Yu Darvish, Defining “Change of Pace”

So, Yu Darvish is off to a pretty good start to 2013. Through eight starts this season, the Ranger’s right-hander currently sports the following statistics:

GS K% BB% HR/FB ERA- FIP- SwgStr%
8 39.0% 8.8% 13.9% 62 56 15.7%

Darvish currently ranks first (or tied for first) among qualified starters in K% and SwgStr%, and he has posted the 6th best adjusted FIP in the league (56 FIP-). After a blazing start, his ERA- has dropped to 20th and his HR/FB now ranks 84th, but overall it’s clear Darvish has been a beast in 2013.

After watching this wonderful footage from Darvish’s dismantling of the Angels last night I was struck by how slow is curveball actually is.

Our own Carson Cistulli isolated his four slow curves from that night — check out the final bender to Mike Trout, resulting in a strikeout in the 6th inning. And, yes, that was 61 mph.

I wondered whether the differential between Darvish’s fastball and curveball was the largest in the league. And, so, to the data I went.

I pulled the average velocity by pitch type from our PITCHf/x leaderboards and calculated the difference between each pitcher’s hardest thrown pitch and their slowest. I eliminated any pitches coded as an eephus pitch to get a more realistic read.

Through May 13, Darvish in fact has the greatest differential between his fastest and slowest thrown pitches at 26.1 mph. That’s 5 mph greater than the next starter, Chad Billingsley (21.1 mph) (note: Max and Min refer to the average velocities for a pitchers fastest and slowest pitches):

Year Name Team IP ERA- FIP- K% Max Min Difference
2013 Yu Darvish Rangers 52.2 62 56 39.00% 93.0 66.9 26.1
2013 Chad Billingsley Dodgers 12 82 118 12.20% 91.0 69.9 21.1
2013 A.J. Griffin Athletics 51.2 88 110 18.40% 89.1 68.3 20.8
2013 Eric Stults Padres 45.1 130 112 15.90% 86.4 66.3 20.1
2013 Wei-Yin Chen Orioles 47.1 73 86 14.40% 90.7 71.2 19.5
2013 Clayton Kershaw Dodgers 55.2 44 75 26.70% 92.5 73.5 19.0
2013 Jeremy Guthrie Royals 47.1 56 114 15.60% 92.5 73.7 18.8
2013 Hyun-Jin Ryu Dodgers 50.1 93 85 24.80% 90.5 72.2 18.3
2013 Hisashi Iwakuma Mariners 51.2 46 75 26.70% 89.6 71.6 18.0
2013 Jorge de la Rosa Rockies 45.1 69 92 15.50% 91.4 73.4 18.0
2013 Brandon Maurer Mariners 34.2 156 145 14.70% 91.7 73.8 17.9
2013 Derek Holland Rangers 49.2 58 57 22.20% 93.3 75.6 17.7
2013 Dan Straily Athletics 21.2 178 96 26.30% 90.8 73.3 17.5
2013 Bronson Arroyo Reds 52.2 97 98 14.00% 87.2 69.8 17.4
2013 Jeff Francis Rockies 30 159 113 16.70% 85.0 67.6 17.4
2013 Zack Greinke Dodgers 11.1 44 47 23.80% 90.8 73.5 17.3
2013 Mike Pelfrey Twins 34.1 149 92 9.50% 91.5 74.3 17.2
2013 Brad Peacock Astros 22 234 184 17.60% 91.4 74.2 17.2
2013 Jon Lester Red Sox 52.2 64 78 21.50% 92.2 75.1 17.1
2013 Tommy Hanson Angels 28 98 146 13.90% 88.4 71.3 17.1
2013 Josh Beckett Dodgers 43.1 143 125 21.00% 91.9 74.8 17.1
2013 Phil Hughes Yankees 40.2 106 101 21.30% 92.2 75.2 17.0
2013 Hiram Burgos Brewers 21 179 138 12.80% 89.7 73.0 16.7
2013 Jordan Zimmermann Nationals 58.2 44 67 17.30% 93.8 77.2 16.6
2013 Freddy Garcia Orioles 12.2 102 139 10.60% 87.4 71.0 16.4

Taking a look at the past four years, Darvish’s differential in 2013 ranks first among all starters since 2010. Darvish’s 2012 ranks 5th on the list:

Year Name Team IP ERA- FIP- K% Max Min Difference
2013 Yu Darvish Rangers 52.2 62 56 39.00% 93.0 66.9 26.1
2010 Chad Billingsley Dodgers 191.2 93 81 20.90% 91.5 67.3 24.2
2012 Chad Billingsley Dodgers 149.2 94 90 20.20% 91.6 67.6 24.0
2011 Chad Billingsley Dodgers 188 116 104 18.30% 91.5 68.3 23.2
2012 Yu Darvish Rangers 191.1 89 74 27.10% 92.7 70.9 21.8
2010 Randy Wolf Brewers 215.2 104 121 15.20% 88.3 66.9 21.4
2012 A.J. Griffin Athletics 82.1 77 95 19.10% 89.7 68.5 21.2
2011 Randy Wolf Brewers 212.1 96 111 14.80% 88.4 67.6 20.8
2013 A.J. Griffin Athletics 51.2 88 110 18.40% 89.1 68.3 20.8
2011 Roy Oswalt Phillies 139 96 89 15.70% 91.5 70.8 20.7
2011 Sean O’Sullivan Royals 58.1 177 148 7.00% 92.0 71.3 20.7
2012 Randy Wolf – – – 157.2 142 121 14.90% 88.6 68.3 20.3
2012 Nathan Eovaldi – – – 119.1 111 108 14.80% 94.3 74.5 19.8
2012 R.A. Dickey Mets 233.2 72 87 24.80% 83.0 63.2 19.8
2010 Jered Weaver Angels 224.1 76 76 25.80% 90.1 70.5 19.6
2010 Adam Wainwright Cardinals 230.1 62 74 23.40% 93.5 74.0 19.5
2010 Roy Oswalt – – – 211.2 69 83 23.10% 92.6 73.2 19.4
2011 Clayton Kershaw Dodgers 233.1 63 67 27.20% 93.2 73.9 19.3
2010 Clayton Kershaw Dodgers 204.1 76 82 25.00% 92.5 73.2 19.3
2010 Jeremy Guthrie Orioles 209.1 90 104 13.70% 92.6 73.3 19.3
2012 Brandon Beachy Braves 81 51 90 21.30% 91.3 72.1 19.2
2012 Wei-Yin Chen Orioles 192.2 96 104 18.80% 91.0 71.8 19.2
2012 Clayton Kershaw Dodgers 227.2 67 78 25.40% 93.0 73.9 19.1
2013 Clayton Kershaw Dodgers 55.2 44 75 26.70% 92.5 73.5 19.0
2010 Zack Greinke Royals 220 100 79 19.70% 93.4 74.4 19.0

Just eye-balling these lists it would appear that larger differentials in velocity are associated with better performance (i.e. ERA-, FIP-, K%). In fact, a pitchers velocity differential is significantly correlated with their K% (.167), HR/FB (.167), ERA- (.158), FIP- (.144), and IFFB% (.131). If we break starters up into various percentiles — based on the differential between their fastest and slowest pitches — we can better see the difference in their performance (weighted by innings pitched):

Metric 90th P-tile 75th P-tile 25th P-tile
ERA- 90 92 100
FIP- 92 93 100
HR/FB 9.0% 9.3% 10.4%
IFFB% 10.4% 9.9% 8.7%
SwStr% 8.7% 8.6% 8.4%
K% 20.2% 19.5% 17.7%

On average, starters with differentials at or above the 75th percentile (15.3 mph) produce adjusted ERAs and FIPs about 7-8 points lower than those at or below the 25th percentile (10.7). Greater differential pitchers also strike out about two percent more of the batters they face (19.5% vs. 17.7 K%).

Between the correlations and the percentile comparisons it’s clear that, while making a difference, overall change of pace isn’t a huge differentiator for pitchers. However, it does have some impact and right now no one is doing it better than Yu Darvish.



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Bill leads Predictive Modeling and Data Science consulting at Gallup. In his free time, he writes for The Hardball Times, speaks about baseball research and analytics, has consulted for a Major League Baseball team, and has appeared on MLB Network's Clubhouse Confidential as well as several MLB-produced documentaries. He is also the creator of the baseballr package for the R programming language. Along with Jeff Zimmerman, he won the 2013 SABR Analytics Research Award for Contemporary Analysis. Follow him on Twitter @BillPetti.


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