## Why Is There No Version of wRC+ Including Baserunning?

Would someone be so good as to please implement this for me? This statistic can and should definitely exist and would be easily derivable from the stats FanGraphs already has. Yes, there is the offensive runs above average figure, but this is not a rate stat, and is not scaled to 100. What the people want is a wRC+ rate stat for offense that includes baserunning — for why should baserunning be excluded if what we want to know is who are the best offensive players on our team, which certainly includes their contributions on the bases or the lack thereof? (I’m looking at you Wilmer Flores; I saw you thrown out three times on the bases in a single game last week.)

I think the argument is clear for adding a baserunning-included linear weights rate stat, and one which is park- and league-adjusted, and one on the intuitive, familiar, and non arbitrary 100 point scale. (I prefer wRC+ because the scale of wOBA seems entirely arbitrary and unintuitive in meaning.) I don’t think the user should need to try to cobble together figures on their own and perform division to determine various players’ offensive contribution per plate appearance, if they have a simple question, such as “Who are the best 8 or 10 or 12 or 14 offensive players on our team, in their careers, or this season?” Despite all the stats given to us, I don’t really see one that answers this without my performing calculations.

That’s right; I have figured out how to calculate this myself, but it would be nice to have it automatically calculated. Here’s how you do it. (There is probably an easier way than this, but this is the way I figured from the materials available.)

Take the Players (Park/League Adjusted) Batting Runs above Average (listed at the bottom of the page under Value, or subtract baserunning runs from offense runs) and divide by wRC+ the percentage above average, converting wRC+ to a percentage and subtracting 100 (i.e. for a wRC+ of 104, divide by .04). This will give you the League Average Batting Runs in the player’s plate appearances. Now that you have this figure, simply add back in the Offensive runs above average (or the Batting Runs above average + Baserunning runs above average) to get the total park- and league-adjusted linear weights runs for the player including baserunning and hitting. And of course, to get our wRC+ baserunning-included statistic, we simply now divide by the league-average runs (which we already calculated, and which has the park and league adjustments built into it because of how it was calculated) and we arrive at the desired baserunning adjusted wRC+. Voila.

As an illustration, take Curtis Granderson, who I have down, as of September 15 having a career 117 wRC+, and a sum total of 192.9 Offense Runs, and 50.9 Baserunning Runs. By the way, we should immediately see that his career wRC+ is going to be seriously under-rating his overall offensive contribution since roughly 1/4 of his career offensive runs above average derive from his good works on the bases.

1) We start by subtracting the Baserunning runs from the Offense runs to get the Batting Runs above average (You can skip this step if you look down under value, where this stat is listed, though you can probably calculate it faster than you can scroll if you’re like me):

192.9 – 50.9 = 142

2) Next, since a wRC+ simply means his batting was 17 percent above league average for his career (park-adjusted), we divide the batting runs only by .17 to get the league-average batting runs, park-adjusted:

142 / .17 = 835.29

3)  Next, we add back the player’s total offensive runs above average, to the league-average figure over that span, park-adjusted for where the player played already, to get the player’s total park- and league-adjusted runs, including baserunning.

835.29 + 192.9 = 1028.19

4) Last, but not least, simply divide by the figure we arrived at in step 2 (the park-adjusted league-average runs a player would have produced in however many plate appearances) to arrive at your magnificently complete new baserunning-included wRC+:

1028.19 / 835.29 = 1.2307, or 123 on the 100 point wRC+ scale.

Thus, in the case of Granderson, his ostensible wRC+ of 117 is significantly under-playing how much better than average he’s been over his career on offense, relative to his opportunities, since his “true” wRC+, including baserunning, is actually 123, not 117.

I can’t see what the argument for baserunning not being included would even be; I understand why one would also want the batting-only figure, but the batting + baserunning figure is surely also important to know, and if I had to only have one, to my mind, I’d unequivocally take the figure that gives total offensive contribution relative to opportunities and adjusted by context, rather than a partial figure that tells me only about batting. Luckily, there’s no real reason to choose; we can and should have both.

You might now be thinking, wait, what about below-average players? (I momentarily had this trivial thought, but the negative runs above average, and the percentage wRC+ below 100 will cancel out, of course.)

A demonstration, using the aforementioned lead-footed Wilmer Flores as our exemplar. Flores, has -7.0 batting runs above average for career, -2.8 Baserunning above average, -9.8 offense above average, and a 95 career wRC+. Here I’ve skip step 1 by just finding Batting Runs on the bottom of the page.

1. -7/.05=140 (which represents league average runs in Flores’s career plate appearances, including adjustments)
2. 140-9.8=130.2 (the number of offensive runs Flores actually contributed, including his base-running miscues.)
3. 130.2/140=.93 or a wRC+ of 93, once we appropriately dock Flores for his base-running.

Now, while this isn’t that complicated for me to calculate, I propose this, or something like it be implemented for a total wRC+ that includes baserunning. Obviously it could be calculated for season stats too just as easily. If you have baserunning runs, Offensive total runs, and wRC+, the figure I’m looking for can easily be implemented. Thanks for reading.

## Hardball Retrospective – What Might Have Been – The “Original” 1992 Padres

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. 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 teams with the biggest single-season difference in the WAR and Win Shares for the “Original” vs. “Actual” rosters for every Major League organization. “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

AWAR – Wins Above Replacement for players on “actual” teams

AWS – Win Shares for players on “actual” teams

APW% – Pythagorean Won-Loss record for the “actual” teams

# Assessment

OWAR: 52.6     OWS: 324     OPW%: .595     (96-66)

AWAR: 37.3      AWS: 246     APW%: .506     (82-80)

WARdiff: 15.3                        WSdiff: 78

The ’92 Friars fiercely engaged the Braves but when the dust settled, the San Diego crew emerged two games behind Atlanta. The Padres led National League in OWAR and OWS. Roberto Alomar (.310/8/76) nabbed 49 bags in 58 attempts and registered 105 tallies. Carlos Baerga (.312/20/105) collected 205 base knocks, rapped 32 doubles and merited his first All-Star selection. Shane Mack supplied a .315 BA and scored 101 runs. Dave Winfield drilled 33 two-baggers, walloped 26 big-flies and plated 108 baserunners. Dave “Head” Hollins manned the hot corner and responded to full-time status with personal-bests in home runs (27), RBI (93) and runs scored (104). John Kruk laced 30 two-base hits and posted a .323 BA. In the final season of a 13-year consecutive Gold Glove Award streak, Ozzie Smith aka “The Wizard of Oz” delivered a .295 BA and succeeded on 43 of 52 stolen base tries. “Mr. Padre” Tony Gwynn contributed a .317 BA with 27 doubles.

Gary Sheffield (.330/33/100) and Fred “Crime Dog” McGriff secured their first invitations to the Mid-Summer Classic and accounted for a substantial chunk of the “Actuals” offensive production. “Sheff” claimed the batting title and placed third in the 1992 NL MVP balloting. McGriff topped the Senior Circuit with 35 bombs while driving in 104 runs.

Tony Gwynn rated sixth among right fielders in the “The New Bill James Historical Baseball Abstract” top 100 player rankings. “Original” San Diego teammates enumerated in the “NBJHBA” top 100 lists include Ozzie Smith (7th-SS), Roberto Alomar (10th-2B), Dave Winfield (13th-RF), Kevin McReynolds (45th-LF), John Kruk (72nd-1B), Ozzie Guillen (74th-SS) and Carlos Baerga (93rd-2B). Fred McGriff (21st-1B), Tony Fernandez (24th-SS) and Gary Sheffield (54th-RF) attained top-100 status among those who played exclusively for the “Actual” 1992 Padres.

 STARTING LINEUP POS OWAR OWS STARTING LINEUP POS OWAR OWS Shane Mack LF 6.17 27.47 Jerald Clark LF -0.67 9.94 Thomas Howard CF/LF 0.05 6.44 Darrin Jackson CF 0.46 13.54 Tony Gwynn RF 1.69 17.86 Tony Gwynn RF 1.69 17.86 John Kruk 1B 4.35 25.38 Fred McGriff 1B 3.6 27.38 Roberto Alomar 2B 5.37 31.53 Tim Teufel 2B -0.48 5.17 Ozzie Smith SS 3.24 22.13 Tony Fernandez SS 1.41 18.31 Dave Hollins 3B 3.61 25.6 Gary Sheffield 3B 5.92 32.28 Sandy Alomar, Jr. C 0.09 8.2 Benito Santiago C 0.81 8.17 BENCH POS OWAR OWS BENCH POS AWAR AWS Carlos Baerga 2B 4.83 28.54 Dan Walters C 0.36 5.43 Dave Winfield DH 3.53 25.75 Kurt Stillwell 2B -1.98 4.93 Kevin McReynolds LF 1.27 12.89 Craig Shipley SS -0.37 1.61 Jerald Clark LF -0.67 9.94 Tom Lampkin C 0.21 1.03 Benito Santiago C 0.81 8.17 Paul Faries 2B 0.19 0.82 Warren Newson RF 0.25 4.04 Guillermo Velasquez 1B 0.08 0.7 Joey Cora 2B 0.66 3.98 Dann Bilardello C -0.3 0.59 Ron Tingley C 0.13 3.36 Jim Vatcher RF 0.02 0.54 Mark Parent C 0.25 1.42 Kevin Ward LF -0.8 0.52 Paul Faries 2B 0.19 0.82 Oscar Azocar LF -1.14 0.44 Guillermo Velasquez 1B 0.08 0.7 Jeff Gardner 2B -0.22 0.27 Gary Green SS 0.08 0.46 Gary Pettis CF -0.08 0.24 Rodney McCray RF 0.09 0.45 Phil Stephenson LF -0.5 0.19 Ozzie Guillen SS -0.01 0.41 Thomas Howard – 0 0.05 Mike Humphreys LF -0.15 0.12 Jim Tatum 3B -0.1 0.08 Luis Quinones DH -0.04 0.02 Jose Valentin 2B -0.03 0

Andy Benes fortified the “Original” and “Actual” Padres rotations with 13 victories and a 3.35 ERA. Rich Rodriguez and Mike Maddux enhanced the “Actuals” bullpen with identical 2.37 ERA’s while southpaw Bruce Hurst contributed to the starting rotation with a 14-9 record. Omar Olivares registered 9 wins with a 3.84 ERA and Bob Patterson posted a career-best 2.92 ERA for the “Originals”.

 ROTATION POS OWAR OWS ROTATION POS AWAR AWS Andy Benes SP 4.22 15.68 Andy Benes SP 4.22 15.68 Omar Olivares SP 1.89 8.33 Bruce Hurst SP 2.56 12.47 Jimmy Jones SP 0.41 4.89 Craig Lefferts SP 1.27 9.7 Ricky Bones SP -0.35 4.22 Frank Seminara SP 0.93 6.47 Greg W. Harris SP 0.4 3.81 Jim Deshaies SP 1.39 5.78 BULLPEN POS OWAR OWS BULLPEN POS AWAR AWS Bob Patterson RP 0.95 7.52 Rich Rodriguez RP 1.6 9.21 Jim Austin RP 1.21 6.79 Mike Maddux RP 1.56 8.9 Mitch Williams RP -0.27 4.99 Jose Melendez RP 1.28 7.3 Mark Williamson RP 0.4 2.48 Randy Myers RP -0.04 7.16 Steve Fireovid RP -0.18 0.3 Larry Andersen RP 0.31 3.6 Matt Maysey RP -0.01 0.08 Greg W. Harris SP 0.4 3.81 Doug Brocail SP -0.23 0 Pat Clements RP 0.22 2.12 Jeremy Hernandez RP 0.05 1.49 Gene Harris RP 0.31 1.37 Tim Scott RP -0.65 0.91 Doug Brocail SP -0.23 0 Dave Eiland SP -0.51 0

Notable Transactions

Roberto Alomar

December 5, 1990: Traded by the San Diego Padres with Joe Carter to the Toronto Blue Jays for Tony Fernandez and Fred McGriff.

Carlos Baerga

December 6, 1989: Traded by the San Diego Padres with Sandy Alomar and Chris James to the Cleveland Indians for Joe Carter.

Shane Mack

December 4, 1989: Drafted by the Minnesota Twins from the San Diego Padres in the 1989 rule 5 draft.

Dave Winfield

October 22, 1980: Granted Free Agency.

December 15, 1980: Signed as a Free Agent with the New York Yankees.

May 11, 1990: Traded by the New York Yankees to the California Angels for Mike Witt.

October 30, 1991: Granted Free Agency.

December 19, 1991: Signed as a Free Agent with the Toronto Blue Jays.

Dave Hollins

December 4, 1989: Drafted by the Philadelphia Phillies from the San Diego Padres in the 1989 rule 5 draft.

Ozzie Smith

Traded by the San Diego Padres with a player to be named later and Steve Mura to the St. Louis Cardinals for a player to be named later, Sixto Lezcano and Garry Templeton. The San Diego Padres sent Al Olmsted (February 19, 1982) to the St. Louis Cardinals to complete the trade. The St. Louis Cardinals sent Luis DeLeon (February 19, 1982) to the San Diego Padres to complete the trade.

# Honorable Mention

OWAR: 47.6     OWS: 298     OPW%: .518     (84-78)

AWAR: 29.2       AWS: 222      APW%: .457    (74-88)

WARdiff: 18.4                        WSdiff: 76

The ’86 Padres ended the season in a virtual tie with the Dodgers. Tony Gwynn (.329/14/51) paced the Senior Circuit with 211 base hits and 107 runs scored. He swiped 37 bases in 46 attempts and collected his first Gold Glove Award. Kevin McReynolds (.288/26/96) began a streak of five successive seasons with at least 20 round-trippers. Ozzie Smith succeeded on 31 of 38 stolen base attempts. Dave Winfield crushed 24 moon-shots and plated 104 baserunners. Johnny Grubb contributed a .333 BA with 13 jacks in a part-time role and John Kruk delivered a .309 BA in his inaugural campaign. Eric Show fashioned a 2.97 ERA and tallied 9 victories for the San Diego starting staff.

# On Deck

What Might Have Been – The “Original” 2002 Blue Jays

# 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

The information used here was obtained free of charge from and is copyrighted by Retrosheet. Interested parties may contact Retrosheet at “www.retrosheet.org”.

Sean Lahman Baseball Archive

## Someone Give Juan Uribe a Job

Todd Frazier has 38 home runs this year. That’s probably a strange way to start off a post about Juan Uribe but hang with me.

Todd Frazier has 38 home runs this year. Todd Frazier also has a wRC+ of 100 this year. That is a pretty remarkable combination. According to wRC+ Frazier has been exactly an average hitter this year despite the fact that he is currently 8th in all of baseball in home runs. This interesting and seemingly unlikely union piqued my curiosity and sent me down a statistical rabbit hole in search of home runs and terrible wRC+’s. At the bottom of that rabbit hole is where I ran into Juan Uribe.

Juan Uribe has not played an MLB game since July 30th. In that game he went 0-for-3 and he was released by Cleveland a few days later on August 6th. This probably wasn’t a surprise to most people as A) Most people probably would be more surprised to learn he was still in the league to begin with, and B) he was running a 54 wRC+ over 259 PA with Cleveland this year.

But I’m not here to argue that someone should give Uribe a job because his current talent level deserves one (although you probably could; he was nearly a 2-WAR player as recently as last year). I’m here to argue for someone to give him a job because Juan Uribe is on the cusp of history. Juan Uribe has 199 career home runs.

You might think that 200 career home runs isn’t that much of a milestone and it’s only because humans love round numbers that we even recognize it as a milestone. And you would be absolutely correct in saying that. But much like Todd Frazier’s 38 home runs this year, Juan Uribe’s 200 career home runs would be fairly unique. In fact they would be entirely unlike anyone before him because Juan Uribe would be the worst hitter to ever hit 200 home runs.

That is the board of directors of the Terrible 200 Club (patent pending) and as you can see Juan Uribe is poised to unseat Tony “why the hell am I standing sideways at the plate” Batista as CEO with one more measly home run, and by a pretty decent margin. Obviously though his bid is now under threat because he is 37 years old, has been without a team for over a month now and was absolutely awful when he did have a team. It is entirely possible, maybe even likely, that he never hits another MLB home run. And it’s not like there is another current player who is a slam dunk to make a run at Batista if Uribe never steps into the batter’s box again:

Brandon Phillips will get to 200 but he is sneaky old. He turned 35 in June, so while he is nowhere near what he was earlier in his career it seems unlikely that he plays long enough to see his career wRC+ fall below 90.

AJ Pierzynksi is all but done at this point. At 39 years old and nearly a win below replacement level this year it’s probably more likely that the ghost of Clete Boyer gets signed and hits 38 home runs to get to 200 as it is Pierzynski hits 12 more in his career.

-Which bring us to James Jerry Hardy. Hardy seemed to be doing his best to crater his wRC+, posting a dreadful 50 last year, but he has rebounded (relatively speaking) to post a 93 so far this year. One has to wonder if he can even get to 200 home runs (he still needs 16 more to get there and he has hit only 26 over his past 1404 PAs), and secondly, if he does, will he post a wRC+ low enough to “best” Batista? You could probably argue that any version of Hardy that is good enough to get to 200 homers is probably also good enough to not decimate his career wRC+.

The easiest solution is for some intrepid and/or awful team to just give Uribe a spot so that he can chase history with each swing. Atlanta, Arizona, Minnesota, what have you guys got to lose? Would a Kickstarter or GoFundMe to pay some of his salary help? It would just be such a shame for the baseball public to be denied a potentially marvelous thing when it’s so close to realization. Like teasing a dog by pretending to throw a ball or every season after the first one of Homeland.

Somewhere Tony Batista is sitting in a recliner, probably in some crazy way that no one else sits in recliners because he is Tony Batista, just waiting for the news that Uribe has been picked up by someone so he can hand the crown to the new king of the Terrible 200 (patent pending). He just needs a little help. Let’s make this happen, MLB.

## Bases Produced and a Consideration of the 2016 AL/NL MVPs

Bases Produced is the keystone stat in a paradigm for baseball statistics that I have been developing, off and on, for the past 18 years.* Bases Produced measures a player’s overall offensive productivity by counting, quite simply, the number of times that player enables either himself or a teammate to advance to the next base. Each time this happens, a player is considered to have “produced a base.” Counting these events is important because producing bases is quite literally the only way that a baseball player can contribute to the scoring of runs by his team. When a player scores a run, after all, he has done nothing more than advance to all four bases in succession.

The Bases Produced system assigns credit for the production of these bases in a way that is based on traditional baseball statistics, but is also an expansion thereof. This expansion enables most traditional numbers to be tied together into a unified whole, evaluated in terms of Bases Produced, rather than remaining the haphazard collection of unrelated counts that they have always seemed to be.

How does it work? To calculate Bases Produced (BP), I first unify all of a player’s productive batting stats into one sub-total called “Batting Bases Produced” (BBP). This counts each base the player reaches on his own base hits, walks, or times hit by pitch:

BBP = 1 * 1B + 2 * 2B + 3 * 3B + 4 * HR + BB + HBP

A player’s success at producing BBP may be contextualized by dividing his BBP by his total number of “Batting Base Production Chances” (BBPC). This total includes all of a player’s plate appearances (PA), except for those times when a player has attempted to lay down a sacrifice bunt (SHA) — where his primary goal is ostensibly to produce bases for his teammates, rather than himself — and also his catcher’s interferences (CI), where the defense literally takes away his ability to put the ball in play.

BBPC = PA – SHA – CI

The ratio of BBP to BBPC then becomes a player’s “Batting Base Production Average” (BBPAVG):

BBPAVG = BBP / BBPC

Secondly, a player may produce bases for himself as a runner, by either stealing bases (SB), advancing on fielder’s indifference (FI), or “gaining” bases (BG). “Gaining Bases” is the term I use for a player who advances a base when the defense attempts to make a play on a runner somewhere else on the basepaths. For example, if a runner tries to score from second on a single, the batter may advance to second when the defense tries to throw out the runner at the plate. In this case, the batter/runner “gains” second base.

Taken altogether, the bases a player produces for himself as a runner are then called “Running Bases Produced” (RBP):

RBP = SB + FI + BG

Lastly, an offensive player can produce bases for teammates who are already on base by either drawing walks, getting hit by a pitch, or by putting the ball in play. Collectively, these bases are known as “Team Bases Produced” (TBP). The number of times a batter enables a teammate to reach home (TBP4) can be intuitively understood as the number of RBIs he has produced for his teammates, without including any that he has produced for himself. Overall, Team Bases Produced expands this concept by including the number of times a player enables his teammates to advance to second (TBP2) or third (TBP3), as well:

TBP = TBP2 + TBP3 + TBP4

While of course the batter depends on the presence — and subsequent baserunning actions — of a teammate on base to produce these bases, I assign the credit for producing them solely to the batter, without whose actions the runner(s) would not be able to advance on the play. The presence of the runners on base, however, is important to recognize when trying to evaluate how successful a batter is at producing team bases; each runner on base therefore counts as one “Team Base Production Chance” (TBPC) for a batter. (Note: When a batter draws an intentional walk, I do not count TBPC for runners whom the batter cannot force ahead to the next base.)

A batter’s Team Base Production Average (TBPAVG) then becomes, generally (and simply):

TBPAVG = TBP/TBPC

Overall, a player’s total Bases Produced (BP) is simply the sum of his Batting Bases Produced, Running Bases Produced and Team Bases Produced:

BP = BBP + RBP + TBP

This number may also be evaluated in terms of the player’s total number of chances to produce bases (BPC), including his Plate Appearances, Team Base Production Chances, and the number of times he enters the game as a pinch runner (PRS):

BPC = PA + PRS + TBPC

Rounding out this approach, I calculate a general measure of “Base Production Average” as the ratio of Bases Produced to Base Production Chances:

BPAVG = BP / BPC

On my website, www.basesproduced.com, I fill in the blanks of this general paradigm with similar breakdowns for “Outs Produced” and “Bases Run” (= bases a player reaches, but does not necessarily produce); interested readers may follow the link to learn all of the gruesome details for themselves. On the same website, I also calculate and update the BP stats for the current MLB season on a daily basis. You are welcome to check it out to follow along and see how they play out in real life.

While the Bases Produced paradigm may not enjoy all of the mathematical sophistication that goes into many modern sabermetric measures of offensive performance, it does have the advantage of reflecting straightforward facts and events that take place in every baseball game that any fan can quickly recognize and easily count for themselves (with or without a smartphone!). A grand slam home run, for instance, counts as 10 BP: 4 for the batter, 3 for the runner at first, 2 for the runner at second, and 1 for the runner at third. 10 Bases Produced is also a pretty good standard for an excellent game of baseball: I’ll mention in passing that there were just 7 performances of 10 BP or greater in last night’s (9/16) slate of 15 MLB games, with 14 BP topping the list (by three different players).

On basesproduced.com, I have also tabulated the same stats, using data from retrosheet.org, going back to the 1922 season. For those who are curious, the highest single-season BP total in history is 1005, by Lou Gehrig in 1927, while the highest BPAVG of all time is Barry Bonds’ .885, in 2004. There are still many bases produced statistics left to be calculated from the very olden days of baseball, however, before any of these numbers might be considered “records.”

Although Bases Produced is not, strictly speaking, a system that was designed to determine who ought to be the “Most Valuable Player” in any given season (whatever you might interpret that to mean), it is fun to use as another data point in the never-ending discussions about who most deserves the MVP award each year. So let’s consider what the system can show us about the best players in the American and National Leagues in 2016.

The AL MVP race has generally been described this season as a five-man horse race between David Ortiz, Mike Trout, Jose Altuve, Josh Donaldson and Mookie Betts. The Base Production Average numbers back that perception up, as all five of those players sit on top of the current AL BPAVG leaderboard, as of September 16th:

Player                             BPAVG      BBPAVG     TBPAVG

1. David Ortiz               .709            .673              .760

2. Mike Trout               .649            .628              .613

3. Jose Altuve              .645             .590             .652

4. Josh Donaldson      .644             .630             .651

5. Mookie Betts            .605             .564             .607

Although these numbers should ideally be normalized to account for the influence of hitter-friendly venues like Fenway Park, Ortiz is still enjoying his best season there ever (his previous season high BPAVG was .697, in 2007), and he’s well ahead of his career BPAVG of .620, too. As far as base-production statistics are concerned, David Ortiz is unambiguously the 2016 AL MVP.

Over in the National League, I have heard many people talk about the great year that Kris Bryant is having, but his performance fails to even register in the NL’s top five base producers, by average:

Player                             BPAVG      BBPAVG     TBPAVG

1. Daniel Murphy         .665            .619              .718

2. Anthony Rizzo         .634            .607              .659

3. Joey Votto                .619             .602             .617

4. Nolan Arenado        .617             .607             .624

5. Freddie Freeman    .612             .612              .597

(9. Kris Bryant             .601             .618             .541)

Daniel Murphy of the Nationals has clearly had the standout year, instead. And it is worth noting that Bryant’s teammate, Anthony Rizzo, is actually doing considerably better than Bryant in overall BPAVG. The big difference amongst these three players can largely be attributed to Bryant’s mediocre TBPAVG, which is near the National League median of .529 (Aledmys Diaz). That difference can, in turn, be attributed to a combination of Bryant’s high strikeout percentage (.219) and very low ground-out percentage (.113). The one outcome of a plate appearance that never produces bases for teammates is a strikeout, and ground outs tend to be about three times as team-productive as fly outs, in those situations where a batter hasn’t succeeded in producing a base for himself. Bryant’s current numbers place him squarely on the wrong side of both of these team-base-production tendencies.

While Kris Bryant has had a great baserunning season this year…these numbers give reason to question any suggestion that he might have been the best player in the league this season — or even, for that matter, the best player on his own team. But at least it is manifestly clear that Joe Maddon has Bryant and Rizzo in the correct order in the Cubs’ lineup. :-)

*While I am not as up on the current literature in baseball statistical analysis as I should be, I do know that others have developed similar statistical measures independently of me, including at least Gary Hardegree, Alfredo Nasiff Fors, and someone named EvanJ on this forum. If there are other similar thinkers out there, then I apologize for my ignorance of their work.

## Three Fringe NL Central Prospects Assigned to the AFL

*College stats taken from thebaseballcube.com, minor-league stats taken from fangraphs.com and MLBfarm.com

Last week, Baseball America released their Arizona Fall League (AFL) rosters. For those not familiar with the AFL, read more here. In short: each August, all 30 MLB clubs select six players from their minor-league rosters to participate in the fall league. While the minor-league playoffs wrap up toward the end of September, the AFL serves as a domestic developmental league starting in October.

The AFL is prestigious, bringing together some of the top minor-league talent each year. Aside from well-known names, organizations tend to also invite rising prospects who have flown under the radar. Although these NL Central prospects have gotten little public hype, their recent numbers have impressed enough to earn an invite to the AFL, making them intriguing names to watch in the coming months.

Barrett Astin – RHP 6’1” 200, Blue Wahoos (Reds AA), Age: 24 (Video)

Astin had a strong 2012 season as a closer during his sophomore year at the University of Arkansas, helping a well-staffed Razorback team to the College World Series. However, he started all five of his appearances in the Cape Cod league that off-season, where he posted an underwhelming 6.23 K/9 and 2.91 BB/9 through 21.2 IP. He went back to college to find himself in the rotation for the majority of the year, though scouts questioned his durability as a starter as he continued to struggle to go deep into games, going more than 6 IP in only one start. He was signed in the 3rd round in the 2013 draft at slot value by the Brewers, soon being dealt to the Reds for Jonathan Broxton a year later.

Despite being omitted from MLB.com’s top 30 Reds prospects this season, the Reds chose to send Astin to the AFL after having an impressive season in AA alternating between the bullpen and the rotation. In 103.1 IP, he posted an 8.39 K/9 (his career high) with a 2.18 BB/9 and a strong 65.02 GB%, numbers that would play well at hitter-friendly Great American Ball Park.  His ERA sits at 2.26, which is best in the Southern League and roughly 40% better than the league’s average ERA. His low BABIP (.246) and high LOB% (78.9%) may lead to some regression when it comes to run prevention, but FIP still has him pegged at an above average 3.37. His 11 starts have yielded similar peripherals to his numbers from out of the bullpen. However he still showed durability issues, only averaging 5.1 IP/GS this year.

The question is the same now as it was the day he was drafted: can he stay a starter? Considering the Reds have Homer Bailey, Anthony DeSclafani, and possibly Cody Reed solidified in the rotation with prospects Amir Garrett and Robert Stephenson expecting to be in the rotation as well, my guess is that Astin’s ticket to the big leagues will be as part of the relief corps for the Reds. His inability to show consistent stamina and his better numbers against righties than lefties (all 8 HR allowed this year have been off of lefties) all indicate he is better suited as a bullpen option. Considering the Reds’ well documented bullpen problems this year, Astin could have his MLB debut with a rebuilding Reds team sometime next year if all goes well. His AFL stint should give a good indication on which direction he is trending heading into his 25th birthday.

James Farris – RHP 6’2” 210, Smokies (Cubs AA), Age: 24 (Video)

Another participant in the 2012 College World Series, Farris started and pitched seven innings in Arizona’s World Series-clinching win. He was drafted in the 15th round by the Astros after a below-average junior campaign, only to return to Arizona for his senior year. He was drafted in the 9th round by the Cubs at the end of the his last and best year playing in the Pac-12.

Baseball America’s draft-day scouting report notes that Farris does not have overpowering stuff and transformed into a smart, command-oriented pitcher over the course of his four seasons with the Wildcats (subscription required). His best pitch is his changeup, with a 85-89 mph fastball, which he mixes speeds to add cut to, and a below-average curveball to round out his arsenal. His lack of an average third pitch gave the Cubs reason to put him in the bullpen, where he has spent all 127 innings in the minors thus far, and is part of the reason he is not a top-30 prospect in a highly talented Cubs farm system according to MLB.com.

The Cubs’ decision to put Farris in late-inning situations out of the bullpen has paid dividends thus far. In his minor-league career, he holds a 2.91 ERA with a 10.70 K/9 to a 2.69 BB/9, despite only holding a 6.95 K/9 throughout his four years starting at Arizona. He has an average ground-ball rate and the ability to suppress power (as he also did in college), only yielding 2 HR in his professional career thus far. Because of his high strikeout rate and low HR/FB%, ERA estimators have been lower than his ERA.

Farris’ performance thus far has been a pleasant surprise considering the bargain the senior signed for only \$3,000. The question surrounding Farris is whether or not he can sustain the numbers he has put up to this point in his career. His sample size has been relatively small, so tracking Farris’ outings in the AFL should shed more light onto the legitimacy what he has done the past couple years. With key pieces Aroldis Chapman, Pedro Strop, Trevor Cahill, and Travis Wood all free agents to be, there could be some room for Farris sometime next year depending on how the Cubs’ off-season and spring training play out.

Corey Littrell – LHP 6’3” 185, Redbirds (Cardinals AA), Age: 24 (Video)

Littrell was drafted out of high school in the 43rd round by the Nationals, but was too committed to the University of Kentucky to sign. After starting for the Cats for three years, he was drafted in the 5th round by the Red Sox for near slot value in 2013. He was traded the next year to the Cardinals in the deal that brought Joe Kelly and Allen Craig to Boston in exchange for John Lackey, Littrell and \$1.75MM in cash.

A lanky pitcher who lost 10 pounds since draft day according to the Memphis Redbirds official roster, Corey has a similar frame to his father and grandfather, who both played professional baseball as well. According to MLB.com, Littrell is the 29th-best prospect in the Cardinals organization. He throws a fastball that sits 88-90 that plays as average because of above-average command down in the zone. He also has three other average offerings: a changeup, a curveball and a cutter with slider-like action. He was a starter until this year, where he has come out of the bullpen in 52/53 appearances between AA Springfield and AAA Memphis.

After a quick and effective stint in AA to start off his 2016 campaign, Littrell struggled with control in AAA with a hefty 5.08 BB/9 paired with a slightly above-average 8.59 K/9 in 51.1 IP. One positive note for Littrell is that he has done well controlling balls in play since his switch to the bullpen. His 2016 ground-ball rate is up to an above-average 51.5%, which is a career high. His run prevention, however, has been subpar due to his high walk rate, yielding a 4.56 ERA and 5.01 FIP in Springfield.

Since the Cardinals bullpen has been average to date according to WAR and the majority of the relievers are controlled through next year, there may not be a spot for Littrell to begin the Cardinals’ 2017 season unless he impresses from here on out. However, if he can regain the control in the AFL that he had before his promotion to AAA and keep it through the beginning of next year, he could become an option for the Cardinals sometime next season.

## Poking Holes In Some of the Best Players of 2016

We are reaching the end of the 2016 fantasy-baseball season, which means two things: 1) It’s time to look ahead to next season a bit, and 2) the sample sizes on many metrics are either stabilized or right around the corner from stabilizing.

With that in mind, let’s take a look at a few of players who are sure to be trendy draft picks in 2017, and see what their potential downfalls might be. This is not to say to avoid these players, but rather to spot a potential weakness so that if you do draft this player, and they start to struggle you can maybe know why, and see if it is a recurring issue, instead of just a bit of bad luck. Let’s meet our contestants:

Rougned Odor

In many ways Rougned Odor has had his coming-out party this season. He has hit 31 home runs through 135 games, adding in 12 steals, to go along with a .282 batting average, and strong production (85 runs and RBI each). He’s been the fifth-best second baseman by the ESPN player rater, and that’s with two players (Daniel Murphy and Jean Segura) with multiple positions above him in the ranks. He even got famous on the national scene with his punch heard round the hot-take tables in May.

However, when looking at his plate discipline, it has somehow got even worse than it was last year. His walk rate has dropped to 3.0 percent (17 walks all season!), while his strikeout rate has climbed above 20 percent (20.9, to be exact). His swing rate on pitches outside the zone is seventh in all of baseball, while his contact on those pitches outside the zone isn’t even in the top 80. That’s a dangerous duo. Talented players who hit the ball as hard as Odor can sustain success while flailing that much for a little while, but in the long haul, it almost always burns you. Odor could certainly make strides to improve his discipline, but coming off the season he is having, why is he going to try to change anything? He may well need a rough season, or at least rough couple of months, to admit he needs to fix the holes in his swing, and you don’t want to deal with that when he does.

Jon Lester

With a record of 16-4 and a 2.51 ERA, Lester is having arguably the best season of his career. He’s a candidate for the Cy Young, and currently trails only Noah Syndergaard and teammate, Kyle Hendricks, in the race for the lowest ERA in all of baseball. There are plenty of signs for regression, though.

For starters, Lester is 32, and while left-handed pitchers seem to age like a fine wine sometimes, that’s only because we forget about the guys who crash and burn and are out of the league by 34. Now Lester is showing no real signs of aging, but he also has signs of regression elsewhere.

His left-on-base rate is currently leading all qualified pitchers, at 85.5 percent. That’s more than 10 percent higher than his career rate, and by far the highest percent of his career. That’s especially amazing considering Lester can’t even throw to first base, meaning runners should be moving around the bases faster on him if anything. He is also one of the FIP-ERA leaders, thanks to a well below-average opponent BABIP (.257).

There’s also the fact that, while not by a huge margin, Lester’s strikeout rate has also dropped this season, while the rest of the league is striking out more batters than ever. If you’re already going to get some regression in terms of ERA and wins, you best not be losing strikeouts, as well.

I don’t think Lester is going to fall off a cliff, but I also don’t think he’ll be repeating his 2016 performance next year.

Michael Fulmer

We’ll start with the most obvious candidate for overdrafting. Fulmer will be a 23-year-old, (likely) coming off an American League Rookie of the Year Award, and pitching for a strong Tigers team. He may well win the AL ERA crown, and will easily have a winning record. Heck, he’s got an outside shot at a Cy Young in what has been a weak year for AL pitchers.

That being said, there are some definite weak spots in his profile, the most obvious being his FIP-ERA. If one were to simply go to the FIP-ERA leaderboard — a good spot to at least start to find potential regression candidates — Fulmer’s name is sitting there in fourth, trailing only Kyle Hendricks (borderline historic ERA), Brandon Finnegan (a guy who would have certainly made this list if he were famous enough), and Ian Kennedy (a professor at Hogwarts in the baseball offseason).

It’s more than just the FIP with Fulmer, though. His left-on-base rate is over 80 percent (81.3, to be exact), and his opponent BABIP is just .251. He allows over 30 percent hard-hit rate and his line-drive rate allowed is nearly 20 percent, which while not terrible, do not portend a Cy Young winner.

With Fulmer, it’s more an accumulation of slights rather than one big one, combined with the fact that he will be an extremely trendy pick. There’s no reason to believe he won’t finish the year 13-8 with a 3.35 ERA and 140 strikeouts, but you’ll have to pay for much better stats than that to land him.

Ryan Braun

Like all of the players on this list, Braun is having one of his best seasons in 2016, which is saying something for the six-time All-Star. Braun is hitting .310 with 27 home runs, both of which are his highest since 2012. He has also stolen 14 bases, and only been caught three times, impressive for a 32-year-old.

But it’s not Braun’s age that is troubling (although it is obviously worth remembering come draft day); it’s his ground-ball rate. Braun is seventh in all of baseball in ground-ball rate, surrounded by names like Jonathan Villar and Cesar Hernandez. He has hit ground balls on 55.6 percent of the balls he has put in play in 2016, and has only hit fly balls on 25.4 percent. Because of that, it’s hard to imagine his home-run totals staying as high as they are right now in 2017.

Braun is currently sporting a HR/FB rate of 28.4 percent, highest in the major leagues. Ask Jose Abreu owners what it is like to own the reigning HR/FB rate champion. Here’s how the last four HR/FB rate champions fared the next season:

2012 Adam Dunn – 41 HRs; 2013 Adam Dunn – 34 HRs

2013 Chris Davis – 53 HRs; 2014 Chris Davis – 26 HRs

2014 Jose Abreu – 36 HRs; 2015 Jose Abreu – 30 HRs

2015 Nelson Cruz 44 HRs; 2016 Nelson Cruz 35 HRs (with 18 games to go)

Only Chris Davis fell off the earth, but none of the four went up. If you’re buying on Braun’s power, you’re basically buying at its highest point, which is never a good idea. Especially with a 32-year-old.

Jose Fernandez

Yes, we are getting a little bit into “snake eating his own tail,” by turning advanced metrics against Jose Fernandez, after spending all of the first couple months using the advanced numbers to show a turnaround was imminent, but it was foolish to ignore Fernandez’s biggest weakness: his line-drive rate allowed.

Opponents are hitting line drives off Fernandez 29.3 percent of the time in 2016, by far the highest percent among qualified pitchers, and a rate that tops even Freddie Freeman, the 2016 league-leader in line-drive rate.

Part of that can be explained by the fact that Fernandez throws so hard, that of course the ball is going to come out faster — that’s just physics. It also isn’t as big a deal to allow such a high line-drive rate, when you also have, by far, the highest strikeout rate in the big leagues this season (34.9 percent). It doesn’t matter how hard you hit it, if you simply can’t hit it.

However, that line-drive rate certainly helps to explain the fact that opponents have a BABIP of .341 off Fernandez this year, a figure that would seem due for some regression in favor of Fernandez if we missed looking at the full picture. Some 2017 drafters may see Fernandez’s .341 BABIP and his 2.27 FIP and assume that his 2017 ERA will drop into the low 2.00s. If Fernandez keeps allowing line drives at the rate he has this season, there’s no reason to think his ERA will drop at all. Now drafting a player with an ERA of 3.00 and the best strikeout rate in baseball is still never a bad idea, but if batters start to elevate their swings against Fernandez, while maintaining that same hard contact, Jose could see his home-run rate jump up quite a bit, even when pitching in pitcher-friendly Marlins Park.

Fernandez allowed just as high a line-drive rate in his 11 starts in 2015, and while some of it may still be noise, it is something to keep an eye on. Especially if you have Fernandez in a long-term keeper league, and he eventually makes a move to somewhere like Fenway where the stadium might be a lot less forgiving than in Miami.

## NY-Penn League Scouting: Chalmers, Shore, Chatham, Dalbec, and Dawson

I watch a lot of baseball. I get to see a lot of players. Some of them will go on to have productive major-league careers, but most will not. The point of this article is to look at some of those who may, at the the very least, reach the show.

This report comes after observing two NY-Penn League (low-A) series in late August/early Sept. and includes players from the Oakland Athletics, Boston Red Sox, and Houston Astros organizations.

I will introduce each player as follows:

Name, Position, Organization, Organizational Prospect Rank, Age

Dakota Chalmers, RHP, Oakland Athletics, Rank: 9, Age: 19

Chalmers was drafted out of a Georgia high school in 2015. He’s a four-pitch pitcher– fastball, changeup, curveball, slider. Though, there’s only a 2-3 mph difference between his slider and curveball and not much of a visible difference. His fastball sat 91-93 when I saw him last week; I’ve seen him as high as 93-95. He has a high-effort delivery and control remains his biggest issue, which I’d say is a pretty good place to be as a 19-year-old. His fastball and curveball/slider look above-average, while his changeup shows potential but still is inconsistent in terms of location. I imagine he didn’t have to throw it that often in high-school competition last year.

Logan Shore, RHP, Oakland Athletics, Rank: 12, Age: 21

Shore’s strength is his command. His fastball sits 90-92 and he also throws a changeup (his best pitch) and slider. He pounds the zone and shows the ability to throw any pitch for a strike in any count. He made one (big) mistake during his last outing – an opposite-field three-run home run– but otherwise was solid. His slider remains his weakest pitch, but when it’s on (and it mostly is) he sees a lot of quick and easy outs. I would imagine he won’t add much velocity in the future as he’s already filled out, but can still see him being an effective pitcher nonetheless.

C.J. Chatham, SS, Boston Red Sox, Rank: 15, Age: 21

Interestingly, Chatham is a tall (6’4) shortstop whose biggest strength is his defense. Many at his size project better as third basemen, but it looks like Chatham has the ability to stay at short. He uses his long frame well to cover ground and also shows good arm strength. At the plate, the first thing that stood out was his aggressiveness as he swung at seven of nine first pitches. He also showed some line-drive power, hitting two doubles (one over the center fielder’s head and one down the left-field line) in the two games I saw.

Bobby Dalbec, 3B, Boston Red Sox, Rank: 21, Age: 21

This guy hits the ball really really hard. I saw him in eight at-bats – three strikeouts and five very well-hit balls. Even his outs were hit hard. He looks like an all-or-nothing type hitter. Lots of doubles and home runs but a lot of strikeouts. A former pitcher in college, Dalbec definitely has the arm to remain at third base. His range looked good too — he made one nice play to his right, a charging backhand near third base while having to throw across his body to get the out.

Ronnie Dawson, OF, Houston Astros, Rank: 18, Age: 21

Another all-or-nothing-type hitter, Dawson was drafted in the second round of the 2016 draft out of Ohio State. He looks like he could have been a running back at OSU too — standing 6’2 and 225 lbs. His power and bat speed definitely show – he smoked a line-drive double down the right-field line when I saw him. But so do the swings and misses – he struck out in his other three at-bats. Defensively, Dawson projects more as left fielder as his arm and speed aren’t two of his better tools. From the eye test, Dawson reminds me of the Indians’ Carlos Santana, except Santana strikes out a lot less (14% compared to Dawson’s 24%).

## An Early Look at the AL MVP Race

[This analysis is also featured in our emerging blog www.theimperfectgame.com]

With less than one month to go, the American League MVP race is very close. While usually nothing is set on stone in early September, during the last few years the AL MVP has been a two-man race (Mike Trout with either Josh Donaldson or Miguel Cabrera). This year, however, features five remarkable candidates: Mookie Betts, David Ortiz, Jose Altuve, Mike Trout and Josh Donaldson. Yes, I expect a few other to grab a few top-five votes (e.g. Cano, Cabrera, Lindor and Machado) but I don’t anticipate the award to fall outside those five players.

Let’s look at the classic, old-school numbers first, which not only are sometimes referenced in casual conversations at local bars and pubs but also frequently (and occasionally unfortunately) followed by voters. I’ve plotted R, RBI, HR, OBP, SLG and SB as percentiles of the entire population. Let’s take a quick look.

If you like well-rounded players, probably this year you’re excited with Altuve, Trout and Betts, who dominate across the board. In an era where stolen bases keep declining, 20+ SB will get you to the 90th percentile. On the other hand, if you’re into true sluggers, then the show Ortiz has put this season should be one to remember. However, then again, these metrics paint only part of the picture — they don’t take into account when or where each event happened nor they include defense or base running on its most complete form.

Let’s take a deeper look at WAR and a quick indicator for each batting, fielding and base-running performance.

 Player WAR wRC+ UZR/150 BsR David Ortiz 4.0 164 0 -7.4 Jose Altuve 6.6 160 -0.4 0.3 Josh Donaldson 7.1 161 10.6 -0.8 Mike Trout 8.1 175 -2 8.0 Mookie Betts 6.6 138 16.4 8.0

Obviously when we move away from batting, David Ortiz loses ground — he only contributes in one aspect of the game, and while he has been outstanding in the batter’s box, likely it will not be enough for him to win. When we adjust by park and league, we realize the Trout – Betts race for the best OF is not as close as I initially thought. Trout has quietly put a(nother) great season on an awful team (again) — he’s already at 8.1 WAR and a 175 wRC+, with both easily leading the league. His defense is slightly below average at best but he compensates by running extremely well. Altuve and Donaldson have had similar seasons offensively. However, Altuve is having a down season in both defense and base-running (remarkably low on Ultimate Base Running (UBR), which measures how frequently and effectively a runner takes an extra base via running). Betts drives his value largely from his defense, where he’s settled in nicely as one of the best OF this year.

One of the metrics I tend to assess when I look at awards is how performance was spread the entire season. I want an MVP to be someone that I rely throughout the year, not only during a hot stretch. Additionally, having a big month can really uplift the numbers and build up a misleading argument in favor of someone. Let’s understand how wRC+ is split by month.

This picture to me is interesting for a couple of reasons. First, part of the argument on Betts’ candidacy is that he’s getting better, and delivering when it matters the most — in the middle of a pennant race. After a below-average March/April, Betts has been a beast since July, when Ortiz cooled off a bit. Now, then again, Mike Trout has also followed an upward-trending curve — peaking at 206 in August — and his lowest point is at 144, which is the highest of all lowest points in the sample. From my perspective, if everything else is equal, I’d rather have a Trout-esque curve than Donaldson’s one, who has the highest single-month wRC+ (213 in June) but also with the largest swing (118 difference between May and June). And then you have remarkably constant Altuve — with the narrowest gap between highest and lowest points throughout the season and at least 140 wRC+ in any given month.

Now, most of what we have shown up to now is context-neutral. An argument could be made that every single game is worth the same, regardless of whether it’s in April or July — what’s really important is to deliver in key, high-leverage situations. There is where true MVPs show their full potential to influence a team and define its fate. As they say, a home run against a non-contender team when you are losing by five runs is not as valuable as a game-winning double against our wild-card-rival’s closer in the 9th inning. I’ll admit neither OPS in high-leverage situation or Win Probability Added (WPA) is the perfect metric to evaluate this, but they provide a very good proxy to how well they have fared in tough, game-changing situations. If you are not familiar with WPA, please click here.

Again we see the usual suspect — Mike ‘King’ Trout — leading not only this graph but the MLB with his 5.66 WPA, closely followed by Josh Donaldson, and they’re the only two players from this sample to have a higher OPS in high-leverage situations than in low-leverage ones. Interestingly, Boston’s Betts and Ortiz’s OPS go down 9% and 15% respectively when the stakes are high. I definitely don’t want to say that Altuve’s 0.841 OPS in high leverage is bad, but I certainly want to recognize Donaldson’s and Trout’s clutchier performance.

Another way of looking at the MVP is to ask yourself: Where would that team be if that player wouldn’t have been part of it? While in essence it is impossible to know for sure the answer, a nice proxy is to measure what percentage of position-player WAR is that player responsible for, i.e. what percentage share does this player represent.

 Player WAR Team WAR % David Ortiz 4.0 28.7 14% Jose Altuve 6.6 18.8 35% Josh Donaldson 7.1 21.4 33% Mike Trout 8.1 17 48% Mookie Betts 6.6 28.7 23%

Well, this is another way to see Mike Trout’s leadership on the field. Almost half of the Angels’ WAR have Trout’s name attached to it, which is amazing. (For reference, the leaders in this table are Khris Davis and Marcus Semien with 122% (2.2 WAR each out of 1.6 Athletics total WAR). Now, Donaldson and Altuve have, too, a remarkable 33% and 35% of their total, but probably Betts falls short again with his 23%.

At the end, when all is said and done, it looks like numbers indicate it should go down to a Donaldson vs. Trout race, just as it was in 2015. Ortiz has had an amazing season but his base-running and defense (or lack thereof) limit his overall impact on his team. Betts is definitely an exciting, five-tool player, but his performance hasn’t been as good as Donaldson’s or as consistent as Trout’s. Additionally, Boston’s talent-loaded team reduces his value (this is the opposite of the Trout-Angels argument – how valuable can you be when your team would perform well, even if you’re not there?). His future is extremely bright though. Finally you have Altuve, who may have a legitimate case but falls (a bit) short on overall performance to Donaldson and Trout. Houston has under-performed and arguably that’s a worse outcome than Trout’s, because we knew the Angels were going to be bad, but we thought the Astros would be better.

Last year, Donaldson built his case with a magnificent August, when he posted a 1.132 OPS and Toronto got to first place in the AL East. This year it was Trout who had a torrid August, but the Angels are not in the wild card race. It surely seems to me as if we are measuring the MVP as a team award. Though I understand the rationale of having an MVP on a winning team, there is more to it. If I had a vote, and still being a few games away from the end of the season, I’d support Trout in his quest for his second MVP (as of today), but it looks like momentum and narrative are gaining traction around Donaldson — who has posted much better numbers than in his MVP season — Altuve — who brings new blood to the MVP discussion and might get an extra push if Houston makes it to the playoffs — and Betts — who is clearly the face of Boston’s extremely talented young generation. They, though, despite great Septembers, will post worse numbers than Trout. Yes, the Angels are a bad team — but to what extend is that Trout’s fault? What else could he have done? When did ‘valuable’ translate into ‘winning by himself beyond reasonable expectations’? When did we change this award to ‘best player on the best team’? In 2012 it was Cabrera’s Triple Crown and in 2015 it was Donaldson’s ‘ability’ to get Toronto to the postseason for the first time in many years. In 2016, Trout has been comprehensively better, avoided any deep slumps during the season, and performed very well under pressure and shown that you can put counting stats up on a bad team. We are running out of excuses this year.

## Examining Baseball’s Most Extreme Environment

“The Coors Effect.”

These three words evoke a strong reaction from most people and are impossible to ignore when discussing the offensive production of a Rockies player. Ask anyone who was around for the Rockies of the ‘90s and they will tell horror stories of games with final scores of 16-14. Ask anyone at FanGraphs and they will laugh and point at the Rockies’ 2015 Park Factor of 118. Heck, ask Dan Haren and see what he has to say:

Suffice it to say that Coors is a hitter’s park. Nobody will argue that. But there have been murmurs recently about another effect of playing 81 games at altitude, an effect that actually decreases offensive production. These murmurs have evolved into a full-blown theory, which has been labeled the “Coors Hangover.”

This theory supposes that a hitter gets used to seeing pitches move (or, more accurately, not move) a certain way while in Denver. When they go on the road, the pitches suddenly have drastically different movement, making it difficult to adjust and find success at lower elevations. In other words, Coors not only boosts offensive numbers at home, it actively suppresses offensive numbers on the road, which can take relatively large home/road splits for Rockies players and make them absolutely obscene.

The concept seems believable, but thus far we have no conclusive evidence of its merit. FanGraphs’ Jeff Sullivan recently tested this theory, as did Matt Gross from Purple Row. Although neither article revealed anything promising, Jeff is still a believer, as he recently shared his personal opinion that the Coors Hangover might simply last longer than any 10-day road trip. With this is mind, I decided to approach the problem by examining the park factors themselves.

If you haven’t read the article about how FanGraphs calculates its park factors, I highly recommend you do so before continuing. The basic approach detailed in that article is the same approach that I use here. As a quick example, the park factor for the Rockies is calculated by taking the number of runs scored in Rockies games at Coors (both by the Rockies and the opposing team) and comparing that to the number of runs scored in Rockies games away from Coors. Add in some regression and a few other tricks, and we have our final park factors.

This method makes a number of assumptions, most of which are perfectly reasonable, but I was interested in taking a closer look at one critical assumption. By combining the runs scored by the Rockies with the runs scored by their opponents, we are assuming that any park effect is having an equal (or at least, an indistinguishable) impact on both teams. This seems like an obvious assumption, but it becomes invalid when the Rockies play on the road. According to the Coors Hangover, Rockies hitters experience a lingering negative park effect after leaving Coors which the opposing team is not experiencing.

In other words, we expect a gap to exist between a hitter’s performance at Coors and his performance at an average park. If the Coors Hangover is true, this gap would be larger for Rockies hitters than anyone else.

Let’s start by taking a look at the park factors we have now. The following tables only contain data from NL teams for simplicity sake.

 Park Factors, 5-year Regressed (2011-2015) Team Total Runs (team + opponent) Park Factor Home Away Rockies 4572 3205 1.18 D-backs 3657 3328 1.04 Brewers 3588 3306 1.04 Reds 3385 3215 1.02 Phillies 3365 3341 1.00 Nationals 3240 3213 1.00 Cubs 3346 3345 1.00 Marlins 3200 3229 1.00 Braves 3086 3199 0.99 Cardinals 3243 3397 0.98 Pirates 3070 3394 0.96 Dodgers 2995 3323 0.96 Mets 3109 3556 0.95 Padres 2936 3440 0.94 Giants 2900 3537 0.92

No surprises. Teams score a ton of runs at Coors and hardly ever score at AT&T Park in San Francisco. Now let’s split up those middle columns to get a closer look at who is scoring these runs.

 Runs Scored, 2011-2015 Team Home Stats Away Stats Team Opponent Team Opponent Rockies 2308 2264 1383 1822 D-backs 1844 1813 1641 1687 Brewers 1823 1765 1619 1687 Reds 1731 1654 1606 1609 Phillies 1676 1689 1576 1765 Nationals 1749 1491 1651 1562 Cubs 1625 1721 1547 1798 Marlins 1541 1659 1464 1765 Braves 1606 1480 1569 1630 Cardinals 1779 1464 1797 1600 Pirates 1586 1484 1688 1706 Padres 1443 1493 1604 1836 Dodgers 1557 1438 1758 1565 Giants 1481 1419 1797 1740 Mets 1482 1627 1817 1739

These are the two pieces of run differential — runs scored and runs allowed — and we generally see agreement between the home and away stats. If a team out-scores their opponents at home, they can be expected to do the same on the road. Good teams are better than bad teams, regardless of where they play. Although, if you subtract a team’s run differential on the road from their run differential at home, the difference will actually be around 100 runs due to home-field advantage. Doing this for all 30 teams yields a mean difference of 83 runs with a standard deviation of 122.

Where do the Rockies fall in this data set? Not only have they scored over 400 more runs at home than the next-best NL team — they have also scored almost 200 runs less on the road than the next-worst NL team. Comparing their home and road run differentials, we see a difference of 483 runs (+44 at home, -439 on the road), or 3.3 standard deviations above the mean. To put it plainly: that’s massive. This is a discrepancy in run differentials that cannot be explained by simple home-field advantage.

Furthermore, I followed the same process of calculating park factors for each team explained above, but I split up the data to calculate a park factor once using the runs scored by each team (tPF), and again using the runs scored by each team’s opponents (oPF). Generally, these new park factors are closely aligned with the park factors from before…except for, of course, the Rockies.

 Alternate Park Factors, 5-year Regressed (2011-2015) Team tPF (Team Park Factor) oPF (Opponent Park Factor) Rockies 1.27 1.10 D-backs 1.05 1.03 Brewers 1.05 1.02 Reds 1.03 1.01 Phillies 1.03 0.98 Nationals 1.02 0.98 Cubs 1.02 0.98 Marlins 1.02 0.97 Braves 1.01 0.96 Cardinals 1.00 0.96 Pirates 0.97 0.94 Padres 0.96 0.92 Dodgers 0.95 0.97 Giants 0.93 0.92 Mets 0.92 0.97

On average, a team’s tPF is about two points higher than its oPF — again, this can be attributed to home-field advantage. The Rockies, however, are in an entirely different zip code with a discrepancy of 17 points. We aren’t talking about home-field advantage anymore. We are talking about something deeper, something that should make us stop and think before averaging the two values to get a park factor that we apply to the most important offensive statistics.

We have no reason to believe that any team should have a 17-point difference between their tPF and oPF; the fact that the Rockies are in this situation either means that they are enjoying hidden advantages at home, or they are suffering hidden disadvantages on the road. To date, we don’t have a theory supporting the former, but we do have one supporting the latter. This is the Coors Hangover.

Does this mean that the Rockies’ Park Factor should actually be their oPF of 110? Should it be some weighted average of different values? I don’t know. But I do know these numbers can’t be ignored. Something is going on here, and we need to talk about it.

## Using Statcast to Analyze the 2015/16 Royals Outfielders

I’m working under the hypothesis that you can use launch angle on balls hit to the outfield to determine an outfielder’s relative strength.

The more I look at the data, the more convinced I’m becoming.

So I downloaded the 2015 and 2016 KC Royals Statcast data to see if I could compare their major outfielders’ performance year to year and see a couple things. What I’ve done is bucket hits to the OF by launch angle (in two-degree increments) and calculate a percentage of that contact resulting in a HIT or an OUT. Simple as that. So what I’m comparing between years is:

1) Are the hit likelihood percentages for each angle by OF reasonably projectable year to year
2) Does improvement in my angle metric result in improvement in other defense metrics

First let’s look at Jarrod Dyson. He’s one of the best outfielders in MLB. He recorded, per FanGraphs, 11 DRS in 2015 and to date has 18 DRS in 2016. His 2015 UZR/150 was 18.4 and in 2016 to date it’s 28.7. So both of the “new-traditional” type defense stats are saying, he’s not only good but he’s getting better in 2016 versus 2015. What does my angular stat suggest?

The red points are for ’16 Dyson while the blue is ’15. The left linear regression equation (with the .837 R2) is 2015 while the right (R2 .7796) is 2016. This shows Dyson as a similar player year to year, but likely a bit better. On the higher-angle fly balls, it does appear that Dyson has done a better job this year tracking them down; however, it also appears that in 2015 he did a bit better catching some of the lower-angled fly balls. So it’s not entirely clear, from this graph, why Dyson is per DRS and UZR having such a better defensive year. To have something like this happen, it could indicate that maybe Dyson is starting to play deeper than before. This would limit the likelihood of him catching the low-angled line drives to the OF, but help track down more true fly balls. I’d certainly be interested to see if Dyson is actually doing that very thing this year.

When it comes to projecting year to year, the R2 for Dyson’s ’15 to ’16 hit likelihood % was: 0.532. In real life this is a pretty strong correlation, so I’d say it’s a reasonable estimator.

How about we look at KC OF defensive darling Alex Gordon:

Again the red points are for ’16 Gordon while the blue is ’15. The left linear regression equation (with the .939R2) is 2015 while the right (R2 .8424) is 2016. It jumps right out to you how much smoother Gordon’s regressions are than Dyson’s. Maybe experience leads to that, who knows. So the 2016 regression line (the dashed one) shows that contact to him in the OF is a bit more likely to land for a hit now in 2016 than it was in 2015. This would suggest that Alex Gordon is having a worse year defensively in ’16 than ’15.

How do DRS and UZR/150 compare? Well, Alex has a DRS of 3 in 2016 and had a DRS of 7 in 2015. So he does seem to be trending a bit lower, though not too much. And he has a UZR/150 in 2016 of 9.9 whereas that was 10.5 in 2015. So in this case it all sort of agrees. Gordon seems to be a step or two slower (age and injuries easily could account for that) and as a result his defense has stepped backward a bit. Interestingly he’s still doing about the same job on balls that are high-likelihood hits — the more difficult plays. It’s really at the end of the spectrum where the balls are unlikely to be hits anyway that Alex seems to be struggling. So maybe the “skills” are still there, but the athleticism has just faded a bit and he can’t run down those long fly balls anymore. This is sort of the opposite of Dyson. Maybe Gordon is in fact playing too shallow, cheating to ensure his reputation for robbing sure hits stays intact while losing a bit of overall range, creating a situation where some balls land that probably should have been outs.

When it comes to projecting year to year, the R2 for Gordon’15 to ’16 hit likelihood % was: 0.778. This is excellent and I think it is clearly visible from the chart just how projectable year to year this would be.

What about All-Star and defensive stalwart Lorenzo Cain?

Again the red points are for ’16 Cain while the blue is ’15. The left linear regression equation (with the .8876 R2) is 2015 while the right (R2 .9073) is 2016. Well this is interesting — it’s just as though you shifted the line up ever so slightly. A 2016 higher trendline would indicate that contact to the outfield around Lorenzo would be more likely than last year to result in a base hit. This would indicate he too has backslid some from his 2015 self. So what do UZR and DRS say? DRS in 2016 is 11 whereas it was 18 in 2015. But UZR/150 is currently 15.4 in 2016 and it was only 14.1 in 2015. So there is a bit of confusion as to Cain’s 2016 performance, relative to ’15. Clearly he is still an excellent outfielder by all measures, but I would lean toward him trending in the negative direction in ’16 and moving forward.

Given the two linear regressions and data sets, you’d have to believe you could use this data to project very accurately the future year. And you’d be right. Cain’s year-to-year R2 checks in at 0.955.

Well what about newcomer Paulo Orlando? he already seems to be living up to the newfound tradition of excellent KC outfield defense:

Paulo Orlando is sort of the exact reverse of Cain. His trend has basically just taken an entire step down. This means balls are less likely to be hits now than before. So do UZR and DRS agree with Orlando taking what appears to be a reasonable step forward? Surprisingly no. DRS from ’15 to ’16 has jumped from 8 to 12, but Orlando has played a lot more innings which more or less would explain that growth. And his UZR/150 went from 14.0 in 2015 to 8.7 now in 2016. So these metrics both seem to think Orlando is the same if not a little worse than in ’15.

Projecting using Orlando’s earlier year is, like with Cain, excellent. There is an R2 of .90 between the two data sets.

So for my questions:

1) Are the hit-likelihood percentages projectable year to year? This seems to be a resounding yes, at least in the case of KC Royals. The R2 was always greater than 0.5 with two instances of the four being over 0.9! I’m starting to believe this really could mean something in regards to defense evaluation.
2) How does my angle measure compare to UZR/DRS? There do seem to be some differences; however, this is basically the norm in the “new” defense evaluations. No universal system has been developed and there are plenty of cases where UZR and DRS themselves have disagreements.

I do think in the end this has some merit and I will be looking further into it. I also think similar work can be done with regards to hit speed, as I already alluded to in my earlier article:

Using Statcast to Substitute the KC Outfield for Detroit’s

I think it’s important to view both the angle and hit speed as two pieces and going forward that’s something I’m hoping to include for these players.