The A’s winning the West after a huge offseason makeover in 2015 might seem like an unlikely achievement, but here are seven reasons why this is not at all unachievable:

1. The New-Look Infield

In 2015 the Athletics will be throwing out a fresh face at each of the four starting infield positions. Here’s a quick look:

Especially from an Athletics fan’s perspective, the left side of this chart looks very nice. The names Moss and Donaldson are very important and dear to you; however, the right side of this chart is actually more productive overall. While Moss and Donaldson have the highest wOBA of the eight players at .351 and .339 respectively, Jed Lowrie and Eric Sogard have the two lowest at .300 and .262 respectively. This averages out to be a wOBA of .313. The Average wOBA for 2015’s infield is .320.

You might be thinking that Lawrie does not compare to Donaldson, and you could be right. The fact of the matter is that Lawrie is a downgrade from Donaldson, but not by all that much, meanwhile, Zobrist is a huge upgrade from Sogard at 2B. And even Sogard is an upgrade from Punto as the UTIL infielder.

Other important categories that favor the 2015 infield are BB%, K%, FB%, Contact%, OPS, OBP, etc. Also, the new infield got quite a bit younger and faster.

The 2015 infield also has a higher average wRC+ at 104 in comparison to 2014’s 102.5. These aren’t huge differences, but the A’s are expecting better years from Lawrie, who was injured a lot in 2014, Davis, who hit 32 HR in 2012, and Semien, who hasn’t really had much of a chance in the majors yet. These moves were necessary, not only to save money, but because the 2014 team didn’t actually win the AL West. I’m now going to compare this new INF to a team that did win the West, the 2012 A’s.

The 2012 INF consisted of Josh Donaldson, Stephen Drew, Cliff Pennington and Brandon Moss. There were other guys in the mix earlier on in the season, i.e. Jemile Weeks, Brandon Inge, however, these were the guys that got it done down the home stretch.

These numbers are almost identical, however the 2015 team has a slight edge in every category. That is despite the fact that the A’s expect growth from the incoming players this season. Even after the significant losses of Josh Donaldson and Brandon Moss the A’s infield is more than capable of pushing them toward another Western division title.

2. The Designated Hitter

The Athletics’ DH numbers from 2014 are not where you want them to be. Yes, Melvin will still use this spot as a “half-rest” day for players like Crisp, Reddick and Lawrie, but the newcomer Billy Butler will most likely fill the spot the majority of the time. Butler is a huge upgrade from the A’s team DH numbers last season in which Callaspo, Moss, Norris, Jaso, Vogt, Dunn, among countless others had at bats. Let’s take a look at the 2014 A’s DH numbers vs. Billy Butler’s 2014 numbers. (he also had a down season):

This chart shows that Butler is a significant upgrade at the DH spot, as he will bring a lot more production to the middle of this lineup. I should also bring up his career numbers, which are a wOBA of .351 and wRC+ of 117. If Butler can get back to his career form, the A’s offense is looking at a huge boost, but even if he doesn’t and repeats his 2014 performance, the DH spot is still getting a nice upgrade.

3. The Rotation

The starting rotation for the A’s no longer consists of Jon Lester, Jeff Samardzija or Jason Hammel, but it is still a very strong group with huge potential. I’m going to compare the projected 2015 group to the 2012 and 2013 rotations that led the A’s to division titles.

2012

2013

Projected 2015 (2014 STATS)

As you can see, the 2015 rotation wins four out of the six categories. They won the majority of the categories already, but this 2015 staff has the potential to be better than these numbers show. In past years, the A’s success had a lot to do with their strong pitching staff — this is a big reason why I believe they will win the west in 2015 — however, we need to take a look at the projected rotations of the four other teams in the division to see how the A’s compare to each of them.

Here are the five teams’ projected rotations for 2015:

Angels

Mariners

Rangers

Astros

Athletics

The Mariners and the Athletics both have really solid pitching staffs. The Mariners have arguably the best pitcher in the American League in Felix Hernandez. The Angels also have a good young ace in Garrett Richards, but he is coming off an injury; it will be interesting to see how he bounces back. Sonny Gray proved that he is a true ace last season, going over 200 innings and pitching extremely well in big games. The numbers do give the A’s a slight edge; they won three of the six categories and the Mariners won two of them. King Felix, Iwakuma and the solid supporting cast are hard to bet against, but 1-5, the A’s have a better staff according to last year’s numbers.

4. Speedee Oil Change

Anytime manager Bob Melvin calls on the bullpen, the A’s should be confident. There are so many capable arms out there that it’s really not fair. Honestly, a starter could go four innings with a lead and that would be enough for this bullpen with Otero, Abad, Cook, O’Flaherty, Clippard and Doolittle in the mix. There are plenty of other options as well that might not get a shot because it’s already crowded with talent out there. The starters, however, are very capable of giving you six or seven innings consistently, which makes this bullpen even that much more deadly, allowing Melvin to create left-on-left matchups or vice versa. The fact of the matter is, if you can’t score, you can’t win. While the starting staff is very solid, getting to the bullpen might not be the opponent’s best option when facing the A’s. Another positive for the A’s has been their ability to fight their way back into ballgames the last few years. With a bullpen like this who can keep the deficit where it is, the probability of achieving a comeback is that much greater.

As shown by the Royals on the successful end and the Dodgers on the opposite end, the strength of your bullpen can make or break your season.

Let’s compare the A’s bullpen to the other teams in the division by highlighting the projected top six bullpen arms for each team:

Angels

Mariners

Rangers

Astros

Athletics

The Mariners and Athletics each won two out of the five categories. The Athletics also came in second in two other categories. Although this chart shows the Mariners and the A’s as pretty evenly matched, the Mariners have a lot of aging players in their pen, so we cannot be sure if they will keep up the good numbers. The Astros got a lot better by adding Luke Gregerson and Pat Neshek, but that still wasn’t enough to make them the best in the division, especially after the A’s went out and traded for the two time All-Star, Tyler Clippard. All of these teams except Texas have a very strong bullpen, so trying to come back from a deficit is going to be a tough feat in this division.

The A’s also have a lot of other options past these six players, probably more so than the other four teams, making injuries less of a factor for them.

5. Coco Crisp

When Coco Crisp is at the top of the lineup, the A’s are a better team. Over the past three seasons there’s no player who has had as much of an overall impact on this team than Coco. Whether it’s at the plate, in the field or in the clubhouse, Crisp’s impact is significant. Despite losing a lot of star players, the A’s will not take a step backward because they still have their most important piece in Crisp. If Crisp would have been traded away this offseason, I don’t believe the A’s would be ready to compete for the AL West title in 2015. There would be too long of an adjustment period, someone else would need to step up big time and fill his shoes. Luckily, the A’s don’t have to worry about that yet. Bottom line: the A’s need Coco Crisp.

6. Depth and Versatility

Having a deep roster is always important in a 162 game season. You will have players go on the DL, it is unavoidable. Being able to replace the injured players with capable major leaguers is key to a team’s success in the long run. Billy Beane has constructed a 40-man roster with tremendous depth, especially with pitching. The A’s have eight or nine guys capable of making the starting rotation, not to mention two others (Jarrod Parker and A.J. Griffin) due back this summer. There are upwards of ten players competing for a spot in the bullpen as well. It will be interesting to see who makes it on to the 25-man roster, but I wouldn’t be surprised if Triple-A Nashville has a stacked opening day roster. Having great options in the minor leagues is key for any team, and the A’s will definitely have that this season with Kendall Graveman, Chris Bassitt, Sean Nolin and Brad Mills, four starters likely to be starting in Triple-A. Also, RJ Alvarez, Eury De La Rosa and Evan Scriber, three above-average bullpen arms will likely be starting down there as well.

The A’s lineup is a very versatile group this season. Eric Sogard, A’s second baseman the last few seasons, has moved into a utility INF role; he plays excellent defense, and for a defensive replacement, he can handle the stick pretty well. Ben Zobrist is known for his ability to play all over the diamond with above-average defense, and also for getting the job done from both sides of the plate; his career wOBA is .344. Craig Gentry and Sam Fuld can play all three outfield positions with ease while providing speed off the bench in pinch running situations. Marcus Semien will likely be the everyday SS, but he can play all over the infield as well. Stephen Vogt will mostly catch, but he can play first base and corner outfield if the A’s need him to. The amount of options the A’s have, if injuries do occur, are limitless. It will be entertaining to see how Bob Melvin constructs his lineup card every day.

7. The Manager

Bob Melvin is the perfect manager for a team of misfits and players who have never played together previously. He will bring this group to play for each other, as a unit, one day at a time. Melvin is great at creating matchups that benefit the team and give them the best chance to succeed. The roster that has been assembled this season is perfect for just that. It is loaded with skilled, versatile players. Bob Melvin has done it before and he will do it again.

Johnny Cueto was very good in 2014. By traditional metrics, he was excellent: 20 wins, 242 strikeouts in 243 2/3 innings, a 2.25 ERA and 0.96 WHIP. By more advanced metrics, he was good but not quite that good: 3.30 FIP, 3.21 xFIP, 3.15 SIERA. Per FanGraphs, Cueto had 4.1 WAR, ranking him 14^th among pitchers. Baseball-Reference had Cueto with 6.4 WAR, which placed him 6^th among pitchers. No matter how you look at it, Johnny Cueto was good in 2014.

Johnny Cueto threw 3659 pitches during the regular season last year, making him one of only six pitchers to throw 3500 or more pitches. [NOTE: for this article, I’m only using major league regular season pitches thrown.] Cueto is not a big guy for a pitcher. He’s listed at 5’11, 215. The other five pitchers to throw 3500 or more pitches last year were David Price (6’6”, 220), Corey Kluber (6’4”, 215), James Shields (6’3”, 215), Max Scherzer (6’3”, 220), and R.A. Dickey (6’3”, 215). Of these six pitchers, Cueto had the greatest increase in pitches thrown from the previous year.

So, based on his high pitch total last year and low pitch total the year before, should we be worried about Johnny Cueto in 2015?

Let’s start with the high pitch total. Using the Baseball-Reference Play Index and the FanGraphs Leaderboards, I gathered some information. The following chart shows the number of pitchers who threw 3500 or more pitches each year going back to 2000, along with the average number of pitches thrown per pitcher in their high-pitch year, the average number of pitches thrown by those same pitchers in the following year, and the difference between the two.

As you might expect, pitchers who throw 3500 or more pitches one year are likely to throw fewer pitches the following year. That’s the nature of regression to the mean. To throw 3500 pitches, a pitcher is likely to be having a good, healthy season. Things happen in baseball and it’s difficult for any group of pitchers to have back-to-back good, healthy seasons. Some are going to get injured and some are going to pitch worse and pitch less. In this case, the average difference was 379 pitches. Over the last fourteen years, pitchers who throw 3500 or more pitches one season have averaged 379 fewer pitches the following season. These days, 379 pitches is about 3 or 4 starts.

What about performance following a 3500-plus pitch season? The following chart shows how pitchers who threw 3500 or more pitches in one season performed in the following season.

Once again, keeping in mind regression to the mean, it’s not surprising to see that these pitchers were worse the following season. Looking at ERA+, 58% of these pitchers were worse in the year following their high pitch total year. The majority (63%) also had lower strikeout rates, but improved walk rates (52% improved their walk rate in the year after their high pitch year).

More specifically, the following chart shows the difference in innings pitched (IP) and runs allowed per 9 innings (RA/9):

Over the last fourteen years, pitchers who threw 3500 or more pitches in one year averaged 228 innings pitched that year. In the following year, they dropped to 205 innings pitched, a difference of 23 innings (this matches up well with the 379 fewer pitches thrown). In their high pitch count year, these pitchers had an RA/9 of 3.87. The following year, their RA/9 went up to 4.08, an increase of 0.21 RA/9.

Is this bad news for Johnny Cueto and the other five pitchers who threw more than 3500 pitches in 2014? Not really. I’ve mentioned regression to the mean a couple times. Based on regression, we would expect these pitchers to pitch fewer innings and have a higher RA/9.

With this in mind, here is a look at these 131 pitchers and their innings pitched and RA/9 in the year after they threw 3500 or more pitches compared to their Marcel projections for that year. Thanks to The Baseball Projection Project, I was able to find Marcel projections going back to 2001. The following chart shows each pitcher’s next-year Marcel projection for innings pitched and RA/9, along with each pitcher’s next-year actual innings pitched and RA/9.

Over the last fourteen years, pitchers who threw 3500 or more pitches in a season were projected by Marcel to pitch 194 innings the following season. They actually pitched 205 innings in that following season, for an increase of 11 innings over their Marcel projection.

When it comes to performance, we find that these pitchers averaged a 3.87 RA/9 in their high pitch total season and were projected by Marcel for a 4.05 RA/9 for the following season. They actually had a 4.07 RA/9 in the following season. It’s a very slight increase of 0.02 RA/9, which shouldn’t be anything to worry about, really.

So it would appear that throwing 3500 pitches in one season should not be a big cause for alarm. The pitchers who have done this recently did not perform any worse than their projections would have expected.

With Cueto, though, there was that other thing that worried me—his large increase in pitches thrown from 2013 (953 pitches thrown) to 2014 (3659 pitches thrown).

With this in mind, I looked at the 131 pitchers in this study to find the pitchers who had the largest increase in pitches thrown from one year to the next. I set the limit at no more than 2000 pitches thrown in the year prior to that pitcher’s 3500-plus pitch season. There were only 10 pitchers, including Cueto, who threw fewer than 2000 pitches in one season and more than 3500 pitches the next season. That screams “small sample size!”

Unfortunately, there is a problem with even this group of comparable pitchers—they aren’t very good matches for the Johnny Cueto Experience. For example, one of them was Barry Zito. Zito only had 92 2/3 innings in the major leagues in 2000, the year before he threw more than 3500 pitches, but he also pitched 101 2/3 minor league innings that year, so there really wasn’t a big increase in the number of pitches thrown from one year to the next. He gets eliminated. The same is true for Steve Sparks, Roy Halladay, Randy Johnson, Noah Lowry, and Adam Wainwright, all of whom had additional minor league innings that would push them over the 2000 pitch limit. Unfortunately, that leaves very little to work with—just three pitchers (Woody Williams, Roy Oswalt, and Chris Capuano).

These three pitchers did throw fewer innings in the year after their 3500-plus pitch year, but to a lesser degree than the group as a whole and they pitched more innings than projected by Marcel. Also, this group actually improved their RA/9 in the year after their 3500-plus pitch year and were much better than their Marcel projection.

Based on throwing 3500 or more pitches, it doesn’t appear there’s anything to worry about with Cueto. Based on such a large increase in pitches thrown from one year to the next, we don’t really know because there just haven’t been many pitchers allowed to do that over the last 14 years. My gut still tells me to be wary but the numbers don’t see a problem.

Jeff Zimmerman posted an interesting article on Friday. It prompted me to try to analyze the relationship between (i) an outfielder’s ability to make plays, and (ii) an outfielder’s ability to save runs. From my analysis below, the relationship is not as hand-in-glove as I initially would have thought.

From what I understood about Jeff’s article, he advanced a new defensive metric called “PMR,” which stands for Plays Made Ratio. Jeff calculated this ratio using data from Inside Edge, which categorizes every ball in play into one of six buckets. Jeff explains:

Most of the fielding data falls into two categories. The zero percentage plays are just that, impossible plays, and make up 23.2% of all the balls in play. Balls in this bucket are never caught and always have a 0% value. The other major range is the Routine Plays or the 90% to 100% bin. Defenders make outs on 97.9% of these plays, which make up 64.0% of all the plays in the field; the 2.1% which aren’t made are mostly errors. In total, 87.2% of all plays are graded out as either automatic hits or outs; it is the final ~13% which really determine if a defender is above or below average.

Between almost always and never, four categories remain. Even though each category has a defined range, like 40% to 60%, the average amount of plays made is not exactly in the middle of each range. Here are the actual percentage of plays made in each of the four ranges.

With these league average values and each individual player’s values, a ratio of number of plays made compared to the league average value can be calculated. To have the same output of stats like FIP- and wRC+, I put Plays Made Ratio on a 100 scale where a value like 125 is 25% better than the league average. Here is the long form formula and Jason Heyward’s value determined for an example.

Plays Made Ratio = ((Plays made from 1% to 90%)/((1% to 10% chances * .063%)+( 10% to 40% chances * .289)+ (40% to 60% chances * .576) + (60% to 90% chances * .805))) * 100

Heyward’s Plays Made Ratio = ((1+10+9+26)/((14*.063)+(16*.289)+(9*.576)+(27*.805)))*100

Heyward’s Plays Made Ratio = (46/32.4)*100

Heyward’s Plays Made Ratio = 142

Heyward had a heck of a season. Of the 66 playable balls hit to him, normally only 32 of them would have been caught for an out. Heyward was able to get to 46 of them, or 42% better than the league average. He has consistently had above league average values with a 133 value in 2012 and 125 in 2013.

Jeff posits that the new PFM metric gives us new insight that FanGraphs current go-to defensive metric (Ultimate Zone Rating) does not:

Now remember this stat [PMR] only looks at how often a fielder would have made the play considering their position on the field. The team could be playing its outfielders back to prevent a double or their infielders in for a bunt which could put the defender out of position. Additionally, it doesn’t look at the final results of the play (at least for now). If Sir Dive Alot is playing in the outfield and he loves to try to catch every ball hit his way, then he will get to a few extra flyballs by diving all the time, but those he doesn’t get to will pass him by for more doubles and triples. Also, an outfielder could be good at making plays while coming in versus going deep; balls which fall in over his head would be more damaging than those which fall for shallow singles. While his Plays Made Ratio may be high, the number of runs he saves, as seen by UZR or Defensive Runs Saved, may be lower by comparison.

This got me thinking about the relationship between a player’s PMR and his UZR, and, more specifically, his RngR. As I understand RngR, it is the component of UZR that estimates the number of runs a player saves, or surrenders, due to his range. RngR isolates the contribution a player’s range makes to his Ultimate Zone Rating by ignoring the contributions from his arm and his ability to limit errors.

Intuitively, it would make sense that a player’s PMR and his RngR would be strongly correlated. In other words, a player whose range allows him to make more plays than average would also be the same type of player whose range would allow him to save more runs than average. A simple two-by-two matrix, with RngR along the left side and PMR along the top would show the following quadrants:

My intuition is that players would fall in either quadrant (2) or quadrant (3). The interesting questions arise with players that would fall in quadrant (1) (those who exhibit poor range, but whose range saves runs), and in quadrant (4) (those who exhibit good range, but whose range does not save runs). There are several explanations for why a player may fall into quadrant (1) or (4).

Jeff noted three possible explanations.  First, a player may be overly aggressive, which would may lead to more outs (a higher PMR) but also more misplays resulting in doubles and triples (a lower RngR). Second, “an outfielder could be good at making plays while coming in versus going deep; balls which fall in over his head would be more damaging than those which fall for shallow singles. While his Plays Made Ratio may be high, the number of runs he saves, as seen by UZR or Defensive Runs Saved, may be lower by comparison.” Third, a player (or his team) may be particularly well adept at positioning himself, which would amplify his RngR rating, but not necessarily his PMR (as Jeff noted when discussing Nick Markakis).

How does the relationship between PMR and RngR look if it is applied to actual players? To find out, I looked at all center fielders who between 2012 and 2014 had at least 70 “total chances” (defined by Inside Edge as balls hit to that fielder where there is between a 1% and 90% likelihood that the ball is caught). That provided me a list of 18 center fielders. Next, I calculated each player’s rate-based RngR/150 (calculated by his total RngR divided by the innings he played in center field, multiplied by nine, multiplied by 150). That revealed the following table:

A scatter chart of this information looks like this. I also added a best-fit line to the scatter plot. My intuition that a player’s RngR/150 would be strongly correlated with his PMR is contradicted by this data. In fact, according to this data, (and based on my very limited skillset at statistical analysis, which may be completely incorrect), only 15% of the runs saved due to these 18 center fielders’ range can be explained by their Plays Made Ratio.

Even more interesting than the two-by-two matrix characterization introduced above, are the points on the scatter plot that are either way above (Juan Lagares and Lorenzo Cain) and way below (Dexter Fowler) the linear trendline.

The data suggest that Lagares/Cain and Fowler have similar range in center field, but that the former use their range to save more runs than the latter. One possible implication of this information is that Fowler is not optimizing his ability and that through better decision-making (such as being more aggressive or less aggressive on fly balls) or better positioning he could save more runs. As discussed earlier, it could also mean that Fowler is not (relatively) adept at playing balls hit over his head or in the gap, which leads to more doubles and triples.

On a larger scale, a possible implication of this data is that teams could significantly improve the amount of runs their center fielders save by (i) coaching their center fielders to make optimal decisions regarding their aggressiveness and (ii) properly positioning their center fielders. I would be curious to analyze what is the optimal amount of aggression a center fielder would have in going after balls hit to the outfield, the optimal way to position himself. For example, is it better to play shallow and be aggressive in cutting off singles (which Lagares has a reputation of doing) or to play deep? Those questions are best answered in a follow-up post/article.

The Oakland Athletics may have finally completed their roster turnover on Wednesday with their most recent deal sending Yunel Escobar to Washington for RP Tyler Clippard. However, you can never know if Billy Beane is finished making moves. With that being said, I’d like to break down the roster from last year to this year and assess whether or not the team will actually regress in 2015. The fact is that the Athletics got quite a bit younger this offseason and acquired many players with a lot of team control remaining. The distant future appears brighter now than it did prior to this offseason, but the main question is, will the Athletics be able to compete in 2015 as well as they would have prior to the roster turnover? Lets take a look at the numbers:

STARTING LINEUP

I will start by comparing the most common nine players in the A’s lineup last year to their projected starting nine this year, using WAR and wRC+:

[All stats give on the chart will represent the 2014 season in the MLB only. In further commentary I may bring up career numbers or minor league numbers for some players.]

2014 AVG WAR = 2.1 / Total wRC+ = 929

2015 AVG WAR = 1.7 / Total wRC+ = 937

As shocking as it may seem, this displays that the A’s should in fact score more runs with their lineup in 2015 than they did with Donaldson, Moss and Cespedes in the heart of their lineup last season. Although, this chart only accounts for 2014 stats, in which Billy Butler (among others) had an off year. If the A’s can get him back to, or even near his 2012 form, in which his WAR was 2.9 and his wRC+ was 139, they could be in for a significant upgrade on offense as a whole. One of the reasons why this lineup has the potential to be more successful even after losing a guy like Donaldson is because of the acquisition of Ben Zobrist. While Brett Lawrie is -4.7 to Donaldson in WAR and -28 to Donaldson in wRC+, Zobrist is +5.4 to Sogard in WAR and +52 to Sogard in wRC+, more than making up for the loss of Donaldson. While the A’s did use a lot of other DH besides Callaspo in 2014, he totaled the greatest amount of plate appearances from that spot, which might lower the 2014 numbers a little.

The average WAR is down slightly from last season, but with Stephen Vogt behind the plate and Marcus Semien most likely getting the every day job at SS, the A’s feel they are upgrading defensively. Semien’s numbers represent his slim 255 plate appearances in the majors last season, but in TripleA his wRC+ was 142. You cannot expect that out of Semien at the major league level, but it shows that he has potential to improve in 2015. The A’s did use a lot of players at each position last season and they will again in 2015; that is why it is important to also take a look at the bench players from last year and the projected bench for this year.

BENCH

While the 25-man roster is not set in stone for 2015 just yet, here is last year’s most commonly used bench players versus next year’s projected bench.

2014 AVG WAR = 1.1 / TOTAL wRC+ = 344

2015 AVG WAR = .48 / TOTAL wRC+ = 367(407)

While these numbers are a bit skewed due to the fact that Canha has not yet reached the majors and also because Jaso was actually a starter while he was healthy, they do give a good idea of what to expect in 2015. Sogard takes over for Punto as the reserve infielder. Fuld and Gentry will most likely platoon in LF, same goes for Vogt and Phegley at C. Since Fuld and Vogt are LH, they will see more time in the starting lineup, leaving Gentry and Phegley on the list of bench players for 2015. Gentry and Phegley will see most their time against lefties, which will likely help their overall numbers. The A’s always do a great job shifting their lineup to create the match ups they want, expect more of the same with platoons and late pinch hitting in 2015.

STARTING ROTATION

The starting rotation is an area where a lot of people say they A’s have question marks. This may be due to the fact that they lost Jon Lester and Jason Hammel to free agency and traded away Jeff Samardzija to the White Sox earlier this off season. However, the A’s held the best record in baseball for months in 2014 with a rotation featuring Sonny Gray, Scott Kazmir, Jesse Chavez, Drew Pomeranz and Tommy Milone. Four of those guys will be returning in 2015, with a slew of other young arms fighting for a spot in the rotation. Anyone from Chris Bassitt, Jesse Hahn, Sean Nolin or Kendall Graveman would be an upgrade or at worst an equal replacement of Milone. Let’s take a look at the numbers for the five players who started the most games for the Athletics last season VS the A’s projected rotation for next season using ERA, WHIP and WAR from the 2014 season:

2014 AVG: ERA = 3.46 / WHIP = 1.22 / Avg WAR = 2.56

2015 AVG: ERA = 3.12 / WHIP = 1.18 / WAR = 1.96

Keep in mind that ERA and WHIP are better when they are lower and WAR is better if it is higher. While this list does not consist of Jon Lester, the A’s were at their best when they still had Chavez and Milone in their rotation. Also, it was a small sample size for Pomeranz, so we cannot expect numbers quite that solid again in 2015. However, with all that being said, the A’s, despite losing All-Stars, should not take more than a tiny step back in 2015. This rotation is still very solid and is in fact younger this year than last. Not only that, the A’s now have a lot more depth with three other pitchers not on this list that could fill a rotation spot, Chris Bassit, Sean Nolin and Kendall Graveman. Also, we cannot forget about the Tommy John rehabbers Jarrod Parker and AJ Griffin, who could make their way back into this rotation before the All-Star break. Both Parker and Griffin were huge contributors to the A’s success in both 2012 and 2013.

BULLPEN

There are a lot of similar faces coming back to the Athletics’ bullpen in 2015. So, instead of continuing with the format I’ve used for position players and the starting rotation I’m quickly going to compare Luke Gregerson and Tyler Clippard, the one main difference in the bullpen for 2015.

Player ERA / WHIP / WAR

Luke Gregerson 2.12 / 1.01 / 0.9

Tyler Clippard 2.18 / 1.00 / 1.5

These numbers are very similar, making Clippard a perfect replacement for Gregerson, taking over the 8th inning duties in front of incumbent closer Sean Doolittle. I don’t think many people expected the A’s to make a move to acquire another back end of the bullpen piece. Even after losing Gregerson, they seemed to have a very solid bullpen, but now it is even more solidified with a proven set-up man in Tyler Clippard. Another important thing to note about Clippard is his ability to create fly balls. His FB% in 2014 was 49.4% also, his IFFB% was 19.3% and that will likely increase mightily with him now pitching in Oakland. He is the perfect pitcher for the o.Co Coliseum. The A’s will pay Clippard more than they would have paid Escobar in 2015, but they are saving money in the long run due to the fact the Escobar is owed 14 million over the next two seasons and Clippard becomes a free agent after this season (in which he will make around 9 million).

Now let’s take a look at 12 potential options for the Athletics bullpen in 2015. Some of them are locks, but the others will either gain a spot due to the fact that they did not make it into the rotation or if they have a solid showing in spring training.

There are a lot of very solid options for the A’s bullpen in 2015. I’d expect to see, Doolittle, Clippard, O’Flaherty, Cook, Otero and Abad for sure, but I expect all of these guys to make an impact at some point, if not this season then in 2016.

TAKEAWAY

The Athletics have a very deep pitching staff. With Sonny Gray and Scott Kazmir headlining the rotation, they have a plethora of options to fill the remaining three spots. Pomeranz, Hahn and Chavez look to be the leading candidates, although Billy Beane himself has mentioned Kendall Graveman as someone he sees making the rotation out of spring training. The A’s also have a very strong bullpen, especially after the recent acquisition of All-Star set-up man Tyler Clippard. After losing Josh Donaldson, Brandon Moss, Yoenis Cespedes and Derek Norris (four All-Stars), the A’s lineup for 2015, according to wRC+ actually got better. It’s not always the big name All-Stars that make a team successful. Oakland has proven this many times in the past, most recently in 2012, right after an offseason makeover similar to this year’s. The one piece that has remained since before the 2012 makeover and after this 2015 makeover, is Coco Crisp. There cannot be enough said about the value of Crisp to the A’s organization. With Crisp healthy in CF and the newly acquired pieces filling in around him, I expect the A’s to be back competing for another American League West division title in 2015.

One of the things I enjoy most about FanGraphs Community–really, I’m not just apple polishing here–is the quality of the comments. After I came up dry trying to explain the increase in hit batters to near-historical levels in recent years, a commenter led me to what I feel is the correct path: Batters are more likely to be hit when the pitcher’s ahead on the count (and thereby more likely to work the edges of the strike zone, where a miss inside may hit the batter), and the steady increase in strikeouts has yielded an increase in pitchers’ counts on which batters get hit. Similarly, on December 30, when I wrote about how larger pitching staffs have adversely affected the performance of designated hitters, I got this smart comment from Jon L., reacting to my contention that the relative (not absolute) rise in DH offensive performance (measured by OPS+) from 1994 to 2007 probably wasn’t related to PEDs because the improvement was relative to increasing offensive levels overall:

I think it was clearly a PED thing. Players were able to build strength and muscle mass to enhance hitting prowess, and were willing to take the hit on baserunning and agility that comes with toting more weight around. And why not? The money’s in hitting. PEDs were more appealing to players with some initial level of slugging ability, and disproportionately benefited DH-type skills.

That made me think about the Steroid Era (or the PED Era, or, as Joe Posnanski put it, the Selig Era). Generally, I avoid this issue. I listen to SiriusXM in my car, and when I’m on MLB Network Radio and the discussion turns to PEDs, I change the station. I’ve had enough of it for this life, and of course it’ll keep going into overdrive every year around this time with all the Hall of Fame posturing. And, of course, there are commentators like Joe Sheehan who attribute the change in offense since drug testing was instituted to changes in contact rate rather than, as he calls them, “sports drugs.” I’m not making a call on any of that here.

But Jon L.’s comment made me look at the era in a different light. As I noted in my piece, between 1994 and 2007, the average OPS+ for designated hitters was 109. Prior to that, it was 104, and since then, it’s been 106. Those 14 years between 1994 and 2007 represent the high-water mark for DH offense. both absolutely and relatively. In the 42 years in which the American League has had a designated hitter, there have been 28 seasons in which the OPS+ for DHs was 105 or higher: Every season from 1994 to 2007, but just half of the remaining 28 years.

So I’m going to start with the years 1994-2007 as my definition of the Steroid Era. I’m not saying they’re the right answer. They do fit in with the record for DHs, and I’d note that those fourteen years account for 23 of the 43 player-seasons, and 14 of the 23 players, hitting 50+ home runs in a season. (And that doesn’t include 1994, when six players–Matt Williams, Ken Griffey Jr., Jeff Bagwell, Frank Thomas, Barry Bonds, and Albert Belle–were on pace for 50+ when the strike ended the season.) But maybe the Steroid Era started, as Rob Neyer recently suggested, in 1987, following Jose Canseco’s Rookie of the Year season. That’s the same starting point the Eno Sarris points to in this article from 2013. Maybe it ended in 2003, the last year before drug testing commenced. I’ll get to that later.

To test Jon L.’s hypothesis, I looked at Bill James’s Defensive Spectrum, which puts defensive positions along a continuum:

DH – 1B – LF – RF – 3B – CF – 2B – SS – C

For purposes of this analysis, let’s just say that the defensive spectrum rates positions as offense-first through defense-first. (It’s more nuanced, having to do with the availability of talent, but that’s not important here.) DHs, obviously, are asked only to hit, not to field. On the other end of the spectrum, players like Clint Barmes and Jose Molina get paid for the glove, not their bat.

For each position, I looked at their relative hitting (measured by OPS+, the only relative metric I could find with positional splits going back to the implementation of the DH). Obviously, overall offense increased across baseball during the Steroid Era. That’s not at issue. Rather, I’m looking for the contours of the increase: Did some types of players benefit more than others? That’s the beauty of relative statistics. Since they average to 100 overall, they’re effectively a zero-sum game. In pretty much identical playing time, Justin Upton’s OPS+ improved from 124 in 2013 to 132 in 2014. That means that the rest of his league, in aggregate, lost 8 points of OPS+ over Upton’s 642 or so plate appearances from 2013 to 2014. Taking that logic to positions, if one position goes up, as the DHs did from 1994 to 2007, another position has to go down.

I compared three ranges of seasons:

• The Steroid Era; fourteen years from 1994 to 2007
• The fourteen prior seasons, 1980-1993
• The seven seasons since

If, as Jon L. suggests, the Steroid Era disproportionately helped sluggers, we’d expect to see OPS+ rise for the left end of the spectrum and fall for the right end. If, as I contended, the increase in DH productivity was more due to the influx of very skilled hitters in the DH role (Edgar Martinez, David Ortiz, Travis Hafner, and others) than something systematic, the change in OPS+ among positions would be pretty random. Here are the American League results (source for all tables: baseball-reference.com):

That’s not as dramatic. Yes, there’s still a shift in OPS+ from the five positions on the right of the defensive spectrum to the four on the left during the Steroid Era, and back again thereafter. But it’s smaller and much less uniform. DHs and left fielders have actually done a bit better since the end of the differently-defined Steroid Era. That’s less compelling.

And the Steroid Era didn’t affect just the American League, of course. Of the 24 player-seasons between 1990 and 2007 in which a player hit 50+ home runs, half the players were in the National League (12 and a third, given that Mark McGwire split time in 1997 between Oakland and St. Louis en route to 58 homers), including all seven seasons of 58+ (seven and a third including McGwire’s 1997). And if you throw the NL into the mix the relationship breaks down more, regardless of how you define the Steroid Era, looking more random than systematic:

The shift of offense to bat-first positions during the Steroid Era is much less pronounced when looking at the two leagues combined, If there were an incontrovertible trend, we’d see plus signs for DHs, first basemen, and corner outfielders in the Steroid Era and minus signs thereafter, and the opposite for infielders and center fielders. That’s not the case.

So while the data aren’t altogether compelling, I’ll concede Jon L.’s point: The Steroid (or PED, or Selig) Era didn’t just boost offenses overall, it changed the contour of offensive performance, shifting some production away from the glove-first positions to the bat-first positions. There was an uptick not only in offensive performance as a whole, but particularly in offensive performance generated designated hitters, first basemen, and corner outfielders. However, the magnitude of the effect is dependent on the league and the years chosen, which indicates that it’s not strong. So I’m sticking with my view that there was an unusual concentration of talent playing DH from the mid-1990s to the mid-2000s. Designated hitters generated more offense, both absolutely and relatively, from 1994 to 2007 than in any other period. The underlying reason may be partly the Steroid Era, but we can say that those years were also the Edgar Martinez era.

With the winter meetings over and Opening Day months away, now is an interesting time to consider the economics of baseball.  Earlier this year, I developed a framework for estimating NBA team values for Mid Level Exceptional, which met with a positive reception.  With some tweaks, it can be adapted to MLB team valuation.

In my franchise valuation methodology, each team is priced based on a multiple of its revenue.  These multipliers reflect future earnings potential: the higher the multiple, the brighter the prospects for earnings growth.  This approach is common in finance; Aswath Damodaran, professor of finance at NYU Stern and author of Musings on Markets, used it to generate a back-of-the-envelope valuation for the recently sold Los Angeles Clippers.

Both Forbes and Bloomberg compute estimates of each MLB franchise’s value and annual revenue.  But I’m wary of their valuations.  Forbes consistently undershoots the sale price of NBA teams.  In January, Forbes pegged the Clippers’ value at $575 million; five months later, they sold for $2 billion. Prices for sports franchises have risen sharply over the past few years, as sports programming has become ever more valuable as live TV viewership dwindles.  The Forbes methodology hasn’t incorporated this shift in the value of broadcast rights, leading me to guess that its MLB valuations are also too low.  Bloomberg’s version reflects the same problem.  It values the average MLB team at 3.4 times revenue, not much higher than Forbes’ 2.9 times revenue.

In my version, I started with Bloomberg’s 2012 estimates of franchise revenue, which include revenue from teams’ stakes in regional sports networks and MLB Advanced Media.  (Forbes’ revenue figures are newer, but exclude these important revenue sources.)  To update the revenue numbers, I increased them by 20%, which is in keeping with MLB’s total revenue growth over the past two years.

Then, I created a range of revenue multipliers, which reflect the team’s market size (approximated by the size of the team’s MSA).  They are based on the multiples implied by recent MLB and NBA franchise sales.  In the model, big-market teams have higher multiples; I conclude that they generate disproportionate value from greater national media exposure, prestige, and ability to attract top free agents.  MLB’s lack of a salary cap makes the big-market advantage even more formidable than in the NBA.

I also chose multipliers that are slightly lower than the multipliers in my NBA team valuation model, since I perceive baseball to be a more mature industry than basketball (which means slower long-term revenue growth; this is analogous to Exxon trading at a lower P/E ratio than Facebook.)  Put the revenue and multipliers together, and the result is a range of estimated sale prices for each team.

Before jumping into the valuations, it’s worth explaining the shortcomings of the model.  The revenue multipliers are my best guesses, and I have no hard proof that they’re correct.  Using 2014 revenue data would be more accurate than assuming that individual teams’ revenue grew 20% since 2012, and multiple years of revenue data would be better than a one-year snapshot.  But to paraphrase Donald Rumsfeld, you go to war with the data you’ve got.  This is why I compute a range of likely values; unlike Forbes or Bloomberg, I don’t see the point of highlighting a single number of dubious accuracy.

With that said, here are the ranges of values for each MLB team.

A couple of findings that jump off the page:

• To no one’s surprise, the New York Yankees are the most valuable team in baseball, with an estimated price tag between $3.4 billion and $5.5 billion.  The Tampa Bay Rays are the least valuable team, with a value ranging between $630 million and $840 million.
• 21 teams have a median value of at least $1 billion; in my earlier research on NBA team valuation, only 11 teams out of 30 were valued as highly.
• The big brother/little brother dynamic of the New York and Chicago teams is reflected in their valuations.  The Yankees are worth more than twice as much as the Mets, and the Cubs are worth 40% more than the White Sox.
• The Boston Red Sox are the highest-valued team in a medium-sized market (with a median value of $2.4 billion), and the St. Louis Cardinals are the highest-valued team in a small market (with a median value of $1.1 billion).  This reflects their recent success on the field, as well as their fan base’s reach beyond their core MSAs.
• The Miami Marlins and Houston Astros appear overvalued in the model, since their recent poor performance and lack of popularity are only partially reflected in their revenue.  Their MSA’s sizes probably overestimate the size of their fan bases.  Furthermore, the model doesn’t reflect team-specific issues like fan disenchantment with a team’s owners (Marlins) or difficulty in making the team’s regional sports network widely available (Astros).

Next time an MLB franchise sells, we’ll have a clearer indication of how accurate this valuation method is.

Some people do not believe in “clutch”. The timing of hits is based on luck. If that is the case, then Allen Craig who hit .454 with runners in scoring position in 2013 is the luckiest man in baseball. But the baseball gods are a fickle bunch, and just as they bestow greatest on Allen Craig they quickly took it away. At the end of 2013, the baseball gods sent the injury plague to Mr. Craig. It was diagnosis as a Lisfranc fracture, and it has morphed Craig from a perfect fit for Fenway Park to a surefire disaster.

Without a doubt Craig is a professional hitter, he has been at all levels of professional baseball. But since that injury, the ability to turn on a baseball as evaded him. He has never been a dead pull hitter but most of his power has historically been to left field. In 2012-2013, nearly 63% of Craig’s long balls were to the left of center field (he hit 35 total home runs in 253 games)[1]. In case you have not heard of Fenway Park, there is a big green wall in left field that is only 310 feet away from home plate, not a bad place for a right handed power hitter. But as car companies know, the new model is not always better. In 2014, Craig devolved into a light hitting outfielder with little power to left field and the inability to crush inside fastballs. In 2013 before the injury, Craig hit .382 (50 of 131)[2] against inside fastballs. Post injury, he hit .189 (28 of 148).

Without the ability to pull the ball, power numbers to left field plummeted. Three of Craig’s eight home runs were to the left field side of center field in 2014[3].

Bostonians beware; shipping up to Boston may be the worst thing for Craig if he continues his trend.  Fenway is a haven for right handed power hitters who can play pepper off the Green Monster. But just a few feet left of Pesky’s Pole; right field at Fenway deepens to 380 feet and walks back to 420 feet before reaching straightaway center field. These are not exactly ideal conditions for a guy who just hit five of his eight home runs to the right of center field in 2014.In fact, only five of Craig’s home runs would have been home runs in Fenway[4].

Acquiring Allen Craig before 2014 would have been a masterful move for the Red Sox who were trying to acquire some depth in the outfield and at first base. But now they might be better off resurrecting the career of Mark Reynolds by letting him play pepper with the Green Monster (ironically the Cardinals signed him earlier this offseason) and shipping Craig out of Boston. If Craig’s 2014 season is any indication of 2015, only having limited power to the right side will not bode well for the Red Sox and Craig. If Craig cannot adjust to the inside fastball, he may be shipping out of Boston even faster than Bobby V.

Introduction first: my name is Walter King and over the next few weeks I plan on sharing my counter to Wins Above Replacement, which I call PEACE: Player Evaluator and Calculated Expectancy.  The engine behind PEACE is Calculated Runs Expectancy, which is what this article will cover.

Calculated Runs Expectancy (CRE) is an analytical model that estimates runs produced by a player, team, or league for any number of games.  CRE operates under the assumption that every single play on the field is relevant to output and thus can be translated into a statistical measure.

In its general form, the Calculated Runs Expectancy formula looks like this:

•  CRE = (√ {[(Bases Acquired) * [(Potential Runs) * (Quantified Advancement) / (Total Opportunities)]] / Outs Made²} * (Total Opportunities) + (Hit and Run Plays) + Home Runs) / Runs Divisor, relative to the league

This formula was reached by following a particular line of logical reasoning, which starts with the assumption that the singular objective of baseball is to win every game (well, duh!).  Winning every game mathematically requires one of two scenarios: either a team allows zero runs, or they score an infinite number of runs, both resulting in one team scoring 100% of the runs, assuring 100% of the wins.  Because the objective is to win the game, and the only way to assure victory is to score the most runs, then the only two ways players can contribute to winning are by scoring runs or by preventing the opponent from doing so.  This sounds painfully simple, but we have to establish that metrics are limited in usefulness if there is no clear link to runs, and therefore wins.  This assumption forces us to define what makes a run in terms of statistics.

With so many different statistics to represent the happenings on the field, it can be tough to form a clear definition.  Keep it simple.  Break down what a run is in the simplest way possible: a run scored is when a player safely touches all four bases, ending by touching home plate.  That’s it.  A team must acquire at least 4 bases in order to score 1 run, so the first formula we can use in our analysis is Bases Acquired:

•  Bases Acquired = TB + BB + HBP + ROE + XI + SH + SF + SB + BT (bases taken)

This is a complete representation of the number of individual bases a hitter acquired, which is often overlooked as valuable information.

My second definition of a run comes directly from Bill James’ Runs Created statistic: to  score a run, a batter needs to first reach base, and then advance among the bases until they reach home plate.  This focus looks at offensive production through the completion of those two smaller goals.  These concepts have already been identified by James using three basic principles: On-Base Factor, Advancement Factor, and Opportunity Factor to calculate runs created.

But what composes these factors?  Well, this is where I venture slightly away from James, attempting to encompass a more complete representation of a hitter in my calculations.  I’ve altered them a bit and given them new names:

• Potential Runs = TOB (times on base) – CS – GDP – BPO (basepath outs)
• Quantified Advancement = TB + SB + SH + SF + BT
• Total Opportunities = PA + SB + CS + BT + BPO

With these now defined, my modified Runs Created formula looks like this:

•  Modified Runs Created = [(TOB – CS – GIDP – BPO) * (TB + SB + SH + SF + BT)] / (PA + SB + CS + BT + BPO)

Bases Acquired and Runs Created are counting statistics, but we want rate statistics.  I believe strongly in the principles of VORP, which asserts that production must always be measured relative to cost in terms of outs.  To amalgamate our measures of offensive production and outs made, we simply divide each by outs made to create two “per out” statistics.

So what we have now are two different measures of a batter’s efficiency; one that calculates bases acquired per out made and another that finds calculated runs scored per out made.  By multiplying the two, we can incorporate two different statistics of efficiency in our evaluation of hitters.  Conceptually, this represents a reconciliation of two different philosophies on how runs are produced.  We’ll call the resulting quantity Offensive Efficiency.

•  Offensive Efficiency = (Bases Acquired * Runs Created) / Outs Made²

I particularly like this formula because the two key components that comprise it are largely considered obsolete by modern sabermetrics.  Both Total Average (bases/outs) and Runs Created are from the 1970s and are throwbacks to better uniforms and simpler ways of thinking.  If you were to approach a stathead today championing total average or runs created as “the answers,” they would first dismiss you, and then suggest more modern metrics.  Much like the struggle sabermetrics saw when first attempting to become a respected pursuit, modern sabermetrics seems to scoff at the idea that older, simpler calculations can be valuable.  But both Total Average and Runs Created per Out are logically sound in their function; they break down the aspects of hitting into real-life objectives that correspond to real-life results.  Offensive Efficiency will definitely tell you which batters performed most efficiently, but it is sensitive to outliers.  To counter this, recall the general CRE equation:

•  CRE = (√ {[(Bases Acquired) * [(Potential Runs) * (Quantified Advancement) / (Total Opportunities)]] / Outs Made²} * (Total Opportunities) + (Hit and Run Plays) + Home Runs) / Runs Divisor, relative to the league

Multiplying Offensive Efficiency by Total Opportunities creates a balance between efficient and high-volume performers.  The next step, inspired by Base Runs, is to add “Hit and Run Plays” along with Home Runs to the equation because those are instances when a run is guaranteed to score.  Hit and Run Plays are my name for situational baserunning plays (found on Baseball-Reference) that result in a batter advancing more bases than the ball in play would suggest.  For example, when a batter hits a single with a runner on first, the runner would be definitely expected to reach second base.  Reaching third or scoring, however, would indicate a skillful play (or a hit and run) by an opportunistic baserunner.  Three stats make up Hit and Runs Plays: 1s3/4 (reaching third or home from first on a single), 2s4 (scoring from second on a single), and 1d4 (scoring from first on a double).

At this point, all that’s left is the Runs Divisor.  If you’re following along at home, an individual batter season without a Runs Divisor would be somewhere between 200-500, while a team single season would typically be between 2000-3000.  The Runs Divisor is specific to each season and league (so the 2014 AL and NL both have unique divisors), and is the average optimal divisor that would result in actual runs scored, relative to the specific league.  Let’s use a 2-team league as an example.  Team A scores a raw CRE of 2500 while scoring 700 actual runs, so their optimal divisor would be 3.57.  Team B, on the other hand, has a raw CRE of 2250 and scored 600, a divisor of 3.75.  The league’s Runs Divisor would be the average of the two: 3.66.  This divisor would be used for every individual player in that league, as well.  Divisors vary every year, but always remain very similar.

A full list of Runs Divisors from the seasons 1975-2014 can be seen here:

The average divisor across that time span was 3.7631, with a standard deviation of just 0.0268.  This provides strong evidence of the relationship between CRE and runs; the two are related in the same way across generations of ballplayers.  When we graph the results of CRE against actual runs for all 1114 teams in that timespan, we can see some very convincing results:

The R2 value (0.9682) corresponds to an average difference between actual and calculated runs of 14.02.  When compared to other run estimators, the differences are significant:

Runs Estimator (Creator), Average, R2

• Base Runs (David Smyth), 18.77, 0.9441             
• Estimated Runs Produced (Paul Johnson), 18.15, 0.9480             
• Extrapolated Runs (Jim Furtado), 18.33, 0.9515             
• Runs Created (Bill James), 20.01, 0.9383                         
• Weighted Runs Created (Tom Tango), 19.37, 0.9443  

The gap between CRE and the 5 other estimators is consistent across the entire span of 40 seasons.

There is a lot of new information to take in here, so feel free to comment below with any questions or feedback.  Part 2 will be uploaded in a few days.

This article is a follow-up to my previous one in which I will work through some examples. You should try to get an intuition on it. If the concept seems too complicated I have to apologize for not explaining myself well because I sincerely think this is very straightforward and no voodoo and could help improve fWAR even further… which is mindboggling if you think about it. It could improve projection systems as well as the correlation of WAR and actual wins while also handling players changing from the AL to the NL or vice versa more elegantly.

I will simply follow my steps 1-4 from my previous article to figure out the proper league adjustment and continue with some WAR calculations. I will use the 2014 season as my guinea pig.

While playing around with it I also stumbled upon a wRC+ adjustment that has to be done because of a) the independence of both leagues and b) the differing league strengths. I will tackle this issue in my next article.

All right, here are steps 1) –4).

1) I need to figure out the wOBA values, R/PA, FIP, R/W, cFIP for each league individually. These can normally be found here. I will not list every single wOBA value here because that doesn’t add much to the explanation and saves me some time.

AL (2014):

wOBA: .312

R/PA: .110

FIP: 3.82

R/W: 9.25

cFIP: 3.16

NL (2014):

wOBA: .308

R/PA: .105

FIP: 3.66

R/W: 8.97

cFIP: 3.10

The exact values for all of MLB found on the Guts! page is conveniently exactly the arithmetic mean of my AL and NL values.

2) All right, we now move on to step 2 which is to figure out the interleague record. I suggested that a 3 year rolling regressed average could be a possibility with years N-1, N and N+1 as inputs. I cannot see into the future, for that reason I will simply use the 2012-2014 interleague record based on pythagenpat. This comes out to a .539 W% for the AL. Conveniently, the actual W% is exactly the same. For demonstration purposes let’s just do a farmer’s regression and call that a “true talent” .530 W%.

3) This is the seemingly tricky part but once you got your head around it is is very easy to grasp. As a reminder: the three necessary “true” replacement levels needed for all WAR calculations are .294 in general for teams – this is where the fixed 1,000 WAR each year comes from – the .380 replacement level for starting pitchers and the .470 for relievers.

Imagine an NL team that is a .500 team within the NL. This team plays a .500 AL team within the AL. That needs to be stressed. Those teams are NOT of equal strength, even if both have a .500 record. Why, you ask? Because if they were, we would not see an advantage for the AL in interleague play. We would see a balanced .500 interleague record. That is not our reality and we can confidently conclude that the NL is the weaker league as of today.

Following this line of thought, what happens if two replacement teams out of each league play each other? Well, this means a .294 NL team plays a .294 AL team. What would the outcome be? A .530 winning percentage in favor of the AL. This comes straight out of the interleague record.

How much better than a .294 W% would this NL team have to be in order to win exactly half of its games against this .294 AL team? This is where the odds ratio comes into play and it spits out a .320 winning percentage. That means if a .320 NL team faces a .294 AL team in an environment, in which the AL wins 53% of all interleague games, we would finally expect parity. A .500 interleague record. This .320 is our new “artificial” replacement level for the NL in 2014.

On the other hand we have to ask the question: How much worse than a .294 can an AL team be when facing a .294 NL team and still win half of its games? Odds ratio says a .270 AL team would still win 50% of all games against a .294 NL team in a context where the AL wins 53% of all interleague games. This .270 is our new “artificial” replacement level for the AL in 2014.

4) Remember that our “regressed” interleague record suggests the AL to be the stronger league, thus worthy of receiving more share of the WAR-pie. Now it is time to figure out how much more they deserve.

We figured out a .270 “artificial” replacement level for the AL. Therefore, we can distribute (.500-.270)*15*162 = 559 WAR towards the AL. This is split up 57/43 between position players and pitchers.

In the National League we found a .320 “artificial” replacement level. Therefore, we can distribute (.500-.320)*15*162 = 437 WAR towards the NL. Same 57/43 split.

Now 559+437 = 996, which is not equal to 1,000. This is because of the odds ratio being non-linear the closer it gets to the extremes but I might be totally mistaken here. This usually is where Tangotiger appears out of the dark and helps out with fancy math or steps in when the math gets hurt. I don’t really see it as a problem.

We could either distribute the remaining 4 WAR 50/50 between both leagues or adjust the replacement levels slightly to arrive at exactly 1,000 WAR. Both would change individual WAR figures only on an atomic level.

I want to point out that this kind of inconsistency is very common in the implementations of WAR. rWAR and fWAR both have some adjustment runs to match inconsistencies like that. This doesn’t even make a difference on a player level. It would not even change a team’s WAR figure by 1/10 I guess.

WAR calculations

After you have come this far you are probably interested in how much certain player’s WAR figure might change. Again, I won’t list every step necessary but only the actual results. If you ask yourself how I have done it, you should take a look here, here and here. If that doesn’t help out, just comment with your question and I will walk you through.

My example will be Mike Trout. I will show the differences of some of the more important and interesting stats as (OLD/NEW). Forgive me for not being a formatting wizard.

NOTE: For sake of better comparison I will present the “new” run values with an exchange rate of 9.117 R/W (currently used). Otherwise 1 run wouldn’t have the same meaning since in my WAR calculations 1 win equals 9.25 runs.( See step 1  ) This makes this an apples to apples comparison.

Trout:

wOBA: (.403 /.402)

wRC+* : (167 / 170)

WAR**: (7.8 / 8.0)

batting: (52.1 / 54.0)

UBR: (3.0 / 3.0) unchanged

wSB: (1.8 / 1.7)

Fld: (-9.8 / -9.8) unchanged

Pos: (1.4 / 1.4) unchanged

Lg: (2.9 / 2.9)

Rep***: (19.9 / 19.9 )

*  I use a slightly different wRC+ calculation here. My league adjustment method would also improve the accuracy of wRC+ as a comparison tool between the two leagues. I will write another article dealing with the modified wRC+ calculation, as well as the wRAA and replacement runs modifications to improve the accuracy of fWAR.

** Fielding runs, UBR and positional adjustment were not changed. These three will never change, the league adjustment however will undoubtedly change, as well as wSB, although the changes would be tiny. It involves complete league stats, i.e. every single player’s stats.

*** The value of replacement runs will never be affected in my league adjustments even though I use different replacement levels for my calculations. Replacement runs will always be based on the .294 baseline. I hope this makes sense to you. If not I point out to the upcoming article of mine.

Outlook

In my next article I will lay out the modifications that have to be applied to wRAA, wRC+, batting runs and the replacement runs. I will show why my modifications make wRC+ more accurate in comparing both leagues and explain why this new league adjustment influences position player WAR more than pitcher WAR. Because right now, the fWAR-process for pitchers leans heavily, not entirely though, towards the independency treatment of both leagues – a cornerstone of my league adjustments.

Also look forward to a table of the players with the biggest and the smallest increase in WAR and the corresponding losses. In both the AL and NL there are players who gain or lose more than others. This has to do with the different run environments is my best educated guess so far. In the NL – the lower scoring league – extra-base hits become slightly more valuable. So does base-stealing. Opposite for the AL. So look forward to my next piece, fellows!

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.