Top 10 Players in PER and Box Plus/Minus | The Boneyard
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Top 10 Players in PER and Box Plus/Minus

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PER (Player Efficiency Rating) and Box Plus/Minus are two advanced statistics that have been developed to try to measure players' relative overall total effectiveness—basically, their good stats minus their bad stats, adjusted for game pace and tweeked by various mathematical algorithms. Each stat has strengths and weaknesses. More specific definitions and explanations of these stats can easily be found via searches.

Using sports-reference.com as my source, every Monday I will post the top ten D1 players in both cumulative seasonal PER and Box Plus/Minus. I might occasionally include some other advanced stats.

Monday, January 5, 2026:

PER

1.Audi Crooks Iowa State 50.5
2.Sarah Strong UConn 45.7
3.Avery Koenen North Dakota State 43.8
4.Hannah Hidalgo Notre Dame 41.4
5.Brooklyn Meyer South Dakota State 41.1
6.Mikayla Blakes Vanderbilt 39.4
7.MiLaysia Fulwiley LSU 39.0
8.Madina Okot South Carolina 38.9
9.Raegan Beers Oklahoma 38.9
10.Alyssa Moreland Brown 38.3


Box Plus/Minus

1.Sarah Strong UConn 29.6
2.MiLaysia Fulwiley LSU 21.0
3.Azzi Fudd UConn 20.3
4.Gianna Kneepkens UCLA 19.5
5.Hannah Hidalgo Notre Dame 19.3
6.Talaysia Cooper Tennessee 19.0
7.Mikayla Blakes Vanderbilt 18.7
8.Kiki Rice UCLA 18.3
9.Madison Booker Texas 18.2
10.Joyce Edwards South Carolina 17.7
 
PER (Player Efficiency Rating) and Box Plus/Minus are two advanced statistics that have been developed to try to measure players' relative overall total effectiveness—basically, their good stats minus their bad stats, adjusted for game pace and tweeked by various mathematical algorithms. Each stat has strengths and weaknesses. More specific definitions and explanations of these stats can easily be found via searches.

Using sports-reference.com as my source, every Monday I will post the top ten D1 players in both cumulative seasonal PER and Box Plus/Minus. I might occasionally include some other advanced stats.

Monday, January 5, 2026:

PER

1.Audi Crooks Iowa State50.5
2.Sarah Strong UConn45.7
3.Avery Koenen North Dakota State43.8
4.Hannah Hidalgo Notre Dame41.4
5.Brooklyn Meyer South Dakota State41.1
6.Mikayla Blakes Vanderbilt39.4
7.MiLaysia Fulwiley LSU39.0
8.Madina Okot South Carolina38.9
9.Raegan Beers Oklahoma38.9
10.Alyssa Moreland Brown38.3


Box Plus/Minus

1.Sarah Strong UConn29.6
2.MiLaysia Fulwiley LSU21.0
3.Azzi Fudd UConn20.3
4.Gianna Kneepkens UCLA19.5
5.Hannah Hidalgo Notre Dame19.3
6.Talaysia Cooper Tennessee19.0
7.Mikayla Blakes Vanderbilt18.7
8.Kiki Rice UCLA18.3
9.Madison Booker Texas18.2
10.Joyce Edwards South Carolina17.7
I prefer to look at these stats filtered for games against top 50 or top 100 teams. I use Torvik for that. I look at BPM and things look very different when you get rid of the cupcake factor.
 
I prefer to look at these stats filtered for games against top 50 or top 100 teams. I use Torvik for that. I look at BPM and things look very different when you get rid of the cupcake factor.

Feel free to post Torvik's top 10 BPMs in this thread if you like.

One long-term purpose for this thread is to see how these advanced stats compare to each other and ultimately how they compare to the human-chosen NPOYs and AAs at the end of the season.
 
I’m not entirely surprised to see the discrepancy between PER and BPM fo Crooks. I don’t have the PER definition at my finger tips, but my sense is that it doesn’t capture defensive stats as well as offensive ones, and that may be what’s at work here.
 
Monday, January 12, 2026:

PER

1.Sarah Strong UConn 47.0
2.Audi Crooks Iowa State 46.9
3.Avery Koenen North Dakota State 44.1
4.Hannah Hidalgo Notre Dame 42.2
5.Brooklyn Meyer South Dakota State 40.6
6.Mikayla Blakes Vanderbilt 39.3
7.Madina Okot South Carolina 38.5
8.Gracie Merkle Penn State 38.1
9.Antoniette Emma-Nnopu Weber State 37.8
10.Lauren Whittaker Gonzaga 37.7

Box Plus/Minus

1.Sarah Strong UConn 29.9
2.MiLaysia Fulwiley LSU 20.7
3.Hannah Hidalgo Notre Dame 20.1
4.Azzi Fudd UConn 19.8
5.Gianna Kneepkens UCLA 19.6
6.Talaysia Cooper Tennessee 19.2
7.Mikayla Blakes Vanderbilt 18.3
8.Kiki Rice UCLA 18.3
9.Madison Booker Texas 18.0
10.Joyce Edwards South Carolina 17.8
 
Monday, January 12, 2026:

PER

1.Sarah Strong UConn47.0
2.Audi Crooks Iowa State46.9
3.Avery Koenen North Dakota State44.1
4.Hannah Hidalgo Notre Dame42.2
5.Brooklyn Meyer South Dakota State40.6
6.Mikayla Blakes Vanderbilt39.3
7.Madina Okot South Carolina38.5
8.Gracie Merkle Penn State38.1
9.Antoniette Emma-Nnopu Weber State37.8
10.Lauren Whittaker Gonzaga37.7

Box Plus/Minus

1.Sarah Strong UConn29.9
2.MiLaysia Fulwiley LSU20.7
3.Hannah Hidalgo Notre Dame20.1
4.Azzi Fudd UConn19.8
5.Gianna Kneepkens UCLA19.6
6.Talaysia Cooper Tennessee19.2
7.Mikayla Blakes Vanderbilt18.3
8.Kiki Rice UCLA18.3
9.Madison Booker Texas18.0
10.Joyce Edwards South Carolina17.8
I pretty much expected Sara to overtake Audi in PER, if not this week then soon because of their respective schedules. PER numbers for Sarah gets easier against Big East competition while Audi's numbers predictably decline against her much tougher conference competition.

Regardless just looking at these lists reveals the Box Plus/Minus as the far better overall measure of a player's effectiveness. I don't know how it is done but you can see all the top 10 are very highly regarded top players, while the PER list contains a few surprises and a few missing that belong there.

The box stats adjust much better for the level of competition, and are much better when comparing players from conferences that are very different in ability.
 
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Regardless just looking at these lists reveals the Box Plus/Minus as the far better overall measure of a player's effectiveness. I don't know how it is done but you can see all the top 10 are very highly regarded top players, while the PER list contains a few surprises and a few missing that belong there.

The PER list includes players from mid-major schools who have probably been putting up big, positive stats for their teams. The BPM list seems to incorporate something like SOS or RPI, since it includes players only from the SEC, Big Ten, ACC and UConn. Maybe I'll investigate this further, or maybe someone here knows why.
 
The PER must really like efficiency metrics because that is the only way I see Grace Merkle's inclusion making any sense.
 
The BPM list seems to incorporate something like SOS or RPI, since it includes players only from the SEC, Big Ten, ACC and UConn.

I've researched this further. Calculating a player's BPM is extremely statistically complicated, but it does include adjustment factors for the team's strength and the team's SOS.

More specifically, the player's "raw BPM" is calculated from box score data. The raw BPM is then adjusted by her position and several other factors.

One of those other factors is her team's "Net Rating" (or "efficiency margin"). The team's Net Rating is computed by subtracting the team's defensive rating (points allowed per 100 possessions) from its Offensive Rating (points scored per 100 possessions). The team's Net Rating is then further adjusted for its SOS.

Finally, these team adjustments factors are applied to each player's raw BPM (in addition to other adjustment factors) to get BPM. Therefore, a player with a raw BPM on a highly ranked team will have a higher BPM than a player with the same raw BPM on a lower ranked team.

Here, for example, are the top 10 WCBB teams sorted by Net Rating (the last column) as of yesterday:

RkSchoolConf AP RankWLPtsOppMOV SOS OSRSDSRSSRSORtgDRtgNRtg
1ConnecticutBig East117089.951.838.1214.5030.6621.9652.62130.2661.0269.24
2UCLABig Ten415187.156.630.4417.4927.3920.5447.93131.7867.1364.65
3South CarolinaSEC317189.753.636.1710.5228.2918.4046.69124.7465.0159.73
4TexasSEC218190.553.237.329.3027.8818.7346.61125.5965.9859.61
5Louisiana StateSEC12162100.155.944.172.0834.7911.4646.25124.2169.8854.33
6MichiganBig Ten914288.957.831.1310.6226.2615.4941.75117.9065.8752.04
7OklahomaSEC514390.860.630.186.9027.559.5237.08111.4468.2443.20
8LouisvilleACC1016382.958.224.6810.1821.7713.1034.86118.7273.6045.12
9TCUBig 121316184.352.132.242.3118.5516.0034.55114.6670.1144.55
10Michigan StateBig Ten1516188.558.230.293.9223.7710.4434.21121.3377.1944.14
 
At this point, Strong is the career leader in box +/- per Basketball Reference.

The PER state can skew based on strength of opponents. Just looking at the two Summit players (Brooklyn Meyer and Avery Koenen) — South Dakota State had a very tough out-of-conference schedule; I think Massey top 25 or 30, while North Dakota State was in the upper 100’s. I’ve only seen Koenen play once, so can’t comment on her, but I’ve seen Meyer many times over the last few years, and I think she will be drafted this spring.
 
RkSchoolConfAP RankWLPtsOppMOVSOSOSRSDSRSSRSORtgDRtgNRtg
1ConnecticutBig East117089.951.838.1214.5030.6621.9652.62130.2661.0269.24
2UCLABig Ten415187.156.630.4417.4927.3920.5447.93131.7867.1364.65
3South CarolinaSEC317189.753.636.1710.5228.2918.4046.69124.7465.0159.73
4TexasSEC218190.553.237.329.3027.8818.7346.61125.5965.9859.61
5Louisiana StateSEC12162100.155.944.172.0834.7911.4646.25124.2169.8854.33
6MichiganBig Ten914288.957.831.1310.6226.2615.4941.75117.9065.8752.04
7OklahomaSEC514390.860.630.186.9027.559.5237.08111.4468.2443.20
8LouisvilleACC1016382.958.224.6810.1821.7713.1034.86118.7273.6045.12
9TCUBig 121316184.352.132.242.3118.5516.0034.55114.6670.1144.55
10Michigan StateBig Ten1516188.558.230.293.9223.7710.4434.21121.3377.1944.14

Can you explain what the difference is between the Net rating above and the NCAA net ratings table - DI Women's Basketball Rankings - NCAA Women's Basketball NET Rankings | NCAA.com
 
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Can you explain what the difference is between the Net rating above and the NCAA net ratings table - DI Women's Basketball Rankings - NCAA Women's Basketball NET Rankings | NCAA.com
NetRtg = OffRtg - DefRtg = (Per 100 PPP) - (Opponent Per 100 PPP);
  • where PPP = Points Per Possession
  • this is the NetRtg in Gael’s post.
The NET (NCAA Evaluation Tool) is similar to Torvik’s Barthag and (Men’s) KenPom — they are essentially variations of the same type of method.
  • At season’s end, the NET and Torvik Barthag values are similar &1;
  • The NET’s description is here, which is not really that helpful;
  • Fortunately, Torvik describes his methodology here.
Torvik and the NET takes OffRtg and DefRtg, applies (proprietary) adjustments to them to come up with Barthag / NET;
  • One of the common factors is a Pythagorean Expectation exponent (Torvik uses 11.5 while the NET’s is undisclosed);
  • As a concept, Barthag/ NET is a team’s probability of winning against the average D1 team.
&1 Certain features of Torvik’s methodology (recency bias and garbage time adjustment) allow it to capture UConn’s metamorphosis and correct for UConn’s propensity to “call off the dogs”.
 
Monday, January 19, 2025

PER

1.Sarah Strong UConn 47.4
2.Audi Crooks Iowa State 45.8
3.Avery Koenen North Dakota State 44.3
4.Hannah Hidalgo Notre Dame 41.6
5.Brooklyn Meyer South Dakota State 40.9
6.Mikayla Blakes Vanderbilt 39.8
7.Mia Nicastro Western Illinois 38.2
8.Lauren Whittaker Gonzaga 38.1
9.Gracie Merkle Penn State 38.1
10.MiLaysia Fulwiley LSU 37.6

Box Plus/Minus

1.Sarah Strong UConn 30.2
2.MiLaysia Fulwiley LSU 20.8
3.Hannah Hidalgo Notre Dame 19.7
4.Kiki Rice UCLA 19.7
5.Azzi Fudd UConn 19.4
6.Gianna Kneepkens UCLA 19.4
7.Talaysia Cooper Tennessee 19.2
8.KK Arnold UConn 18.4
9.Mikayla Blakes Vanderbilt 18.3
10.Madison Booker Texas 18.2
 
Monday, January 26, 2026

PER

1.Sarah Strong UConn 46.1
2.Avery Koenen North Dakota State 44.4
3.Audi Crooks Iowa State 43.6
4.Brooklyn Meyer South Dakota State 42.9
5.Hannah Hidalgo Notre Dame 40.5
6.Lauren Whittaker Gonzaga 39.2
7.MiLaysia Fulwiley LSU 39.0
8.Gracie Merkle Penn State 38.0
9.Mikayla Blakes Vanderbilt 37.0
10.Raegan Beers Oklahoma 36.2

Box Plus/Minus

1.Sarah Strong UConn 29.1
2.MiLaysia Fulwiley LSU 22.4
3.Hannah Hidalgo Notre Dame 19.5
4.Kiki Rice UCLA 19.4
5.Gianna Kneepkens UCLA 19.3
6.Azzi Fudd UConn 19.0
7.Madison Booker Texas 18.5
8.Talaysia Cooper Tennessee 18.5
9.Charlisse Leger-Walker UCLA 17.9
10.KK Arnold UConn 17.6
 

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