Georgia Tech and Their Pink Uniforms

I took some film of the Georgia Tech women's basketball team on February 8, 2008 during their game against N. C. State. The pink uniforms were in honor of the late coach Kay Yow.

After some YouTube experimentation, I was able to load the videos up onto YouTube. Therefore, you get to watch my amateur cinematography.

More Potential College Prospects and the Senior Prospects Metric

Mighty, or midget, or both?

The author of the Chasing the Title blog - even though his real name isn't that much of a secret I don't feel I have permission to state it yet - has boldly gone where only a few of us out there are going, namely, trying to say "these are the players you should be watching this year".

It's tough, because it opens you up to ridicule. Trust me, I know.

As far as I know, there are only three people doing this kind of stats-based analysis: me, the Chasing the Title author, and bullsky who is the author of WNBA Draft Net. Our lists of players look pretty much the same; the only difference is in what order should players be placed?

However, there are time when our lists don't match. I see a player at the Chasing the Title blog who isn't even on my list at all! There were three such players named. That means that I go back to my little spreadsheet, put the stats in, and see how they compare.

Here are three players on the Chasing the Title blog that weren't in my spreadsheet. But they are now. Let's look at how my metrics rate them.

Jenna Green, center, UC Santa Barbara

Well, there's a simple reason Jenna Green didn't make it to my list: she's returning not from her first, but her second medical redshirt year. Which means that we'll be comparing her against a class of people who are actually two years younger than her. Green is two years closer to her peak value, which automatically drops her a lot in my spreadsheet's evaluation.

On the other hand, in her rates of blocks and steals, Green is actually quite good. Furthermore, in terms of efficiency, she does well in producing value per minutes played.

In terms of assists vs. turnovers, she suffers - but all players except guards suffer, so she's in good company. The strength of her conference - the Big West - causes her rating to take a hit because she performs against a lesser class of players.

In her "rebounds expected per 100 rebounds a game for both teams total", however, Green suffers. She doesn't rebound as efficiently as a lot of centers - some of those centers are from schools in conferences as relatively weak at the Big West. My spreadsheet looks at her rebound rate, concludes "this is truly sub-par rebounding for a center", and assigns her a big hit against.

In the end, Green doesn't even make the Top 100 of my spreadsheet - the two medical redshirt years hurt her even more than her rebounding. If you decide to treat her as players aged two years younger, however, she'd jump to the 50s on my list.

Laura Kurz, shooting forward, Villanova

I never liked the term "small forward". There's nothing small about most of those ladies. For Laura Kurz, we might make a relative exception.

Kurz is another person who is missing a year - she sat out a year after transferring to Duke. Kurtz, however, is young enough to compare favorably with her "cohort", so to speak.

In terms of blocking-stealing-rebounding, her rates per game are frankly not impressive enough to stand out overall. She has too many turnovers for her assists - she had 32 assists compared to 71 turnovers in 2007-08.

However, Kurz is small compared to other forwards at 6'1" tall - she'd be at best a Jennifer Lacy type of forward, with Lacy's D-class output. Could Laura Kurz rally grapple against a Ruth Riley-Ann Wauters combo guarding the basket, much less a Candace Parker-Lisa Leslie tandem?

It doesn't look good for Kurz, and I have her at #99 on my list.

Brianne O'Rourke, point guard, Penn State

In O'Rourke's case, some things hurt her and some things help her. She doesn't have very many blocked shots - even for a guard - but Kristi Toliver doesn't have blocked shots either, and Toliver is #3 on my list.

Furthermore, O'Rourke handles the ball well if you compare assists to turnovers. So where does she take her hits?

She actually takes a double hit because of one factor - Brianne O'Rourke is five feet six inches tall. Just like my spreadsheet doesn't trust anyone over 6'6" - the "Katie Feenstra clause" - it turns up its nose at particularly small players. The spreadsheet basically says, "okay, here's a penalty, now prove that you're good". Good players will overcome the penalty; poorer players won't.

Because of her height, it's harder to get rebounds - not impossible if you like to fight, but harder. In "rebounds expected per 100 rebounds a game for both teams total", O'Rourke falls below the acceptable minimum. When O'Rourke players for you, you don't have five players scrapping for rebounds, you might have - as Queenie might say - "four rebounders and one midget". Rebounding is a skill that O'Rourke can't bring to the table. It impairs the team and has to be made up for elsewhere.

This puts O'Rourke in the 50s in my spreadsheet. If she has a good senior year - and I mean a really good one - she might approach WNBA draft territory. But if her senior year is like her junior year, odds are she won't be drafted.

Making Use of Usage

Usage is defined as the percentage of a team's possessions that an individual player uses. Basically, it approximates the "space" a player takes up in the team's offence.

If a team only had five players that were exact clones of each other, each would have the exact same usage rate: 20 percent. The stat is estimated by:

usage = 40 x (field goal attempts + (0.44 x free throw attempts) + (0.33 x assists) + turnovers) x (team pace / league pace) / minutes played

Unfortunately, calculating usage requires two other stats - team pace and league pace. The problem with calculating college usage is determining what "league" pace is. I've decided that when I use usage in evaluating college players, I'll ignore the "team pace / league place" part - in short, by assuming that team pace/league pace = 1.00. This introduces an error in a statistic which is essentially an estimation, but as long as the rules are laid out ahead of time, we'll go forward.

According to Ken Pomeroy at Basketball Prospectus, it seems to be an iron law that usage doesn't change much over time. If a player is a role player in college, she'll be a role player in the WNBA.

Furthermore, there's another rule about usage, this one formulated by Dean Oliver. Players who have high usage rates - somewhat over 25 percent - are performing under their maximum efficiency. It makes sense, as they have to carry more of a team's offense. You're not seeing the player at their best.

On the other hand, players with low usage rates - 15 percent or less - are already performing at their peak. If their usage goes above at what they're used to doing, they'll perform less well. Low usage players are performing at better than their maximum efficiency.

The end result is that players with high usage should be rewarded, and players with low usage should be penalized. I went to my Senior Prospects Metric and decided to reward any player with a usage above 25 percent, and to penalize players with usage below 15.

Players with Usage Greater than 25 Percent

Angel McCoughtry, Louisville - 32.69 percent
Robyn Fairbanks, Utah Valley State - 31.35 percent
Krystal Ellis, Marquette - 29.01 percent
Sade Logan, Robert Morris - 28.27 percent
Kendra Appling, Tennessee State - 27.16 percent
Shavonte Zellous, Pittsburgh - 26.36 percent
Megan Frazee, Liberty - 26.28 percent
Kristi Little, Duquesne - 25.86 percent
Obiageli Okafor, Tennessee State - 25.79 percent
Shantia Grace, South Florida - 25.21 percent
Erin Kerner, Quinnipiac - 25.20 percent

That doesn't mean any of the above players are going to be great WNBA players. Which players had the same unadjusted-for-pace average of the 2008 Draft Class? Valeriya Berezhynska with 30.76 percent and Jolene Anderson of Wisconsin with 30.55 percent. (Candace Parker had a respectable 28.60 percent at Tennessee.)

Adding in usage to the SPM moves Angel McCoughtry up from #8 to #5 on my list. And here's my newest Top 25:

Some More College Players and the Senior Prospects Metric

So who's this "Mandy Morales" girl?

I've added the Preseason Top 25 players has sparked a lot of discussion.

I have my own draft list of over 100 players, but there were players on bullsky's list that weren't on my list. I looked at the one's missing on my list, and then ranked them using the Senior Prospects Metric.

So does the newest WNBA Draft website find some diamonds...or does it dig some coal?

Robyn Fairbanks, Utah Valley State

Strengths: Averaged 23.8 points a game for Utah Valley State. Good rebounder and shot blocker.
Weaknesses: Plays for a weak conference - as a matter of fact, Utah Valley State does not belong to a conference. Short for a center.

Projection: Will not be drafted. I have her as #48 in my list of players. However, she should definitely show up at a WNBA pre-draft camp. Someone should at least give her a chance, given the dearth of centers.

Lindsay Wysdom-Hilton, Purdue

Strengths: Blocked Shots. Steals. All of the great defensive stats that would translate to NBA success if she were male.
Weaknesses: ACL tear led her to miss all of 2007-08. She's one year ahead of the other players in development.

Projection: I'm a moron for missing her. I had to evaluate Purdue's strength in 2007 as equal to 2008, since I don't have the Sagarin ratings for 2006-07, and they're not on the internet. If we assume that Purdue stands relatively to other teams the same way in 2006-07 as 2007-08...she jumps to #2. A first round pick, and the highest ranked forward.

Candace Byngham, Louisville

Strengths: Biggest contribution in my metric is in steals. Also a good rebounder.
Weaknesses: Age - older players take a bigger hit. Furthermore, at 6-1, she's small for a forward.

Projection: I have her at #39, which puts her as a candidate for the third round of the WNBA draft.

Jhasmin Player, Baylor

Strengths: Few turnovers and high steals ratio. Something tells me she's a great ball handler.
Weaknesses: She only played 21 games last year with a torn ACL. Therefore, we're looking at a smaller sample size of games. Games earlier in the season are usually against non-conference foes, and the Big East was the baddest conference around last year.

Projection: #15. On the border of second round/first round. She really needs to stand out as a senior.

Aisha Mohammed, Virginia

Strengths: She's a great rebounder.
Weaknesses: First, she'll be 23 on draft day. She's suffered a knee injury. She has a lot of turnovers (even for a center) and she doesn't have many blocked shots at all for someone playing the five position.

Projection: #115. She's not going to be drafted unless some team is absolutely desperate for a relatively unskilled rebounder. We'll see her in international play if her knees hold out.

Marshae Dotson, Florida

Strengths: High steal ratio. Good rebounder despite her size.
Weaknesses: High number of turnovers. Really too short to be a forward in the WNBA.

Projection: #81. The statistics say that she'll be one of the last people invited to a WNBA camp, and one of the first ones cut.

Mandy Morales, Montana

Strengths: Excellent assist to turnover ratio. As in 129 assists and 69 turnovers. Furthermore, she was the leading scorer for her team.
Weaknesses: Only age and that some might look down on the Big Sky conference.

Projection: bullsky over at the 2009 WNBA Draft site really found a diamond in the rough. Her stats project her as a first rounder, and she goes to #10 on my list. Someone should keep their eye on Mandy Morales.

Best Performances by NCAA Coaches in the Post-Season

I'm going to replicate the work of an interesting blog called, "Yet Another Basketball Blog " and examine the success rates of women's basketball coaches. Like Dan Hanner, I'll look at the current leaders in appearances plus wins in the NCAA, but I'm changing the time frame to five years instead of ten years. The current school of each coach is listed, with annotations if the coach has changed schools

NCAA Appearances - Wins - Coach - School

5-24 Pat Summitt Tennessee
5-18 Geno Auriemma Connecticut
5-15 Tara VanDerveer Stanford
5-14 Gail Goestenkors (Duke 4/Texas 1) Texas
5-14 Sylvia Hatchell North Carolina
5-13 C. Vivian Stringer Rutgers
5-12 Brenda Frese Maryland
5-11 Kim Mulkey Baylor
5-10 Andy Landers Georgia
5-8 Melanie Balcomb Vanderbilt
5-7 Charli Turner Thorne Arizona State
5-6 Muffet McGraw Notre Dame
5-6 Sherri Coale Oklahoma
4-6 Pam Borton Minnesota
5-4 Doug Bruno DePaul (IL)
5-4 Jim Foster Ohio State
3-5 Kristy Curry (Purdue 3/Texas Tech 2) Texas Tech
5-3 Dawn Staley (Temple 5) South Carolina
5-2 Wendy Larry Old Dominion (VA)
3-4 Gary Blair Texas A&M
3-3 Sharon Versyp (Maine 3/Purdue 2) Purdue
3-3 Elaine Elliott Utah
4-2 Jeff Mittie TCU
4-2 Bill Fennelly Iowa State
5-1 Don Flanagan New Mexico
3-3 Deb Patterson Kansas State
4-1 Joanne Boyle (Richmond 2/Cal 3) California

We now want to develop an idea called "PASE", which is used both by Henner in the blog and by ESPN. PASE stands for "performance above seed expectations".

There aren't many benchmarks we can use to compare college coaches. Win-loss record certainly won't do it. A team's win-loss record depends seemingly on two things:

a) strength of other teams in conference, and
b) strength of non-conference schedule.

However, since 1994, the women's NCAA tournament has had 64 teams, with teams receiving a rank from "1" to "16" dependent on subjective assessment of strength (and later, Sagarin RPI). What we can do is compare a team's performance based on their seed with the performance of all the other teams that had that same seed in past tournaments.

We come up with an "expected win" count per seed - looking back over performance, we can say that a "6" seed is expected to win 1.03 games. Your average six seed makes it to the second round. If you're a college coach and can take your sixth-seeded team into the Sweet Sixteen, that's a plus. If you lose your first round game, that's a minus.

First, here are the expected wins associated with NCAA seed in the women's tournament:

1 - 3.73
2 - 2.65
3 - 2.38
4 - 1.78
5 - 1.15
6 - 1.03
7 - 0.85
8 - 0.48
9 - 0.60
10 - 0.35
11 - 0.37
12 - 0.23
13 - 0.13
14 - 0
15 - 0
16 - 0.02

Why isn't the #1 seed worth more wins? Because if you make it to the Final Four, you'll probably bump against the other #1 seeds.

Note that the #8 seed has had particularly bad luck. History shows that the #9 seed does better than the #8 seed.

The #14 and #15 seeds have never won a first round game in the history of the 64-team tournament. As for #16 being 0.02, I refer you to March 14, 1998, a dark day in Stanford history as they became the only #1 seed - in women's or men's play - to lose to a #16 seed as Harvard beat a Stanford team with two injured leading scorers by the score of 71-67 in the first round.

So given their assigned seeds, which coaches have done the best? We will look over the last five years only and determine how well, per year, each coach does above the expected number of wins.

Best Coaches vs. PASE (minimum 3 appearances in five years)

Pat Summitt 1.29
Tara VanDerveer 0.60
C. Vivian Stringer 0.59
Geno Auriemma 0.57
Brenda Frese 0.38
Pam Borton 0.34
Andy Landers 0.27
Muffet McGraw 0.14
Kim Mulkey 0.13
Charli Turner Thorne 0.13

This is pretty much a list of the big names in women's college ball - the best coaches are the ones with longevity, and they have that longevity because they can keep coming to the NCAA tournament and performing beyond expectations. Pat Summitt has won a few championships recently. That helps, and we see that on the average Coach Summitt gets one game beyond where she's expected to finish.

Worst Coaches vs. PACE (minimum 3 appearances in five years)

Don Flanagan -0.16
Wendy Larry -0.24
Tom Collen -0.25
Gary Blair -0.29
Joanne Boyle -0.40
Deb Patterson -0.52
Kay Yow -0.53
Sylvia Hatchell -0.54
Sherri Coale -0.73
Jim Foster -1.24

Flanagan is a good coach at a weak school. Tom Collen wasn't so weak that Arkansas didn't run off and hire him, and the same case goes for Joanne Boyle at California. Surprisingly, Sylvia Hatchell of North Carolina and Jim Foster have had some lousy post-season results. I wonder how well Sherri Coale of Oklahoma will do once Courtney Paris leaves the Sooners.

My next goal is to examine the relationship between a team's success and the number of McDonald's All-Americans it is able to sign. That should be fun.

2008-09 - The Best Senior Prospects

I've now concluded my analysis of the current Division I women's basketball prospects. All of these women will be seniors during the 2008-09 season. For blog readers which may be math-phobic, you might want to skip down to the very end where the players are actually named.

Why do an analysis?

Good question. The point is basically to try to determine which players deserve a closer look.

Whenever someone who likes statistics creates a ranked list, contention is the outcome. "How dare you claim that Player X is #16 when it's plain that X is better than Player Y who is #6?"

The goal is to look both with the eyes and with the stats. Your eyes can actually fool you as much as statistics can. In order to really know which players are the best, you would have to watch each of the named players for the entire college season. This is impossible for all but maybe a handful of reporters, and even they can only watch so many players in the day. Statistics do two things:

a) they isolate players that have distinguished themselves in some way in the boxscores and,
b) they point to a player's flaws as well as a player's strengths.

The Starting Point

I began with Hollinger’s idea that blocks, steals, rebounds, and three point shooting were important in a prospect. I determined that we should look at NCAA Division I players who were juniors in the 2007-08 season. Only players who were in the Top 100 players in any of the four categories indicated above should be considered. This left me with 108 players to look at, and I was able to locate statistics for 105 of those players. (Florida A & M and St. Mary’s (California), I’m pointing the finger at you.)

Ashley Paris, sister of Courtney Paris, was mentioned in a previous comment, so I added her to my list. As my attention is drawn to other players, I'll add them to my analysis.

Blocks, Forwards, Rebounds

One expects forwards and centers to be able to get rebounds, with guards at a disadvantage. All players should be able to get steals, with guards (being more agile and quick) having the best shot. Three point shooting would be primarily a guard skill.

I decided that the scale for blocks, steals, and rebounds would be linear. It would be based on number of blocks per game, steals per game, and rebounds per game. A good player should be good in all of these defensive skills at the college level. Therefore, these three factors were multiplied together. Ed Weiland has a stat called RSB40, which he uses as a multiplicative factor so I feel that I'm on the right track.

This decision hurts guards – because we don’t expect them to rebound - but the guards get a chance to be rewarded later.

Furthermore, the factor for steals was doubled. Hollinger weighted steals more heavily than any other factor in his analysis, and the numbers that come out at the end match up with “common sense” when I make the same decision.

The problem is that at some point in the analysis, you have to decide what number of blocks, steals, or rebounds per game corresponds to a factor = 1.0. Looking over the top 100 finishers in these categories, I made the following decisions:

1.5 blocks per game = factor of 1.0
2.2 steals per game = factor of 2.0 (remember, the factor for steals is doubled)
8.1 rebounds per game = factor of 1.0.

If you want different results, set different factors. I'm going to stand on these values, which would be pretty impressive if they were per-game.

Three-point Shooting

We ranked players by percentage, not by number of shots made. 0.343 percentage = 1.00 factor. Players were required to make at least an average of two three-pointers a game to sort out those people with high three-point shooting but low numbers of baskets – if they didn’t meet the average qualification, they got a zero factor.

Age

The age of players is definitely important, because older players are closer to their peaks. The youngest player among the candidates had a 11/18/1987 birthday. I began penalizing players who had birthdates before 11/18/1986. A person born a quarter of a year before that date would have 0.25 removed from the final score; a person born on 11/18/85 would have 1.0 removed from the final score. A player closer to their eventual peak should be devalued.

For some players, I was not able to find their ages. Those players were not penalized in the system. There was one player who was born in “January 1987”, I assigned her a birthdate of 1/15/87. She didn't suffer any age penalty according to my current rules.

Wins Efficiency

Certainly, we should reward players who can score, but by how much? There are so many metrics like PER and Efficiency. I don’t agree with “Efficiency” because I believe it overvalues crappy shooting. I therefore used the “Wins Score” metric, but divided it by Games Played – Wins Score is not divided by Games Played – to create a more Efficiency-like metric called “Wins Efficiency”. I divided the final Wins Efficiency results by 10 to award points. A player with a score of 10 in “Wins Efficiency” gets 1.00 point.

WPPR

This is a variation on Hollinger’s Pure Point Rating. The problem with Pure Point Rating is that if you apply the formula to WNBA players, they have terrible values. The reason is because in the NBA, the average team’s amount of turnovers is 2/3 of the amount of its assists. In the WNBA, players turn the ball over more and for an average team, turnovers and assists are equal. I simply removed the 2/3 multiplicative factor from Hollinger’s formula. In the NBA, assist/turnover ratio is misleading as to a player’s true value as a guard; in the WNBA assist/turnover ratio is more accurate.

My variation – WPPR – rewards players who have more assists than turnovers. A good guard will have a high WPPR and all players are rewarded 1/5 of their WPPR if the value is positive. In general, this decision rewards guards and punishes forwards and centers (who have more turnovers than assists), but the best forwards and centers at the college level can avoid turnovers. Still, they’ll have a negative number. If a forward or center has a negative WPPR, 1/10th of the WPPR is removed from the final score.

However, a guard who has a negative WPPR in the women’s game is someone whom one should be wary of. If a guard has a negative WPPR, 1/5th of the WPPR is removed from the final score.

50-50 players

Fifty-fifty players are players who have 50 blocks and 50 steals in a season in the men’s college games turn out to be excellent college players, according to Hollinger. Players are given a special 1.00 factor bonus for meeting this benchmark. Only two players qualify for this benchmark in the 2007-08 season: Demetress Adams of South Carolina and Jessica Bobbitt of Belmont. This is an additive factor, so no one is penalized for not reaching the mark. (Except for maybe Chante Black of Duke, who missed the 50-50 mark by one steal. I decided not to give her the point; your mileage may very.)

Conference Strength

Players were rewarded for playing against good teams and punished for playing against bad teams. The final score is multiplied by a factor equivalent to the strength of their conference. Conference strengths are determined by Jeff Sagarin’s ratings for women’s NCAA basketball. The central mean method was used, with the Big Twelve’s 86.55 rating converted to a 1.00 factor, and partial ratings converted linearly.

Final Factor

Equals:

blocked factor*steals factors (which is x2) * rebounds factor
plus three point factor
plus age factor (negative for older players)
plus Wins Efficiency factor
plus PPR factor
plus 50-50 factor….

all multiplied by conference strength factor.

Known Flaws

Hollinger points to several "red flags" that might disqualify a player. One of the flaws is the flaw of a player not receiving a certain number of rebounds per height. Hollinger states that "a player in the X range of height should receive at least Y rebounds per game". I wasn't able to do create these height categories because I don't have enough information about the range of heights in the NBA (and WNBA) to determine the appropriate cutoff points.

Furthermore, schools play at many different "paces". The word "pace" has a specific meaning in the APBRmetric community. I was not able to obtain this information and was not willing to calculate it for, oh, about 300 schools. Maybe someday, there will be a master women's college database. Until then, the speed at which schools play, the number of possessions per team, etc. is not taken into account.

(* * *)

So who are the leaders after all this number crunching? Lets look at the top twenty overall players:

Top Twenty Overall Players



We have Courtney Paris of Oklahoma as number one, and that's a good start. I'd expect Angel McCoughtry to be number two, but she ends up as #8 on the metric. However, Kristi Toliver at #2 should be no surprise.

I wonder if my formula gives too much credit to guards. Then again, I have approximately 20 centers, 30 forwards and fifty guards on my list so I shouldn't be surprised that guards are overrepresented. I would say the only surprises are Kristi Cirone of Illinois State and Jenna Schone of Miami of Ohio, both from small schools.

Top Ten Centers



I'm very surprised that the quality of centers drops of quickly after Courtney Paris. I don't know if centers get short shrift for having low WPPR or for the fact that most of the centers in women's college basketball don't seem to produce a lot of Wins Efficiency.

Top Twenty Forwards



Forwards also drop off quickly, although not as quickly as centers do. There are four good forwards to be had early in the draft. Most of the good centers come from the big schools.

Top Twenty Guards



Guards are the quality position in the metric. Even down at the twentieth position, there are decent guards. Furthermore, the small schools (Tiera DeLaHoussay at Western Michigan, Shantia Grace at South Florida) provide a large number of great guards. If you want to sneak up on someone in the draft, you can probably get a quality guard from an overlooked school.

I also wondered if guards are the prime position for players to make their presence known in women's basketball. The talent level at the women's college game is thinner than in the men's game, and it's more likely that tall (but not truly athletic) women have been pushed into the post positions in high school.

(* * *)

I'll try to keep this list updated, and to have the senior stats analyzed at the end of the 2009 season. Oddly enough, Hollinger says that good NBA players have a decrease in their statistics between their junior and senior years. So if one of your favorite juniors has a bad year...well, don't lose hope. And if one of your favorite juniors isn't ranked high on my list...well, 2008-09 has the power to change everything.