Measuring slumps – let’s make up a stat

June 18th, 2010 by pbr

One of my on-going interests in baseball stats, aside from HBPs – which is, and will always be the one true measure of baseball greatness – is trying to look at player’s batting stats in a way that doesn’t exclusively focus on season totals or season long averages.  Like the old joke about the 6 foot tall statistician who drowned crossing the river with an average depth of 4.78 feet.  (The funny part was that he went to 2 decimal places).  So last year I came up with the measure of batting consistency called BACON, which attempts to get a handle on day to day batting consistency.  You can read about that here.  Or just look at this years numbers here.  Or you can keep reading this.

Well, recently I’ve seen a couple of articles on streakiness, and the statistical view of it is that streakiness doesn’t exist.  Like this article, except you need a subscription to read it.  The general point of it is that hot streaks don’t really mean anything because the outcome of any give plate appearance is more correlated with the player’s long term results than his short term results (his current hot or cold streak).  But, that doesn’t really show that hot streaks don’t exist, just that there’s no reason to assume that they’d continue.  It goes back to the basic problem of whether you think stats are telling you what has already happened or if they’re telling you what’s going to happen.   (My general rule was the only thing I trust to tell me what is going to happen is the TV Guide – before it crossed me).

So, the fact of the matter is that Dustin Pedroia and Mark Reynolds have had 4 different streaks of at least 15 consecutive plate appearances without a hit this year.  That seems to imply a general tendency toward slumpiness this season.   On the other hand, Troy Tulowitzki, Billy Butler, Michael Young and Marco Scutaro have over 250 plate appearances this year, but haven’t gone more than 10 straight without getting a hit.  They’ve been good at staying out of slumps.  And I want to measure that.

(Note: for purposes of the rest of this, I’m excluding intentional walks from plate appearances, just because an intentional pass shouldn’t count toward how many consecutive plate appearances you’ve gone without a hit, and I don’t want to keep saying “plate appearances excluding intentional walks” a million times, and it made my query run a lot faster to take those out.)

So here’s how we do it – take every plate appearance for each batter this season, in order.  Assign each plate appearance a number equal to how many plate appearances they’ve gone without a hit at that point.  Add up all those numbers, and divide by the number of plate appearances.  Low numbers mean the batter is good at staying out of prolonged hitless slumps, high numbers mean they’ve been prone to forgetting how to hit the ball for relatively long stretches.  Longer hitless streaks hurt worse.  For example, if two batters go 3 for 10, but one’s plate appearance look like this: 1, 1, 1, 0, 0, 0, 0, 0, 0, 0 and the other’s look like this: 1, 0, 0, 1, 0, 0, 1, 0, 0, 0; the first one looks worse because he had a 7 PA hitless streak.  That gets calculated as (1+2+3+4+5+6+7)/10 = 2.8 for the first one and (1+2+1+2+1+2+3)/10 = 1.2 for the 2nd one.  For lack of a better word, I’m calling that result the Slump Proneness Analyis Metric – or SPAM.  In keeping with the cured meat theme. And, with all respect to the canned Hormel miracle from Austin Minnesota.  If you haven’t been to their museum, you should go.

Now that we’ve defined and named it, lets try it on the whole league, and try not to worry when it comes out with Ronny Paulino as the best player with over 125 PAs.

Here are the 30 best players this year at avoiding hitless streaks (125 PA minimum):

Batter SPAM 15+ PA hitless streaks Longest Hitless Streak Total PAs (without IBBs)
Ronny Paulino (FLA) 1.83 0 8 172
Vladimir Guerrero (TEX) 2.03 0 13 259
Reid Brignac (TB) 2.07 0 8 166
Eric Hinske (ATL) 2.07 0 9 138
Troy Tulowitzki (COL) 2.08 0 10 265
Ichiro Suzuki (SEA) 2.17 0 13 291
Billy Butler (KC) 2.18 0 10 285
Andre Ethier (LAD) 2.19 1 15 209
Robinson Cano (NYY) 2.23 0 14 282
Ivan Rodriguez (WSH) 2.24 0 12 157
Adrian Beltre (BOS) 2.25 0 12 270
Joe Mauer (MIN) 2.26 0 12 238
Michael Young (TEX) 2.26 0 10 298
Mike Fontenot (CHC) 2.28 0 12 134
Carlos Guillen (DET) 2.28 0 14 141
Brandon Phillips (CIN) 2.29 0 12 296
Ryan Braun (MIL) 2.29 0 14 289
Mike Aviles (KC) 2.34 1 15 147
Justin Morneau (MIN) 2.35 0 14 266
Kevin Kouzmanoff (OAK) 2.35 1 16 274
Ben Zobrist (TB) 2.37 0 11 283
Rafael Furcal (LAD) 2.39 0 12 181
Miguel Cabrera (DET) 2.42 1 17 268
Brennan Boesch (DET) 2.43 1 16 176
Manny Ramirez (LAD) 2.43 0 10 169
David DeJesus (KC) 2.45 0 12 283
Angel Pagan (NYM) 2.45 0 13 259
Cristian Guzman (WSH) 2.46 0 12 235
Rod Barajas (NYM) 2.48 0 11 191
Evan Longoria (TB) 2.48 1 16 284

And, here are the 30 most slump-prone batters this year (125 non IBB PAs minimum):

Batter SPAM 15+ PA hitless streaks Longest Hitless Streak Total PAs (without IBBs)
Akinori Iwamura (PIT) 7.62 3 40 193
Dexter Fowler (COL) 6.35 2 36 167
Luis Valbuena (CLE) 5.54 2 20 166
Jeff Clement (PIT) 5.45 2 21 134
Carlos Pena (TB) 5.37 2 29 267
Gerald Laird (DET) 5.23 3 18 150
Lou Marson (CLE) 5.09 1 23 159
Aramis Ramirez (CHC) 5 3 21 197
Brandon Wood (LAA) 4.97 2 17 134
Brendan Ryan (STL) 4.88 3 19 186
Mark Reynolds (ARI) 4.82 4 17 269
Rob Johnson (SEA) 4.8 1 24 128
Chone Figgins (SEA) 4.73 2 30 285
Carlos Ruiz (PHI) 4.61 2 25 177
Tony Gwynn (SD) 4.6 2 24 180
Carlos Quentin (CWS) 4.6 1 27 230
Mark Teixeira (NYY) 4.58 3 23 297
Edwin Encarnacion (TOR) 4.55 1 17 130
Adam Lind (TOR) 4.53 3 22 271
Casey Kotchman (SEA) 4.5 2 20 198
Garrett Atkins (BAL) 4.49 2 20 149
Cliff Pennington (OAK) 4.38 1 30 254
Travis Hafner (CLE) 4.29 2 26 236
Aaron Hill (TOR) 4.23 2 20 234
Nate McLouth (ATL) 4.23 2 16 204
Jeff Francoeur (NYM) 4.22 2 26 245
Brad Hawpe (COL) 4.21 2 21 185
Andy LaRoche (PIT) 4.18 2 17 196
Drew Stubbs (CIN) 4.16 2 25 250
Mark Kotsay (CWS) 4.14 1 15 150

It seems to work pretty well, but may benefit from some refinements at some point. The same method could be easily applied to teams, or we could look at on-basing instead of just hitting.

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