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Feasting on Garbage: Early Strength of Schedule and Team Offense

The Oakland Athletics and the Colorado Rockies are two of the most productive offenses in the league this year, both ranking in the top 5 teams by wRC+. By contrast, the Brewers and Cardinals have been below-average so far, with a 93 wRC+ and 96 wRC+ respectively. Could the strength of these teams’ early schedules be a factor in these varying levels of production?

To evaluate this, I tabulated the actual innings pitched by opponents of the Athletics, Rockies, Brewers, and Cardinals so far in 2014, and then tabulated the anticipated innings for upcoming opponents in June, assuming 9 innings per game. (You could pick any four teams you wanted; these were the ones that interested me). To evaluate the quality of the pitching staffs faced, I used SIERA (published here at FanGraphs) to evaluate the runs the pitching staffs would have been expected to give up, on average, in light of their actual skill sets. Last year, SIERA explained 63% (by r2) of the variance in runs given up by team pitching staffs, making it a good choice for this exercise. Because the pitchers faced in a game are largely outside an opposing team’s control, I used the current, team-average SIERA for each pitching staff, and weighted each inning of a team opponent by that value. I totaled the weighted values to get an aggregate SIERA for the collective opponents of each team.

Let’s start with quality of opposing pitchers for each team in the two months so far:

Opponent SIERA
Lg. Avg. Athletics Rockies Brewers Cardinals
3.73 3.86 3.65 3.58 3.62
AVG RUN EFFECT +7 -4 -8 -6

SIERA can be a difficult statistic to appreciate because it operates on a tighter curve than other pitching statistics (ERA, FIP), and small differences have a surprisingly large effect on runs allowed.  Remember that as with most pitching metrics, however, lower is better.

Let’s work from the league-average SIERA so far this year — 3.73 — to make some overall observations. First, the Rockies’ production is quite impressive, as they were facing above-average pitching skills yet managed to generate a 110 wRC+. The Athletics, on the other hand, generated the same 110 wRC+ as the Rockies, but the quality of competition was entirely different. For the past two months, they’ve had the privilege of teeing off on opponents with an average staff SIERA of 3.84. That is literally like facing a team slightly worse than the Astros (3.83 SIERA) every day for two months.

Contrast that with the task faced by the Brewers and Cardinals so far. To date, the teams faced by those two clubs have posted an aggregate SIERA of 3.58 (Brewers) and 3.62 (Cardinals). On average, that’s like facing a top-10 pitching staff every day for two months. Is it all that surprising, then, that these two teams, widely thought to be above-average offensively when the season began, have struggled to live up to offensive expectations so far?

How does this difference actually affect runs scored? That is a tricky fact to isolate. Drawing a zero-coefficient, least-squares line, each .01 of SIERA has been worth about half of a run so far in 2014. (That rate is comparable to the entire season of 2013, suggesting that this ratio stabilizes fairly quickly). By that measure, as shown in the above table, we would expect their tough schedule to have cost the Brewers almost a win (8 runs) over average in runs scored so far, and almost a win-and-a half as compared to Oakland (15 runs difference). The Cardinals are not far behind.

But that is just the average runs lost, and does not account for the outliers. It probably won’t surprise you to learn that the largest deviations (residuals, technically) from the relatively modest average tend to come from teams at the bottom half of the pitching barrel. When these teams have a bad day, they are really bad, and they are prone to getting blown out. These teams include the White Sox, the Rangers, and the Astros — teams that, as it so happens, have been well-represented on the Athletics’ schedule to date. Certainly, we should expect good teams to blow bad teams out, but when your offensive success consists substantially of beating up bad pitching, it’s hard to say how good your offense really is. The Brewers and Cardinals, on the other hand, have enjoyed healthy servings of the Braves, the Cubs, the Reds, and also each other. All of those teams are in the top half of the league by SIERA, and none of them has a tendency toward outlier scores that allow an opponent to super-size their run differential.

What’s particularly interesting, though, is that this imbalance is about to change in the month of June. Here is how it looks right now:

Opponent SIERA
Lg. Avg. Athletics Rockies Brewers Cardinals
3.73 3.67 3.48 3.87 3.75
AVG RUN EFFECT -3 -13 +7 +1

Things project to be different this month. In June, it is the Brewers’ turn to feast on garbage pitching, as they essentially get to bat against the Astros pitching staff for the entire month (3.87 SIERA). The Cardinals aren’t quite as fortunate, although they still get to face slightly below-average pitching (akin to facing the Rays every day), whereas the Athletics at least have to face a top-half schedule by aggregate SIERA. The poor Rockies, on the other hand, fare worst of all, with a schedule that could not be more grueling: the Braves, Brewers, Cardinals, Dodgers, and Nationals, among others. If the Rockies still come out of June with an above-average wRC+, we can safely say that they are probably a true-talent, above-average ball club, at least when healthy.

The point of all this is not to say that Oakland is some kind of fluke. That team’s out-sized run differential is also a credit to excellent pitching, and it is not Oakland’s fault that it was assigned what turned out to be a favorable early schedule. Yet, this analysis provides yet another reason to be careful when relying upon early-season run differentials.  Before you get too enamored with a team’s production to date, take a close look at the opponents a team has played. You may find that a team’s seemingly-extraordinary results appear to be less so, when you properly weight the skills of the opponents who allowed those results to come about.

Follow Jonathan on Twitter @bachlaw.

Jonathan writes a weekly column about the Brewers at Disciples of Uecker. He has also published at Baseball Prospectus.  

The “Exceptional” Kyle Lohse

After the 2012 season, Kyle Lohse declined the qualifying offer of the St. Louis Cardinals, and hit the free agent market.  Lohse’s 2012 season was exactly what any starter would want in a contract year: a career-best 2.86 ERA over 211 innings.  It completed a comeback from a rough 2010 in which Lohse battled arm trouble, and had one of his worst seasons. 

Many commentators felt that that Lohse’s 2012 campaign was a one-time affair.  Lohse’s ERA benefited from an unusually low .262 Batting Average on Balls in Play (BABIP), and the usually reliable pitching statistic of Fielding Independent Pitching (FIP) dinged him for it, pegging his real performance at 3.51 — almost three quarters of a run higher.  Furthermore, Lohse spent 2012 at Busch Stadium, a pitcher’s park, and got to have his pitches called by Yadier Molina, perhaps the best catcher in the game.  

But was Lohse’s low BABIP in 2012 truly a fluke? 

Let’s start by comparing Lohse to other Cardinals starters with at least 150 IP that year.  Like Lohse, they pitched their home games in the same pitcher’s park, and also took their signs from Yadier Molina:

Kyle Lohse 211 0.262 2.86 3.51
Jake Westbrook 174.2 0.312 3.97 3.8
Adam Wainwright 198.2 0.315 3.94 3.1
Lance Lynn 169 0.316 3.67 3.47

Of all Cardinals starters that year, Kyle Lohse had the best starter BABIP by 50 points, and was the only one below the league BABIP average.  Interesting.  But, one season proves nothing.  So, let’s look at 2011, again for Cardinal starters with at least 150 IP:

Kyle Lohse 188.1 0.269 3.39 3.67
Chris Carpenter 237.1 0.312 3.45 3.06
Jake Westbrook 183.1 0.313 4.66 4.25
Jaime Garcia 194.2 0.318 3.56 3.23

In 2011, Kyle Lohse’s BABIP was a mere seven points higher than his 2012 BABIP, and still absurdly low.  Once again, Lohse’s BABIP was by far better than any other Cardinals starter, and well below league average.  Is this still a fluke?  Does Yadi just save his best calls for his friend Kyle?

Perhaps, the key is to get Lohse away from Molina and Busch Stadium.  Fortunately for our purposes, the Milwaukee Brewers indulged this notion, signing Lohse at the conclusion of 2013 Spring Training.  Miller Park, where the Brewers play, is a hitter’s park where the fly balls go a long way and batters get more hits.  Furthermore, in 2012, the Brewers had one of the worst defenses in baseball.  The stage seemed to be set for a substantial BABIP regression.

The 2013 season is now almost complete for the Brewers.  Yet, as of the time this article was written, here are the statistics for Brewers starters with at least 150 IP in 2013:

Kyle Lohse 184.2 0.284 3.46 4.1
Yovani Gallardo 161.2 0.299 4.18 3.95
Wily Peralta 172.1 0.292 4.49 4.28

Lohse’s BABIP did regress a bit.  Yet, Lohse’s BABIP is not only the lowest of the three qualifying Brewers starters, but still notably below the .294 BABIP average of baseball. 

One last comparison: other NL Central starters play in many of the same stadiums that Kyle Lohse does.  How does his BABIP compare to starters who have also spent the last three years pitching at least 450 innings exclusively for NL Central teams?

Kyle Lohse 0.271 3.22 3.75
Bronson Arroyo 0.278 4.13 4.63
Mike Leake 0.284 3.87 4.21
Homer Bailey 0.292 3.76 3.67
Yovani Gallardo 0.293 3.79 3.83
Jake Westbrook 0.307 4.23 4.15

There he is again.  The lowest BABIP in the NL Central for starters over the last three years belongs to Kyle Lohse.

What is going on?  Does Kyle Lohse simply possess The Will to Pitch? 

Certainly, many of you might claim Kyle Lohse is the beneficiary of nothing more than good luck.  It is almost an article of faith among observers that BABIP is essentially a random attribute beyond the pitcher’s control, benefiting substantially from defense.  One could also argue I am using arbitrary endpoints.  While Kyle Lohse had a terrific pitching BABIP from 2011–2013, his major league BABIP was .364 in 2010.  Move the goalposts, some would say, and get a different result.  Finally, Derek Carty suggests that BABIP can take as long as 8 years (~3729 batters) to stabilize into a predictable indicator of a pitcher’s ability, which is another way of saying that it never really stabilizes at all, and is therefore indicative of nothing.

As to Kyle Lohse, that view may be correct.  But I suspect it is not.  Rather, I suspect that Kyle Lohse’s career renaissance has actually been driven in part from his ability to limit the damage caused by balls put into play.  To explain why, I’ll first address the arguments I just made in favor of his performance being unsustainable.

First, let’s talk about BABIP.  Although it common to attribute BABIP entirely to luck, it is more complicated than that.  Tom Tango and his colleagues found, for example, that BABIP was 44% luck.  The remainder (majority) of BABIP was attributed to a combination of the pitcher, the park, and fielding.  The pitcher was given 28% of the credit for his BABIP, but that is just an average; many observers suspect that a small class of pitchers has a unique ability to control their BABIP by inducing less effective contact.  Strikeout pitchers are one example. So, while it is common to dismiss good BABIPs as flukes, it is intellectually lazy to do so, particularly if a pitcher is generating low BABIPs on a consistent basis. 

Second, let’s address arbitrary endpoints.  Am I excluding Kyle Lohse’s dreadful 2010 season from my endpoints?  Yes.  Why? A few reasons.  First, because Lohse was injured that year and dealing with arm trouble that he finally was able to resolve.  In fact, the 2010 season was the culmination of a few injury-plagued seasons for Lohse.  But since the 2011 season that followed, Lohse has consistently pitched at least 180 innings per year and also consistently been effective, more so than he was ever was before.  Since 2011, his walk rates have been the best of his career, as have the ratio of his strikeouts to walks, both attributes that everyone agrees are controlled primarily by the pitcher’s ability.  Also, as Russell Carleton has found, a pitcher’s recent BABIP performance tends to be more predictive of their BABIP going forward.  So, what some would call an arbitrary endpoint (the beginning of Lohse’s 2011 season), I would call appropriate, and indicative.    

Finally, there is the issue of sample size.  Although I have no quarrel with the method Derek Carty used to conclude that a pitcher’s BABIP can take 3729 batters to stabilize, Kyle Lohse has faced over 2400 batters in the past three years.  That is not trivial sample, particularly when it spans home stadiums at opposite ends of the park factor spectrum. 

My suspicions about Lohse are further confirmed when you consider the differential between his RA9-WAR and his fWAR.  FanGraphs bases fWAR for pitchers entirely on their FIP.  However, FanGraphs also recognizes that FIP, while effective in evaluating most pitchers, does not properly evaluate pitchers who actually possess the skill to limit the damage on balls put into play.  Rather than toss FIP and fWAR aside, FanGraphs last year began publishing RA9-WAR as an alternative metric to allow a comparison between the number of runs that actually come across the plate while a pitcher is on the mound, versus those that FIP is willing to credit to the pitcher as having personally prevented.  The differential between a pitcher’s RA9-WAR and fWAR tells you how much of that pitcher’s run prevention cannot be explained by the three “true” outcomes of home runs, walks, and strikeouts.  Niftily, FanGraphs also estimates how the other runs were prevented — through BABIP (BIP-Wins) and by runners stranded (LOB-Wins).  Both RA9-WAR and fWAR are also park-adjusted.

Let’s start with the entire time period of 2011-2013.  For starters with 450 IP, Lohse’s RA9-WAR / fWAR differential is one of the top 10% in the game.

Name RA9-WAR BIP-Wins LOB-Wins FDP-Wins RAR WAR RA9 / fWAR Differential
Jered Weaver 17 6.1 -0.1 6 102 10.9 6.1
Jeremy Hellickson 9 4.6 0.6 5.2 37.2 3.8 5.2
Hiroki Kuroda 14 1.7 2.6 4.3 90.4 9.7 4.3
Clayton Kershaw 21.9 5.6 -1.5 4.1 152.9 17.8 4.1
Bronson Arroyo 6.6 2.3 1.7 3.9 23.3 2.6 4
Kyle Lohse 11 3.6 0.2 3.8 66.1 7.2 3.8
Ervin Santana 8.2 4.6 -0.9 3.7 41.9 4.5 3.7
R.A. Dickey 11.8 3.2 0 3.2 80 8.6 3.2
James Shields 15.5 2 1 3 117.3 12.5 3

Lohse’s differential has intensified in 2012-2013.  Over the last two years, among those with 300 IP pitched, only one starter in baseball had a larger RA9-WAR / fWAR differential (last column) than Kyle Lohse:

Name RA9-WAR BIP-Wins LOB-Wins FDP-Wins fWAR RA9-WAR minus fWAR
Clayton Kershaw 14.6 4.3 -0.9 3.4 11.2 3.4
Kyle Lohse 8.3 2.4 0.9 3.3 5 3.3
Hiroki Kuroda 10.3 1.6 1.1 2.7 7.6 2.7
Bronson Arroyo 6.7 1.3 1.1 2.5 4.2 2.5
Jarrod Parker 7.2 2.1 0.2 2.3 5 2.2
Jordan Zimmermann 8.3 1.1 0.8 2 6.4 1.9
Ervin Santana 3.5 3.4 -1.6 1.9 1.7 1.8
R.A. Dickey 8.2 2.3 -0.4 1.9 6.4 1.8
Chris Sale 11.3 0.8 0.8 1.6 9.7 1.6

That guy’s name is Clayton Kershaw, and he is pretty good.  In fact, Kershaw and Lohse have beat their FIP by basically the same amount over the past two years.  Unlike Kershaw, Lohse has pitched one of those seasons at home in Miller Park.

Overall, it is safe to say Lohse is showing a strong and consistent ability to beat his FIP, and over the last few years, is doing so better than almost any starter in baseball.  He is doing so by generating balls in play that are uniquely unsuccessful at becoming hits, and which his defense seems unusually capable of being able to field for outs.

How is he doing this?  It certainly is not his strikeout rate.  Lohse is not anybody’s idea of a strikeout pitcher.

What Lohse does do, however, is control the count, minimize walks, and consistently pitch from ahead.  This quality makes Lohse an extremely enjoyable pitcher to watch: despite topping out at 90 mph, he pounds the zone and challenges hitters.  His BB/9 over the last three years has ranged from 1.62 to 2.01.  During that same time frame, only Cliff Lee is more likely than Kyle Lohse to throw a first-pitch strike, which Lohse did 67.5% of the time.  The fact that Lohse is throwing first-pitch strikes against 2/3 of the batters he faces without getting killed suggests that he is putting those strikes in locations where batters want no part of them.  In short, Lohse has terrific control and consistently finds himself in counts where he and his catcher have the luxury of choosing their pitch.

Does Lohse’s control affect the quality of the ball being put into play against him?  It very well may.  Although his sample size could have been larger, Russell Carleton found that pitcher BABIPs correlated with the pitch counts the hitters were facing when they put the bat on the ball.  The more favorable the count to the pitcher, the less likely the hitter will get on base from his hit.  Kyle Lohse’s three best counts for limiting batter wOBA this year?  Why, those would be 0-2, 1-2, and 0-1.  And the three counts Kyle Lohse faces far less than any others?  Those would be 3-0, 3-1, and 3-2. 

The bottom line is that Kyle Lohse is an exception among aging starters: a pitcher who has gained effectiveness in his mid-thirties through terrific control that not only forces hitters to beat him, but also apparently limits the damage even when batters do hit the ball.  Should the Brewers make Lohse available at the trade deadline next year, contenders would be foolish not to give him a close look, particularly with Lohse under control through 2015.  When the difference between collecting a pennant and going home can be a batted ball just out of reach, it makes sense to have a pitcher with a demonstrated knack for putting the ball in the defender’s glove.  

Does Your Team Have a Winning Core? Profiling Sustainable Roster Construction

Thanks to an atrocious month of May, the 2013 Milwaukee Brewers were abruptly transformed from a fringe contender into a rebuilding baseball club.

Most people agree that the Brewers need to build a new core, but what does that mean? Many teams have young players in the midst of an above-average season, but that doesn’t necessarily translate to sustainable success for the roster as a whole. And the opinions expressed about so-called core players are usually subjective and not expressed in a way that allows direct comparisons between teams.

We could really use a metric to compare the rosters of teams who are developing potentially sustainable talent with those who aren’t. My effort to do this is called Core Wins, which summarizes the extent to which a team’s success is being driven from players most likely to constitute core talent, as opposed to players on their way out the door, probably in decline, or both.

To do this, we need define what it means to be a core player, and specifically the factors by which we evaluate a core player’s respective contributions to the team.

The Core Player

In my view, core players do three things: (1) contribute significantly to their team’s success, (2) do so while under extended team control, and (3) do so at or before they reach their peak ages of likely productivity. Each of those attributes needs to be mathematically summarized to reduce these contributions to a measurable value.

The first factor is the easiest: a core player is expected to contribute, and to do so above what could be found in an entry-level minor-league call-up. A major league player’s ability to do so over the course of a season is commonly summarized in some version of the wins above replacement (WAR) metric, which attempts to combine the player’s batting, fielding, and if applicable, pitching contributions. A counting statistic also fits our needs best, since we are looking for aggregate contributions over the course of a single season. So, we’ll use WAR, as calculated by Fangraphs (fWAR).

The second factor, team control, is more complicated. Player control comes in two primary forms: (1) players under club control due to the terms of baseball’s collective bargaining agreement, and (2) players who have signed freely-negotiated contracts. The collective bargaining agreement keeps players under club control for at least six major league years. Free agent contracts range from one-year stop-gaps to those lasting a decade or longer. Most ballclubs are a collection of young players under sustained club control, long-term (and typically expensive) free agents, and stopgap players on value contracts. But teams with a sustainable core should be drawing significant production from players who will actually be around in future years. If too much production is coming from departing or declining players, the club is asking for trouble.

The third factor — player age — is less significant, but still important. Younger players are cheaper than older players, and thus easier to afford and keep around. Younger players are less frequently injured, meaning they will be in the lineup more often. Younger players who have not yet reached their peak production age will also probably continue to improve, whereas players beyond their peak age will probably decline.

However, age can be overemphasized. The primary advantage of youth— extended club control — is already being considered. Moreover, mature players signed to long-term contracts tend to be some of the most valuable players in the game — Joey Votto, Felix Hernandez, and their peers. And while prospects are important, most ballclubs would strongly prefer Joey Votto over a 22-year old prospect who may, but probably won’t, someday turn into Joey Votto. So while age matters, it is not as important as control.

So to summarize: we need to weigh player value, but do it in a way that primarily emphasizes team control while still placing some value on a player’s age.


Player Contributions

All WAR figures were drawn from Fangraphs. The figures for batting fWAR (which incorporates fielding) and pitching fWAR were combined into one spreadsheet for each team year. When a player generated values for both batting (plus fielding) and pitching WAR, those values were summed, including the effect of any negative values. Once a net value was obtained for all players on a team roster for the year, all zero or net negative WAR values were disregarded.

Player Control Index

Player control numbers were drawn primarily from Cot’s Contracts, and cross-checked with Baseball Reference, other sources, and common sense as needed. Cot’s provides individual player contract data from 2009 onward, so only data from 2009 through 2012 was used. Control years were weighted identically, regardless of whether they arose from the CBA or a free agent contract. A player subject to a club option was considered to be under club control for that year. The author’s best estimate of remaining club control was necessary in a few cases when contract details were unclear, but not surprisingly, most of those players were fringe contributors that would not constitute core talent anyway.

A player was assigned one control year if his contract expired after the current season, two control years if his contract expired after the following season, and so on. For practical reasons — including the frequent shuffling from the minors experienced by young players, and the oft-diminishing returns of the longest contracts — the maximum number of control years considered for a player was 5. A Control Index was then calculated for each player in each roster year, with the number of control years as numerator, and an assigned denominator of 2 — for the minimum years that would constitute extended organizational control. So, for example, a player with an expiring contract would have a Control Index of 0.5 (1 season left divided by 2), and a typical player in their final pre-arbitration year would have a Control Index of 2.0 (4 seasons of control divided by 2). The maximum Control Index is 2.5.

Age Index

A player’s “baseball age” — their age on July 1 of a given season — was drawn from Fangraphs. An Age Index was then calculated for each player using an assigned value for a typical peak performance age as the numerator and the player’s baseball age for each season as the denominator. There has been some debate on the overall peak performance age for ball players, but, taking a strong hint from one of my reviewers, I used 27. To give some sense of the value range, the Age Index in 2012 for Mike Trout would have been 1.35 (27/20) and for Livan Hernandez would have been 0.73 (27/37).

Determining Core Win Value

In my formula, Core Win value is a weighting exercise. To calculate a player’s Core Win value to a roster, I multiplied the player’s net fWAR for each season by the Control Index and the Age Index. The Control Index has a greater range (0.5 to 2.5) and thus a greater potential weight than the Age Index, which seems appropriate for the reasons stated above. The combined effect of these indices means young prospects that produce at a level of 2 fWAR or higher are weighted the most heavily. This makes sense: players who promptly adjust to the difficulty of the major leagues, yet still have years of probable improvement ahead of them, all while under extended team control, are those most likely to constitute a sustainable core of talent for the ballclub.


Now that we have a formula for Core Win Value, we need to decide what it means to have a winning core. That cut-off is ultimately in the eye of the beholder, but I looked to the gold standard: the Tampa Bay Rays. The Rays are widely acclaimed for their ability to acquire and maintain control of young talent, often through early buy-outs of free agent years, combined with club options that retain team flexibility. This has been particularly true over the years covered by this study: 2009 through 2012.

To provide some contrast with the Rays, we will also consider the roster construction during that same time period of the New York Mets and the Oakland Athletics.

The Gold Standard: The Rays

Not surprisingly, the Core Wins formula likes the Rays very much. Indeed, three characteristics of the Rays between 2009 and 2012 suggest a working definition of a team with a strong, sustainable core: (1) the Rays consistently feature five or more players producing a Core Win Value of 5 or higher per season, which is my working definition of a “Core Player”; (2) they have accomplished this feat in multiple consecutive years (all four years I studied, in fact) and (3) at least two of these Core Players were usually pitchers.

Let’s start with 2009. For ease of viewing, in each of these tables, I’ve bolded wins figures for potential Core Players (five or more Core Wins). I’ve also italicized the names of pitchers who cross the Core Wins threshold, to distinguish them from position players.

2009 Tampa Bay Rays

Name fWAR Age Control Years Control Index Age Index Core Wins
Evan Longoria 7.5 23 5 2.50 1.17 22
Ben Zobrist 8.5 28 5 2.50 0.96 20
James Shields 3.5 27 5 2.50 1.00 9
Matt Garza 2.9 25 5 2.50 1.08 8
Jason Bartlett 5.3 29 3 1.50 0.93 7
Carl Crawford 5.6 27 2 1.00 1.00 6
B.J. Upton 2.1 24 4 2.00 1.13 5
David Price 1.3 23 5 2.50 1.17 4

In 2009, the Rays won 84 games, featuring seven players that delivered 5 Core Wins or more. This depth, plus MVP-level performances from Evan Longoria and Ben Zobrist, prepared the Rays for the eventual departure of Carl Crawford, whose dwindling team control was removing him from the team’s core. Note that the team’s two best pitchers in 2009, James Shields and Matt Garza, were both under team control for 5 more years. David Price generated only 1.3 fWAR in 2009, and thus barely missed the Core Wins cut, but he was on the upswing.

2010 Tampa Bay Rays

Name fWAR Age Control Years Control Index Age Index Core Wins
Evan Longoria 7.6 24 5 2.50 1.13 21
David Price 3.9 24 5 2.50 1.13 11
Ben Zobrist 3.7 29 5 2.50 0.93 9
B.J. Upton 3.8 25 3 1.50 1.08 6
John Jaso 2.3 26 5 2.50 1.04 6
Sean Rodriguez 2.1 25 5 2.50 1.08 6
Matt Joyce 1.7 25 5 2.50 1.08 5
James Shields 1.7 28 5 2.50 0.96 4
Carl Crawford 7.4 28 1 0.50 0.96 4
Matt Garza 1.5 26 4 2.00 1.04 3

In 2010, the Rays maintained 7 players at a Core Win level of 5 or more, culminating in 96 team wins and a first-place finish in the AL East. Only one pitcher (David Price) made the Core Win cut-off of 5 this time, but James Shields just missed it. Matt Garza regressed a bit (and was promptly traded to the Cubs for more prospects, without any negative effect). Carl Crawford, despite an MVP-level year of 7.4 fWAR, is discounted out of the team core by the Core Wins formula, due to his team control ending that year.

2011 Tampa Bay Rays

Name fWAR Age Control Years Control Index Age Index Core Wins
Ben Zobrist 6.2 30 5 2.50 0.90 14
Evan Longoria 6.2 25 4 2.00 1.08 13
David Price 4.3 25 5 2.50 1.08 12
Matt Joyce 3.5 26 5 2.50 1.04 9
James Shields 4.4 29 4 2.00 0.93 8
Desmond Jennings 2.3 24 5 2.50 1.13 6

2011 featured more of the same. Carl Crawford was gone, but the Rays did not miss him, as the formula anticipated. Six Rays met the Core Win threshold, two of them pitchers (Price, Shields). Superstar contributions by Zobrist and Longoria, combined with ascending contributions from four others — including Price and Shields — resulted in a highly-successful season from Tampa Bay’s controlled talent, and others. The Rays won 91 games and made a wild-card playoff appearance.

2012 Tampa Bay Rays

Name fWAR Age Control Years Control Index Age Index Core Wins
Ben Zobrist 5.8 31 4 2.00 0.87 10
David Price 4.8 26 4 2.00 1.04 10
Desmond Jennings 3.3 25 5 2.50 1.08 9
Matt Moore 2.4 23 5 2.50 1.17 7
Evan Longoria 2.5 26 5 2.50 1.04 6
Alex Cobb 2.0 24 5 2.50 1.13 6
Jake McGee 2.0 25 5 2.50 1.08 5
James Shields 3.9 30 3 1.50 0.90 5

By 2012, the Rays had developed an astonishing eight players that crossed our Core Win threshold. An incredible five of these players — over half the team’s core, under our formula — were starting pitchers with at least four years of team control remaining. This means that the Rays’ entire starting rotation was under long-term control. Despite a hamstring injury that kept him out for over three months, Evan Longoria still contributed 2.5 fWAR to the effort, and his new contract provided the team with the long-term control to keep him in the team’s core. The 2012 Rays won 90 games: not enough for even a wildcard in the American League that year, but a terrific season nonetheless.

Before the 2013 season, the Rays dealt James Shields to Kansas City for the bat of Wil Meyers and other prospects. As of the publication of this article, Fangraphs projects them to win 93 games in 2013, on a payroll of only $62 million. In sum, the Rays have been, and continue to be, the prototypical team that demonstrates what it means to have a sustainable core of controlled talent.

By Stark Contrast, the New York Mets

The Mets have been bad for years, and the Core Wins formula identifies major flaws in roster construction as a possible culprit.

2009 New York Mets

Name WAR Age Control Years Control Index Age Index Core Wins
David Wright 3.4 26 5 2.50 1.04 9
Johan Santana 3.2 30 5 2.50 0.90 7
Angel Pagan 2.8 27 4 2.00 1.00 6

Dreadful: there is no other way to describe the 2009 Mets. That year, the Mets spent $140 million for 70 team wins, generating only three Core Players under our formula. Even those players gave only ok performances. From a Core Wins perspective, this roster was terrible. One of the three players to meet the Core Wins threshold, and the only starting pitcher — Johan Santana — is heading past his probable prime.

2010 New York Mets

Name WAR Age Control Years Control Index Age Index Core Wins
Ike Davis 3.1 23 5 2.50 1.17 9
Johan Santana 3.6 31 5 2.50 0.87 8
Angel Pagan 5.1 28 3 1.50 0.96 7
David Wright 3.5 27 4 2.00 1.00 7
Jon Niese 2.1 23 5 2.50 1.17 6
Mike Pelfrey 2.2 26 4 2.00 1.04 5

The results for the Mets weren’t much better in 2010 — 79 wins — but their roster at least improved. Six players made Core Player-type contributions, and two of those players were starting pitchers. If these performances proved to be sustainable over multiple years, or at least into 2011, the Mets had some reason for optimism.

2011 New York Mets

Name WAR Age Control Years Control Index Age Index Core Wins
Daniel Murphy 2.8 26 5 2.50 1.04 7
Jon Niese 2.1 24 5 2.50 1.13 6
Ruben Tejada 1.6 21 5 2.50 1.29 5
Ike Davis 1.3 24 5 2.50 1.13 4
Jose Reyes 5.8 28 1 0.50 0.96 3
David Wright 1.7 28 3 1.50 0.96 3

But it didn’t work out. In 2011, the Mets were right back to a pathetic three Core Player performances, with only one starting pitcher among them. In fact, the Mets’s strongest core performance in 2011 came from 2.8-win Daniel Murphy. Not good. Ike Davis promptly regressed out of the core, David Wright fought injuries, and Johann Santana didn’t play all year, which is why Core Wins discounts the value of aging players. Although Jose Reyes provided a superstar WAR of 5.8 and a batting title, as a departing free agent, that performance provided no ongoing value to the team, and the Core Wins formula discounts it accordingly. It all amounted to 77 wins, and low expectations for the following season.

2012 New York Mets

Name WAR Age Control Years Control Index Age Index Core Wins
Jon Niese 2.7 25 5 2.50 1.08 7
David Wright 7.4 29 2 1.00 0.93 7
Ruben Tejada 1.7 22 5 2.50 1.23 5
Matt Harvey 1.5 23 5 2.50 1.17 4
R.A. Dickey 4.4 37 2 1.00 0.73 3

Validating this expectation, the 2012 Mets did even worse, winning only 74 games. Only three players could pass the Core Wins threshold, and one of their best players — R.A. Dickey — could not even quality as a Core Player, despite 4.4 fWAR. The Core Wins formula discounts the going-forward value of 37-year-old performances, and Dickey’s 2013 performance with the Blue Jays has validated that skepticism.

But, the Mets get enough bad news, so let’s focus on some positive aspects. In 2012, David Wright performed at an MVP level. And while the Mets had only four Core Win players in 2011, two of them are starting pitchers, which is an important positive from our study of the Rays. In fact, one starter, Jon Niese, was signed to an early long-term contract a very Rays thing to do, putting a competent starter under extended team control. Matt Harvey also looks to be a championship-caliber ace, and remains under maximum team control.

So far, 2013 is not being kind to the Mets either — Fangraphs currently projects them to finish with 76 wins — but there are hints that things may soon be looking up, particularly if their farm system can continue to develop strong rotation talent, as many project that it will.

Trending in the Right Direction: The Oakland Athletics

Finally, let’s conclude with what turns out to be a Goldilocks example: the team that like the Mets, tried and failed to improve their core, but stuck with it and seems to have gotten the hang of it lately: the Oakland Athletics.

2009 Oakland Athletics

Name WAR Age Control Years Control Index Age Index Core Wins
Brett Anderson 3.6 21 5 2.50 1.29 12
Ryan Sweeney 3.9 24 5 2.50 1.13 11
Rajai Davis 3.7 28 5 2.50 0.96 9
Kurt Suzuki 3.1 25 5 2.50 1.08 8
Dallas Braden 2.7 25 5 2.50 1.08 7
Andrew Bailey 2.3 25 5 2.50 1.08 6

In terms of roster-building, the 2009 Athletics took a fairly solid approach: they ended up with six potential Core Players, and three of them are starting pitchers. All these players offered at least five years of team control. However, the 2009 Athletics also underscore that just because your wins are coming from the right place does not mean you are getting enough of them. The best performance in this group is still only 3.9 fWAR — good, not great. The 2009 Athletics won only 74 games, although at least they didn’t have to pay Mets prices to get there.

2010 Oakland Athletics

Name WAR Age Control Years Control Index Age Index Core Wins
Daric Barton 4.8 24 5 2.50 1.13 14
Cliff Pennington 3.4 26 5 2.50 1.04 9
Gio Gonzalez 2.9 24 5 2.50 1.13 8
Brett Anderson 2.4 22 5 2.50 1.23 7
Dallas Braden 3.3 26 4 2.00 1.04 7
Trevor Cahill 1.6 22 5 2.50 1.23 5

In 2010, the Athletics were better. Leveraging some of the previous year’s young talent, they ended up 81-81. There were six core-type player performances, and four of them pitchers: ordinarily, a good thing. But notably, there was not a significant amount of improvement from 2009’s core contributors. In fact, the strongest core contributors in 2010, Daric Barton and Cliff Pennington, were marginal contributors the year before, raising the possibility of fluke performances. And, only two core performances came from position players, which didn’t leave much room for error going forward in the scoring department. So, the 2010 Athletics showed hints of a developing core, but a fragile one.

2011 Oakland Athletics

Name WAR Age Control Years Control Index Age Index Core Wins
Gio Gonzalez 3.2 25 5 2.50 1.08 9
Jemile Weeks 1.7 24 5 2.50 1.13 5
Trevor Cahill 2 23 4 2.00 1.17 5

And indeed it was. The Athletics rotation was devastated by injuries in 2011: Dallas Braden needed shoulder surgery, and Brett Anderson needed Tommy John surgery. That would be a tough blow for any team, but particularly for Oakland, which did not have much behind them. What was left of the rotation (and roster) collapsed to three core-type players. The two core bats of consequence in 2010, Daric Barton and Cliff Pennington, immediately regressed and revealed themselves to be one-year wonders. The only developing bat remaining was an average, but unspectacular debut by Jemile Weeks, whose own performance later proved unsustainable.

Although two out of the three core players were starting pitchers, there was little to support it. Brandon McCarthy actually had a very good year (4.5 fWAR), but since he was completing a 1-year-deal at the time, he offered the A’s no core value.

Things looked bleak. Fortunately, the A’s stuck to their guns and kept developing young talent. Then, 2012 happened.

2012 Oakland Athletics

Name WAR Age Control Years Control Index Age Index Core Wins
Josh Reddick 4.5 25 5 2.50 1.08 12
Jarrod Parker 3.4 23 5 2.50 1.17 10
Tommy Milone 2.8 25 5 2.50 1.08 8
Yoenis Cespedes 2.9 26 4 2.00 1.04 6
Brandon Moss 2.3 28 5 2.50 0.96 6
Sean Doolittle 1.6 25 5 2.50 1.08 4

2012 found the Athletics again having restocked their core, this time with a balance of bats and pitching talent. Five core players are represented, and their values are not all projection, either: Josh Reddick produced 4.5 fWAR, Jarrod Parker generated 3.4 fWAR, and two other controlled players produced close to 3 fWAR. Two core players are starting pitchers. Furthermore, in 2012, the A’s finally enjoyed a little luck. They outplayed their Pythagorean expectation by a few wins, got 2+ win performances from non-core starters on short-term deals — Brandon McCarthy and Bartolo Colon — and ended up with 94 wins and an AL West title, on top of what appeared to be developing core.

If you thought that the Athletics were finally getting the hang of this roster-building thing, you may be right. The Athletics have spent much of 2013 on top of the AL West, and Fangraphs currently projects them to finish with 91 wins — on a budget of $62 million. A very Rays-like experience all around, which corresponds with quality roster construction.


The Core Wins metric profiles the extent to which team performances are being delivered by so-called Core Players, and also tracks the progression of players in and out of the club’s core over time. Even herculean performances by impending free agents (see Carl Crawford, 2010) tend to wash out of the metric, while young players who initially impress, but fail to sustain (see Ike Davis, 2011) also fall out of the measured core, despite their built-in advantages of youth and team control. As such, Core Wins strikes me as useful and if nothing else, an improvement over the prevailing practice of eyeballing the roster and cherry-picking performances by younger players.

Because it is based on WAR (a counting statistic), Core Wins is primarily backward-looking. But, the general method can also be used prospectively. For example, if you input projections from your preferred player projection system, you could forecast the extent to which your team is likely to get future contributions from sustainable sources — a useful thing to know when deciding between trades, farm system call-ups, or free agent signings. Similarly, if you want to focus on particular positions of concern — (third base, starting rotation) — or skill sets (batter OBP, pitcher FIP) — you can adjust the Age Index to account for the peak performance ages corresponding with those particular positions or skills. Those analyses can be retrospective or prospective.

Of course, superior roster construction does not guarantee superior performance, as the Oakland A’s can attest. Previously healthy players can be felled by injury, and promising talents too often fail to sustain early achievements. But in general, developing Core Players makes good sense, and certainly seems to be delivering results for the league’s most efficient ballclubs. So if your favorite team seems incapable of stacking success, you might check to see how good of a job the front office has been doing in generating Core Wins.

Special thanks to Paul Noonan and Tom Tango, who both offered helpful comments on the general direction of this article. All errors are entirely my own, including some table pasting errors in the original version. Thanks to Andrew Yuskaitis for pointing those out. They have now been corrected.

Rebuilding on a Crash Diet: The Brewers and a Calamitous May

To describe May, 2013 as an awful month for the Milwaukee Brewers would not do it justice.

In fact, the Brewers were downright putrid, winning only six games the entire month.  Their record in May was so bad (6-22) that it tied the worst month in franchise history: the August turned out by the 1969 Seattle Pilots, who ended the following season in bankruptcy, followed by a permanent road trip to become the Milwaukee Brewers.

The Brewers ended the month of April only a half game out of first place.  The Brewers ended the month of May 15 games behind the St. Louis Cardinals, managing the impressive feat of losing 14.5 games in the standings in one month.  Now that is a tailspin.

CoolStandings.Com currently gives the Brewers a 1 in 250 chance of making even the wild-card play-in game.  GM Doug Melvin admitted there is no chance the Brewers will be buyers this year at the trade deadline.  Rather, they will either be in a sell mode, seeking high-ceiling prospects a few years away, or keeping the assets they have, presumably only if they cannot get anything in return.  In short, the Brewers are suddenly rebuilding, and are focusing on  stocking up their farm system and developing controllable rotation talent.

But, rebuilding is a complicated topic in small markets like Milwaukee.  As Wendy Thurm has noted, the Brewers, with their limited geographic reach, have one of the smallest television contracts in the league.  Thus, the Brewers rely upon strong attendance to deliver profits for Mark Attanasio and his ownership group.  In recent years, the Brewers’ attendance fortunately has been some of the most impressive in baseball, particularly in comparison to the size of the Milwaukee metropolitan area.  Over the last five years, the Brewers have consistently approached or exceeded three million fans, despite challenging economic times.  So, one thing the Brewers cannot afford is a collapse akin to the mere 1.7 million fans they drew in 2003 during a terrible season — not if they want to make the investments in future talent required to make the franchise a perennial contender.

So, the Brewers face an obvious challenge: the team needs to lose enough games to obtain a prime draft position, and thereby maximize its chances to draft a top-ceiling player with minimum bust potential.  At the same time, the Brewers need to avoid losing in any drawn-out fashion, because a corresponding and sustained decline in attendance could hemorrhage desperately-needed cash from their balance sheet.  As Ryan Topp and others have argued, this need to maintain attendance in the short term seems to be one reason why the Brewers have systematically traded away what previously was an excellent farm system, with the apparent goal of maintaining the aura of a competitive team.

How does one navigate this problem?  Well, the best solution could be to experience a May like the Brewers just suffered.  Doing so addresses two problems: (1) it abruptly puts the team on course to get a top 5 draft pick, and (2) it achieves this result so abruptly, and in this case so early in the season, that the fan base can still — at least in theory —enjoy much-improved baseball for the remainder of the season without jeopardizing that draft slot.  In short, when you can take your medicine over the course of one month, instead of over an entire season, you really ought to do it.

As to the draft:

Thanks to May, the Brewers currently have the fifth-worst record in baseball at 23–37.  As of the morning of June 8, 2013, FanGraphs predicted that the Brewers will end the season tied for baseball’s fourth-worst record with the New York Mets at 73–89.  Provided that 2013’s top five draft picks all reach agreement with their teams, the Brewers are on pace for a top-5 draft slot in 2014.

The Brewers have not had a top-5 pick in the Rule 4 draft since 2005, when they picked some guy named Ryan Braun.  Before 2013, the top five slots in the draft provided, among others, Buster Posey (#5, 2008), Stephen Strasburg (#1, 2009), Manny Machado (#3, 2010), Dylan Bundy (#4, 2011), and Byron Buxton (#2, 2012) — the types of superstar prospects the Brewers have been denied for years, and which they need to anchor their next generation of players.  At the end of April, and before May occurred, the Brewers were on track for yet another mid-round pick slot.

As to the rest of the season:

It is unlikely that the Brewers will continue to suffer the combination of injuries and dreadful rotation pitching that helped ruin their May.  FanGraphs seems to agree, predicting that the current Brewers roster (or something like it) will essentially play .500 baseball for the rest of the season, even while maintaining one of the five worst records in the game.

Average baseball is not contending baseball, but average baseball at least would offer Brewers fans — already pleased with Miller Park’s immunity from rain delays — a reasonable likelihood of seeing a win on any given day.  In 2009, the Brewers were able to bring in over three million fans, despite finishing under .500 overall.  In 2010, the Brewers ended up eight games under .500, but still brought in 2.7 million fans.  It remains to be seen whether playing .500 baseball for the rest of the 2013 season would be sufficient to keep fans coming through the Miller Park turnstiles, but if so, the increasing remoteness of May could be a significant factor, particularly if the team can convince fans that “one bad month” does not represent the current Miller Park experience or true caliber of the team.

Of course, it is also possible that the Brewers will be able to trade significant assets at the deadline in exchange for the prospects Doug Melvin wants.  If so, their projected record could, and probably would decline.  (This is necessarily not a bad thing, given that 68.5 wins is the average cut-off to secure a top 5 draft spot from 2003 through 2012).  If that happens, the Brewers will have a further challenge on their hands in trying to provide even average baseball for their fans, and maintain the attendance they need.

That said, the Brewers’ remarkable close to 2012 — an incredible .610 winning percentage from August through October — was accomplished after trading away Zack Greinke and calling up minor league talent to plug gaps in the rotation left by Greinke’s trade and Shaun Marcum’s injuries.  If the Brewers are once again able to make advantageous trades at the deadline, and also able to play even .500 ball for the rest of the year, they are still in a position to do so without hurting their chances to get the impact player they need in the 2014 Rule 4 draft.

If they can pull both of these things off, much of the thanks should be given to the horrible month of May.