UConn stats.... What the heck is N.I.S.E. ? Why does it matter...?

[YKCornelius]

0.44 and 0.475 are regression shortcuts used in basketball analytics in the calculation of Possessions from Box Scores.

• Ken Pomeroy uses 0.475;
• 0.44 is considered a precursor (Dean Oliver’s “four factors” which appear in SRSCB, and which are the precursor of KenPom’s system).

I became aware of these approximations when I was constructing certain analyses based on Play By Play Info such as a rotations analysis which some are contemplating &1 on attempting.

The motivation for the regression factors is ease of use from Box Scores and the fact that:

• (U.S.) Box Scores don’t record missed FGA’s with awarded foul shots.

&1 Rather than use play-by-play, one could use a rewindable (recorded) box score.

NycUcWbbFan, thanks for the added info. Do you have the rationale behind .44 as opposed to higher or lower value regression shortcuts?

 

[OldSchooler]

First, let me say I like N.I.S.E. as it currently stands. What you say makes sense. However, if a player gets fouled on a 2 point shot and only makes 1 free throw, or if a player gets to the line on a non-shooting foul and misses how does that get counted? Or if a player gets fouled on a 3-pointer and makes only 1 or 2 of the free throws? Unfortunately, properly accounting made/missed free throws becomes a huge pain in the buttocks. Maybe a job for AI.

It's my understanding that a player fouled in the act of shooting is not charged with an FGA, unless the shot goes in.

 

[OldSchooler]

N.I.S.E. is short for Net Index of Scoring Efficiency. It is a totally made up (by me) measure of scoring efficiency or points scored per shot taken. Last season, Paige took a total of 537 shots and 126 free throws. That is 663 total scoring attempts. She scored 756 points. Her N.I.S.E. was 1.14, which is very good. (756 divided by 663) For every shot or free throw taken, Paige scored 1.14 points. Anything above 1.0 is wonderful.

So what was the N.I.S.E. for the rest of the team?
Azzi = 405 shots & free throws for 462 points. NISE = 1.14 (same as Paige)
Ashlyn = 271 shots & free throws for 308 points. NISE = 1.13
K.K. = 235 shots & free throws for 218 points. NISE = 0.92
Jana = 217 shots & free throws for 200 points. NISE = 0.92

What about Sarah? Saving the best on the team for last.
Sarah = 537 shots & free throws for 657 points. NISE = 1.22 (which is really, really great..!)

What about Serah, the transfer from Wisconsin?
Serah = 618 shots and free throws for 577 points. NISE = 0.93

Conclusions:
1. What matters is not points scored, but how many shots & free throws it took to score those points. (efficiency)
2. Sarah, Paige & Azzi leading in NISE makes sense. All are quality shooters & don't take many bad shots.
3. Serah's NISE should improve to over 1.0 with outside shooters to keep the defense honest. Fewer double teams.
4. Free throws only score 1 point each, but are made at 80% or so rate. Baskets score 2 or 3 pts but are harder to make.
5. I'm soooo ready for the season to start that I'm making up stats.... 😀

Go Huskies..!!

On edit:
Hannah Hidalgo from ND has NISE of 0.98 (770 shots & free throws for 761 points) Her per game average for points was 23.8 ppg.
As requested, in 2024, Caitlin Clark took 1,150 shots & free throws. She scored 1,234 points. NISE was 1.07. Scoring per game was 31.6 ppg.

David - Thanks for this revealing and helpful stat, and for opening up an interesting line of discussion that I hope will continue to be a boon to fans. It's also confirmation of an intangible that UConn fans have believed all along -- that the Huskies recruiters and coaching staff really are a cut above all the rest.

 

[NycUcWbbFan]

NycUcWbbFan, thanks for the added info. Do you have the rationale behind .44 as opposed to higher or lower value regression shortcuts?

Being basically curve-fit factors, 0.44 and 0.475 factors reflect the games Dean Oliver (NBA,?) and Ken Pomeroy (MCBB, pre-2003?) used for the curve-fitting exercises.

And I suppose one can make inferences about the two types of games data sets (i.e. Ken Pomeroy’s data set implies more possessions).

 

[Gael]

Here are the Her Hoop Stats PPSA (aka lesser NISE) calculations for UConn's players in the first game against Louisville, sorted from highest to lowest.

Player	MIN	USG%	PPP	PPSA
KK Arnold	30:00	16.7%	1.18	1.30
Sarah Strong	38:00	23.5%	1.05	1.11
Kayleigh Heckel	23:00	25.0%	1.08	1.08
Jana El Alfy	8:00	17.6%	0.67	1.00
Azzi Fudd	38:00	25.6%	0.91	1.00
Serah Williams	21:00	16.7%	0.50	0.67
Ashlynn Shade	28:00	15.6%	0.50	0.50
Allie Ziebell	6:00	7.7%	0.00	0.00
Ice Brady	9:00	0.0%	--	--
Totals	200:00	--	0.88	0.96

 

[hoophuskee]

What I was trying to say (badly) player A takes 5 3 pointers and makes two…they have six points on two makes whiles player B takes 5 2 pointers and makes 3, resulting in six points on three makes. If you divide by total shots, they are both 1.0. The additional missed shot from player A is a scoring opportunity for the other team. The efficiency of a high volume 3 point shooter will look better than it really is when you consider the full outcome of the shot. If the miss results in a turnover, there is more to consider than just the outcome of the shot.

Not if UCONN gets the OReb.
Or if the other team turns the ball over more.
In fact the more shots UCONN takes at the basket vs the other team only taking 2's - pace of play could also be of benefit.