As an avid reader of FanGraphs, I’ve been following the ongoing mock draft and thought it would be interesting to compare the results to the dollar value rankings I created using Steamer’s 2015 projections.

I downloaded the draft spreadsheet partway through the 16^th round, just after pick 183 (Chase Headley). Here is a breakdown, position-by-position. I’ve included the overall pick and the dollar value for that player based on 2015 Steamer projections in parentheses.

Third Base

Four third basemen were drafted in the second and third rounds: Anthony Rendon (14^th–$26), Josh Donaldson (25^th–$24), Adrian Beltre (26^th–$30), and Evan Longoria (32^nd–$17). Rendon being the first third baseman drafted isn’t a surprise. He had a terrific 2014 season (111 R, 21 HR, 83 RBI, 17 SB, .287 AVG). Steamer projects regression in all of those categories, so his value drops from what he did last year. He also has youth on his side, being just 25 years old in 2015, and is in a good Washington Nationals’ lineup. His $26 valuation is based on second base eligibility (25 games there in 2014), so it is a little higher than if he were only eligible at third base.

Josh Donaldson was taken nine picks later and is projected for similar value, but in a different shape (more homers and RBI, fewer steals and a lower batting average). Adrian Beltre was taken with the very next pick and has the most valuable projection according to Steamer. He’s also heading into his age 36 season and saw his homers drop from 30 in 2013 to last year’s 19. Finally, Evan Longoria was taken six picks after Beltre. Longoria’s rate stats last year were well below his career averages (he hit .253/.320/.404), but Steamer sees a bounce-back to better numbers in 2015. Here are the projections for the upcoming season for these four players:

573 AB, 85 R, 19 HR, 71 RBI, 11 SB, .279 AVG—Anthony Rendon (14^th–$26)

558 AB, 83 R, 27 HR, 88 RBI, 5 SB, .264 AVG—Josh Donaldson (25^th–$24)

576 AB, 82 R, 24 HR, 94 RBI, 1 SB, .297 AVG—Adrian Beltre (26^th–$30)

567 AB, 78 R, 25 HR, 85 RBI, 3 SB, .256 AVG—Evan Longoria (32^nd–$17)

I would argue that Rendon, Donaldson, and Beltre belong on their own tier (in whatever order you prefer), with Longoria moving down to the next group.

In round 5, two more third basemen were taken within three picks of each other: Kyle Seager (57^th–$15) and Nolan Arenado (59^th–$18). The projections for these two are very similar:

562 AB, 75 R, 21 HR, 78 RBI, 7 SB, .262 AVG—Kyle Seager (57^th–$15)

571 AB, 73 R, 20 HR, 82 RBI, 3 SB, .282 AVG—Nolan Arenado (59^th–$18)

Seager has three straight years with similar production and will be 27 in 2015. He seems a good bet to hit that projection. He’s also played 155 or more games in each of the last three seasons. Arenado has two years in the big leagues, with the 133 games he played in 2013 being a career high (111 games last year). He’s younger and has the Coors Field advantage. I think you could go either way here and, as I mentioned above, I think Longoria fits better with these two than he does with the top three.

At the end of round 6 and into round 7, three more third basemen came off the board: David Wright (69^th–$8), Todd Frazier (75^th–$8), and Pablo Sandoval (78^th–$19). Wright is coming off an ugly season that saw him hit .269/.324/.374 with 8 homers in 134 games, but it was only two years ago that Wright slugged over .500, so he’s a good candidate to bounce back at least somewhat. He will be 32 years old, though. Todd Frazier had a very big 2014 season when he set career-highs in runs, homers, RBI, steals, and tied his career best in batting average. Steamer projects all of those numbers to come down this year. Sandoval projects to be significantly better than Wright or Frazier. He is moving to a better ballpark for hitters and a better lineup to produce runs and RBI. Let’s look at the three-year averages for these players:

515 AB, 69 R, 16 HR, 71 RBI, 13 SB, .294 AVG—David Wright (will be 32)

517 AB, 69 R, 22 HR, 73 RBI, 10 SB, .259 AVG—Todd Frazier (will be 29)

503 AB, 60 R, 14 HR, 72 RBI, 0 SB, .280 AVG—Pablo Sandoval (will be 28)

Over the last three years, they are essentially even in RBI. Wright and Frazier have the potential to steal some bases, while Sandoval won’t do anything for you there. Frazier comes with the best potential for home run production, but lowest batting average. For 2015, I would take Sandoval, then Frazier, then Wright.

Three more third basemen were taken in rounds 9, 10, and 11: Manny Machado (108^th–$12), Matt Carpenter (109^th–$5), and Josh Harrison (125^th–$12).

This is an interesting trio of players taken within 17 picks of each other. Machado is going to be 22 years old. He only played in 82 games last year, but played in 156 games in 2013 and scored 88 runs with 14 homers, 71 RBI, 6 steals, and a .283 batting average as a 20-year-old. With youth on his side, he has the greatest potential of these three, so it’s not surprising he was taken ahead of Carpenter and Harrison.

The Steamer projection for Carpenter seems low on runs, in particular. Carpenter scored 126 runs in 2013 and 99 last year, but is projected for just 81 this year, despite a .368 on-base percentage. He won’t hit many homers or steal many bases, so his value is in runs scored and a solid batting average. He’s boringly consistent.

Josh Harrison had a good 2014 season (77 R, 13 HR, 52 RBI, 18 SB, .315 AVG). If he were five years younger and played for the Red Sox, he would be getting the Mookie Betts love as a multi-position guy who can contribute in all five hitting categories. Unfortunately, Harrison doesn’t have a great history before 2014. In his three previous seasons with the Pirates, Harrison hit .250/.282/.367 with 7 homers and 13 steals in 532 at-bats. He’s projected by Steamer to be as valuable as Machado in 2015, but I believe the risk is higher. If you’re looking at these three on draft day, Machado is likely the best option, then you have to decide whether you want to go with the boringly consistent Matt Carpenter or the higher potential but bigger risk of Josh Harrison.

The last two third basemen taken at this point of the draft were Kris Bryant (155^th—[-$13]) and Chase Headley (183^rd–$6). Bryant is projected for negative value because Steamer has him getting roughly a half-season of major league playing time. His hitting production (39 R, 16 HR, 42 RBI, 5 SB, .261 AVG IN 267 AB) would move him way up the third base rankings if he were to get 500 at-bats. Chase Headley was taken in the 16^th round but his projections are pretty close to David Wright’s and Wright was taken ten rounds earlier. Here’s the comparison:

512 AB, 66 R, 16 HR, 65 RBI, 9 SB, .275 AVG—David Wright (69^th–$8)

518 AB, 69 R, 17 HR, 67 RBI, 8 SB, .257 AVG—Chase Headley (183^rd–$6)

If you believe in Steamer, you can pass on Wright in the earlier rounds and take Headley much later.

Final notes: I believe there’s a clear top three at third base in Rendon, Donaldson, and Beltre, which becomes a top two if Rendon is slotted at second base. Longoria belongs with Seager and Arenado in the next grouping. You could move Sandoval up to this group if you are encouraged by his move to the Red Sox. David Wright and Todd Frazier could be combined with Manny Machado, Matt Carpenter, and Josh Harrison to form a diverse group that will give you different options depending on your team needs and willingness to take some risks in your draft. Kris Bryant’s outlook is mainly dependent on playing time. Chase Headley is a fallback option that would allow you to bypass guys like David Wright and Todd Frazier in the earlier rounds. Finally, Pedro Alvarez had not yet been drafted when I downloaded the draft spreadsheet. He is a risky pick but could be as valuable as the David Wright/Todd Frazier/Chase Headley group.

As an avid reader of FanGraphs, I’ve been following the ongoing mock draft and thought it would be interesting to compare the results to the dollar value rankings I created using Steamer’s 2015 projections.

I downloaded the draft spreadsheet partway through the 16^th round, just after pick 183 (Chase Headley). Here is a breakdown, position-by-position. I’ve included the overall pick and the dollar value for that player based on 2015 Steamer projections in parentheses.

Second Base

At the top of the rankings for second base, there are two clear-cut guys, according to Steamer projections: Robinson Cano and Jose Altuve. Cano (7^th–$29) was taken with the 7^th pick of the 1^st round, ahead of Jose Abreu, Jose Bautista, and Edwin Encarnacion, all of whom project to be more valuable. Cano saw a big drop-off in home run production in his first season in Seattle. After hitting 25 or more homers for five consecutive years, Cano hit just 14 in 2014. Steamer projects him to hit 18 in 2015. Jose Altuve (15^th–$32) was taken 8 picks later, but projects to be more valuable, thanks mainly to his potential for 30 or more steals and a higher batting average. Personally, I would not have taken Cano ahead of Abreu, Bautista, or Encarnacion. I’m not sure he’s still a 1^st-round pick. I could see an argument for taking him ahead of Altuve, despite the Steamer projections.

Three more second basemen were taken in rounds 3 and 4: Jason Kipnis (36^th–$12), Ian Kinsler (40^th–$22), and Dee Gordon (45^th–$14). Gordon has his own unique set of skills, so I’ll set him aside for a moment and compare Kipnis to Kinsler. Kinsler was clearly better than Kipnis last year, although Kipnis was dealing with injuries. Here are their three-year averages:

552 AB, 78 R, 12 HR, 67 RBI, 28 SB, .261 AVG—Jason Kipnis

628 AB, 97 R, 16 HR, 79 RBI, 17 SB, .269 AVG—Ian Kinsler

This is how Steamer projects them for 2015:

540 AB, 71 R, 13 HR, 62 RBI, 20 SB, .253 AVG—Jason Kipnis ($12)

612 AB, 87 R, 16 HR, 67 RBI, 14 SB, .266 AVG—Ian Kinsler ($22)

The x-factor is that Kipnis will be 28 in 2015 and Kinsler will be 33. If you expect Kinsler to start an early-30s fade, then perhaps Kipnis is your guy. I would have taken Kinsler first.

As for Gordon, he’s one of very few players in baseball who can be expected to steal 50 or more bases (he had 64 last year). If you’re willing to take the hit in home runs and RBI to get the bulk of your steals from one guy, he’s a good option. Otherwise, you’ll likely be looking at needing two or three players to get you 50 steals.

Two more second basemen were taken in the 7^th round: Brian Dozier (76^th–$11) and Dustin Pedroia (81^st–$17). Brian Dozier has hit 18 and 23 homers in the last two seasons, albeit with a low .240s batting average. He also scored a surprising 112 runs last year and stole 21 bases. Dustin Pedroia has seen his power drop, going from 21 homers in 2011 to 15 to 9 to last year’s 7. His steals have also fallen off considerably, from 26 to 20 to 17 to 6. Here are their 2015 Steamer projections:

576 AB, 78 R, 16 HR, 63 RBI, 16 SB, .240 AVG—Brian Dozier ($11)

553 AB, 78 R, 10 HR, 68 RBI, 10 SB, .283 AVG—Dustin Pedrioa ($17)

Even though Pedroia’s projected to be more valuable, I could see going with Dozier based on recent trends. Also, he’s three years younger.

In rounds 9 and 10, three more second basemen were drafted: Howie Kendrick (99^th–$2), Javier Baez (112^th–$5), and Kolten Wong (114^th–$1). Steamer projects Kendrick to drop back to his 2013 levels, as opposed to what he did in 2014. It’s a significant drop of 30 runs scored, 17 RBI, 6 steals, and about 20 points of batting average, which considerably drops his value. Baez is a high-risk, high-reward player. He could hit the 23 homers projected by Steamer (with a 30% strikeout rate) or he could strike out 40% of the time and be sent back to the minors for more seasoning. We just don’t know. Wong hit 12 homers and stole 20 bases in 113 games in 2014, so he has the most potential as a HR/SB dual-threat. He’s also projected for less playing time than Kendrick, which cuts into his value.

The next three second basemen drafted would appear to be “safer” picks than the previous three: Daniel Murphy (123^rd–$8), Ben Zobrist (129^th–$12), and Neil Walker (139^th–$11). These three also project to be more valuable than the previous three. Let’s look at their projections:

572 AB, 67 R, 9 HR, 56 RBI, 11 SB, .277 AVG—Daniel Murphy ($8)

540 AB, 75 R, 12 HR, 61 RBI, 9 SB, .262 AVG—Ben Zobrist ($12)

501 AB, 67 R, 17 HR, 66 RBI, 3 SB, .273 AVG—Neil Walker ($11)

Zobrist gets a slight bump because he has shortstop eligibility (31 games played there in 2014). With the shortstop replacement level, Zobrist is worth $12. Based on the second base replacement level, he’s worth $10. With these three players, you can expect more homers from Walker, more steals from Murphy, and you have the multi-positional eligibility of Zobrist.

One could easily argue that any or all of these three players could be drafted before the previous group of three (Kendrick, Baez, and Wong). In this mock draft, Kendrick was taken 40 picks ahead of Walker, but Walker projects to be better in runs, HR, and RBI, with fewer steals and a very similar batting average.

The final second baseman taken by this point of the draft was Chase Utley (176^th–$6). After struggling with injuries for the previous four seasons, Utley played in 155 games last year. He will be 36 in 2015 and Steamer projects him to play in 136 games. He is projected to have similar value as Daniel Murphy, who was taken 53 spots ahead of Utley. Here are their 2015 Steamer projections:

572 AB, 67 R, 9 HR, 56 RBI, 11 SB, .277 AVG—Daniel Murphy (123^rd pick–$8)

544 AB, 65 R, 14 HR, 62 RBI, 7 SB, .258 AVG—Chase Utley (176^th pick–$6)

If you believe in these projections, there really is no reason to take Murphy in the 11^th round if you can get Utley in the 15^th. Let’s look at their three-year averages:

608 AB, 78 R, 9 HR, 67 RBI, 15 SB, .288 AVG—Daniel Murphy

455 AB, 65 R, 13 HR, 64 RBI, 10 SB, .272 AVG—Chase Utley

Here, you can make an argument for taking Murphy well ahead of Utley, with their respective ages (30 for Murphy, 36 for Utley) adding to that argument.

Final notes: Robinson Cano and Jose Altuve are the top tier guys. In this draft, Kipnis was next off the board, but I would have had Kinsler on a tier of his own, with Kipnis dropping down to a tier including Dee Gordon, Brian Dozier, and Dustin Pedroia, with arguments for Ben Zobrist, Neil Walker, Dan Murphy, and perhaps Howie Kendrick being in the mix. Javier Baez and Kolten Wong are there for owners who like to gamble a bit, with Utley left over for those who miss out on the rest and believe he can stay healthy enough to contribute in 2015.

As an avid reader of FanGraphs, I’ve been following the ongoing mock draft and thought it would be interesting to compare the results to the dollar value rankings I created using Steamer’s 2015 projections.

I downloaded the draft spreadsheet partway through the 16^th round, just after pick 183 (Chase Headley). Here is a breakdown, position-by-position. I’ve included the overall pick and the dollar value for that player based on 2015 Steamer projections in parentheses.

First Base

Four first basemen were taken in the first round: Miguel Cabrera (4^th–$48), Paul Goldschmidt (6^th–$36), Jose Abreu (8^th–$35), and Edwin Encarnacion (10^th–$36). After Cabrera is off the board, the next three guys are almost identical in value, according to Steamer. At this point, it becomes the preference of the owner. Goldy and Abreu are younger than Encarnacion and should hit for a better average with similar production in the HR/RBI department, so it makes sense to see them go ahead of Edwin, but there really isn’t much difference among them.

In the second round, Freddie Freeman (18^th–$24) went before Anthony Rizzo (21^st–$29) in what could be considered a questionable selection. Steamer likes Rizzo over Freeman by $5 in value. Below are their respective projections for 2015:

560 AB, 81 R, 24 HR, 83 RBI, 3 SB, .284 AVG—Freeman ($24)

541 AB, 85 R, 30 HR, 89 RBI, 6 SB, .271 AVG—Rizzo ($29)

Rizzo has Freeman beat in every category except for average.

Let’s look at their average stats over the last two years (since Rizzo has only played full seasons over the last two years):

579 AB, 91 R, 20 HR, 94 RBI, 2 SB, .303 AVG—Freeman

565 AB, 80 R, 28 HR, 79 RBI, 6 SB, .258 AVG—Rizzo

This tells a different tale, as Freeman now has the edge in runs, RBI, and average, with Rizzo leading in homers and steals. Another factor is the outlook for their respective teams. The Cubs look to have a much better offense than the Braves, which should allow Rizzo to score and drive in more runs. I would have gone for Rizzo before Freeman.

Five more first basemen were taken in rounds 4, 5, and 6: Albert Pujols (47^th–$22), Victor Martinez (48^th–$26), Adrian Gonzalez (53^rd–$22), Joey Votto (61^st–$16), and Prince Fielder (72^nd–$23).

Based on past history, I believe you have to take Pujols, Martinez, and Gonzalez before Votto and Fielder. Votto played in just 62 games last season and doesn’t have the power you’d like to get from a first baseman. Fielder played in just 42 games and is coming off major surgery that included having his neck bones fused together. His production was already dropping before the injury, so he really is a question mark for 2015.

Back to Pujols, Martinez, and Gonzalez: Steamer likes V-Mart the best of the three and he is coming off a terrific season (.335, 87 R, 32 HR, 103 RBI), but is also heading into his age 36 season (Pujols will be 35, Gonzalez will be 33).

Let’s look at what they’ve done over the last three seasons (seasonal averages):

544 AB, 74 R, 25 HR, 91 RBI, 5 SB, .273 AVG—Pujols

569 AB, 77 R, 19 HR, 96 RBI, 1 SB, .321 AVG—Martinez

601 AB, 76 R, 22 HR, 108 RBI, 1 SB, .290 AVG—Gonzalez

It’s close. There’s enough of a range of outcomes with all three hitters that they could finish the season in any order.

Rounds 7 through 9 saw four more first baseman get drafted, starting with Carlos Santana, taken with the 77^th pick. Here I’m not sure of the league specifications. For my dollar values, I have Santana only eligible at first base (94 game played in 2014) or third base (26 games played). He did play 10 games at catcher. If he’s only eligible at first base or third base, I have him worth $8. If he’s eligible at catcher, his value jumps to $21 based on positional scarcity. Anyway, the four first baseman taken here were Santana (77^th–$8), Chris Davis (79^th–$13), Lucas Duda (105^th–$5), and Steve Pearce (106^th–$17).

Steamer 2015 projections:

490 AB, 74 R, 21 HR, 73 RBI, 4 SB, .245 AVG—Carlos Santana ($8)

483 AB, 71 R, 30 HR, 79 RBI, 3 SB, .242 AVG—Chris Davis ($13)

534 AB, 69 R, 24 HR, 75 RBI, 3 SB, .234 AVG—Lucas Duda ($5)

514 AB, 77 R, 23 HR, 74 RBI, 6 SB, .270 AVG—Steve Pearce ($17)

Again, Santana is much more valuable if you can slot him at catcher. Davis is a big risk considering he had by far the worst year of any of these players in 2014 (.196, 65 R, 26 HR, 72 RBI, 2 SB), but he also has the highest ceiling, having hit 53 homers with a .286 average in 2013. Duda had a breakout 2014 season, hitting 30 homers and driving in 92 runs last year. Steamer sees regression to 23 and 74 in 2015. Of these four players, Steve Pearce had the best rate stats in 2014 (.293/.373/.556) and best wRC+ (161). He’s projected for a career-high 586 plate appearances in 2015. Consider the Orioles have an open spot for him to be an everyday player after losing Nelson Cruz and Nick Markakis in the offseason, if you expect Pearce to get the playing time, he’s your guy. My order for these four players would be Pearce, Davis, Santana, and Duda (unless Santana has catcher eligibility).

The next four first basemen could be right up there with the previous group, based on Steamer projections: Ryan Zimmerman (120^th–$11), Mark Trumbo (135^th–$12), Justin Morneau (163^rd–$14), and Eric Hosmer (166^th–$17). The projections:

508 AB, 70 R, 19 HR, 71 RBI, 3 SB, .275 AVG—Ryan Zimmerman ($11)

526 AB, 67 R, 29 HR, 81 RBI, 4 SB, .246 AVG—Mark Trumbo ($12)

479 AB, 68 R, 19 HR, 74 RBI, 2 SB, .295 AVG—Justin Morneau ($14)

573 AB, 76 R, 19 HR, 77 RBI, 7 SB, .278 AVG—Eric Hosmer ($17)

With Zimmerman, you have to worry about his health, as he only played in 61 games last year. He also had the lowest HR/FB% of his career, at 7.8%. In 2012 and 2013, he hit 25 and 26 home runs, so he could bounce back and be just fine. Trumbo played in just 88 games last year and hit 14 homers after back-to-back seasons of 30 or more. Steamer expects him to bounce back to 29 homers, albeit with a low batting average. Morneau is the oldest of this bunch, at 34 years old, but has the Coors Field advantage and should hit for the best batting average. Hosmer is the youngest of this bunch (25), but is also coming off a bad year rate-stat wise (.270/.318/.398).

The interesting thing to notice is that this group of four, taken in rounds 10-14, is projected to be similar to the previous group of four, taken in rounds 7-9. There’s a difference of 90 picks between Santana at 77 and Hosmer at 166, but little difference in their projections, with Hosmer actually projecting better.

Final Notes: The top four of Miggy, Goldschmidt, Abreu, and Encarnacion are a tier above Freeman and Rizzo. Then you have Pujols, Martinez, and Gonzalez, with the wild cards of Votto and Fielder fitting in just below them. Beyond that, I’d expect diverse opinions when it comes to Santana, Davis, Duda, Pearce, Zimmerman, Trumbo, Morneau, and Hosmer. Davis is the most volatile. Pearce could be a late-bloomer, like Jose Bautista. Santana is likely the most predictable but is much more valuable if he can be played at catcher than first base, while Zimmerman and Trumbo are coming off injury-shortened years.

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!

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.