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.





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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Prince Rogers Nelson
10 years ago

Nothing Compares 2 Yu

Alexander Nevermind
10 years ago

I Would Die 4 YU

Bobby Ayala
10 years ago

Yu make me feel like a natural woman

Jo-Jo Reyes
10 years ago
Reply to  Bobby Ayala

Stop with the puns Yu guys.