I’ve used the concept of “Championship Leverage” a lot over the past year or so, but I realize that I’ve never really explained it very well. Here’s an attempt to correct that…

Championship Leverage obviously owes its name to the baseball stat invented by Tom Tango to measure the cruciality of a given base-out-inning situation. It starts with the win probability of each team in a series, given the current “state” of the series (home team in Game 4 up 2-1, road team in Game 6 down 3-2, etc.). The state of the series not only tells you how often each team can expect to win (assuming evenly-matched opponents with true .500 talent levels and a .600 home-court advantage), but also how much that probability can swing based on the outcome of the next game of the series, which in turn tells you the most important games in the series.

Here’s a table for a 2-2-1-1-1 best-of-7 series (”Exp Δ” means the expected swing in series win probability for the game, based on the home team having a 60% chance of winning the game):

Game #

Home W

Home L

Home SerW%

Swing w/ W

Swing w/ L

Exp Δ

1

0

0

0.532

0.128

-0.192

0.154

2

1

0

0.660

0.132

-0.199

0.159

2

0

1

0.340

0.122

-0.182

0.146

3

2

0

0.843

0.100

-0.150

0.120

3

1

1

0.539

0.154

-0.231

0.185

3

0

2

0.207

0.100

-0.150

0.120

4

3

0

0.942

0.058

-0.086

0.069

4

2

1

0.693

0.163

-0.245

0.196

4

1

2

0.307

0.141

-0.211

0.169

4

0

3

0.058

0.038

-0.058

0.046

5

3

1

0.904

0.096

-0.144

0.115

5

2

2

0.552

0.208

-0.312

0.250

5

1

3

0.144

0.096

-0.144

0.115

6

3

2

0.760

0.240

-0.360

0.288

6

2

3

0.240

0.160

-0.240

0.192

7

3

3

0.600

0.400

-0.600

0.480

Here is the same table, but for a 2-3-2 best-of-7 series:

Game #

Home W

Home L

Home SerW%

Swing w/ W

Swing w/ L

Exp Δ

1

0

0

0.532

0.128

-0.192

0.154

2

1

0

0.660

0.132

-0.199

0.159

2

0

1

0.340

0.122

-0.182

0.146

3

2

0

0.843

0.100

-0.150

0.120

3

1

1

0.539

0.154

-0.231

0.185

3

0

2

0.207

0.100

-0.150

0.120

4

3

0

0.942

0.058

-0.086

0.069

4

2

1

0.693

0.163

-0.245

0.196

4

1

2

0.307

0.141

-0.211

0.169

4

0

3

0.058

0.038

-0.058

0.046

5

3

1

0.856

0.144

-0.216

0.173

5

2

2

0.448

0.192

-0.288

0.230

5

1

3

0.096

0.064

-0.096

0.077

6

3

2

0.840

0.160

-0.240

0.192

6

2

3

0.360

0.240

-0.360

0.288

7

3

3

0.600

0.400

-0.600

0.480

And finally, a 2-2-1 best-of-5 series:

Game #

Home W

Home L

Home SerW%

Swing w/ W

Swing w/ L

Exp Δ

1

0

0

0.539

0.154

-0.231

0.185

2

1

0

0.693

0.163

-0.245

0.196

2

0

1

0.307

0.141

-0.211

0.169

3

2

0

0.904

0.096

-0.144

0.115

3

1

1

0.552

0.208

-0.312

0.250

3

0

2

0.144

0.096

-0.144

0.115

4

2

1

0.760

0.240

-0.360

0.288

4

1

2

0.240

0.160

-0.240

0.192

5

2

2

0.600

0.400

-0.600

0.480

Using these numbers, you can measure how important a game is within a series. However, the “Championship” part of Championship Leverage involves also taking into account the playoff round in which the game took place. Again assuming each team is morally a .500 ballclub, your generic probability of a title before the 1st round would be 6.3% (1/16), which goes to 12.5% if you win, then 25%, then 50%, then 100% if you win the championship.

That’s where a table like this comes from:

Date

Game #

Home

Pts

Road

Pts

Winner

hW

rW

Hm p(Ser)

Hm p(Champ)

Leverage

4/29/2012

1

MEM

98

LAC

99

LAC

0

0

53.2%

6.7%

0.75

5/2/2012

2

MEM

105

LAC

98

MEM

0

1

34.0%

4.2%

0.71

5/5/2012

3

LAC

87

MEM

86

LAC

1

1

53.9%

6.7%

0.90

5/7/2012

4

LAC

101

MEM

97

LAC

2

1

69.3%

8.7%

0.95

5/9/2012

5

MEM

92

LAC

80

MEM

1

3

14.4%

1.8%

0.56

5/11/2012

6

LAC

88

MEM

90

MEM

3

2

76.0%

9.5%

1.40

5/13/2012

7

MEM

72

LAC

82

LAC

3

3

60.0%

7.5%

2.33

5/15/2012

1

SAS

108

LAC

92

SAS

0

0

53.2%

13.3%

1.49

5/17/2012

2

SAS

105

LAC

88

SAS

1

0

66.0%

16.5%

1.55

5/19/2012

3

LAC

86

SAS

96

SAS

0

2

20.7%

5.2%

1.16

5/20/2012

4

LAC

99

SAS

102

SAS

0

3

5.8%

1.4%

0.45

That’s the Clippers’ first 2 rounds of the playoffs. From “Hm p(Ser)”, you can see what the home team’s probability of winning the series was going into any given game (derived from the charts I listed above), which is multiplied by the generic probability of a championship if they won the series in question to arrive at “Hm p(Champ)”. Using that stat and the possible swings in Championship Probability with a win or a loss, you can calculate an “Exp Δ” stat at the championship level (rather than at the series level as we did before).

Once you’ve done that, calculate the average Exp Δ for every game in a given playoff season (the average in 2012 so far is 0.026; last year’s average was 0.047) and divide the game’s Exp Δ by the average Exp Δ across all games that year. The result is Championship Leverage Index, where 1.00 represents a game with an average impact on the championship (going back to our example, Game 7 of the Clippers-Grizzlies series had 2.3 times as much impact as the average game). Here are the highest-leverage games since the NBA went to a 16-team playoff in 1984:

Date

Game #

Home

Pts

Road

Pts

Winner

Leverage

6/17/2010

7

LAL

83

BOS

79

LAL

9.24

6/23/2005

7

SAS

81

DET

74

SAS

9.00

6/12/1984

7

BOS

111

LAL

102

BOS

8.24

6/22/1994

7

HOU

90

NYK

84

HOU

7.43

6/21/1988

7

LAL

108

DET

105

LAL

7.25

6/12/2011

6

MIA

95

DAL

105

DAL

6.18

6/9/1985

6

BOS

100

LAL

111

LAL

5.69

6/20/2006

6

DAL

92

MIA

95

MIA

5.60

6/15/2010

6

LAL

89

BOS

67

LAL

5.54

6/14/1998

6

UTA

86

CHI

87

CHI

5.31

6/13/2003

5

NJN

83

SAS

93

SAS

5.11

6/2/1996

7

SEA

90

UTA

86

SEA

5.10

6/20/1993

6

PHO

98

CHI

99

CHI

4.96

6/9/2011

5

DAL

112

MIA

103

DAL

4.94

6/2/2002

7

SAC

106

LAL

112

LAL

4.80

5/30/1987

7

BOS

117

DET

114

BOS

4.70

6/3/2001

7

PHI

108

MIL

91

PHI

4.66

6/4/2000

7

LAL

89

POR

84

LAL

4.62

6/3/1990

7

DET

93

CHI

74

DET

4.56

6/7/1985

5

LAL

120

BOS

111

LAL

4.55

6/4/1995

7

ORL

105

IND

81

ORL

4.55

6/6/2005

7

MIA

82

DET

88

DET

4.50

6/18/2006

5

MIA

101

DAL

100

MIA

4.48

6/19/1994

6

HOU

86

NYK

84

HOU

4.46

6/13/2010

5

BOS

92

LAL

86

BOS

4.43

By using Championship Leverage Index, you can weight team and player performances by the importance of the game itself. For instance, here are the team PPG margin leaders in this year’s playoffs, both raw and weighted by leverage:

Rank

Team

Games

Raw MOV

Rank

Team

Lev Gm

Lev MOV

1

SAS

8

13.8

1

SAS

7.0

15.3

2

MIA

9

7.2

2

OKC

8.2

7.5

3

OKC

8

7.1

3

IND

10.5

4.8

4

IND

9

6.6

4

MIA

9.6

4.0

5

BOS

10

3.5

5

BOS

11.8

3.6

6

MEM

7

0.7

6

PHI

11.8

-0.1

7

DEN

7

0.4

7

DEN

6.7

-1.8

8

PHI

10

0.3

8

MEM

7.6

-1.9

9

CHI

6

-1.7

9

CHI

5.3

-2.3

10

LAL

11

-3.1

10

LAL

12.6

-2.8

11

LAC

11

-4.6

11

LAC

12.3

-4.0

12

ATL

6

-4.7

12

ATL

5.3

-5.4

13

DAL

4

-6.5

13

DAL

2.3

-5.9

14

ORL

5

-10.8

14

ORL

3.7

-10.7

15

NYK

5

-14.0

15

NYK

2.9

-16.8

16

UTA

4

-16.0

16

UTA

2.3

-18.7

San Antonio leads both, but below them you see differences that are explained by the timing of each team’s good/bad performances. By playing better in less important games, Miami ranks lower in the leveraged MOV rankings than the raw ones, while teams like Oklahoma City and Indiana move up because more of their PPG differential was accrued in games that had more crucial implications. The difference between raw and leveraged MOV can even be turned into a “clutch” metric:

Rank

Team

Raw MOV

Lev MOV

Clutch

1

SAS

13.8

15.3

1.5

2

LAC

-4.6

-4.0

0.6

3

DAL

-6.5

-5.9

0.6

4

OKC

7.1

7.5

0.4

5

LAL

-3.1

-2.8

0.3

6

BOS

3.5

3.6

0.1

7

ORL

-10.8

-10.7

0.1

8

PHI

0.3

-0.1

-0.4

9

CHI

-1.7

-2.3

-0.6

10

ATL

-4.7

-5.4

-0.7

11

IND

6.6

4.8

-1.8

12

DEN

0.4

-1.8

-2.3

13

MEM

0.7

-1.9

-2.6

14

UTA

-16.0

-18.7

-2.7

15

NYK

-14.0

-16.8

-2.8

16

MIA

7.2

4.0

-3.2

There are a lot of cool things you can do with a stat like this, and hopefully this served as a primer for those wanting to learn more about the Championship Leverage concept and how it’s calculated.