Prentiss Hubb

Notre Dame · 31 available appearances · 2019–2019 · Linked seasons and observed career performance

Available player-season logs · Partial career coverage

Logged performance

1039:12 minutes logged

Games logged: 31
Minutes per game: 33:31
Points per game: 8.2
Rebounds / assists per game: 3.1 / 4.1

Box line on three bases
Basis	Pts	Reb	Ast	Stl	Blk	TO	PF
Per game	8.2	3.1	4.1	1.0	0.4	2.0	1.9
Per 40 minutes	9.7	3.7	4.9	1.2	0.5	2.4	2.3
Season totals	253	96	127	31	12	62	60

Shooting with attempts
Shot	Made–att.	Pct	Att/G	Att/40
FG	91–278	32.7%	9.0	10.7
2P	50–121	41.3%	3.9	4.7
3P	41–157	26.1%	5.1	6.0
FT	30–46	65.2%	1.5	1.8

Derived rates (computed here from the box line, not published statistics)
eFG%	Estimated TS%	3PA / FGA	FTA / FGA	AST / TO
40.1%	42.4%	56.5%	16.5%	2.05

Accepted logged appearances only; absent games are not zeroes. † marks a rate on fewer attempts than the floor (field goal 100, three-point 50, free throw 25). Floors are descriptive, not model output. Definitions

Speculative compensationβ

No revenue-sharing budget existed before the 2025–26 season, so no compensation figure is modelled for earlier seasons. The logs on this page are from seasons before 2025–26. A speculative best guess with an 80% range exists for 2025–26 only, for players on a modelled team; this player's by-season table below shows it for 2025–26 if one is published, and the linked athlete page has the full card.

Model-implied value and speculative compensation by seasonβ

Net points per 100 possessions by season, newest first. The speculative compensation best guess for 2025–26 only sits right after the team, so it is the first figure after the season. Ordered by season, not by value or dollars. Compensation is a best guess of a share of the school’s own revenue-sharing budget, not observed pay.
Season	Team	Speculative compensation (best guess)	Compensation 80% range	Split role	Net	Range	Evidence	Offense	Defense	Wins above replacement	On-court possessions	Sample and flags
2022	Notre Dame	None: no budget that season	—	development: in model selection	+1.1	+0.1 to +2.2	Sufficient evidence	+1.7	-0.6	Not computed for this season	3,314
2021	Notre Dame	None: no budget that season	—	development: in model selection	-0.8	-1.7 to +0.1	Sufficient evidence	+1.6	-2.3	Not computed for this season	2,570	2020-21 season, shortened by COVID-19 cancellations; kept in the model fit with this flag.
2020	Notre Dame	None: no budget that season	—	development: in model selection	-0.2	-0.9 to +0.6	Sufficient evidence	+1.0	-1.1	Not computed for this season	3,164
2019	Notre Dame	None: no budget that season	—	development: in model selection	-1.0	-2.2 to -0.1	Sufficient evidence	+0.0	-1.0	Not computed for this season	3,236

Open circle: limited evidence. Filled circle: sufficient evidence. Vertical line: zero. This table makes no claim about change over time, so it has no change column or trend. Seasons 2011 to 2017 have a point value but no range, so none is shown. Compensation exists for 2025–26 only; earlier rows say that no budget existed. NIL methodology · Compensation methodology

Game log

Trend over the 31 listed games, oldest to newest. Horizontal position follows the date. Minutes and points start at zero; game eFG% scales to its own range. Each row has its own vertical scale, so compare the printed values, not the graphics. Faint line: five-game rolling mean.
Measure	Last five	Listed games	Range
Minutes	38:48	33:31	18:57–39:59
Points	9.0	8.2	0.0–18.0
Game eFG%	38.6%	37.4%	0.0%–83.3%

Game log, box line per game. eFG% is computed for the game from field goals and three-pointers; it is blank without a field-goal attempt.
Season and date	Opponent	Result	Min	Pts	Reb	Ast	Stl	Blk	TO	PF	FG	3P	FT	eFG
2019 · 2019-03-13	Louisville	L 53–75	37:39	6	3	1	2	0	1	2	2-9	0-5	2-2	22.2%
2019 · 2019-03-12	Georgia Tech	W 78–71	39:50	9	6	6	1	0	2	2	2-8	1-3	4-5	31.3%
2019 · 2019-03-09	Pittsburgh	L 53–56	37:44	7	3	7	0	1	2	1	3-9	1-5	0-0	38.9%
2019 · 2019-03-06	Clemson	L 62–64	39:59	7	3	4	2	0	5	3	3-9	1-7	0-0	38.9%
2019 · 2019-03-03	Louisville	L 61–75	38:46	16	1	6	2	0	2	1	6-13	4-10	0-0	61.5%
2019 · 2019-02-25	Florida St	L 61–68	39:13	17	7	3	1	0	2	0	6-15	3-6	2-4	50.0%
2019 · 2019-02-23	Virginia Tech	L 59–67	36:28	2	2	7	1	0	1	3	1-8	0-5	0-0	12.5%
2019 · 2019-02-19	Wake Forest	L 68–75	37:08	11	2	9	1	0	4	3	4-10	2-7	1-2	50.0%
2019 · 2019-02-16	Virginia	L 54–60	39:45	7	3	2	0	0	1	2	3-8	1-4	0-0	43.8%
2019 · 2019-02-10	Georgia Tech	W 69–59	33:33	13	5	3	0	0	1	1	4-7	1-3	4-4	64.3%
2019 · 2019-02-06	Miami FL	L 47–62	31:03	3	3	2	3	0	6	1	1-7	1-6	0-0	21.4%
2019 · 2019-02-02	Boston College	W 79–73	35:54	16	2	4	1	2	2	4	6-10	2-5	2-2	70.0%
2019 · 2019-01-28	Duke	L 61–83	35:28	13	3	4	0	0	2	0	5-13	3-7	0-0	50.0%
2019 · 2019-01-26	Virginia	L 55–82	35:04	7	1	5	0	1	1	3	3-7	1-4	0-1	50.0%
2019 · 2019-01-22	Georgia Tech	L 61–63	23:34	0	2	5	1	0	1	2	0-4	0-3	0-0	0.0%
2019 · 2019-01-15	North Carolina	L 69–75	31:42	18	3	6	0	1	4	4	6-16	4-10	2-3	50.0%
2019 · 2019-01-12	Boston College	W 69–66	39:16	16	6	5	3	0	3	4	5-15	4-8	2-2	46.7%
2019 · 2019-01-05	Syracuse	L 62–72	28:20	0	2	4	1	1	2	3	0-4	0-4	0-0	0.0%
2019 · 2019-01-01	Virginia Tech	L 66–81	37:24	3	3	3	0	0	2	0	1-10	1-8	0-0	15.0%
2019 · 2018-12-29	Coppin St	W 63–56	31:12	2	3	2	2	0	1	1	1-7	0-4	0-0	14.3%
2019 · 2018-12-20	Jacksonville	W 100–74	30:09	3	2	7	0	1	2	2	1-7	1-6	0-0	21.4%
2019 · 2018-12-18	Binghamton	W 69–56	33:34	0	2	7	2	1	4	0	0-6	0-2	0-1	0.0%
2019 · 2018-12-08	UCLA	L 62–65	35:35	7	6	4	1	0	2	2	2-12	1-4	2-2	20.8%
2019 · 2018-12-04	Oklahoma	L 80–85	32:22	4	2	3	1	0	1	2	2-9	0-4	0-2	22.2%
2019 · 2018-11-27	Illinois	W 76–74	32:23	10	3	3	2	0	4	2	3-7	1-3	3-5	50.0%
2019 · 2018-11-24	DePaul	W 95–70	32:32	15	5	5	1	1	1	2	6-12	2-7	1-2	58.3%
2019 · 2018-11-20	Duquesne	W 67–56	34:55	14	4	1	1	1	1	2	5-10	3-6	1-2	65.0%
2019 · 2018-11-17	William & Mary	W 73–64	31:04	11	4	2	1	1	1	1	5-10	1-3	0-2	55.0%
2019 · 2018-11-14	Radford	L 60–63	26:12	5	2	1	0	1	0	2	1-6	0-2	3-4	16.7%
2019 · 2018-11-08	Chicago St	W 89–62	22:27	6	2	4	0	0	1	1	2-7	1-4	1-1	35.7%
2019 · 2018-11-06	IL Chicago	W 84–67	18:57	5	1	2	1	0	0	4	2-3	1-2	0-0	83.3%

Secondary NCAA-derived SportsDataverse archive; reconstructed player statistics. Corroborated against Kaggle dates, participants, scores and overtime; incomplete identity coverage is retained.