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.
