Advanced Player Stats

True Shooting %, Usage Rate, Game Score, and 15+ advanced metrics for 500+ qualifying D1 players with national percentile rankings.

		Basic				Shooting					Usage			Rebounding		Defense		Composite
#	Player	PPG	RPG	APG	MPG	TS%	eFG%	3P%	FTR	3PAr	USG%	TOV%	AST%	ORB%	DRB%	STL%	BLK%	GmScr	GS SD
1	Cameron Boozer Duke·FVolume Scorer	22.4	10.2	4.0	33.4	66.2%	60.7%	39.1%	0.537	0.265	29.8%	12.9%	24.9%	13.7%	23.9%	2.5%	2.2%	21.1	6.5
2	AJ Dybantsa BYU·FVolume Scorer	25.5	6.8	3.7	34.8	60.9%	55.0%	33.1%	0.490	0.245	33.9%	12.8%	22.1%	5.2%	18.7%	1.7%	1.1%	19.2	8.6
3	Caleb Wilson North Carolina·FGlass Cleaner	19.8	9.4	2.7	31.5	63.7%	59.0%	25.9%	0.616	0.092	27.9%	11.2%	17.3%	11.8%	23.8%	2.7%	4.3%	18.8	5.0
4	Darius Acuff Jr. Arkansas·GVolume Scorer	23.4	3.1	6.4	35.1	61.2%	56.2%	44.0%	0.372	0.350	29.3%	10.2%	31.9%	1.7%	9.1%	1.3%	0.9%	18.5	7.4
5	JT Toppin Texas Tech·FVolume Scorer	21.8	10.8	2.1	34.8	57.6%	56.7%	28.1%	0.251	0.133	31.2%	11.6%	13.6%	15.3%	23.6%	2.2%	5.1%	18.3	7.5
6	Hannes Steinbach Washington·FGlass Cleaner	18.5	11.8	1.6	34.5	64.4%	60.2%	34.0%	0.436	0.146	24.2%	12.0%	8.9%	15.5%	27.8%	1.8%	3.9%	17.7	6.1
7	Ebuka Okorie Stanford·GVolume Scorer	23.2	3.6	3.6	35.1	59.7%	52.8%	35.4%	0.449	0.354	31.2%	8.8%	23.8%	2.6%	11.2%	2.6%	1.0%	17.6	9.2
8	Tyler Tanner Vanderbilt·GFloor General	19.5	3.6	5.1	33.4	62.0%	54.8%	36.8%	0.469	0.347	26.8%	10.9%	29.4%	1.7%	11.9%	4.0%	1.0%	17.4	6.8
9	Bruce Thornton Ohio State·GPlaymaker	20.0	5.0	3.8	36.5	67.4%	62.7%	40.0%	0.396	0.361	23.8%	8.0%	21.2%	3.6%	14.7%	1.9%	0.7%	17.4	6.8
10	Nick Martinelli Northwestern·FVolume Scorer	23.0	6.2	2.0	35.6	60.9%	55.2%	41.7%	0.418	0.205	31.2%	7.0%	13.1%	8.7%	15.2%	1.3%	1.2%	17.4	7.3
11	Labaron Philon Jr. Alabama·GVolume Scorer	22.0	3.5	5.0	30.9	63.4%	58.5%	39.9%	0.408	0.419	30.2%	12.8%	32.3%	2.2%	10.2%	2.0%	0.5%	17.0	6.5
12	Aidan Kehoe Navy·CGlass Cleaner	15.3	10.9	2.4	34.4	69.1%	72.9%	-	0.553	0.000	20.1%	13.9%	15.5%	15.0%	26.2%	2.2%	4.8%	16.8	7.4
13	Dior Johnson Tarleton State·GVolume Scorer	23.5	3.2	1.6	25.9	65.4%	57.1%	54.0%	0.613	0.186	38.2%	10.9%	17.3%	3.7%	14.0%	2.6%	0.5%	16.6	9.8
14	Quinn Denker Northern Colorado·GVolume Scorer	20.1	5.3	7.0	35.7	58.6%	53.4%	34.7%	0.353	0.291	28.1%	11.9%	37.8%	2.8%	16.1%	1.1%	1.1%	16.5	8.7
15	Graham Ike Gonzaga·F	19.8	8.0	2.4	31.2	63.3%	59.0%	33.8%	0.352	0.169	27.7%	10.3%	13.7%	8.8%	23.0%	1.3%	3.2%	16.3	7.3
16	Jack Karasinski Bellarmine·FVolume Scorer	21.6	4.7	1.2	29.8	70.1%	65.3%	41.2%	0.509	0.451	31.8%	8.5%	8.7%	5.4%	17.6%	1.2%	1.3%	16.3	5.6
17	Zuby Ejiofor St. John's·FRim Protector	16.3	7.3	3.5	30.0	62.0%	56.0%	30.5%	0.694	0.158	24.7%	13.8%	22.8%	13.5%	16.4%	2.1%	7.2%	16.2	6.7
18	Logan Duncomb Winthrop·CVolume Scorer	18.2	8.7	1.6	24.7	65.8%	58.7%	0.0%	0.823	0.010	30.5%	12.3%	15.5%	17.2%	25.1%	1.8%	3.7%	16.1	8.0
19	Camren Hunter Central Arkansas·GVolume Scorer	21.3	4.3	2.7	31.7	61.9%	58.0%	37.8%	0.309	0.398	30.7%	9.7%	19.6%	4.7%	13.4%	2.4%	1.2%	16.1	8.7
20	Javontae Campbell Bowling Green·GFloor General	19.3	4.4	5.1	35.3	58.1%	51.9%	32.3%	0.508	0.165	27.8%	14.4%	29.6%	4.3%	11.9%	4.4%	0.8%	16.0	6.9
21	Rafael Castro George Washington·FRim Protector	15.3	9.1	1.8	27.7	66.0%	62.8%	-	0.781	0.000	23.8%	14.2%	12.4%	12.9%	28.0%	3.6%	7.9%	15.9	6.8
22	Boopie Miller SMU·GFloor General	19.1	3.8	6.4	35.2	61.6%	54.4%	41.4%	0.441	0.378	25.2%	14.2%	30.6%	2.0%	11.1%	2.3%	0.2%	15.9	7.0
23	Justin Neely UNC Greensboro·F	17.7	11.4	2.4	33.0	64.1%	55.6%	31.9%	0.780	0.143	25.3%	14.7%	15.3%	9.5%	34.8%	1.5%	0.7%	15.8	7.1
24	Nick Boyd Wisconsin·GVolume Scorer	20.7	3.8	4.3	32.0	59.5%	53.8%	36.5%	0.370	0.318	30.7%	8.8%	28.7%	3.1%	12.0%	1.8%	0.1%	15.8	8.2
25	Juke Harris Wake Forest·GVolume Scorer	21.4	6.5	1.9	35.1	58.9%	52.7%	33.2%	0.472	0.497	28.9%	8.4%	11.6%	4.8%	19.4%	2.0%	0.7%	15.7	6.2

Player Analytics Glossary

A complete reference to the advanced efficiency metrics, rate stats, and composite scores used to evaluate individual NCAA Division I basketball player performance. Every metric is calculated from official box score data with national percentile rankings. Select a category below to explore how each metric is calculated and what the benchmarks mean.

True Shooting % (TS%)

PTS / (2 × (FGA + 0.44 × FTA))

The most comprehensive single measure of shooting efficiency, accounting for two-pointers, three-pointers, and free throws in one number. The 0.44 coefficient approximates the number of possessions used by free throw attempts (since and-ones and technical free throws don’t end a possession). A D1 average is roughly 54% — elite shooters reach 62%+, while anything below 50% signals significant inefficiency. TS% is the primary efficiency metric on the Hub scatter plot.

Effective FG% (eFG%)

(FGM + 0.5 × 3PM) / FGA

Adjusts traditional field goal percentage to account for the extra value of three-pointers. A made three is worth 50% more than a made two, so this formula credits shooters accordingly. Unlike TS%, eFG% does not include free throws — it isolates pure shooting from the field. An eFG% above 55% is strong; below 45% is a major concern. The gap between a player’s FG% and eFG% reveals how much of their value comes from three-point shooting.

Free Throw Rate (FTR)

FTA / FGA

Measures how often a player gets to the free throw line relative to their field goal attempts. A high FTR indicates a player who attacks the basket, draws contact, and generates easy points from the line. An FTR above 0.450 is excellent — think aggressive slashers and physical post players. Below 0.200 suggests a perimeter-oriented player who rarely draws fouls. FTR is especially valuable for evaluating players whose raw scoring numbers don’t capture how much pressure they put on opposing defenses.

3-Point Attempt Rate (3PAr)

3PA / FGA

The proportion of a player’s field goal attempts that come from beyond the arc. A 3PAr above 0.550 indicates a primary three-point shooter; below 0.200 signals a player who operates almost exclusively inside the arc. This metric reveals shot selection and offensive role — a stretch big with a 3PAr of 0.400 provides very different spacing value than a traditional post player at 0.050. Pair with eFG% to distinguish high-volume shooters from efficient ones.