Bellarmine - 2024 | Sports Data

Model Outputs

2025-2026 latest available

Catalog

No materialized model snapshot for 2024 yet, so this section is showing the latest available team-model rows.

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 →	1051 (#242)	-
Bradley-Terry Recency Bradley-Terry Recency Static Bradley-Terry with exponential recency weights on newer games. HCA +62 elo More →	1016 (#325)	HCA +62 elo
Margin Recency Margin Recency Margin regression with exponential recency weights on newer games. HCA +2.9 More →	+7.1 (#246)	HCA +2.9
Pythagorean Pythagorean Pythagorean win expectation from raw points scored and allowed. More →	0.985 (#155)	-
Efficiency Efficiency Tempo-adjusted efficiency version of Pythagorean ratings. NetEff +33.3 More →	0.991 (#86)	NetEff +33.3
Adjusted Efficiency Adjusted Efficiency Opponent-adjusted efficiency model with separate offensive and defensive components. AdjNet +23.2 More →	0.936 (#179)	AdjNet +23.2
Log Adjusted Log Adjusted Log-scale adjusted efficiency model that downweights blowout leverage. AdjNet +22.5 More →	0.935 (#183)	AdjNet +22.5
Points Off/Def Points Off/Def Raw points regression with separate offensive and defensive team parameters. AdjO 78.3 \| AdjD 62.5 More →	0.808 (#171)	AdjO 78.3 \| AdjD 62.5
Points Off/Def Recency Points Off/Def Recency Off/def points regression with exponential recency weights. AdjO 63.4 \| AdjD 60.7 More →	0.561 (#283)	AdjO 63.4 \| AdjD 60.7
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 →	0.705 (#213)	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 →	0.685 (#229)	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 →	1088 (#182)	RD 350 \| GP 1

2024 Schedule & Results

Date	Vs/At	Opponent	Result	Score
2023-12-09	vs	Asbury	W	90 - 65

2024 Roster

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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%
Hayley Harrison	-	1	31.0	20.0	3.0	4.0	1.0	2.0	1.0	16.0	13.0	-	-	-	50.0	44.4	0	-	62.5	62.5
Miyah Brown	-	1	25.0	16.0	10.0	8.0	4.0	0.0	4.0	14.0	20.0	22	3.1	3.2	57.1	0.0	0	0.46	57.1	57.1
Lucy Robertson	-	1	23.0	14.0	4.0	1.0	1.0	2.0	0.0	9.0	13.0	-	-	-	66.7	0	66.7	-	67.8	66.7
Hope Sivori	-	1	18.0	9.0	3.0	3.0	4.0	0.0	1.0	6.0	12.0	-	-	-	50.0	75.0	0	-	75.0	75.0
Paetynn Gray	-	1	18.0	8.0	6.0	3.0	2.0	0.0	0.0	6.0	13.0	-	-	-	66.7	0.0	0	-	66.7	66.7
Jayla Butler	-	1	17.0	8.0	1.0	2.0	5.0	0.0	2.0	5.0	9.0	-	-	-	60.0	0	100.0	-	68.0	60.0
Claire Knies	-	1	23.0	6.0	0.0	0.0	1.0	0.0	2.0	8.0	-3.0	-	-	-	37.5	0.0	0	-	37.5	37.5
Mackenzie Keelin	-	1	20.0	5.0	8.0	3.0	0.0	0.0	4.0	10.0	2.0	-	-	-	20.0	16.7	0	-	25.0	25.0
Ashlee Harris	-	1	15.0	2.0	0.0	1.0	1.0	0.0	0.0	3.0	1.0	1	0.1	0.1	33.3	0.0	0	0.17	33.3	33.3
Aly Avrach	-	1	10.0	2.0	4.0	0.0	0.0	0.0	2.0	6.0	-2.0	-	-	-	16.7	0.0	0	-	16.7	16.7

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