It may have been John Steinbeck who said that everyone in California is from somewhere else. Or it may have been some other dude. In any case, the San Diego Padres’ roster exemplifies the melting pot that is the Golden State. Ten of their 14 core major leaguers (where I’m defining “core” to mean the 14 players that make up the starting lineup, starting rotation, and closer) are trade acquisitions:

C     Derek Norris

1B   Yonder Alonso

2B   Jedd Gyorko

3B   Will Middlebrooks

SS   Alexi Amarista

LF  Justin Upton

CF   Wil Myers

RF   Matt Kemp

S1   Andrew Cashner

S2   Ian Kennedy

S3   Tyson Ross

S4  Odrisamer Despaigne

S5   Brandon Morrow

CL   Joaquin Benoit

The Padres core is as heavily dependent on trade imports as any I’ve ever seen. And while this may be a recipe for cooking up a world championship, it hasn’t been, at least not recently. No world champ in the last ten years has had that many core players (including DHs for the AL teams) acquired by trades:

White Sox (2005)    7

Cardinals (2006)     5

Cardinals (2011)      5

Red Sox (2007)       4

Giants (2012)           3

Phillies (2008)         2

Yankees (2009)       2

Giants (2014)           2

Giants (2010)           1

Red Sox (2013)        1

It’s possible that a couple of home-grown Padres could replace two of their trade imports – Cory Spangenberg might effectively discard Middlebrooks (either by winning the hot corner himself or by pushing Gyorko to third). Free agent pickup Clint Barmes could displace Amarista, one of the few major league players whose job Clint Barmes genuinely threatens. But even if the Padres close the numerical gap, they are still pursuing what is at best an unusual route to victory.

The top four trade-dependent world series winners listed above had established superstars around which to build: Frank Thomas for the White Sox; Albert Pujols for the Cards; and Dustin Pedroia, David Ortiz and MannybeingManny for the Sawx. (In the White Sox’ case, all the activity indeed produced a championship for the prophetically-monickered Big Hurt, but too late. A foot injury sidelined him at the end of July, and he would never again take a swing in anger for the south-siders.) The Padres roster has no such anchor tenant – their only established home-grown regular is Jedd Gyorko, he of the 2.5 career WAR.

While new general manager A. J. Preller’s hyperactivity has generated much of the hot stove heat this winter (and the best hot stove headline thus far), the wheeling and dealing began before he took over. Alonso, Amarista, and the top three starters all arrived under the previous administration. So while Preller’s moves look like a radical restructuring of the roster, they can also be seen as simply finishing the grim task that his predecessor Josh Byrnes started.

Because this is what happens when prospects degenerate into suspects. (Younger or more sensitive readers may wish to avert their eyes now.) This list goes a long way toward explaining why Preller has been treating his roster like a cat treats a new sofa. Not one of the top ten players on it is with the Padres major league club today; indeed, only the not-yet-immortal Logan Forsythe is even in the majors. Donavan Tate’s tire fire has been well-chronicled – the reboot failed, and he did not play organized ball in 2014. Nor did Simon Castro or James Darnell. Wynn Pelzer pitched for the Camden Riversharks. Cory Luebke’s had two more Tommy John surgeries than you’ll ever have. The rest of that erstwhile top ten are tilling the soil of other teams’ farms, generally without significant yield. (Ok, younger and sensitive readers, you can open your eyes.)

None of this is Preller’s fault (or Byrnes’, for that matter – these were Kevin Towers picks), but this is the hole out of which Preller must dig, and they don’t make many shovels this large. Preller had essentially two choices on assuming the helm of the S.S. Friar: (a) put a motley cast of young low ceiling players and affordable, declining vets on the field and wait for the farm to resprout; or (b) make trades like Jim Bowden on Red Bull and hope to field a competitive team in a division with two perennial playoff contenders.

Preller chose the latter, ill-advisedly in my view, until I read a recent Joe Sheehan newsletter (yes, you should subscribe). Sheehan made a number of points about the Padres current situation; the one relevant here is that the Pads are stuck with a relatively bad TV deal, and thus are unusually dependent on attendance for revenue. Preller needs to get butts in the seats, and that won’t happen if he puts a AAA team on the field, even if he distracts the fans with dollar beer nights and kazoo-playing clowns shooting T-shirts into the sparsely populated upper deck. Sheehan believes that  in order to fund a sustainable scouting and development-based franchise, Preller paradoxically needs to increase the age and cost of the major league roster in the short term.

I don’t like Preller’s odds. Look at the Padres’ core again – there isn’t a single position player on it that doesn’t have either injury or on-base issues, except Upton. The rotation doesn’t have a #1 starter, although perhaps Ross can develop into one. On the other hand, he’s already 27. The Padres play in the same division as the Los Angeles Dodgers, who may bolster their farm system by purchasing Cuba once the messy embargo-lifting details are sorted out. The Giants don’t have the Dodgers’ financial resources, but they remain one of the consistently best run organizations in the game, with two franchise players (Posey and Bumgarner) who are still a long way from old.

But Preller presumably knew the job was dangerous when he took it, and at least he has attacked his task with vigor and focus. Sometimes guys don’t get hurt, and sometimes the batted balls find grass rather than gloves. That’s why they play the games, and San Diego’s 2015 campaign promises to be more interesting than most, whether it’s ultimately successful or not.

This article is a response to Noah’s thought inspiring articles about a modification to the FIP-based pitching  fWAR and  his issues with the fWAR league adjustments in which I want to lay out a possible solution to the somewhat “flawed” league adjustments currently used. My method could be applied to a divisional context as well therefore I won’t address it specifically.  I am not a native speaker therefore please do not take any offense in grammar or spelling mistakes.

Let’s start with the basics of the current concept. 1,000 WAR has to be given out each year to all players implying a replacement level of .294. Even if for some reason every player on all the current 25-man rosters happened to be abducted by aliens this would not change. Even if both leagues consisted entirely of “replacement” players, 1,000 WAR would be handed out. This is our model and it is a great one because it includes context so beautifully and effortlessly.

Here is a little thought experiment: Say these aliens are huge fans of the NL for some reason and decide to abduct the entire league’s player population. We would be left with the untouched AL (we assume the AL and NL are of exactly equal strength for this thought experiment). Again, 1,000 WAR has to be distributed among all big league players. If our current model is handling league adjustments correctly we would expect to see 0 WAR in the NL and 1,000 WAR in the AL. Unfortunately, the current fWAR model wouldn’t spit out a result coming close to this.

Here is why: Even in a reality where about 88% of all games are played internally in a given league a great portion of the fWAR calculation is based on treating MLB as being ONE league instead of two rather independent leagues. The consequences can be strongly seen in my thought experiment. Because every player in the NL would be a replacement player we could hardly find a hint of the changed talent level in the NL’s stats. This is because replacement level hitters are facing replacement level pitching and my guess would be that the NL’s overall batting line and R/G would barely change – even if the talent changed dramatically. Now wOBA is calculated using both leagues and the offensive output by these replacement hitters would be weighted as if they put up these numbers against actual major league competition. Thus, the NL would be undeservedly credited with batting runs and run prevention for the pitchers (again versus replacement hitters).

This is certainly an exaggeration but it is still true with one league being weaker. The only way we would notice the changed talent level would be the interleague record against the AL. In a perfectly balanced world with two equally strong and talented leagues we were to see a .500 record and our 1,000 WAR could be handed out 50/50 between the AL and NL and 57/43 between position players and pitchers. What would the interleague record be? What would it have to be? The answer is pretty easy: .294 aka replacement level. Now this is interesting and it seems like we are going somewhere. Here seems to lie the key for the proper league adjustments because how much WAR should be handed out to a league that wins at a replacement level against a “true” major league? Sounds pretty darn like a league full of replacement players which are by definition worth 0 WAR. And this 0 WAR should be the correct answer based on our assumptions in this thought experiment.

How do we get there?

1) Calculate every aspect that goes into WAR (R/PA, wOBA, FIP, etc) separately for both leagues. In fact we have to treat both leagues as independent. This would mean 500 WAR for each league per default, distributed 57/43 between position players and pitchers.

2) Figure out the interleague record. I would suggest using something like a 3 year regressed rolling average (Just like the 5 year rolling regressed park factors on FG that can actually change a player’s WAR retroactively if his home park happens to play very hitter – or pitcher friendly in the immediate future) I will use a .525 record in favor of the AL for an example later on.

3) Based on the “true” replacement levels of .294 for teams, .380 for starters and .470 for relievers we calculate an “artificial replacement level” for the weaker and the stronger league via the odds ratio.  Using the .525 interleague record for the AL as an example this will come out to an artificial replacement level of

.315 for NL teams / .274 for AL teams

.404 for NL starting pitchers / .357 for AL staring pitchers

.495 for NL relievers / .445 for AL relievers.

To help interpret these numbers think about it this way: The .475 NL is the weaker league. A “replacement team” would have a .294 record in the NL (forget about interleague for a moment). If this team plays against a .294 AL team, we would expect a .500 W% IF both leagues are equally strong. But we already established that the AL wins at a .525 clip when two teams with “equal” records IN their respective leagues match up. The .315 “artificial” replacement level for the NL means that we expect a .315 NL team to win 50% of all games a against a .294 AL team. Thus, we can conclude that the replacement level bar to clear should be put a little higher in the NL because it seems easier to accumulate value in the weaker league. On the other hand the opposite is true for the AL, where the replacement level bar should be put a little lower for the same opposite reasons and to be consistent with handing out 1,000 WAR each year.

4) Derive  the correct distribution of WAR for both leagues based on the artificial replacement levels. In my thought experiment at the beginning we would have a 0/1,000 WAR distribution, because replacement level would actually be .500 for the NL using my methodology in 3). A balanced league would have a 500/500 WAR distribution with a replacement level of .294 for both leagues. With the AL winning at a .525 clip against the NL this means a WAR distribution close to 450/550 in favor of the AL.

The WAR distribution for 2014 on FG was 472/528 in favor of the AL.

Conclusion

There are really some beautiful and elegant side effects. The independence of both league’s calculations would mean interleague adjustments are not necessary at all. This is because even if there are about 12% interleague games, pitchers and hitters are only compared to the stats that other players in the same league have put up – interleague included. The adjustment takes place when we evaluate the interleague record because this is the only direct way to measure difference in strength/talent. The current league adjustments are a little bit flawed in my opinion because wOBA and the run environment is calculated for the entire MLB and interleague records are not taken into consideration at all. Therefore a stiff replacement level is used for all years. My methodology addresses these problems and scales an artificial replacement level for each year and league based on a multi-year regressed interleague record while still keeping the overall replacement level for all of MLB to .294 and 1,000 WAR each year.

To be honest with you I am not a huge fan of divisional adjustments because of small samples and differing opponents. In an entire season’s interleague schedule there should be a lot more signal. I think when applying divisional adjustments we would have to regress heavily. I am not entirely sold yet to include a possibly very complicated divisional adjustment when its heavily regression doesn’t give us much to learn from anyway. But I am open to be sold the other way.

Look forward to a follow-up in which I walk through some real life examples and present some of the changes my methodology brings. Feel free to comment and discuss! Prost!

After the 2013 season, Clay Buchholz was kind of interesting. He put up some crazy good numbers with an ERA/FIP/xFIP line of 1.74/2.78/3.41. It was clear that Buchholz was good in 2013, putting up a 3.2 WAR while being limited to just 108 innings of work. This may have caused some to be weary of Buchholz following the 2013 season. Sure he was good during the Red Sox championship run, but he also had trouble staying on the field. Combine that with several outliers, a lot of luck (.254 BABIP, 83.3% LOB%), and it was easy to see that there were a lot of red flags in Buchholz’s performance.  While we shouldn’t discredit 108 innings of awesome work, we also shouldn’t put all of our weight on it either. Buchholz’s 2014 season taught us that as well.

Buchholz’s 2014 season looked pretty bad.

In 2014, Buchholz put up an ERA/FIP/xFIP line of 5.34/4.01/4.04. The first thing that pops out is that awful ERA. However, ERA isn’t everything, and there’s a compelling argument that it’s not the most trustworthy statistic. However, we do know that run prevention is some kind of a skill. Buchholz’s RA9-WAR between 2013 and 2014 fell from 5.0 t0 -0.5. There was some bad luck as well. In order for Buchholz’s skillset to work he needs to have a low BABIP, and the seasons in which he has been successful his BABIPs were somewhere in the .250-.260 range. In 2014 his BABIP was .315, which was the highest it’s ever been aside from a 75- inning stint early in his career.  This is not entirely Buchholz’s fault, however it’s clear that he took a step back as a pitcher in 2014.

However, peripherally Buchholz actually seems in line with his career norms.

Buchholz has proven that he’s the type of pitcher who succeeds by outperforming his FIP, and for the most part he has done a decent job of doing just that. In his career year of 2011, he had nearly a 1.30 ERA-FIP differential, and in 2013 the trend was the same, with his ERA being a whole run lower than his FIP. It’s clear that this is how Buchholz has made himself an above-average starting pitcher. That’s not to say that this is not a skill set that can’t work. Matt Cain has always outperformed his FIPs, and done so at an elite level. Shelby Miller looks like the type of pitcher who may do the same thing. There are exceptions to everything, and it’s clear that there are some pitchers who can do a good job of beating out their FIPs. Buchholz may or may not be one of those pitchers.

It is clear that Buchholz, for a good chunk of his career, has masked his average to below-average peripherals by doing a good job of preventing runs from scoring. That eventually caught up with him in 2014 when his luck ran out. Regression from the 2013 season was inevitable. Buchholz increased his K% in from 16% in 2012 to 23%. This is what made his peripherals look really good in 2013. However, an increase in strikeouts isn’t always sustainable as the increase in strikeout rate usually doesn’t carry over into the next season.

Buchholz never struck out batters at such a high clip in his career and given that this was a small sample — 108 innings — regression in 2014 was predictable. However, it’s not like Buchholz regressed to something that was godawful in 2014. In fact, he actually regressed to something that was pretty similar to what he has always been. There were a couple of concerns throughout the season in terms of his ability to repeat his delivery, which is quite concerning, but at the end of the day the stuff hadn’t changed that much from 2013 to 2014.

Whiffs Per Swing: 

There was a decrease in his ability to get whiffs on two of his pitch categories. However, the decreases weren’t that extreme. One could label an 8% change on Whiffs per Swing on his off speed stuff as drastic, but at the same time this only regressed Buchholz back to getting strikeouts at a typical 16-17% rate rather than the 23%. At the end of the day, Buchholz’s skill set isn’t about striking guys out. His approach is about not walking too many guys, making weak contact and keeping the ball in the park. He has never excelled at being a command artist, in fact in some parts of his career he has been quite lousy at keeping his walk rate down as well as keeping the ball in the park. If a pitcher is not going to strike guys out at a high rate, in order to be elite he has to be able to excel at either keeping the ball in the park or not walking guys. Buchholz has been very okay at both keeping the ball in the park and not giving up walks.

Buchholz has built up a conventional reputation of being something special by posting low ERAs, a no hitter, and maybe some post-season dramatics. However, Buchholz may just be a mediocre pitcher masked by some stellar defense. He doesn’t have that stellar walk rate and he doesn’t seem immune to home runs like Matt Cain in his prime. However, Buchholz in 2014 wasn’t as bad as many thought he was. Sure a 4.06 FIP in 2014 — where pitching rules — isn’t the prettiest figure, but at the same time there are still plenty of teams that would consider the figure very serviceable. Positive regression is likely for Buchholz, however asking him to come back to those pretty looking ERAs is asking a lot. By FIP Buchholz has never been anything elite, and he has proven that he is nothing elite. Buchholz is what he is, a very serviceable pitcher with some highlights in his career such as postseason heroics and a no-hitter. Buchholz is not terrible nor is anything spectacular; he is somewhere in between.

In Part 1 of the “Trying to Improve fWAR” series, we focused on how using runs park factors for a FIP-based WAR leads to problems when calculating fWAR, and suggested the use of FIP park factors instead.  Today we’ll analyze a different yet equally important problem with the current construction of FanGraphs Wins Above Replacement for both position players and pitchers: league adjustments. When calculating WAR, the reason we adjust for league is simple; the two leagues aren’t equal.  The American League has been the superior league for some time now, and considering that all teams play about 88% of their games within their league, the relative strength of the leagues is relevant when trying to put a value on individual players.  If a player moved from the American League, a stronger league, to the National League, a weaker league, we’d expect the player’s basic numbers to improve; yet, if we properly adjust for quality of league when calculating WAR, his WAR shouldn’t change significantly by moving into a weaker league.

The adjustments that FanGraphs makes for strength of league are unclear.  The glossary entry “What is WAR?” and the links within it don’t seem to reference adjusting for the strength of a player’s league/division at all.  The only league adjustment is within position player fWAR, and is described as “a small correction to make it so that each league’s runs above average balances out to zero”.  Not exactly a major adjustment. Rather than evaluating FanGraphs’ methods of adjusting for league, let’s instead look at the how the two leagues compared in fWAR for both pitchers and position players in 2014:

Interestingly, AL pitchers seem to get a much greater advantage than AL position players from playing in a superior league.  Yes, the AL does have a DH, but the effect of having a DH should be in the form of the AL replacement level RA/9 being higher than the NL replacement level RA/9.  Having a DH (and hence a higher run environment) does not mean that the league should have more pitching fWAR.  Essentially, somewhere in the calculation and implementation of fWAR, the WAR of AL pitchers is being inflated by around 13% and the WAR of NL pitchers is being deflated by the same amount. Meanwhile, AL position players don’t benefit at all from playing in a superior league.  In order to accommodate for league strength, the entire American League should benefit from playing in the stronger league, not just the pitchers.  In order to find out what the league adjustment should be (at least for the 2015 season), let’s look at each league’s interleague performance since 2013:

The “Regressed Winning Percentage” is simply the league’s interleague Winning Percentage regressed to the mean by a factor of .1, meaning that 90% of the league’s interleague WP% is assumed to be skill.  Each league’s interleague winning percentage is regressed slightly to ensure that we aren’t overestimating the differences between the two leagues.  Part of the reason we regress each league’s interleague winning percentage is because the interleague system is admittedly not perfect; while NL teams believe that the AL has an inherent advantage because of their everyday DH, AL teams complain about having pitchers who can’t bunt and a managerial style that is strategically difficult for their managers.  While both sides have valid points, interleague games probably don’t hurt one side significantly more than the other, meaning that the vast amount of data that comes from interleague games is reliable as long as it is properly regressed.

Just knowing each league’s regressed interleague winning percentage, however, is not enough.  We also need to know the percent of games each league plays within its own league.  Why?  The more games the league plays against the other league, the less playing in a superior league matters; the only reason we have to adjust for strength of league in the first place is because of the disparity in competition between the leagues. In a 162-game season, a team plays exactly 20 games against interleague opponents, meaning that 142 of 162 games, or 87.7% of a team’s schedule, is intra-league.  Therefore, in order to find each league’s multiplier, the following equation is used:

League Multiplier = 2 * ((.877 * Regressed WP%) + ((1-.877) * Opponent Regressed WP%))

In this calculation, the “Opponent Regressed WP%” is simply the opposing league’s Regressed WP%.  This is incorporated into the formula because each league plays 12.3% of its games (20 games) against the other league.  Without further ado, here are the league multipliers:

As expected, the American League comes out as the stronger league, albeit by a smaller margin than its advantage in fWAR (remember, the AL’s league multiplier in fWAR was 1.056).  Still, there are other adjustments that can be made besides adjusting for league. In the same way that the superiority of the American League is no secret, the fact that all divisions are not created equal is relatively obvious to most baseball fans.  The AL East has long been considered the best division in baseball, and their inter-division record backs up that reputation; they have a .530 inter-division winning percentage over the last two seasons (only including games in their own league), best in the American League.  Using the same process we used to calculate the league multipliers, division multipliers were calculated as shown below, with the data from the 2013-2014 seasons:

One difference between this calculation and the league multiplier calculation was that, in this calculation, not all games were used when determining what percent of a division’s games were intra-division; because we already adjusted for league earlier, the 20 interleague games on each team’s schedule were ignored from the calculation.  The .535 figure in column 6 is simply the number of games each team plays against its own division, 76, divided by the number of non-interleague games each team plays, 142.  In addition, the “Interdivision Opponent Regressed WP%” is the average opponent each division faces while playing out of division in non-interleague games.  The AL East, for example, plays the AL Central and AL West in its remaining intra-league games, so the .487 inter-division opponent regressed WP% is calculated by taking a simple average of the AL Central’s Regressed WP%, .489, and the AL West’s Regressed WP%, .484.

Now that we have both divisional and league multipliers, we can derive each division’s total (observed) multiplier by simply multiplying the two:

How do these multipliers, which were fairly easy to calculate, compare with the multipliers implied in FanGraphs’ WAR calculations?  Below, the multipliers are compared in bar graph form:

As you can see, the current construction of fWAR artificially helps certain divisions while hurting others.  Let’s get a closer look at the problem by graphing how much fWAR inflates each division’s pitchers and position players relative to the multipliers we just calculated:

Upon viewing the chart, a theme emerges: Pitching WAR at FanGraphs is in need of serious repair.  Pitching fWAR dramatically overvalues the American League.  All three American League divisions have Pitching fWAR Multipliers at least 4.5% higher than they should be, while each Pitching fWAR Multipliers for the National League are all at least 6% lower than they should be.

Is this just a random aberration for 2014?  Probably not; in 2013, the American League’s Pitching fWAR Multiplier was 1.095, not much lower than 2014’s 1.127 (and nowhere near the 1.038 value we got).  For whatever reason, Pitching fWAR overvalues American League pitchers and undervalues their National League counterparts.  The strongest National League division, the NL Central, suffers the most from this calculation error, while the weaker American League divisions (the AL Central and AL West) experience the greatest benefit.  Fans of the Reds and Brewers in particular should take solace in the fact that their teams were hurt the most by not only the errors discussed here but also the park factor miscalculation discussed in Part 1 (hint: fWAR seriously undervalues Cueto).

As the chart shows, position player fWAR overvalues the National League, albeit to a lesser extent.  Position player fWAR suffers an almost entirely different problem then Pitcher fWAR: Unlike pitcher fWAR, which seems to over-adjust for league, position player fWAR doesn’t adjust for strength of league and division at all.  This inflates the fWAR of players/teams in weaker divisions – the NL East and NL West, for example – while deflating the fWAR of players in stronger divisions, like the AL East.

While the issue with position player fWAR is more obvious – a lack of league and divisional factors – the problem with pitching fWAR is less clear.  Perhaps part of the problem is how replacement level is calculated.  I am not familiar enough with the FanGraphs’ process of calculating WAR to know if there is a clear, fixable mistake.  Either way, hopefully this article will inspire change in the way that fWAR is calculated for both pitchers and position players, with the changes to position player fWAR being much simpler to incorporate.

There are different ways to rank players for fantasy baseball. These rankings will depend on the league settings and your personal beliefs as to what is the best method. Currently, there are two main methods that immediately come to mind. Here at FanGraphs, Zach Sanders has posted his method to determine rankings and dollar values using z-scores, which takes into account the standard deviation for that player’s statistics compared to the set of players in the sample. Others prefer a method called Standings Gain Points. With Standings Gain Points, you will need to have an expectation of the end-of-year standings in the different categories based on previous year’s data.

I wanted to find a quick-and-easy way to rank starting pitchers and I remembered reading a post at Tom Tango’s site about the usefulness of “strikeouts minus walks” for pitchers (K-BB). Similarly, some writers here at FanGraphs have begun to use K%-BB% in their posts. I decided to look at a few options to see which is the best “quick-and-easy” way to rank starting pitchers.

For each of the last three seasons, I used the z-scores method to determine the top 100 starting pitchers for comparison purposes. More specifically: I found the sum of the standard deviations for four traditional categories for starting pitchers: wins, strikeouts, ERA, and WHIP (saves were not included because I was just looking at starting pitchers). I’ll use Clayton Kershaw as an example. I divided Kershaw’s wins (21) by the standard deviation of all pitchers’ wins in this sample (3.5) to get the z-score for Kershaw in the wins category (6.0). I did the same for strikeouts (239/41.9=5.7).

For ERA and WHIP, I had to do a little more work. Again, using Kershaw as an example. I took the ERA of the group of pitchers (3.27), subtracted Kershaw’s ERA (1.77), multiplied by Kershaw’s innings pitched (198.3), then divided by nine. The result was 33. This gave me Kershaw’s number of runs saved (runs allowed below what a pitcher with a league average ERA would allow in that many innings). This may be confusing to some. That number, 33, is how many more earned runs Kershaw would have to allow to have a league average ERA. In 2014, Kershaw pitched 198.3 innings and allowed 39 earned runs for an ERA of 1.77. Had he allowed 33 more earned runs, he would have allowed 72 earned runs. Allowing 72 earned runs in 198.3 innings would give him an ERA of 3.27, which is the average of this group of pitchers I’m working with.

I did a similar thing for WHIP. I took the WHIP of the group of pitchers (1.18), subtracted Kershaw’s WHIP (0.86), then multiplied by Kershaw’s innings pitched (198.3). This gave me the number of base runners saved for Kershaw (64). This means had Kershaw allowed 64 more base runners (walks or hits) in the same number of innings pitched, his WHIP would have been league average.

Once I found runs saved and base runners saved for each pitcher, I found the standard deviation of the group of pitcher for each metric. I then divided that pitcher’s runs saved and base runners saved by the standard deviation of the group of pitchers to get the z-scores for ERA and WHIP. Because I was only dealing with starting pitchers, I did not use saves, but if relievers had been included, saves would be done the same way as wins and strikeouts. Once I found the z-scores for wins, strikeouts, ERA, and WHIP, I added them together to get a total number for each pitcher. The pitchers were ranked by this total number for fantasy purposes.

Once I had this total for each pitcher, I ran correlations with each pitcher’s total number based on z-scores and some potential “quick-and-easy” methods to rank these pitchers. I started off with four potential methods: raw strikeouts, K-BB, K%, and K%-BB%. The following shows the correlation for these four methods with the total number figured above (the sum of the z-scores for the four fantasy pitching categories for starting pitchers).

For 2014:

0.80    K-BB

0.72    Strikeouts

0.65    K%-BB%

0.58    K%

For 2013:

0.78    K-BB

0.72    Strikeouts

0.67    K%-BB%

0.56    K%

For 2012:

0.77    K-BB

0.71    Strikeouts

0.55    K%-BB%

0.44    K%

Of these four methods, K-BB has the highest correlation, followed by raw strikeouts. This makes sense because starting pitchers in fantasy baseball get some of their value from the innings they pitch. They need to pitch to get those wins and strikeouts. The other two options (K% and K%-BB%) don’t factor in playing time, so it’s not surprising that they don’t correlate as well as K-BB and raw strikeouts.

With this in mind, I took K% and multiplied by innings pitched for an additional metric, along with (K%-BB%)*IP for another. Below, I’ve included these two options.

For 2014:

0.83    (K%-BB%)*IP

0.80    K-BB

0.78    K%*IP

0.72    Strikeouts

0.65    K%-BB%

0.58    K%

For 2013:

0.81    (K%-BB%)*IP

0.78    K-BB

0.77    K%*IP

0.72    Strikeouts

0.67    K%-BB%

0.56    K%

For 2012:

0.80    (K%-BB%)*IP

0.77    K-BB

0.76    K%*IP

0.71    Strikeouts

0.55    K%-BB%

0.44    K%

As you can see, (K%-BB%)*IP comes out on top, but the more simple K-BB is close and the point is to find a “quick-and-easy” method. The next idea I had was to incorporate home runs allowed. Keeping it simple, I created K-BB-HR and compared it to the others.

For 2014:

0.83    (K%-BB%)*IP

0.82    K-BB-HR

0.80    K-BB

0.78    K%*IP

0.72    Strikeouts

0.65    K%-BB%

0.58    K%

For 2013:

0.81    (K%-BB%)*IP

0.81    K-BB-HR

0.78    K-BB

0.77    K%*IP

0.72    Strikeouts

0.67    K%-BB%

0.56    K%

For 2012:

0.80    K-BB-HR

0.80    (K%-BB%)*IP

0.77    K-BB

0.76    K%*IP

0.71    Strikeouts

0.55    K%-BB%

0.44    K%

This method (K-BB-HR) is right there with (K%-BB%)*IP. It’s not quite as simple as K-BB, but it is quite simple. Using K-BB is very simple and will get you close to the more complex methods to rank starting pitchers. If you want to take it one step farther, use K-BB-HR.

So, without further ado, here are the top 20 pitchers ranked by K-BB-HR using Steamer projections for 2015:

1. Clayton Kershaw (164)
2. Chris Sale (152)
3. Max Scherzer (151)
4. Felix Hernandez (141)
5. Stephen Strasburg (137)
6. Yu Darvish (136)
7. Madison Bumgarner (135)
8. Corey Kluber (132)
9. David Price (123)
10. Matt Harvey (121)
11. Zack Greinke (118)
12. Jon Lester (116)
13. Masahiro Tanaka (111)
14. Cole Hamels (110)
15. Adam Wainwright (106)
16. Johnny Cueto (106)
17. James Shields (105)
18. Jeff Samardzija (102)
19. Jordan Zimmermann (101)
20. Ian Kennedy (100)

That’s a pretty good-looking list and easy to figure using three basic statistics and subtraction.