San Francisco

2026 · descriptive season · nil-value-v3 · Team results

Roster valuesβ

Roster values for San Francisco, 2026: retrospective net points per 100 possessions and, for 2025–26 only, a speculative compensation best guess. Players are listed by possession share, not by value or dollars.
Player	Possession share	Net and range	Wins above replacement and range	Speculative compensation (best guess)	Compensation 80% range
Ryan Beasley	79.0%	+1.5range +0.3 to +2.7Sufficient evidence	+5.5range +4.8 to +6.3Sufficient evidence	$110KspeculativeLoading	$40K to $280KLoading
Tyrone Riley IV	75.5%	+2.3range +1.1 to +3.6Sufficient evidence	+5.7range +5.0 to +6.3Sufficient evidence	$135KspeculativeLoading	$54K to $355KLoading
Vukasin Masic	54.8%	-0.2range -1.6 to +1.1Sufficient evidence	+3.3range +2.7 to +3.8Sufficient evidence	$39KspeculativeLoading	$5K to $115KLoading
David Fuchs	53.3%	+1.4range -0.1 to +2.8Sufficient evidence	+3.7range +3.1 to +4.3Sufficient evidence	$72KspeculativeLoading	$22K to $190KLoading
Legend Smiley	52.2%	-0.0range -1.7 to +1.5Sufficient evidence	+3.2range +2.6 to +3.8Sufficient evidence	$42KspeculativeLoading	$6K to $120KLoading
Junjie Wang	51.6%	+0.2range -1.2 to +1.3Sufficient evidence	+3.2range +2.8 to +3.7Sufficient evidence	$44KspeculativeLoading	$7K to $120KLoading
Guillermo Diaz Graham	43.7%	+3.0range +2.0 to +4.1Sufficient evidence	+3.5range +3.1 to +3.9Sufficient evidence	$105KspeculativeLoading	$41K to $285KLoading
Mookie Cook	38.9%	-0.2range -1.3 to +1.1Sufficient evidence	+2.3range +2.0 to +2.7Sufficient evidence	$31KspeculativeLoading	$5K to $87KLoading
Veniamin Abosi	28.8%	-0.0range -1.6 to +1.2Sufficient evidence	+1.7range +1.4 to +2.0Sufficient evidence	$25KspeculativeLoading	$5K to $72KLoading
Saba Gigiberia	11.8%	-0.1range -1.0 to +0.8Limited evidence	+0.7range +0.6 to +0.8Limited evidence	$13KspeculativeLoading	$4K to $34KLoading
Aidan Braccia	5.9%	Fewer than 300 on-court possessions; no value is estimated. Insufficient evidence	Not applicable	$5Kspeculative, floor onlyLoading	$2K to $12KLoading
Weilun Zhao	4.1%	Fewer than 300 on-court possessions; no value is estimated. Insufficient evidence	Not applicable	$5Kspeculative, floor onlyLoading	$2K to $12KLoading
Sean Blakely Drummond	0.3%	Fewer than 300 on-court possessions; no value is estimated. Insufficient evidence	Not applicable	$5Kspeculative, floor onlyLoading	$2K to $12KLoading

Ranges are the 10th to 90th percentile of whole-game resampling draws. They are not calibrated intervals. Compensation is a speculative best guess of a share of the school's own revenue-sharing budget, not observed pay.

Team pool and per-player compensation · 2025–26β

Team Pool: $680K; 80% Range $305K to $1.55M
Tier: Mid and high major; low to medium evidence
Team Pool, Scenarios Z1 to Z4: $645K to $860K; Scenario medians
Players with Figures: 13; 3 floor only

The widest scenario range, $390K to $1.90M, is wider than the 80% Range above (soft check S5). Methodology

Players in possession-share order
Player	Poss. Share	Speculative Best Guess	80% Range	Median % of Team Pool	Scenarios Z1 to Z4	Floor Part	Evidence and Flags
Ryan Beasley	79.0%	$110K	$40K to $280K	16.6%	$105K to $140K	$5K	Sufficient evidenceRange driven by assumption, not data
Tyrone Riley IV	75.5%	$135K	$54K to $355K	18.2%	$125K to $165K	$5K	Sufficient evidenceRange driven by assumption, not data
Vukasin Masic	54.8%	$39K	$5K to $115K	7.1%	$37K to $49K	$5K	Sufficient evidenceRange driven by assumption, not data
David Fuchs	53.3%	$72K	$22K to $190K	10.8%	$68K to $90K	$5K	Sufficient evidenceRange driven by assumption, not data
Legend Smiley	52.2%	$42K	$6K to $120K	7.5%	$39K to $52K	$5K	Sufficient evidenceRange driven by assumption, not data
Junjie Wang	51.6%	$44K	$7K to $120K	7.6%	$41K to $55K	$5K	Sufficient evidenceRange driven by assumption, not data
Guillermo Diaz Graham	43.7%	$105K	$41K to $285K	13.7%	$97K to $130K	$5K	Sufficient evidenceRange driven by assumption, not data
Mookie Cook	38.9%	$31K	$5K to $87K	5.5%	$29K to $38K	$5K	Sufficient evidenceRange driven by assumption, not data
Veniamin Abosi	28.8%	$25K	$5K to $72K	4.4%	$24K to $32K	$5K	Sufficient evidenceRange driven by assumption, not data
Saba Gigiberia	11.8%	$13K	$4K to $34K	2.2%	$12K to $16K	$5K	Limited evidenceRange driven by assumption, not data
Aidan Braccia	5.9%	$5K	$2K to $12K	0.8%	$5K to $6K	$5K	Floor only: below 300 possessions, no value estimateRange driven by assumption, not data
Weilun Zhao	4.1%	$5K	$2K to $12K	0.8%	$5K to $6K	$5K	Floor only: below 300 possessions, no value estimateRange driven by assumption, not data
Sean Blakely Drummond	0.3%	$5K	$2K to $12K	0.8%	$5K to $6K	$5K	Floor only: below 300 possessions, no value estimateRange driven by assumption, not data
Team Pool	—	$680K	$305K to $1.55M	100%	$645K to $860K	—

What-if calculator on the team page

School Finances · School-Reported (EADA)

Self-reported to the U.S. Department of Education. Expenses cover coaching, travel, aid and operations, not player pay.

School-reported finances, 2024-25, University of San Francisco (NCAA Division I without football)
Measure	School-Reported Amount	Note
Men's basketball revenue	$6,432,687	Equals expenses (budget identity)
Men's basketball expenses	$6,432,687
Athletics department expenses	$27,558,534
Athletics department revenue	$27,558,534

Coverage and exclusions

28 of 31 games passed lineup acceptance (90%). 100% of accepted on-court possessions are bound to registered players.

3 of 31 audited games did not pass lineup acceptance; per-game reasons are counted for the whole season (see methodology), not per team

Program revenueβ

EADA year 2024-25 (alternate pairing to repo season 2026; fiscal-year mapping unconfirmed) · self-reported by the school, no external review · EADA revenue is any money used to pay expensesrevenue equals expenses

Reported revenue	$6,432,687	Reported by the school; not NIL pay and not a model output.
Reported expenses	$6,432,687	Reported by the school.

EADA link: matched to the registry school by a unique institution name and state.

No model-implied dollar range is shown (status: suppressed). The published dollar status could not be loaded; see NIL methodology for the reason.

Schedule loadβ

Counts are descriptive and associational, with neutral formatting. Consecutive away games is the only travel proxy; distance, time zones and injuries are not modeled.

Rest days before each game

Rest days	Games
First game (no rest value)	1
1 day	1
2 days	3
3 or more days	26

Rest is capped at three days. Descriptive count.

Load summary

Mean games in prior 7 days	1.4
Games with fewer rest days than the opponent	1
Longest run of consecutive away games	2

Association between rest and margin, all teams

Pooled association passes the declared test (gate S1); not causal and not specific to this team · 0.86 points of margin associated with each extra rest day, range +0.33 to +1.41

This is an association, not a causal lever, pooled across Division I and not specific to this team.

Manifest 13921361a27d · NIL methodology