Algosys College Basketball

Search teams and players

Type a team or player name. Results are alphabetical; players show their 2026 team. Up and Down move, Enter opens the highlighted result or the first one, Esc closes.

San Diego

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

Roster valuesβ

Roster values for San Diego, 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.
PlayerPossession shareNet and rangeWins above replacement and rangeSpeculative compensation (best guess)Compensation 80% range
Toneari Lane65.8%-2.8range -4.4 to -1.4Sufficient evidence+2.7range +2.0 to +3.3Sufficient evidenceLoadingLoading
Adrian McIntyre61.2%+1.8range +0.7 to +2.8Sufficient evidence+4.0range +3.6 to +4.4Sufficient evidenceLoadingLoading
Juanse Gorosito61.0%+0.4range -1.0 to +1.9Sufficient evidence+3.6range +3.0 to +4.1Sufficient evidenceLoadingLoading
Ty-Laur Johnson56.7%-0.6range -1.7 to +0.5Sufficient evidence+3.1range +2.6 to +3.5Sufficient evidenceLoadingLoading
Vuk Boskovic55.4%-0.9range -1.9 to -0.1Sufficient evidence+2.9range +2.5 to +3.2Sufficient evidenceLoadingLoading
Dominique Ford38.2%-0.8range -1.9 to +0.4Sufficient evidence+2.1range +1.8 to +2.4Sufficient evidenceLoadingLoading
Assane Diop37.7%-0.9range -2.1 to +0.4Sufficient evidence+2.0range +1.7 to +2.3Sufficient evidenceLoadingLoading
Tim Moore34.4%-0.7range -1.8 to +0.4Sufficient evidence+1.9range +1.6 to +2.2Sufficient evidenceLoadingLoading
Gavin Ripp29.8%+0.0range -0.8 to +1.2Sufficient evidence+1.8range +1.6 to +2.0Sufficient evidenceLoadingLoading
Alejandro Aviles21.2%+0.7range -0.4 to +1.8Sufficient evidence+1.4range +1.2 to +1.5Sufficient evidenceLoadingLoading
Darrae Goodwin14.6%+0.4range -0.2 to +1.2Limited evidence+0.9range +0.8 to +1.0Limited evidenceLoadingLoading
Brandon Benjamin8.5%+1.0range +0.2 to +1.7Limited evidence+0.6range +0.5 to +0.6Limited evidenceLoadingLoading
Emmanuel Callas6.7%Fewer than 300 on-court possessions; no value is estimated. Insufficient evidenceNot applicableLoadingLoading
Piotr Winkowski6.3%Fewer than 300 on-court possessions; no value is estimated. Insufficient evidenceNot applicableLoadingLoading
Neel Beniwal1.1%Fewer than 300 on-court possessions; no value is estimated. Insufficient evidenceNot applicableLoadingLoading
Josh Hecht0.8%Fewer than 300 on-court possessions; no value is estimated. Insufficient evidenceNot applicableLoadingLoading
Nick Del Bosque0.4%Fewer than 300 on-court possessions; no value is estimated. Insufficient evidenceNot applicableLoadingLoading
Caleb Versher0.3%Fewer than 300 on-court possessions; no value is estimated. Insufficient evidenceNot applicableLoadingLoading

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

Loading Speculative compensation

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.

Loading School finances

Coverage and exclusions

27 of 31 games passed lineup acceptance (87%). 100% of accepted on-court possessions are bound to registered players.

  • 4 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$5,328,384Reported by the school; not NIL pay and not a model output.
Reported expenses$5,328,384Reported 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 daysGames
First game (no rest value)1
1 day1
2 days3
3 or more days26

Rest is capped at three days. Descriptive count.

Load summary

Mean games in prior 7 days1.4
Games with fewer rest days than the opponent1
Longest run of consecutive away games2

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