My 2025 WBB Top 32 Commit Classes by Composite Rankings - Iowa #8

[HawksGoneWild1]

Now that #1 ranked player (Chavez) committed to OK, I can post My 2025 WBB Top 32 Commit Classes by Composite Rankings.

The purpose of this project is to use a simple formula to calculate all player scores to take out as much bias as possible.

Got tired of looking at ESPN's Rankings that looked liked they devalued us compared to other teams that look lower than us with player rankings.

Had a problem properly evaluating player rankings from one site to another. It was frustrating trying to get a good handle on some players. Ex: Layla Hays was ranked #24 on one site and not rated on another site of 100 players. Makes you scratch your head.

Decided to figure the avg between all sites for a composite ranking for each player and then sort on the composite ranking for best rated players on a team. Then gave an uniformed weighted score or value to each player called 'Rank Score'. Now these teams can be sorted based on their sum of 'Rank Score' for all their players to find their ranking against all other teams.

The same small formula drives every players 'Rank Score' which is based on their Avg Rank score. The higher up the Avg Rank for a player the higher their Rank Score. Meaning the lower your Avg Rank, it "exponentially" reduces their Rank Score.

All player Rank Scores are added up for each team to arrive at their 'Composite Rank/Sum'. Teams were then sorted in descending order for their end Class of 2025 Ranking.

Iowa's incoming Class of 2025 with headliner Addie Deal ends up at #8.

There will be at lease 8 additional postings to get all 32 team details listed (could only get ~4 teams to a post). 32 teams are listed because I found at least 4 Top 25 list that had these teams listed on them.

---

Each grid contains the following columns:
Player Team Rank, Name, 8 Ranking Sites (1 col ea), MISC, LOW, Avg Rank and Rank Score

Key - 8 Ranking Sites Column Abbr:
ASGR = ASGR Basketball
PN = Prospect Nation
JRAS = Jr All Star
ESPN
TSB = Top Spot Basketball
247S = 247 Sports
BSBB = Blue Star Basketball
ON3 = On3

MISC = A special ranking column created to handle special cases. This acts like a 9th ranking site for the Avg Ranking column.
1) A player that's NOT ranked on any site. 207 was lowest ranked player from all sites. Ranking #208 was used as lowest ranking for those players with no ranking found and their Rank Score wil be 0.
2) Players rated higher than their current ranking. This happens a lot to injured players when they were rated higher and then after the injury their ranking went down to non-playing time.

LOW = Lowest ranking from all 9 possible rankings (including MISC column).

Avg Rank = Composite player ranking across all ranking sites (including MISC column).
Rank Score = Weighted score of the 'Avg Rank' column. There are big differences between a #1 rated player, a #40 player, a #75 player and a #150 rated player. Thus, a simple uniformed player 'Rank Score' formula was used in this calculation to take out as much bias as possible. That formula is: (208-Avg Rank)^5.44

---

This is NOT a perfect system. However, it's the best way I could figure out how to compare a team against other teams (eg only having one highly player vs another team having 4 moderate rated players).

I'm open to suggestions to make the rankings better or more fair.

EDIT: (4/8/25) Discovered Brandie Harrod decommitted from Auburn after a coaching change. She committed to Kansas State. Kansas State now moves up from #16 to #12.

My 2025 WBB Top 32 Commit Classes by Composite Rankings List

1​	LSU
2​	Tennessee
3​	Stanford
4​	Oklahoma
5​	South Carolina
6​	Kansas
7​	UNC
8​	Iowa
9​	Cincinnati
10​	UCLA
11​	USC
12​	Kansan St
13​	Illinois
14​	Miss St
15​	Washington
16​	Georgia
17​	Texas
18​	Uconn
19​	Duke
20​	Notre Dame
21​	Alabama
22​	Michigan
23​	Utah
24​	California
25​	Oregon
26​	Miami
27​	Kentucky
28​	Indiana
29​	NC St
30​	Michigan St
31​	Maryland
32​	Creighton

 

[HawksGoneWild1]

Rk

'25-'26 LSU

ASGR

PN

JRAS

ESPN

TSB

247S

BSBB

ON3

MISC

LOW

Avg Rank

Rank Score

1​

ZaKiyah Johnson

8​

4​

6​

13​

8​

12​

19​

4​

4​

9.25​

3.183E+12​

2​

Grace Knox

5​

15​

13​

6​

17​

11​

6​

13​

5​

10.75​

3.054E+12​

3​

Divine Bourrage

13​

9​

20​

12​

7​

8​

7​

7​

10.86​

3.045E+12​

4​

Bella Hines

23​

26​

33​

30​

41​

23​

24​

23​

28.57​

1.825E+12​

AVG

9.75​

14.86​

#1

Composite Rank/Sum

1.111E+13​

Rk

'25-'26 Tennessee

ASGR

PN

JRAS

ESPN

TSB

247S

BSBB

ON3

MISC

LOW

Avg Rank

Rank Score

1​

Deniya Prawl

30​

10​

16​

14​

7​

6​

15​

6​

14.00​

2.790E+12​

2​

Mia Pauldo

25​

18​

10​

11​

10​

16​

26​

14​

10​

16.25​

2.619E+12​

3​

Jaida Civil

20​

19​

38​

32​

35​

13​

13​

26.17​

1.962E+12​

4​

Lauren Hurst

35​

82​

42​

42​

50​

47​

35​

49.67​

9.240E+11​

5​

Mya Pauldo

86​

54​

56​

56​

29​

49​

29​

55.00​

7.669E+11​

AVG

18.60​

32.22​

#2

Composite Rank/Sum

9.061E+12​

Rk

'25-'26 Stanford

ASGR

PN

JRAS

ESPN

TSB

247S

BSBB

ON3

MISC

LOW

Avg Rank

Rank Score

1​

Hailee Swain

16​

5​

21​

9​

8​

7​

10​

5​

10.86​

3.045E+12​

2​

Lara Somfai

2​

14​

16​

40​

2​

18.00​

2.491E+12​

3​

Alexandra Eschmeyer

28​

37​

31​

31​

33​

34​

28​

32.33​

1.626E+12​

4​

Carly Amborn

70​

69​

68​

18​

18​

56.25​

7.334E+11​

5​

Nora Ezike

42​

49​

77​

84​

75​

39​

39​

61.00​

6.169E+11​

AVG

18.40​

35.69​

#3

Composite Rank/Sum

8.513E+12​

Rk

'25-'26 Oklahoma

ASGR

PN

JRAS

ESPN

TSB

247S

BSBB

ON3

MISC

LOW

Avg Rank

Rank Score

1​

Aaliyah Chavez

1​

1​

2​

1​

1​

1​

3​

1​

1​

1.38​

3.932E+12​

2​

Keziah Lofton

50​

23​

15​

50​

9​

34​

11​

17​

9​

26.13​

1.964E+12​

3​

Brooklyn Stewart

147​

50​

82​

62​

58​

50​

79.80​

2.930E+11​

AVG

20.00​

35.77​

#4

Composite Rank/Sum

6.189E+12​

 

[HawksGoneWild1]

Rk

'25-'26 South Carolina

ASGR

PN

JRAS

ESPN

TSB

247S

BSBB

ON3

MISC

LOW

Avg Rank

Rank Score

1​

Agot Makeer

5​

7​

5​

4​

4​

5​

4​

5.00​

3.571E+12​

2​

Ayla McDowell

7​

29​

27​

22​

53​

27​

15​

23​

7​

25.38​

2.009E+12​

AVG

5.50​

15.19​

#5

Composite Rank/Sum

5.579E+12​

Rk

'25-'26 Kansas

ASGR

PN

JRAS

ESPN

TSB

247S

BSBB

ON3

MISC

LOW

Avg Rank

Rank Score

1​

Jaliya Davis

15​

25​

18​

17​

34​

24​

24​

22​

15​

22.38​

2.195E+12​

2​

Keeley Parks

33​

20​

19​

29​

19​

17​

27​

24​

17​

23.50​

2.123E+12​

3​

Libby Fandel

97​

51​

44​

45​

32​

32​

53.80​

8.002E+11​

4​

Tatyonna Brown

208​

208​

208.00​

0.000E+00​

AVG

68.00​

76.92​

#6

Composite Rank/Sum

5.118E+12​

Rk

'25-'26 UNC

ASGR

PN

JRAS

ESPN

TSB

247S

BSBB

ON3

MISC

LOW

Avg Rank

Rank Score

1​

Nyla Brooks

21​

28​

12​

20​

21​

5​

19​

5​

18.00​

2.491E+12​

2​

Taliyah Henderson

19​

24​

24​

23​

20​

30​

19​

23.33​

2.134E+12​

3​

Taissa Queiroz

63​

84​

76​

63​

74.33​

3.678E+11​

AVG

29.00​

38.56​

#7

Composite Rank/Sum

4.993E+12​

Rk

'25-'26 Iowa

ASGR

PN

JRAS

ESPN

TSB

247S

BSBB

ON3

MISC

LOW

Avg Rank

Rank Score

1​

Addie Deal

17​

8​

22​

18​

5​

9​

25​

14​

2​

2​

13.33​

2.843E+12​

2​

Layla Hays

24​

51​

49​

70​

62​

21​

21​

46.17​

1.041E+12​

3​

Journey Houston

40​

31​

61​

85​

24​

79​

43​

19​

19​

47.75​

9.865E+11​

AVG

14.00​

35.75​

#8

Composite Rank/Sum

4.870E+12​

 

[HawksGoneWild1]

Rk

'25-'26 Cincinnati

ASGR

PN

JRAS

ESPN

TSB

247S

BSBB

ON3

MISC

LOW

Avg Rank

Rank Score

1​

Dee Alexander

6​

13​

4​

8​

2​

15​

28​

9​

2​

10.63​

3.065E+12​

2​

Caliyah DeVillasee

64​

68​

66​

57​

40​

44​

40​

56.50​

7.268E+11​

3​

Kali Barrett

123​

123​

123.00​

3.134E+10​

4​

Paige Whitted

183​

183​

183.00​

4.025E+07​

5​

Joya Crawford

190​

190​

190.00​

6.740E+06​

AVG

107.60​

112.63​

#9

Composite Rank/Sum

3.823E+12​

Rk

'25-'26 UCLA

ASGR

PN

JRAS

ESPN

TSB

247S

BSBB

ON3

MISC

LOW

Avg Rank

Rank Score

1​

Sienna Betts

4​

3​

1​

2​

4​

2​

1​

3​

1​

2.50​

3.817E+12​

2​

Lena Bilic

208​

208​

208.00​

0.000E+00​

AVG

104.50​

105.25​

#10

Composite Rank/Sum

3.817E+12​

Rk

'25-'26 USC

ASGR

PN

JRAS

ESPN

TSB

247S

BSBB

ON3

MISC

LOW

Avg Rank

Rank Score

1​

Jazzy Davidson

3​

2​

3​

3​

3​

3​

2​

2​

2​

2.63​

3.804E+12​

AVG

2.00​

2.63​

#11

Composite Rank/Sum

3.804E+12​

Rk

'25-'26 Kansas St

ASGR

PN

JRAS

ESPN

TSB

247S

BSBB

ON3

MISC

LOW

Avg Rank

Rank Score

1​

Jordan Speiser

62​

14​

17​

10​

36​

19​

29​

18​

10​

25.63​

1.994E+12​

2​

Aniya Foy

39​

41​

53​

46​

21​

52​

42​

21​

42.00​

1.195E+12​

3​

Brandie Harrod

87​

38​

83​

59​

38​

66.75​

4.965E+11​

4​

Gina Garcia Safont

208​

208​

208.00​

0.000E+00​

AVG

69.25​

85.59​

#12

Composite Rank/Sum

3.685E+12​

 

[HawksGoneWild1]

Rk

'25-'26 Illinois

ASGR

PN

JRAS

ESPN

TSB

247S

BSBB

ON3

MISC

LOW

Avg Rank

Rank Score

1​

Destiny Jackson

59​

39​

28​

25​

51​

28​

20​

20​

35.71​

1.463E+12​

2​

Manuella Fernandes

22​

45​

61​

22​

42.67​

1.169E+12​

3​

Cearah Parchment

72​

64​

40​

46​

40​

55.50​

7.533E+11​

4​

Naomi Benson

89​

98​

69​

69​

85.33​

2.305E+11​

5​

Erica Finney

208​

208​

208.00​

0.000E+00​

AVG

84.75​

97.88​

#13

Composite Rank/Sum

3.616E+12​

Rk

'25-'26 Miss St

ASGR

PN

JRAS

ESPN

TSB

247S

BSBB

ON3

MISC

LOW

Avg Rank

Rank Score

1​

Madison Francis

10​

21​

8​

39​

16​

26​

9​

20​

8​

18.63​

2.447E+12​

2​

Jaylah Lampley

26​

76​

72​

38​

28​

66​

48​

26​

50.57​

8.956E+11​

3​

Nataliyah Gray

91​

95​

91​

93.00​

1.623E+11​

AVG

41.67​

54.07​

#14

Composite Rank/Sum

3.505E+12​

Rk

'25-'26 Washington

ASGR

PN

JRAS

ESPN

TSB

247S

BSBB

ON3

MISC

LOW

Avg Rank

Rank Score

1​

Brynn McGaughy

12​

17​

25​

21​

22​

16​

12​

18.83​

2.432E+12​

2​

Bryn Martin

43​

67​

30​

68​

62​

64​

30​

55.67​

7.489E+11​

3​

Nina Cain

93​

89​

95​

71​

70​

70​

83.60​

2.488E+11​

4​

Sienna Harvey

208​

208​

208.00​

0.000E+00​

AVG

80.00​

91.53​

#15

Composite Rank/Sum

3.430E+12​

Rk

'25-'26 Georgia

ASGR

PN

JRAS

ESPN

TSB

247S

BSBB

ON3

MISC

LOW

Avg Rank

Rank Score

1​

Zhen Craft

27​

16​

23​

64​

11​

41​

12​

11​

27.71​

1.873E+12​

2​

Aubrey Beckham

55​

55​

47​

49​

59​

38​

38​

50.50​

8.979E+11​

3​

Meghan Yarnevich

85​

86​

81​

54​

54​

76.50​

3.365E+11​

4​

Jocelyn Faison

79​

79​

79.00​

3.031E+11​

5​

Harrissoum Coulibaly

208​

208​

208.00​

0.000E+00​

AVG

113.67​

121.17​

#16

Composite Rank/Sum

3.410E+12​

 

[HawksGoneWild1]

Rk

'25-'26 Texas

ASGR

PN

JRAS

ESPN

TSB

247S

BSBB

ON3

MISC

LOW

Avg Rank

Rank Score

1​

Aaliyah Crump

9​

12​

7​

5​

20​

5​

13​

8​

5​

9.88​

3.129E+12​

AVG

5.00​

9.88​

#17

Composite Rank/Sum

3.129E+12​

Rk

'25-'26 UConn

ASGR

PN

JRAS

ESPN

TSB

247S

BSBB

ON3

MISC

LOW

Avg Rank

Rank Score

1​

Kelis Fisher

18​

22​

37​

27​

18​

16​

21​

16​

22.71​

2.173E+12​

2​

Gandy Malou-Mamel

31​

79​

73​

74​

45​

31​

60.40​

6.307E+11​

3​

Blanca Quinonez

208​

208​

208.00​

0.000E+00​

AVG

85.00​

97.04​

#18

Composite Rank/Sum

2.804E+12​

Rk

'25-'26 Duke

ASGR

PN

JRAS

ESPN

TSB

247S

BSBB

ON3

MISC

LOW

Avg Rank

Rank Score

1​

Emilee Skinner

29​

6​

9​

7​

10​

31​

5​

5​

13.86​

2.801E+12​

AVG

5.00​

13.86​

#19

Composite Rank/Sum

2.801E+12​

Rk

'25-'26 Notre Dame

ASGR

PN

JRAS

ESPN

TSB

247S

BSBB

ON3

MISC

LOW

Avg Rank

Rank Score

1​

Leah Macy

34​

11​

11​

19​

13​

14​

4​

15​

4​

15.13​

2.703E+12​

AVG

4.00​

15.13​

#20

Composite Rank/Sum

2.703E+12​

 

[HawksGoneWild1]

Rk

'25-'26 Alabama

ASGR

PN

JRAS

ESPN

TSB

247S

BSBB

ON3

MISC

LOW

Avg Rank

Rank Score

1​

Lourdes Da Silva Costa

44​

50​

51​

44​

48.33​

9.671E+11​

2​

Ace Austin

74​

44​

52​

55​

39​

39​

52.80​

8.288E+11​

3​

Joy Egbuna

56​

97​

37​

37​

63.33​

5.655E+11​

4​

Tianna Chambers

94​

94​

73​

73​

87.00​

2.140E+11​

AVG

48.25​

62.87​

#21

Composite Rank/Sum

2.575E+12​

Rk

'25-'26 Michigan

ASGR

PN

JRAS

ESPN

TSB

247S

BSBB

ON3

MISC

LOW

Avg Rank

Rank Score

1​

McKenzie Mathurin

41​

33​

35​

37​

35​

37​

33​

36.33​

1.434E+12​

2​

Ciara Byars

81​

36​

32​

65​

32​

53.50​

8.087E+11​

3​

Jessica Fields

76​

77​

89​

99​

76​

85.25​

2.314E+11​

AVG

47.00​

58.36​

#22

Composite Rank/Sum

2.474E+12​

Rk

'25-'26 Utah

ASGR

PN

JRAS

ESPN

TSB

247S

BSBB

ON3

MISC

LOW

Avg Rank

Rank Score

1​

Leonna Sneed

82​

32​

44​

26​

56​

30​

46​

26​

45.14​

1.077E+12​

2​

Avery Hjelmstad

54​

53​

46​

47​

36​

36​

47.20​

1.005E+12​

3​

Ella Todd

92​

88​

66​

66​

82.00​

2.667E+11​

AVG

42.67​

58.11​

#23

Composite Rank/Sum

2.349E+12​

Rk

'25-'26 California

ASGR

PN

JRAS

ESPN

TSB

247S

BSBB

ON3

MISC

LOW

Avg Rank

Rank Score

1​

Aliyahna Morris

66​

30​

26​

24​

23​

25​

23​

32.33​

1.626E+12​

2​

Isis Johnson Musah

71​

71​

71.00​

4.205E+11​

3​

Taylor Barnes

145​

98​

93​

72​

72​

102.00​

1.042E+11​

4​

Grace McCallop

166​

83​

83​

124.50​

2.844E+10​

AVG

62.25​

82.46​

#24

Composite Rank/Sum

2.179E+12​

 

[HawksGoneWild1]

Rk

'25-'26 Oregon

ASGR

PN

JRAS

ESPN

TSB

247S

BSBB

ON3

MISC

LOW

Avg Rank

Rank Score

1​

Janiyah Williams

51​

34​

57​

53​

6​

37​

21​

6​

37.00​

1.404E+12​

2​

Sara Barhoum

68​

57​

90​

63​

57​

69.50​

4.462E+11​

AVG

57.00​

69.50​

#25

Composite Rank/Sum

1.851E+12​

Rk

'25-'26 Miami

ASGR

PN

JRAS

ESPN

TSB

247S

BSBB

ON3

MISC

LOW

Avg Rank

Rank Score

1​

Danielle Osho

65​

71​

39​

54​

65​

39​

58.80​

6.688E+11​

2​

Soma Okolo

52​

76​

52​

64.00​

5.514E+11​

3​

Camille Williams

112​

36​

36​

74.00​

3.728E+11​

4​

Meredith Tipper

100​

87​

93​

87​

93.33​

1.597E+11​

5​

Natalie Wetzel

130​

79​

79​

104.50​

9.147E+10​

AVG

67.33​

90.61​

#26

Composite Rank/Sum

1.844E+12​

Rk

'25-'26 Kentucky

ASGR

PN

JRAS

ESPN

TSB

247S

BSBB

ON3

MISC

LOW

Avg Rank

Rank Score

1​

Kaelyn Carroll

47​

42​

59​

15​

18​

32​

14​

14​

32.43​

1.621E+12​

AVG

14.00​

32.43​

#27

Composite Rank/Sum

1.621E+12​

Rk

'25-'26 Indiana

ASGR

PN

JRAS

ESPN

TSB

247S

BSBB

ON3

MISC

LOW

Avg Rank

Rank Score

1​

Maya Makalusky

45​

43​

34​

35​

59​

57​

34​

45.50​

1.064E+12​

2​

Nevaeh Caffey

83​

62​

67​

67​

62​

69.75​

4.418E+11​

AVG

48.00​

57.63​

#28

Composite Rank/Sum

1.506E+12​

 

[HawksGoneWild1]

Rk

'25-'26 NC St

ASGR

PN

JRAS

ESPN

TSB

247S

BSBB

ON3

MISC

LOW

Avg Rank

Rank Score

1​

Adelaide Jernigan

36​

46​

78​

66​

55​

36​

56.20​

7.347E+11​

2​

Destiny Lunan

53​

72​

65​

60​

73​

53​

64.60​

5.390E+11​

AVG

44.50​

60.40​

#29

Composite Rank/Sum

1.274E+12​

Rk

'25-'26 Michigan St

ASGR

PN

JRAS

ESPN

TSB

247S

BSBB

ON3

MISC

LOW

Avg Rank

Rank Score

1​

Jordan Ode

88​

35​

29​

33​

98​

51​

29​

55.67​

7.489E+11​

2​

Amy Terrian

117​

58​

89​

53​

53​

79.25​

2.999E+11​

3​

Anna Terrian

124​

73​

73​

98.50​

1.243E+11​

AVG

51.67​

77.81​

#30

Composite Rank/Sum

1.173E+12​

Rk

'25-'26 Maryland

ASGR

PN

JRAS

ESPN

TSB

247S

BSBB

ON3

MISC

LOW

Avg Rank

Rank Score

1​

Rainey Welson

80​

63​

34​

64​

29​

23​

23​

48.83​

9.508E+11​

2​

Addi Mack

106​

55​

100​

100​

55​

90.25​

1.845E+11​

AVG

39.00​

69.54​

#31

Composite Rank/Sum

1.135E+12​

Rk

'25-'26 Creighton

ASGR

PN

JRAS

ESPN

TSB

247S

BSBB

ON3

MISC

LOW

Avg Rank

Rank Score

1​

Ava Zediker

129​

47​

70​

69​

63​

68​

47​

74.33​

3.678E+11​

2​

Neleigh Gessert

109​

60​

91​

82​

54​

54​

79.20​

3.006E+11​

3​

Kendall McGee

122​

49​

49​

85.50​

2.288E+11​

4​

Norah Gessert

158​

92​

91​

91​

113.67​

5.523E+10​

5​

Avery Cooper

131​

131​

131.00​

1.830E+10​

AVG

111.00​

122.33​

#32

Composite Rank/Sum

9.707E+11​

 

[ObeseMuffins]

I am hoping Deal can play above her ranking. I hope she’s a gym rat like CC.

 

[HIWILLE]

Wow, thanks for your hard work.👍

 

[exspec]

 

[AMAYS]

ESPN usually has a post-McDonald’s AA game update to their rankings. Will be interesting to see if there is movement with Hays and Houston.

 

[JEDN]

Great work. Thanks!

 

[wneff]

Sure looks like you're cooking the books on the Iowa players with their "Misc" ranking.
As well as giving rankings of 208 to foreign players.

 

[HawksGoneWild1]

Sure looks like you're cooking the books on the Iowa players with their "Misc" ranking.

Just my attempt to uncook the books.

Addie Deal rated #2:

Addie Deal at Mater Dei Recruit Rank History

247sports.com

Layla Hays rated #21:

Iowa WBB 2025 Recruiting Thread

@BraydonRoberts5 what about Taliyah Henderson — any idea of her schedule? Last I heard staff had taken at least two visits to AZ and they went well. I don’t think she has been to campus yet (tho I could have missed it). Also, do you know the nature of Deals injury? Thx as always for the good...

iowa.forums.rivals.com

Journey Houston rated #19:

Iowa WBB Class of 2025 Top Targets

Taking a look at some of Iowa WBB's top targets for the 2025 class, including a pair of Davenport, Iowa prospects.

iowa.rivals.com

I did ask hawkbuck about Journey's injury and rankings fall back in November of last year. Here is his reply:

Journey fell in the rankings because she simply did not play this past summer while rehabbing from her knee injury. Rankings are subjective, in nature, and if not seen, the fall was expected. Journey is a kid who gives max effort, relies on deceptive quickness and shear determination to impact the game. Her offensive skills, as it pertains to shooting, overall, were and will continue to be a work in progress. She certainly could have used another summer on the club circuit with her Attack team that would have further developed her skills, but it just didn't happen for circumstances out of her control. Journey, because of this setback, may not (with hesitation) be the impact player we hoped she would be as a freshman. But, this kid has the motor, the drive, determination to be great that every coach would want in a player. She will rebound and run the floor at a high level. She will guard and she knows her strengths. Getting her to be a double digit scorer at the next level, if that is what she needs to be, will the question yet to be answered.

I also stated in my original post:
MISC = A special ranking column created to handle special cases. This acts like a 9th ranking site for the Avg Ranking column.
1) A player that's NOT ranked on any site. 207 was lowest ranked player from all sites. Ranking #208 was used as lowest ranking for those players with no ranking found and their Rank Score wil be 0.
2) Players rated higher than their current ranking. This happens a lot to injured players when they were rated higher and then after the injury their ranking went down to non-playing time.

That being said, all of our players were injured at some point over their junior years. Believe they are now playing at the same level or better than their prior higher ranking.

Not trying to cook the books. Just happened to notice our commit's rankings as I was researching other events on the forum and made a note that their rankings were at a higher point than currently stated now.

As far as I can tell, the ranking sites could be cooking the books on us by possibly using injuries to devalue us. So there's more than one way to look at this.

Just reran the #s without the MISC column filled in and we would still be slotted at #8.

Rk

'25-'26 Iowa

ASGR

PN

JRAS

ESPN

TSB

247S

BSBB

ON3

MISC

LOW

Avg Rank

Rank Score

1​

Addie Deal

17​

8​

22​

18​

5​

9​

25​

14​

5​

14.75​

2.732E+12​

2​

Layla Hays

24​

51​

49​

70​

62​

24​

51.20​

8.764E+11​

3​

Journey Houston

40​

31​

61​

85​

24​

79​

43​

24​

51.86​

8.566E+11​

AVG

17.67​

39.27​

#8

Composite Rank/Sum

4.465E+12​

 

[Rocket_Fran]

This is awesome, thanks for doing this! Would you consider a sort of retrospective-based weighting to the individual sites based on their on-court success So, the more accurate and reliable recruiting sites weigh higher when applying it to future rankings.

I don't know the best way of doing it but I think one could use All American 1st team to - Honorable Mention - there's your top 25. Then, use first and second team all conference honors to round out the top 100. Obviously if one wanted to expand further they could but that isn't totally necessary. Compare the *real* top 100 to the 100 of the sites. Only problem is that does produce errors temporally they are not equal. Top 100 is just one class, real top 100 is multiple.

The rankers can't see everyone everywhere and be at all places at all times. So, they only get credit for true positives (i.e. hit rate), and don't have deductions for false negatives (e.g. not in the top 100 but player ended up 3rd team all-american. Recruiters aren't deducted accuracy points for false positives (rated them 50th but they never started a single game). Injuries are a big confounding variable in this analysis but removing false positives as a contributor would mostly remove that confounder. This somewhat takes care of the different classes issue. Since we are only giving credit for true positives then the sites only get credit for saying someone is top 100 and them actually playing early.

For simplicity and time one could just group 10-15 players in tiers and assign accuracy that way which would also take out any need for further inspection and bias along the spectrum.

I haven't thought it through but using this method could also determine what I call essentially is group think ranking bias. This bias could be applied intra or inter-recruit rankings since they are independent. Example: two sites may have the same 100 recruits in the top 100 but completely flip flopped. Alternatively, two sites could only have 50% of the same recruits in the top 100 but for the ones that do they have the exact same rank.

This way, you could quickly identify trends between the sites and relate that back to accuracy and potentially weighting. Lastly, you may be able to take out some sites entirely. If there's a couple sites who look like they are basically mirror clones of site A then why keep including them in the results? It's more leg work with no pay-off. Just weight Site A a little bit more.

I like the previous 5 years because A) it's essentially a whole class and B) these sites have some dynamicity to them with different people determining the rankings or the same person getting better/worse over time.

What do you think? If someone has a different/better idea I'm all ears. I think I have a few ways to automate these processes through excel/coding so it wouldn't be a time sink on my end. Would be an interesting base line to add each consecutive year to the data. I wrote this across 4-5 separate instances so apologies if it lacks a coherent train of thought.

 

[ssparks1333]

Just my attempt to uncook the books.

Addie Deal rated #2:

Addie Deal at Mater Dei Recruit Rank History

247sports.com

Layla Hays rated #21:

Iowa WBB 2025 Recruiting Thread

@BraydonRoberts5 what about Taliyah Henderson — any idea of her schedule? Last I heard staff had taken at least two visits to AZ and they went well. I don’t think she has been to campus yet (tho I could have missed it). Also, do you know the nature of Deals injury? Thx as always for the good...

iowa.forums.rivals.com

Journey Houston rated #19:

Iowa WBB Class of 2025 Top Targets

Taking a look at some of Iowa WBB's top targets for the 2025 class, including a pair of Davenport, Iowa prospects.

iowa.rivals.com

I did ask hawkbuck about Journey's injury and rankings fall back in November of last year. Here is his reply:


I also stated in my original post:
MISC = A special ranking column created to handle special cases. This acts like a 9th ranking site for the Avg Ranking column.
1) A player that's NOT ranked on any site. 207 was lowest ranked player from all sites. Ranking #208 was used as lowest ranking for those players with no ranking found and their Rank Score wil be 0.
2) Players rated higher than their current ranking. This happens a lot to injured players when they were rated higher and then after the injury their ranking went down to non-playing time.

That being said, all of our players were injured at some point over their junior years. Believe they are now playing at the same level or better than their prior higher ranking.

Not trying to cook the books. Just happened to pick our commit's rankings as I was researching other events on the forum and made a note that their rankings were at a higher point than currently stated now.

As far as I can tell, the ranking sites could be cooking the books on us by possibly using injuries to devalue us. So there's more than one way to look at this.

Just reran the #s without the MISC column filled in and we would still be slotted at #8.

Rk

'25-'26 Iowa

ASGR

PN

JRAS

ESPN

TSB

247S

BSBB

ON3

MISC

LOW

Avg Rank

Rank Score

1​

Addie Deal

17​

8​

22​

18​

5​

9​

25​

14​

5​

14.75​

2.732E+12​

2​

Layla Hays

24​

51​

49​

70​

62​

24​

51.20​

8.764E+11​

3​

Journey Houston

40​

31​

61​

85​

24​

79​

43​

24​

51.86​

8.566E+11​

AVG

17.67​

39.27​

#8

Composite Rank/Sum

4.465E+12​

Dude! Thank you for all the hard work. Interesting to see where Iowas recruiting "misses" ended up ranked, and committed. If we could have landed anyone of them ( Skinner, Speiser, McC, etc) this class could have been more special. Obviously!

 

[HawksGoneWild1]

As well as giving rankings of 208 to foreign players.

Do you have a ranking service for that to compare to the current list? If they're not listed in the 8 rankings list, how do you propose to rank them?

 

[nu2u]

This is NOT a perfect system

Not acceptable!

 

[Hawkeyes2133]

ESPN usually has a post-McDonald’s AA game update to their rankings. Will be interesting to see if there is movement with Hays and Houston.

Ya im interested too. I've seen hays slid down a little. She was in the 50s at a time.

Journey put up some nice stats when the girl on her team going to lsu got hurt.

Well see

 

[herkhatescy2]

How can you rank a team #11 with only one recruit? Just curious.

 

[HawksGoneWild1]

How can you rank a team #11 with only one recruit? Just curious.

Well, I did state this.
Rank Score = Weighted score of the 'Avg Rank' column. There are big differences between a #1 rated player, a #40 player, a #75 player and a #150 rated player.

I forgot to add the following into my original post and I did a edit to put it in now.
Thus, a simple uniformed player 'Rank Score' formula was used in this calculation to take out as much bias as possible. That formula is: (208-Avg Rank)^5.44

TLDR - That's just how the exponential formula placed a value/score on them and how it gave less weight to lower rated players. I had different teams pegged to be below certain teams throughout the list. To get the top teams, middle teams and lower teams to come into their spots it's just how other teams in between them fell into place and I can live with that. See the other rankings below of why I can live with it.

USC was #11 in my list. If you noticed her avg rank was 2.63 across 8 rating services. If you're ranked 2.63, then you're most likely a TOP McD All-American. You're value is considerably higher than a #20, it's going to be much higher than #40 and it's going to be way higher than #80. This means you need to use a rating system that exponentially decreases a player's score the lower they are ranked.

Rk	'25-'26 USC	ASGR	PN	JRAS	ESPN	TSB	247S	BSBB	ON3	MISC	LOW	Avg Rank	Rank Score
1	Jazzy Davidson	3	2	3	3	3	3	2	2		2	2.63	3.804E+12

These McDAA players are going to bring more to the table and make other players around them better than non-McDAAs. This is why you have UConn and South Carolina stacked with these type of players. These are CC and JuJu type players. Hard to tell who will excel and who will just show up. If these type of players go down, it can devastate your team (eg USC this year).

Once you start getting players around say ~40ish, they can start to be a bust or not as advertised. AJ Edijer #39 out of high school. It doesn't mean a #11 player can't be a bust. You're just dealing with odds of something happening here. These players have a highly decreased value/score tied to them than McDAAs. You start getting near 80ish rated and you have bench players on very good teams.

Anyways, this is just the way the formula worked out to get LSU #1, TN #2 and Stanford #3. Think my first 6 come very close to a match others with this one simple formula.

Here's USC at #5 on National Signing Day for women's college basketball in 2024 on Nov 13, 2024. This came out 2 days later to eval those signings. USC only had Jazzy Davidson at that time. You can see us at #9 had 3 players and a McD All-American also. They valued 1 McD All-American over our 1 McD All-American + 2 other players, Also, Journey Houston was rated at #19 when she committed. Both Houston and Hays avg ~50 on signing day. You see they didn't give much value to these #50 rated players of ours.



Here's USC at #15. So I'm not so far off. As you can tell it's very subjective. I wanted to take that bias out as much as possible. By: 1) Calcing a player's avg rank from multiple services. 2) Using a formula that then adds up those avg ranked scores to see where the chips may fall.
This is a link to jrallstar.com rankings:

The Top 2025 Recruiting Classes: Where Each Program Stands Post-Signing Day - Jr. All-Star Basketball

With National Signing Day now behind us and most players signing yesterday, here are our current 2025 Recruiting Class Rankings! With a few Top 100 […] More

jrallstar.com

So it's not total nutwaggery if that's what you're thinking.

 

[herkhatescy2]

Well, I did state this.
Rank Score = Weighted score of the 'Avg Rank' column. There are big differences between a #1 rated player, a #40 player, a #75 player and a #150 rated player.

I forgot to add the following into my original post and I did a edit to put it in now.
Thus, a simple uniformed player 'Rank Score' formula was used in this calculation to take out as much bias as possible. That formula is: (208-Avg Rank)^5.44

TLDR - That's just how the exponential formula placed a value/score on them and how it gave less weight to lower rated players. I had different teams pegged to be below certain teams throughout the list. To get the top teams, middle teams and lower teams to come into their spots it's just how other teams in between them fell into place and I can live with that. See the other rankings below of why I can live with it.

USC was #11 in my list. If you noticed her avg rank was 2.63 across 8 rating services. If you're ranked 2.63, then you're most likely a TOP McD All-American. You're value is considerably higher than a #20, it's going to be much higher than #40 and it's going to be way higher than #80. This means you need to use a rating system that exponentially decreases a player's score the lower they are ranked.

Rk	'25-'26 USC	ASGR	PN	JRAS	ESPN	TSB	247S	BSBB	ON3	MISC	LOW	Avg Rank	Rank Score
1	Jazzy Davidson	3	2	3	3	3	3	2	2		2	2.63	3.804E+12

These McDAA players are going to bring more to the table and make other players around them better than non-McDAAs. This is why you have UConn and South Carolina stacked with these type of players. These are CC and JuJu type players. Hard to tell who will excel and who will just show up. If these type of players go down, it can devastate your team (eg USC this year).

Once you start getting players around say ~40ish, they can start to be a bust or not as advertised. AJ Edijer #39 out of high school. It doesn't mean a #11 player can't be a bust. You're just dealing with odds of something happening here. These players have a highly decreased value/score tied to them than McDAAs. You start getting near 80ish rated and you have bench players on very good teams.

Anyways, this is just the way the formula worked out to get LSU #1, TN #2 and Stanford #3. Think my first 6 come very close to a match others with this one simple formula.

Here's USC at #5 on National Signing Day for women's college basketball in 2024 on Nov 13, 2024. This came out 2 days later to eval those signings. USC only had Jazzy Davidson at that time. You can see us at #9 had 3 players and a McD All-American also. They valued 1 McD All-American over our 1 McD All-American + 2 other players, Also, Journey Houston was rated at #19 when she committed. Both Houston and Hays avg ~50 on signing day. You see they didn't give much value to these #50 rated players of ours.

View embedded media

Here's USC at #15. So I'm not so far off. As you can tell it's very subjective. I wanted to take that bias out as much as possible. By: 1) Calcing a player's avg rank from multiple services. 2) Using a formula that then adds up those avg ranked scores to see where the chips may fall.
This is a link to jrallstar.com rankings:

The Top 2025 Recruiting Classes: Where Each Program Stands Post-Signing Day - Jr. All-Star Basketball

With National Signing Day now behind us and most players signing yesterday, here are our current 2025 Recruiting Class Rankings! With a few Top 100 […] More

jrallstar.com

So it's not total nutwaggery if that's what you're thinking.