Stanton - 2026 | Sports Data

Model Outputs

2025-2026

Catalog

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

Model	Output	Notes
Bradley-Terry Bradley-Terry Static logistic paired-comparison model with one team strength parameter. More →	882 (#484)	-
Bradley-Terry Recency Bradley-Terry Recency Static Bradley-Terry with exponential recency weights on newer games. HCA +62 elo More →	978 (#520)	HCA +62 elo
Margin Recency Margin Recency Margin regression with exponential recency weights on newer games. HCA +2.9 More →	-40.6 (#697)	HCA +2.9
Pythagorean Pythagorean Pythagorean win expectation from raw points scored and allowed. More →	0.000 (#751)	-
Efficiency Efficiency Tempo-adjusted efficiency version of Pythagorean ratings. NetEff -58.6 More →	0.000 (#595)	NetEff -58.6
Adjusted Efficiency Adjusted Efficiency Opponent-adjusted efficiency model with separate offensive and defensive components. AdjNet -51.0 More →	0.002 (#706)	AdjNet -51.0
Log Adjusted Log Adjusted Log-scale adjusted efficiency model that downweights blowout leverage. AdjNet -50.4 More →	0.002 (#707)	AdjNet -50.4
Points Off/Def Points Off/Def Raw points regression with separate offensive and defensive team parameters. AdjO 49.5 \| AdjD 85.2 More →	0.037 (#714)	AdjO 49.5 \| AdjD 85.2
Points Off/Def Recency Points Off/Def Recency Off/def points regression with exponential recency weights. AdjO 55.9 \| AdjD 66.6 More →	0.274 (#621)	AdjO 55.9 \| AdjD 66.6
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 →	0.055 (#693)	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 →	0.076 (#681)	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 2 More →	846 (#463)	RD 350 \| GP 2

2026 Schedule & Results

Date	Vs/At	Opponent	Result	Score
2025-11-11	@	Cal Lutheran	L	50 - 104
2025-11-22	@	Caltech	L	30 - 75

2026 Roster

Show Alumni

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%
Alexus Behn	-	1	34.9	12.0	4.0	5.0	4.0	0.0	15.0	23.0	-13.0	-	-	-	21.7	11.1	20.0	-	23.8	23.9
Sophia Laurel	-	1	35.1	10.0	4.0	1.0	1.0	1.0	4.0	14.0	-1.0	-	-	-	28.6	28.6	0	-	35.7	35.7
Tuana Akgun	-	2	29.2	7.5	9.5	1.0	1.0	0.0	2.5	8.0	8.5	-	-	-	37.5	0.0	37.5	-	38.4	37.5
Melissa Castro	-	2	28.9	7.0	4.5	2.0	0.5	0.0	2.5	7.5	4.0	-	-	-	33.3	16.7	75.0	-	41.8	36.7
Mary Gonzales	-	1	31.6	5.0	6.0	0.0	0.0	0.0	3.0	4.0	4.0	-	-	-	50.0	0	33.3	-	47.0	50.0
Jang Wei	-	2	22.6	4.5	1.5	1.0	0.5	0.0	2.0	6.5	-1.0	-	-	-	23.1	25.0	50.0	-	32.4	30.8
Paule Balanzategui	-	2	26.9	4.5	6.0	1.5	0.5	0.5	3.0	9.0	1.0	-	-	-	16.7	20.0	50.0	-	22.8	19.4
Jane Doe	-	2	26.8	2.0	3.5	0.5	1.0	0.0	4.0	4.0	-1.0	-	-	-	12.5	25.0	25.0	-	20.5	18.8
Naia Beltran	-	1	20.0	2.0	1.0	2.0	1.0	0.0	1.0	12.0	-7.0	-	-	-	8.3	0.0	0	-	8.3	8.3
Mizuki Higushijima	-	1	9.6	0.0	1.0	0.0	0.0	1.0	1.0	0.0	1.0	-	-	-	0	0	0	-	0	0

Numbers/Game vs RAPM

Not enough players with both Numbers/Game and RAPM to plot.

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