack did.

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 a single team’s hitting efficiency by conference opponent, or how a freshman’s role grew over the season. 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 and hockey 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.