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Starting Pitcher Strength of Schedule: Does It Matter?

One aspect of probability that I am always cognizant of is that you can get extreme outcomes in small sample sizes.

Baseball players only play 162 games a season, and the most healthy starting pitcher only plays in a fifth of those games.

As a result, starting pitchers who maintain the ideal health and are talented enough to never have their start skipped only have 32 starts a year. (In reality, the average amount of starts for pitchers that threw 100 innings in 2014 was 26.5 starts, while the median amount of starts for that same group was somewhat higher with 27 starts.)

The point remains — that’s only 27 starts a year in which a pitcher’s success is judged upon. From an analysis perspective, a lot of work has been done to develop stats that have more of a predictive quality in relation to their correlation with a pitcher’s future performance than some classical statistics.

Starting Pitcher Strength of Schedule?

While there have been statistics that have been developed to account for batted ball luck, HR-to-flyball variation, and strand rate, there really have not been any statistics made to account for the quality of opponents that a pitcher sees over the course of a season.

This article will look at two questions when it comes to starting pitcher strength of schedule:

  1. Should we worry about strength of schedule for starting pitchers?
  2. How much does strength of schedule matter for starting pitchers?

Does Strength of Schedule Matter?

In order to see if strength of schedule matters, we have to deconstruct how schedules are created.

Obviously, teams in the American League play each other more than they play teams in the National League, and teams in the AL West play each other more than they play teams in the AL Central.

Because there is schedule inequity (i.e. not all teams play each other the same amount of time), it opens the gate for unbalanced schedules (i.e. some teams have easier schedules than others).

When we look at the graph below, it’s quite stark how many more runs some divisions scored in 2014 than others. The worst American League division scored more runs than the best National League division, and it’s impressive to visually see the disparity in talent that is brought up as a topic of conversation when it comes to the imbalance of power between divisions and leagues.

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Just like they are on a team-by-team basis, runs scored can be deceptive, if you try to use it as an indication of true offensive talent. Remember the 2013 Cardinals? They were third in baseball in total runs scored, but they were only seventh in baseball in wRC+. This dissonance is a result of their 137 wRC+ with runners in scoring position. To get a better idea of each division’s offensive talent, lets look at wOBA by division:

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Compared to our initial graph that looked at the amount of runs scored by division, you still see a large amount of variance when it comes to offense by division.

How Much Does Strength of Schedule Matter?

As we can see from the last two graphs, because of the unbalanced schedules, strength of schedule matters for starting pitchers when it comes to their league — and especially the division they play in.

Baseball Prospectus has a very useful statistic in their sortable stats page that lets you look at the quality of opponent for pitchers based off of the metric that you’ve chosen.

For our purposes, we’ll use true average (TAv) which “is a measure of total offensive value scaled to batting average. Adjustments are made for park and league quality [i.e. the year, not whether it’s the AL or NL], as such the league-average mark is constant at .260.”

True average works well for this process because it looks at the true talent of the player and takes away any park effects that may be present in a park-agnostic statistic. For now, we just want to look at the effect that the opposition’s talent has on starting pitcher performance. If we use a stat like wOBA, we mix our variables and unintentionally look at the opposition’s true talent and whatever park effects may be present.

To put into context the variance that exists when it comes to the strength of schedule for starting pitchers, Brad Peacock (among all pitchers that threw more than 100 innings in 2014) had the most difficult strength of schedule for starters with a .268 oppTAv (opponents’ true average). Yangervis Solarte and Colby Rasmus had a .268 TAv last year. Brad Hand had the easiest strength of schedule –the hitters that he faced had a .250 oppTAv.

The chart below shows the oppTAv for all pitchers in 2014, but the third column (SoS+) also shows the strength of schedule for each starting pitcher relative to the league average opponent’s TAv.

PlayeroppTAvSoS-
Brad Peacock0.2683%
Nick Tepesch0.2683%
Collin Mchugh0.2673%
Rubby De La Rosa0.2673%
Ricky Nolasco0.2673%
Colby Lewis0.2673%
Hector Noesi0.2673%
Nick Martinez0.2673%
Jeff Samardzija0.2662%
Chris Archer0.2662%
Kevin Gausman0.2662%
Kyle Gibson0.2662%
Jake Peavy0.2662%
Alex Cobb0.2652%
Brett Oberholtzer0.2652%
James Shields0.2652%
Hiroki Kuroda0.2652%
Clay Buchholz0.2652%
Scott Feldman0.2652%
Felix Hernandez0.2642%
Dallas Keuchel0.2642%
Sonny Gray0.2642%
Jake Odorizzi0.2642%
Jered Weaver0.2642%
Bud Norris0.2642%
Jeremy Guthrie0.2642%
David Phelps0.2642%
Scott Carroll0.2642%
Chris Young0.2642%
Corey Kluber0.2631%
Garrett Richards0.2631%
Masahiro Tanaka0.2631%
Hisashi Iwakuma0.2631%
Matt Shoemaker0.2631%
Julio Teheran0.2631%
Danny Salazar0.2631%
John Lackey0.2631%
Jesse Chavez0.2631%
Chris Tillman0.2631%
Carlos Carrasco0.2621%
Yu Darvish0.2621%
Max Scherzer0.2621%
Jarred Cosart0.2621%
Jason Vargas0.2621%
Drew Hutchison0.2621%
Trevor Bauer0.2621%
A.j. Burnett0.2621%
Chase Anderson0.2621%
Hector Santiago0.2621%
Alfredo Simon0.2621%
Ubaldo Jimenez0.2621%
Marco Estrada0.2621%
Miguel Gonzalez0.2621%
Phil Hughes0.2610%
Anibal Sanchez0.2610%
Jose Quintana0.2610%
Scott Kazmir0.2610%
Rick Porcello0.2610%
Justin Verlander0.2610%
Wei-yin Chen0.2610%
Roenis Elias0.2610%
Wily Peralta0.2610%
J.a. Happ0.2610%
R.a. Dickey0.2610%
Kevin Correia0.2610%
John Danks0.2610%
Jacob Degrom0.260%
Jon Lester0.260%
Marcus Stroman0.260%
Adam Wainwright0.260%
Ian Kennedy0.260%
Tyler Skaggs0.260%
Yordano Ventura0.260%
Jon Niese0.260%
Danny Duffy0.260%
Mike Leake0.260%
Jason Hammel0.260%
Doug Fister0.260%
Yovani Gallardo0.260%
Josh Tomlin0.260%
Shelby Miller0.2590%
David Buchanan0.2590%
Trevor Cahill0.2590%
Aaron Harang0.2590%
Bartolo Colon0.2590%
Johnny Cueto0.2590%
David Price0.2590%
Chris Sale0.2590%
Franklin Morales0.258-1%
Dillon Gee0.258-1%
Josh Beckett0.258-1%
Edinson Volquez0.258-1%
Wade Miley0.258-1%
Homer Bailey0.258-1%
Tyler Matzek0.258-1%
Mat Latos0.258-1%
Tyson Ross0.258-1%
Madison Bumgarner0.258-1%
Yusmeiro Petit0.258-1%
Mike Minor0.257-1%
C.j. Wilson0.257-1%
Tim Lincecum0.257-1%
Kyle Lohse0.257-1%
Ryan Vogelsong0.257-1%
Tom Koehler0.257-1%
Drew Smyly0.257-1%
T.j. House0.257-1%
Mark Buehrle0.257-1%
Henderson Alvarez0.257-1%
Tim Hudson0.257-1%
Ervin Santana0.257-1%
Lance Lynn0.257-1%
Alex Wood0.257-1%
Zack Greinke0.257-1%
Jordan Zimmermann0.257-1%
Eric Stults0.256-1%
Kyle Kendrick0.256-1%
Matt Garza0.256-1%
Nathan Eovaldi0.256-1%
Jeff Samardzija0.256-1%
Roberto Hernandez0.255-2%
Edwin Jackson0.255-2%
Jorge De La Rosa0.255-2%
Josh Collmenter0.255-2%
Charlie Morton0.255-2%
Zack Wheeler0.255-2%
Michael Wacha0.255-2%
Andrew Cashner0.255-2%
Cole Hamels0.255-2%
Stephen Strasburg0.255-2%
Hyun-jin Ryu0.255-2%
Jake Arrieta0.255-2%
Travis Wood0.254-2%
Jordan Lyles0.254-2%
Dan Haren0.254-2%
Tanner Roark0.254-2%
Vance Worley0.254-2%
Jeff Locke0.253-3%
Brandon Mccarthy0.253-3%
Gio Gonzalez0.253-3%
Francisco Liriano0.252-3%
Gerrit Cole0.252-3%
Clayton Kershaw0.251-3%
Brad Hand0.25-4%

A SoS- of 3% means that starting pitcher had a schedule that was three percent more difficult than league average in 2014, while a SoS- of -3% means that a pitcher had a schedule that was three percent easier than league average in 2014.

While the most that strength of schedule mattered in 2014 was three percent in the positive direction and four percent in the negative direction, those small inefficiencies are what make the difference in baseball — and Fantasy Baseball.

If you can get a pitcher like Jon Lester, who left two of the most difficult divisions in baseball for the easiest division in baseball, at a price that doesn’t reflect that particular variable, you’ll be able to generate a more adequate depiction of Lester’s value than the other people in your leagues.

The next step will be to come up with an adjusted FIP that bakes into itself the quality of opponent that each pitcher faces. But for now, knowing the relative strength of schedule for each starting pitcher is a good place to start.

Jon Lester Photo Credit: Keith Allison

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Devin Jordan

Devin Jordan is obsessed with statistical analysis, non-fiction literature, and electronic music. If you enjoyed reading him, follow him on Twitter!
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