🐻⬇️🏀

UC Santa Cruz

Season: 2026 2025 2024 2023 2022 2020
Also known as: UC Santa Cruz
Program History

Model Outputs

2025-2026
Catalog

Output is shown as model rating with league rank in parentheses when available.

Model Output Notes
Bradley-Terry Recency Bradley-Terry Recency Static Bradley-Terry with exponential recency weights on newer games. HCA +95 elo More → 998 (#291) HCA +95 elo
Margin Recency Margin Recency Margin regression with exponential recency weights on newer games. HCA +3.3 More → +6.8 (#213) HCA +3.3
Pythagorean Pythagorean Pythagorean win expectation from raw points scored and allowed. More → 0.099 (#526) -
Adjusted Efficiency Adjusted Efficiency Opponent-adjusted efficiency model with separate offensive and defensive components. AdjNet -14.8 More → 0.153 (#576) AdjNet -14.8
Log Adjusted Log Adjusted Log-scale adjusted efficiency model that downweights blowout leverage. AdjNet -14.4 More → 0.157 (#574) AdjNet -14.4
Points Off/Def Points Off/Def Raw points regression with separate offensive and defensive team parameters. AdjO 65.1 | AdjD 73.2 More → 0.323 (#533) AdjO 65.1 | AdjD 73.2
Points Off/Def Recency Points Off/Def Recency Off/def points regression with exponential recency weights. AdjO 79.1 | AdjD 74.9 More → 0.594 (#158) AdjO 79.1 | AdjD 74.9
Core Ensemble Core Ensemble Equal-logit blend of Elo, recency BT, recency margin, log-adjusted pyth, and points off/def. Blend of Elo, BT, Margin, PythLog, PtsOD More → 0.648 (#191) Blend of Elo, BT, Margin, PythLog, PtsOD
Recency Ensemble Recency Ensemble Equal-logit blend of Elo, recency BT, recency margin, log-adjusted pyth, and recency points off/def. Blend of Elo, BT, Margin, PythLog, PtsOD More → 0.625 (#207) Blend of Elo, BT, Margin, PythLog, PtsOD
Dynamic Bradley-Terry Dynamic Bradley-Terry Time-evolving paired-comparison model with latent team strength drift. RD 350 | GP 1 More → 991 (#240) RD 350 | GP 1

2026 Schedule & Results

Date Vs/At Opponent Result Score
2025-11-04 vs San Fran. St. W 98 - 67

2026 Roster

Minutes by Position

The surface stays filled across the five on-court roles. Use the labels or legend to isolate how each player absorbs guard-to-big minutes.

Player Pos GP MIN PTS REB AST STL BLK TO FGA Numbers PM PM/G PM/40 FG% 3P% FT% RAPM TS% eFG%
Zion Sensley - 1 24.9 23.0 14.0 1.0 1.0 0.0 0.0 12.0 27.0 - - - 66.7 60.0 66.7 - 78.6 79.2
CJ Shaw - 1 24.7 20.0 6.0 1.0 4.0 0.0 1.0 15.0 15.0 -84 -4.4 -28.4 53.3 20.0 75.0 0.16 59.7 56.7
Colin Smith - 1 20.3 13.0 5.0 0.0 0.0 1.0 2.0 7.0 10.0 -9 -0.8 -16.1 42.9 50.0 60.0 -0.11 57.0 50.0
Miro Little - 1 25.5 12.0 7.0 3.0 0.0 1.0 1.0 2.0 20.0 - - - 100.0 100.0 77.8 - 100.7 125.0
Hosana Kitenge - 1 18.4 11.0 9.0 1.0 1.0 0.0 0.0 9.0 13.0 - - - 44.4 0.0 75.0 - 51.1 44.4
Aidan Mahaney - 1 22.4 9.0 3.0 3.0 1.0 0.0 0.0 5.0 11.0 - - - 60.0 100.0 66.7 - 71.2 70.0
Jason Fontenet II - 1 23.4 6.0 5.0 3.0 0.0 1.0 4.0 8.0 3.0 -3 -0.1 -1.0 25.0 0.0 50.0 1.18 30.7 25.0
Kyle Haughy - 1 3.8 2.0 2.0 0.0 0.0 0.0 0.0 1.0 3.0 - - - 100.0 0 0.0 - 53.2 100.0
Michael Simcoe - 1 10.5 2.0 4.0 0.0 0.0 0.0 0.0 2.0 4.0 - - - 50.0 0 0.0 - 41.0 50.0
Isaac Shingange - 1 5.6 0.0 0.0 0.0 0.0 0.0 1.0 0.0 -1.0 - - - 0 0 0 - 0 0
John Simcoe - 1 3.8 0.0 2.0 0.0 0.0 0.0 1.0 0.0 1.0 - - - 0 0 0 - 0 0
Luke Zuffelato - 1 16.9 0.0 1.0 1.0 0.0 0.0 1.0 7.0 -6.0 - - - 0.0 0.0 0 - 0.0 0.0

Numbers/Game vs RAPM

X-axis = Numbers/Game (PTS+REB+AST+STL+BLK-TO-FGA), Y-axis = RAPM.

Advanced: Numbers = PTS+REB+AST+STL+BLK-TO-FGA, PM = total +/-, PM/G = per game, PM/40 = per 40 minutes, RAPM = Regularized Adj Plus-Minus, TS% = True Shooting, eFG% = Effective FG