Algosys College Basketball

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San Diego

WCC · 2025–26 · Raw net rank 297 · Raw net -8.7 · Tempo 68.8 · 31 Division I games · Through 2026-03-06

All seasons
Record
10–21
10 wins
AdjNet
-6.7β
rank 262 of 365
AdjO
106.1β
rank 223 of 365
AdjD
112.8β
rank 285 of 365
AdjT
68.9β
SOS
0.7β
rank 136 of 363
NCSOS
-3.2β
WAB
-13.4β
rank 288 of 363
RR
-0.6β
Exp. W
10.6β

Scouting Report

Scouting report with national ranks
CategoryOffenseDefenseD-I Average
Efficiency
Efficiency / 100 poss.104.5259113.2296108.5
Four Factors
Effective FG%51.218654.731851.4
TO / 100 poss.17.523718.95516.7
Offensive rebound %28.126134.532830.6
FTA / FGA %25.835838.828335.0
Shooting
3P%34.614132.710133.9
2P%50.822158.535651.7
FT%74.79573.525272.5
Style
3PA / FGA %43.740.139.5
Assists / FGM %59.157.352.6
Blocks / opp. 2PA %7.510.19.5
Point Distribution
3-pointers %37.228.531.4
2-pointers %47.050.848.8
Free throws %15.820.719.8

Best · Median · Worst · Mark: further right is a better national rank, of 365 teams.

Rotation Depth

Effective Rotation Size
10.6
Top Three Share of Minutes
38%
Bench Share of Minutes
42%
Players with Logged Minutes
18

Bench: players outside the top five in minutes. Definitions

Schedule and Results

Schedule and results
DateOpponentResultPoss.SiteRecord
11-07Idaho St.L 68–7163.3Home0–1
11-12IdahoW 78–7467.0Home1–1
11-18GramblingW 78–6874.0Home2–1
11-21UC RiversideL 71–8568.0Home2–2
11-25California BaptistL 61–7670.7Neutral2–3
11-30Long Beach St.L 72–7663.0Away2–4
12-05San Jose St.L 69–8673.0Away2–5
12-09Southern CaliforniaL 81–9478.5Home2–6
12-13Northern Ariz.W 78–6966.5Home3–6
12-19UC San DiegoW 82–8067.4Away4–6
12-22WashingtonL 56–8674.0Away4–7
12-28PacificW 66–5464.0Home5–7
12-30GonzagaL 93–9979.7Home5–8
01-02San FranciscoL 64–7471.4Away5–9
01-04Santa ClaraL 70–9874.5Away5–10
01-08PepperdineW 83–6367.2Home6–10
01-10PacificL 70–7769.9Away6–11
01-15Seattle UL 64–7572.3Away6–12
01-21Washington St.W 96–9270.0Home7–12
01-24Santa ClaraL 73–8574.6Home7–13
01-28PepperdineW 92–88 1OT80.3Away8–13
01-31Oregon St.L 76–78 1OT81.8Home8–14
02-04Saint Mary's (CA)L 60–8763.8Away8–15
02-07LMU (CA)L 63–8363.1Away8–16
02-11PortlandW 71–5863.7Home9–16
02-15San FranciscoL 79–9269.1Home9–17
02-21LMU (CA)L 65–7762.1Home9–18
02-25Oregon St.L 82–92 1OT74.3Away9–19
02-28PortlandL 74–8769.2Away9–20
03-05LMU (CA)W 66–6261.3Neutral10–20
03-06Seattle UL 56–5861.1Neutral10–21

Roster · 18 players

Player logs in 31 of 31 team games

Team roster, per game
Column groupsMinutesPer GameShootingLast Five Team Games
PlayerGMPGTeam Min%PtsRebAstStlBlkTOPF2P2P%3P3P%FTFT%eFG%TS%FTA/FGAGMPGMin%Pts/G
Toneari Lane3127:0466.9%11.02.00.70.60.10.61.242–9444.7%75–17742.4%32–3884.2%57.0%59.3%14.0%532:51 +5:4780.1%14.0 +3.0
Adrian McIntyre3125:0061.8%10.03.62.91.20.01.61.394–15361.4%24–7731.2%49–6773.1%56.5%59.5%29.1%532:48 +7:4880.0%10.4 +0.4
Juanse Gorosito3124:3560.7%10.62.82.21.00.11.31.545–10642.5%66–17338.2%40–4883.3%51.6%54.6%17.2%523:58 −0:3758.4%12.0 +1.4
Vuk Boskovic3120:4151.1%6.13.22.10.40.31.72.926–4557.8%38–10735.5%23–3959.0%54.6%55.9%25.7%520:27 −0:1449.9%8.2 +2.1
Ty-Laur Johnson2228:5550.7%15.02.84.22.20.33.03.095–19648.5%23–7829.5%71–8682.6%47.3%52.9%31.4%————
Dominique Ford2917:1239.7%5.62.10.70.90.00.71.245–9348.4%10–4522.2%†41–5475.9%43.5%49.8%39.1%420:38 +3:2640.3%5.5 −0.1
Assane Diop2718:2139.5%5.54.70.90.20.41.12.541–7852.6%17–4240.5%†16–2759.3%55.4%56.5%22.5%524:32 +6:1259.9%7.2 +1.7
Gavin Ripp2517:0033.9%3.92.41.40.50.11.02.234–6949.3%†4–1625.0%†18–2378.3%†47.1%†51.5%†27.1%†531:51 +14:5177.7%7.8 +3.9
Tim Moore2914:2733.4%5.52.30.30.40.70.51.546–8156.8%11–5520.0%34–4575.6%46.0%51.0%33.1%510:29 −3:5925.6%4.2 −1.3
Alejandro Aviles2310:3319.3%2.41.60.80.40.30.51.321–5141.2%†0–20.0%†13–1776.5%†39.6%†45.5%†32.1%†33:37 −6:565.3%0.0 −2.4
Darrae Goodwin1314:0614.6%4.32.30.60.20.40.81.710–1662.5%†10–3231.3%†6–966.7%†52.1%†53.9%†18.8%†————
Emmanuel Callas186:569.9%0.71.10.20.50.10.11.25–1241.7%†0–0—2–450.0%†41.7%†43.6%†33.3%†310:00 +3:0414.6%0.0 −0.7
Brandon Benjamin186:289.3%2.40.80.80.40.10.60.317–3056.7%†1–333.3%†7–1070.0%†56.1%†58.8%†30.3%†34:25 −2:036.5%0.7 −1.8
Piotr WinkowskiUnder 100 minutes144:545.5%1.51.10.20.30.70.50.78–1650.0%†0–0—5–1145.5%†50.0%†50.4%†68.8%†————
Neel BeniwalFew gamesUnder 100 minutes71:380.9%0.40.30.00.00.00.10.30–0—1–250.0%†0–0—75.0%†75.0%†0.0%†————
Josh HechtFew gamesUnder 100 minutes71:120.7%0.30.00.00.00.00.30.11–250.0%†0–10.0%†0–0—33.3%†33.3%†0.0%†————
Nick Del BosqueFew gamesUnder 100 minutes50:460.3%0.00.00.00.00.00.20.20–10.0%†0–0—0–0—0.0%†0.0%†0.0%†————
Caleb VersherFew gamesUnder 100 minutes21:480.3%0.00.50.50.00.00.00.50–10.0%†0–0—0–0—0.0%†0.0%†0.0%†————
Team, games with logs · per game31 games72.830.715.5not published2.612.2not published530–104450.8%280–81034.6%357–47874.7%51.2%54.7%25.8%
Sum of players72.827.715.57.72.611.718.5Shooting residuals: see Player Lines Against Team Totals.
Residual: team minus players0.0+3.10.0not published0.0+0.5not published

G: logged games. Team Min%: share of team minutes on court (40 per game plus overtime). 2P, 3P and FT show made–attempted. † Few attempts: under 100 FGA, 50 3PA or 25 FTA. Few G: under 10 games. <100 min: under 100 minutes. Last Five Team Games columns show change from the season value and hide on narrow screens.

Speculative Compensation · 2025–26β

Estimated share of the school's revenue-sharing budget for men's basketball. Not observed pay.

Team Pool
$735K
80% Range $330K to $1.65M
Tier
Mid and high major
low to medium evidence
Team Pool, Scenarios Z1 to Z4
$705K to $775K
Scenario medians
Players with Figures
18
6 floor only
Players in possession-share order
PlayerPoss. ShareSpeculative Best Guess80% RangeRange GraphicMedian % of Team PoolScenarios Z1 to Z4Floor PartEvidence and Flags
Toneari Lane65.8%$14K$2K to $99K2.0%$13K to $15K$4KSufficient evidenceRange driven by assumption, not data
Adrian McIntyre61.2%$180K$67K to $530K21.9%$175K to $190K$4KSufficient evidenceRange driven by assumption, not data
Juanse Gorosito61.0%$98K$22K to $280K13.7%$95K to $105K$4KSufficient evidenceRange driven by assumption, not data
Ty-Laur Johnson56.7%$54K$4K to $155K9.3%$52K to $57K$4KSufficient evidenceRange driven by assumption, not data
Vuk Boskovic55.4%$41K$3K to $135K7.9%$39K to $43K$4KSufficient evidenceRange driven by assumption, not data
Dominique Ford38.2%$33K$4K to $100K5.9%$32K to $34K$4KSufficient evidenceRange driven by assumption, not data
Assane Diop37.7%$31K$3K to $99K5.7%$29K to $32K$4KSufficient evidenceRange driven by assumption, not data
Tim Moore34.4%$30K$4K to $90K5.4%$28K to $31K$4KSufficient evidenceRange driven by assumption, not data
Gavin Ripp29.8%$42K$9K to $125K6.2%$41K to $44K$4KSufficient evidenceRange driven by assumption, not data
Alejandro Aviles21.2%$41K$13K to $115K5.3%$39K to $43K$4KSufficient evidenceRange driven by assumption, not data
Darrae Goodwin14.6%$27K$9K to $72K3.6%$26K to $28K$4KLimited evidenceRange driven by assumption, not data
Brandon Benjamin8.5%$22K$8K to $60K2.7%$21K to $23K$4KLimited evidenceRange driven by assumption, not data
Emmanuel Callas6.7%$4K$2K to $10K0.5%$4K in all four$4KFloor only: below 300 possessions, no value estimateRange driven by assumption, not data
Piotr Winkowski6.3%$4K$2K to $10K0.5%$4K in all four$4KFloor only: below 300 possessions, no value estimateRange driven by assumption, not data
Neel Beniwal1.1%$4K$2K to $10K0.5%$4K in all four$4KFloor only: below 300 possessions, no value estimateRange driven by assumption, not data
Josh Hecht0.8%$4K$2K to $10K0.5%$4K in all four$4KFloor only: below 300 possessions, no value estimateRange driven by assumption, not data
Nick Del Bosque0.4%$4K$2K to $10K0.5%$4K in all four$4KFloor only: below 300 possessions, no value estimateRange driven by assumption, not data
Caleb Versher0.3%$4K$2K to $10K0.5%$4K in all four$4KFloor only: below 300 possessions, no value estimateRange driven by assumption, not data
Team Pool—$735K$330K to $1.65M100%$705K to $775K—

What-If Calculator · Speculativeβ

Re-runs the 4,000 joint draws with your inputs. The address saves your inputs.

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 Diego (NCAA Division I-FCS)
MeasureSchool-Reported AmountNote
Men's basketball revenue$5,328,384Equals expenses (budget identity)
Men's basketball expenses$5,328,384
Athletics department expenses$37,050,500
Athletics department revenue$37,050,500

Team Totalsβ

Season per game, 31 games, 2,159 possessions. Observed box-score sums, unadjusted. Definitions

Team and opponent season totals
StatisticTeamOpponents
Points72.878.8
Field goals made26.127.5
Field goals attempted59.857.2
3-pointers made9.07.5
3-pointers attempted26.122.9
Free throws made11.516.3
Free throws attempted15.422.2
Offensive rebounds9.911.0
Defensive rebounds20.825.3
Total rebounds30.736.3
Assists15.515.8
Blocks2.63.4
Turnovers12.213.2
StealsNo team figure. Players sum to 238.
Personal foulsNo team figure. Players sum to 575.
Minutes (derived)202.4not published

31 of 31 games have player logs. Residual is team total minus sum of players. Team rebounds and team turnovers have no player owner, so a residual is expected. Definitions

Player lines against team totals
StatisticStatusTeam Total, Logged GamesSum of PlayersResidualToleranceTeam Total, Games Without Logs
PointsReconciled2,2572,2570±11.30
Field goals madeReconciled8108100±4.00
Field goals attemptedReconciled1,8541,8540±9.30
3-pointers madeReconciled2802800±2.00
3-pointers attemptedReconciled8108100±4.00
Free throws madeReconciled3573570±2.00
Free throws attemptedReconciled4784780±2.40
Offensive reboundsNo player splitnot publishednot publishednot publishednot publishednot published
Defensive reboundsNo player splitnot publishednot publishednot publishednot publishednot published
Total reboundsResidual shown953858+95±76.20
AssistsReconciled4794790±2.40
BlocksReconciled80800±2.00
TurnoversResidual shown378362+16±11.30
StealsNo team figurenot published238not publishednot publishednot published
Personal foulsNo team figurenot published575not publishednot publishednot published
Minutes (derived)Reconciled6,2756,255+20.0±125.50

Model-Implied Player Valueβ

Ranges are the 10th to 90th percentile of whole-game resampling draws. They are not calibrated intervals.

Open circle: limited evidence. Filled circle: sufficient evidence. Line: zero. No graphic: insufficient evidence.

Net points per 100 possessions, in possession-share order.
PlayerPoss. ShareNet and RangeβRange Graphic
Toneari Lane65.8%-2.8range -4.4 to -1.4Sufficient evidence
Adrian McIntyre61.2%+1.8range +0.7 to +2.8Sufficient evidence
Juanse Gorosito61.0%+0.4range -1.0 to +1.9Sufficient evidence
Ty-Laur Johnson56.7%-0.6range -1.7 to +0.5Sufficient evidence
Vuk Boskovic55.4%-0.9range -1.9 to -0.1Sufficient evidence
Dominique Ford38.2%-0.8range -1.9 to +0.4Sufficient evidence
Assane Diop37.7%-0.9range -2.1 to +0.4Sufficient evidence
Tim Moore34.4%-0.7range -1.8 to +0.4Sufficient evidence
Gavin Ripp29.8%+0.0range -0.8 to +1.2Sufficient evidence
Alejandro Aviles21.2%+0.7range -0.4 to +1.8Sufficient evidence
Darrae Goodwin14.6%+0.4range -0.2 to +1.2Limited evidence
Brandon Benjamin8.5%+1.0range +0.2 to +1.7Limited evidence
Emmanuel Callas6.7%Fewer than 300 on-court possessions; no value is estimated. Insufficient evidence
Piotr Winkowski6.3%Fewer than 300 on-court possessions; no value is estimated. Insufficient evidence
Neel Beniwal1.1%Fewer than 300 on-court possessions; no value is estimated. Insufficient evidence
Josh Hecht0.8%Fewer than 300 on-court possessions; no value is estimated. Insufficient evidence
Nick Del Bosque0.4%Fewer than 300 on-court possessions; no value is estimated. Insufficient evidence
Caleb Versher0.3%Fewer than 300 on-court possessions; no value is estimated. Insufficient evidence

Team Aggregate of Player Values, 2026β

Summary of the player values below. Split role: descriptive: not used in fit or gates. Model nil-value-v3. Definitions · NIL Methodology

Average value over five on-court slots, net points per 100 possessions
+8.8range +7.9 to +9.7Sufficient evidence

200 joint draws. The range is the 10th to 90th percentile. Player ranges cannot be added.

12 valued players hold 96.9% of on-court time. 6 without a value (under 300 possessions) hold 3.1%, counted at replacement level -9.5. Limited-evidence players hold 4.6%.

NIL Methodology · Scheduling and Model-Implied Value