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:

Should we worry about strength of schedule for starting pitchers? 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.

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:

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.

Player oppTAv SoS- Brad Peacock 0.268 3% Nick Tepesch 0.268 3% Collin Mchugh 0.267 3% Rubby De La Rosa 0.267 3% Ricky Nolasco 0.267 3% Colby Lewis 0.267 3% Hector Noesi 0.267 3% Nick Martinez 0.267 3% Jeff Samardzija 0.266 2% Chris Archer 0.266 2% Kevin Gausman 0.266 2% Kyle Gibson 0.266 2% Jake Peavy 0.266 2% Alex Cobb 0.265 2% Brett Oberholtzer 0.265 2% James Shields 0.265 2% Hiroki Kuroda 0.265 2% Clay Buchholz 0.265 2% Scott Feldman 0.265 2% Felix Hernandez 0.264 2% Dallas Keuchel 0.264 2% Sonny Gray 0.264 2% Jake Odorizzi 0.264 2% Jered Weaver 0.264 2% Bud Norris 0.264 2% Jeremy Guthrie 0.264 2% David Phelps 0.264 2% Scott Carroll 0.264 2% Chris Young 0.264 2% Corey Kluber 0.263 1% Garrett Richards 0.263 1% Masahiro Tanaka 0.263 1% Hisashi Iwakuma 0.263 1% Matt Shoemaker 0.263 1% Julio Teheran 0.263 1% Danny Salazar 0.263 1% John Lackey 0.263 1% Jesse Chavez 0.263 1% Chris Tillman 0.263 1% Carlos Carrasco 0.262 1% Yu Darvish 0.262 1% Max Scherzer 0.262 1% Jarred Cosart 0.262 1% Jason Vargas 0.262 1% Drew Hutchison 0.262 1% Trevor Bauer 0.262 1% A.j. Burnett 0.262 1% Chase Anderson 0.262 1% Hector Santiago 0.262 1% Alfredo Simon 0.262 1% Ubaldo Jimenez 0.262 1% Marco Estrada 0.262 1% Miguel Gonzalez 0.262 1% Phil Hughes 0.261 0% Anibal Sanchez 0.261 0% Jose Quintana 0.261 0% Scott Kazmir 0.261 0% Rick Porcello 0.261 0% Justin Verlander 0.261 0% Wei-yin Chen 0.261 0% Roenis Elias 0.261 0% Wily Peralta 0.261 0% J.a. Happ 0.261 0% R.a. Dickey 0.261 0% Kevin Correia 0.261 0% John Danks 0.261 0% Jacob Degrom 0.26 0% Jon Lester 0.26 0% Marcus Stroman 0.26 0% Adam Wainwright 0.26 0% Ian Kennedy 0.26 0% Tyler Skaggs 0.26 0% Yordano Ventura 0.26 0% Jon Niese 0.26 0% Danny Duffy 0.26 0% Mike Leake 0.26 0% Jason Hammel 0.26 0% Doug Fister 0.26 0% Yovani Gallardo 0.26 0% Josh Tomlin 0.26 0% Shelby Miller 0.259 0% David Buchanan 0.259 0% Trevor Cahill 0.259 0% Aaron Harang 0.259 0% Bartolo Colon 0.259 0% Johnny Cueto 0.259 0% David Price 0.259 0% Chris Sale 0.259 0% Franklin Morales 0.258 -1% Dillon Gee 0.258 -1% Josh Beckett 0.258 -1% Edinson Volquez 0.258 -1% Wade Miley 0.258 -1% Homer Bailey 0.258 -1% Tyler Matzek 0.258 -1% Mat Latos 0.258 -1% Tyson Ross 0.258 -1% Madison Bumgarner 0.258 -1% Yusmeiro Petit 0.258 -1% Mike Minor 0.257 -1% C.j. Wilson 0.257 -1% Tim Lincecum 0.257 -1% Kyle Lohse 0.257 -1% Ryan Vogelsong 0.257 -1% Tom Koehler 0.257 -1% Drew Smyly 0.257 -1% T.j. House 0.257 -1% Mark Buehrle 0.257 -1% Henderson Alvarez 0.257 -1% Tim Hudson 0.257 -1% Ervin Santana 0.257 -1% Lance Lynn 0.257 -1% Alex Wood 0.257 -1% Zack Greinke 0.257 -1% Jordan Zimmermann 0.257 -1% Eric Stults 0.256 -1% Kyle Kendrick 0.256 -1% Matt Garza 0.256 -1% Nathan Eovaldi 0.256 -1% Jeff Samardzija 0.256 -1% Roberto Hernandez 0.255 -2% Edwin Jackson 0.255 -2% Jorge De La Rosa 0.255 -2% Josh Collmenter 0.255 -2% Charlie Morton 0.255 -2% Zack Wheeler 0.255 -2% Michael Wacha 0.255 -2% Andrew Cashner 0.255 -2% Cole Hamels 0.255 -2% Stephen Strasburg 0.255 -2% Hyun-jin Ryu 0.255 -2% Jake Arrieta 0.255 -2% Travis Wood 0.254 -2% Jordan Lyles 0.254 -2% Dan Haren 0.254 -2% Tanner Roark 0.254 -2% Vance Worley 0.254 -2% Jeff Locke 0.253 -3% Brandon Mccarthy 0.253 -3% Gio Gonzalez 0.253 -3% Francisco Liriano 0.252 -3% Gerrit Cole 0.252 -3% Clayton Kershaw 0.251 -3% Brad Hand 0.25 -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.

#Cubs Jon Lester in 2014 after falling behind a batter 2-0 18 strikeouts 17 walks — Ace of MLB Stats (@AceballStats) January 16, 2015

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