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The Chicago Cubs and the Terrible, Horrible, No Good, Very Bad Junior Lake Season

The future is bright on the North side of Chicago. As Rany Jazayerli pointed out this week on Grantland, that future is especially bright in the batter’s box. With an absolute stud to build around, arguably the best bat in the minor leagues right now, a 24-year old short stop who has been worth over ten wins in his career so far, and seemingly every single middle infield prospect in the world, even the biggest curmudgeon on TV seems to be enthusiastic about the Cubs future. However, if this article were written a year ago today, there would be another name that Cubs fans would be screaming at me for leaving off, Mr. Junior Lake. While Lake was not the biggest prospect in the Cubs’ system, when he burst on to the scene last July, excitement was high. It took him only five games to tie a major league record, as no player has ever had more than the 12 hits he garnered through his first five games.

After six games, Lake had an article of his own on FanGraphs, (deservedly) espousing his hot start, while (prophetically) looking at a few red flags in Lake’s game (more on that later). In those six games he slashed .519/.536/.852 with a pair of home runs and three doubles. He even continued to hit well for the remainder of the season, finishing with a wRC+ of 110 and despite below average defense in the field, was still worth over a win to the Cubs for the 64 games in which he appeared. Coming into this season, Lake was a 23-year old with a decent upside who had Cubs’ fans excited for the future.

Cut to: August 13, 2014.

Lake has lost his regular spot in the lineup, and no one has deserved to lose their spot in the lineup more this season. Now Lake may bounce back to be a decent contributor for future iterations of the Cubs (again, more on that in a little bit), but his numbers this season are incredibly poor. Check out his ranks (these will all be from the bottom ranks, as in first really means the worst in the league) in these essential statistics in 2014:

Stat Lake in 2014 League Rank (min. 300 PAs)
OBP .246 2nd
wOBA .269 11th
LD% 15.6 8th
wRC+ 66 10th
fWAR -0.7 6th

Being near the bottom of one of these statistics is hardly a death knell (check out the killer lineup that could be created with the lowest line-drive rates in the league), but if you’re at the bottom of the barrel across the board, it’s fair to say you’ve Schruted up your season in a big way.

So what has been the cause of Lake’s collapse at the plate? The answer is pretty simple. All that’s needed is a trip over to the plate discipline graphs on this very website. Once again using from the bottom ranks, check out some of Lake’s plate discipline statistics:

Stat Lake in 2014 League Rank (min. 300 PAs)
K% 33.4% 3rd
BB% 3.3% 7th
BB 10 T-1st
BB/K 0.10 1st
O-Swing% 43.0% 7th
Z-Swing% 77.2% 7th
Swing% 58.1% 4th
O-Contact% 43.3% 5th
Z-Contact% 74.5% 4th
Contact% 61.6% 3rd
F-Strike% 69.5% 1st
SwSt% 21.7% 1st

As someone who is neither here nor there on the Cubs (aside from a long-term bet in which I have them making the playoffs before the Astros), I think it’s fair to look at this man who has somehow combined a Pablo Sandoval-esque lack of patience at the plate with a Chris Carter-esque lack of contact on those reckless swings, and simply say, “I’m not even mad, that’s amazing.”

That duo isn’t easy to pull off. Of the top thirty highest swing rates among hitters with at least 300 plate appearances, only one other hitter (Mike Zunino) has a contact rate in the sixties, and Zunino’s swing rate is lower and his contact rate is higher than Lake’s. Not to mention that Zunino’s defense has actually made him worth two wins for the Mariners this season, while only George Springer has committed more errors in the outfield than Lake in 2014.

Lake’s Brooks Baseball profile is maybe the most depressing thing in Chicago since this guy.

His performance against fastballs is described as, “an exceptionally aggressive approach at the plate (-0.19 c) with a disastrously high likelihood to swing and miss (30% whiff/swing),” against breaking pitches, “an exceptionally aggressive approach at the plate (-0.32 c) with a disastrously high likelihood to swing and miss (50% whiff/swing),” and against offspeed pitches, yeah, “an exceptionally aggressive approach at the plate (-0.65 c) with a disastrously high likelihood to swing and miss (55% whiff/swing).”

The man clearly needs a private lesson with Wade Boggs, although that might not even be enough. Interestingly enough, in his aforementioned piece on Lake last year, FanGraphs’ Bradley Woodrum spotted a couple of potential flaws that Lake would have to fix. Woodrum mentions Lake’s lack of plate discipline in the minor leagues, but he also touches on two other drawbacks to Lake’s game: his extremely “loud” swing, and his struggles with low sliders.

As far as the “loud” swing, scouting player’s swings is not my specialty, but his swing actually does seem a little toned down in 2014. Check out the gif used in last year’s FanGraphs piece showing Lake’s bat going crazy as the pitch comes in. Now here’s his toned down approach taken from the middle of this season. Well, toned down until he swings and misses, at least:

While his hands are still moving, they don’t seem to be doing so with the same reckless abandon as last year. That would seem to be a good sign, one that Lake is willing to tinker with his swing to get better results. As I said though, I am far from a scout, and would be curious to hear feedback on what others think about his swing.

In terms of his struggles with sliders, those have only been exasperated in 2014, as he has derived the fourth-lowest value against sliders this season, at -7.4 runs. And considering that that pitch value is a cumulative statistic and the three men in front of him all have more than 100 more plate appearances than Lake in 2014, it’s fair to say Lake has struggled as much as any hitter in baseball against the slider in 2014.

With Arismendy Alcantara having made a far smoother transition to the outfield (believe it or not, Lake was yet another middle infield prospect originally), and Jorge Soler/Kris Bryant due to be called up in the not-too-distant future, one has to wonder whether Lake’s shot at as a member of the Cubs is through with. The best option likely would have been to send him down to Triple-A about a month ago, as sitting on the bench in the major leagues will neither help his confidence, nor give him the chance to get in regular swings every day, and begin to tinker with his swing etc.

There is some evidence that Lake far prefers to play left field instead of center field, slashing .312/.333/.561 in his 44 games in left field, but this seems a little bit more noise than signal. However, given that the Cubs really have no motivation to win at this point, their best option may well be to put Chris Coghlan, their current left fielder and a useful piece for say the Oakland A’s, on waivers, and see what they have with Lake in left field for the remainder of the season. With a month and a half of season left, the Cubs could see if those splits really are statistically significant, and if they were, the Cubs could have yet another piece of their future lineup in place. And if not, there are plenty of reinforcements on the way.


Bringing Bill James’ Famous Arbitration Case to 2014

“I helped prepare arbitration cases for George three straight years in the 1980’s… George had led the American League in errors the first year that we prepared a case for him. We were wondering what to do about that, so I drew up an exhibit entitled ‘What Was the Cost of George Bell’s Errors?’ The exhibit showed that while Bell had led the league in errors with 11, none of the errors had actually cost his team anything. Of the 11 errors, only about three led to unearned runs, all had occurred in games which Toronto had won anyway, and in those three games, Bell had driven in something like seven runs.”

Bill James, The New Bill James Historical Abstract

 

The case that Bill James made for George Bell in 1985, and later informed his readers about when he released his Historical Abstract, always fascinated me. As someone who is a big believer that fielding metrics have a long way to go (especially behind the plate), this arbitration case was my Zihuatanejo, that far away place that always gave me hope that errors were really as pointless a statistic as they seemed.

However, as Bill James points out in the rest of George Bell’s player ranking, the fact that nothing came of Bell’s errors in 1985 (his first arbitration year), as well as 1986 and 1987, when James used the same exhibit, was rather noteworthy. Although errors are definitely not the be all and end all of fielding statistics, one would have to imagine that some ill had to come of them, at some point, right?

With the All-Star break upon us, and sadly no real baseball for the last four days, the chance to finally look into this idea of how much errors actually cost the erring player’s team, presented itself. At the halfway point, there were exactly 20 players who had committed 10 or more errors in 2014. Since there was time to kill without baseball on, I decided to pour over some box scores and figure out just how much each of those leading “error-men” had cost their teams. Using baseball-references fielding game logs, it was easy to find the games in which each player had made their errors, and then going through the play-by-play made it (usually) straightforward as to whether their error led to a run or not.

For this study, I created a chart with columns for all of the parts mentioned in Bill James arbitration case: total errors, unearned runs as a result of those errors, games that the team lost when that player committed an error, and RBI in those games that were lost. The final column (RBI in games lost) was tweaked a tiny bit due to the inclusion of one other column. The column added was one called “true losses.” This was the measure of how many games the team lost by equal to, or fewer runs, than the player’s error cost the team. For example, if Pedro Alvarez made an error that cost his team three runs, and the Pirates lost 4-3, that would be a true loss. Or, if Derek Dietrich made an error that cost his team one run, and the Marlins lost 3-2, that would also be a true loss. Finally, if the game went to extra innings and was a loss, any error worth one run or more was counted as a true loss. Therefore, if Josh Donaldson committed an error which cost his team only one run and then the A’s lost 10-8, but that final came in extra innings, then that would still count as a true loss because the extra innings would have never occurred (hypothetically).

Now this is obviously not a foolproof study. There is no way to say for sure that the error committed for one run was any more the cause of the loss than the pitcher who gave up the home run the next inning. It is also starting to get into a bit of a messy “Butterfly Effect” situation, meaning that there is no way of knowing how the rest of the game (or our lives, bro) would be different if Jose Reyes hadn’t booted that grounder in the fifth inning.

However, it was a fun study to put together, and it can be revealing into how little (or in poor Starlin Castro’s case, how much) errors truly change a game. Here’s the official chart:

What Was the Cost of Player X’s Errors?

Name Errors UER from E Team L’s True L’s RBI in True L’s
Pedro Alvarez 3B 20 11 11 4 4
Josh Donaldson 3B 15 6 5 1 0
Ian Desmond SS 15 10 8 2 2
Asdrubal Cabrera SS 14 12 9 1 0
Jose Reyes SS 13 7 9 2 0
Brandon Crawford SS 13 6 5 0 0
Lonnie Chisenhall 3B 13 6 5 0 0
Everth Cabrera SS 13 7 6 0 0
Brad Miller SS 13 7 5 1 0
Martin Prado 3B 12 13 8 2 2
Jonathan Villar SS 12 14 8 0 0
David Wright 3B 11 5 4 1 0
Starlin Castro SS 11 12 6 5 0
Jean Segura SS 11 8 1 0 0
Elvis Andrus SS 11 7 8 0 0
Yan Gomes C 11 4 6 0 0
Chris Owings SS 11 8 7 2 1
Derek Dietrich 2B 11 6 5 1 0
Jarrod Saltalamacchia C 10 5 7 1 0
Hanley Ramirez SS 10 7 7 1 0

Key: UER from E – unearned runs from errors; Team L’s – team losses; True L’s – true losses (described above); RBI in True L’s – how many RBIs the player had in said True Loss games

 

Let’s tackle this table column by column.

Well, I don’t think a historiography of each player’s name is necessary in today’s article, so let’s skip over to the position column. It is interesting to note how many left-side of the infield players there are atop the error leaderboard. There’s nobody from the outfield to be found (the “top” outfielder per errors is Sports Illustrated cover boy, George Springer with seven), and there are only three players that don’t hail from third base or short stop as their main position. One branch off of this study that could be interesting would be to look at whether or not there was a correlation between a player’s position on the diamond, and how frequently an error led to runs or “true losses.” My gut instinct would be to guess no, but maybe errors in the outfield are often for more bases, and therefore more likely to lead to a run – just a hypothesis.

Jumping over to the errors column, Alvarez’s 20 errors stood out, as the difference between his total and the second place total is the same as the difference between second place total and the bottom of our table. In fact, seeing that high total made me curious as to just how many errors it would take to get into the record books. Well, if you’re including the entire history of baseball, the answer is: like a bajillion. Obviously the game was entirely different, but it’s hard to imagine that Herman Long’s 122 errors in 1889 weren’t embarrassing even back then. The record for errors in a single season since 1952 is 44 by Robin Yount in 1975, and the record since 1980 is Jose Offerman with 42 in 1992. So while Alvarez’s 20 errors may be pacing the league by a good margin now, it’s fair to say he won’t be joining even the modern record books this season.

The next column looks at unearned runs derived from each player’s errors, and the variance is quite extreme. With a range from only four runs (it’s interesting to note that the catchers have the two lowest unearned runs tallies, maybe that positional study would provide some analysis after all) all the way up to 14, there doesn’t seem to be too close of a connection between the amount of errors and the amount of unearned runs. For instance, Josh Donaldson has committed three more errors than Jonathan Villar in 2014, but Villar’s errors have led to eight more runs. This brings up the question of whether unearned run prevention is simply luck, or whether some teams (and pitchers) respond better after an error is committed in the field.

The A’s are one of baseball’s best teams, and have an excellent pitching staff, so it isn’t too surprising that Donaldson’s unearned runs are among the lowest, especially in comparison to how many errors he has committed. On the other end of the spectrum are players like Altuve and Castro who play on rebuilding teams, and it is unsurprising to see their names next to some of the highest unearned run totals. However, there is most certainly a lot to be said for luck playing a role in how many unearned runs come along after an error. For example, teammates Asdrubal Cabrera and Lonnie Chisenhall find themselves on opposite ends of the spectrum in terms of unearned runs after errors, a definite sign of the role random chance plays in unearned run prevention.

One other note on the extreme variance in unearned runs tied to errors. The variance could also come as the result of what kind of error was made. A bobbled ball that never even gets thrown across the infield does only one base of harm; whereas, an overthrow (many of Alvarez’s errors) may lead to two bases of harm. One could also try to really dig deep into this data and see if younger, more inexperienced players were more likely to commit errors late in games, when the pressure was ratcheted up, and maybe those errors were more likely to be costly. However, with this study, the idea is simply to get a feel for another way of looking at errors, and the main point that remains here is that there is a lot of luck to whether a player’s error costs his team a run or not.

There isn’t a whole lot to be said about the team losses column, as committing an error does indeed swing the pendulum (or WPA chart) towards a loss, but so minimally that it wouldn’t even bother one of Poe’s victims. For instance, implying that Jean Segura (only one team loss in games he committed an error) timed his errors better than Elvis Andrus (eight team losses in games he committed an error) is really just saying that the Brewers are better than the Rangers; which they are, but that doesn’t reflect on the individual player at all. That comparison is especially interesting given that Andrus’ errors have actually led to fewer unearned runs than Segura’s.

The next column, the “true losses” column, is where the fallacy of the error as a statistic truly shows its colors. The only players who cost their teams more than two wins in the first half (with teams having played well over 90 games in 2014, so far) were the league leader, Alvarez, and the incredibly unlucky Starlin Castro. Castro’s case could be an entire article itself, and the poor timing of his errors is remarkable. The fact that the Cubs have only lost six games in which he has committed an error, and five of those can be considered “true losses” is very much a statistical anomaly. Consider that in this chart there are 124 team losses outside of Castro’s Cubs. Of those 124 losses, 19 were true losses, or just over 15 percent. In Castro’s case, over 83 percent of his team losses were true losses, such a far outlier it warrants special attention.

Even when including Castro’s remarkable true loss numbers, the percent of losses that could be considered, even hypothetically, the erring player’s fault is merely 18.5 percent, and that’s not even accounting for all the games that the team’s still won in which one of  the listed player’s committed an error. This is a good time to point out that this study obviously does not take into account any of the good, run-saving plays that these fielders make, and even still the total impact on a team is minimal. As seen in Pedro Alvarez’s row, he drove in plenty of runs in those games in which he cost his team, and with his strong range, some of those errors he made likely would have been singles, with the majority of third baseman failing to even get to the ball. Josh Donaldson and David Wright stand out as particularly strong cases of top-notch fielders who, because of their strong range, get to more groundballs, but get to them in difficult positions, thus increasing the likelihood of an error.

All of this being said, let’s not take too much away from the potential impact of an error. It is indeed a mistake, and can have a negative impact on the team in ways more than just the scoreboard. For instance, for every error made, that is an extra batter that the pitcher has to face, and therefore, more pitches on his final pitch count. If the bases were clear before the error, the pitcher has to pitch out of the stretch now, and the threat of a potential steal is in play. If a certain player is prone to errors, it may also lead to his pitcher not having confidence in his defense behind him, and therefore getting himself in trouble by trying to do too much on the mound. Other fielders may feel that they have to cheat in the commonly erring fielder’s direction if there is likely to be a mistake made, which can mess up a team’s defensive positioning. Finally, there’s the fact that for all of us here at FanGraphs who realize the harm in relying on errors too much as a statistic, there are still those in baseball who do rely on it, and committing enough errors in the field, may lead to a player riding the pine for a few days.

In the end, it’s fair to say that errors are one metric out of many. They have historically been overused, and hopefully the chart above, has made it clear that frequently an error won’t really cost the team anything.

And if your error did cost your team, well, you’re probably Starlin Castro.


The Unique Path to Success in Oakland

 Two roads diverged in a wood, and I–

I took the one less traveled by,

And that has made all the difference.

– Robert Frost

There are many things that stand out about this year’s Oakland A’s. Their incredible run differential has reached a near historic level, their breakout star from last year has proven that last season was no fluke, and the top three starters are pitching at incredible levels. They’ve been marauding through the American League like Heisenberg’s nemesis through Janjira. However, there’s one aspect of this team that flies under the radar: of their current 25-man roster, only two players were acquired through the amateur draft – Sonny Gray and Sean Doolittle. The rest were acquired through a mix of trades, free agency, waiver claims, purchases, and even one conditional deal.

Billy Beane made his name a while ago by not being afraid to stray from the pack, and in fact looking for those market inefficiencies that could save him a buck or two with the low payroll A’s. By trading for players who may have disappointed at other spots across Major League Baseball, or claiming players put on waivers, Beane is once again finding talent in the most frugal way possible. So is this a new phenomenon in Oakland? Let’s see what the numbers say. Here’s the acquisitional (who says you can’t invent words?!) breakdown of the Oakland A’s roster the last thirteen years.* This includes any hitters who made at least 100 plate appearances and any pitchers who pitched in at least ten games in addition to this year’s current 25-man roster.

* Why thirteen years? Because, Moneyball, of course!

A’s Roster Construction Since 2002
Year AD* FA** T*** AFA^ WC^^ P^^^ CD’ R5” MD”’
2014 2 4 13 1 2 2 1 0 0
2013 4 4 16 1 4 2 1 0 0
2012 7 9 16 2 2 1 0 0 0
2011 6 7 17 0 1 1 0 0 0
2010 9 7 12 1 2 1 0 0 0
2009 11 6 14 1 3 2 0 0 0
2008 8 5 16 1 2 3 0 0 0
2007 10 5 10 1 4 2 0 0 0
2006 8 5 15 0 1 0 0 0 0
2005 10 4 15 0 1 0 0 0 0
2004 8 7 11 0 1 0 0 0 0
2003 8 6 9 2 0 0 1 1 0
2002 6 8 16 2 0 0 0 0 1

AD*= Players acquired through amateur draft;  FA**= Players acquired through free agency;  T***= Players acquired through trades;  AFA^= Players acquired through amatuer free agency;  WC^^= Players acquired through waiver claims;  P^^^= Players acquired through purchases;  CD’= Players acquired through conditional deals;  R5’’= Players acquired through the rule 5 draft;  MnD’’’= Players acquired through minor league draft

 

While the A’s have always built their roster through trades more than through the draft (the only years those numbers were even tied was in 2007 and 2003; every other year there were more players acquired via trade than draft), the trend is becoming more and more evident as of late. On the A’s current 25-man roster, there are a measly two players who the A’s acquired through the amateur draft versus sixteen acquired through trades. Granted, the number acquired through the draft was bound to be a bit smaller so far this season than in previous years since a 25-man roster was used this season, instead of qualified players (again, players who had either 100 plate appearances or ten games in which a player pitched in that given season), which totaled between 27 and 37 in the previous twelve seasons. However, given that the season with the second lowest number of players acquired via the draft was last season, there definitely appears to be a trend here.

Now the question becomes, “how does this compare to the league as a whole?”

Usually Beane is at the forefront of certain trends, so if the A’s roster composition varies greatly from the rest of the league, could it be the start of a league wide trend, especially given the A’s incredible success so far? To answer that question, data on all 30 teams’ roster composition was collected for the 2013 season. Given the same requirements as the previous A’s seasons (100 plate appearances or ten games pitched), how did other rosters across Major League Baseball look last year?

League Wide Roster Construction in 2013
Team AD* FA** T*** AFA^ WC^^ P^^^ CD’ R5”
BOS 26.47 35.29 26.47 5.88 0.00 5.88 0.00 0.00
STL 65.63 12.50 18.75 0.00 0.00 3.13 0.00 0.00
OAK 12.50 12.50 50.00 3.13 12.50 6.25 3.13 0.00
ATL 33.33 10.00 33.33 6.67 16.67 0.00 0.00 0.00
PIT 28.57 21.43 42.86 3.57 0.00 3.57 0.00 0.00
DET 18.75 40.63 31.25 6.25 3.13 0.00 0.00 0.00
LAD 21.88 34.38 34.38 6.25 0.00 3.13 0.00 0.00
CLE 13.79 24.14 58.62 3.45 0.00 0.00 0.00 0.00
TBR 22.58 29.03 41.94 0.00 3.23 3.23 0.00 0.00
TEX 29.03 32.26 19.35 12.90 0.00 3.23 0.00 3.23
CIN 40.00 23.33 23.33 10.00 3.33 0.00 0.00 0.00
WSN 37.50 25.00 31.25 3.13 3.13 0.00 0.00 0.00
KCR 33.33 13.33 36.67 6.67 3.33 6.67 0.00 0.00
BAL 25.81 12.90 35.48 3.23 9.68 6.45 0.00 6.45
NYY 25.81 35.48 22.58 9.68 3.23 0.00 0.00 3.23
ARI 16.13 25.81 45.16 9.68 3.23 0.00 0.00 0.00
LAA 37.84 29.73 21.62 5.41 5.41 0.00 0.00 0.00
SFG 33.33 36.67 10.00 6.67 10.00 3.33 0.00 0.00
SDP 31.43 20.00 40.00 0.00 2.86 0.00 2.86 2.86
NYM 31.58 31.58 13.16 13.16 10.53 0.00 0.00 0.00
MIL 39.39 36.36 12.12 3.03 6.06 3.03 0.00 0.00
COL 36.36 27.27 21.21 12.12 3.03 0.00 0.00 0.00
TOR 24.32 21.62 45.95 2.70 2.70 2.70 0.00 0.00
PHI 35.00 37.50 20.00 7.50 0.00 0.00 0.00 0.00
SEA 27.27 30.30 30.30 9.09 0.00 0.00 0.00 3.03
MIN 33.33 33.33 12.12 6.06 9.09 0.00 0.00 6.06
CHC 11.43 42.86 22.86 11.43 8.57 0.00 0.00 2.86
CHW 30.00 33.33 20.00 10.00 6.67 0.00 0.00 0.00
MIA 30.30 24.24 39.39 6.06 0.00 0.00 0.00 0.00
HOU 15.00 22.50 40.00 5.00 10.00 0.00 0.00 7.50

That’s a lot of numbers, so let’s take a step back and look at some of the numbers that stick out. First of all, instead of using raw totals, percentages have been used to even out the variance among how many players each team had qualify for this roster construction study. It’s also important to note that the highest and lowest percentage in each column has been bolded (this was used only for the three primary ways of acquiring players – the amateur draft, free agency, and trades). One may think of the old adage, “there’s more than one way to skin a cat” when looking at the top of the league. Apparently this adage holds true for baseball roster construction, as well as cat mutilation, as the St. Louis Cardinals – you know, that franchise that has won four of the last ten NL pennants with a pair of titles, and has the self-proclaimed best fanbase in baseball – has gone the complete opposite direction as the A’s to build their squad, relying more on the amateur draft than any other team in baseball, and doing so with great success. Then there are last year’s World Series champions, the Boston Red Sox, who were among the league leaders in players brought in through free agency.

One consistent, league-wide trend was that teams at the bottom of the league standings had far more players qualify for the 100 plate appearance/ten games pitched minimums. This is a bit of a “chicken or the egg” type observation, where the cause can sometimes be confused with the effect. There are several teams among the league’s cellar dwellers that went through numerous players throughout the season in an attempt to find effective players (the “throw the spaghetti at the wall and see what sticks” approach Jonah Keri has referenced on multiple occasions). This would be your Marlins, Astros, and Cubs. However, there are also teams among the lower tier of the standings that were forced into more personnel choices due to injuries; your Phillies, Blue Jays, and Angels. Whatever the reason, it is noticeable that nearly all the teams at the top of the standings at the end of the year have fewer players qualified for the 100 plate appearance/ten games pitched minimums thanks to good health and a clear vision – two staples of successful franchises (interestingly enough the one team that was an exception to this rule in 2013 was the Boston Red Sox; however, given their disaster of a 2012 season, it’s not as surprising to see that they tinkered a bit with their roster throughout the season).

The data supports what many baseball fans would already think, which is that the teams with higher payrolls usually are among the most reliant on free agents, and, in order to compete, the smaller market teams need to find other ways to build their rosters. For example, the top eight teams who built through free agency were: the Cubs, the Tigers, the Phillies, the Giants, the Brewers, the Yankees, the Red Sox, and the Dodgers. Of those eight, the Tigers, Philles, Giants, Yankees, Red Sox, and Dodgers make up the top six teams by payroll in 2014. The Cubs are in the middle of a complete roster overhaul, and Theo Epstein seems to be constructing a team built for flipping at the deadline for future prospects, so cheap free agents are a prime commodity. The Brewers are the odd team out, and would make for an interesting case study.

On the flip side, the top nine teams created by trading players were: the Indians, the A’s, the Blue Jays, the Diamondbacks, the Pirates, the Rays, the Astros, the Padres, and the Marlins. Of those nine, the A’s Pirates, Rays, Astros, Padres, and Marlins made up the six lowest teams by payroll in 2013; the Indians were not far off, with only the 21st biggest payroll of 2013; and the Blue Jays and Diamondbacks both have super aggressive front offices that prefer to bring in players via (usually poor) trades.

There is, of course, the caveat that while this study looks at general roster construction it does not have the nuance to differentiate between a team that is loaded with free agents that are big money free agents (like the Yankees and Red Sox) versus a team loaded with replacement level free agents (like the Cubs). If each player’s salary was totaled by how he was acquired, and then turned into percentages of roster construction again, this would show us how much each team is truly investing into each method of roster construction from a financial point of view. This could be used to compliment Jonah Keri and Neil Payne’s recent study that looked at roster construction. In their piece, Keri and Payne look at roster construction through the lens of a stars and scrubs roster versus a balanced roster. Although there might be some discrepancy based on the arbitrary 100 plate appearance and ten games pitched cut-offs, the data likely wouldn’t be vastly skewed from the current results.

Todd Boss, of Nationals Arm Race did an interesting study somewhat similar to this one, looking at the core players (the 5-man starting rotation, the setup and closer, the 8 out-field players, and the DH for AL teams) for the playoffs teams in 2013, and put the teams into four different categories of roster construction: draft/development, trade major leaguers, trade prospects, and free agency. The results were similar to what was found here, and help to support the idea that the arbitrary cut-offs of 100 plate appearances and 10 games pitched didn’t have a negative impact on the study. The only slightly different result was that Boss found the Rays to be relying more on the draft than on trades.

Having looked at the league-wide breakdown for roster construction last season, let’s take a look at roster construction from an historical perspective. To make a long story short, when Curt Flood took on Major League Baseball, and eventually the Supreme Court, in his fight to turn down a trade to Philadelphia (who can blame him?), he opened up the Floodgates (couldn’t help myself) for the eventual implementation of free agency in baseball. So, has successful (being judged by the extremely arbitrary “ringz” perspective) roster construction changed since then? Let’s take a look with yet another chart (Marshall Eriksen would be proud), this time looking at the past 40 World Series winners, and how each team was constructed.

Roster Construction of World Series Winners Since 1974
Year Team AD* FA** T*** AFA^ WC^^ P^^^ CD’ R5” MD”’ DC+ XD++
2013 BOS 26.47 35.29 26.47 5.88 0.00 5.88 0.00 0.00 0.00 0.00 0.00
2012 SFG 37.50 37.50 15.63 6.25 3.13 0.00 0.00 0.00 0.00 0.00 0.00
2011 STL 39.39 33.33 21.21 3.03 0.00 3.03 0.00 0.00 0.00 0.00 0.00
2010 SFG 31.25 50.00 15.63 3.13 0.00 0.00 0.00 0.00 0.00 0.00 0.00
2009 NYY 21.88 43.75 12.50 15.63 0.00 6.25 0.00 0.00 0.00 0.00 0.00
2008 PHI 29.63 44.44 14.81 3.70 3.70 0.00 0.00 3.70 0.00 0.00 0.00
2007 BOS 20.00 46.67 23.33 0.00 3.33 6.67 0.00 0.00 0.00 0.00 0.00
2006 STL 16.13 41.94 32.26 0.00 0.00 3.23 0.00 6.45 0.00 0.00 0.00
2005 CHW 14.81 40.74 40.74 0.00 3.70 0.00 0.00 0.00 0.00 0.00 0.00
2004 BOS 9.09 39.39 30.30 0.00 12.12 6.06 3.03 0.00 0.00 0.00 0.00
2003 FLA 10.00 30.00 50.00 10.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
2002 LAA 35.71 28.57 14.29 7.14 14.29 0.00 0.00 0.00 0.00 0.00 0.00
2001 ARI 10.00 50.00 20.00 6.67 0.00 3.33 0.00 0.00 0.00 0.00 10.00
2000 NYY 25.00 31.25 31.25 9.38 3.13 0.00 0.00 0.00 0.00 0.00 0.00
1999 NYY 20.00 36.00 32.00 12.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
1998 NYY 16.00 44.00 28.00 12.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
1997 FLA 6.45 29.03 38.71 16.13 0.00 0.00 0.00 0.00 3.23 0.00 6.45
1996 NYY 12.12 27.27 39.39 18.18 0.00 3.03 0.00 0.00 0.00 0.00 0.00
1995 ATL 40.00 40.00 16.00 4.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
1994 BOO XX XX XX XX XX XX XX XX XX XX XX
1993 TOR 29.63 37.04 33.33 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
1992 TOR 44.00 20.00 24.00 0.00 0.00 0.00 0.00 8.00 0.00 4.00 0.00
1991 MIN 33.33 29.63 33.33 0.00 0.00 0.00 0.00 3.70 0.00 0.00 0.00
1990 CIN 32.00 12.00 52.00 0.00 0.00 0.00 0.00 4.00 0.00 0.00 0.00
1989 OAK 32.14 32.14 32.14 3.57 0.00 0.00 0.00 0.00 0.00 0.00 0.00
1988 LAD 32.14 35.71 28.57 0.00 0.00 3.57 0.00 0.00 0.00 0.00 0.00
1987 MIN 33.33 11.11 51.85 3.70 0.00 0.00 0.00 0.00 0.00 0.00 0.00
1986 NYM 30.77 11.54 50.00 7.69 0.00 0.00 0.00 0.00 0.00 0.00 0.00
1985 KCR 38.46 19.23 26.92 11.54 0.00 3.85 0.00 0.00 0.00 0.00 0.00
1984 DET 42.86 17.86 28.57 3.57 0.00 7.14 0.00 0.00 0.00 0.00 0.00
1983 BAL 32.14 21.43 32.14 10.71 0.00 3.57 0.00 0.00 0.00 0.00 0.00
1982 STL 19.23 3.85 65.38 7.69 0.00 3.85 0.00 0.00 0.00 0.00 0.00
1981 LAD 43.48 17.39 21.74 8.70 0.00 8.70 0.00 0.00 0.00 0.00 0.00
1980 PHHI 39.29 14.29 39.29 3.57 0.00 3.57 0.00 0.00 0.00 0.00 0.00
1979 PIT 32.00 12.00 40.00 16.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
1978 NYY 18.18 13.64 63.64 4.55 0.00 0.00 0.00 0.00 0.00 0.00 0.00
1977 NYY 18.18 13.64 63.64 4.55 0.00 0.00 0.00 0.00 0.00 0.00 0.00
1976 CIN 28.00 N/A 44.00 28.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
1975 CIN 33.33 N/A 45.83 20.83 0.00 0.00 0.00 0.00 0.00 0.00 0.00
1974 OAK 25.00 N/A 37.50 29.17 0.00 8.33 0.00 0.00 0.00 0.00 0.00

#DC+= Players acquired through free agent draft compensation;  #XD++= Players acquired through the expansion draft

The first note that needs to be made is regarding the 1997 Marlins and 2001 Diamondbacks. Both rosters had skewed roster construction due to how soon after the team’s inception they were able to win a championship. The Marlins have by far the lowest reliance on the amateur draft, and the Diamondbacks have tied for the highest reliance on free agents, but both of these numbers were driven up (or down) by the limited time for drafting and moving along prospects before their championships.

After accounting for the 2001 Diamondbacks season, the steady rise of reliance on free agents since the mid-seventies is notable – up until three years ago that is. It’s hard to tell whether baseball is undergoing an actual “grass roots” movement, with teams relying less and less on big market free agents to succeed, or if this is simply a three-year blip in the radar, but it is certainly notable that the last three World Series winners have had notably lower reliance on free agents than the previous seven years’ winners. The 2011 Cardinals, 2012 Giants, and 2013 Red Sox have not, however, relied on trades, but instead their farm systems more so than other winners of this millennium (not including the 2002 Angels).

In fact, excluding the fluky 2003 Marlins, there has not been a World Series winner as reliant on trades as the 2013 A’s (50 percent) since the mid-eighties Twins and Mets. What’s even more troubling for the A’s is that there hasn’t been a team to use the draft and free agency combined as little as the 2013 A’s since the 1982 Cardinals, a team built during the dawn of free agency.

When judging by championships, in fact, the picture of baseball as a sport in which you need to be in a big market, with the ability to sign big name free agents becomes unfortunately evident. The roster composition of nearly all of the World Series winners this century is quite similar to that first group of teams mentioned above as big market teams built through free agency. This is no surprise to any real baseball fans, however. Look at the cities that have hosted World Series parades since the Yankees’ dynasty of the nineties began. Sure, there are the success stories in Florida and Arizona, but other than that it’s a who’s who of big market teams. While the Cardinals play themselves off as plucky little underdogs, their payroll was the eleventh largest in baseball last year, almost exactly twice that of the A’s.

That’s why this year’s A’s team could be so special. If they are able to continue their regular season success, and finally make the breakthrough they have been struggling so much to make in recent years, they could continue the recent trend of teams moving away from a strictly free agent diet to fulfill their championship dreams. Of course, this has been the case for a couple of years in Oakland now, and it hasn’t happened yet. However, with the top three in the A’s rotation looking as good as any in baseball right now, baseball’s secret superstar at third, and the fact that it is the 25th anniversary of the last A’s World Series title, suddenly it doesn’t seem that unlikely that the A’s could make ole Bobby Frost proud this October.