New Post at Swish Appeal

I have a new post up at the Swish Appeal website. This one explains the concept of WNBA Most Valuable Player Shares.

Go visit Swish Appeal and send them some love!

"Favorite Toy" Projections of Dream Players

A long time ago, I wrote a post on Pleasant Dreams called "Does Any Dream Player Have a Shot at 5000 Points?". I'm going to revisit that post, as pilight keeps his projections page updated.

He uses a predictive tool modeled after Bill James's "Favorite Toy" to estimate a player's chance of reaching an important statistical benchmark. Let's look at the Dream players he currently has on his list:

Chamique Holdsclaw

Has a 54 percent chance of reaching 5000 points scored.

Iziane Castro Marques

Has a 36 percent chance of reaching 5000 points scored.
Has a 15 percent chance of reaching 6000 points scored.
Has an 11 percent chance of reaching 6263 points scored. (The current WNBA career record.)
Has a 3 percent chance of reaching 8000 points scored.

Sancho Lyttle

Has a 30 percent chance of reaching 3000 rebounds.
Has a 20 percent chance of reaching 3307 rebounds. (The current WNBA career record.)
Has a 5 percent chance of reaching 4000 rebounds.
Has a 29 percent chance of reaching 750 steals.
Has a 4 percent chance of reaching 1000 steals.

Michelle Snow

Has a 27 percent chance of reaching 3000 rebounds.
Has a 12 percent chance of reaching 3307 rebounds. (The current WNBA career record.)
Has a 9 percent chance of reaching 500 blocks.

Erika de Souza

Has a 3 percent chance of reaching 3000 rebounds.
Has a 6 percent chance of reaching 500 blocks.

Angel McCoughtry

Has a 7 percent chance of reaching 750 steals, based on her single-year of output.

Visit pilight's page to see how well your favorite players stack up.

Thinking of the 2010 WNBA Draft

With the 2010 WNBA Draft, the Atlanta Dream will draft at the #10, the #23 and the #36 positions. This comes from the regular season record, which was ranked as fourth best in a heavy-parity league this year. The question then becomes if the Dream can find anyone good at the 10-spot.

I read something interesting at 82games.com which provides a sort of "blunt force" metric to evaluate draft choices. The highly simplistic metric goes:

rating = points/game + assists/game + rebounds/game

From there, players fall into several categories depending on their ratings:

Star: 16 or more
Solid: 12.5 to 16
Role player: 8 to 12.5
Deep bench: 4 to 8
Bust: 0 to 4
DNP: "did not play"

(The numbers above are adjusted to the shorter WNBA game.)

I then applied the metric to the various draft picks. The first column states how many picks at that rank have existed. For example, the WNBA has been around for 13 years and there have been 13 #1 picks.

					Role	Deep
Pick Total	Pick		Star	Solid	Player	Bench	Bust	DNP
13	1		11	2	0	0	0	0
13	2		5	4	1	3	0	0
13	3		6	1	4	1	1	0
13	4		5	5	2	1	0	0
13	5		3	4	4	2	0	0
13	6		4	3	4	1	1	0
13	7		1	1	5	5	1	0
13	8		0	3	5	4	1	0
13	9		0	2	5	6	0	0
13	10		2	2	4	4	0	1
13	11		3	1	5	3	1	0
13	12		1	0	4	5	1	1
13	13		0	3	2	3	3	2
13	14		0	2	5	4	2	0
13	15		0	0	3	4	5	1
13	16		1	1	0	4	5	2
13	17		0	1	3	3	4	2
13	18		1	0	1	6	5	0
13	19		0	0	2	5	2	4
13	20		1	0	0	5	4	3
13	21		0	0	3	3	4	3
13	22		0	1	2	5	2	3
13	23		0	0	2	4	3	4
13	24		0	0	2	6	2	3
13	25		0	0	4	4	2	3
13	26		0	0	2	6	3	2
13	27		1	1	1	5	1	4
13	28		1	0	3	1	2	6
13	29		0	1	1	2	6	3
13	30		1	1	0	4	3	4
13	31		0	1	1	4	3	4
13	32		1	0	0	4	3	5
12	33		0	0	2	0	1	9
12	34		0	1	2	2	1	6
12	35		1	0	0	2	3	6
12	36		0	0	1	0	2	9
12	37		0	0	0	0	5	7
12	38		0	0	0	2	0	10
11	39		0	0	0	3	0	8
8	40		0	0	1	3	0	4
7	41		0	1	0	0	1	5
7	42		0	0	0	1	3	3
5	43		0	0	1	1	1	2
4	44		0	0	0	0	1	3
4	45		0	0	0	1	1	2
4	46		0	0	0	0	3	1
4	47		0	0	0	0	1	3
4	48		0	0	0	1	0	3
4	49		0	0	0	1	0	3
4	50		0	0	1	0	1	2
3	51		0	0	1	0	0	2
3	52		0	0	0	0	1	2
3	53		0	0	0	1	1	1
3	54		0	0	0	1	0	2
3	55		0	0	0	0	0	3
3	56		0	0	0	0	2	1
3	57		0	0	0	1	0	2
3	58		0	0	0	0	1	2
3	59		0	0	0	0	0	3
3	60		0	0	0	0	0	3
3	61		0	0	0	0	0	3
3	62		0	0	0	0	0	3
3	63		0	0	0	1	0	2
3	64		0	0	0	0	0	3

Some thoughts about the above:

* The bust at the #3 spot was Sandora Irvin.
* The bust at the #6 spot was Cindy Blodgett.
* The bust at the #7 spot was Shona Thorburn.
* The bust at the #8 spot was Sun Min-Jung.
* At #10, Molly Creamer is the highest draft pick never to play in the WNBA. Not surprisingly, she was drafted by the New York Liberty in 2003.
* The bust at #11 is Danielle Crockrum.
* At #12, Alison Curtin never played for Houston. She was drafted in 2003. No telling whether or not Ashley Walker is a bust yet or not.
* At #16, Nicky Anosike is the outlier - the good player among the bad.
* At #18, Tammy Sutton-Brown is the outlier.
* At #20, Sheri Sam is the outlier.
* At #27, the star is Adrienne Goodson and the solid player is Cathrine Kraayeveld.
* At #28, the star is Brandy Reed.
* At #29, the solid player is Dominique Canty.
* At #30, the star is Charde Houston, the solid player is Tamika Whitmore.
* At #32, the star is Taj McWilliams.
* At #34, the solid player is Sandy Brondello.
* At #35, the star is Jia Perkins.

The modern draft ends at the #39 position. Let's look at each of the draft spots.

The #10 Pick: It is actually quite possible to get good players at #10. Two stars (Katie Douglas, Michelle Snow) and two solid players (Rebekkah Brunson, Loree Moore) have come out of this spot. It's unlikely that a bust player will come out of the #10 spot, but this is the level in the draft where there's the fractional chance that one's draft pick might not play a single game. (The aforementioned Molly Creamer.)

Marynell Meador's reputation for drafts can be questioned. In 2008, her first pick was Tamera Young, but she passed up Nicky Anosike. Then again, who saw Nicky Anosike coming. In 2009, she picked Angel McCoughtry, and the #1 position has always yielded at the very least a solid player. You could end up with anything at the #10 spot.

The #23 Pick: One thing I've noticed is that all of the star players generally get picked in the first round, the #1 through #13 positions. Afterwards, all you usually get is role player...until around the #27 spot of the draft. Then, there's a smattering of star picks afterwards, testimony to teams that just got lucky.

Why is that? Here's my theory. For the early part of the draft, teams stick to a list of pretty much the top 20 players or so - players about which everyone is in agreement. These players are the group of "great players". Some are great, but by the early twenties the "name picks" have run out. Everyone is guessing.

However, if all of the players you wanted are gone - now is the time when you just have to guess. Or, if you don't guess, you take risks. You stray away from the tried and true, and you pick that player out of the Ohio Valley Conference who is 6-2 or 6-3 but led the NCAA in blocks. All that are left are the risky picks at this stage, and sometimes, risk turns into reward.

The #36 Pick: Unfortanately, even the risky picks are gone by the time you get to the #36 picks. 11 out of 12 of the #36 picks in history were either busts or never played at all. The only player that was any good was Kara Wolters. Everything else has been disaster.

This is the level where you should be thinking of a local player. Odds are good that the person you pick will never see a WNBA court. You could pick someone from Georgia, or Georgia Tech, or Kennesaw State, or Mercer. Why not use the #36 pick for publicity, if nothing else? Either that, or sign some Eurostar and at least keep the drafting rights to that player.

(* * *)

Drafting is more of an art than a science. Still, in the second and third rounds, there's no reason not to just spin the roulette wheel. But that very first pick has to pay off, because if it doesn't, your entire draft could be flushed away - nothing more than a bad memory.

PER Ratings: Atlanta Dream

On Swish Appeal, I've gone ahead and posted the WNBA's player efficiency ratings for 2009.

If you're interested, here's how the various players of the Atlanta Dream did in PER:

Angel McCoughtry (23.2)
Sancho Lyttle (21.3)
Erika de Souza (20,0)
Chamique Holdsclaw (14.8)
Michelle Snow (14.6)
Ivory Latta (13.5)
Tamera Young (12.1)
Coco Miller (10.4)
Iziane Castro Marques (9.8)
Shalee Lehning (8.7)
Jennifer Lacy (5.8)
Armintie Price (4.2)

If the numbers aren't pleasing, you might want to beat up on John Hollinger, the guy who created the formula.

Attendance as a Predictive Metric

Ethan over at Actionless Activity does an attendance analysis of the 2009 WNBA Regular Season.

His idea: look at attendance numbers and see if they indicate anything about the team: he sets a mark that if a team claims an average of 7,000 fans per game, it should be profitable and if it claims an average of 10,000 fans per game it is probably a "legitimate" franchise with a long term future in the WNBA.

However, given the average attendance figures for the year, you would have to conclude that every team is profitable except for one (*) - the Connecticut Sun - and two teams, the Sparks and Mystics would be "legitimate".

The problem with the analysis is that attendance numbers...really don't say much of anything. First, every league inflates its attendance - they all do it, not just the WNBA. Even football has been known to give away tickets to move crowds which are close to selling out up to official "sellout" status. I've seen "crowds" of 16,000 at pro baseball games that looked like if the crowd rushed the field to attack the players I'd give the players even money on winning the fight.

In some places in the WNBA - like Connecticut - attendance figures are roughly accurate. (Connecticut doesn't have much incentive to lie.) In other places, like Detroit, the attendance figures might as well be drawn up out of hat. Attendance figures can indicate how confident the team is in the product, or...the lack of confidence in the product: "hey, if we blow up these figures to 7,000 maybe someone will take notice!"

Part of the problem with attendance is that there is no official "turnstile attendance" - how many actual fannies are in seats during any one game. If those numbers are kept, we'll never see them. (No league will ever show them - do you really want people to know the difference between claimed attendance and physical attendance?)

Another problem is what do you do about season ticket holders that miss games? The STHs have paid for all of their seats. So why wouldn't you count them in attendance? If someone has paid for a seat but doesn't show up to take it, the franchise has their money and it's almost as good as someone attending. Maybe those people would have attended if something hadn't kept them from coming - or maybe they wanted to support the team without the labor of showing up for games.

One poster at RebKell stated that one should not assume that once attendance reaches a certain point, profitability is implied. (Except if you have 250,000/game.) One must factor in the cost of:

* arena rental: if you also own the arena (Connecticut), you need fewer attendees to remain profitable

* who controls parking rights? concession rights? For example, Atlanta doesn't get any concession rights - if you buy a hotdog at Philips Arena (**) not a single bite of that dog goes to the Atlanta Dream. If teams get a cut from other sources, it lowers the number of tickets you have to sell to remain profitable.

My understanding is that Los Angeles has something called "premium seating" - if you're a ticket holder of a premium seat (for the Lakers, for example), all of that money goes into a pool and each of the arena franchises gets a slice of that cash. The Sparks get only a tiny sliver of Jack Nicholson's money - but that tiny sliver is enough.

My conclusion: looking at attendance is, at best, an imperfect metric. What might be more interesting to look at is change in attendance as a metric. For example, in 2008, the Comets claimed attendance dropped by 1,581. They had moved to a new arena, of course, but their team high in their new digs (7,261) wasn't even as high as their claimed average from the year before (8,166). That should have been a sign of trouble right there.

____


(*) - I stole this joke from a story about the Dallas Cowboys.

Some lout watches the owner of the Connecticut Sun walk down the street. "Hey, dumbass!" he shouts. "I hope you like losing a million a year on a WNBA team!"

The man takes off his hat. "I guess I better do something, then, or I'll be bankrupt in a thousand years."

(**) - I prefer bratwurst, myself.

Worst Coach in the W?

With Sue F. of the They're Playing Basketball blog asking what makes a good coach, it sparked me (no pun) to write a post about bad coaches.

One of the great haters of the WNBA - who shall be known by his appropriate initials of "B. S." - wrote an ESPN post that characterized what could indicate a team that has a bad coach:

1. A team with a poor record in close games.
2. A team that couldn't take care of the ball, reflected in turnovers.
3. A team that gave up too many offensive rebounds.
4. A team that couldn't put together win streaks.
5. A team that gave up too many 3-point shots.
6. A team that had a bad road record.
7. A team that doesn't have a consistent rotation.
8. A team where the coach makes egregiously stupid moves.

I decided to do some number crunching.

"Close games" I could measure - Swanny's Stats had the most recent stats that indicated team records in games decided by six points or less. (Yeah, B. S. used five points or less. I don't have better stats, and B. S. can bite me.)

Turnovers, opponent 3-point shooting and road records are provided at the WNBA website. Offensive rebounds allowed per game are provided at each of the team sites. I created a new stat called "Streak Wins", which is a team's total wins counting only winning streaks of three games or more. For example if a team has winning streaks of 5, 3, 1, and 1 the team has eight streak wins.

I only judged 10 coaches - John Whisenant, Rick Mahorn and Anne Donovan all get free passes. For the remaining 10 coaches, they were judged on a 0 through 9 scale in each of the categories above. I don't have a metric for rotation consistency, and no metric on earth can measure stupidity.

This leaves me a number I call the "B. S. Coaching Number." It measures how bad a coach you are. Finishing from #1 to #10 are:

1. Marynell Meadors
2. Steven Key
3. Dan Hughes
4. Jennifer Gillom
5. Michael Cooper
6. Julie Plank
7. Brian Agler
8. Mike Thibault
9. Corey Gaines
10. Lin Dunn

"But Pet," you might ask, "aren't you just measuring how bad a team is instead of how bad a coach is?" Good point. I decided to correlate my final scores with the teams "losing percentage", or 1 minus winning percentage. The correlation was 0.40 - which is about a medium correlation. This means that a team's overall record accounts for *some* of a coach's placement on the list, but not *all* of it.

This very primitive metric - it's not scaled, which means that you get an extra point whether you finish 1 percent or 100 percent above your nearest competitor - seems to confirm at least in the #1 and #2 positions what people already believe about certain WNBA coaches: you can't find a WNBA message board without anyone screaming about Meadors and/or Key. Dan Hughes's poor placement, on the other hand, might have to do more with the team than with him. Likewise, Lin Dunn's good placement - the "least bad of the bad" - might have to do more with the Fever than with her.

I might have to think about this a bit more. But it's a nice mental exercise.

The Dream: Net Plus/Minus and Who You Want on the Court

There's a statistic that you'll find in WNBA box scores called "Plus/Minus". It is usually listed with a +/- symbol. I explained how this works on a messageboard, and I'll just cut and paste:

Assume that Betty Basketball (no relation to Betty Lennox) comes into a game two times.

The first time she comes in, the score is 20-20. When the coach finally substitutes for her, the score is 35-30 in favor of Betty's team. In effect, the team increased its lead by five when Betty was on the court. We give Betty a +5 for this interval.

The second time she comes in, her team is ahead 65-50. When she leaves, the score is 70-65 - clearly Betty's presence wasn't helping anything. The lead decreased by 10 points when Betty was out there. We give Betty a -10 for this interval.

These were the only two times Betty was on the court during the game: we add all of her plus-minuses together for each time she was on the court: +5 + (-10) = -5

Next to the box score, there will be a "-5" next to Betty's name. The implication is that the team did five points worth of worse when Betty was out there.

The whole point of +/- is to draw some sort of conclusion as to whether or not the presence of a particular player helps or hurts a team.

Of course, plus-minus isn't a perfect stat for a few reasons. The first is that plus/minus can depend greatly on who you're with on the court. If you're surrounded by All-Stars, your team will certainly do better when you take the court...but it might not be so much because of you. It might just be the fact that the other four players are picking the team up, and she's just along for the ride. Likewise, a player's plus-minus might be dragged down because she has crappy teammates.

The second problem is that the method above calculates something called raw plus/minus. Raw plus/minus is the first step, and everybody can do it. You just count points, and it requires no skill greater than addition or subtraction. However, raw plus/minus, being a cumulative statistic, rewards players proportionally to how much time they get on the court. The more time you spend on the court, the higher your numbers become (in either direction).

We can't fix the first problem, but we can fix the second one. We simply equalize the stats. In other words, we project all players to 40 minutes per game. This gives people who aren't on the court a lot a chance to shine.

WNBA Leaders in Net Plus/Minus
(values as of June 28, 2009)

1. Lindsey Harding, Mystics: + 48.3
2. Sue Bird, Storm: + 36.0
3. Barbara Farris, Shock: + 35.1
4. Lauren Jackson, Storm: + 31.2
5. Chelsea Newton, Monarchs: + 30.4
6. Candice Wiggins, Lynx: + 27.2
7. Candice Dupree, Sky: + 23.9
8. Jia Perkins, Sky: + 22.9
9. Seimone Augustus, Lynx: + 21.6
10. Katie Douglas, Fever: + 19.8

The numbers above pass the "smell test" - you would expect the players on the list to be in the top 10 in something, and the list isn't swamped with a bunch of no-name players.

Now, let's look at the Net Plus/Minus Leaders for the Atlanta Dream

Atlanta Dream Leaders in Net Plus/Minus
(values as of June 28, 2009)

1. Tamera Young, + 25.1
2. Sancho Lyttle, + 8.7
3. Iziane Castro Marques, + 5.7
4. Jennifer Lacy, + 3.0
5. Coco Miller, + 1.7
6. Shalee Lehning, - 0.1
7. Erika de Souza, - 2.0
8. Chamique Holdsclaw, - 5.0
9. Nikki Teasley, - 5.9
10. Michelle Snow, - 6.9
11. Angel McCoughtry, - 12.1

You might not expect Tamera Young at the top, but the net score indicates that the team performs better with Young on the floor than without her, despite Young's dismal shooting percentage. Are Little Smooth's teammates simply forced to pick up the slack when she's there, or does she bring something to the Atlanta Dream that can't be qualified in a box score?

Furthermore, Shalee Lehning suporters can take heart. Angel McCoughtry has only played about 11 more minutes than Shalee, but Angel's net plus/minus is significantly lower. Shalee, as a matter of fact, is right in the middle of the Dream in net plus/minus.

Initially, I wondered if individual style would have a lot to do with net plus/minus. Sue Bird is seen as a player who does the "little things" that don't show up on a box score but which help her team, whereas Angel McCoughtry is the "put-the-whole-team-on-my-back" kind of player - if Angel does poorly, so does the team.

However, the streaky Izi Castro Marques is #2 in net plus/minus for the Dream. Clearly, net plus/minus tells us more than we knew before - but we know not to rely too much on one metric.

Diamond Ratings for 2009

The idea behind the Diamond Ratings is explained in this post. What we're looking for are "diamonds in the rough" - players who might actually be pretty good if they were just given enough minutes per game. These are the kind of players you start looking at when you're thinking of making a trade - or if they're on your team, they're the kind of players you should think of giving more minutes.

Basically, here's how it is calculated:

a) Figure out what the Wins Score would be if the player played 33 1/3 minutes and divide that by games played,
b) Subtract their current Wins Score per games played,
c) Add the difference between their win score per 33 1/3 minutes divided by games played and the league's average win score per 33 1/3 minutes divided by games played.

A and B determines how close the player comes to a starter. C gives them credit if they're producing more than an average player.

We then exclude certain groups of players. Anyone who's playing less than 2.5 minutes per game is chopped off -- we don't have enough data on them. Anyone playing more than 21 minutes per game is chopped off -- they're earning close to starter's minutes already.

Top 10 Diamond Rating (*) Performers in 2009
(as of June 24, 2009)

1. Kristi Cirone, Sun, 28.59
2. Eshaya Murphy, Fever, 27.42
3. Khadijah Whittington, Fever, 21.88
4. Megan Frazee, Silver Stars, 21.62
5. Katie Mattera, Silver Stars, 20.12
6. Janelle Burse, Storm, 19.21
7. Kiesha Brown, Sun, 18.99
8. Lindsay Wisdom-Hylton, Sparks, 18.21
9. Sidney Spencer, Liberty, 18.19
10. Courtney Paris, Monarchs, 17.86

(*) - This is an adjusted version of the Diamond Rating for the WNBA.

Rookies are in bold face type. By the way, the leader in Diamond Rating last year? Sancho Lyttle, with 18.05.

The 1800 Club

I first heard about the 1800 Club from Frisco Del Rosario. Members of the 1800 Club are known to be good all-around shooters.

So how do you know if your favorite player is an 1800 club member? Simple: you add their field goal percentage, 3-point field goal percentage, and free throw percentage. You then multiply that by 1000.

Out of all WNBA players who have played 500 or more minutes in a career, the club only has one member: Simone Edwards, who had a FG % of 478, a FT % of 647....and a 3-Pt % of 1000 (1-for-1 lifetime) to end up at 2125. If you put the restriction that the person has to have made at least 10 3-point attempts in a career, the best finisher is Elene Tournikidou at 1758. Jennifer Azzi, who just went into the Women's Basketball Hall of Fame, had a career number of 1741.

Even though it's hard to keep up that level of shooting for a career, it's good to look at seasonal numbers.

1800 Club Contenders 2008

(minimum 100 minutes and 10 3-point attempts)

1. Lisa Willis, 1856
2. Roneeka Hodges, 1785
3. Erin Phillips, 1782
4. Ebony Hoffman, 1751
5. Kara Lawson, 1751
6. Sidney Spencer, 1734
7. Jamie Carey, 1733
8. Deanna Nolan, 1690
9. Jia Perkins, 1680

The best performer for the Dream last year? Betty Lennox with 1628.

New Project: The Hall of Fame Projector

I've been looking at the Hall of Fame Probability indicator from Basketball-Reference.com with a bit of envy and I wanted to do the same thing for WNBA players. Since WNBA players have not appeared in great enough numbers yet at the Women's Basketball Hall of Fame to give an indication of the kinds of stats that make a Hall of Fame, I decided to look at the NBA model.

The creator of the predictor used a pool of 668 players. Of the 668 players, 78 had been elected to the (Men's Basketball) Hall of Fame and 590 had not. He then ran a statistical model - probably a multivariate regression using something which is a lot more powerful than an Excel spreadsheet. He found a formula that takes the following values

player height
NBA points per game
NBA rebounds per game
NBA assists per game
NBA All-Star Game selections
NBA MVP "award shares"
NBA championships won

for any player whose final season was after 1959-60. The creator comes up with this formula.

Probability of Hall of Fame selection

Step 1: Find X, where X = a*height + b*PPG + c*RPG + d*APG + e*(all star selections) + f*(MVP award shares) + g*(championships). The values of a through f are in the link.

Step 2: Calculate e^X/(e^x + 1). The value, expressed as a number between 0 and 1, is the Hall of Fame probability.

My question: until we have enough WNBA Hall of Famers to determine a formula of our own, could we use this NBA's formula in the meantime?

There were two problems. The first is the fact that a NBA game is 48 minutes long, and a WNBA game is 40 minutes long. This means that points, assists, and rebounds per game would have to be expanded to a 48-point game for WNBA players. One could either multiply the per-game values by 48/40 - or, much more easily, multiply the game-based coefficients by 48/40 or 1.20.

The other problem is the height problem. We can't generalize NBA height to WNBA height. We would have to run our own complex height-based multivariate regression based on data we don't have. Therefore, we either have to throw height out of the equation, or equalize it.

Throwing height out of the equation in Step 1 might mess up Step 2 completely. Therefore, we will equalize instead and assume all players are "average height". The average NBA height is 6 feet 7 inches. We multiply (-0.20518 * 79) to get -16.209. The "height based part" of our equation is therefore always "equalized" to a number around -16.209.

We come up with a new formula:

WNBA Hall of Fame Probability Calculator

Step 1: Calculate X = -16.2 +
0.54 * points per game +
0.45 * rebounds per game +
0.47 * assists per game +
0.49 * number of All-Star/Olympic selections since 1997 +
3.18 * MVP shares +
1.03 * WNBA championships.

Step 2 : Calculate

Prob (WNBA Hall of Fame) = e ^ x / (1 + e ^ x)

e is the "Euler number" on the calculator. ^ means "to the power of". The above equation would be read "e to the power of x divided by quantity one plus e to the power of x").

(* * *)

Okay. We have a formula. But does it mean anything? We'll try to calculate Chamique Holdsclaw's Hall of Fame Probabililty.

Chamique's career totals are 17.66 ppg, 8.28 rpg and 2.6 apg. We get those from her career statistics.

For Number of All-Star selections, Holdsclaw's number is five (5). This number would include any appearances on your nation's Olympic team during a year when the All-Star Game wasn't played. This number only counts appearances from 1997 and beyond.

WNBA championships is simple. Holdsclaw has never appeared on a WNBA championship team. The number is zero.

Now we have this strange number called "MVP shares". It's a way to look at how popular a player was in MVP voting.

Example: Jane Doe was named WNBA MVP in 2010. She received 500 votes. Rhonda Roe received 100 votes for MVP that year. How many MVP shares did each player earn in 2010?

We consider the number of votes earned by the WNBA MVP winner for any given year to be the basis of a share. For the 2010 example above, 500 votes equals one WNBA MVP share. Therefore, Jane Doe receives 500/500 MVP Shares in 2010, or 1.0. (The MVP for the year always gets 1.0.) Rhonda Roe gets 100/500 = 0.2 of a share for that year - Rhonda's performance is "twenty percent" of the MVP's for the purposes of MVP voting.

The following quotients are number of votes Holdsclaw received divided by number of votes earned by the WNBA MVP winner that year.

1999 24/397
2000 18/527
2001 8/563
2002 169/482
2003 71/406
2004 0/425
2005 17/327
2006 0/508
2007 0/473

The sum of all those fractions is 0.69. This is how many MVP Shares Chamique Holdsclaw has earned in her career.

Let's do the calcuation:

X = -16.2 + (0.54 * 17.66) + (0.45 * 8.28) + (0.47 * 2.6) + (0.49 * 5) + (3.18 * 0.69) + (1.03 * 0) = 2.93

e ^ 2.93 / (1 + e ^ 2.93) = 18.73/19.73 = 0.9493

According to the formula above, Holdsclaw has a 94.3 percent chance of being named to the WNBA Hall of Fame (if it existed) based on her current statistics.

I'd love to have a value of this metric for every WNBA player. I'll run some numbers for the Atlanta Dream roster to see what comes up.




Notes:

1) If you follow the original link, you'll notice that there's a negative coefficient next to height. This makes sense, because this has the effect of punishing players for beyond-average height and rewarding players with below-average height.

2) Also note the large coefficient associated with MVP shares. This also makes sense, since the best measure of how good a player is should be reflected by the number of MVP votes they've received over their career. By definition, a Hall of Fame player should be someone who is thought to be "MVP worthy".

3) I would set a minimum of 175 games played to be even considered for an accurate calculation. The people at basketball-reference use 400 games, a somewhat equivalent number. Chamique's 225 games fit the bill.

Does the WNBA Have Fewer Close Games than the NBA?

There was an article in the New York Times called "The No-Stats All Star". The article is about Houston Rocket forward Shane Battier, and the fact that he has the ability to take great players and turn them into so-so players - usually by forcing them into areas of the court where they shoot less well. However, his ability to do this is not reflected well in traditional basketball statistics.

It was written by Michael Lewis, the author of "Moneyball", the book that brought advanced sports statistics to public attention. There was a line in the article that caught my attention.

A team scores on average about 100 points a game, but two out of three N.B.A. games are decided by fewer than 6 points — two or three possessions. The effect of this, in his mind, was to raise significantly the importance of every little thing that happened.

I was very surprised. I looked up the 2008-09 NBA regular season stats at basketball-reference.com. Sure enough, the average team in an NBA game scored 100.0 points.

A team scored about 76.3 points in the average regular season WNBA game. However, in the 276 games that took place in the WNBA in 2008 from the start of the regular season to the end of the finals, only 66 games were decided by five points or less - that's only one out of four games. 25 percent of all games being close (WNBA) is a big difference from 66 percent of all games being close (NBA).

What can we take away from this? Is there an explanation for why this is true? I don't know one, but this might be one of the reasons that the WNBA is not seen to be as "exciting" as the NBA.

Evaluating the WNBA Draft With Adjusted Wins Score

A long time ago, I introduced a linear metric called "Wins Score", which is one of my favorite metrics. I like it better than the WNBA's metric (Efficiency) because I believe that Efficiency overrates poor shooters.

Since then, there has been some by people involved in the statistical side of basketball whether "Wins Score" overrates rebounders. The Wins Score metric gives players one point for every rebound, regardless of whether the rebound was offensive or defensive. The argument - as well I understand it - is that in a lot of cases, rebounds will sort of come as a byproduct of just being there. Someone has to end up with the ball after a missed shot, or it just goes out of bounds.

Adjusted Wins Score (AWS) adjusts for the rebounds. It gives 0.7 points for all offensive rebounds, and 0.3 to the more numerous defensive rebounding. The resulting formula:

Adjusted Wins Score=

total points scored
+ 0.7 * offensive rebounds
+ 0.3 * defensive rebounds
+ steals
+ 0.5 * (assists + blocks)
- field goal attempts
- turnovers
-0.5 * (personal fouls + free throw attempts)

The correlation of Adjusted Wins Score to total teams wins is around 0.9 percent, if I recall correctly. The metric has a high degree of predictability "after the fact".

However, if you want to use AWS to predict the future, you have to make some tough decisions. The first is how to project future performance of a player based on past performance. The second problem is how to project initial performance for players who have never played before - draftees.

So how do you evaluate the draftees? We now have 12 years of data, which should allow us to make some kind of predictions. We can simply look at a draft position - #1, #2, #3 - and see how players have done historically in their first year of play. We could then compare a drafted player after the season to her ancestors, so to speak. We would compare Angel McCoughtry to every previous #1 draft pick between 1997 and 2008, we'd compare Marissa Coleman to every previous #2 draft pick, and so forth. Depending on how those comparisons measured up, we could then sort out which GM organizations drafted well and which didn't.

My first idea was to take the average AWS of all #1 picks, the average AWS of all #2 picks, etc. The problem is that very high values or very low ones would skew the average. If we go by the rule of mathematical average, you'd have to conclude that the Los Angeles Sparks organization were geniuses in drafting Candace Parker. They shouldn't be given huge amounts of credit for that - Candace Parker was clearly someone who would be a great player in the WNBA right away. We can at the very least give the Sparks organization credit in not passing up Parker, so they should at least get credit for doing something right.

Instead of using an average AWS, I would use the median AWS. The median of a set of numbers is just the dividing line between the top half of the values and the bottom half. If the set has an odd number of values, the median is the one that's smack in the middle; if the set has an even number of values the median is the average of the "highest low" and the "lowest high".

In those cases where a draft pick didn't play in the year when she was drafted, or never made it to the WNBA at all, I assigned a value of 0.0 for that given draft position and year. It seems fair, since AWS is an additive metric and the missing player had neither added nor subtracted from the team's total AWS.

For the top 39 positions in the AWS, here is the median AWS:



What do these numbers tell us?

First, let's look at how the players in 2008 stacked up in AWS. My grading scale:

50.0 AWS and above: A
10.0 - 49.9 AWS: B
0.0 - 9.9 AWS: C
-9.9 to -0.1 AWS: D
-10.0 AWS and below: F

Second, let's compare the sample median scores to some actual players: according to the table, the median AWS for a #1 draft pick is 38.4. Historically, they have proven to be "B" players in their first year of play. In 2008, Shameka Christon of the Liberty had a AWS of 38.1. We would expect Angel McCoughtry to be about as good in 2009 as Shameka Christon was in 2008 - probably a B or B+ player, not expected to be a superstar immediately. If Angel McCoughtry is at least this good, we give the Atlanta Dream organization kudos. If she proves to be sub-par, we knock the organization.

Third, there is some unevenness in the results. #6 picks have proven to be historically better than #5 picks - possibly because they don't have to bear the expectations that come with being a Top Five draft pick. #11 is also a good position for some reason. #7 has not been good, with most of the players drafted at #7 having a poor AWS in their first year. (And this year's #7 pick? Courtney Paris.)

Fourth, you can see that once you get to around to the #8 pick, the value of the drafted player in the first year is almost negligible. One you get past the mid second round, any player that puts up even average numbers should be considered an example of smart drafting. It might be the case that GMs make their reputations in the second and third rounds of the draft and not in the first rounds, where the good choices are fairly obvious.

It will be interesting to see how well this years draft picks turn out. I'll be watching, and calculating.

The WNBA: Most Interesting and Least Interesting Teams

The Wages of Wins has an entry on what they call "The Least Interesting Team in the NBA". The post was written in 2007 and at the time, the Milwaukee Bucks were considered the NBA's Least Interest Team, at least according to the authors.

This begs the question of how you mathematically quantify the term "interesting". This is the definition that the Wages of Wins uses:


interesting (adj.) - A team in the NBA which either

a) contends for a title every year, or
b) contends for a high draft pick every year.


The author claims that in any year following a league, there are only two sets of interesting stories. The first set of stories deal with teams that have a chance to win the league championship. The second set of stories deal with horrible teams and their horribleness, and what they might be able to do with a high draft pick the following season.

Therefore, to be "uninteresting" is to not be in column A and not in column B. Not bad enough to contend for a good draft pick; not good enough to be a title contender. The least interesting team would be one that was a bland average.

Let's therefore look at a mathematical way to define "average". A team is "average" is if is within one standard deviation of the average number of wins for the year. A team is "interesting" if it is beyond one standard deviation of wins in either direction.

Example: In 2003, the average number of the wins for a team in the WNBA was 17. The standard deviation of wins for all teams that year was 4.819. Therefore, any team that wins more than 17 + 4.819 times in a year or wins less than 17 - 4.819 times is considered "interesting". In which case the interesting teams that year were:

Detroit - 25 wins
Los Angeles - 24 wins
Washington - 9 wins
Phoenix - 8 wins.

Each of those teams in 2003 earns one point; the other teams in that year earn zero points each. We add up the total points scored over the 12 years of the WNBA's existence, and here's what we get:

0 points: New York, Miami, Portland
1 point: Atlanta, Chicago
2 points: Charlotte, Minnesota, Orlando/Connecticut, Sacramento
3 points: Cleveland, Indiana, Phoenix
4 points: Detroit, Utah/San Antonio, Washington
6 points: Houston
7 points: Los Angeles

(teams in bold face are teams active since 1997)


The results were definitely not what I expected. According to the rules above, the New York Liberty is tied for the least interesting team in the WNBA's existence. The only teams tied with it are Miami and Portland, two defunct teams that never had the time to build a reputation one way or another. New York's existence, if you look at wins and standard deviations of wins, has been 12 years of mediocrity.

Looking at New York's wins per year, they generally tend to be a team that manages to finish just slightly (one or two games) above average every year. And yet this "uninteresting" team has challenged for the WNBA title not once, but four times. They've never won, but they've tied with Detroit for most WNBA finals appearances. If that isn't interesting, then I don't know what is.

It looks like the authors of the Wages of Wins Journal has a system which might not apply to the WNBA. And even if it does apply, I'm sure that we could all agree that even if a WNBA isn't "interesting", it's always interesting to hypothesize how such a team might become interesting. New York, with its four finals appearance, might have solved that problem.

WNBA Seasons and the Noll-Scully Measure

One of the ways that you can measure the competitveness of any league - basketball, baseball, football, whatever - is through something called a Noll-Scully measure. I decided that I would apply the Noll-Scully measure to the history of the WNBA.

The way Noll-Scully is developed is to look at a "perfectly competitive" league and compare the league in question to this imaginary league mathematically. We will define perfectly competitive league as a league where "all teams are equal". They are equally competitive to the point that when any two teams play each other on a neutral court, you literally cannot predict who will win. Neither team has a discernable edge and you could predict just as accurately by flipping a coin.

So if such a league existed, and that league was set up the same way as the WNBA where every team played 34 games, what would we expect the league standings to be? You might be tempted to say, "every team will have a 17-17 record at the end of the year"...but you'd be wrong, and the reason you'd be wrong is that you have to take into account what randomness really means. To claim that every team would finish 17-17 for the year would be akin to claiming that for every trial in which you flipped a coin 34 times, you would always get 17 heads and 17 tails. As an experiment, make a trial of flipping a coin four times. Do you always get two heads and two tails with each trial.

The answer is "no": this perfectly competitive league will be affected by a random scatter. (I'll just simply say "binomial distribution" and "standard deviation" and let you go to sleep.) For any of the teams in this perfectly competitive league, the chances are 68 percent that the team will win between 14 and 20 games in a year. (After all, if you were to flip a coin 34 times, you might not get 17 heads but you'll probably get something pretty close to it.) The chances are 95 percent that the team will win between 11 and 23 games. For such a team to either win 10 or less games or 24 or more games would be very rare - the chances would be 4.6 percent or less.

If we know something about how the distribution of wins in a perfectly competitive league "scatter", we can compare the scatter of our test league to this perfectly competitive league and come up with the Noll-Scully measure.

Noll-Scully measure = (standard deviation of wins in test league)/(standard deviation of wins in perfectly competitive league).

Binomial distributions - "random chance distributions" - have a rather tight and regular scatter. If our test league is exactly like a perfectly competitive league, the numerator and denominator become equal, and the Noll-Scully measure becomes equal to 1.00. 1.00 is perfection; non-perfect leagues - in other words, every league - will have a Noll-Scully higher than 1.00.

Here are some commonly accepted Noll-Scully measures for professional leagues:

National Football League: 1.48
National Hockey League: 1.70
National League (baseball): 1.76
American League (baseball) 1.78
National Basketball Association: 2.89

These numbers sort of make sense. The NFL's low Noll-Scully indicates that the NFL is a very competitive league. Every year, it takes until the very last week of play to eliminate some of the teams from playoff contention. The NBA, however, is a very low-competitive league, which is divided into "have" teams and "have-not" teams - except maybe for the #8 playoff spot, one can usually tell right away which teams will be competitive and which teams won't.

And now, the heart of the matter: Here are the year-per-year Noll-Scully measures for the WNBA:

1997 1.40
1998 2.14
1999 1.62
2000 2.22
2001 1.96
2002 1.64
2003 1.65
2004 1.26
2005 2.00
2006 2.12
2007 1.53
2008 1.84

Using a "weighted mean", where the weight of 2008 is "12", the weight of 2007 is "11", etc., the weighted Noll-Scully measure of the WNBA is 1.78.

This is very surprising. This indicates that the WNBA is much more competitive than the NBA - it's a lot harder to tell right away who the best teams are in the WNBA. It takes longer to sort out the playoff picture in the WNBA than it does in the NBA, where at the beginning of the year you can usually pencil the Celtics and Lakers in automatically.

Some observations:

1. 1997 was the most competitive year of WNBA history. If you look at the final regular season standings, it was a 28-game season and no team won more than 18 games. Only three games separated the first place team from the last place team in the Eastern Conference.
2. You would expect the N-S measure to increase every year of league expansion. In 1998, the measure jumped to 2.14 as weak, non-competitive teams were thrown into the mix. With another 1999 expansion, there's an aberration as the N-S measure falls, but in 2000 with the advent of a 16-team league, the NS goes up again as four teams are added to the WNBA. Indeed, 2000 was the least competitive year according to N-S.
3. All other things being equal, after an expansion you would expect an immediate decline in the N-S. From 2000 to 2002 - the years of the 16-team WNBA - the N-S measure goes down every year. The bad teams are given a chance to sort themselves out and become competitive.
4. From 2005 on, the league hasn't been very competitive. In 2005, Charlotte and San Antonio finished in the dog house. In 2006, the league expanded which weakened competitive balance. (Chicago finished 5-29.) The league's balance got better in 2007 when Charlotte was contracted out of the league, but the addition of Atlanta in 2008 made things less competitive again - the Eastern Conference, for example, was much weaker than the Western.

Does this prove anything? No, but it's an interesting way to look at changes from year to year. My prediction is that with the strengthening of the Atlanta Dream in the off-season and with the tightening of roster sizes necessitated by the recession that the league will become more competitive and the N-S measure will drop. We shall see.

Note: This isn't the first time I've written about Noll-Scully - I also wrote about it last year. That's the problem with the flu, it fries your brain.

Age vs. Ability in the WNBA, Part II

Janie Fincher of the WPBL has probably passed peak age.

If you've noticed in the comment section of yesterday's post, Rebkellian and all-around good guy pilight takes issue with the results. To quote:

"The problem is that you're only considering players who are still in the league at each given age. The only players who make rosters at 35 are those who are above average. Most players are long since out of the league [by age 35]."

In short, pilight's statement was that I had introduced a selection bias, which according to the fine folks at Wikipedia is "the distortion of a statistical analysis, due to the method of collecting samples". According to pilight, my particular form of error was a participant bias.

One can't look at all 35 year olds and claim that they'd be representative of any group of players that, by hook or crook, could theoretically reach 35 years old and be playing in the WNBA. All of the players who are age 35 in the WNBA are good players - they'd have to be to have lasted 12 + years in the league. When you compare 22 year old players to a group of 35 year old players, the 22 year old players will have some good, a lot of average, and many bad players but the 35 year olds generally won't have bad players left in it anymore. The groups being compared were unequal to begin with, and the conclusions drawn will be biased.

As someone once said, "Statistics is the most non-intuitive branch of mathematics." I found myself forced to agree with pilight - I would have to abandon my beloved hypothesis that WNBA players get better with age and seek some other approach. Maybe the new approach would yield the same conclusion...but maybe it wouldn't.

So what will be the new approach? What we'll do is compare selected groups of players across brief intervals of time. We'll compare a group of players who are age n (let n be whatever year of age you want) and then look at those same players at age (n+1).

We'll toss out any player from this group who didn't play 500 minutes in either year. Let's assume n = 23. We are looking at all of the players in the WNBA who played 500 minutes both at age 23 and at age 24. We compare their performances at age 23 with their performances at age 24.

If the group turned in better performances at age 24 than at 23, we have reason to conclude that your typical player will play better at age 24 than at age 23. If they turned in worse performances at age 24 than at 23, then we can conclude the opposite is true.

We do this for every pair of years for which we have data.

Results

Start Age	End Age	Candidates	Average Change in Wins Score
19	20	1	32.00
20	21	4	-6.12
21	22	18	25.19
22	23	51	14.90
23	24	66	5.11
24	25	72	6.95
25	26	71	-6.60
26	27	54	15.43
27	28	50	0.14
28	29	45	-14.72
29	30	39	-1.41
30	31	38	-7.96
31	32	32	-6.80
32	33	33	6.33
33	34	23	-2.67
34	35	15	-10.20
35	36	12	-27.79
36	37	6	7.17
37	38	2	-24.25
38	39	1	-53.50

This begins to look like what we expect: a bell curve which rises to a certain point and then progressive declines.

Column F is the number of candidates. Note that there is only one WNBA player who played more than 500 minutes both at age 19 and age 20 - Ann Wauters. Likewise, Teresa Edwards is the only WNBA player who played more than 500 minutes both at age 38 and age 39. (This information does not look good for Sheryl Swoopes's employment prospects.)

Column G is the rise in average Wins Score per year. Aside from some little glitches, the year-per-year win score rises, sort of stays the same between age 27 and age 28, and then begins to decline year per year. This implies that the peak age for a WNBA player is...oh, I don't know, somewhere between age 27 and age 28. Afterwards, performance declines.

The only skewing of the results might be due to some players playing more minutes than others, even in the 500+ minute category. Wins Score is a linear metric - it rewards players for everything good they do and punishes them for every bad thing they do. Players who play a lot of minutes will tend to higher Wins Scores simply because they have more of an opportunity to accumulate points. The most minutes ever played in a WNBA season is around 1,200.

In general, the correlation between age progression and Win Score changes is about 0.94 for ages 21 to 27 and -0.81 for ages 28 to 35. Those are very good results.

So sadly (for me anyway), I was wrong and pilight is right: player performance begins to decline after age 28, and by age 38 players are at the far right end of the bell curve. All I managed to prove in my initial analysis is that the kinds of players that are still around after age 28 tend to be the better type of players. Looks like I learned three things:

a) that I was right if I redefined my question of my first analysis to "are the players still around at older ages the ones that have been historically the best"?
b) that pilight was right if I try to answer the question that I wanted to answer in the first place, which isn't the one in question a) and
c) a big heaping dose of humility.

Age vs. Ability in the WNBA

The WNBA Veterans have beaten the clock.

I've been very much interested in WNBA age scales. An age scale is a graph with the age of the player on one axis (easy to determine) and some talent metric on the other axis (hard to determine). The idea is that a player's performance can be projected across time.

If you look at any of baseball's proposed age scales, those graphs will be bell curves. As the "average player" ages, he becomes better and better until he peaks at age 28 and reaches the top of the little hill on the graph. Then, the player slowly declines as "it's all downhill after age 28" with all the little injuries and better players attriting the player's natural talent.

There are a lot of 28 year olds in baseball. There aren't so many 38 year olds, and this age-related athletic decline explains why.

Figuring out the age of WNBA players was easy. Now, I just needed a metric to evaluate them. I decided to use the Wins Score metric. (If you want to know what "Wins Score" is, just follow the link.) I like the metric better than I like the WNBA's official player metric - "Efficiency".

I limited to this analysis to ages where at least 50 seasons were played. For example, there are only 20 seasons from 36-year olds, so we won't look at an "average" 36 year old. Not enough data.

I expected a bell curve - what I got was shocking.

Player Age	Mean Wins Score
21	13.36
22	18.51
23	26.72
24	34.58
25	36.92
26	35.78
27	41.79
28	41.21
29	46.78
30	43.78
31	47.26
32	47.28
33	60.24

Holy crap. The values imply that as a WNBA player gets older, she just gets better and better and better...on the average. The values also implies that the best WNBA players - the ones who could most influence an arithmetic mean, or simple average - are on the upper end of the age scale.

I tried this analysis on John Hollinger's PER. I weighted PER by minutes played. Pretty much I got the same result, except that the numbers were less eye-popping as the range of values for PER is tighter for that of Wins Score - a great PER is above 30; a great Wins Score is above 300.

If you look at ages for which I didn't have 50 seasons, the values get even higher. At 34 the average Wins Score is 63.28; at 35 it's 65.92! Only at age 36 do we see a decline.

Since this flies in the face of what we'd expect - that the best players should be about 28 or 29 and then fall apart as they get older - we need some sort of explanation.

Here are some hypotheses:

1) The WNBA coaches and GMs are excellent managers of talent. They simply weed out every player who isn't good and keep weeding them out. (Okay, you can stop laughing.)

2) Marginal players become discouraged by sitting on the bench and leave the WNBA before they hit 30. Possible. But I don't know any WNBA player that didn't love the game so much that she wouldn't play it even for limited minutes.

3) Financial pressures: I think this a great reason. Note that there are two jumps, one at age 23 and another at age 24. The jump disappears at age 25. My understanding is that after three years, players stop being rookies and stop getting paid with the rookie scale. Anyone in the WNBA who hasn't shown a coach or GM something after three years gets weeded out of the league. After 25, you are an official WNBA survivor.

Since WNBA veteran salaries are in a narrow band - between $50K and $100K at the uppermost - there are different pressures than in multimillion dollar pro sports. When a NBA player is being paid $10 million a year you play him even if he isn't any good. Those kinds of pressures don't exist in the WNBA. Since the star factor is also diminished - WNBA players aren't national superstars - there's no pressure to play a popular player whose skills are diminishing. (Look at what happened to Sheryl Swoopes in Seattle. If it had been Sam Swoopes playing for the Oklahoma Thunder that situation would have been handled much differently.)

Furthermore, since it's very hard to establish a career on just $45 K for a limited number of years, there's more pressure on players to bail out and do something more lucrative.

I don't know if hypothesis #3 is the truth, but I think it comes closer to the truth than the other two explanations. "Locked into six years at multimillion dollar contract" disappears. "Can't waive her because she's a national superstar" disappears. In short, they don't pay or play you in the WNBA at age 33 unless you deserve to be paid and played.

4) Tighter band of athletic skill. When I say "tighter band", I mean that the deviation in athletic skill is smaller than it is in MLB or the NBA. That deviation comes from gender. The most athletic player in the NBA is probably more dominant over his average counterpart. Whereas the difference is smaller between the WNBA's average player and its most dominant player. Men are taller and have more muscular power than women do...and when that power disappears with age, male players are more substantially diminished. This leads to a discussion about....

5) Game expertise. In the NBA, it's much more natural for one player to take over the game with superior athletic ability, but I suspect it's more difficult in the WNBA. Therefore, knowledge of the tiny idiosyncracies of the game becomes more valuable.

Which refs can I work with a smile and a wink? (Becky Hammon)
Which rookie can I provoke into doing something stupid? (Plenette Pierson)
How many game situations like this have I seen before? (Vickie Johnson)

Those 35 year old WNBA players become as wily as jungle cats. They'll outsmart you. It's one of the only ways to gain an advantage in the WNBA where you can't use natural strength to have your way with your opposition (at least, until Brittney Griner shows up).

Furthermore, once the Allen Iverson type player in the NBA loses a step...he's lost. He's always ignored fundamentals for athleticism and his decline becomes that much sharper when he gets older. Since WNBA players don't have that advantage, they have to know the game to a greater degree than their male counterparts.

(* * *)

All in all, it adds up to one fact. WNBA players have beaten the clock. They're like fine wine; they just get better with age. However, if genetics catch up, we'll see the stars of tomorrow begin to dominate the game with athleticism.

Update: To see how wrong this post really was, go here to the update.