Joe Morgan’s Secret Socialist Baseball Regime

A popular theme this preseason was parity.  Truth be told, it’s been quite popular since the 2014 preseason projections forecast the smallest disparity between the best and worst teams at least going back to 2005Since then, the term has been so worn out that BuzzFeed included it on their end-of-the-year list of “words that need to be stricken from the Saber community” (source needed).

While the AL was the main driver of parity-related conversation, it might be worth mentioning that the results show that the AL was more lopsided than it was in 2015 while the NL’s gap was more compressed compared to the previous season despite the existence of the Chicago Cubs World Champion Chicago Cubs.  It’s not that that’s incredible — projection systems are conservative and variables such as sequencing and luck are still unpredictable.  Reflections of these points can be seen in Texas’ record in one-run games, or the Phillies and Braves performing better than they expected, or the Twins performing more like the Phillies and Braves were expected to.

It’s possibly reasonable to think that, as front offices skew more towards advanced analytics, the trend of increased parity will continue.  Of course that’s too simple of a statement as revenue sharing and luxury-tax measures have played their part in balancing out the competitive environment as well.  But as front offices progress it’s more likely that the true-talent level at the major-league level will span a smaller range, fewer and fewer at-bats will go to poor players, and the top players should be more evenly distributed throughout the league, speaking in terms of true talent.

This article, however, is not really about anything based in analytics or reality and I don’t know how to segue from my intro into delivering to you what I set out to do any better than asking you to assume some truly ridiculous prerequisites:

  1. MLB and the owners of all the teams only care about the viewer’s experience
  2. Unpredictable variables have become somewhat predictable. This includes some luck, breakouts, injuries, and rapid declines or dips based on smaller injuries.  This does not mean, however, that Runs and RBIs are predictable; it just works out perfectly by FanGraphs WAR
  3. The public is unaware of the predictability of baseball and there is an Illuminati-type presence in baseball headed by a board of trustees that includes, ironically, but obviously, Joe Morgan
  4. Payrolls are dictated by the outcomes that MLB knows will happen and are strictly performance-based – by FanGraphs WAR
  5. Rosters are reconstructed every single year
  6. Reconstructing rosters has no effect on luck or sequencing or ballpark effects (maybe all ballparks have the same dimensions)
  7. The DH is in both leagues but is only reserved for a portion of games throughout the year; teams are required to allocate at least 140 PA to pitchers
  8. Dave Stewart somehow managed to mess up his last season as the Diamondbacks GM (They just happened to be the last team I constructed and there wasn’t enough WAR left to make them as good as the other teams — the Cubs got dinged by this, too.)

What I did was export all the data I felt was relevant from the leaderboards and build 30 rosters based on the average number of Plate Appearances, Games Started, Innings Pitched, and WAR.  The numbers for the league break down like this:

Offense

PA (Non Pitchers): 179,218 (5,974.93/team)

WAR (Non Pitchers): 572 (19.07/team)

PA (Pitchers): 5,366 (178.87/team)

WAR (Pitchers): -2.6 (-0.09/team)

Pitching

GS: 4856 (161.87/team)

IP: 43306.3 (1443.54/team)

WAR: 429.5 (14.32/team)

The only other things I wanted to be consistent with reality were the distribution of plate appearances by position and accounting for the IP by position players.  The first caveat doesn’t work out perfectly, but you’re not going to find a team that received 1,500 PA from their catchers and only 900 from all three outfield positions combined.  The second one, however, I believe I perfected.

After I had built the 30 rosters I realized they were only distinguished by a roster number, so in order to assign each roster a team, I simply took an alphabetical list of the team names and went down one by one with a random number generator and matched that team and random number to the roster with the corresponding number.

Here’s a link!

Who was on your favorite team?  Considering the public doesn’t know about the basically flawless projection systems, how did your team do compared to how you thought they would do? How much would this affect the way you watch the game?  How much would this affect your team loyalty?  Would you enjoy this?  Is this the dumbest exercise you’ve ever seen?  Is Joe Morgan a genius for complaining about the lack of dynasties while he secretly pulled strings to get all teams to be perfectly balanced, competitively, thereby creating a socialist baseball regime?

 

illuminati

I’ll do this again when the 2017 rosters and projections are set so we can follow up on “equal” roster construction.


Hardball Retrospective – What Might Have Been – The “Original” 1921 Tigers

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

The 1921 Detroit Tigers 

OWAR: 49.3     OWS: 289     OPW%: .553     (85-69)

AWAR: 40.4      AWS: 212     APW%: .464     (71-82)

WARdiff: 8.9                        WSdiff: 77  

The “Original” 1921 Tigers paced the Junior Circuit in OWAR and OWS. Detroit finished third in the American League, ten games in arrears to the Red Sox. Harry “Slug” Heilmann (.394/19/139) collected his first batting title, smashed 43 two-baggers and topped the leader boards with 237 safeties. Ty Cobb (.389/12/101) continued to mash opposition offerings. “The Georgia Peach” tallied 197 base knocks, 124 runs, 37 doubles and 16 triples while recording an OBP of .452 and a .596 SLG. Baby Doll Jacobson (.352/5/90) contributed 211 base hits, 38 doubles and 14 triples to Detroit’s powerful lineup. Ray “Rabbit” Powell (.306/12/74) legged out 18 three-base hits to lead the League and scored 114 runs. Powell and outfield mate Bobby Veach (.338/16/128) established personal-bests in almost every major offensive category. Lu Blue supplied a .308 BA with 103 runs scored and 33 two-baggers in his inaugural campaign while fellow first-sacker Wally Pipp (.296/8/103) drilled 35 doubles.

Ty Cobb placed runner-up to Willie Mays among center fielders in the “The New Bill James Historical Baseball Abstract” top 100 player rankings. “Original” Tigers teammates registered in the “NBJHBA” top 100 ratings include Harry Heilmann (16th-RF), Bobby Veach (33rd-LF), Carl Mays (38th-P), Donie Bush (51st-SS), Lu Blue (77th-1B), George H. Burns (79th-1B), Wally Pipp (83rd-1B) and Baby Doll Jacobson (85th-CF).

“Actuals” backstop Johnny Bassler rated forty-seventh.

  Original 1921 Tigers                                Actual 1921 Tigers

STARTING LINEUP POS OWAR OWS STARTING LINEUP POS OWAR OWS
Bobby Veach LF 5.23 22.64 Bobby Veach LF 5.23 22.64
Ty Cobb CF 5.74 25.77 Ty Cobb CF 5.74 25.77
Harry Heilmann RF 4.1 28.09 Harry Heilmann RF 4.1 28.09
Lu Blue 1B 1.61 16.87 Lu Blue 1B 1.61 16.87
Joe Sargent 2B 0.04 3.37 Ralph Young 2B -0.3 8.76
Donie Bush SS -2.03 6.82 Ira Flagstead SS 0.4 5.92
Eddie Foster 3B 1.78 13.05 Bob Jones 3B 1.22 12
Frank Gibson C 0.34 4.11 Johnny Bassler C 2.38 12.73
BENCH POS OWAR OWS BENCH POS OWAR OWS
Baby Doll Jacobson CF 3.64 25.11 Donie Bush SS -1.41 5.77
Ray Powell CF 3.17 24.19 Joe Sargent 2B 0.04 3.37
Wally Pipp 1B 1.49 14.75 Chick Shorten CF -0.39 3.06
Bob Jones 3B 1.22 12 Larry Woodall C 0.38 2.48
Charlie Deal 3B 0.66 11.21 Eddie Ainsmith C 0.22 2.38
Fred Nicholson LF 1.52 10.17 Herm Merritt SS 0.32 1.45
George H. Burns 1B 1.5 8.58 Sam Barnes 2B -0.02 0.17
Ira Flagstead SS 0.4 5.92 Clyde Manion C -0.01 0.13
Ossie Vitt 3B -0.37 3.6 Jackie Tavener SS -0.05 0.04
John Peters C -0.21 2.59 George Cunningham RF -0.01 0.01
Larry Woodall C 0.38 2.48 Clarence Huber 3B 0 0.01
Herm Merritt SS 0.32 1.45 Sammy Hale -0.04 0
Frank Walker CF -0.37 0.63
Sam Barnes 2B -0.02 0.17
Clyde Manion C -0.01 0.13
Jackie Tavener SS -0.05 0.04
George Cunningham RF -0.01 0.01
Clarence Huber 3B 0 0.01
Sammy Hale -0.04 0

Carl “Sub” Mays (27-9, 3.05) topped the American League in victories, games (49), saves (7) and innings pitched (336.2). Clarence Mitchell fashioned a 2.89 ERA and notched 11 wins while splitting time among the bullpen and starting rotation. Dutch H. Leonard contributed a 3.75 ERA with an 11-13 record for the “Actuals”.

  Original 1921 Tigers                                Actual 1921 Tigers 

ROTATION POS OWAR OWS ROTATION POS AWAR AWS
Carl Mays SP 7.27 34.42 Dutch H. Leonard SP 3.02 13.14
Clarence Mitchell SP 2.59 16.23 Red Oldham SP 2.13 10.76
Red Oldham SP 2.13 10.76 Hooks Dauss SP 1.3 9.82
Hooks Dauss SP 1.3 9.82 Howard Ehmke SP 0.65 8.14
BULLPEN POS OWAR OWS BULLPEN POS AWAR AWS
Lou North RP 0.41 6.23 Jim Middleton SW -0.73 4.25
Slicker Parks RP -0.18 0.85 Slicker Parks RP -0.18 0.85
Jim Walsh RP 0.04 0.25 Jim Walsh RP 0.04 0.25
George Boehler RP 0.06 0.15 Dan Boone RP 0.01 0.16
Lefty Stewart RP -0.58 0
Bert Cole SP 0.75 5.71 Bert Cole SP 0.75 5.71
Carl Holling SP -0.65 4.81 Carl Holling SP -0.65 4.81
Suds Sutherland SP -0.17 2.87 Suds Sutherland SP -0.17 2.87
Bernie Boland SP -1.58 0 Pol Perritt SP -0.02 0.59
Doc Ayers SP -0.29 0
Lefty Stewart RP -0.58 0

 

Notable Transactions

Carl Mays 

Before 1914 Season: Returned to Providence (International) by the Detroit Tigers after expiration of minor league working agreement.

Before 1914 Season: Obtained by the Boston Red Sox from Providence (International) as part of a minor league working agreement.

July 30, 1919: the Boston Red Sox sent Carl Mays to the New York Yankees to complete an earlier deal made on July 29, 1919. July 29, 1919: The Boston Red Sox sent a player to be named later to the New York Yankees for Bob McGraw, Allen Russell and $40,000. 

Baby Doll Jacobson 

Before 1915 Season: Purchased by the Detroit Tigers from Chattanooga (Southern Association).

August 18, 1915: Traded by the Detroit Tigers with $15,000 to the St. Louis Browns for Bill James. 

Ray Powell 

July 10, 1917: Purchased with Wally Rehg by the Boston Braves from Providence (International).

Clarence Mitchell

October 16, 1917: Selected off waivers by the Brooklyn Robins from the Cincinnati Reds.

Wally Pipp

August, 1912: Purchased by the Detroit Tigers from Kalamazoo (Southern Michigan). (Date given is approximate. Exact date is uncertain.)

February 4, 1915: Purchased with Hugh High by the New York Yankees from the Detroit Tigers.

Honorable Mention

The 2003 Detroit Tigers 

OWAR: 14.8     OWS: 195     OPW%: .400     (65-97)

AWAR: 7.1       AWS: 129      APW%: .265    (43-119)

WARdiff: 7.7                        WSdiff: 66

 

The “Original” 2003 Tigers finished last in the AL Central, 17 games behind the White Sox. However the “Actuals” finished 47 games off the pace with a ghastly 43-119 record.

Juan Encarnacion (.270/19/94) established career-highs in RBI and doubles (37). Frank Catalanotto contributed a .299 BA with 34 two-base knocks. Robert Fick registered a personal-best with 80 ribbies and Dave R. Roberts pilfered 40 bags. The bullpen featured John Smoltz (1.12, 45 SV) and Francisco Cordero (2.94, 15 SV).

On Deck

What Might Have Been – The “Original” 1979 Mets

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”.

Seamheads – Baseball Gauge

Sean Lahman Baseball Archive


The State of the Yankees

As a Yankees fan (albeit one that has only witnessed their 2009 World Series), I have never been more excited about the team’s present and future. With the MLB roster slowly filling with good, young talent, and with even more stirring circumstances in the minors, the Yankees have the potential to be another powerhouse team.

The Team

Right now, the Yankees are in the midst of a revolution. Out with the old (A-Rod and Teixeira) and in with the new (Sanchez, Judge and Austin). Despite missing out on the playoffs, they will feature a well-rounded lineup at the start of next year.

It’s safe to say that Gary Sanchez won’t enjoy quite the success he did in the last two months of this season. Actually, he won’t come close. This isn’t to say he will play poorly, it’s just that he played so well that he can’t come back to those levels. However, Sanchez will no doubt still be one of the better-hitting catchers in the MLB with average to plus defense behind the dish, so they will already be better in that position in 2017 than they were in 2016.

The Yankees infield is the most likely to change the least with only Greg Bird slotting in at first base. Didi Gregorious, Starlin Castro and Chase Headley are each under team control until at least 2018, and there isn’t anyone challenging them for their spots at the moment. At first base, though, I say it is most likely that Bird gets the spot because it is possible that Tyler Austin beats him out in spring training. Austin is more likely to be used as a quasi-utility player as he can play at first, in right field and DH.

In the outfield everything could remain the same as the end of the season with Hicks or Judge in right, Ellsbury in center and Gardner in left. It could also see some changes. Gardner and Ellsbury both have the potential to be traded over the offseason with Gardner the more likely of the two. There are options to fill those gaps if trades do happen. Mason Williams could fill in until Clint Frazier is (hopefully) ready later in the season. Hicks, Austin and Judge could also fill the holes if needed.

The Yankees pitching is the most worrisome issue. The starting pitching, that is. Masahiro Tanaka performed well in 2016, so there is no reason to think otherwise for the next year. Beyond that, though, are question marks. Nate Eovaldi will probably be a non-tender after his Tommy John surgery. Pineda had his usual ups and downs. Sabathia is still getting older. Then there are numerous options in Luis Severino, Chad Green, Luis Cessa and Bryan Mitchell. Severino will be given the longest look because of his end to the 2015 season, but it’s a toss-up from there.

The bullpen in New York is still a quality one despite trading away Aroldis Chapman and Andrew Miller. Dellin Betances is one of the best in the game, so that’s a good start. Tyler Clippard, Adam Warren and whoever misses out on the rotation gig will presumably fill in the rest with a lefty thrown in.

The Minors

Now comes the most exciting part of the Yankees. With a system that starts with four top-30 prospects despite Sanchez already graduating, the Bombers are on their way to a good future. Frazier is in AAA and still needs to put up good at-bats before he gets the call to the majors, but that time will come soon enough. Gleyber Torres and Jorge Mateo will likely start the year in AA, so they won’t be seen until 2018 most likely, especially with the likes of Gregorious and Castro blocking them. Beyond their top three guys, the Yankees still have plenty of players who could make a major-league impact once it’s their time. Simply, there is a lot to be excited about when it comes to the team’s future.

The Yankees will have the 17th overall pick in next year’s draft, so they will be in familiar position after having the 16th and 18th picks in the two previous years. Their first-round picks in recent years have both been ones that I personally like, but who wouldn’t? James Kaprielian is shining in the Arizona Fall League and Blake Rutherford looks like a steal at the 18th pick, especially after his hot start to his pro career. This year will hopefully prove to be another that produces some good picks.

The Offseason

With the Yankees pretty much set with position players, there’s no reason to add any pricey free agents. It also wouldn’t be wise to block some of their young players out to prove themselves or ones that are close to ready in the minors. Pitching is another story.

As I stated before, their starting pitching has question marks when it comes to Sabathia’s age, Pineda’s consistency and Severino bouncing back. There also aren’t many pitchers on the free-agent market that stand out. Overpaying for Rich Hill would be contradictory to what the Yankees are trying to do in becoming younger, but his dominance when healthy is something that can’t be questioned. It wouldn’t be a bad move to sign him, but it would add yet another question mark to their rotation due to his injury history. Signing him also wouldn’t help any towards getting under the luxury tax, which Steinbrenner would like to do.

The only free-agent acquisition that I would like to see is a top-notch reliever, which means one of Chapman, Kenley Jansen or Mark Melancon. Jansen is likely going back to the Dodgers and Melancon would be yet another righty for the bullpen. A reunion with Chapman would be the best move. Pairing him with Betances again would put the bullpen in great shape. It’s just that it will cost a lot.

In terms of trading, I am one that is all for trading Gardner, Ellsbury and/or Brian McCann. Ellsbury’s contract probably means he’s staying, but Gardner will be easy to move if Brian Cashman can get the right return. Some reports have said that a swap of him for a middle-of-the-rotation pitcher could work, and that would be just what the Yankees need. McCann will have high demand this offseason with multiple teams needing catchers and not enough free agents to go around. The Yankees will have to eat a good chunk of his contract to get anything of value in return, but it shouldn’t be a problem as they’d be shedding a good portion of his $17-million-per-year contract. It would also give younger players like Bird, Austin and Judge a chance to DH.

The Braves have been said to want a reunion with McCann but won’t trade Mike Foltynewicz for him. The Yankees will do well if they can eat about half of his contract and get a couple middling prospects with some upside.

With such a deep farm, the Yankees also have the ability to trade for a front-of-the-rotation starter. Landing one of the top guys on the trade market probably isn’t in their best interests, though. To get one of Chris Sale, Chris Archer or Sonny Gray will cost a good portion of what the Yankees were able to get for Chapman and Miller. Instead, they should look to trade from depth for a guy that is a step down from the others. With Torres looking like the better middle-infield prospect, trading Mateo as the headliner of a package for a starter would be a good move and won’t impact the team’s future too much.

In Summary

In an ideal scenario, the Yankees will sign one of the top relievers to pair with Betances, stand pat on other free agents and see how Cashman can work the trade market for a third straight offseason. The Yankees likely aren’t a top contender next season, but the potential is there. If things break right with Judge, Bird, Sanchez and the rotation, they could find themselves at the top of the A.L. East. Right now, though, they should look to continue development of their top-three farm system and look at 2018 as the year to really contend.


The Home Run Conundrum, Part II: Less Is More

In Part I, one of the major observations was that a group of smaller-statured players seemed to be using backspin as a distance tool. I was curious how the increase in home runs would look when broken down by physical size. In addition to using Statcast data from Baseball Savant, I downloaded player heights and weights from MLB rosters and created size quintiles. While I expected to see significant contribution from smaller-sized players, the magnitude of what is occurring was quite surprising:

Size Quintile Home Runs
 (1= smallest) 2015 2016 Change
1 410 522 112
2 714 1,025 311
3 1,036 1,132 96
4 1,277 1,420 143
5 1,469 1,512 43
Totals 4,906 5,611 705

Note: Size based on height * weight. Since pitchers skewed the quintiles due to their above-average size, they were excluded in making the quintile groups; however, their HRs are included in order to tie back to HR totals.

Now that is democratization of power! While interesting, the obvious question is: How are the smaller players hitting all the additional home runs? Is it more distance through exit velocity (EV) and/or launch angle (LA), more pulled balls, more fly balls, or just better-hit fly balls? Let’s take a look:

Distance, EV and LA

Change from 2015 to 2016
Balls Hit >=90 MPH, >=15 Deg.
Quintile EV (MPH) LA Distance (ft)
1 0.08 -0.09 -0.96
2 0.26 0.32 2.99
3 0.54 -0.56 3.75
4 0.44 0.16 3.88
5 0.58 0.46 3.06

Note: Balls hit at Coors Field excluded

Although the data above would support a slight increase in homers overall, there is no smoking gun as to what might be happening within the smaller player groups. If smaller players are not hitting the ball that much harder or further, maybe it could be that they are hitting more homers to the pull side.

Pulled HR and Hits

Pulled Home Runs Change and Mix
Quintile            Change 2015 2016
1                   63 85% 78%
2                242 77% 77%
3                   69 77% 77%
4                153 68% 71%
5                   18 65% 64%
Total/Avg                545 71.8% 72.2%

Although the location mix of homers did not change significantly from the prior year, smaller players in both years hit a much higher percentage of their homers to the pull side than average. The more important metric to consider with respect with the pull factor is what is happening to the mix of well-hit fly balls.

            Pulled Balls Hit >=90 MPH And >=15 Deg
Quintile 2015 2016 Change
1 35.7% 36.2% 0.5%
2 36.3% 38.4% 2.1%
3 38.1% 40.3% 2.2%
4 35.0% 37.0% 2.0%
5 35.6% 36.7% 1.1%

Again, more data supporting a slight overall increase in homers – More well-hit fly balls hit to the pull side and more of those balls going for homers. No real support here for what might be happening with the smaller players. What about well-hit fly balls in general:

Size Quintile Well Hit Fly Balls >=90 MPH + >=15 Deg.)
 (1= smallest) Change % Change
1 442 16%
2 881 22%
3 -175 -3%
4 66 1%
5 13 0%

Now we’re getting somewhere! Smaller players experienced a significant increase in well-hit fly balls in 2016. What about fly balls in general, not just those of the well-hit variety:

Change in Total Fly Balls

2015 – 2016

Quintile Change % Chng
1 385 10.4%
2 979 20.5%
3 -146 -2.5%
4 127 2.1%
5 199 3.4%

The last two charts kind of sum it up – smaller players are hitting more fly balls in general as well as more well-hit fly balls that are going for homers. Before closing, I’d like to show two other tables which I believe are meaningful for both the home-run question as well as hitting in general. In a certain respect, it appears hitters are making better contact. The following chart shows the volatility (via standard deviation) for EV, LA, and Distance.

Changes in Volatility
Changes in Std. Dev.    2015-2016
Quintile EV LA Distance
1 -0.65 0.18 -5.44
2 -0.31 0.60 -4.56
3 -0.36 0.46 -5.22
4 -0.21 0.56 -5.77
5 -0.18 0.67 -5.43

Since EV is up in terms of MPH but down in terms of volatility, this would indicate players in general are making better contact. The same is true for distance – higher average distance but lower volatility. However, the increase in volatility of launch angle would seem to indicate quite the opposite – that players are using a lower ball-contact point in order to achieve the higher number of fly balls. Take a look at pop-ups over the past two seasons:

Change in Pop-Ups
Quintile Change % Chng
1 59 4.9%
2 211 13.7%
3 -34 -1.8%
4 41 2.2%
5 -8 -0.4%

While not up across the board, it is very interesting that there is a significant increase in pop-ups in the group responsible for the largest increase in homers.

After considering the data above, I was curious how the homers looked broken down by age. The increase in homers of the younger players was equally surprising:

         HR Breakdown By Age
Age 2015 2016 Change
21-23 125 285 160
24-26 905 1,474 569
27-29 1,321 1,352 31
>=30 2,555 2,500 -55

I checked for the obvious relationship between size and age; however, the 24-26 age group was reasonably well distributed in terms of size so there is likely something additional going on with the younger players. Whether it is a power focus earlier in their development, a selection bias through the draft or some other factor I’m not sure. Maybe I’ll get into that another time.

Summary

This is a very interesting issue to consider and while I’m sure there will be much more written on the topic, it certainly takes some possibilities such as a juiced ball completely out of consideration. Now that would be a conspiracy! That umpires are throwing out juiced balls for the little guys! Except that the balls would have to be so stealthy that they don’t get hit significantly harder or further – they just hit bats of the smaller players for well-hit fly balls more often.

For me, the really interesting part is the underpinning driver – that advanced metrics have changed the market which values the players. Whether consciously or not, players are changing to align with the market to maximize their value. Even more interesting is what the future holds – what is the cost of the hyper-focus on power and loft and what are the unintended consequences that have yet to come to light?  As far as the home-run issue, at least in terms of player size and age, less certainly has been more.


The Future of the Angels

Any fan that is somewhat invested in the game of baseball understands the importance of putting both a good team on the field at the MLB level while also sustaining an adequate farm system. The Angels have done neither.

This is a hard thing to do when the best player in baseball plays for you, but Arte Moreno and the Angels have managed to do that. Let’s take a look at how they ended up in this dire situation.

MLB Team

One thing the Angels do have (sorta) is a good core of players. They have Mike Trout, Kole Calhoun, Andrelton Simmons and Garrett Richards. Trout and Simmons play at premium positions, Calhoun is a good two-way player and Richards has ace potential (given his arm doesn’t snap). They even have quality players in Yunel Escobar and C.J. Cron who are quietly productive. With the exception of Escobar, each of those players are controllable for the next several years. The team isn’t devoid of talent behind Mike Trout like some believe. They also have a few starting pitchers who have either shown success or are promising in Matt Shoemaker, Tyler Skaggs, and Andrew Heaney. The only problem is that Heaney is done for next year already and Skaggs may be too if his stem-cell therapy doesn’t work.

The holes in the team are at second base, left field, starting pitching spots that aren’t guaranteed and the bullpen. Especially the bullpen. The only true bright spot of their bullpen is Cam Bedrosian while everyone else is expendable at best (unless Huston Street can return to form).

Now that we’ve looked at their roster heading into next season, how can they fix it?

Win-Now Options

Moreno has to let Billy Eppler spend some money this offseason. He isn’t paying Jered Weaver or C.J. Wilson anymore. That leaves them with roughly $30 million to spend before hitting the luxury tax, which Moreno has made clear he won’t go over. One thing this offseason is sure to provide is offense.

Obvious fixes would be to sign Yoenis Cespedes and one of Justin Turner — which would move either him or Escobar to second — or Neil Walker. The Angels can’t go another year with below-replacement-level players at those positions if they truly want to win. The smart route for them would be to avoid what are likely to be excessive bidding wars for Cespedes and Turner. Walker is a great fit at second for them. He offers good offense from the left side to couple with their righty-heavy lineup as well as average defense to a team that has seen paltry turnouts at the position.

As for left field, their are plenty of corner outfielders on the market. However, instead of paying too much for a reunion with Mark Trumbo, the Angels should look at Ian Desmond, Dexter Fowler and Carlos Gomez. Desmond and Fowler failed to garner much interest last offseason, so an offense-heavy free-agent class should keep their price tags down. As for Gomez, he was DFA’d by the Astros before playing somewhat better with the Rangers. Gomez is the higher risk/reward player while Fowler is the closest to a sure bet to perform consistently. Desmond is a wild card since he just converted to the outfield and profiled as below average in center. Shifting him to left with Trout in center could improve his defense, so he is also a viable option. The Angels took huge risks in the past that didn’t turn out well, so Fowler is probably their best option. Plus he adds another lefty bat against righties.

If the Angels can manage to add both Walker and Fowler, their offense would actually fill the basic requirements for a successful team. They would have a leadoff hitter in Fowler, and a number two in Calhoun, with Trout in the three slot. Either Cron or Pujols will bat fourth and fifth with Walker behind them. Then some mix of Escobar, Simmons and their catcher in the 7-9 spots.

As for fixing the rotation, that will be much harder, and they might just have to wait out the storm or go over the luxury tax. Overpaying for Jeremy Hellickson or Ivan Nova would be a bad move and Rich Hill isn’t a good fit for a fairly old roster that already has its risks. Henderson Alvarez could be a good bounce-back candidate after missing 2016 following a shoulder surgery. Andrew Cashner could be an option in a pitcher-friendly park in Anaheim, though the one in San Diego didn’t do him much good. Going into the season with Richards, Shoemaker, Skaggs, Nolasco and one of Alex Meyer, Nate Smith or Daniel Wright isn’t the end of the world. It just has its risks.

What about the farm?

Minor Leagues

According to RosterResource.com, the Angels have 11 home-grown players on their 40-man roster. That’s definitely on the lighter side compared to most teams, but it isn’t quite as extreme as the Padres’ six. They also only have six free-agent signings, which isn’t too large of an amount. The part of their roster that stands out is their eight waiver claims. The fact is that the Angels didn’t have the depth to fill their own roster with players already within their organization.

Going back to the team’s free-agent signings, six isn’t a large amount as I stated before. However, some of their more recent signings have been very costly (both in terms of monetary and baseball value). In the offseason prior to the 2012 and 2013 regular seasons, they signed Albert Pujols, C.J. Wilson and Josh Hamilton. Each of these players cost north of $15 million per season. Each also cost the team draft picks. They lost two first-round picks and a second-round pick.

The teams’ most recent top picks most likely aren’t going to make an impact with the MLB team. For one, Sean Newcomb definitely won’t since he’s on the Braves now. Taylor Ward and Matt Thaiss were both very weak first-round picks. Ward won’t hit and Thaiss is basically limited to first base.

This isn’t to say the Angels have nothing in the minors. Jahmai Jones is promising but very young and a few of their other 2016 picks could develop into good MLB players, including Brandon Marsh and Nonie Williams. Their farm isn’t deep enough to trade for any key pieces though, and they shouldn’t do that even if they did have the pieces. Eppler has a chance to use a top-10 pick this year as well as future picks to try and build the strong farm that the Angels have lacked for so long, so he can’t waste that opportunity on a middling team.

In Summary

I feel that I’ve laid out the best-case scenario for the Angels next year with potential signings of Walker and Fowler, who would fit in very nicely with their current lineup. Any team with Mike Trout has a chance to be successful after all. They will need to sign those two players first, and then the rotation needs to have luck on its side with the injury situations. The bullpen is a clear gap in the roster, so safe signings over the offseason could complement Bedrosian and possibly Street. Their farm system is also a clear work in progress, but it isn’t empty in terms of talent. That talent is just a little ways off at the moment. Overall the future of the Angels seems dreadful, but if things break right they can be a contender next year. Their overall run differential was only 18 apart from the first-place Rangers, so they at least played in close games. Now all that needs to be done is execution by the players and by the front office to bring a winner back to Anaheim.


The Small Things Do Matter: Lack of Hustle by the Cubs

Despite their World Series Game 5 win, the Cubs came under fire Monday morning for their lack of hustle. On Mike & Mike ESPN Radio, Mike Greenberg commented that the Cubs seem to be lacking hustle, as evidenced by slow home-plate-to-first-base times by Jorge Soler on his Game 3 triple and Anthony Rizzo on his Game 5 double. Soler assumed a fly out or foul ball, and Rizzo assumed a go-ahead home run. Buster Olney added, “It’s interesting you say that, because I had a conversation with one of the veterans in this World Series…and that’s exactly what he said. ‘This is the World Series, how can that happen? It’s a different generation.’”

That’s right, this is the World Series, the biggest baseball stage, the postseason when even average fans tune in to watch and learn from some of the best players in the game. In today’s baseball market worth billions, where players in their late teens and early twenties are paid 10 or more times the average American salary, maybe a lapse in hustle or a hard 90-time is acceptable, if not necessary, during the regular season of 162 games in order to avoid injury or excessive exhaustion. You wouldn’t want your star player pulling a hamstring on a routine infield groundball in August, would you? Of course not. But c’mon, this is the World Series! Most of these players have never played for higher stakes. Is the “lack of hustle” a generational problem? What ever happened to the commitment Joe DiMaggio had to playing hard just in case someone was watching for the first or last time?

Your organization hasn’t won a title in 108 years. Why wouldn’t you approach every play as if it was the last? You’d think they would in the World Series; especially given Game 5 could have been the last. Greenberg and Olney failed to even mention Javier Baez’s Game 5, second-inning strikeout where he refused to run to first base on a dropped third strike, looking increasingly frustrated with his World Series offense (2 for 18 with 7 strikeouts, and 16 runners left on base after that at bat).

Let’s take a look at some numbers by analyzing the Win Expectancy (WE) for the Cubs before and after each of these three plays (Soler’s triple, Baez’s strikeout, and Rizzo’s double) to show the importance of maximizing every opportunity and play. These three players were caught up in the moment and took things for granted on the biggest stage of their sport, a time when small mental errors could make the ultimate difference in winning a game and the championship. We’ll also look at the WE if each play had ended with a different outcome. All WE are obtained from The Book: Playing the Percentages in Baseball, by Tom Tango, Mitchel Lichtman and Andrew Dolphin.

Jorge Soler (Age 24)

Game 3, Series tied 1-1

Bottom 7th, Cubs trailing 1-0 with 2 outs

Triple to right field

WE before at bat: 29.1%

WE after triple: 35.0%

WE after next batter ends inning 26.5%

For the sake of this article, let’s assume (which is assuming a lot with an inside-the-park home run) Soler runs hard out of the batter’s box and rounds the bases for an inside-the-park home run, tying the game. The WE for the Cubs would have jumped to 52.2%, a major swing (nearly double) from where that inning ended. The Cubs lost that game 1-0 to give the Indians a 2-1 series lead.

Javier Baez (Age 23)

Game 5, Indians leading series 3-1, Cubs one loss from elimination

Bottom 2nd, Cubs trailing 1-0 with 2 outs and a runner on 1st

Strikeout, dropped third strike (ball in dirt), Baez does not run to 1st base

WE before at bat: 41.9%

WE after strikeout: 39.4%

Again, for the sake of this article, let’s assume Baez runs hard to first on the dropped third strike and reaches base, which rarely ever happens in Major League Baseball. However, this is the World Series and Game 5 is an elimination game. You never know what can happen. Though the data for the number of baserunners reaching on a dropped third strike isn’t available, such instance would be scored with either a passed ball or wild pitch on the play (a battery error). Take a look at the number of passed balls and wild pitches in MLB over the past 10 seasons, as provided by Baseball Reference.

Year Passed Ball (per game) Wild Pitch (per game)
2016 0.08 0.37
2015 0.07 0.36
2014 0.07 0.35
2013 0.07 0.36
2012 0.08 0.32
2011 0.07 0.32
2010 0.06 0.34
2009 0.06 0.33
2008 0.06 0.32
2007 0.07 0.31
Average 0.069 0.338

A catcher is scored with a passed ball on average every 14-15 games, while a pitcher is scored with a wild pitch on average every third game. If anyone can provide data for the number of times a batter has reached base on a dropped third strike, it would only strengthen the claim that Baez’s chances of reaching base were slim. Regardless, remember it’s an elimination game in the World Series. For the sake of proving a point, let’s look at the scenarios if Baez had reached base.

The ball did skip quite a distance from catcher Robert Perez, so let’s take a look at WE if Baez reached first, leaving the Cubs with runners on first and second with 2 outs: 44.1%.

How about if Roberto Perez threw the ball away into right field, causing a 1st and 3rd situation (let’s note that catcher errors are also very rare): 45%.

Even though Baez reaching first base on a dropped third strike (which was far from guaranteed by running) would have only added about 5-6% to the Cubs WE, there is no excuse for Baez to have a lapse of effort and allow Perez an easy, no-pressure throw to first base because there was no runner hustling down the line. At the very least, run hard and make it look good for the millions of people watching. Not to mention the thousands of people who spent a week’s wage on tickets to Wrigley. They, along with your teammates, want to see you running to first base instead of walking back to the dugout.

Instead, the Cubs were left with their two weakest hitters (David Ross and Jon Lester) to lead off the next inning, which resulted in a 1-2-3 inning for Indians starting pitcher Trevor Bauer, who took a 1-0 lead into the fourth inning.

Anthony Rizzo (Age 27)

Game 5, Indians leading series 3-1, Cubs one loss from elimination

Bottom 4th, game tied 1-1, 0 outs, first batter after Kris Bryant game-tying home run

Double to right field

WE before at bat (after Bryant home run): 56.3%

WE after double: 63.4%

The back-to-back extra-base hits certainly turned the momentum of the game in the Cubs’ favor. Two batters (Bryant and Rizzo) increased the Cubs’ WE from 43.7% to 63.4%, a major increase in a game they eventually won 3-2 to force a trip back to Cleveland for Game 6.

A better throw from right field would have made a very close play at second base, so let’s look at the WE for the Cubs had Rizzo been thrown out at second base on his hit off the right-field ivy: 53.4%. His lack of hustle from home plate to first base, as he admired what he thought was a go-ahead home run, could have cost the Cubs 10% on their WE.

Conclusion

There is certainly no guarantee that any individual exertion of hustle will lead to a different outcome in a baseball game. Running out a groundball will not guarantee an infield hit, but it puts pressure on the fielder to make a clean play. Running hard on a fly ball has no measurable effect on whether a fielder will catch it or not, but it puts the runner in the best possible position to advance an extra base on a rare dropped ball. Running hard to first base after a dropped third strike does not make a difference in the outcome of the play 99% of the time, and it certainly doesn’t change the 0-for-1 with a strikeout in the box score. But it puts pressure on the catcher to retrieve the ball in a clean manner and make an accurate throw to first base. Let’s not forget that hustle is the right thing to do. It’s more about the precedent, not the result. It’s about the example we want MLB players on the biggest stage to set for younger players worldwide. DiMaggio likely wouldn’t recognize some aspects of today’s game. He, like many other players, never took anything for granted. Despite the fact that Soler and Rizzo still ended up with extra-base hits, and Baez most likely would have been thrown out at first base anyway, shouldn’t we hope that on this kind of stage the very best will play the game to its absolute potential?


Linearization and Fantasy Baseball

Among the astounding phenomena abundant throughout calculus, linearization remains one of the least glamorous. It’s incredibly simple, taught in less than a day, and a more precise (and more complicated) method can often be substituted for it. On the other hand, it’s an incredibly powerful tool and one with weighty implications for fantasy baseball. Because of the concept’s relative simplicity, a reader with even the most basic inkling of what calculus actually is should be able to understand the idea of it, so don’t let a fear of mathematics deter you.

First, let’s think about graphs, functions, and derivatives. Put simply, continuous functions, whether they’re linear, quadratic, or exponential, will generally experience some rate of change — slope. Think of it as the change in the y direction per unit change in the x direction between two points. This is considered a secant line, or the average rate of change between two points. More interesting, however, is the concept of the tangent line, or the instantaneous rate of change at a given point. Note that the tangent line only touches the function at one point rather than two, meaning that we can easily evaluate and analyze the rate of change when comparing two points on a curve. Importantly, the magnitude of the slope of the tangent line tells us the rate by which the function is increasing or decreasing. So the greater the slope, the faster it is increasing (perhaps indicating an exponential function), and the lesser the slope, the more it is decreasing (a negative quadratic).

In calculus, the formula for linearization is:

L(x) = f(a) – f'(a)(x – a)

Here, given some value of a, we get a y-value, or f(a). From there, we subtract the product of the derivative of f(a) and the difference between the value we are estimating, x, and the value we already have, a. This gives the linear approximation and we get a pretty good estimate.

When rendered down to its most basic essence, linearization is a glorified form of estimation that gives credence to gut instinct through a formula. Using the tangent line at a certain point, one can make very incremental estimations, but it’s important to note that they must be very small. The farther from the initial point a that one travels to find an approximation of y, the less accurate the result will be.

It seems that this would have little application to baseball, but that’s incorrect. Recently, I started toying with a couple of formulas that could actually have some importance in the realm of amateur fantasy baseball with the usage of a regression line for an entire player’s career in pretty much any statistic.

L(x) = f(k) – f'(a)(x – a)

Here, f(k) is the actual value at the known point (k), f'(a) is the derivative of the predicted point on the regression line, x is the point for which we are predicting the value, and a is the value we start from.

L(x) = f(a) – f'(a)(x – a)

Differing here, f(a) is the predicted value at the regression line, f'(a) is the derivative of the predicted point on the regression line, x is the point for which we are predicting the value, and a is the value we start from.

I don’t know which would work best, but my guess is that first formula would be most accurate due to its mix of actual and predicted values. Neither of them would be terribly precise, but it’s a heck of a lot better than relying on what you feel might be best.

Regardless of which formula you might prefer, the implications of the linearization idea as applied to fantasy baseball are apparent. Probably best used for 10-day predictions, linearization mixes short-term performance with long-term talent to assess how well a player might perform for a short period of time — whether he’s likely to continue streaking, slumping, or somewhere in between. Rather than having to rely on gut instinct or dated and/or biased statistical analysis, a fantasy player could rely on some concrete math to make short-term decisions. This would be especially helpful in leagues that play for only a month, or can only alter their rosters once a week, or even at the end of a highly competitive season (perhaps making the risky move of dropping a slumping MVP for the streaking rookie).

It’s understandable if it’s unclear how to use one of the formulas at this point. To simplify matters, let’s use formula 1 to demonstrate how this might work in regard to something as simple as batting average. So what you might have is a regression line for a player of rolling 10-game predicted batting averages plotted along with actual values. In this case, x-values are 10-game rolling averages by each 0.01 (the intervals are arbitrary). So 1.1 is the x-value at 110 games played, while 1.2 is the x-value at 120 games. Let’s just say for simplicity that the player has played 110 games in his career, had an actual average of .264 during the last 10-game stretch, and the derivative of the regression line at this point is 0.12. We want to guess his average for the next 10 games, up to career game number 120.

L(1.2) = .264 – (0.12)(1.2 – 1.1)

L(1.2) = .254

We’d expect him to hit .254 over the next 10 games. Hopefully that makes some sense. Obviously it’s still in development and I haven’t done a whole lot of research yet, but expect some to come out later along with some clarifying material if necessary. Confusion is to be expected, but with some explanation applied linearization could potentially help a lot of people out next season in fantasy.


The Case for No Starting Pitchers in the National League

I’ve watched many a baseball game over my lifetime (that’s 50+ years), and I’ve cringed every time I see a National League manager send his starting pitcher up to bat any time prior to the seventh inning. Especially with runners on base! Doesn’t he know that pitchers can’t hit? Doesn’t he know that if he would just pinch-hit for the lame-batting starter he’d improve his team’s chances of winning?

So, after years of pondering this problem for five seconds at a time every couple of days, I decided to see if I could build a solid quantitative case for never letting a pitcher come to the plate for a National League team (obviously this is not an issue for the American League with their designated hitters). How would this change the look of the team’s pitching staff? And more importantly, how many more games would a team expect to win in a season if they adopted a “pitchers never bat” strategy?

The answer to the first question is pretty easy. The staff would “look” different. There were would be no more “starting pitchers.” A team’s pitching staff would consist only of “relievers.” Sure, one of the “relievers” would throw the first pitch of the game and could technically be called a “starter,” but given that he’ll be taken out of the game as soon as his spot in the batting line-up comes up, he’s effectively a “reliever,” just like the other 10 or 11 guys on the staff.

Now, the conventional wisdom would say that the current starting pitchers, especially the “aces,” get in a groove, and can give you six or seven solid innings. Why would anyone take them out the game in the second or third inning? Well, let’s do a “cost-benefit” analysis and see if we can make a case for “The Pitchers Never Bat” strategy.

 

Key Components of the Case:

The two primary components of the analysis are 1) how many more runs would a team expect to score in a season by pinch-hitting for every pitcher, and 2) how many more runs would a team expect to give up in a season because their starting pitchers are no longer going six, seven, or more innings in an outing? Or, maybe the team adopting such a strategy would actually give up FEWER runs per year by giving up on the century-old strategy of planning for the starting pitcher to pitch deep into the game.

A third component of the analysis could include the benefit of being able to choose from any of the team’s entire staff (probably 11 or 12 pitchers) and use only the ones that look like they’ve got their “stuff” while warming up before the game, instead of sticking with the “starter” who is scheduled to pitch today because it’s his turn in the “rotation.”

A fourth component of the analysis could include the benefit a team could achieve because the other team can no longer stack their starting batting order with a lot of lefties (to face a right-handed starter), or with lot of righties (to face a left-handed starter), because the team with no “starters” will pinch-hit for their first pitcher after one, two, or three innings. So, in total, the “handedness battle” tilts slightly more in favor of the team implementing the new strategy.

A fifth component could include the cost (or benefit) of reducing the size of the pitching staff by one or two, and adding one or two more everyday players, who would be needed to pinch-hit in the early innings.

A sixth component could be an added benefit that batters will not be able to get “used to” a pitcher by seeing them multiple times in a single game. Under the new strategy batters will see each pitcher once, or, at most, twice in a game.

I’m going to focus on the two primary components above, and let the lessor components alone for now. Perhaps others can weigh in on how to quantify the potential impacts of these changes.

 

Component #1: How much more offense will the “Pitchers Never Bat” strategy create?

This is the easiest of the components to quantify. I will use the wOBA (weighted On Base Average) statistic as defined and measured by FanGraphs to evaluate this component. Let’s start with some basic information and rules-of-thumb.

Using data from the National League for the 2015 season I find that pinch-hitters have a wOBA of .275 across the entire league, while pitchers, when batting, had a wOBA of just .148 across the entire league. The difference in wOBA between pinch-hitters and pitchers is .127 (that’s .275 minus .148.) Note that all position players in the NL combined for an average wOBA of .318 in 2015. I’m assuming that our new pinch-hitters won’t get anywhere near that figure, but will be comparable to the 2015 pinch-hitters, who came in way lower, at .275.

Now, let’s assume we can replace every pitcher’s plate appearance (PA) with a pinch-hitter. This improvement of .127 in wOBA needs to be applied 336 times per season, because that was the average number of times that a National League team sent their pitchers up to the plate in 2015. And lastly, we need to know two rules of thumb from FanGraphs that are needed to complete the analysis of the first component: 1) every additional 20 points in wOBA is expected to result in an additional 10 runs per 600 plate appearances, and 2) every 10 additional runs a team expects to score in season translates into one additional win per year. OK – so, let’s do the math:

If 20 additional points of wOBA translates into 10 runs per 600 PA, then our new pinch-hitters who are now batting for pitchers will provide the team with 63.5 incremental runs per 600 PA (which equals 127/20 * 10.) And since these pinch-hitters will be coming to the plate 336 times, not 600 times, we need to reduce the 63.5 incremental runs per season down to 35.6 incremental runs per season (which is 336 / 600 * 63.5).

Finally, the last step is to take our 35.6 incremental runs per season and translate that into incremental wins per year using the rule-of-thumb that ten runs equates to one win. Therefore, our 35.6 extra runs results in an expected 3.6 incremental wins per year. That’s a decent-sized pick-up in expected wins.

OK, so now, what about the pitching staff? Will replacing the conventional pitching staff with a staff consisting of no starters and all relievers cause the runs allowed to increase, and if so, by how much? Enough to offset our 3.6 extra wins that we just picked up on offense?

 

Component #2: How many more runs will pitchers give up using the “Pitchers Never Bat” strategy?

Imagine, for the moment, that a GM is to build his pitching staff from scratch. (We’ll worry about how to transition from a conventional staff to an all-reliever staff later.) And let’s just assume he’ll pick just 11 pitchers. (Most NL teams use 12-man staffs while some use 13, so that will give the team one or two additional position players.) Currently, starting pitchers typically throw 160-200 innings per season, and relievers tend to throw 50-80 innings per season. But with the new all-reliever strategy, and using only 11 pitchers, each of our new guys will need to average around 130 innings each, with perhaps some pitching as much as 160, and some as low as 100 innings per year. So, the GM is looking for 11 guys who can each contribute 100-160 innings per season. Each outing will be for about one to three innings for each pitcher. How will they fare?

Let’s look at the National League’s pitchers for 2015. Starting pitchers had an aggregate WHIP (Walks Plus Hits per Inning Pitched) of 1.299, while relievers, in total, recorded an identical WHIP of 1.299. So my takeaway from this is that the average starter was equally as good (or bad) as the average reliever. From this, I am going to take a leap of faith, and assume that a staff of 11 new-style relievers could be expected to perform equivalently. (And that doesn’t even factor in some of the lesser elements of the new strategy, as mentioned above, such as Components 3 and 4 of the analysis.)

From this, albeit simplified, evaluation of Component #2, I estimate that a team moving to an all-reliever pitching staff will have an expected change in Runs Allowed of zero, and therefore the change will neither offset, nor supplement, the offensive benefit evaluated in Component #1.

 

Conclusion and Final Thoughts

In summary, using the two primary components of my analysis, I estimate that adopting a “Pitchers Never Bat” strategy in the National League (a.k.a. an “All Reliever Pitching Staff” strategy) will improve a team’s offense by an expected 36 runs per year, which will increase the team’s expected win total by 3.6 games. I estimate that the impact on runs allowed will be near zero. Some lesser elements, Components #3 through #6, could also add some additional value to the strategy.

Implementing the strategy does not necessarily need to be a complete, 100% adoption of the “pitchers never bat” rule. Modifications can be made. Perhaps a pitcher is doing well through two innings and comes to bat with two out and no one on base. In this case the manager could let the pitcher bat, so that he can stay in and pitch another two or three innings. This would change the name of the strategy to something like the “Pitchers Very, Very Rarely Bat” strategy.

As far as transitioning to an all-reliever staff from a conventional staff, it could be done over time, or only in part, such that a team could maintain, say, its two top aces, and complement them with eight or nine relievers. This way, the aces could pitch as they do now, going six-plus innings, every fifth day, while limiting the “Pitchers Never Bat” strategy to the three out of the five days when the two starters are resting.

Finally, let’s try to put a dollar value on this new strategy. The guys at FanGraphs, and other places, have tried to estimate how much teams are willing to pay for each additional win. Without going into all the various estimates and approaches at trying to answer that question, let’s just go with a simple $8 million per win. I’m sure it could be argued to be more or less, but let’s just put $8 million out there as a base case. If that’s true, a 3.6-win strategy, such as the “Pitchers Never Bat” strategy, is worth about $29 million per year. Go ahead and implement the strategy now, and, if it takes, say, three years before any of the other NL teams catch on, you’ve just picked up a cool $87 million (3 * 29 million).

And if the other components of the analysis (#3 through #6) are quantified and it can be determined that they add another 0.5 wins per year, which I think is quite doable, then we can get the total up to 4.1 wins per year, for a value of $33 million per year, or just around a cool $100 million over the first three years. And that’s how you make $100 million without really trying!


Predicting the Next 300-Game Winner

With the special attention pitchers receive today, such as pitch counts, innings limits, as well as the host of PITCHf/x data that can notify teams of when a pitcher is fatigued, it seems like they days of 300-game winners have come and passed. And for the most part, some of this is true. We’ve seen pitchers be shut down during their earlier years to prevent injuries, such as the Nationals keeping a close eye on Stephen Strasburg. When we think of 300 wins, the math isn’t that hard. It’s some combination of 15+ seasons of 15+ wins over an entire career. Let’s dive in to what further breaks down these pitchers.

I gathered data on pitchers who finished their careers after 1980 as well as pitchers younger than that; I did this to avoid looking at pitchers such as Cy Young who are a little tough to compare to the modern day, with rule changes and the different run-scoring environments. In my query, I looked at pitchers with at least 250 wins. This gave me more data, and since 250-win pitchers are reasonably close to 300, it will allow me to get at what exactly creates a pitcher of this caliber.

My list included 19 names:

Greg Maddux

Roger Clemens

Steve Carlton

Nolan Ryan

Don Sutton

Phil Niekro

Gaylord Perry

Tom Seaver

Tom Glavine

Randy Johnson

Tommy John

Bert Blyleven

Fergie Jenkins

Jim Kaat

Mike Mussina

Jamie Moyer

Jim Palmer

Andy Pettitte

Some of these guys were absolute iron men, pitching over 5000 innings in their career. Maddux did this, as well as Carlton, Ryan, and Sutton. Most of this group barely reached 12 wins per season, showing that they reached the 300-club with longevity, not necessarily dominance. The other guys on this list, by default, either had higher win totals or pitched forever, but without racking up a ton of innings (Kaat, Moyer). Surprisingly, or perhaps not, only four of the 19 pitchers did not pitch for 20 seasons, so again, dominance might not be the key factor — instead, longevity.

I then looked at where these pitchers were at when they were 30 years old. Thirty years seems to be about a halfway point, but the data indicates otherwise. In fact, only three of these 19 pitchers had at least 150 wins at 30. This again drives home the point that these pitchers do not necessarily have to be untouchable every single year they pitched; it just means they have to be pitchers that stay healthy and can pitch for a long, long time. At the same time, the average pitcher on this list had 115 wins at 30, so they did need to have a productive youth in terms of racking up wins.

Here is a table displaying the careers of our 19 pitchers:

screen-shot-2016-10-26-at-4-07-45-pm

The amazing part, at least in my opinion, is that these pitchers almost seemed to get better with age, at least in terms of wins. I know that wins is not a good stat for tracking the effectiveness of pitchers, but since we are talking the 300-win club, it is what we have in front of us. Anyways, 17 of these 19 pitchers had more wins after 30 than they did before. Again, this hammers home the idea that longevity and durability is more important than complete dominance. Yes, you have to be a good, if not great, pitcher, but you also have to stay healthy.

So when looking at current pitchers that possibly have a chance at 300, I filtered through active pitchers fulfilling a few different qualifications. First, the pitcher must have at least 190 innings pitcher per year, including years of injuries (this helps get at longevity and durability). Also, the pitcher must also average at least 12 wins per year. I came up with a group of pitchers who where close to matching these requirements. From this list of 14 pitchers, I think eight or so have the best chance of eclipsing 300.

Here is a table of possible contenders:

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This list includes: Clayton Kershaw, Chris Sale, Justin Verlander, Madison Bumgarner, David Price, Rick Porcello, Jon Lester, and Felix Hernandez. CC Sabathia, although at 223 career wins, does not make this list, since I don’t think he has 5-8 more seasons of decent pitching in front of him. I will go into each pitcher in more detail to describe what each pitcher needs to do to have a chance.

I’m going to start with Lester. Lester is currently at 146 wins, with 2003 regular-season innings pitched. He has been great through his first 11 seasons, in nine of which he was a full-time starter. In those nine seasons, he failed to pitch 200 innings just once, when he posted 191.2 innings pitched. He has been an iron man, and at age 32, the recipe is simple. He just needs to stay healthy and he needs his game to age well. This is going to be a repetitive theme, but to be honest, that’s what we would expect. Things helping Lester? Well, playing for the Cubs is one. Not only do they have a great defense, but they also create great run support, which can help Lester pick up a lot of wins. He was 19-5 this past year, matching his career high in Boston in 2010.

Now on to Justin Verlander. After an injury-riddled 2015, Verlander was great this year, posting a 16-9 record and an ERA of 3.04 (FIP of 3.48). Currently, he sits at 173 wins and is 33 years old. I mentioned his injury struggles in 2015. He only pitched 133 innings. In his 11 years as a full-time starter, that was the only the second time he failed to reach 200 innings pitched. People may worry that Verlander is starting to lose his velocity, which could mitigate his effectiveness, but in 2016, he struck out batters at a career-high rate and also had a career-best strikeout to walk ratio. Verlander is back with the elite, and if he can avoid injury trouble, he deserves to be in the discussion for a possible 300-win flirtation.

I’ll now move on to Clayton Kershaw. Kershaw has been the best pitcher in baseball for the past five years, and has only struggled with injuries for this past year, when he hit the DL with back issues. He still picked up 12 wins, and looked like peak Kershaw when he came back. Kershaw continues to strike out hitters and not allow walks, and in his shortened 2016, he posted a career-best FIP. Kershaw currently sits at 126 wins, and is 28 years old, in the middle of his prime. I think there are two factors that could keep Kershaw from getting close. The first one is his back. The Dodgers shut Kershaw down for half the year, and hopefully it heals, but if it is one of those lingering injuries that can also affect his timing a delivery as well as his overall health, he won’t be able to age his game to the necessary limits needed to hit 300. Also, he should get more wins. I’m not sure this will be a big factor now that the Dodgers have Andrew Friedman at the helm, but if he cannot get the run support he needs, that could lead to two or three fewer wins every year.

Chris Sale is next. Sale sits at 74 wins and is 27. He has some work to do. He has been relatively healthy, however, over his five full years as a starter. I think the best bet for Sale is to get out of Chicago, or at least the White Sox, and get on a team that can give him some good defense and offense. His win totals just aren’t high enough, but he is young enough where if he finds a new team and can age well, he might be able to hit 250.

I’ll do Bumgarner next. He really hasn’t had any injury trouble in his six years as a full-time starter. He is 27 and has 100 wins. He is a little harder to project, but I would say he’s got a better shot than Sale. I mean, he is already at 100 and only 27. Kershaw might have a leg up on him, but MadBum has been able to stay healthy. To be honest, Kershaw had been healthy too before this year, which somewhat shows that pitching 20 full seasons does not happen to often. Anyways, Bumgarner hasn’t quite been as dominant as some of the other names on this list, but he has been very good, and has stayed healthy. He is on a solid team with a good defense. The conditions are correct, he just needs to age well and stay healthy. I still like Kershaw’s odds a little more, but Bumgarner’s are not far behind.

Now I’ll move on to David Price. Price is 31, has 121 wins, and has pitched relatively healthy for seven full seasons. He is on the Red Sox now, which — although their poor defense won’t help some of his pitching metrics, they should give him the run support he needs. He wasn’t terrible this year; I have a feeling people think he fell off the map. He had 17 wins, and a ERA of 3.99 and a FIP of 3.60. His ERA and FIP were at career highs, but the FIP really wasn’t too far off what we’d expect. I’d credit the higher ERA to playing in Fenway with not the best defense behind him. Price may not be as dominant as he once was, but the Red Sox should give him support. He might be a little behind pace, but he could be the next CC Sabathia or Mike Mussina, where upon retirement, we say, “I didn’t realize he had 260 wins!” For the record, I doubt CC gets there, but the point is that if Price can stay healthy and moderately effective on a team that will support him, he may be able to move up in the wins chart. Will he hit 300? I don’t see it, but realistically, I’m not sure any of these guys will.

Now I’ll move on to the other Red Sox pitcher on this list: Rick Porcello. Porcello had a modest beginning in Detroit, but his FIP always seemed to outperform his ERA, so he has that going for him. Porcello is only 27 and somehow has 107 wins already. Although he is on the Red Sox, who can support him, Porcello really hasn’t been able to stay healthy over his career, and only eclipsed 200 innings pitched in a season twice: 2014 in Detroit, and this past season in Boston. Still, he is young, and if he can hang around awhile, he might be able to pick up 100 wins or more if he can stay decent on an offensive team. Again, he doesn’t need to contend for the Cy Young, but he has to stay relatively effective, so he keeps his starting spot and racks up wins.

Finally, I move on to my dark horse, King Felix Hernandez. Felix is only 30, but has been a full-time starter for 11 years. He sits at 154 wins. I feel like as a baseball community, we tend to forget about Felix. He has been very durable, although he hit the DL this past season by injuring his calf when celebrating a win. But hey, forgive the guy; he plays in Seattle, who hadn’t given him much help until recently. He is my dark horse on the list. He now plays on a good Seattle team, so he should be able to pick up wins. He might not be as good as he once was, but if he can stay effective, he has the best chance of anyone on this list. He can age well, he has stayed healthy, and he now plays on a winning team. The conditions are there, and I think he has the best shot of anyone on this list.

Realistically, if I had to choose between none of them winning 300 or one of them winning, that would be a much harder choice than picking one out of the group. Realistically, do I think any of these guys have a shot? Sure, but a shot is a lot different than actually getting there. Who knows, maybe one of these guys will age well and will stay healthy. Your guess may be as good as mine.


Hardball Retrospective – What Might Have Been – The “Original” 1902 Orphans

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

The 1902 Chicago Orphans 

OWAR: 37.4     OWS: 280     OPW%: .527     (74-66)

AWAR: 29.9      AWS: 203     APW%: .496     (68-69)

WARdiff: 7.5                        WSdiff: 77  

The 1902 “Original” Orphans finished in third place, ten games behind the Reds. Bill Bradley (.340/11/77) thrived against opposing hurlers, notching career-bests in base hits (187), runs scored (104), doubles (39), home runs and batting average. “Bad” Bill Dahlen drilled 25 two-baggers and swiped 20 bags. Danny Green delivered a .302 BA and pilfered 35 bases. Jimmy “Pony” Ryan slashed 32 two-base knocks and produced a .320 BA. Johnny “Noisy” Kling succeeded on 25 stolen base attempts. Jimmy “Rabbit” Slagle executed 41 thefts and supplied a .315 BA for the “Actual” Orphans.

Bill Dahlen rated twenty-first among shortstops in the “The New Bill James Historical Baseball Abstract” top 100 player rankings. “Original” Orphans teammates registered in the “NBJHBA” top 100 rankings include Frank Chance (25th-1B), Johnny Evers (25th-2B), Jimmy Ryan (26th-CF), Joe Tinker (33rd-SS), Bill Bradley (46th-3B), Johnny Kling (48th-C) and Tom Daly (55th-2B). “Actuals” second-sacker Bobby Lowe placed fifty-sixth.

  Original 1902 Orphans                          Actual 1902 Orphans

STARTING LINEUP POS OWAR OWS STARTING LINEUP POS OWAR OWS
Jimmy Ryan LF/CF 3.26 18.59 Jimmy Slagle LF 5.11 22.25
Davy Jones CF 2.67 13.4 Davy Jones CF 2.67 13.4
Danny Green RF 3.52 20.73 John Dobbs RF/CF 0.8 8.31
Frank Chance 1B 2.66 12.37 Frank Chance 1B 2.66 12.37
Tom Daly 2B -1.87 10.46 Bobby Lowe 2B 0.79 10.24
Bill Dahlen SS 4.65 21.9 Joe Tinker SS 3.31 16.58
Bill Bradley 3B 5.38 25.61 Charlie Dexter 3B -0.47 4.12
Johnny Kling C 2.47 17.06 Johnny Kling C 2.47 17.06
BENCH POS OWAR OWS BENCH POS OWAR OWS
Charlie Irwin 3B 0.74 17.4 Dusty Miller LF -0.25 3.95
Joe Tinker SS 3.31 16.58 Art Williams RF -0.33 2.46
Harry Wolverton 3B 0.41 10.43 Larry Schlafly RF 0.46 2.15
Frank Isbell 1B -0.32 9.1 Bunk Congalton RF -0.99 1.55
Art Nichols 1B 0.09 8.68 Johnny Evers 2B -0.17 1.27
Malachi Kittridge C 0.59 8.44 Hal O’Hagan 1B -0.06 1.09
Duke Farrell C -0.06 5.46 Jack Hendricks RF 0.19 0.91
Dusty Miller LF -0.25 3.95 Germany Schaefer 3B -2.27 0.71
Art Williams RF -0.33 2.46 Sammy Strang 3B 0.07 0.42
Larry Schlafly RF 0.46 2.15 Jim Murray RF -0.52 0.27
Zaza Harvey RF 0.15 1.58 Mike Jacobs SS -0.15 0.18
Bunk Congalton RF -0.99 1.55 Mike Lynch CF -0.34 0.14
Johnny Evers 2B -0.17 1.27 Snapper Kennedy CF -0.06 0.14
Germany Schaefer 3B -2.27 0.71 Ed Glenn SS -0.08 0.1
Jim Murray RF -0.52 0.27 Mike Kahoe C -0.11 0.09
Mike Jacobs SS -0.15 0.18 Pete Lamer C -0.06 0.07
Mike Lynch CF -0.34 0.14 Dad Clark 1B -0.31 0.05
Snapper Kennedy CF -0.06 0.14 Chick Pedroes RF -0.1 0.03
Jim Delahanty RF -0.14 0.09 R.E. Hillebrand RF -0.06 0.01
Pete Lamer C -0.06 0.07 Joe Hughes RF -0.05 0
Dad Clark 1B -0.31 0.05
Chick Pedroes RF -0.1 0.03
R.E. Hillebrand RF -0.06 0.01
Joe Hughes RF -0.05 0

Jack W. Taylor (23-11, 1.29) paced the National League in ERA, shutouts (8) and WHIP (0.953). Mal “Kid” Eason contributed 10 victories with a 2.76 ERA and Carl Lundgren (9-9, 1.97) completed 17 of 18 starts during his rookie campaign. Jock Menefee (12-10, 2.42) and Pop Williams (11-16, 2.49) rounded out the rotation for the “Actuals”.

  Original 1902 Orphans                         Actual 1902 Orphans

ROTATION POS OWAR OWS ROTATION POS OWAR OWS
Jack Taylor SP 7.47 31.24 Jack Taylor SP 7.47 31.24
Mal Eason SP 0.55 12.06 Jock Menefee SP 1.82 14.41
Carl Lundgren SP 0.89 10.79 Pop Williams SP 0.7 13.84
Tom Hughes SP 1.4 9 Carl Lundgren SP 0.89 10.79
BULLPEN POS OWAR OWS BULLPEN POS OWAR OWS
Jim St.Vrain SP 0.51 5.85 Jim St.Vrain SP 0.51 5.85
Bob Rhoads SP -1.48 3.4 Bob Rhoads SP -1.48 3.4
Jack Katoll SP -1.74 3.04 Frank Morrissey SP 0.05 2.12
Alex Hardy SP -0.29 1.16 Mal Eason SP 0.13 1.41
Fred Glade SP -0.49 0.27 Alex Hardy SP -0.29 1.16
Jim Gardner SP -0.1 1.01
Fred Glade SP -0.49 0.27

 

Notable Transactions

Bill Bradley 

Before 1901 Season: Jumped from the Chicago Orphans to the Cleveland Blues. 

Bill Dahlen 

January 25, 1899: Traded by the Chicago Orphans to the Baltimore Orioles for Gene DeMontreville.

March 11, 1899: Assigned to the Brooklyn Superbas by the Baltimore Orioles. 

Danny Green 

Before 1902 Season: Jumped from the Chicago Orphans to the Chicago White Sox. 

Jimmy Ryan

Before 1902 Season: To the Washington Senators in unknown transaction.

Charlie Irwin

July 11, 1901: Released by the Cincinnati Reds.

July 12, 1901: Signed as a Free Agent with the Brooklyn Superbas.

Honorable Mention

The 1966 Chicago Cubs 

OWAR: 43.3     OWS: 235     OPW%: .510     (83-79)

AWAR: 27.1       AWS: 176      APW%: .364    (59-103)

WARdiff: 16.2                        WSdiff: 59

The “Original” 1966 Cubs placed fourth with a record north of .500 yet fifteen games off the pace of the Giants. Ron Santo (.312/30/94) merited Gold Glove honors for the third straight season and paced the circuit with 95 bases on balls and a .412 OBP. Lou Brock aka “The Franchise” tallied 94 runs and topped the National League with 74 stolen bases. “Sweet Swingin’” Billy L. Williams socked 29 long balls and registered 100 runs scored. Al “Red” Worthington (2.46, 16 SV) fashioned a 1.018 WHIP and secured the late-inning leads. Ernie “Mr. Cub” Banks contributed 23 two-baggers and a .272 BA. Ken Holtzman collected 11 victories while furnishing an ERA of 3.79 in his inaugural season.

On Deck

What Might Have Been – The “Original” 1921 Tigers

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”.

Seamheads – Baseball Gauge

Sean Lahman Baseball Archive