One Estimator to Rule Them All: xxFIP – Part 2: xBB%

If you missed part 1 of the segment, I advise you read it first here! xxFIP may just even be the best estimator known to date for in-season estimation! Last week I looked at the xK% portion of the formula, and now it’s time to move on to xBB%.

Walk rates are a lot different from strikeout rates. They are very hard to come up with expected values for, especially in medium-to-large samples. This may be due to pitching around a hitter or maybe due to a pitcher losing his command altogether. The stats we use to get these expected strikeouts and walks assume that each event happens randomly (based on the rates the pitcher throws them), but in reality, balls might be bunched together more often for these reasons. Intentional walks are obviously one of these problems as well. Still xBB% can be a very useful tool, as it eliminates the catcher and the umpire.

xBB% was created with the same events (or stats) as xK%. (Balls In Play)/Pitch, SwStr%, Fouls/Pitch, (Zone-Looking)/Pitch, and (Outisde Zone Looking)/Pitch.

Here are the respective coefficients that were determined from the same sample as xK% (2008-2012):

-.548
-.073
0.01
.094
0.48

As you can see, BIP/Pitch is the most detrimental to BB% (obviously, since a BIP means no walk) and OL/Pitch has the greatest coefficient (since it should be a ball). ZL/Pitches is the next highest as about 14% of pitches in the zone are called balls. Fouls are next since you can’t strike out on a foul which keeps the PA alive and the chance of a walk alive. Lastly, a swing and a miss is always a strike and can strike out a hitter, so it has the next-lowest coefficient.

Now, xBB% does not have as great of a correlation to BB% as we saw with xK%, but this xBB% is still the best one to date, and the most predictive.

A graph is pretty boring so why don’t I just tell you the R^2 is around 0.65 for xBB% to BB%. Not near as nice as xK%, but we want xBB% to be predictive, which means we don’t really care too much about the R^2.

We know xBB% s going to outperform BB% in small samples due to the the components of xBB% stabilizing much faster than BB’s themselves, but where is this point? In previous xBB% formulae, I have not seen anything past 50IP.

Here are the results (RMSE) of BB% and xBB% for 2012-2013 which is out of sample.

Screen Shot 2013-12-09 at 2.28.09 AM

The table suggests that BB% takes over in predicability at around 150 IP, that is higher than I might have thought! Though 2011-2012 suggests it occurs around 110 IP. Either way, I would still use xBB% over a season just due to the fact that it is independent of the catcher and umpire, as well as pitching around a batter and intentional walks.

Here is the xBB% leaderboard for 2013:

Rank
Name
xBB%
1Bartolo Colon0.039
2Jordan Zimmermann0.045
3Bronson Arroyo0.047
4Hisashi Iwakuma0.05
5Dillon Gee0.051
6Cliff Lee0.055
7David Price0.058
8Cole Hamels0.06
9John Lackey0.062
10Patrick Corbin0.062
11Kevin Correia0.064
12Kyle Kendrick0.066
13Julio Teheran0.067
14Miguel Gonzalez0.067
15Derek Holland0.067
16Andy Pettitte0.068
17Ervin Santana0.068
18R.A. Dickey0.068
19Kris Medlen0.068
20Adam Wainwright0.068
21Jerome Williams0.069
22Jhoulys Chacin0.07
23Roberto Hernandez0.07
24Mike Leake0.071
25Mike Minor0.071
26Kyle Lohse0.071
27Matt Harvey0.072
28Jeremy Guthrie0.072
29Matt Garza0.073
30Travis Wood0.073
31Mat Latos0.073
32CC Sabathia0.073
33Chris Sale0.073
34Clayton Kershaw0.074
35Eric Stults0.075
36Ricky Nolasco0.076
37Jose Fernandez0.076
38Andrew Cashner0.077
39Jason Vargas0.078
40Dan Haren0.079
41Paul Maholm0.079
42Jarrod Parker0.079
43Doug Fister0.08
44Rick Porcello0.08
45Matt Cain0.08
46Hyun-Jin Ryu0.081
47Homer Bailey0.081
48Jered Weaver0.081
49Dallas Keuchel0.081
50Dan Straily0.082
51Scott Kazmir0.082
52Justin Verlander0.083
53Edwin Jackson0.084
54Hiroki Kuroda0.085
55Jon Lester0.085
56Tommy Milone0.085
57Bud Norris0.085
58Mark Buehrle0.086
59Felix Hernandez0.086
60Anibal Sanchez0.086
61A.J. Griffin0.087
62Max Scherzer0.088
63Jose Quintana0.088
64Madison Bumgarner0.088
65Jeff Samardzija0.088
66James Shields0.088
67Shelby Miller0.088
68Jeremy Hellickson0.089
69Juan Nicasio0.089
70Jorge de la Rosa0.089
71Mike Pelfrey0.09
72Zack Greinke0.09
73Chris Tillman0.09
74A.J. Burnett0.091
75Wade Miley0.091
76Justin Masterson0.092
77Ian Kennedy0.092
78Lance Lynn0.093
79Joe Saunders0.094
80Francisco Liriano0.094
81Tim Lincecum0.095
82Stephen Strasburg0.096
83Wily Peralta0.096
84Edinson Volquez0.099
85Felix Doubront0.101
86Scott Feldman0.101
87Erik Bedard0.104
88Gio Gonzalez0.104
89C.J. Wilson0.104
90Ryan Dempster0.106
91Ubaldo Jimenez0.109
92Matt Moore0.114
93Yovani Gallardo0.115
94Yu Darvish0.115
95Jeff Locke0.118
96Lucas Harrell0.127

Now that we have xK% and xBB%, we can sub these into our trusty xFIP formula to get an idea of the simplified xxFIP formula. We don’t have xHR/FB yet, so I’ll just leave it as xHR/FB.

xFIP = 3*BB/IP -2*K/IP +13*lgHR/FB*FB + C

xK% and xBB% can be converted to IP by just multiplying by TBF (total batters faced)/IP (usually around 4.3) for each pitcher. After subbing in xK% and xBB% we get:

(0.318*BIP/P – 2.825*SwStr% – 0.834*Foul/P – 1.152*ZL/P + 1.22*OL/P)*(TBF/IP) +13*xHR/FB*FB + C

C in this equation is usually very close to cFIP (FIP constant) but it varies a bit year to year from cFIP.

If you assumed 4.3 TBF/IP we would get:

1.367*BIP/P – 12.148*SwStr% – 3.586*Foul/P – 4.953*ZL/P + 5.246*OL/P + HR term + C

A little more teaser for xxFIP for you. Here are the top 10 relievers with over 50 IP:

Rank
Name
xxFIP
1Koji Uehara2.12
2Aroldis Chapman2.15
3Greg Holland2.26
4Kenley Jansen2.42
5Craig Kimbrel2.53
6Trevor Rosenthal2.54
7Glen Perkins2.61
8Ernesto Frieri2.65
9Neal Cotts2.75
10Kelvin Herrera2.78

There will be one more part to the series where we will briefly go over xHR/FB and reveal results of xxFIP testing!

 

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Author: Chris Carruthers

Chris has been a baseball (and Jays) fan for 15 years. He has also played since the age of 6, working his way through Little League and Babe Ruth Baseball as a catcher and first baseman. He got interested in sabermetrics after viewing the movie Moneyball. His continuous self-learning in sabermetrics and advanced stats is driven by his engineering background and love for numbers. Chris's go-to website is FanGraphs, where he has had a few previous community submissions. Chris also enjoys music and plays guitar in his sparse spare time from his studies. He also follows hockey and his favourite team, the Calgary Flames. Follow Chris on Twitter @CCBreakingBlue.

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