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Pitching Efficiency Part I: Tanaka, Sale and the Maddux Game

This column is about macro-level stuff that really does matter.  But, it is not necessarily fantasy-relevant.  By definition, therefore, that makes it reality relevant.  So, if you wanna stay on your side of the looking glass with the Mad Hatter (no relation to the MadProf, btw) or you wanna stay plugged into the matrix with the world’s second or third worst actor (Seriously Keanu Reeves in general or Hayden Christensen in Star Wars? Lame-o-rama. Even Natalie Portman could not avoid the lameness that Christensen spread…), be my guest.  But, I’m addressing an important stat that will generate more than one article this summer.

This is inspired by a great, brief commentary on the Sale-Tanaka duel between the Yankees and Red Sox on 27 April here (http://razzball.com/fanduel-the-maddux-papi-less-red-sox/).  The author (“Cinthree”) described Tanaka’s performance as the year’s first Maddux (CGSO, <100 pitches).  Cinthree ranted on and echoed the concerns I noted about the flaccid Red Sox 2017 lineup in my post earlier this season here (https://www.majorleaguefantasysports.com/2017/03/20/raise-the-flag-the-madprofs-stone-cold-assessment-of-the-boston-red-sox/ ).

By rights, fantasy owners would have preferred to have had Chris Sale pitching that night.  He struck out ten in 8 innings.  Tanaka struck out three.  Sale gave up eight hits and two ER.  Tanaka gave up three and zero, respectively.  Tanaka got a win.  Both got a QS and Sale delivered on the K, K/BB, etc. (In fact. Both delivered on the K/BB since neither walked a batter, their K/BB are equally irrational numbers).  This was an epic matchup.

  • Tanaka threw 97 pitches. 72 were strikes.  He faced 29 batters.
  • Sale threw 109 pitches. 82 were strikes.  He faced 31 batters (Hembree picked up the last three).
pitches strikes batters s/p p/b
Tanaka 97 72 29 0.74 3.34
Sale 109 82 31 0.75 3.52

So the K/pitches thrown were essentially the same.  But Tanaka was more efficient. Sale is throwing a lot of pitches this season.   See my column last week on this (https://www.majorleaguefantasysports.com/2017/04/24/studs-and-duds-part-deux-the-madprof-unleashes-higgs-boson/ ).  Kinda makes you wonder:  Can a stud pitcher remain successful if he’s throwing significantly more pitches than average?  To address this will take more than one column.

So, OK, this is round one of a piece that will have a couple of successors.  There are several issues to address with regard to pitching prowess and how matters for (or transcend) fantasy.  Starting with the Sale-Tanaka duel, we might want to ask how many CG and how many shutouts are there, to begin with? CG are uncommon.  Madduxes are rare.  To achieve the latter, a pitcher must balance K potential with efficiency.   I have to look.  But, I’d bet that most Maddux games do not produce fantasy-relevant outcomes.  This is because they may not generate K.  Sure, they will have a positive impact on ERA, WHIP, W, CG, etc.  But two of those are ratio stats and wins depends on your bullpen much of the time.  So, the marginal impact of a W or QS is not as meaningful as 10 K…usually.

In the aftermath of Bill James, Moneyball, etc., we’d expect CG and therefore CGSO to dwindle since we live in an era of 100-pitch counts and batters taking pitches to get that pitch count up early and often. Teams like the 2004 Red Sox killed opposing pitching because they would take pitches or foul them off and work pitchers up toward that 100 pitches thrown number.  This would get the pitcher yanked in favor of the bullpen and life tended to be good.

Working the pitch count entails taking pitches and protecting the plate by fouling them off.  So, to produce a Maddux, a pitcher needs to do more than just strike batters out. He has to get them out on as few pitches as possible. That means a pitcher needs to induce swings that put balls in play.  Otherwise, for every pitch taken, the likelihood of a Maddux on pitch count alone diminishes. So a Maddux entails getting batters to do what Billy Bean, Theo Epstein, and Bill James have instructed batters not to do: swing early and often.

Granted, something is afoot.  Back in 2015, Jeff Sullivan demonstrated that the first-pitch swing rate had begun to increase. (“Return of the First-Pitch Swing,” Fangraphs: http://www.fangraphs.com/blogs/return-of-the-first-pitch-swing/):  “For years, batters were OK with the idea of falling behind 0-and-1. They wanted to practice early-count selectivity. It’s always important to be selective, but this year, we’ve seen hitters collectively counter the trend. They’re swinging more at the first pitch they see, and they’re having success.”  In this respect, Tanaka may have benefited from this increased aggressiveness.

I won’t go into a granular analysis of each at bat of the 27 April contest between the Sox and Yankees right now.  But, in this article, I begin an analysis of several data points.  I’ll first put the Maddux game into historical perspective.  Then, I’ll look at pitching trends over time and this year to see how pitchers stack up in their capacity to deliver a Maddux game.

The Maddux in Perspective

Depending on your psyche, if you are a pitcher, you want either to mow down batters with as many strikeouts as possible or you want to Maddux the hell out of them and get them to swing early and often at crummy pitches that will be easy outs. Ideally, this would make for a 27 pitch game (one pitch per batter). If you struck out the side with no balls, you’d throw an 81 pitch (9 innings x 9 strikes) game. A Maddux game is 9 innings with no more than 99 pitches thrown (an average of 11 pitches per inning).

To begin, how many complete games occur and how many shutouts occur?  Since 2002 (when Fangraphs started collecting all of the data I need for this article), the numbers have been dwindling.

 

Season CG ShO SV SHO%
2002 214 87 1224 0.41
2003 209 72 1198 0.34
2004 150 69 1230 0.46
2005 189 63 1254 0.33
2006 144 63 1201 0.44
2007 112 43 1198 0.38
2008 136 54 1184 0.40
2009 152 63 1202 0.41
2010 165 59 1204 0.36
2011 173 75 1243 0.43
2012 128 69 1261 0.54
2013 124 57 1266 0.46
2014 118 65 1264 0.55
2015 104 51 1292 0.49
2016 83 36 1276 0.43
average 147 62 1233 0.42

MLB has averaged 147 CG and 62 SHO per season since 2002.    The patterns demonstrate a general downward trend.  Based on this, looking for complete games, let alone CGSOs is a daunting task. The numbers above don’t even take Madduxes into consideration.  But, we can tweak a bit to make Maddux estimates. With the number of CG dwindling, we need to look at both the number of CGSOs and, more broadly, the tendency of any pitcher to deliver an 11 pitch inning.

How often do Madduxes occur?  The following table offers aggregate pitching numbers across the MLB since 2002.  If we use the 11 P/IP measure as the definition of a Maddux inning, we can generate a sense of how rare a Maddux inning has been since 2002.  (This is legal, kosher and Poker according to Hoyle.  Maddux averaged 11 P/IP.  Sometimes he had three-pitch innings, others weren’t so hot.)

Season Pitches Balls TBF IP P/BF P/IP BF/IP
2002 459730 171952 123795 28758.2 3.71 16.0 4.30
2003 459013 170814 123603 28615.2 3.71 16.0 4.32
2004 461000 172221 123439 28437.1 3.73 16.2 4.34
2005 463109 169673 125021 29135.1 3.70 15.9 4.29
2006 458593 170295 122956 28292 3.73 16.2 4.35
2007 457279 169873 122237 28134.1 3.74 16.3 4.34
2008 459230 170389 121818 28198.2 3.77 16.3 4.32
2009 463274 172565 121839 28257.1 3.80 16.4 4.31
2010 471397 174100 124129 29061 3.80 16.2 4.27
2011 470897 171883 124544 29299.1 3.78 16.1 4.25
2012 461057 167803 121548 28617.2 3.79 16.1 4.25
2013 463397 168341 121434 28676.1 3.82 16.2 4.23
2014 464644 166657 122052 28992 3.81 16.0 4.21
2015 452423 162371 119243 28223.1 3.79 16.0 4.23
2016 449680 163238 116837 27412 3.85 16.4 4.26
average 460982 169478 122300 106742 3.8 16.2 4.29

MLB starters have averaged 16.2 P/IP (roughly 3.8 P/batter faced) with a standard deviation of 0.14 P/IP.  If we assume that MLB pitchers form a normal, bell-curve distribution around that 16.2 P/IP average, then 11 P/IP is simply freaking nuts.  In general, two-thirds of all pitchers would fall between 16.06 and 16.34 P/IP.  95% would fall between 15.92 and 16.48 P/IP.  Finally, about 99% of pitchers would average between 15.78 and16.62 P/IP.  (It may not be completely kosher to assume a bell curve distribution.  But, for round one of this analysis, it works as a starting point).

To cut to the quick:  a Maddux inning is ridiculously rare.  So, back to the Sale-Tanaka duel. What Tanaka did is statistically breathtaking.  Funny thing was that Sale’s performance was barely different.  But, he did not pitch the complete game.

This begs the question that I’ll follow up with in my next piece:  how efficient are pitchers in inducing batters to swing?   While Jeff Sullivan confirmed that batters seem to be getting less patient, swinging strikes are not the only or best weapon in a pitcher’s arsenal.

Fantasy baseball values those strikeouts.  But, from a pitching efficiency perspective a lazy fly or routine grounder achieves the same result with a lot less work.   More to come on this.  For now, tip your hats to Tanaka and Sale.

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Unrepentant Red Sox fan and all things Boston. Deflategate was a joke. Boston Latin School is awesome. Harvard and Johns Hopkins alma maters... Besides that... Stanley D. and Nikki Waxberg Professor of Politics and Law at Washington and Lee University. Wrote for Ron Shandler's Shandler Park for two summers and have been on board with MLFS since 2011. Been at Washington and Lee since 1990 with a brief hiatus (2010-2013) in the Middle East. Currently developing that last word in Fantasy Baseball analysis. Married to Flor, Dad to William and Alex, and adopted daughter Reem. Soon to be father and law to Meaghann. Alpha male to the world's super-pup, Humphrey. Life is not bad.

2 Comments

2 Comments

  1. Mark Rush

    May 1, 2017 at 7:19 pm

    Comment from your gentle author: After all that, Ivan Nova pitched an even better Maddux on 29 April.

  2. Pingback: Pitching Efficiency, Strikeouts and Dollars. The MadProf and Maddux…

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