Swish Appeal

WNBA Opening Day Demographics

2012-05-17T21:04:42Z

Some factoids about the 2012 opening day rosters

Note that suspended players are not included

Oldest players

Youngest Players

17 players born in the 1970's made opening day rosters compared to 10 born in the 1990's. This will almost certainly be the last year 1970's born players outnumber 1990's born players. No player born in the 1960's has played in the league since 2007.

By Draft Class

1997 1

1998 2 +1 undrafted

1999 3 +2 undrafted/assigned

2000 1 + 1 undrafted

2001 7

2002 4

2003 3 + 1 undrafted

2004 9 + 1 undrafted

2005 8 + 1 undrafted

2006 8 + 2 undrafted

2007 7

2008 15

2009 13

2010 11

2011 13 + 1 undrafted

2012 14 + 3 undrafted

No player from the 1997 Elite draft has played in the league since 2009.

By School

11 Connecticut

10 Tennessee

7 Duke

7 Rutgers

6 Stanford

5 LSU

4 Maryland

4 North Carolina

4 Ohio State

3 Notre Dame

3 USC

3 Virginia

2 Auburn

2 Florida

2 Georgia

2 Georgia Tech

2 Kansas

2 Miami (FL)

2 Oklahoma

2 Purdue

2 Texas Tech

Many others with one

By draft round

87 First

21 Second

11 Third

13 Undrafted/Assigned

No 4th round pick has played in the league since 2008. The last four round draft was 2002.

By Zodiac

18 Taurus

15 Aquarius

15 Virgo

14 Cancer

12 Libra

10 Aries

10 Leo

8 Capricorn

8 Gemini

8 Sagittarius

7 Pisces

7 Scorpio

Note that these are Tropical, not Sidereal

Durability: WNBA Players and Career Games Started

2012-05-17T17:37:38Z

I decided to take a look at the an often-overlooked statistic: "games started". Instead of looking at which players in WNBA history have started the most games, I decided to look at start percentage.

Were there players who have started every game they have played? To me, this was an interesting stat because the mythical lifespan of players moves from rookie working her way off the bench to starter to returning to the bench in old age. This magical graph of start percentage per season would look like a parabola, or a hill. I was more interested in players whose graph was a plateau - players who started at the top of the hill and stayed there.

Top Ten Career Start Percentages, WNBA History (Minimum 100 Games)

1. Sue Bird (2002- SEA) - 324 per 324 games (1.000)
2. Lauren Jackson (2001- SEA) - 308 per 308 games (1.000)
3. Diana Taurasi (2004- PHO) - 261 per 261 games (1.000)
4. Sophia Young (2006- SAS) - 200 per 200 games (1.000)
5. Cappie Pondexter (2006-09 PHO, 2010- NYL) - 197 per 197 games (1.000)
6. Cheryl Ford (2003-09 DET) - 196 per 196 games (1.000)
7. Vicky Bullett (1997-99 CHA, 2000-02 WAS) - 186 per 186 games (1.000)
8. Seimone Augustus (2006- MIN) - 164 per 164 games (1.000)
9. Cynthia Cooper (1997-2000, 2003 HOU) - 124 per 124 games (1.000)
10. Lindsey Whalen (2004-09 CON, 2010- MIN) - 263 per 264 games (0.996)

Whalen missed one game in her first season. She must be kicking herself.

But as you can see, the above list could be someone's short list for Hall of Fame candidates. Clearly, these are all great players. There are two teams - Seattle and Minnesota - that have two of the players on this list on their current roster.

Okay, there are some players who are just great. But let's go back to our example. What about the players in the middle of this list? What kind of players are there who have played 100 games and have a starting percentage of approximately 50 percent?

Middle of the Pack Career Start Percentages, WNBA History (Minimum 100 Games)

1. Tamera Young (2008-09 ATL, 2009- CHI) - 62 per 119 games (0.521)
2. Essence Carson (2008- NYL) - 69 per 134 games (.515)
3. DeMya Walker - (2000-02 POR, 2003-09 SAC, 2010-11 CON, 2011 WAS) - 154 per 302 games (.510)
4. Erin Phillips (2006, 2008-09 CON, 2011 IND) - 53 per 105 games (.505)
5. Brooke Wyckoff (2001-02 ORL, 2003, 2005 CON, 2006-09 CHI) - 121 per 242 games (.500)
6. Vanessa Nygaard (1999 CLE, 2000-01 POR, 2002 MIA, 2003 LAS) - 53 per 107 games (.495)
7. LaToya Thomas (2003 CLE, 2004-06 SAS, 2007-LAS, 2008 DET, 2008 MIN) - 74 per 150 games (.493)
8. Janell Burse (2001-03 MIN, 2004-07, 2009 SEA) - 114 per 233 games (.489)
9. Chantel Tremitiere (1997 SAC, 1998-99 UTA, 2000 IND) - 49 per 101 games (.485)
10. Mery Andrade - (2000-02 CLE, 2004 CHA) - 67 per 140 games (.485)

DeMya Walker will be with her fifth team this year, the New York Liberty. Young is best described as a "mixed/utility" player. Only three of these players are on a WNBA roster, although Erin Phillips was on a team that was a WNBA Finals contender last year.

So what about the players with 100 games that have the fewest number of starts?

Bottom Ten Career Start Percentages, WNBA History (Minimum 100 Games)

1. Laurie Koehn (2005-08 WAS) - 0 per 120 games (0.00)
2. Tricia Bader-Binford (1998-99 UTA, 1999-2002 CLE) - 0 per 100 games (0.00)
3. Nicky McCrimmon (2000-03 LAS) - 1 per 125 games (0.01)
4. Sandora Irvin (2005-06 PHO, 2007-08 SAS, 2010 CHI, 2011 ATL) - 2 per 111 games (0.02)
5. Jamie Carey (2005-08 CON) - 3 per 105 games (0.03)
6. Kisha Ford (1997-98 NYL, 1999 ORL, 2000-01 MIA) - 4 per 124 games (0.03)
7. K. B. Sharp (2003-04 NYL, 2006-07 IND, 2008-09 CHI) - 6 per 178 games (0.03)
8. Shay Murphy (2007-08 MIN, 2008 DET, 2008 WAS, 2009-10 IND, 2010-11 CHI) - 4 per 109 games (0.04)
9. Kelly Mazzante (2004-06 CHA, 2007-09 PHO, 2011 ATL) - 9 per 200 games (0.05)
10. Kristin Haynie (2005-07 SAC, 2008 ATL, 2009 DET, 2009 SAC) - 8 per 160 games (0.05)

Murphy is still with the Chicago Sky. The rest are pretty what you'd expect, even though Sandora Irvin and Kelly Mazzante both saw time on a roster last year. Haynie's start count might be smaller if she didn't spend a full season in Atlanta.

Nicky McCrimmon was on the first season of American Idol - I might be wrong about that, though.

Murphy should be known as "Ms. WNBA" - America's Guest. She's worn five uniforms with WNBA teams, and with three teams in the same year (2008).

Two factors I noticed: players either had blocks of time with one team (Koehn, McCrimmon, Carey) - this suggests that their coaches liked them as bench warmers as long as they could pay them rookie salaries, but no one wanted to pay them for realsies. Other players (Bader-Binford, Ford) had the advantage (?) of playing either in the expansion era or with awful teams that couldn't be choosy.

It just goes to show you that you can make WNBA history - whether you start every game, carry Gatorade and towels on the bench, or, well, just be somewhere in the middle.

Final 2012 Opening Day WNBA Rosters By Playing Style

2012-05-17T12:00:57Z

A number of WNBA teams finalized their rosters yesterday and with that we updated our previous training camp rosters to the best of our ability last night.

The following are links to updated rosters for each team by playing style.

2012 Eastern Conference WNBA rosters (as of midnight PST May 18, 2012)

2012 Western Conference WNBA rosters (as of 7:30 p.m. PST May 17, 2012)

The links have now been updated to indicate players who:

are still on the roster,
have been cut,
are expected to miss the season due to injury,
are affected by the Olympics, either missing the season or returning after the Olympic break.

The official deadline to reduce rosters to 11 players is today at 2 p.m. EDT, which gives those teams that have yet to announce their final roster a few more hours to get that done.

Were there any cuts that surprised you? Or any players that made a roster you didn't expect? Feel free to leave your thoughts or updates on the rosters in the comments.

WNBA GM Survey is Out: 10 of 12 GM's believe that Minnesota will repeat as 2012 Champions

2012-05-17T00:01:59Z

WNBA GM Survey is Out: 10 of 12 GM's believe that Minnesota will repeat as 2012 Champions

That's right ladies and gents. 83% of the GM's (or 10 of 12 after figuring out the math) believe the Lynx will make it two in a row for this year. In addition, all but one GM believes that Minny will also win the Western Conference. For the East, 5 GM's thought Indy will win it; 3 thought Connecticut will, and every other team had 1 GM. Yes, the Mystics got a vote in as Eastern Champs and I bet that same GM was the one who voted the Mystics as a team that would be in the playoffs. I wonder who that is.

Among players, three each thought Sparks forward Candace Parker and Lynx guard Seimone Augustus will be the 2012 MVP. Seattle guard Sue Bird won the most superlatives in this survey though, being named the best point guard, best passer, best leader, best IQ , best at making her teammates better, and best future head coach of a hoops team someday. She was tied for 2nd as the first franchise player to build around too. I think some of these same GM's just forgot to make her MVP and best three point shooter when it counts.

Among rookies, Sparks forward Nneka Ogwumike is the GM's presason rookie of the year. No surprise.

To add to more Mystics homerism, Crystal Langhorne was tied for the most underrated player in the league, and was second for best post moves (behind Connecticut center Tina Charles) so I'm glad she is getting some respect league-wide.

These are fun reads and allow some homerism and friendly trash talk. Enjoy reading the survey.

A Statistical Framework For Team Building

2012-05-16T21:07:16Z

After tying the mark for all-time women's professional basketball coaching wins after a win against the Chicago Sky on September 11, 2011, Seattle Storm coach and GM Brian Agler offered brief insight related to what it takes to build a winning team.

"When we first got this opportunity I thought Sue and Lauren could be the best at their positions in the world, so what kind of people and players are going to be a good match with those guys?" Agler said. "Obviously, they're both quality people but they also are finesse players so you need to put toughness and tenacity and physical play and people to do the dirty around them. That's what we have and everybody plays and accepts their role. The longer we've played the tighter we've gotten."

Although building around Bird and (a healthy) Jackson might sound relatively simple formula for success to many, Agler was confronted with a more difficult team building challenge this WNBA off-season.

Faced with salary cap constraints and aging stars, it was obvious that the Storm had to make changes. Yet compounding the challenge was the desire to remain competitive in the present - with star center Lauren Jackson missing half the season due to the Olympics - while simultaneously building for a sustainable future. To do that, Agler pursued a combination of experience and youth this off-season.

It's a delicate balance and, as Agler suggested in an article by Jayda Evans of the Seattle Times, what he's attempting to do is something that's very difficult in any professional sport.

"What we're trying to do is very difficult - maintain a high level of competitive drive and success by striving to win championships but yet still get younger with the roster," said Agler, whose Storm has reached the WNBA playoffs a league record-tying eight consecutive times. "Those two things really don't work together sometimes. We're trying to do it."

Bird and Jackson are clearly still the focal points of the Storm's plans this season, but they're clearly preparing to move in a new direction. Only time will truly tell how well Agler did balancing competitiveness with building for the future, but it does raise the question

So that raises the question of what goes into building a competitive team and how we might evaluate the off-season changes of Agler's or any team. I have my thoughts on the Storm's path that I'll share (sooner than) later, but the following is the framework I'm going to use for season previews that I've described before and is essentially all described in our statistics glossary. This is just an update to some of that thinking with the use of additional statistics that I didn't previously have available to me for the WNBA.

Four Factors strengths and weaknesses

Before scrutinizing the performances of a team's individuals, it actually helps to take stock of how the team performed as a whole.

Although win percentage, point differential, or defensive/offensive ratings are all strong indicators of success, for an analysis of how well a team is put together it really helps to have a sense of specific strengths and weaknesses relative to opponents. The best way to do that while still keeping an eye on wins and losses is to use Dean Oliver's Four Factors to create a statistical profile for a team.

Ken Pomeroy summarizes Oliver's Four Factors well and Ray Floriani has provided plenty of game examples throughout the WNBA and NCAA women's basketball season at Swish Appeal with his "stat analyses", but for now I want to revisit the matter of weighting each one of those to determine strengths and weaknesses.

The Four Factors are as follows: effective field goal percentage, turnover percentage, offensive rebounding, free throw rate. But as one might suspect, not all of those things are created equal. Take, for example, the Four Factors for this 59-49 win by the New York Liberty over the Chicago Sky on August 4, 2011 summarized statistically by Ray:

Team	eFG%	Tov%	OReb%	FT Rate
Sky	35%	23%	34%	31%
Liberty	41%	11%	29%	20%
NYL Differential	+6%	+12%	-5%	-11%

Four Factors for Liberty vs Sky on 8/4/11.

So, what do we make of this? On the surface of it, the game looks like a wash - each team "won" a category with each essentially dominating one and playing closer in another. But common sense might tell us that a few of those battles were more important than others, as described here previously.

Oliver has established weights to determine how important each of those Four Factors are that help shed some light on how the Liberty won that particular game, which you can find here. But to summarize, those weights are as follows:

Shooting efficiency: 10
Turnover percentage: -8.4
Offensive rebounding percentage: 4.1
Free throw rate: 2.0

So applying those weights to that game, we'd get the following weighted differentials to help us determine the most significant factors in winning the game:

eFG%	Tov%	Oreb%	FTR
+60	+100.8	-20.1	-22

Weighted Four Factors differentials for the NYL in their win against Chi on 8/4/11.

In other words, although each team won two of four categories, the Liberty "won" more significant Four Factors and thus established more significant advantages that helped them win the game.

Balance

The same analysis above can be applied to looking at a team's performance relative to their opponents over the course of a season: championship teams tend to establish significant advantages in at least three of four categories relative to opponents, average teams in two of four significant categories, and poor teams have negative differentials in all four.

So the best teams are built to win as many of those categories as possible. And after looking at the team level, we can look at the individual players that make up a team.

Versatility

In addition to balance on the team level, a team's versatility in terms of their ability to put combinations on the floor that can produce those Four Factors stats is rather significant to a team's success. Perhaps another way of saying that is that you want the players that not only have a clear division of labor but also a distributed one - a team that relies on one player heavily to produce one of the Four Factors is going to be easier to neutralize than one that has multiple contributors to that area (as you might guess, this is especially true of offensive rebounding, which is usually dominated by post players who get in foul trouble more often). Conversely,if you have multiple players that can contribute to the Four Factors, your roster is more adaptive than a team that relies heavily on one player to produce Four Factors stats.

Quality

What might matter even more than balance and versatility is having players who can make high quality contributions to the Four Factors. Having four players who are capable of rebounding is not bad on an 11-player roster; having four rebounders as the 8th, 9th, 10th, and 11th players on the bench might not be a formula for success.

There are a number of ways to look at player productivity, but one that I'll rely on early on is 4-year Regularized Adjusted Plus Minus (RAPM). To oversimplify, 4-year RAPM numbers basically tell us how many points a player's team allowed and scored when that player was on the court independent of who they were playing with over a 4-year span.

The advantages of using RAPM over a four-year span have been described here at length (with soundtrack), but one major asset is that RAPM helps us figure out which players are productive defensively, something that is extremely difficult to measure otherwise. With all the player movement and personnel changes that happen over four years, it's a rather valuable statistic. Obviously, younger players haven't played for four years so I'll also use 1-year RAPM numbers when applicable.

Complementarity

Another way to look at quality is relative to players' style of play - as important as it is to win those Four Factors with quality players, so is having the different types of players that are particularly adept at doing those things. For example, a team full of wings who can contribute to the Four Factors is nice, but they'll inevitably struggle when they facing a team with interior players that they don't match up with well. Championship rosters tend to have a mix of quality players across the spectrum of playing styles who allow them to adjust to different situations fluidly.

The SPI Playing Styles framework has already been described elsewhere and the importance of that to this discussion should be somewhat obvious: 11-player rosters have made versatility even more important in the WNBA in terms of both individual player skill sets and types of players.

So another important way to assess the quality of a team's roster is not only by standard metrics, but with a breakdown of whether each player is a) above average in each area of the Four Factors b) relative to other similar players by SPI Playing Style.

The Structure of Championship Teams

The basic idea is that flexibility and productivity matter more than simply being "deep" in the WNBA. Realistically, most teams are only going to play 7-9 players consistently to begin with but have to find a way to manage injuries, fatigue, leaving little room for development unless those top rotation players are outstanding enough to fill the Four Factors on their own (e.g. Seattle, 2010).

In ideal terms, a team that can put five players on the floor that can contribute to the Four Factors at an above average level on the interior and perimeter while scoring points is better off than a team for which that contribution is concentrated in one place because it's easier to find a way to establish advantages against a range of opponents.

Over the last few years, Minnesota (2011), Seattle (2010) Indiana & Phoenix (2009) had rosters that exemplified all of these qualities entering the season in terms of the players who can contribute to those Four Factors. They all ended up in the WNBA Finals. Those squads stand in direct opposition to Atlanta (2010 & 11), which has been an interesting example of a team that had multiple players that contributed to the team's dominance in two of those Four Factors (offensive rebounding and turnover percentage), which ended up fueling impressive finishes to the season and consecutive runs to the WNBA Finals.

2012 will be the first year since the 11-player rosters have been implemented that we'll have an Olympic break prior to which some key foreign players will be absent, which could make this matter of roster flexibility - specifically the ability to make up for the absent player by productively changing style of play if not replacing them - even more important.

Percent valuable contributions

The best way to examine how much a team might lose due to a player absence is to look at percentage of valuable contributions for each player on the team or the weighted value of everything they contributed to the team from assists to missed shots. That's all described here, but this is essentially what that would look like (using the 2011 Tulsa Shock as an example):

In the case of teams that will be missing players due to injury or Olympic absence, this helps to understand how significant that loss is statistically.

Projecting player contributions

But along with knowing how valuable a player was to their team's success last year, we also might want to know how much value a player contributes relative to the proportion of team minutes they received. Valuable contributions ratio (VCR) helps to figure out a few things in that regard:

Whether a player is contributing quality minutes when they're on the court in terms of their statistical contribution relative to the percentage of team's minutes they receive.
Whether a player can be expected to play starter, rotation, or reserve-level minutes, even if they changed teams.
Players who are poised to have a breakout season in terms of how well they produced in the minutes they received in the previous season and whether they'll be able to maintain their productivity if given more minutes.
How much room rookies have for development.

Essentially, VCR answers the question: "How much more (or less) would this player contribute to the team if given more minutes, assuming they don't improve their game?" That's why VCR is also useful for determining the Most Improved Player award: if a player does improve their game in some way (e.g. basketball IQ, strength, skill, etc) the rate of statistical contribution relative to the proportion of team minutes they receive would increase. That's different than players averaging more because they played more - it's a measure of whether they did more damage in whatever time they did get.

Just win baby

That's a lot of information to digest at once, which probably makes it seem a lot more overwhelming that it really is (which is why it deserved its own post rather than trying to cram the explanations into the team analyses). As I said at the outset, it's really a list of stats that I use to look at the structure of teams that will be used as a reference point in the future.

So to summarize in the broad statistical terms, the teams with the broadest range of playing styles that can establish advantages by contributing to the Four Factors at above average rates tend to win championships. Conceptually, the teams with the greatest diversity of quality players have won the WNBA title for the past three years.

In real plain terms, teams with the most talent do well even in a league where team play might be of higher value than one-on-one play.

All we're doing with all this statistical stuff is defining what "talent" is and how to turn a collection of talent into a great team. While it might seem overwrought, all of this might assume even more importance than usual as we enter a season that might be heavily shaped by who is present come playoff time.

WNBA Live Access to Begin Charging Fees

2012-05-16T13:38:21Z

WNBA Live Access to Begin Charging Fees

WNBA fans will have to pay $4.99 for access to games not otherwise shown on national television for the 2012 season, a price increase of infinity percent - since previously, WNBA Live Access was free. More at the link, above.

If you show up to the WNBA site on May 18th expecting to see one of your favorite games, you'll need either a debit card, credit card, or PayPal account. (Either that, or a friend at the game who can shout very, very loudly.)

Tulsa Shock Roster Down To 11 After Veteran Cuts - Amber Holt And Shanna Zolman Released

2012-05-15T19:47:21Z

The Tulsa Shock have released a pair of five-year league veterans in Amber Holt and Shanna Zolman, announced today.

Last season Holt was a staple in the starting lineup, starting in 24 of the 26 games in which she played. She averaged 6.7 points, 3.1 rebounds and 2.4 assists in Tulsa's trying season. Holt's came to Tulsa in a 2010 Draft Day trade with the Connecticut Sun.

Shanna Zolman (formerly Crossley) was a face on many a Shock broadcast last year as she took her position behind the mic after sustaining a season-ending knee injury. Zolman's contribution to the offense ranked her third on the team in points per game (9.7) in 2010. She sunk an easy 42.2 percent of her 3-point attempts as she put her nickname "Layup" on display.

With the cuts, the Tulsa Shock roster is down to 11. Three rookies - Glory Johnson, Riquna Williams and Lynetta Kizer - join the fold in the youthful organization. Tulsa will have to drop someone off the roster after the 2012 Olympic break to make way for Liz Cambage, should she be ready to return to the states after the Australian Olympic campaign.

TULSA SHOCK 2012 OPENING NIGHT ROSTER
PLAYER	POS	FROM	YRS
Chante Black	C	Duke	2
Karima Christmas	G-F	Duke	1
Glory Johnson	F	Tennessee	R
Temeka Johnson	G	Louisiana State	7
Lynetta Kizer	C	Maryland	R
Jennifer Lacy	F	Pepperdine	6
Ivory Latta	G	North Carolina	5
Jene Morris	G	San Diego State	1
Kayla Pedersen	F	Stanford	1
Scholanda Dorrell	G	Louisiana State	6
Riquna Williams	G	Miami	R

On Big Games vs. Team Wins

2012-05-15T17:00:49Z

It's been a long and busy winter for me, one which has taken me away from women's basketball and towards the dubious goal of "career advancement". (Oh, the things you'll do for money.) With a temporary respite in chasing the mighty dollar, I'm hoping to get back into the swing of things, despite the fact that a good six months or so has been eaten from my basketball knowledge. (*)

The focus of this article is an article that Bill James wrote several years ago comparing pitchers Mitt Pappas to Don Drysdale. The former is not in the Baseball Hall of the Fame but the latter is despite identical win-loss records. James's contention is that the greater amount of variance in Drysdale's won-loss record makes him the more valuable player. By variance, I mean that there is a greater "spread" among Drysdale's seasons - he has several good seasons and several crappy ones, whereas Pappas is Mr. Consistency. However, this variance means that it's very likely for Drysdale to have a breakout season which pushes him over the top, whereas Pappas is less likely to have such seasons.

Variance in WNBA career performance, however, is an article for another time. I began to think about variance in game performance, particularly the kind of variance where one player has - or appears to have - a breakout game. Generally, most WNBA teams have a "go to" player who scores the majority of their points. You can probably name your team's go-to player. Sometimes, that player - or some other player - will score a whole lot of points.

Furthermore, I began to think about a certain kind of variance, the kind of variance represented in cases where a player scored the bulk of a team's points during the game - the kind of games which are basically one-player games, where someone explodes against another team. Clearly, if your best player scores 30 or 40 points against some other team, your team is more likely to win the game, right? And clearly, these are the kinds of performances a fan wants to see.

But do those performances actually lead to victories? As it turns out, the Atlanta Dream have a certain Ms. A. McCoughtry who is known for scoring a lot of her team's points. I don't have hard data but I would estimate that the team's best player usually scores about 20 percent of her team's total points in a game. So I created a chart of all of Atlanta's games for the 2011 season. I listed the total score and compared that score to the score of the highest scoring player, and then calculated a percentage. Furthermore, I marked the result of the game, whether or not it was a loss or a win for the team.

	Atlanta

	Opponent	Score	High	Player	Pct	Result
1	New York	88	19	Castro Marques	21.6%	L
2	Washington	90	20	De Souza	22.2%	L
3	San Antonio	74	19	McCoughtry	25.7%	L
4	New York	79	18	McCoughtry	22.8%	W
5	Minnesota	85	27	McCoughtry	31.8%	L
6	Minnesota	64	10	3 players	15.6%	L
7	Chicago	71	14	McCoughtry	19.7%	W
8	Phoenix	83	24	McCoughtry	28.9%	L
9	San Antonio	86	19	Miller	22.1%	L
10	New York	87	27	De Souza	31.0%	W
11	Chicago	69	17	McCoughtry	24.6%	L
12	New York	69	17	McCoughtry	24.6%	L
13	Chicago	76	24	McCoughtry	31.6%	W
14	Indiana	84	19	Harding	22.6%	W
15	Washington	86	33	McCoughtry	38.4%	W
16	Tulsa	76	37	McCoughtry	48.7%	W
17	Los Angeles	89	22	McCoughtry	24.7%	W
18	Connecticut	92	36	McCoughtry	39.1%	L
19	New York	75	24	McCoughtry	32.0%	L
20	Seattle	70	17	McCoughtry	24.3%	W
21	Washington	72	19	McCoughtry	26.4%	W
22	Phoenix	95	25	McCoughtry	26.3%	L
23	Seattle	92	17	2 players	18.5%	W
24	Los Angeles	84	23	McCoughtry	27.4%	W
25	Connecticut	94	26	McCoughtry	27.7%	W
26	Connecticut	87	22	McCoughtry	25.3%	L
27	Chicago	83	22	McCoughtry	26.5%	W
28	Indiana	86	20	McCoughtry	23.3%	W
29	Indiana	92	28	McCoughtry	30.4%	W
30	Washington	81	30	McCoughtry	37.0%	L
31	Washington	95	19	Price	20.0%	W
32	Tulsa	73	19	McCoughtry	26.0%	W
33	Connecticut	85	35	McCoughtry	41.2%	W
34	Indiana	93	30	McCoughtry	32.3%	W

P1	Connecticut	89	21	Harding	23.6%	W
P2	Connecticut	69	12	4 players	17.4%	W
P3

P1	Indiana	74	17	Harding	23.0%	L
P2	Indiana	94	30	Castro Marques	31.9%	W
P3	Indiana	83	26	McCoughtry	31.3%	W

P1	Minnesota	74	33	McCoughtry	44.6%	L
P2	Minnesota	95	38	McCoughtry	40.0%	L
P3	Minnesota	67	22	McCoughtry	32.8%	L
P4
P5

Looking at the chart, you can tell that McCoughtry is clearly the go-to player for Atlanta. Four times during the season, she topped the 40 percent mark of total points scored for her team. During a game against Tulsa, she almost scored 50 percent of her team's points!

So how did Atlanta do in those four games? Two were regular season games, both won by the Dream. But two of those games were WNBA Finals games, and both of those games were lost by the Dream. McCoughtry scored 44.6 percent of the total points for the Dream in Game 1 - scoring 33 against the Lynx - but losing.

I decided to run a correlation between the proportion of total team points scored and whether or not the Atlanta Dream won. Correlation indicates the mathematical relationship between two variables. The question then becomes, "If someone scores a huge percentage of the team's points, is the Dream more likely to win?" A correlation of 0.5 (one is the highest) would be a strong indicator; a correlation of zero would be random chance and a correlation of -1 would be an inverse relationship.

What was the correlation? -0.06! That's virtually identical to random chance - no correlation at all between the proportion of total points scored and whether or not the Dream won. This shouldn't surprise anyone, and I didn't expect a correlation (but wished to explore if one existed). There can be several theories as to why there's no correlation:

1) There is more than one way to win a basketball game. One can distribute scoring duties among several players rather than have one player bear the burden.
2) Just because one player carries the burden, doesn't mean the other team doesn't have a player that can have pull similar duties. Ask Cappie Pondexter about Angel McCoughtry in 2010.
3) If a team is flattened, one player carrying 30 percent of the scoring burden doesn't mean much.
4) A team may have one player carrying the burden simply because there are no other good players on the team. I'm sure if you looked through some Tulsa box scores you'd find some player who had "good" nights relative to the rest of the team - but the Shock were terrible in 2011.

But surely, those nights when McCoughtry is the best player - as opposed to say, De Souza or Harding - should mean something. So I ran a correlation comparing wins or losses to games where McCoughtry is the best player. There should be a relationship between wins there, right.

No. The correlation was -0.02. If McCoughtry is the best player, she's going to be the best player during both wins *and* losses.

It just goes to show. Women's basketball is not a one-woman game, not yet. At least, not until Griner shows up.

(*) The reply from some is "what knowledge?"