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WHIP or SWIP?
by Ray Flowers (Guest Writer)
Mar 20, 2004

WHIP, a “roto-geek” stat that has become all the rage in the past ten years (you measure WHIP by adding Walks and Hits and dividing by Innings Pitched). Of course this is a valuable stat and it can be one of many predictive tools one uses to forecast the success of a pitcher. But we believe that the vagaries of hits allowed (a ball lost in the sun, a wind gust, a misstep by a fielder, coaches positioning of players) can possible offer a “false” positive in terms or a pitchers overall effectiveness. 

How do we remedy this situation? We propose a new stat, one we call SWIP.

What does SWIP stand for?

S- Strikeouts  
W- Walks  
IP- Innings Pitched
 

Why SWIP? If you don’t let a guy hit the ball, or give him a free-pass, he cant score. Therefore you are a “better” pitcher if you let batter put the ball in play because you are less susceptible to be negatively influenced by the performance of others. Here is a list of SWIP from some of last year’s top pitchers. 

  Ks BB IP SWIP

Schilling

194

32

168

.96

Prior

245

50

211.1

.92

P. Martinez

206

47

186.2

.85

Wood

266

100

211

.79

Schmidt

208

46

207.2

.78

Halladay

204

32

266

.65

Hudson

162

61

240

.42

 

What becomes apparent in this instance is that the HIGHER the SWIP, the more effectively the pitcher presumably performed. We say presumably because SWIP, like any other tool , is but one measure of the effectiveness of a ballplayer. This system is also more biased toward power pitchers as it places a greater emphasis on K’s (Tim Hudson scores a rather poor .42 SWIP last year, but by almost all other statistical measurements had a very successful year, so the system is not full proof). But it stands to reason, does it not, that the fewer times a batter puts the ball in play the fewer hits he can accumulate? Therefore the pitcher who successfully limits the amount of balls in play stands a greater chance of given up fewer baserunners, and by inference, fewer runs. 

Just what designates a great total for SWIP? A general table for SWIP would be as follows:

.90 and UP… Excellent season, Hall of Fame worthy.
.70 to .89… An all-star performance, worthy of Cy Young Consideration.
.50 to .69… Borderline all-star to serviceable starting pitcher, a guy you’d like to have on your staff.
.35 to .50… A guy who’s hanging on to the 4th or 5th rotation spot.
.20 to .34… His major league days are numbered.
Below .20… Toledo Mudhens here I come! 

Lets look at some of the top seasons from 1995-2002 as benchmarks. 

  K BB IP SWIP

Schilling 2002

316

33

259.1

1.09

R. Johnson 2001

372

71

249.2

1.21

P. Martinez 2000

284

32

217

1.16

Hampton 1999

177

101

239

.32

Brown 1998

257

49

257

.81

Clemens 1997

292

68

264

.86

Smoltz 1996

276

55

253.2

.87

Maddux 1995

181

23

209.2

.75

 

As we can see anything over 1.00 represents a truly historic season (Schilling, Johnson and Pedro averaged 324 K’s while going an aggregate 67-17, or on average roughly 22 -6). We can also see that a CY Young season usually hovers around the .80 total. The .32 SWIP of Hampton is a result of his inordinate amount of walks, something that should have been taken into consideration as an extremely obvious harbinger that he was due for a fall (In fact, he went from 22-4 to 15-10, while his ERA went from 2.90 to 3.14, and his SWIP went down to .24 in 2000…suggesting that his 1999 SWIP was accurate in saying that his 1999 season was, at least record wise, a fluke).  

Lets take a glance of some of the other “franchise” pitchers 2003 seasons. 

  K BB IP SWIP

R. Johnson

125

27

114

.86

Mussina

195

40

214.2

.72

Loaiza

207

56

226.1

.67

Brown

185

56

211

.61

Mulder

128

40

186.2

.47

Colon

173

67

242

.44

Maddux

124

33

218.1

.42

 
What can we infer from this list? Well, despite his high ERA (4,26) and WHIP (1.33) last year, Randy Johnson was still pretty darn effective. On the flip side, can a “power” pitcher like Bartolo Colon be said to be wearing down when his SWIP was only 0.44 up barely from his 2002 season of .34, but down from the previous two years of .50, .54? Maddux did have a very solid year, but was it luck, ideal positioning by his teammates, or does his style of pitching simply defy explanation? 

Lets look at a group of the “must have” youngsters of 2004. 

  K BB IP SWIP

Vazquez

241

57

230.2

.80

Santana

169

47

158.1

.77

Beckett

152

56

142

.68

Webb

172

68

180.2

.58

D. Willis

142

58

160.2

.52

Odalis Perez

141

46

185.1

.51

Perhaps all the pundits predicting great things from Vazquez with his move to the Yankees are correct in seeing a breakout year. Perhaps the same can be said for Santana and Beckett…but Webb, Willis, and O. Perez…the jury’s still out. 

Who had some of the poorest SWIP figures last year? A few of these pitchers will surprise you. 

  K BB IP SWIP

V. Zambrano

132

106

214

.12

R. Franklin

99

61

212

.18

Hampton

110

78

190

.17

Lowe

110

72

230.1

.19

R. Ortiz

149

102

212.1

.22

Zito

146

88

231.2

.25

Buehrle

119

61

230.1

.25

Moyer

129

66

215

.29

 
Zito, didn’t he just win the CY Young? We admit that we were a bit shocked so see the Zen master on this list, a huge bounce back is in order for him. Didn’t Russ Ortiz win 21 games? Yes he did, but his other totals (3.81 ERA, 1.32 WHIP and the 4th highest run support in the NL) should have alerted you that last season, barring an unforeseen jump for Russ, was an anomaly. Lowe had 17 wins, but again, look at his other figures (4.47, 1.42, #1 run support in the AL). On the flip side there’s Jamie Moyer (see Maddux comment above). 

One last mention of last years SWIP totals…under no circumstances would we recommend picking the following player, even if he starts of April with a 4-0 record and a 24 inning scoreless streak. 

Damian Moss

57

63

115

-.04

MINUS .04 ! The only word that comes to mind in this case is… atrocious. 

So have we pioneered a new way to revolutionize the scouting of pitchers? Of course we haven’t. Sabermaticians have pioneered many a pitching stats to gauge the effectiveness of hurlers that will make this formula seem like it was written by a 3rd grader. But isn’t that kind of the point? SWIP is something that is readily understood and it’s a formula that doesn’t require calculus or an abacus to solve, so isn’t it a more practical tool that we might all use? To that end , have we provided another tool to be included in the cache of already existing measurements of pitchers? We believe we have. SWIP, less so than any of the other “standard” fantasy categories (W, L, ERA, WHIP) appears to be a more complete reflection of the effort put forth by the pitcher in the previous year. Give it a look, we think you’ll agree.

 

 


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