Backs Against the Wall

The 100-82 loss by the Dream to the Mercury was the second game of a back-to-back. Let's look at all of the disadvantages that a team has playing back to back. (Note: I'm not trying to establish a narrative of "oh, we would have beaten the Mercury if we weren't playing the back-to-back" - the Merc have to play back-to-backs just as much as anyone else in the WNBA does.)

* A player's muscles are still retaining lactic acid from the night before.
* The player is flying across the country in a coach seat. Imagine a 6'4" woman crammed into a cattle car with wings.
* NBA players have chartered planes. WNBA players get to deal with crying babies in the seat behind.
* Time zone changes. Meaning little or no sleep.
* Bad food.
* Little time to prepare for the game.

We can make guesses about how a team is affected by a back-to-back game - but what really happens? Are teams playing on a back-to-back significantly worse? What are there average margins of victory? Does it matter if the second game of the back-to-back is at home or on the road?

I took at look at every game in the WNBA for the 2009 season which was either:

- the second game of a back to game, or
- the third game in four nights, which can be just as bad. Those games are the fourth game in the game-game-off-game sequence, else they'd be counted as just ordinary back-to-backs.

There are 53 such games this year in the WNBA. Fifty have been played, three are left to play. First, let's see how many such games each team will end up playing:

Chicago: 6
Connecticut: 6
Washington: 6
Los Angeles: 5
Minnesota: 5
Atlanta: 4
Detroit: 4
New York: 4
Sacramento: 4
Indiana: 3
Phoenix: 2
San Antonio: 2
Seattle: 2

To add insult to injury, for the Silver Stars and the Storm one of those two "back-to-back" games is actually the third game of a game-game-off-game series, adding a slight cushion of rest.

Did the WNBA schedulers put this fix in for San Antonio or Seattle? Probably not. One can guess that schedules have to do a lot with arena availability and travel conditions - but the Silver Stars and Storm probably smiled when they looked at
their schedules.

Next up: the average margin of victory for these teams on back-to-back games:

Phoenix: 13.50
Detroit: 13.00
Los Angeles: 3.40
Seattle: 1.50
Connecticut: -0.17
Indiana: -2.00
Sacramento: -2.75
Washington: -4.80
New York: -5.50
San Antonio: -8.00
Minnesota: -11.20
Chicago: -12.00
Atlanta: -12.67

Average: -3.20

On the average, it seems that playing that back-to-back game can take three points off your average. Since the best teams (Indiana, Phoenix) are winning by 3.81 points per game and 3.75 points per game respectively, a back-to-back team can turn a good team into an average team and an average team into a horrible one.

Note that some teams come out quite well in the average because either:

a) they got hot when they were having most of their back-to-backs (Los Angeles),
b) played a small sample size of games (Phoenix), or
c) had big wins that skewed the average (Detroit).

The next question: how do back-to-backs impact the win-loss record of a team:

Back-to-Back Win-Loss Records
(includes Game 3 of game-game-off-game series)

Record: 21-29, .420
Home: 11-4, .733
Away: 10-25, .286

It looks like that when it comes to playing a back-to-back game, knowing that you're going to play that game at home helps. Even when playing those back-to-back games, teams playing them at home win almost 3 out of 4 times. However, teams playing back-to-back games on the road lose 1 out of 4 times.

I then looked at the mathematical correlation between the number of back-to-backs played by a team and its winning percentage. You'd expect an "inverse correlation" - the more back-to-backs, the greater the negative impact on winning percentage.

The correlation is -0.47122, which is a medium correlation - and veering into large territory. So do lots of back-to-back games cause bad records? No. Correlation does not equal causation - the two factors might be related to an unknown third factor. However, it is something to think about as we come into crunch time at the end of the seasons:


Final Back-to-Backs of 2009 WNBA Season

September 10, Detroit plays Game 2 of Back-To-Back: opponent New York
September 12, Atlanta plays Game 2 of Back-To-Back: opponent Washington
September 13, Washington plays Game 2 of Back-To-Back: opponent New York.

Winning Streaks

After starting their season 0-2, the Indiana Fever embarked on an 11 game winning streak that propelled them to the top of the Eastern Conference after just 13 games of a 24-game season. They've been there ever sense, and so far, no one has come close to matching that run.

From what I can tell, here are the longest streaks of the season over the last six years of WNBA play. The lines are in the following format:

year-team-total wins over season-longest win streak

2009 Fever ??? 11
2008 Silver Stars 24 7
2007 Shock 24 7
2006 Sun 26 12
2006 Sparks 25 8
2005 Sun 26 8
2005 Monarchs 25 7
2004 Sparks 25 7

(The Shock had two different seven-game streaks in 2007.)

This leads to an interesting thought exercise:

"Suppose you were told that some time during the 2009 WNBA season, a team would win 11 straight games. You are not told the name of the team. You are not told where in the season this happens. Where do you suppose the team would finish? How many wins would that team have after 34 games?"

Answering this question is not a mathematically straightforward one for several reasons. Conventional probability is good for answering the question, "probability p of winning a game, what is the chance that the team would win x consecutive games from opening day", but it's not good about answering questions about winning streaks where the streak could happen anywhere in the season. That question is more about combinatorics than about probability.

We're also approaching the question from the probability end when it should be approached from the statistics end. I always think of the difference between the two as being that in probability, you start with probability p and ask about a result, where in statistics, you start with a result and ask about probability p.

Furthermore, a team's "true probability" might not have anything to do with the number of games it wins. To claim that probability and wins should be linked up would be like claiming that if you flip a coin 20 times, you will get 10 heads and 10 tails in every trial. For example, a teams "true probability" could be winning 12 games in a 34 game season, but it could finish with say 14 wins or 10 wins as probability is the opposite of deterministic.

I therefore created a spreadsheet that could simulate 10,000 34-game seasons. One could input a "true probability" of the team winning each of those games and the spreadsheet could also count the longest win streak. My approach was to then see how many seasons out of 10,000 had a winning streak of a given number of games.

I looked at probabilities between p = 0.25 (9-win season) and p = 0.75 (26-win season), incrementing by 0.025 with each observation of 10,000 seasons. We count the number of streaks in 10,000 trials and come up with a probability of having a streak of a given size given that the true probability of victory is "p".

(* * *)

For example, suppose the true probability of some team winning a game is 0.25 - that the team we're looking at is a 9-win, bottom of the barrel team on the average. In a simulation of 10,000 seasons, only in 9 of those seasons did the team put together an 11-game (or more) winning streak, or in just 0.09 percent of the trials. If you encounter a team with an 11-game winning streak, it's very unlikely that that team's true win probability is 0.25.

We can probably (there's that word again) start talking confidently about how good a team really is when we get to the 10 percent of all trials level. For example, if the team's true winning probability is 0.55 - where the team averages 19 wins a season - we get a case where in 13.31 percent of all trials (1,331 times out of 10,000) do we get an 11-game winning streak. We can probably say with more confidence that our team should be better than average if can win 11 games in a season.

What probability p is associated with 25 percent of the trials having an 11-game winning streak? When p = 0.625, 27.49 percent of all trials/seasons yield an 11-game winning streak. p=0.625 means that the team should win at least 21 games.

If you want to find the probability where you have a 50 percent chance of a team having an 11-game win streak, look at the case where p = 0.725. In that case, 56.63 percent of all trials/seasons contained an 11-game win streak. p = 0.725 is associated with a 25-win season. The 50 percent mark - where the chances of having an 11-game win streak are 1 in 2 - is a team that should win somewhere between 24 and 25 games in a 34-game season.

As it turns out, the Indiana Fever are 20-8 right now, which is a .714 winning percentage. If the win all of their remaining games at that rate, they would finish 24-10, or right on the money. If someone asked "how many games in a season should a team that has an 11-game winning streak win?" an answer of "24 games or so" is a very good guess.

Here's another way to think about it. Indiana won 11 games. That left 23 games that didn't belong to the win streak. Even if they finished those 23 games at only .500 or so, that would give them about 22 or 23 wins total. Once Indiana went 11-2, and then lost, it seemed that Indiana was pretty much foreordained to finish with a 20+ win season. But you don't need probability or an Excel spreadsheet to tell you that.

Longest Tenures With a Given Team

There are at least a couple of players who have played in every season of the WNBA - Lisa Leslie is retiring this year and I don't think Tina Thompson will ever retire - but it's also an accomplishment to spend several years with the same team, year after year, until you're almost identified with the franchise itself.

Here are the WNBA leaders in the category of most seasons with a particular team. Names in bold are still playing in the WNBA, just not necessarily for the listed team.

Twelve Seasons

Lisa Leslie, Sparks, 1997-2006, 2008-present
Tina Thompson, Comets, 1997-2008 (*)

Eleven Seasons

Mwadi Mabika, Sparks, 1997-2007
Ticha Penicheiro, Monarchs, 1999-present

Ten Seasons

Sheryl Swoopes, Comets, 1997-2000, 2002-07

Nine Seasons

Deanna Nolan, Shock, 2001-present
Lauren Jackson, Storm, 2001-present
Tamecka Dixon, Sparks, 1997-2005
Vickie Johnson, Liberty, 1997-2005
Yolanda Griffith, Monarchs, 1999-2007

Eight Seasons

Andrea Stinson, Sting, 1997-2004
Becky Hammon, Liberty, 1999-2006
Coco Miller, Mystics, 2001-08
Janeth Arcain, Comets, 1997-2003, 2005
Murriel Page, Mystics, 1998-2005
Ruthie Bolton, Monarchs, 1997-2004
Sue Bird, Storm, 2002-present
Tamika Catchings, Fever, 2002-present

(*) - Thompson's tenure was interrupted only by the Houston Comets folding.

Statistical Gas

Chamique Holdsclaw is 12th in field goals blocked so far this year with 12 blocked. The most blocked? Sophia Young of San Antonio with 19 field goals blocked.

Jennifer Lacy is 8th in percentage of field goals blocked (minimum 40 attempts). 12.1 percent of her field goal attempts are rejected. The leader is Chante Black of Connecticut with 22.0 percent of her shots blocked, over 1 in 5.

There are five WNBA players that haven't had a shot blocked this season (minimum 40 attempts). Kelly Mazzante (Phoenix), Erin Thorn and Brooke Wyckoff (Chicago), Kiesha Brown (Connecticut) and Edwige Lawson-Wade (San Antonio).

There are seven players this year who have had four shots blocked in the same game. One of those is Chamique Holdsclaw, who had four shots blocked in our recent loss at New York. Janel McCarville blocked two of those shots.

In terms of assists vs. field goals, Michelle Snow is 10th in percentage of assisted field goals, with 78.3 percent. Sancho Lyttle is 19th with 71.1 percent (all stats minimum 20 field goals). Brooke Wyckoff leads with 100 percent of her field goals assisted - 21 made, with all 21 coming off assists from other Sky players.

If you're looking for players who make their own shots - who have the highest percentage of unassisted field goals with a minimum of 20 field goals - Angel McCoughtry is 4th (70.0 percent), Iziane Castro Marques is 7th (63.6 percent) and Chamique Holdsclaw is 11th (62.4 percent). Kristi Harrower of Los Angeles leads with 77.3 percent of her field goals unassisted.

As for "assist pairs" - the Dream doesn't even make the top 20. The best assist pair is Sue Bird to Lauren Jackson. That combo has been responsible for 30 Seattle field goals.

(All stats from Swanny's Stats.)

Club 76

Only seven WNBA players belong to the exclusive Club 76. This is the list of players who have made 76 or more three-point field goals in a season.

Club 76
WNBA Players with 76+ 3-Point Goals, Season

1. Diana Taurasi, 2006 Mercury, 121
2. Diana Taurasi, 2007 Mercury, 95
3. Diana Taurasi, 2008 Mercury, 89
4. Katie Smith, 2000 Lynx, 88
4T. Katie Smith, 2001 Lynx, 88
6. Allison Feaster, 2002 Sting, 79
7. Katie Smith, 2003 Lynx, 78
7T. Nicole Powell, 2008 Monarchs, 78
9. Becky Hammon, 2008 Silver Stars, 77
10. Crystal Robinson, 1999 Liberty, 76
10T. Tamika Catchings, 2002 Fever, 76
10T. Katie Smith, 2008 Shock, 76

This post was inspired by a similar post on the basketball-reference.com blog. Only two WNBA players have multiple seasons in Club 76 - Diana Taurasi and Katie Smith. They have also been able to do it back-to-back, with Taurasi making the club three years in a row. (Maybe four times, if she has a good 2009 season.)

The NBA equivalent is the 220-Three Club - hitting 220 3-pointers in a season. Adjusting for season length and game length, you get 76 3-pointers.

A couple of facts also inspired by the blog post:

1. There is regression to the mean: No player who made the club in successive years ever hit more 3-pointers in the following year - so far when you reach Club 76, there is always a decline in 3-pointers the next year:

2. 3-point field goal percentage in Club 76 seasons: 38.4 percent
3-point field goal percentage in the next season *: 36.4 percent

The (*) indicates that we are not including the 2009 values for the players who made the club in 2008 - Diana Taurasi, Nicole Powell and Becky Hammon. But once again, it seems that we are seeing regression to the mean, which is a fancy of way of saying that players who perform extraordinarly well in one area of their game one year will have seasons that follow that are closer to average.

So we'll make a prediction: Taurasi won't hit 89 3-pointers in 2009, Powell will fall short of 78 and Becky Hammon will have to fight to renew her membership in Club 76. (Her time overseas has most likely doomed her.) On the other hand, if there's one player you can never count out, it's Diana Taurasi.

UPDATE: The ABL members of this club are...Dawn Staley, Crystal Robinson, Teresa Edwards, Niesa Johnson and...Katie Smith. With 44 games in a season, the ABL's club is less exclusive...but Robinson hit 90 3-pointers in the 1996-97 season for the Colorado Xplosion and topped it in the 1997-98 season with 102. She hit 46.6 percent of her 3-point attempts that season.

4/2009 - Dream 67, Sun 62: Kudos and Cuddles

We don't have another game until a four-game homestand that starts on Friday with a return match against the Washington Mystics. Unforunately for me, I'll be missing three of the those games, and most likely, I won't even be watching them on WNBA Live Access.

Why? I'm going to a wedding in the metropolitan New York area - Jersey City. So I won't be around for those days and blogging will be either light or non-existent. I suspect I'll have to play some catch-up.

Anyway, let's look at how both teams did on Sunday:

For the Sun:

Lindsay Whalen: 16 points, 7 points, and 5 assists. She was 7 for 8 from the free throw line, but had a -8 plus/minus.
Erin Phillips: 11 points, 6 rebounds, 4 steals. Four personal fouls, and 1-for-6 from 3-point range.
Barbara Turner: 5 points in 23 minutes played with a +6 plus/minus.

As for the Dream....

Best Players

Sancho Lyttle: How could you not give the honors to someone who scored 20 points and 15 rebounds? She's the Dreamer of the Game, without a doubt. Lyttle had a +12 plus/minus. I suspect that the league is waking up to how good Sancho Lyttle really is - she might have won a starting spot, who knows?
Erika de Souza: 10 points, 4 rebounds. A +9 plus/minus. She was 4-for-5 in shooting with two blocked shots. Rumor has it that Sancho and Erika simply intimidated the Sun out of a win.
Coco Miller: 6 points and 3 assists in 18 minutes played.

The Murky Middle

Chamique Holdsclaw: Mique had 12 points in 5-for-13 shooting. Two assists, two rebounds, two steals, two personal fouls, three turnovers.
Nikki Teasley: Only two points, but 5 assists. A +6 plus/minus, but four personal fouls.
Iziane Castro Marques: She cooled off a little, with 4 points, 4 rebounds and 3 assists. She led the club in plus/minus with +13. Only played 20 minutes.
Jennifer Lacy: 4 points, 3 rebounds. Only played 10 minutes, 2-for-5 shooting, -4 in plus/minus.
Michelle Snow: Snow also only got 10 minutes. 4 points, 2 rebounds, 3 personal fouls.

Not Up To Snuff

Angel McCoughtry: Kind of unfair to put Angel down here because she didn't hurt the team, but she didn't help it with 2-for-7 shooting as a -10 plus/minus.
Tamera Young: One point. A pair of shanked free throws. Only six minutes of playing time.
Shalee Lehning: Okay, now the Kansas State fans are going to start a lynching party. 10 minutes played. No shots taken. A couple of assists, but three turnovers. Lehning gets the Still Snoozing award - because I figured it's mean to award a "Bad Dream" award when we win.

They Make It Personal

Bring your mouth guard.

(Note: since nothing is going on in the WNBA right now - and I mean nothing - I'm going to fill the hours with posting some very weird WNBA career and season statistics.)

Most Personal Fouls Per Minute Played in WNBA Career *

1. Tausha Mills, 0.248
2. Vanessa Hayden, 0.224
3. Summer Erb, 0.210
4. Katye Christensen, 0.198
5. Teana Miller, 0.192
6. Jennifer Lacy, 0.187
7. Heidi Burge, 0.185
8. Jessie Hicks, 0.181
9. Tasha Humphrey, 0.179
10. Maylana Martin, 0.177

* - minimum 500 career minutes