No this isn’t a piece for accountants so please don’t give up on it or go to sleep!  It is an MVP discussion, and there is always a lot to talk about with the MVP, the very definition of which is vague, entitling anyone to interpret it how they wish.  There are perpetual questions– is it for an outstanding player or one who can meet some criteria of clutch?  Can a pitcher be more valuable than an everyday player?  Must a candidate play on a contender?

This article lays out a framework for quantifying these issues.   As described, a definitive answer requires a little more data than we now have, but it’s possible to have this data for an interesting quantitative measure of the MVP.

Let’s start with principles:

• The objective is to win a championship. I don’t expect this to be controversial, is it?  As we’ll get into, this doesn’t mean a non-contender can’t win, but it will be more difficult for them to do so.
• Context and chances matter. We aren’t trying to pick the best player, we’re trying to pick the most valuable.  We’re not trying to forecast the future, we’re looking back at the past.  Whether a player benefits from the luck of situations or of opportunities, the player who capitalizes upon his luck seems to this author to have been more valuable than an unlucky player who doesn’t have as many such opportunities.  Dave Studeman and Dave Cameron have written well on this topic.  If you don’t agree, take it up with them.  (For future research – must an author’s first name begin with the letter “D” to believe this?)  Further, the context of a player’s team matters – clinching a pennant on the last day is more valuable based upon context than an April rout or a meaningless September game between call-ups.

Introducing CPA

Accordingly, we’ll take something old, Win Probability Added, (WPA) and dust off and tweak something else old, Championship Leverage Index (CLI) to make up a new statistic to measure value – Championship Probability Added (CPA).  Our formula is CPA = sum of all daily WPA x CLI.

The facets of WPA are discussed thoroughly in another Studeman article.  Suffice to say, it captures a hitter’s or pitcher’s contribution to the probability of his team winning a game, which we can take as a player’s value to his team in the particular game.

As for the importance of the game to the team, Studeman and Sky Andrecheck have developed a measure of the game’s importance, the Championship Leverage Index, how the outcome of a game affects a team’s championship probability, but, as Studeman pointed out in his WPA article, the new wild-card format makes calculation of CLI difficult.

Fortunately, FanGraphs has a big part of the answer in their playoff probability table, which daily measures a team’s playoff and championship probabilities.  The day to day changes in these probabilities are indicative of each game’s importance, although a full measure of a game’s importance would require running the simulations 15 more times to determine the change in probability for each game’s alternative outcome.

There are different measures of championship probability in these tables based upon projections or upon random (coin toss) probabilities for a season’s balance.  The projection-based probabilities may be more accurate, but, for our purpose, measuring the value of each game, the coin toss probabilities are more useful.  1) The projection-based probabilities are more volatile early in the season as they vary not only with the game’s outcome, but with players’ individual performance which in turn affect his team’s projections.  Thus early-season games are weighted more highly than late games.  2) A player’s individual impact can be diminished because it already has been factored into a team’s projections.

The 2015 MVP Race by CPA

For now, without the complete probabilistic simulations, we’ll try to approximate the value of a game by taking the absolute value of a daily change in a team’s championship probabilities.  We use the absolute value of the daily change since it measure’s the game’s importance whether or not a team wins.  Without this, a player would be penalized if his team loses a game, even if he has a big (valuable) game (high WPA).

For now, the daily changes must be recorded from FanGraphs by hand, so we’ll run with an illustrative example rather than a definitive analysis.  Let’s start with the top two players by WAR in each league:

In the AL, both the Angels and Jays were in contention, although, the Jays’ chances became markedly better later in the season.

While the Angels had a low probability, there was still a lot of opportunity for Mike Trout to benefit from swings in their chances in the end, but he couldn’t make up all the ground on Josh Donaldson’s high WPA during the Jays high CLI second-half run.

On the NL side, WAR leader, Bryce Harper, had his CPA affected by the Nats dropping out of playoff contention.

Harper’s early-season lead fell by the wayside as Kershaw’s performance improved from its negative start and the Dodgers remained in the championship hunt.

So, definitive MVP stat?  Not yet, but hopefully a step in that direction.  Calculating a probabilistic CLI would be a big help.  Improvements to WPA to incorporate base running and fielding would help too.

Thoughts?

Red = High swinging-strike rate (swing and a miss / total pitches)
Yellow = Medium ; Green = Low

The size of the circle also represents how high the whiff rate is

Numbers are in Feet, with -X being inside (handedness neutral) and Z being height in feet above the center of the strike zone (as per PITCHf/x strike zone top and bottom)

Some observations (and probably repetition of prior research):

1) Four-seam fastballs are great between 0.8 to 1.4 feet above the middle of the zone and between -.5 and .5 across the plate (i.e., if you want to get a swing and a miss on a four-seamer, throw it high and right down the middle). Will have similar views for GB% and HR% soon.

2) Sliders, changeups and curveballs all need to be thrown low in the zone; doesn’t appear to matter inside or outside, though changeups need to be around the plate (or they don’t get swings).

3) There is almost nowhere you can throw a two-seamer to get swings and misses, though down and in and basically high appear to be the best places to get strikes.

More to come if you think this is interesting!

Recently, Dave Cameron examined a planned back-pick by Russell Martin and the Blue Jays in Game 1 of the ALDS.  The play didn’t have a chance to happen because Delino DeShields put a 2-1 change up in play.  Not just in play, but on the ground to directly where the second baseman Ryan Goins would have been had he not been breaking for second in anticipation of the pick.  Dave wrote a great article that covered the play in depth, so feel free to go read it here.  In this article, I analyze the strategy of calling for a set pickoff attempt. What I found not only vindicates Martin and the Jays, but also questions one of my longest-held beliefs about pickoffs.

My strategy for evaluating the set pickoff was to calculate the break-even point (BEP) for a pickoff attempt using Run Expectancy (RE), similar to previous analyses on bunting and stealing. To calculate the BEP for a given pickoff attempt, I calculated the RE benefit (to the defense) of an out and the weighted RE cost of a safe call or an error.  This sounds simple enough, but calculating the RE after an error involved some guesswork.

Although errors can result in multiple outcomes, I chose to pick one outcome for each base to simplify the analysis. Thus, I assumed 2 bases for all runners on an errant throw to first, 1 base for all runners on an error to second, and, after much thought, 2 bases for runners on second and 1 base for runners on the corners on an error to third. If you have data that can replace these assumptions, please let me know.  Otherwise, be cognizant of my assumptions when you attempt to make use of the findings.  For example, if there is a slow runner on second, the BEP for a pickoff attempt to a corner will be overly conservative (inflated).  Additionally, I didn’t differentiate between pickoff attempts from the pitcher and the catcher.  The pitcher has a shorter, unobstructed throw, and favorable balk rules when picking to second or third, but still has to deal with the risk of a balk, especially to first, along with the added difficulty of throwing off the mound.  Finally, while calling for a back-pick from the catcher can put a defender out of position, I chose to ignore this factor because a) I assume it is rare for a hitter to find the vacated hole, and b) the defense can choose to avoid contact.

In order to weight the cost of a failed pickoff attempt appropriately, I had to estimate what the error rate would be on attempts.  While we do have data on pitcher error rates on pickoff attempts (around 0.95%), the data are only from throws to first.  Set pickoff plays are more challenging for the defense, so the error rate should be higher than on typical attempts to first.  My solution, in lieu of empirical data from actual set pickoff attempts, was to estimate catchers’ throwing error rates from the 2015 season.  I chose this strategy for two reasons: First, catchers are one of the primary players who can attempt a set pickoff, so it made sense to sample from their performance.  And second, catchers accumulate a large portion of their assists under similar conditions to the pickoff attempt (for example, in 2015 nearly 40% of all catcher assists came from caught stealing).  Thus, I expected catcher throwing error rates to approximate the error rates we would observe on set pickoff plays.

While not a perfect method, I estimated catcher throwing error rate as Throwing Errors / Assists + Throwing Errors + Stolen Bases.  The mean throwing error rate in a sample of catchers (n = 38) who played at least 500 innings in 2015 was 3.6%.  Do you accept that set pickoff plays will result in 3.8 times more errors than typical pickoff throws to first? If not, adjust your own estimates accordingly.

Using the estimated throwing error rate for catchers, the formula for estimating the BEP on a set pickoff attempt is RE cost / (RE cost – RE benefit). In this equation, RE benefit = RE after a pickoff – RE before a pickoff; RE cost = RE before a failed attempt – RE with a failed attempt, and RE with a failed attempt = (RE of a safe call *.964) + (RE of an error *.036).  Using the RE tables found here, I generated Table 1 below.

Table 1.  Success rate required to attempt a pick at each base.

Table 1 presents the BEP for the defense of (successful pickoffs / attempts) X 100.  In other words, Table 1 provides the minimum expectation of success required for the defense to attempt a set pickoff and it be a break-even strategy. Unfortunately, it is difficult to guess how successful set pickoff attempts typically are.  In Dan Malkiel’s study of pickoffs to first, he found that righties and lefties were successful about 2% and 4% of the time, respectively.  However, Malkiel’s study sampled situations with base-stealers on first, so the stolen-base rate was between 17% and 21%.  It’s impossible to know what percentage of successful pickoffs occurred when the runner intended to steal, but it’s safe to say 2% and 4% success rates are a little high if the runner on first isn’t planning on going. Set pickoffs usually work differently than throws to first, since neither the pickoff nor the steal are always expected. Therefore, the data on picks to first can only serve as a point of reference, helping to calibrate expectations rather than serving as predictions themselves.

One way to assess if teams are over- or under-utilizing set pickoffs is to compare their pickoff to error ratios with the BEPs for that metric. Unfortunately, I could only find data for one special case of the set pickoff: a catcher back-pick to first.  In the Malkiel study, successful back-picks were 96% of back-picks plus errors.  If we assume an error puts the runner on third, the BEP for pickoffs/pickoffs + errors is 50%, suggesting that catchers have room to get much more aggressive in attempting to pick runners off first.  Without more data, it’s difficult to comment further on current MLB behaviour regarding set pickoff plays. Nevertheless, the estimates in Table 1 provide interesting insights into the risks and rewards of pickoff plays. Below, I list six lessons that can be gleaned from Table 1.  At least two of these lessons fly directly in the face of my own long-held beliefs, and maybe yours too!

Lesson 1

If, at any time, the defense notices that it has better than a 15% chance of picking off a runner, they should attempt the pickoff.

Lesson 2

Pickoff attempts require greater confidence with two outs, with three exceptions.  Often, the required success rate is over 5%, requiring a fairly egregious mistake by the runner to warrant a throw. The exceptions to this rule are with a runner on first, a runner on second, or a pick to second with runners on first and second.

Lesson 3

A runner on second with no runner ahead of him should probably be targeted frequently.  The BEPs are consistently low for attempting the pickoff to second, while the runner is motivated to be aggressive by the chance to score a run or steal third. Even failed attempts have the favorable by-product of keeping the runner close, a factor not considered in Table 1.

Lesson 4

Throwing behind the runner on first with runners on first and second or the bases loaded is dangerous.  This doesn’t mean it’s a bad play if the runner on first opens the door, but the defense should be really confident to make the throw.

Now for the lessons that go against everything I thought I knew…

Lesson 5

Pitchers should throw over to third with runners on 1^st and 3^rd in a steal situation.  Ever since the MLB outlawed the fake-to-third move, pitchers haven’t been allowed to bluff the throw in hopes of catching the runner breaking from first.  Based on Table 1, it seems strange that pitchers ever faked the throw to begin with.  With no one out, the defense would only need to pick the runner off third 8 times per 1000 attempts, or nail the runner stealing second 3 times per 100 attempts, or a combination of the two to break even.  Additionally, if the runner on first breaks for second it’s an easier throw from third than from first, which was often the result with the fake-to-third move.  While many old-school baseball people will object to throwing over to third, the common refrain “he’s not going anywhere!” doesn’t necessarily apply to the 1^st and 3^rd steal situation.  The runner could be trying to get closer to home so he can steal on the catcher’s throw to second, making it the perfect time to throw over.  Although the third baseman’s positioning will sometimes make a true pickoff attempt at third difficult, the rules do not require the pitcher to throw directly to third.  Thus, teams can make legitimate efforts to get the runner on third when the situation allows it, while other times making throws away from the base solely to catch the runner on first breaking for second.

Lesson 6

The situation that requires the lowest probability of success to attempt a pickoff is when there is a runner on third with no one out.  The defence needs to nab merely 2 runners out of every 1000 attempts to break even. And get this, the BEP on pickoff attempts to third with 0 out is lower than the BEP for typical throws to first, even with the much lower error rate on throws to first (0.95%), and even after adjusting the assumed cost of an error to one base.  Holding probability of success constant, the pickoff attempt to get a runner on third with 0 out is the least risky pickoff attempt possible. The LEAST risky.

Of course, a runner who is on third with no one out should be taking no chances.  But that doesn’t mean a pickoff will never work…

My name is Rich Rieders and I am a 2015 graduate of Rutgers Law School. Over the winter, I participated in Tulane University’s 9th Annual Baseball Arbitration Competition and we finished in 2nd place overall out of 40 teams.

In order to prepare for the competition, I created a database (going back to 2008) consisting of all arbitration awards and players who signed 1-year contracts avoiding arbitration along with their respective statistics. Using regression analysis, I was able to determine which statistics correlate most with salary. In turn, I have created a projection system that can accurately predict arbitration salaries. My projections are more accurate than the ones featured on MLBTradeRumors.

I will be releasing my 2016 projections once the season is over and all awards are announced.

The goal of this article is to properly explain how arbitration salaries are determined and how to choose the best comparative baseball salaries (comps) as outlined in Article VI, Section E, Part 10(a) of the CBA. You can think of the comps as legal precedent. The closer the comps are to the player’s stats, the more comps you have and the more recent those comps are, the stronger your argument.

First and foremost, the purpose of the arbitration process is to compensate the player for his actual results on the field, not to give him a salary based on what we expect he will produce in the upcoming season. We concern ourselves with only the traditional stats. I know this is a complete departure from the way we normally think here on FanGraphs, but salary arbitration is a completely different animal. In essence, arbitration salaries are determined by the accumulation of traditional counting stats.

For our purposes, there are six types of players who are up for arbitration in a given offseason and each type has its own separate valuation. The six types of players are:

(1) First-year-eligible SP

(2) SP who have previously been through the arbitration process

(3) First-year-eligible RP

(4) RP who have previously been through the arbitration process

(5) First-year-eligible position player

(6) Position players who have previously been through the arbitration process.

I will explain, in detail, how to properly choose player comps for each of the six group of players. In this segment, we will focus just on the starting pitchers.

For a SP who is arbitration eligible for the first time, here are the statistics that correlate most with eventual salary:

Platform IP: 60.83%

Platform GS: 57.59%

Platform SO: 54.41%

Platform W: 53.12%

Career IP: 50.56%

Career SO: 47.45%

Career W: 42.76%

Career GS: 37.10%

When initially looking for player comps, these are statistics we are going to focus on. Keep in mind that although ERA is not listed, it is nonetheless important as ERA is still one of the default statistics during a hearing and the first basis for comparison. Note that rate stats almost always have a very low correlation since rate stats do not take into account playing time.

Let’s use Atlanta Braves starter, Shelby Miller, as an example of a first-year-eligible SP.

Shelby Miller is arbitration-eligible for the first time going into 2016 with 3 years and 30 days of service time (3.030). In his platform season (2015), Miller made 33 starts recording 6 wins, 171 SO with a 3.02 ERA in 205.1 IP. Over his career, Miller has compiled 575 IP, 32 W, 483 SO with a 3.22 ERA in 96 GS. The objective here is to find the players who avoided arbitration by signing a 1 year contract with statistics that are most similar to Miller’s. The more recent, the better. The best way to do that is to set a floor and a ceiling and then work your way towards the middle.

From Miller’s perspective, let’s look at Miguel Gonzalez’s 2014 platform season. Like Miller, Gonzalez posted a low win total despite a very strong ERA. Gonzalez made 26 starts, recorded 10 wins, 111 SO with a 3.23 ERA in 159 IP. Over his career, Gonzalez compiled 69 starts, 30 wins, 308 SO with a 3.45 ERA in 435.2 IP. Although their ERA and win totals are extremely close, Miller bests Gonzalez in all the most important categories and has significantly more playing time and strikeouts. Therefore, we can definitively state Miller should receive more than Gonzalez did. As such, Gonzalez’s 2015 salary of 3.45 million should be the floor.

From Atlanta’s perspective, let’s look at Chris Tillman’s 2014 platform season. Like Miller, Tillman pitched a similar amount of innings and games with a pretty low ERA. In his platform season, Tillman made 34 starts recording 13 wins, 150 SO and a 3.34 ERA in 207.1 IP. Over his career, Tillman compiled 45 W, 680.1 IP, 511 SO with a 4.00 ERA in 118 GS. Although Miller has the better ERA, Tillman is superior in all the other major categories. Hence, we can conclude that Miller will receive less than Tillman. We can use Tillman’s 2015 salary of $4.315 million as the ceiling.

Given the above, Shelby Miller is likely to receive somewhere between $3.45 million and $4.315 million. Now that we have a range, let’s find someone towards the middle.

In 2011, Justin Masterson made 33 starts with 12 W, 158 SO, 3.21 ERA in 216 IP. Over his career he made 87 starts, with 28 W, 485 SO, 3.92 ERA in 613.2 IP. Those numbers are quite similar across the board with Miller having a better ERA, but fewer IP. Masterson’s 2012 salary was $3.825 million. Alex Cobb ($4.0 million in 2015),  Travis Wood ($3.9 million in 2014) and Steven Strasburg ($3.975 million in 2014) are all good comps as well.

As for my model, Miller projects to receive $3,859,816 +/- $145,351 which is perfectly in line with the comps above. MLBTradeRumors projects him at $4.9 million, which is not only significantly higher than the above comps, but would beat the record for a first-year player by nearly 600K.

For a player who has already been through the arbitration process before, the valuation is completely different as career statistics are no longer used the 2nd, 3rd, 4th, etc. time around (except in a few rare cases). This group of players are the most difficult to project since we use fewer variables due to the exclusion of career stats and how there are fewer SP across the league than relievers or position players. Nonetheless, we can still get a pretty good idea what their eventual salary will be.

For an SP who has previously been through the arbitration process, the stats that correlate most with eventual salary are:

(1) Platform W: 69.12%

(2) Platform RA9-WAR: 64.04%

(3) Platform SO: 60.97%

(4) Platform fWAR: 58.93%

(5) Platform IP: 58.34%

(6) Platform GS: 49.75%

For example, let’s look at Angels SP Garrett Richards who is arbitration eligible for the second time going into 2016. As a Super-2 going into 2015, Richards received a $3.2 million salary. That figure includes everything he had done in his career up to that point. Thus, when determining his 2016 salary, we don’t need to focus on previous seasons. We need only determine what his 2015 season was worth and give him a raise. In his platform season (2015), Richards made 32 starts recording 15 wins, 176 SO, 3.65 ERA, 2.5 fWAR and 2.8 RA9-WAR in 207.1 IP. We want to find the players whose stats are most similar to Richards.

First let’s discuss Matt Garza’s 2010 platform season (a bit old, but still useful) where he made 32 starts recording 15 wins, 150 SO, 3.91 ERA, 1.9 fWAR and 2.8 RA9-WAR in 204.2 IP. Other than the strikeout numbers, we have a virtually identical season. As such, Richards is likely to receive a raise higher than Garza’s $2.6 million raise going into 2011. We can consider a raise of $2.6 million to be his floor.

Next let’s look at C.J. Wilson’s 2010 platform season (again old, but useful still) where he made 33 starts recording 15 wins, 170 SO, 3.35 ERA, 4.1 fWAR and 5.1 RA9-WAR in 204 IP. Wilson has the same amount of wins and virtually the same number of SO although Wilson has a clear advantage in fWAR and RA9-WAR with a slightly better ERA so it’s pretty safe to say that Richards is likely to get a raise lower than Wilson’s $3.9 million raise. The $3.9 million should be the ceiling.

Homer Bailey’s 2012 platform season is a great final comparison. Bailey made 33 starts recording 13 wins, 168 SO, 3.68 ERA, 2.7 fWAR and 2.8 RA9-WAR in 208 IP. Both players are virtually identical statistically. Bailey received a raise of $2.925 million so Richards is likely to receive a very similar raise himself. Shaun Marcum ($3.1 million in 2011), Jordan Zimmerman ($3.050 million in 2011) and Max Scherzer ($2.975 million in 2013) are all good comps as well.

Therefore, we can be certain that Richards will receive a raise somewhere between $2.6 million and $3.9 million. As for my model, Richards projects to receive a raise of $2,923,484 for a total salary of $6,123,484+/- $336,500 and, unsurprisingly, that is perfectly in line with the comps above. MlbTradeRumors is projecting a raise of $3.6 million for a total salary of $6.8 million which I think is a bit generous given the comps we have at our disposal, but not unreasonable.

Next up: Relief Pitchers.

In “Hardball Retrospective: Evaluating Scouting and Development Outcomes for the Modern-Era Franchises”, I placed every ballplayer in the modern era (from 1901-present) on their original team. Therefore, Dan Quisenberry is listed on the Royals roster for the duration of his career while the Tigers declare Charlie Gehringer and the Senators claim Goose Goslin. I calculated revised standings for every season based entirely on the performance of each team’s “original” players. I discuss every team’s “original” players and seasons at length along with organizational performance with respect to the Amateur Draft (or First-Year Player Draft), amateur free agent signings and other methods of player acquisition.  Season standings, WAR and Win Shares totals for the “original” teams are compared against the “actual” team results to assess each franchise’s scouting, development and general management skills.

Expanding on my research for the book, the following series of articles will reveal the finest single-season rosters for every Major League organization based on overall rankings in OWAR and OWS along with the general managers and scouting directors that constructed the teams. “Hardball Retrospective” is available in digital format on Amazon, Barnes and Noble, GooglePlay, iTunes and KoboBooks. The paperback edition is available on Amazon, Barnes and Noble and CreateSpace. Supplemental Statistics, Charts and Graphs along with a discussion forum are offered at TuataraSoftware.com.

Don Daglow (Intellivision World Series Major League Baseball, Earl Weaver Baseball, Tony LaRussa Baseball) contributed the foreword for Hardball Retrospective. The foreword and preview of my book are accessible here.

Terminology

OWAR – Wins Above Replacement for players on “original” teams

OWS – Win Shares for players on “original” teams

OPW% – Pythagorean Won-Loss record for the “original” teams

Assessment

The 1977 Pittsburgh Pirates          OWAR: 53.6     OWS: 347     OPW%: .524

GM Joe Brown acquired all of the ballplayers on the 1977 Pirates roster. Based on the revised standings the “Original” 1977 Pirates tied for second place with the Phillies, one game behind the Cardinals. Pittsburgh topped the Senior Circuit in OWS during consecutive campaigns (1977-78).

Dave Parker (.338/21/88) collected his first batting title and paced the League with 215 base knocks and 44 two-baggers. “Cobra” merited his first All-Star nomination and Gold Glove Award while placing third in the NL MVP balloting. Mitchell Page (.307/21/85) pilfered 42 bags and finished runner-up in the Rookie of the Year vote. Don Money cracked a career-best 25 circuit clouts and received his third All-Star nod. Al “Scoop” Oliver contributed a .308 BA with 19 round-trippers. Richie Zisk clubbed 30 long balls and knocked in 101 baserunners. Willie Randolph laced 11 triples and tallied 91 runs.

Willie Stargell swatted 13 big-flies despite missing almost two-thirds of the 1977 campaign. “Pops” ranks ninth among left fielders according to Bill James in “The New Bill James Historical Baseball Abstract.” Teammates enumerated in the “NBJHBA” top 100 rankings include Parker (14^th-RF), Randolph (17^th-2B), Oliver (31^st-CF), Manny Sanguillen (42^nd-C), Dave Cash (50^th-2B), Money (55^th-3B), Richie Hebner (56^th-3B), Zisk (69^th-RF), Freddie Patek (73^rd – SS), Bob Bailey (79^th – 3B), Tony Armas (89^th-RF) and Rennie Stennett (90^th-2B).

John “Candy Man” Candelaria earned his lone All-Star appearance with a 20-5 record along with a League-best 2.34 ERA. Dock Ellis supplied 12 victories and an ERA of 3.63. Rick Langford surpassed the 200-innings mark while losing 19 of 27 decisions. The bullpen subdued late-inning rallies by the opposition, co-anchored by Gene Garber (2.35, 19 saves) and Kent Tekulve (10-1, 3.06).

The “Original” 1977 Pittsburgh Pirates roster

Honorable Mention

The “Original” 2012 Pirates    OWAR: 46.1     OWS: 303     OPW%: .597

The Bucs seized the National League pennant with 97 victories and topped the circuit in OWS. Andrew McCutchen (.327/31/96) led the League with 194 base hits, earned a Gold Glove Award and finished third in the 2012 National League MVP balloting. Aramis Ramirez (.300/27/105) drilled a League-leading 50 doubles. Pedro Alvarez went yard 30 times while Jose A. Bautista launched 27 bombs in an injury-shortened campaign. Jeff Keppinger delivered a .325 BA in a utility role. Paul Maholm posted a record of 13-11 with a 3.67 ERA and fellow hurler Bronson Arroyo accrued 12 wins with a 3.74 ERA in 202 innings.

On Deck

The “Original” 1931 Athletics

References and Resources

Baseball America – Executive Database

Baseball-Reference

James, Bill. The New Bill James Historical Baseball Abstract. New York, NY.: The Free Press, 2001. Print.

James, Bill, with Jim Henzler. Win Shares. Morton Grove, Ill.: STATS, 2002. Print.

Retrosheet – Transactions Database

Seamheads – Baseball Gauge

Sean Lahman Baseball Archive

Last month, I wrote about the two leagues, noting that

1. The American League, perceived as being bad this year, was actually a good deal better than the National League overall, and
2. The perception of the American League’s weakness was due to a near-record level of parity, with neither great nor bad teams.

Let’s start with the second point. At the time of the post, through games of September 5, the standard deviation of winning percentages among American League clubs was the lowest it has been in the 30-team era. Projected onto a 162-game season, the standard deviation of wins for American League teams was 7.8, barely eking out 2007’s 7.9 as the most egalitarian distribution of wins since 1998.

Since September 5, a .500 record has become a black hole, exerting irresistible gravity throughout the American League galaxy:

• Of the teams with the six best records in the league on that date–the Royals, Blue Jays, Yankees, Astros, Rangers, and Twins–only Toronto and Texas had a winning record the rest of the season.
• Baltimore, the sixth-worst team in the league as of the morning of September 6, tied the Jays for the best record in the East thereafter. Boston, then the third-worst team, went 15-12 the rest of the way.
• Cleveland, four games below .500 at the time, scrambled to finish 81-80.

Overall, parity in the already-equality-loving Junior Circuit increased, by so much that I looked beyond the post-1998 30-team era. I calculated the standard deviation of winning percentages for every league-season since 1901. I then multiplied the standard deviations by 162 to arrive at the standard deviation of wins over a 162-game season. Yes, I know, most of those seasons were shorter than 162 games, but that’s OK; I’m just looking to turn the standard deviation of winning percentages, which is not an intuitive figure (e.g., American League, 1930, 0.1107), into something that is recognizable (17.9 wins). Here are the ten seasons in baseball history with the highest parity, that is, the lowest standard deviation of wins:

The 2015 American League is the most egalitarian, populist, tax the rich/feed the poor, Kumbaya-singing league in baseball history. As I suggested in September, it’s the Sweden of leagues.

(The National League finished 2015 with a standard deviation of 13.1 wins, ranking it 102 out of 230 league-seasons in terms of parity. It was the ninth-most unequal among 36 league-seasons since the expansion to 30 teams in 1998. For Gini coefficient detractors, the most unequal league ever was the 1909 National League, which featured the 110-42 Pirates, 104-49 Cubs, and 92-61 Giants, along with the 55-98 Dodgers, 54-98 Cardinals (Yadi was hurt), and 45-108 Braves.)

Now, as to the other point, the American League’s superiority over the National League despite its group hug ethic, here’s a chart.

Twelve years and running.

1. The Pirates finished the year with a 98-64 record, the second best in all of baseball. That ties them with the 1979 and 1908 clubs for the third most wins in franchise history. (The 1909 Pirates won 110 and the 1902 club won 103.) The Pirates’ record, however, included a losing record against two of the worst teams in the game, the Cincinnati Reds (8-11) and the Milwaukee Brewers (9-10).

Let’s break that down. In games in which the Reds didn’t play the Pirates, they were 53-90. In games in which the Brewers didn’t play the Pirates, they were 58-85. So in their non-Pirates games, the two clubs combined for a 111-175 record, a .388 winning percentage. Had they played at that pace in their 38 games against the Pirates, they would have won .388 x 38 games = 15 games, losing 23. Turned around, the Pirates would have gone 23-15 against the Reds and Brewers.

The Pirates were 81-43 in their games that weren’t against Cincinnati or Milwaukee. Had they gone 23-15 against the two clubs–that, is had they been as successful as the rest of the teams in the majors were–their record would have been 104-58. That would have given the Pirates the best record in baseball. They would be enjoying four off days, looking forward to Wednesday’s wild-card game between the Cardinals and Cubs to see whom they’d face at home to kick off the Division Series on Friday.

2. The Pirates had four relief pitchers who pitched at least 60 innings: Mark Melancon, Tony Watson, Jared Hughes, and Arquimedes Caminero. Of the four, the pitcher with the lowest average leverage index when entering a game was Caminero, wasting his namesake’s leverage expertise.

In “Hardball Retrospective: Evaluating Scouting and Development Outcomes for the Modern-Era Franchises”, I placed every ballplayer in the modern era (from 1901-present) on their original team. Therefore, Tony Perez is listed on the Reds roster for the duration of his career while the Red Sox declare Wade Boggs and the Rockies claim Troy Tulowitzki. I calculated revised standings for every season based entirely on the performance of each team’s “original” players. I discuss every team’s “original” players and seasons at length along with organizational performance with respect to the Amateur Draft (or First-Year Player Draft), amateur free agent signings and other methods of player acquisition.  Season standings, WAR and Win Shares totals for the “original” teams are compared against the “actual” team results to assess each franchise’s scouting, development and general management skills.

Expanding on my research for the book, the following series of articles will reveal the finest single-season rosters for every Major League organization based on overall rankings in OWAR and OWS along with the general managers and scouting directors that constructed the teams. “Hardball Retrospective” is available in digital format on Amazon, Barnes and Noble, GooglePlay, iTunes and KoboBooks. The paperback edition is available on Amazon, Barnes and Noble and CreateSpace. Supplemental Statistics, Charts and Graphs along with a discussion forum are offered at TuataraSoftware.com.

Don Daglow (Intellivision World Series Major League Baseball, Earl Weaver Baseball, Tony LaRussa Baseball) contributed the foreword for Hardball Retrospective. The foreword and preview of my book are accessible here.

Terminology

OWAR – Wins Above Replacement for players on “original” teams

OWS – Win Shares for players on “original” teams

OPW% – Pythagorean Won-Loss record for the “original” teams

Assessment

The 1979 Montreal Expos          OWAR: 53.9     OWS: 327     OPW%: .572

GM Jim Fanning acquired 88% (23/26) of the ballplayers on the 1979 Expos roster. Based on the revised standings the “Original” 1979 Expos captured the first pennant in franchise history with 93 victories while topping the National League in OWAR and OWS.

Gary “Kid” Carter paced Montreal with 28 Win Shares and 5.2 WAR. The Hall of Fame backstop slugged 22 round-trippers and commenced a run of 10 consecutive All-Star appearances. Third-sacker Larry Parrish (.307/30/82) clubbed 39 two-baggers en route to a four-place finish in the N.L. MVP balloting. Andre “The Hawk” Dawson displayed his five-tool talent, blasting 25 long balls and nabbing 35 bags. Gary Roenicke swatted 25 big-flies while platooning in left field. Warren Cromartie delivered career-highs with 181 base knocks and 46 doubles. Ellis Valentine contributed 21 jacks and Tony Scott swiped 37 bases.

Tim Raines received the proverbial “cup of coffee” in 1979 with six pinch-running appearances. “Rock” pilfered 808 bases during a career that spanned 23 seasons. He ranks eighth among left fielders according to Bill James in “The New Bill James Historical Baseball Abstract.” Teammates listed in the “NBJHBA” top 100 rankings include Carter (8^th-C), Dawson (19^th-RF) and Parrish (53^rd-3B).

Steve Rogers (13-12, 3.00), the Expos first-round selection in the June 1971 Amateur Draft, hurled a League-leading 5 shutouts and achieved his third All-Star invitation. Dan Schatzeder posted a 10-5 mark with a 2.83 ERA. David Palmer fashioned a 2.64 ERA with a record of 10-2 in his rookie campaign. Scott Sanderson contributed 9 victories along with a 3.43 ERA. Byron McLaughlin collected 7 wins and 14 saves working in a variety of roles while portsider Shane Rawley saved 11 contests.

The “Original” 1979 Montreal Expos roster

Honorable Mention

The “Original” 1985 Expos     OWAR: 55.8     OWS: 320     OPW%: .556

Montreal claimed the National League East division title by a five-game margin over New York while pacing the Senior Circuit in OWAR and OWS. Tim Raines stole 70 bases in 79 tries and batted .320 with 115 runs scored. Raines (35 WS) and Gary Carter (33 WS) surpassed the 30 Win Share plateau as the “Kid” blasted 32 moon-shots. Tim Wallach dialed long-distance 22 times and earned his first Gold Glove Award. Tony Bernazard supplied career-bests with a .301 BA, 169 hits, 17 home runs and 73 RBI. Andre Dawson collected his sixth consecutive Gold Glove Award and drove in 91 runs. Bob James anchored the bullpen staff with 32 saves, 8 victories and a 2.13 ERA. Shane Rawley provided 13 wins with a 3.31 ERA in 31 starts while Joe Hesketh delivered a 2.49 ERA and a record of 10-5 in his freshmen year.

On Deck

The “Original” 1977 Pirates

References and Resources

Baseball America – Executive Database

Baseball-Reference

James, Bill. The New Bill James Historical Baseball Abstract. New York, NY.: The Free Press, 2001. Print.

James, Bill, with Jim Henzler. Win Shares. Morton Grove, Ill.: STATS, 2002. Print.

Retrosheet – Transactions Database

Seamheads – Baseball Gauge

Sean Lahman Baseball Archive

Baltimore Orioles first baseman, Chris Davis, is in the final year of his contract where he is making $12 mil/yr. At age 29, Davis has had a roller-coaster of a career starting in Texas where he burst onto the scene hitting 17 & 21 homers in his first two seasons. After which, he declined dramatically the next season hitting just under .200 for Texas. The following season he was traded to Baltimore where he revived his career and met his long-awaited potential. Today, Davis is one of the biggest power hitters in the game. He hit 53 homers in 2013 and 43 homers thus far in ’15. With Davis on the market we know clubs will be interested in his bat along with some other big FA names such as Yoenis Cespedes, Justin Upton and Alex Gordon.

The problem with Chris Davis is that he’s somewhat inconsistent. As a power hitter we can take a look at his slugging percentage, which will give us a better indication of his extra-base hits and power numbers. In seasons with at least 300+ at-bats he slugged an average of .507 but has a standard deviation of .091. It seems he has struggled to find consistency with his hitting, especially last season when he hit .194, with 23 homers and a slugging mark of .404. Compared to his .286/53 HR/.634 campaign in 2013, it is a huge difference.

If we take a look at a similar power hitter in Nelson Cruz, in his seasons with at least 300 ABs he slugged a similar average of .515 with a SD of only .04. Cruz is more of a model of consistency and has been less risky than Davis. Besides his one season of slugging .460, Cruz was always in the mid-.500s. Which is great for a power hitter. This is a big reason I am not a huge fan of Chris Davis. He just hasn’t shown a high level of consistency.

Another is his strikeout rate, which is extremely high. Since his first full season in Baltimore, Davis struck out 169, 199, 173, 182 times over the last 4 seasons. That’s good for a 31% K-Rate. Easily one of the worst in the league. His 196 strikeouts this season also happen to lead the league! Although he strikes out a ton, he gets the job done by driving in runs. Which at the end of the day could be seen as more important. Davis drove in 138 runs in 2013, 86 in ’14 and so far 110 in ’15. Did I mention he’s also eighth in the league with 118 runs created. A stat used to measure how valuable a player is to contributing runs to his team. So with great power comes great responsibility. Davis may strike out but he can really drive the ball. To me, he’s a high-reward/high-risk guy.

Interested teams:
Chris Davis is so valuable to the Orioles in terms of producing runs it’s hard to imagine him being let go without a fight. I think the Orioles will absolutely make him an offer. He’s already making $12m/yr and the O’s have a lot of money coming off the books this winter (Orioles’ Payroll). Only $41m is committed to next season ($119m payroll this year). So I can see them raising their price tag to about $18m/yr. The O’s are in a position to win now with Machado/Jones and a fairly young team so if they aren’t getting Davis they will no doubt be spending on others.

Other teams I can see having interest in Davis would be Seattle, San Diego, and Houston. Seattle is the kind of team to pay up for hitting; I could see them doing that with Davis as they did with Cano/Cruz. They need offense but they already have Trumbo at first base who has been decent. If they could move Trumbo I could see them making a play for Davis. Having Cano-Cruz-Davis would be quite powerful. They’re losing some money with Rodney & Jackson coming off the books. Seattle could be really interesting to watch.

The Padres I could see showing some interest but only if they lose Justin Upton and keep Wil Myers in CF. I think they’ll try to re-sign Upton who has had a good year playing in Petco Park. They’ve played Myers at 1B occasionally because of his injury concerns. With no DH, it’s harder to maneuver players around. Yet, again, AJ Preller is a magician so no one can really predict what he will do next. I think the Padres’ concern would be as Davis gets older he could regress on the defensive side of the ball and offensively. Petco Park is a pitcher’s palace so if Davis’ power dropped off his value would really take a hit. Putting Davis in as a full-time DH later in his career would help him maintain his power and consistency like it has for David Ortiz and A-Rod.

The Astros are a wild card I think. I said in the Cespedes post, they have a ton of cash to spend but only if they’re willing to spend it. They love guys who hit home runs. That’s basically their back end of the lineup (Carter, Valbuena, Rasmus). Their 1B Chris Carter ($4.5m) is a mini Chris Davis (low avg/high power) and he will be headed to arbitration. But in the offseason I think they will look to upgrade. They’ll obviously want to replace him with someone more improved. But Davis will cost them a lot; as a more analytics front office I’m not sure if they would see the value in paying up for him. Then again, pairing Davis, who hits lefty, with Correa/Altuve would really help them score runs along with mixing and matching their lineup.

Honorable Mentions: 
The last two teams I looked into with 1B trouble were the Cardinals and Pirates. St. Louis has Matt Adams coming off the DL and we’re not sure if he’s 100% just yet. We know Brandon Moss is not a long-term solution. The Pirates’ Pedro Alvarez has been super inconsistent with Pittsburgh. I think they’ll look to upgrade or float around some other names during the offseason. To be honest, I think Adam Lind would be a great addition to the Cardinals or Pirates instead of Davis. Adam Lind has a club option for $8m this offseason; he hits lefty and has had a solid year for Milwaukee. Overall, I don’t think these two teams will end up throwing money at Chris Davis but they may need 1B help next season. Its baseball, anything can happen.

In the end, I believe Davis enjoys playing in Baltimore. Due to his success there, the favorable ballpark and the DH factor I think he should stay. For his long-term career he should seriously consider staying in the AL with the DH factor. But I think another club will come in and make a play to acquire Davis. Power/RBIs come at a premium these days. Dan Duquette, GM of the Orioles, has experience and knows what he’s doing. If the price tag became too high I think he will definitely consider looking elsewhere on the market. Possibly an Adam Lind or Mark Trumbo.

Similar players such as Nelson Cruz signed a 4yr/57m (14m AVV) at age 34 and Albert Pujols signed a 10y/240m (24mAAV) in 2011. This sets a decent basis for Davis. In terms of his contract, I think Davis could get a 5-year deal worth about $18-21 million a year. His WAR for this season is 4.2 which puts him in the ballpark for this. So, it’s near our estimation. Personally, I would not give Davis $20m for 5 years. I think that’s going overboard but some teams are more into his skill than others. Power really comes at a premium in today’s game and Davis has a ton of it. I wouldn’t be surprised if it went to 6 years, but I just don’t see as many teams bidding on Davis right now. Scott Boras is his agent which will probably drive up the asking price. That may turn off the Orioles which could lead to another club coming in and swooping up Crash Davis. I think it’s favored to be the Orioles or Mariners come signing day.

Projection: 5 years, $100 million

There is a treasure trove of data sitting on FanGraphs which to my (limited) knowledge is little used. These data are the Inside Edge fielding stats. We have UZR and DRS, but no IERS (Inside Edge Runs Saved), despite the general availability of the data.

UZR essentially guesses, based on batted-ball profile, what the probability is that a play will be made, which given the lack of true batted-ball data will take time to stabilize. Inside Edge has the benefit of stacking each play in a probability bucket. Here is a list of the probabilities by POS, Probability Bucket and Year that we have IE data:

I then took each player’s stats and based on their position and year, computed the expected number of plays they should have made and compared that to the actual number of plays they had made. In other words, a RF in 2014 should make 86% of his plays in the 60-90% range, so if he had 100 plays there and made 92, he made 6 extra plays. Here’s what 2015 Top 30 looks like in that lens:

Note that IE seems to like Arenado and Longoria a lot more than DRS, however the list is pretty consistent with DRS, esp with Simmons and Hechavarria in the 2/3 spots. I didn’t control for team bias in the results, so it may be favouring certain teams (Jays players seem to be getting a large boost, see Martin, Revere, Pillar, Tulo and Donaldson all on the top 30). Go Jays Go! Yankees Suck!

The next step is a little less mathematical, in that I attempt to ascribe an average run saved based on position. Based on linear weights, a single is worth roughly .5, a double roughly .75 and a triple roughly 1. Thus, a catcher and pitcher can save at most .5 runs each play they make. Second basemen and shortstops will save .5 on most plays, but will get a bump when they convert a double play. A third baseman will prevent some doubles as well as convert some double plays. Outfielders will be preventing some mix of singles, doubles and triples (and the occasional home run). So, based just on my gut feelings on the matter, I ascribed the following run values to each position:

C/P: .5 Runs Saved

1B/2B/SS: .6 Runs Saved

3B: .65 Runs Saved

OF: .75 Runs Saved

Based on these values (estimated runs saved), these are the top fielders (catchers excluded) from 2012-2015 and 2015, respectively:

And the Worst (2012-2015 followed by 2015):