Some 23-24 Basketball stats

[slugboy]

As of January 2024, two ACC teams are ranked. Is that as low as we’ve been in decades?

School	Pre	11/13	11/20	11/27	12/4	12/11	12/18	12/26	1/1	1/8	1/15	1/22
UNC	19	20	14	17	9	9	11	9	8	7	4	3
Duke	2	9	9	7	22	21	21	16	14	12	7	12
Virginia	-	-	24	-	-	22	22	-	-	-	-	-
NC State	-	-	-	-	-	-	-	-	-	-	-	-
Florida State	-	-	-	-	-	-	-	-	-	-	-	-
Boston College	-	-	-	-	-	-	-	-	-	-	-	-
Georgia Tech	-	-	-	-	-	-	-	-	-	-	-	-
Louisville	-	-	-	-	-	-	-	-	-	-	-	-
Virginia Tech	-	-	-	-	-	-	-	-	-	-	-	-
Notre Dame	-	-	-	-	-	-	-	-	-	-	-	-
Wake Forest	-	-	-	-	-	-	-	-	-	-	-	-
Clemson	-	-	-	-	24	13	18	18	16	21	-	-
Pitt	-	-	-	-	-	-	-	-	-	-	-	-
Miami (FL)	13	12	10	8	15	24	-	-	-	-	-	-
Syracuse	-	-	-	-	-	-	-	-	-	-	-	-

[thead] [/thead]

Provided by CBB at Sports Reference: View Original Table
Generated 1/24/2024.

 

[slugboy]

Not great at offense, bad at defense — it’s been a struggle so far

		Over				Conf		Home		Away			Rati				Per																				Adva
Rk	School	G	W	L	W%	W	L	W	L	W	L		ORtg	DRtg	NRtg		FG	FGA	FG%	3P	3PA	3P%	eFG%	FT	FTA	FT%	ORB	TRB	AST	STL	BLK	TOV	PF	PTS	PTS		SRSV	SOS	Pace
1	UNC	19	16	3	.842	8	0	9	0	4	0		114.9	95.0	+19.9		28.5	62.2	.459	7.6	21.3	.356	.519	18.9	24.8	.761	11.8	41.3	14.1	5.9	4.8	10.4	16.8	83.5	69.1		22.59	8.11	72.2
2	Duke	18	14	4	.778	5	2	9	2	3	2		118.7	97.3	+21.4		28.7	59.5	.482	8.3	21.9	.378	.551	16.1	21.6	.746	9.9	35.9	16.3	7.1	4.0	9.3	15.7	81.7	67.0		18.94	4.22	68.9
3	Clemson	18	13	5	.722	3	4	7	2	3	3		116.4	105.4	+11.1		29.3	60.8	.481	8.5	23.6	.360	.551	13.7	17.7	.771	10.3	37.9	15.8	4.9	4.6	10.4	16.1	80.7	73.1		15.13	7.46	68.4
4	Wake Forest	19	13	6	.684	5	3	11	0	1	4		113.0	99.5	+13.5		27.9	59.4	.471	8.7	22.9	.382	.544	15.6	19.5	.798	8.6	34.8	12.3	5.9	4.5	11.2	15.6	80.2	70.6		14.22	4.64	70.0
5	Miami (FL)	18	12	6	.667	3	4	9	2	1	3		113.4	100.3	+13.1		30.2	61.6	.491	9.3	23.7	.393	.567	12.6	16.4	.767	9.4	37.1	15.4	7.9	3.4	12.8	13.1	82.4	72.9		12.95	3.45	72.2
6	Pitt	19	12	7	.632	3	5	7	5	4	1		111.5	97.4	+14.1		26.6	61.5	.433	9.3	27.3	.340	.508	13.7	20.1	.685	12.7	39.4	14.2	6.7	5.1	10.1	15.5	76.3	66.6		12.77	3.14	68.4
7	Virginia Tech	19	12	7	.632	4	4	9	1	1	4		107.9	99.8	+8.2		25.1	54.3	.462	8.3	22.9	.361	.538	15.5	19.4	.799	7.9	33.4	15.1	5.6	2.7	12.6	15.8	73.8	68.3		11.72	6.14	68.4
8	Florida State	19	12	7	.632	6	2	7	4	3	2		104.9	99.8	+5.1		27.3	61.6	.442	6.4	19.6	.327	.494	16.1	23.1	.695	10.6	35.3	13.1	9.2	3.9	11.9	20.0	77.0	73.3		11.11	7.37	72.9
9	Virginia	18	13	5	.722	4	3	10	0	1	4		104.9	93.2	+11.7		24.5	54.8	.447	6.6	18.1	.363	.507	9.9	15.2	.653	8.3	32.1	15.7	8.3	4.7	8.2	13.3	65.5	58.2		10.48	3.21	62.5
10	NC State	18	13	5	.722	5	2	9	2	3	1		109.5	99.4	+10.1		27.9	62.4	.448	7.1	21.6	.330	.505	14.3	19.8	.725	10.9	36.1	13.3	8.2	2.9	9.8	17.4	77.3	70.2		10.21	3.10	70.2
11	Syracuse	19	13	6	.684	4	4	9	1	2	3		103.1	99.0	+4.0		27.2	61.3	.443	6.8	21.1	.323	.498	14.2	19.4	.732	10.1	37.0	13.6	8.9	4.8	12.4	15.4	75.3	72.4		9.11	8.72	73.1
12	Boston College	19	11	8	.579	2	6	7	3	3	3		110.4	104.9	+5.5		28.2	61.3	.460	7.7	21.6	.355	.523	12.9	17.1	.756	9.4	34.8	13.8	6.4	3.8	9.7	15.9	77.0	73.2		8.58	4.74	69.3
13	Georgia Tech	19	9	10	.474	2	6	5	5	2	4		104.4	107.2	-2.9		26.2	60.9	.430	7.9	24.8	.320	.496	12.7	18.8	.673	12.4	38.5	13.4	4.8	4.4	12.5	17.0	73.1	75.1		4.86	6.86	68.2
14	Notre Dame	18	7	11	.389	2	5	5	6	1	4		94.6	98.1	-3.5		22.3	55.5	.402	7.2	24.1	.297	.467	10.9	15.5	.706	10.3	37.8	9.7	5.5	2.6	13.6	15.2	62.8	65.1		2.84	5.18	65.0
15	Louisville	19	6	13	.316	1	7	5	6	1	5		103.3	110.7	-7.4		25.3	57.8	.437	5.9	18.3	.322	.488	16.5	22.4	.735	10.2	34.8	11.4	5.3	2.7	12.1	16.9	72.9	78.1		-0.35	4.86	70.1

[thead] [/thead]

Provided by CBB at Sports Reference: View Original Table
Generated 1/24/2024.

 

[awbuzz]

Looking like the ACC might be lucky to get 5 teams into the big dance.

 

[Golden Tornadoes]

Not great at offense, bad at defense — it’s been a struggle so far

		Over				Conf		Home		Away			Rati				Per																				Adva
Rk	School	G	W	L	W%	W	L	W	L	W	L		ORtg	DRtg	NRtg		FG	FGA	FG%	3P	3PA	3P%	eFG%	FT	FTA	FT%	ORB	TRB	AST	STL	BLK	TOV	PF	PTS	PTS		SRSV	SOS	Pace
1	UNC	19	16	3	.842	8	0	9	0	4	0		114.9	95.0	+19.9		28.5	62.2	.459	7.6	21.3	.356	.519	18.9	24.8	.761	11.8	41.3	14.1	5.9	4.8	10.4	16.8	83.5	69.1		22.59	8.11	72.2
2	Duke	18	14	4	.778	5	2	9	2	3	2		118.7	97.3	+21.4		28.7	59.5	.482	8.3	21.9	.378	.551	16.1	21.6	.746	9.9	35.9	16.3	7.1	4.0	9.3	15.7	81.7	67.0		18.94	4.22	68.9
3	Clemson	18	13	5	.722	3	4	7	2	3	3		116.4	105.4	+11.1		29.3	60.8	.481	8.5	23.6	.360	.551	13.7	17.7	.771	10.3	37.9	15.8	4.9	4.6	10.4	16.1	80.7	73.1		15.13	7.46	68.4
4	Wake Forest	19	13	6	.684	5	3	11	0	1	4		113.0	99.5	+13.5		27.9	59.4	.471	8.7	22.9	.382	.544	15.6	19.5	.798	8.6	34.8	12.3	5.9	4.5	11.2	15.6	80.2	70.6		14.22	4.64	70.0
5	Miami (FL)	18	12	6	.667	3	4	9	2	1	3		113.4	100.3	+13.1		30.2	61.6	.491	9.3	23.7	.393	.567	12.6	16.4	.767	9.4	37.1	15.4	7.9	3.4	12.8	13.1	82.4	72.9		12.95	3.45	72.2
6	Pitt	19	12	7	.632	3	5	7	5	4	1		111.5	97.4	+14.1		26.6	61.5	.433	9.3	27.3	.340	.508	13.7	20.1	.685	12.7	39.4	14.2	6.7	5.1	10.1	15.5	76.3	66.6		12.77	3.14	68.4
7	Virginia Tech	19	12	7	.632	4	4	9	1	1	4		107.9	99.8	+8.2		25.1	54.3	.462	8.3	22.9	.361	.538	15.5	19.4	.799	7.9	33.4	15.1	5.6	2.7	12.6	15.8	73.8	68.3		11.72	6.14	68.4
8	Florida State	19	12	7	.632	6	2	7	4	3	2		104.9	99.8	+5.1		27.3	61.6	.442	6.4	19.6	.327	.494	16.1	23.1	.695	10.6	35.3	13.1	9.2	3.9	11.9	20.0	77.0	73.3		11.11	7.37	72.9
9	Virginia	18	13	5	.722	4	3	10	0	1	4		104.9	93.2	+11.7		24.5	54.8	.447	6.6	18.1	.363	.507	9.9	15.2	.653	8.3	32.1	15.7	8.3	4.7	8.2	13.3	65.5	58.2		10.48	3.21	62.5
10	NC State	18	13	5	.722	5	2	9	2	3	1		109.5	99.4	+10.1		27.9	62.4	.448	7.1	21.6	.330	.505	14.3	19.8	.725	10.9	36.1	13.3	8.2	2.9	9.8	17.4	77.3	70.2		10.21	3.10	70.2
11	Syracuse	19	13	6	.684	4	4	9	1	2	3		103.1	99.0	+4.0		27.2	61.3	.443	6.8	21.1	.323	.498	14.2	19.4	.732	10.1	37.0	13.6	8.9	4.8	12.4	15.4	75.3	72.4		9.11	8.72	73.1
12	Boston College	19	11	8	.579	2	6	7	3	3	3		110.4	104.9	+5.5		28.2	61.3	.460	7.7	21.6	.355	.523	12.9	17.1	.756	9.4	34.8	13.8	6.4	3.8	9.7	15.9	77.0	73.2		8.58	4.74	69.3
13	Georgia Tech	19	9	10	.474	2	6	5	5	2	4		104.4	107.2	-2.9		26.2	60.9	.430	7.9	24.8	.320	.496	12.7	18.8	.673	12.4	38.5	13.4	4.8	4.4	12.5	17.0	73.1	75.1		4.86	6.86	68.2
14	Notre Dame	18	7	11	.389	2	5	5	6	1	4		94.6	98.1	-3.5		22.3	55.5	.402	7.2	24.1	.297	.467	10.9	15.5	.706	10.3	37.8	9.7	5.5	2.6	13.6	15.2	62.8	65.1		2.84	5.18	65.0
15	Louisville	19	6	13	.316	1	7	5	6	1	5		103.3	110.7	-7.4		25.3	57.8	.437	5.9	18.3	.322	.488	16.5	22.4	.735	10.2	34.8	11.4	5.3	2.7	12.1	16.9	72.9	78.1		-0.35	4.86	70.1

[thead] [/thead]


Provided by CBB at Sports Reference: View Original Table
Generated 1/24/2024.

Just confirms what I wrote in another post:

• We take the second most 3s in the conference
• We have the second worst made 3pt.% in the conference
• We have the second worst made FT % in the conference
• We have the worst steal-per-game average in the conference
• 4th most turnovers-per-game in the conference (this has been noticeably addressed the past couple games)
• 3rd most fouls-per-game in the conference

All that to show we have a young team who hasn't yet learned to play defense with their feet, make the extra pass, and hit the open shot. Hard to win many games like that. However, on the blue moon occurrence that it all comes together, it is quite a thing of beauty. Such as is life with these Jekyll and Hyde Jackets. Another offseason of maturation and the growing pains off this year and possibly next will start to pay dividends though. We saw it in football this year too, it takes time to teach a group of guys how to win. It takes even more time to teach a team how to win who have only played 19 games together. Continuity is key for CDS; the more we can keep our core together, the quicker we learn how to win and finish games. I'm not down on CDS one bit. I think he did a great job getting guys into the program who can contribute. It just takes time for them to mesh and learn to play with each other, much less to win a game. It will happen, it just takes time.

 

[gte447f]

Don’t be surprised if the ACC still has multiple teams in the elite eight and beyond. It’s not a guarantee, but I won’t be surprised.

 

[AUFC]

These past few years have been pretty bad. We're at the point where you need to go 14-6 in conference play to dance as an at-large. And that didn't even cut it for Clemson last season. Wake missed the dance going 13-7 the year before.

 

[Root4GT]

These past few years have been pretty bad. We're at the point where you need to go 14-6 in conference play to dance as an at-large. And that didn't even cut it for Clemson last season. Wake missed the dance going 13-7 the year before.

The ACC teams have too many Quad 3 and 4 losses in OOC games the past several years which makes it nearly impossible to make up NET rankings during the ACC Conference play. This year there are a total of 24 Quad 3 & 4 losses in the ACC. The site didn't break out OOC vs Conference games. The Big 12 has 7. The Big East has 12 (Depaul has 5 of the 12). The SEC has 12 QUAD 3 & 4 losses. The Big 10 has 9 Q 3 & 4 losses. The Pac 12 has 25 such losses.

Don't expect a lot of ACC or Pac 12 teams in the NCAAT this year.

 

[Peacone36]

These past few years have been pretty bad. We're at the point where you need to go 14-6 in conference play to dance as an at-large. And that didn't even cut it for Clemson last season. Wake missed the dance going 13-7 the year before.

There are deeper metrics at play with those two examples. That Wake Forest team played a SOS of 92nd with zero meaningful OOC victories. The same can be said for Clemson last season with the exception of them defeating Penn State.

With the ACC being down in recent years fifth out of the P6 twice and below the MW last season, teams need to be more aggressive in their scheduling to pick up meaningful victories.

 

[alagold]

Great piece of data.Pretty easy to see our problem just looking at the stats.
SHOOTING-2nd from last in--- FG%, FT%, 3s % Throw in 3rd most PFouls and last in steals --we WERE making up for much by OFF REBs advantage but that's gone now. Just a very average team.

 

[Root4GT]

There are deeper metrics at play with those two examples. That Wake Forest team played a SOS of 92nd with zero meaningful OOC victories. The same can be said for Clemson last season with the exception of them defeating Penn State.

With the ACC being down in recent years fifth out of the P6 twice and below the MW last season, teams need to be more aggressive in their scheduling to pick up meaningful victories.

The key is winning the OOC games. The ACC has not done well in that regard the past several years.

 

[stinger78]

The ACC has lost a lot of great coaches the last 20 years or so and has struggled to replace them. It is a major mystery to me. Why is this? Why do top MBB coaches not want to coach in the ACC?

 

[slugboy]

The ACC has lost a lot of great coaches the last 20 years or so and has struggled to replace them. It is a major mystery to me. Why is this? Why do top MBB coaches not want to coach in the ACC?

Roy Williams and Krzyzewski retired. Both schools could have hired just about anyone. Syracuse probably could get a great Boeheim replacement if they wanted them.

Hamilton and Larrañaga got old.

An ACC school could have gotten Pitino—they didn’t try as far as I know.

I don’t think coaches don’t want to coach in the ACC. But, they’re the main attraction in the Big East and they’re on a pedestal in the Big 12. The SEC and B1G will throw $$$ at them. The athletic departments in the ACC got lazy and unfocused

Mostly, it’s ineptitude

 

[slugboy]

Here are some advanced stats—people have posted some better stats—Peacone posted some nice info from KenPom. It’s not opponent-adjusted, so some things are a bit out of whack, like Emmer Nichols is at the top. That might make a difference in the middle, too.

As much as we’d like a big guy in the middle, I’d love a seasoned version of Nait—running point is a lot to put on a Freshman. If Abram had panned out and fit here, an athletic and above average PG for the ACC would probably have this team doing really well right now. I’m not even going back to the old days—add Alvarado to this mix and we’re scoring, playing hard, and doing much better on defense. Nait plays a little less than 30 minutes per game—I think he’s great, but we fall behind when he goes out, so we need him to play full games without fading away, or we need someone else who can do what he does.

(As always, JMHO, and I’m the most amateur basketball fan in the world)

This is sorted by Player Efficiency Rating:

Rk	Player	G	GS	MP	PER	TS%	eFG%	PProd	ORB%	DRB%	TRB%	AST%	STL%	BLK%	TOV%	USG%		OWS	DWS	WS	WS/40		OBPM	DBPM	BPM
1	Emmer Nichols	2	0	4	23.3			1	28.0	82.3	55.4	0.0	0.0	0.0		0.0		0.0	0.0	0.0	.217		5.7	0.7	6.4
2	Baye Ndongo	16	16	466	20.9	.622	.620	190	9.8	22.8	16.4	7.2	2.0	5.1	21.4	23.0		0.7	0.7	1.4	.120		1.6	2.1	3.8
3	Tyzhaun Claude	19	4	315	18.0	.547	.508	110	15.6	17.8	16.7	10.6	1.5	2.7	14.9	16.0		0.7	0.4	1.1	.134		2.0	1.2	3.2
4	Kyle Sturdivant	19	0	361	17.7	.555	.500	163	1.9	11.2	6.6	31.4	1.9	0.0	15.7	22.9		0.9	0.3	1.2	.131		2.6	0.7	3.2
5	Kowacie Reeves	19	19	622	14.7	.587	.555	200	3.6	10.8	7.2	4.7	1.2	2.4	11.4	17.6		1.1	0.5	1.6	.104		3.0	0.6	3.6
6	Naithan George	16	15	459	13.2	.512	.488	171	1.5	5.7	3.6	33.0	0.9	0.7	16.5	19.1		0.8	0.2	0.9	.083		2.3	-0.9	1.3
7	Dallan Coleman	19	0	420	12.7	.545	.535	124	4.8	8.1	6.5	5.9	1.4	1.3	6.0	15.8		0.8	0.3	1.1	.100		1.3	0.7	2.1
8	Miles Kelly	19	19	616	12.6	.458	.419	260	4.4	15.7	10.1	12.6	1.1	0.9	11.1	27.0		0.4	0.6	1.0	.065		0.4	-0.6	-0.2
9	Carter Murphy	5	0	26	10.8	.600	.600	5	0.0	21.1	10.7	0.0	0.0	8.3	0.0	9.6		0.0	0.0	0.1	.109		0.3	2.5	2.7
10	Tafara Gapare	16	6	243	10.0	.438	.417	67	4.6	10.8	7.8	5.4	1.2	7.6	12.0	18.9		0.0	0.3	0.3	.042		-2.4	1.8	-0.7
11	Ibrahima Sacko	13	2	137	7.1	.407	.450	24	11.4	17.6	14.6	2.5	3.0	1.6	27.8	11.8		-0.1	0.2	0.1	.026		-4.4	1.6	-2.8
12	Ebenezer Dowuona	15	10	144	5.2	.475	.500	22	10.1	6.1	8.1	2.4	0.4	3.0	27.0	9.0		0.0	0.1	0.1	.015		-3.7	0.2	-3.6
13	Amaree Abram	7	4	88	4.6	.356	.314	31	2.5	18.7	10.7	21.2	0.7	1.2	16.9	26.8		-0.1	0.1	-0.1	-.027		-6.1	-1.2	-7.3
14	Marcos San Miguel	1	0	2	-51.9	.000	.000	1	56.0	0.0	27.7	0.0	0.0	0.0	33.3	74.8		-0.1	0.0	-0.1	-1.017		-37.1	-28.5	-65.6

Provided by CBB at Sports Reference: View Original Table
Generated 1/25/2024.

Here’s the glossary:
Rk -- Rank
G -- Games
GS -- Games Started
MP -- Minutes Played
PER
▼ -- Player Efficiency Rating
TS% -- True Shooting Percentage
A measure of shooting efficiency that takes into account 2-point field goals, 3-point field goals, and free throws.
eFG% -- Effective Field Goal Percentage; this statistic adjusts for the fact that a 3-point field goal is worth one more point than a 2-point field goal.
PProd -- Points Produced; an estimate of the player's offensive points produced.
ORB% -- Offensive Rebound Percentage; an estimate of the percentage of available offensive rebounds a player grabbed while they were on the floor.
DRB% -- Defensive Rebound Percentage; an estimate of the percentage of available defensive rebounds a player grabbed while they were on the floor.
TRB% -- Total Rebound Percentage
An estimate of the percentage of available rebounds a player grabbed while they were on the floor.
AST% -- Assist Percentage
An estimate of the percentage of teammate field goals a player assisted while they were on the floor.
STL% -- Steal Percentage
An estimate of the percentage of opponent possessions that end with a steal by the player while they were on the floor.
BLK% -- Block Percentage
An estimate of the percentage of opponent two-point field goal attempts blocked by the player while they were on the floor.
TOV% -- Turnover Percentage; an estimate of turnovers per 100 plays.
USG% -- Usage Percentage; an estimate of the percentage of team plays used by a player while they were on the floor.
OWS -- Offensive Win Shares; an estimate of the number of wins contributed by a player due to their offense.
DWS -- Defensive Win Shares; an estimate of the number of wins contributed by a player due to their defense.
WS -- Win Shares; an estimate of the number of wins contributed by a player due to their offense and defense.
WS/40 -- Win Shares Per 40 Minutes; an estimate of the number of wins contributed by a player per 40 minutes (average is approximately .100).
OBPM -- Offensive Box Plus/Minus
A box score estimate of the offensive points per 100 possessions a player contributed above a league-average player, translated to an average team

 

[SecretAgentBuzz]

Here are some advanced stats—people have posted some better stats—Peacone posted some nice info from KenPom. It’s not opponent-adjusted, so some things are a bit out of whack, like Emmer Nichols is at the top. That might make a difference in the middle, too.

As much as we’d like a big guy in the middle, I’d love a seasoned version of Nait—running point is a lot to put on a Freshman. If Abram had panned out and fit here, an athletic and above average PG for the ACC would probably have this team doing really well right now. I’m not even going back to the old days—add Alvarado to this mix and we’re scoring, playing hard, and doing much better on defense. Nait plays a little less than 30 minutes per game—I think he’s great, but we fall behind when he goes out, so we need him to play full games without fading away, or we need someone else who can do what he does.

(As always, JMHO, and I’m the most amateur basketball fan in the world)

This is sorted by Player Efficiency Rating:

Rk	Player	G	GS	MP	PER	TS%	eFG%	PProd	ORB%	DRB%	TRB%	AST%	STL%	BLK%	TOV%	USG%		OWS	DWS	WS	WS/40		OBPM	DBPM	BPM
1	Emmer Nichols	2	0	4	23.3			1	28.0	82.3	55.4	0.0	0.0	0.0		0.0		0.0	0.0	0.0	.217		5.7	0.7	6.4
2	Baye Ndongo	16	16	466	20.9	.622	.620	190	9.8	22.8	16.4	7.2	2.0	5.1	21.4	23.0		0.7	0.7	1.4	.120		1.6	2.1	3.8
3	Tyzhaun Claude	19	4	315	18.0	.547	.508	110	15.6	17.8	16.7	10.6	1.5	2.7	14.9	16.0		0.7	0.4	1.1	.134		2.0	1.2	3.2
4	Kyle Sturdivant	19	0	361	17.7	.555	.500	163	1.9	11.2	6.6	31.4	1.9	0.0	15.7	22.9		0.9	0.3	1.2	.131		2.6	0.7	3.2
5	Kowacie Reeves	19	19	622	14.7	.587	.555	200	3.6	10.8	7.2	4.7	1.2	2.4	11.4	17.6		1.1	0.5	1.6	.104		3.0	0.6	3.6
6	Naithan George	16	15	459	13.2	.512	.488	171	1.5	5.7	3.6	33.0	0.9	0.7	16.5	19.1		0.8	0.2	0.9	.083		2.3	-0.9	1.3
7	Dallan Coleman	19	0	420	12.7	.545	.535	124	4.8	8.1	6.5	5.9	1.4	1.3	6.0	15.8		0.8	0.3	1.1	.100		1.3	0.7	2.1
8	Miles Kelly	19	19	616	12.6	.458	.419	260	4.4	15.7	10.1	12.6	1.1	0.9	11.1	27.0		0.4	0.6	1.0	.065		0.4	-0.6	-0.2
9	Carter Murphy	5	0	26	10.8	.600	.600	5	0.0	21.1	10.7	0.0	0.0	8.3	0.0	9.6		0.0	0.0	0.1	.109		0.3	2.5	2.7
10	Tafara Gapare	16	6	243	10.0	.438	.417	67	4.6	10.8	7.8	5.4	1.2	7.6	12.0	18.9		0.0	0.3	0.3	.042		-2.4	1.8	-0.7
11	Ibrahima Sacko	13	2	137	7.1	.407	.450	24	11.4	17.6	14.6	2.5	3.0	1.6	27.8	11.8		-0.1	0.2	0.1	.026		-4.4	1.6	-2.8
12	Ebenezer Dowuona	15	10	144	5.2	.475	.500	22	10.1	6.1	8.1	2.4	0.4	3.0	27.0	9.0		0.0	0.1	0.1	.015		-3.7	0.2	-3.6
13	Amaree Abram	7	4	88	4.6	.356	.314	31	2.5	18.7	10.7	21.2	0.7	1.2	16.9	26.8		-0.1	0.1	-0.1	-.027		-6.1	-1.2	-7.3
14	Marcos San Miguel	1	0	2	-51.9	.000	.000	1	56.0	0.0	27.7	0.0	0.0	0.0	33.3	74.8		-0.1	0.0	-0.1	-1.017		-37.1	-28.5	-65.6

Provided by CBB at Sports Reference: View Original Table
Generated 1/25/2024.

Here’s the glossary:
Rk -- Rank
G -- Games
GS -- Games Started
MP -- Minutes Played
PER
▼ -- Player Efficiency Rating
TS% -- True Shooting Percentage
A measure of shooting efficiency that takes into account 2-point field goals, 3-point field goals, and free throws.
eFG% -- Effective Field Goal Percentage; this statistic adjusts for the fact that a 3-point field goal is worth one more point than a 2-point field goal.
PProd -- Points Produced; an estimate of the player's offensive points produced.
ORB% -- Offensive Rebound Percentage; an estimate of the percentage of available offensive rebounds a player grabbed while they were on the floor.
DRB% -- Defensive Rebound Percentage; an estimate of the percentage of available defensive rebounds a player grabbed while they were on the floor.
TRB% -- Total Rebound Percentage
An estimate of the percentage of available rebounds a player grabbed while they were on the floor.
AST% -- Assist Percentage
An estimate of the percentage of teammate field goals a player assisted while they were on the floor.
STL% -- Steal Percentage
An estimate of the percentage of opponent possessions that end with a steal by the player while they were on the floor.
BLK% -- Block Percentage
An estimate of the percentage of opponent two-point field goal attempts blocked by the player while they were on the floor.
TOV% -- Turnover Percentage; an estimate of turnovers per 100 plays.
USG% -- Usage Percentage; an estimate of the percentage of team plays used by a player while they were on the floor.
OWS -- Offensive Win Shares; an estimate of the number of wins contributed by a player due to their offense.
DWS -- Defensive Win Shares; an estimate of the number of wins contributed by a player due to their defense.
WS -- Win Shares; an estimate of the number of wins contributed by a player due to their offense and defense.
WS/40 -- Win Shares Per 40 Minutes; an estimate of the number of wins contributed by a player per 40 minutes (average is approximately .100).
OBPM -- Offensive Box Plus/Minus
A box score estimate of the offensive points per 100 possessions a player contributed above a league-average player, translated to an average team

Wait…are you trying to tell me Emmer Nichols isn’t our best player? The stats clearly show it!

 

[57jacket]

In our last 7 losses, the average loss is by 8 points. The largest was 11 at FSU. We certainly have not been blown out by anybody. A basket here, a stop there, and we would have a winning record. We are MUCH better than our record.

 

[Tommy_Taylor_1972]

Wait…are you trying to tell me Emmer Nichols isn’t our best player? The stats clearly show it!

Emmer had it going until he broke his leg in practice last Friday coming down from a dunk on one leg, breaking the tibia and fibula with compound fracture. He will be out for the season and likely be a medical red shirt this year. Will be stronger with his new titanium leg.

 

[lv20gt]

In our last 7 losses, the average loss is by 8 points. The largest was 11 at FSU. We certainly have not been blown out by anybody. A basket here, a stop there, and we would have a winning record. We are MUCH better than our record.

Well, that is certainly one way to look at things. Any particular reason to not include the 14 and 35 point losses (or the 3 point loss to UML for that matter)?

But even if you want to just use the last 7, for whatever reason, then you would also want to look at the wins. In our last 7 wins the average margin was 6.43 points and that is including a 21 point win against Alabama A&M (which would have actual justification as being viewed as an outlier compared to middle of the road SEC or BIg 12 teams). So in general, our wins have been closer than our losses.

Without that outlier game our wins are by an average margin of 4 points over the last 6. Only one of those wins even reached the 8 point margin that was the average margin in our defeats considered. And if anyone wants to know why I used just those wins, it is to match the 7 that was used for our losses. If you want to include the last 7 wins that weren't including the outlier against AAM, the margin of victory would actually goes down to 3.86.

For all games the average MOV when we win has been 7.8 points per game. The average MOV when we have lost has been 10.8. So by pretty much any reasonable view our wins are closer than our losses, and that is including games against GSU (4-15) and AAM (3-15) that would normally be viewed as opportunities to pad the MOV against overmatched teams.

Even if you want to try and account for outliers and say the Cinci game was just one bad night. Not including that game the MOV in our losses is 8.11 still higher than our MOV in our wins. If you throw out the outlier games against overmatched teams (GSU and AAM), the MOV in our wins is 3.86. If you want to include 2 outlier games in our losses just to match up the MOV without either the Cinci or UGA games is 7.375 so it still isn't really close. Our wins by any way you look at it were closer than our losses.

Here is a breakdown by how many games were by different number of possessions.

In one possession games (MOV 3 or less) we have 4 wins (Howard, PSU, UMass, Clemson) and only 1 loss (UML)
In two possession games (MOV 4 to 6) we have 2 wins (Duke, Hawaii) and 1 loss (@Duke)
In three possession games (MOV 7 to 9) we have 1 win (MSU) and 5 losses (Nevada, BC, ND, UVA, and Pitt)
double digit games we are 2 wins (GSU and AAM) and 3 losses (Cinci, UGA, FSU)

So if you're talking about outcomes of games being difference based on a small number of things going differently, that is 3 times more opportunities for that to change a win to a loss than the other way.

 

[57jacket]

Well, that is certainly one way to look at things. Any particular reason to not include the 14 and 35 point losses (or the 3 point loss to UML for that matter)?

But even if you want to just use the last 7, for whatever reason, then you would also want to look at the wins. In our last 7 wins the average margin was 6.43 points and that is including a 21 point win against Alabama A&M (which would have actual justification as being viewed as an outlier compared to middle of the road SEC or BIg 12 teams). So in general, our wins have been closer than our losses.

Without that outlier game our wins are by an average margin of 4 points over the last 6. Only one of those wins even reached the 8 point margin that was the average margin in our defeats considered. And if anyone wants to know why I used just those wins, it is to match the 7 that was used for our losses. If you want to include the last 7 wins that weren't including the outlier against AAM, the margin of victory would actually goes down to 3.86.

For all games the average MOV when we win has been 7.8 points per game. The average MOV when we have lost has been 10.8. So by pretty much any reasonable view our wins are closer than our losses, and that is including games against GSU (4-15) and AAM (3-15) that would normally be viewed as opportunities to pad the MOV against overmatched teams.

Even if you want to try and account for outliers and say the Cinci game was just one bad night. Not including that game the MOV in our losses is 8.11 still higher than our MOV in our wins. If you throw out the outlier games against overmatched teams (GSU and AAM), the MOV in our wins is 3.86. If you want to include 2 outlier games in our losses just to match up the MOV without either the Cinci or UGA games is 7.375 so it still isn't really close. Our wins by any way you look at it were closer than our losses.

Here is a breakdown by how many games were by different number of possessions.

In one possession games (MOV 3 or less) we have 4 wins (Howard, PSU, UMass, Clemson) and only 1 loss (UML)
In two possession games (MOV 4 to 6) we have 2 wins (Duke, Hawaii) and 1 loss (@Duke)
In three possession games (MOV 7 to 9) we have 1 win (MSU) and 5 losses (Nevada, BC, ND, UVA, and Pitt)
double digit games we are 2 wins (GSU and AAM) and 3 losses (Cinci, UGA, FSU)

So if you're talking about outcomes of games being difference based on a small number of things going differently, that is 3 times more opportunities for that to change a win to a loss than the other way.

Good grief. How much effort did you go to in making that analysis? LOL. My point was simply we have been competitive. That bothers you?

 

[Root4GT]

Well, that is certainly one way to look at things. Any particular reason to not include the 14 and 35 point losses (or the 3 point loss to UML for that matter)?

But even if you want to just use the last 7, for whatever reason, then you would also want to look at the wins. In our last 7 wins the average margin was 6.43 points and that is including a 21 point win against Alabama A&M (which would have actual justification as being viewed as an outlier compared to middle of the road SEC or BIg 12 teams). So in general, our wins have been closer than our losses.

Without that outlier game our wins are by an average margin of 4 points over the last 6. Only one of those wins even reached the 8 point margin that was the average margin in our defeats considered. And if anyone wants to know why I used just those wins, it is to match the 7 that was used for our losses. If you want to include the last 7 wins that weren't including the outlier against AAM, the margin of victory would actually goes down to 3.86.

For all games the average MOV when we win has been 7.8 points per game. The average MOV when we have lost has been 10.8. So by pretty much any reasonable view our wins are closer than our losses, and that is including games against GSU (4-15) and AAM (3-15) that would normally be viewed as opportunities to pad the MOV against overmatched teams.

Even if you want to try and account for outliers and say the Cinci game was just one bad night. Not including that game the MOV in our losses is 8.11 still higher than our MOV in our wins. If you throw out the outlier games against overmatched teams (GSU and AAM), the MOV in our wins is 3.86. If you want to include 2 outlier games in our losses just to match up the MOV without either the Cinci or UGA games is 7.375 so it still isn't really close. Our wins by any way you look at it were closer than our losses.

Here is a breakdown by how many games were by different number of possessions.

In one possession games (MOV 3 or less) we have 4 wins (Howard, PSU, UMass, Clemson) and only 1 loss (UML)
In two possession games (MOV 4 to 6) we have 2 wins (Duke, Hawaii) and 1 loss (@Duke)
In three possession games (MOV 7 to 9) we have 1 win (MSU) and 5 losses (Nevada, BC, ND, UVA, and Pitt)
double digit games we are 2 wins (GSU and AAM) and 3 losses (Cinci, UGA, FSU)

So if you're talking about outcomes of games being difference based on a small number of things going differently, that is 3 times more opportunities for that to change a win to a loss than the other way.

This is silly. We have shown we can compete with meh ACC teams and occasionally beat better ACC teams. We have shown we can compete and lose to meh ACC teams.

We are clearly a lower tier ACC team again this year. We were a bottom level ACC team the past 2 years. Expecting this year's team to dramatically jump in the ACC standings was way too optimistic. Heck our three best players based on performance are all three new to the team this year and two are freshmen. (Ndongo, Reeves and George).

Kelly has been a disappointment for what we were all hoping for as a player. He is still our leading scorer though.

We have 2 top 100 recruits signed for next year, that is a talent infusion we badly need.

This year is about learning what CDS will accept from players, who can preform to the standards he sets and how to compete individually and collectively.

Have fun watching the good if you can!

PS, have you changed anyone's mind banging you drum on this topic yet?

 

[TampaBuzz]

Emmer had it going until he broke his leg in practice last Friday coming down from a dunk on one leg, breaking the tibia and fibula with compound fracture. He will be out for the season and likely be a medical red shirt this year. Will be stronger with his new titanium leg.

can they give him a bionic leg (maybe 2)?