Archive for August, 2013

MLB Past and Future Payrolls

I’m a big fan of Bill Simmons’ BS Report podcast. Some of my favorite parts are when Bill talks about trade possibilities between teams. It’s always fun to try and step into a general manager’s shoes and imagine what they can and can’t do to improve their teams. During one of these shows, Jonah Keri was on, and he and Bill were doing a pretty good job of breaking down the options that some MLB teams had in the coming years. It seemed like Jonah had a great command of the restrictions on some of these teams and even what the free agent market is going to look like at various points in the future. I found myself trying to picture and organize all this information in my head. I was inspired to map all this out in a big visualization.

Also, I just wanted to find out how screwed my beloved Phillies are in the coming years.

The image below is a link to the visualization:

MLB Payrolls Thumbnail

The first thing you can do is to click the arrows or use the left and right arrow keys to scroll through past and future years. I collected data back to 1998, when the Baltimore Orioles led the league in payroll with players like Mike Mussina and Rafael Palmeiro. Scrolling back to the present day shows a lot of story lines: how the Yankees expanded their payroll way faster than the rest of the league in the early 2000s, fire sales of the Marlins in 2006 and to a lesser extent in 2013, and the Dodgers’ rapid leapfrog to post the absolute largest payroll this year.

When you scroll to future years, the 2013 payroll hangs around as a ghost image to provide a rough benchmark of what you might expect the team to eventually pay. The solid bars drop down to show the contracts that the teams are currently obligated to pay in that particular year. Here, you can clearly see the Dodgers and Angels leading the league in earmarked money over the next few seasons. Going all the way to 2023 shows that the Reds have actually signed the longest contract so far.

Clicking on a team in that upper chart will show a time series of that team’s payrolls over the years broken out by player. For example, clicking on the Reds shows large green boxes way out into the future. Clicking on any of those boxes will show you that first baseman Joey Votto can expect to be paid $25M to play baseball in the year 2023. Each color in these bottom charts corresponds to a position.

There are some caveats here. I grabbed the data from Baseball Reference who gets their data from Cot’s Baseball Contracts. As far as I can tell, the data is not updated very regularly because I know of a couple contract extensions that have not made it onto their pages yet. Those contracts won’t be displayed here.

Also, when a player misses a whole season to injury, that player’s salary doesn’t show up on the Baseball Reference page. I took care to add the biggest instances of these missed seasons back into the data by hand, but I’m sure I didn’t get them all. There’s also the question of whether those salaries really should be here. I believe most teams take out insurance policies on players and thus they aren’t responsible for paying injured players. Since I have no details about that sort of thing, I just tried to include all the missed seasons I could find.

Lastly, teams sometimes agree to pay part of a player’s salary when they trade them away to another team. A good recent example of that is the Cubs paying most of Alfonso Soriano’s salary while he plays for the Yankees. The Baseball Reference site has good information about these arrangements in the current and future years. But the site does not have information about past arrangements. Again, I took care of a couple of the biggest discrepancies by hand (hello Mike Hampton!), but I’m sure there are lots still in there.

Despite those couple issues, I believe this chart does a great job of showing a snapshot of the MLB economy. I learned a lot just clicking around the whole thing while building it. I think it’s a great indication that you’re building something interesting if you constantly get distracted playing with the thing instead of working on it.

Trade Ian Kinsler

The 2014 Rangers have an interesting predicament.  The same predicament they currently have, but it will be more pronounced, more necessary to solve in the off-season.

They have two shortstops and a second baseman.  One shortstop, Elvis Andrus, is locked up for a long, long time.  And the other shortstop, Jurickson Profar, is most likely going to move over to second base permanently, giving the Rangers what should be  a very good and young middle infield, for many years.

I’m assuming the Rangers keep Profar at 2b, rather than move him to the outfield or trade him for another top prospect.  It would mean Ian Kinsler either must change positions, or more logically, be traded to a team who will value him more highly since he can man second base for said team.

Kinsler has been on a decline the past few years, whether it’s due to injury or diminishing skills.  Or perhaps a combination of both.  For example: The league average wOBA in the American League this season is .318.  Over the past two seasons, Kinsler’s wOBA’s have been .327 and .330, respectively.  The .330 has been over the course of 97 games in 2013, so he has some room to improve upon that.  But there is only so much he can do with only a month and a half of the season remaining.

Best option for 2014?  Trade Ian Kinsler.  There are certainly obstacles.  He is going to turn 32 next season.  He, as I mentioned, isn’t hitting like he used to hit, as just two years ago, he posted a 7-WAR season with a .364 wOBA.  He is guaranteed four more seasons, and $62 million on his current contract (including the 2018 option which has a $5 million buyout).  So most teams will be wary of committing that kind of money to a player who is past his prime, and probably past the point of “good” nowadays.  Above-average, maybe.  But I can’t see Kinsler being worth much more than 3 wins in a season moving forward, and he might be worth even less than that.

There is one team that could use a 2B next season though, and has a fairly new obsession with throwing around money: the Los Angeles Dodgers.  Mark Ellis has a $1 million buyout on his 2014 option and is going to be turning 37 next summer.  There is no doubt that Ian Kinsler will be an upgrade at 2B for the Dodgers over Ellis (And at $5 million, Ellis might even be worth a utility role).  If the Dodgers don’t bring home a championship this season after spending an absurd amount of money in 2013 (and beyond), there will be even more pressure to win next year.

In comes the potential acceptance of either the remaining Ian Kinsler money or most of it, without having to give up much.  Maybe a prospect with some upside.  But they definitely won’t have to surrender a bonafide prospect of any kind.

The Rangers COULD decide to just move Kinsler to 1B or a corner outfield spot.  But a .330-ish wOBA at first base would be below the league average at the position.  And even though .330 would be a little above average in left or right field, he would be learning a new position.  That might not go well.  There is a not-miniscule chance Ian Kinsler is a below-average player in 2014 if he is moved off of 2B, especially if it is to the outfield.

The Rangers would probably be just as good bringing back David Murphy as one of the outfielders, rather than moving Ian Kinsler out there.  Murphy is a solid defender, and even though he’s been terrible at the plate in 2013, he should be very cheap next year and regress back closer to his normal offensive numbers.

The other outfield spot could be solved with a platoon, potentially a minor leaguer, depending on who is ready (if anyone), a stop-gap, maybe even Nelson Cruz.  Although, knowing that Cruz was just suspended, I would simply let him walk.

They can solve their outfield situation in a better manner than using Ian Kinsler to fill one of the two voids.

And they can find a 1B for a year that’ll hit like Kinsler probably will in 2014.

Overall, the best bet for the Rangers is to move on from Kinsler, assuming there is a team that wants or needs a 2B badly enough.


The Folly of Pitching to Contact

‘Pitching to contact’ and ‘throwing ground balls’ are classic baseball buzzwords. Twins pitching coach Rick Anderson has essentially built a career around this philosophy. It seems like every time a young pitching phenom arrives and starts striking hitters out, people start talking about how he needs to pitch to contact. The strategy has been around since this guy played, and while Kirk Rueter pitched in his last game in 2005, Kevin Correia is still hanging around and Jeremy Guthrie signed a three-year deal last offseason. And, lest we forget, Aaron Sele got a Hall of Fame vote. To take a more in-depth look at the merits of pitching to contact I grouped all 394 starting pitchers from 2002 onward (the batted ball era) who had thrown 200 or more innings, and organized them by Contact% into eight groups. The following spreadsheet details the results of my study. Groups 1-4 are classified as contact pitchers, while groups 5-8 are strikeout pitchers.

Group Contact range xFIP- ERA- WAR/200 IP RA9-WAR/200 IP GB% K% BB% HR% BABIP FB velo FB% Pitches/IP
MLB 80.0—82.2 101 103 2.4 2.3 43.0 16.8 7.9 2.8 0.295 90.3 59.3 16.2
Group 1 85.2—89.9 109 112 1.7 1.5 44.7 11.8 6.8 2.9 0.299 89.2 64.3 15.8
Group 2 84.0—85.2 106 110 2.1 2.0 43.7 13.8 7.2 2.8 0.300 89.6 61.7 16.0
Group 3 83.1—84.0 106 112 2.0 1.7 44.0 14.6 7.3 2.8 0.295 89.3 59.0 15.9
Group 4 82.1—83.1 105 110 2.4 2.0 42.4 15.6 7.6 2.8 0.299 89.4 60.1 16.2
Group 5 81.0—82.0 105 106 2.3 2.3 42.4 16.8 8.3 2.7 0.290 90.0 60.3 16.4
Group 6 79.7—80.9 100 101 3.0 3.0 43.2 18.4 7.5 2.7 0.292 90.5 59.0 16.0
Group 7 78.0—79.6 98 98 3.0 3.1 43.1 19.5 8.2 2.6 0.290 91.1 58.8 16.2
Group 8 71.3—77.8 89 90 3.8 3.7 42.1 22.7 8.2 2.5 0.290 91.9 58.5 16.2

Of the Group 1 pitchers, only 5 had an xFIP- better than the league average, and only 6 had an ERA- better than league average.  Two of these were posted by aging control artists Rick Reed and David Wells, who had success on the strength of their walk rates of 4.0% and 3.7%, respectively. Chien-Ming Wang rode his 59.5 GB% to a 98 xFIP- and 99 ERA-. Overall, Nate Cornejo was more typical of the group than these three. xFIP- went down with decreasing contact, and except for a small blip between groups 2 and 3 (both contact groups), so did ERA-.

There is a strong connection here between fastball velocity and contact rates, but there is also a strong connection between fastball usage and contact rates. Group 1 had both the slowest average fastballs and the highest use of fastballs. As anyone watching Gerrit Cole and the Pirates can tell, contact rate has almost as much to do with fastball usage as fastball velocity.

Though the contact pitchers had lower walk rates than the strikeout groups, their strikeout rates were far below average. The separation between strikeout and walk rates was better for the strikeout pitchers, with an average separation of 11.3, compared to 6.7 for the contact pitchers. In terms of K/BB, the strikeout pitchers posted a 2.4 K/BB, and the contact pitchers were at 1.9 K/BB. The old adage that groundball pitchers prevent home runs did not bear out. While the contact pitchers had a groundball rate of 43.7% compared to 42.7% for the strikeout pitchers, the contact pitchers had a HR% of 2.8, and the strikeout pitchers had a HR% of 2.6. Home runs are connected to contact.

The contact pitchers also slightly underachieved their peripherals. The ERA- for the contact groups was an average of 4.5 points higher than their xFIP-, while the ERA- for the strikeout groups was on average less than 1 point higher. The contact pitchers had an average BABIP of .298 compared to the .291 for the strikeout pitchers. High strikeout pitchers can often sustain slightly lower BABIP than their counterparts.

The connection between contact and efficiency is slight. The difference in Pitches/IP was the biggest between group 1 and group 5. The difference of 0.6 Pitches/IP translates to only 120 pitches per 200 IP. While the pitch count and innings limit debate has overtaken the nature of starting pitching, pitching to contact does not seem to be the answer. Teams and pitching coaches that are advocating pitching to contact as a means to pitch longer in games are essentially sacrificing a lot of quality for a tiny amount of quantity. And with 12 or 13 man pitching staffs being the rule of the day, this strategy seems absurd.

Despite mounting evidence that pitching to contact is a futile strategy, teams keep encouraging their young pitchers to stash away their strikeout stuff in the name of efficiency. Young pitchers Nathan Eovaldi and Gerrit Cole currently own the 3rd and 4th fastest fastballs among starting pitchers. Both of them, and Cole in particular, posted very high strikeout rates in the minor leagues. Yet both of them own strikeout rates well below the NL average, and Cole and Eovaldi’s respective xFIP- rates of 99 and 101 are decidedly average.  I know, almost anybody with a good fastball can rack up a lot of strikeouts in the minors, and Eovaldi in particular has a limited repertoire that may keep him from reaching his potential. But shouldn’t young pitchers focus on developing strikeout pitches rather than trying to get ground balls? After all, fastball velocity peaks early and Cole and Eovaldi will probably have a tougher time getting outs on contact when they aren’t throwing 96. While Mike Pelfrey has carved out a decent career for himself, I’m sure most teams hope for more out of their top pitching prospects.

An Introduction to GRIT

Earlier in the month I had an idea. It all stemmed from the idea of quantifying the un-quantifiable. I was going to record grit.

A lot of times we hear about how gritty a player is, but it’s tossed around with no real proof. Sure Nick Punto dives into first a lot, but is that really more gritty than stupid? Is a guy like David Eckstein really the grittiest of all gritty players, or can it be a guy we don’t really notice?

To figure all of this out I, along with some help, wrote a formula. The formula is imperfect, because of a lack of reliable sources for things like headfirst slides and broken-up double plays, but it tries and does its job. The formula is as follows:


Where InfH stands for Infield Hits and 1stS3 means first to third on a single, we have found a way to see a players GRIT (Game Rating In Testosterone.) All this stat is designed to show is who works harder to score a run for their team, it doesn’t show you who is better or worse, but it does show who tries.

Using this formula my small team of experts has found David Eckstein to have a career GRIT of 172.16, which is very impressive over a 10-year career, but it’s no Juan Pierre, who has amassed a career GRIT of, wait for it, 1582.

We also found the difference between Martin Prado and Justin Upton, who was the subject of criticism from Diamondbacks GM Kevin Towers who said he wasn’t gritty enough prior to trading him for Prado. We found out that Kevin Towers may have been wrong.

Using their numbers the formula says that Prado has put together a GRIT of 57.93 in his career, where Upton has a GRIT of 68.65, despite playing in one less season. So, Kevin Towers, you may need to rethink your strategy.

Also invented was TeamGRIT, a stat that uses numerous numbers to calculate how hard a team works for each run.

A disclaimer here before I list the GRITs: I am not trying to say that some teams work harder than others, nor am I saying that a high GRIT is more or less valuable than a low GRIT, all these numbers illustrate is that some teams are more comfortable with power numbers to win games, while others are more inclined to small ball.

The formula used is


The following are the AL leaders prior to games played on August 7th 2013

Royals – 90.57 (9th in wins)

Indians – 74.77 (6th in wins)

Red Sox – 73.92 (1st in wins)

A’s – 70.57 (5th in wins)

Blue Jays – 61.73 (10th in wins)

Rangers – 56.52 (4th in wins)

Astros – 55.70 (15th in wins)

White Sox – 51.62 (14th in wins)

Rays – 51.10 (2nd in wins)

Angels – 48.98 (12th in wins)

Twins – 46.97 (13th in wins)

Yankees – 45.59 (8th in wins)

Orioles – 40.49 (7th in wins)

Tigers – 30.30 (3rd in wins)

Mariners – 25.90 (11th in wins)

The most interesting numbers to me are those of the Royals and the Tigers. On opposite ends of the spectrum, one is a team that absolutely crushes the ball, everything that comes their way, the Tigers hit it, and they’re fine with it. They don’t feel the need to manufacture runs the way that the Royals do. The Royals seem to grind more to score their runs. More than any other team in the league by a large margin. They, like the Astros at 55 GRITs, are doing everything in their power to score more runs. It doesn’t always work, but there’s something to be said about a team that works to get extra runs and extra outs. If anything, they’re less comfortable with a lead than the Tigers. That isn’t to say the Tigers get lazy, just that they tend to not have to try so much.

In the NL there appears to be a negative correlation between GRIT and wins; I assure you, this is just a coincidence.

NL leaders prior to games played on August 7th 2013

Pirates – 80.83 (2nd in wins)

Rockies – 77.08 (8th in wins)

Marlins – 76.31 (15th in wins)

Brewers – 73.57 (14th in wins)

Mets – 67.33 (11th in wins)

Giants – 64.21 (12th in wins)

Padres – 62.53 (9th in wins)

Phillies – 57.06 (10th in wins)

Dodgers – 51.83 (4th in wins)

Cardinals – 47.67 (3rd in wins)

Nationals – 45.03 (7th in wins)

Cubs – 44.79 (13th in wins)

Diamondbacks – 42.38 (6th in wins)

Reds – 39.99 (5th in wins)

Braves – 31.12 (1st in wins)

The only thing these numbers definitively tell us is that there is a lot more GRIT in the American League, which is a deviation from the stereotype of hard-hitting AL clubs. The longball is less important in the American League, whereas manufacturing runs is a lot more emphasized. In the National League one team stands out from the pack: The Pirates.

They have a GRIT of 80.83 while also being in 2nd place, they are the only team in the top 5 of wins who is also in the top 5 of GRIT. The Pirates also hit a fair amount of home runs, but that’s not enough for them. They aren’t comfortable with just a lead. They want more of a lead. They try their damnedest to score more runs than anyone else by any means necessary. Is this because they spent so many years as a losing team? Possibly, but that’s just a theory.

As I said before, these numbers are not proof that any team is better than another, nor are they proof than any player is better than another, just that some teams and players are GRITtier than others.

So there you have it, your introduction to GRIT.

Pettitte vs. Buehrle

Note: I have no idea if I’m the first to do this, but quite frankly I don’t care.

I feel as though this will be the article where my disclaimer is put to full use, as this seems to be a comparison that is an easy one to make, but no one (at least from what I can tell) is making it. Andy Pettitte and Mark Buehrle have very similar career ERA’s (3.88 and 3.85, respectively); they were both late-round draft picks, as I covered earlier, and…well, that’s basically where the similarities end.

Buehrle is obviously famous for his durability, having never gone on the DL, and while Pettitte has had some durability issues recently, he’s been pretty durable for his career, with 10 seasons of 200 innings in his first 14 years in the majors. Obviously, Pettitte has considerably more innings pitched (3255.1 to 2829.1), as he’s existed for five more years.

One key difference between the two is peripherals. Pettitte’s career K% is a solid¹ 17.4%, whereas Buehrle’s is, well, a less solid 13.8%. While Buehrle also has better career control than Pettitte (5.5% to 7.3% BB%), Pettitte is a little more groundball inclined (48.6% to 45.4% GB%).

Put it all together, and Pettitte has a career xFIP² of 3.70, whereas Buehrle’s sits at 4.21, a 51-point difference that would suggest that these two men are not very similar. Their respective WAR values (4.1 WAR/200 IP for Pettitte, 3.3 for Buehrle) also works to support this conclusion. However, this is FanGraphs WAR, based off of FIP; looking at their Baseball-Reference WAR–i.e. runs-allowed WAR–it would appear that Buehrle is better than Pettitte (3.8 to 3.6)³.

While we’re on the subject, let’s see some other pitchers in that general vicinity of career rWAR/200 innings, that I may or may not have picked selectively to further my argument⁴.

Nolan Ryan–3.0

Ted Lyons–3.2

Gaylord Perry–3.4

Steve Carlton–3.5

Phil Niekro–3.6

John Clarkson–3.7

Bert Blyleven–3.8

Fergie Jenkins–3.8

Are several of these pitchers people from the days of yore whom you’ve never heard of? Yes. Are they all Hall of Famers? Also yes.

So why is Pettitte considered to to have a strong case for the Hall of Fame, while Buehrle is borderline at best? It all comes back to that key pitcher stat: wins. Because, as the article cites, Pettitte is part of an elite group: only 46 pitchers have 250 career wins, and 32 of them are in the Hall⁵. Buehrle, meanwhile, is toiling away with a meager 182 wins, only 157th all-time.

Obviously, wins are a completely meaningless statistic, and Pettitte having that many career wins is almost entirely circumstantial. The above article mentions that Pettitte played on playoff teams for 14 of his first 17 seasons, compared to only two for Buehrle’s first 13 seasons, and, of course, Pettitte has played most of his career with one of the best closers of all time, whereas Buehrle played much of his career with a guy who partakes in, uh, unusual fowl ingestion techniques.

There’s also the fact that Pettitte played most of his career in New York, the attention pimp⁶ of cities; while Chicago is one of the larger cities in the U.S., its media shrivels up and dies in comparison to the Big Apple’s. How much this contributed to Pettitte’s alleged divaism–and confirmed indecisiveness–will never be known; what we do know is that Buehrle is humble about himself and his achievements, probably more so than Pettitte.

In many ways, the situation with Pettitte and Buehrle mirrors that of NFL linebackers Ray Lewis and London Fletcher; both Lewis and Fletcher have very similar career stats, but the former is a surefire Hall of Famer, while the latter has more of an outside shot. Some have theorized that the reason for Lewis’s increased fame are twofold: first, that he came from a high-profile school (Miami) as a high-round draft pick (26th in the first round), as opposed to a low-profile school (John Carroll) as an undrafted free agent; obviously, since both Pettitte and Buehrle are both very low-round draft picks (22nd and 38th, respectively) from very low-profile schools (San Jacinto and Jefferson, respectively), this is obviously irrelevant.

And the second reason for Lewis being more popular than Fletcher? Well, this. In short, what Mr. Easterbrook’s theory states is that Lewis–and possibly, by connection, all similarly-inclined athletes–act the way they act in order to promote their own fame, and build up a case for the Hall of Fame. This could easily be applied to to Pettitte and Buehrle; the former is considerably more self-promoting, while the latter is much more willing to give his teammates credit.

So, while this may have been a largely pointless article, the main message remains clear–two pitchers are very similar in most respects, instead of their reputation, and that reputation may have a lasting effect on their immortality. Why are men judged by their reputations instead of their accomplishments? Now there’s a question worth answering.⁷


¹Remember, this was mainly accrued during the steroid era, when that level was (roughly) average.

²Please note that xFIP only goes back to 2002, and Buehrle’s and Pettitte’s careers (and their career ERAs cited above) go back to 2000 and 1995, respectively.

³If aggregate WAR values are more your thing: Buehrle has nearly 20 fewer career wins than Pettitte by fWAR (47.3 to 67.0) but is less than five wins worse than him by rWAR (54.0 to 58.5).

Twain was right.

⁵Of the 14 that are not, 8 are still eligible or have not yet become eligible: Pettitte, Greg Maddux, Roger Clemens, Tom Glavine, Randy Johnson, Mike Mussina, Jamie Moyer, and Jack Morris.

⁶I.e. one that makes attention whores out of the famous.

⁷Believe me when I say I did not intend that to sound as deep as it did.

In Defense of Striking Out: Ideal Strikeout Rates for Hitters

Strikeout rates have climbed since 2006, while league wOBA has dropped.  Responses to ballooning strikeout rates have been mixed. One response is to trade one of your best hitters, while another is to lead the MLB in home runs. Some clubs are more averse to strikeouts than others.

It’s no secret that Diamondbacks GM Kevin Towers hates strikeouts. Since taking over in 2010, Towers has discarded every Diamondbacks player who struck out 100 times or more from the 2010 club that set the major-league record for strikeouts in a season by striking out 24.7% of the time. His 2013 squad’s 18.5% strikeout rate is 10th-lowest in the majors. However, the decreased strikeout rate has not resulted in increased offense. The 2010 D-Backs scored 4.40 runs per game, posting a .325 wOBA and 93 wRC+, a shade better than that of the more contact-driven 2013 Diamondbacks who currently average 4.17 runs per game with a .313 wOBA and 92 wRC+. While the 2010 team had the 4th-best walk rate at 9.5%, the 2013 Diamondbacks are just 13th at 8.1%. Though the 2010 Diamondbacks struck out more, they also walked more, and made more quality contact, as shown by a .312 BABIP% and .166 ISO which were 2nd and 4th in the majors, respectively. The 2013 team has a .301 BABIP% and .135 ISO, good for 10th and 23rd in the majors. A look at the plate discipline numbers shows that the 2013 Diamondbacks swing at more pitches out of the strike zone and make more contact on those swings than the 2010 team.

2010 O-Swing% Z-Swing% Swing% O-Contact% Z-Contact% Contact% Zone% F-Strike% SwStr%
Diamondbacks 27.6% 64.7% 44.6% 57.9% 84.2% 75.4% 45.8% 58.5% 10.6%
2013 O-Swing% Z-Swing% Swing% O-Contact% Z-Contact% Contact% Zone% F-Strike% SwStr%
Diamondbacks 31.4% 64.8% 46.4% 68.6% 87.8% 80.6% 44.9% 59.9% 8.7%

If a hitter can cut his strikeout rate while maintaining his walk rate and power production, that is special. However, there is usually a tradeoff between power/walks and contact. After all, not everyone can be vintage Albert Pujols. To dig deeper into the balance between power and contact, I separated MLB hitters by strikeout percentage into five groups, with 30 hitters per group. I limited the study to qualified hitters, to eliminate the presence of pitchers and small sample size hitters. Not surprisingly, the first group was the clear leader in home run rate.

  19.7 7.9 2.6 0.313 0.296  
Group 1 K% BB% HR% wOBA BABIP% WAR Total PA
  27.2 8.7 4.4 0.336 0.305 61.7 13008
Group 2 K% BB% HR% wOBA BABIP% WAR Total PA
  20.7 8.6 2.5 0.337 0.323 65.9 12962
Group 3 K% BB% HR% wOBA BABIP% WAR Total PA
  17.1 8.1 3.0 0.342 0.313 68.9 13510
Group 4 K% BB% HR% wOBA BABIP% WAR Total PA
  14.3 8.5 2.4 0.342 0.313 71.5 13895
Group 5 K% BB% HR% wOBA BABIP% WAR Total PA
  10.4 7.0 2.1 0.317 0.284 51.9 13187

I included WAR even though it includes defensive and baserunning values because I thought that the contact-heavy hitters in group 5 might make up for their offensive deficiencies by being better defenders or baserunners. However, the total WAR for each group tracked offensive production for the most part. The first four groups are very close together with regards to wOBA. As I expected, the most strikeout-heavy group owned the highest walk and home run rates. Group 2 made up for its lower home run rate with a higher BABIP%. The rates of doubles were very close in all groups, ranging from 4.5% in group 5 to 5.2% in group 3. Group 5 had the lowest homerun and walk rates. Despite group 5’s ability to put the ball in play, the contact generated was of a lesser quality due to higher contact rates on pitches out of the zone. With the exception of Edwin Encarnacion, Adrian Beltre, and Buster Posey, none of the hitters in group 5 had more than 20 weighted runs above average (wRAA). The group average was 0.9 wRAA. Though group 5 had the lowest WAR of any group by a wide margin, they had the 3rd most plate appearances.

As the above table shows, there is not a significant negative connection between higher strikeout rates and offensive production. In fact, the most contact-heavy hitters are far less productive offensively than their more strikeout-prone counterparts. Of course, the plate approach of Chris Davis would not work for Marco Scutaro and vice versa. The idea of an ideal groundball rate for individual hitters has been posited. I would suggest that there is also an ideal strikeout rate for individual hitters. The following is a list of five hitters who I believe would benefit from a more or less contact-friendly approach.

Matt Holliday has trimmed his strikeout rate from 19.2% in 2012 to 14.4% this year. However, he has also trimmed his wRC+ from 141 to 137. His BABIP% is down from .337 to .312, but this is likely due to a less formidable batted ball profile, as his xBABIP% has dropped from .328 to .304. His Line Drive/Infield Fly ratio is down from 89/11 to 58/16. Furthermore, his home runs on contact has dropped from 5.7% to 4.8% and his overall homerun rate has dropped from 4.9% to 3.5%. His flyball distance has decreased from 305.15 to 294.66. A look at the PITCHf/x data shows that Holliday is swinging more and making more contact on those swings. His Swing% has jumped from 47.2 to 49.9 and his Contact% has gone from 78.5 to 81.8. His O-contact% has gone from 65.0 to 66.1 and his Z-contact from 86.1 to 89.0. While Holliday is striking out less while walking at the same rate, his swings have been noticeably less aggressive, and his overall offensive production is down.

Mike Moustakas has reduced his strikeouts even more than Matt Holliday, going from 20.2% in 2012 to 13.6% in 2012 while essentially maintaining his walk rate. However, his offensive production is down significantly, from 90 wRC+ to 79 wRC+. His home run rate has dropped from 3.3% to 2.6%, and his home runs on contact is a paltry 3.3% compared to 4.5% in 2012. His fly ball distance has dropped to 279.2 to 274.6. Moustakas’ increased contact rate has come largely from swings on balls outside of the zone, as he has seen as increase in O-Contact% from 63.7 to 74.3. During GM Dayton Moore’s tenure, the Royals have had an emphasis on putting the ball into play. Their 16.4 K% since 2007 is the lowest in the league over that time frame. However, they have only a 92 wRC+ over that span, good for 21st in the league and their BB% of 7.0 is dead last. While the Royals’ emphasis on contact appears to have helped Eric Hosmer, its application to Moustakas has had a negative impact on his production.

Adrian Gonzalez has undergone a significant change since being traded from the Padres. While playing in the spacious Petco Park Gonzalez posted home run rates between 3.8-5.9% and walk rates of 8.2-17.5%. His wRC+ numbers ranged from 123 to 156. His home run rate dipped to 3.8% in his first year at Fenway, his lowest since his first full season, but a still solid walk rate of 10.3% and a .380 BABIP% led him to an excellent 154 wRC+. Since then, his ability to draw walks and hit for power have plummeted. From 2012 to the present, Gonzalez has a 2.9 HR% and a 6.7 BB%. While Gonzalez has posted his three best contact rates since 2011, his O-Contact% has been between 70.1 and 75.9, well above his career rate of 67.1. Though Gonzalez has slightly improved his power production from 2012, his 126 wRC+ remains a far cry from his peak years. In Gonzalez’ best years, he had strikeout rates in the 17-20% range. He can still be a productive player, but the make-contact approach has taken away much of his power and walks.

Asdrubal Cabrera is posting career high strikeout and fly-ball rates in 2013. Unfortunately for him, this approach has not led to an increased power output, as his home runs on contact, average fly ball distance, and ISO are virtually unchanged from 2012. The 22.0% strikeout rate has conspired to cut his wRC+ from 113 to 91. In an effort to hit for more power, Cabrera’s contact rate has gone from 84.0% to 78.6%, a career-low figure, and his walk rate has dropped from 8.4% to 5.8%, also a career low. Though Cabrera’s BABIP%  has dropped from .303 to .286, his xBABIP% is up from .319 to .334, suggesting that he can be productive when he puts the ball in play. Not yet 28, it is time for the Indians shortstop to go back to the plate approach that made him a productive hitter in 2009-12, controlling the strike zone with a more level swing. In picture form, here is a swing from 2011 when Cabrera had a K% of 17.8 and a 119 wRC+.

 Yoenis Cespedes has improved his home runs on contact from 5.9% in 2012 to 6.4% in 2013. However, because of the jump in his strikeout rate from 18.9% to 23.9% his overall home-run rate remains at 4.3% and his ISO is basically the same. His wRC+ is only 96, compared to 136 in his debut season. Cespedes is hitting more fly balls at 47.7% compared to 39.9%, and their average distance is the same, but those fly balls have come at the expense of line drives and ground balls, which has caused his xBABIP% to sink from .305 to .279 and his actual BABIP% to go from .326 to .255. Because Cespedes is relatively new to the league, I wanted to see if pitchers are attacking him differently. However, Cespedes has been pitched to in largely the same fashion as 2012, but with slightly more fastballs and less changeups. Cespedes has been less able to hit those fastballs, as he is only 0.37 runs above average per 100 fastballs, compared to 1.71 last year. Cespedes has been seeing slightly more pitches out of the zone, as his Zone% has decreased from 46.2% to 45.1%, but his O-Zone Swing% is mostly the same. For the most part, Cespedes has been getting beat in the strike zone, as his Z-Contact% down from 84.2% to 81.0%. Because Cespedes’ raw power and athleticism are so impressive, there is a temptation to be overaggressive at the plate. He will likely always be an aggressive hitter, but if he can cut his strikeout rate to his 2012 level, it will be worth the decrease in home runs on contact.

Unlike many people, I do not think that strikeouts are inherently bad. For some hitters, the increased strikeouts are the cost of home runs and walks. Other hitters would be well served to put more balls in play while suffering a loss of power. However, start implementing a one-size fits all approach of strikeout avoidance and you’ll end up like the Royals.

CarGo and the Value of Plate Discipline

Note: I have no idea if I’m the first to do this, but quite frankly I don’t care.

As you’ve probably grown sick of hearing¹, Brewers center fielder Carlos Gomez is undeterred by his team’s general shittiness and is having a terrific season–his 5.7 WAR² is a very close third in the NL, and one of the players he’s behind may be out for a while. While he’s always been an excellent defender (50.8 career UZR prior to this season), his bat has never been particularly good (his career-best wRC+ prior to this season was 105, last year).

A massive improvement on offense has been the driving force behind his MVP-type numbers, as his wRC+ of 133 this year sits at 16th in the NL; this can be attributed to an increase in power (.235 ISO, compared to .150 for his career) and an uptick in BABIP (.350, compared to .311 for his career). Many of the articles listed in the first footnote cite these as reasons behind his improvement. One element of his game that has not improved, however, and is getting startlingly little coverage from the media, is his plate discipline; his walk and strikeout rates sit at 6% and 25.1%, respectively, meaning his BB/K of 0.24 is 6th-worst in the NL.

Now, should Gomez end up leading the league³ in WAR with that kind of plate discipline, how revolutionary would that be? I decided to find out. I looked up every NL WAR leader going back to 1910 (when strikeouts for batters⁴ were first recorded) and recorded their strikeouts and walks, then calculated each batter’s K/BB⁵ and ranked them from lowest to highest; the top 10 are listed below.

2013 Carlos Gomez* 144 34 4.24
1988 Andy Van Slyke 126 57 2.21
2011 Matt Kemp 159 74 2.15
2012 Ryan Braun 128 63 2.03
1984 Ryne Sandberg 101 52 1.94
1971 Willie Stargell 154 83 1.86
2005 Andruw Jones 112 64 1.75
1970 Tony Perez 134 83 1.61
1978 Dave Parker 92 57 1.61
1941 Pete Reiser 71 46 1.54
*ZiPS Projection

The average K/BB was 0.85, meaning Gomez’s⁶ is nearly 400% worse.

Now, any fan of baseball–sabermetrically inclined or otherwise–knows that this year (and in recent years), plate discipline has been at an all-time low. Knowing this, I decided to measure each player differently. I gathered up all the league-average K/BB’s for every year going back to 1910, then divided each WAR leader’s K/BB by the league-average K/BB for the respective year, and created K/BB-, in the style of ERA-. I then ranked each batter’s K/BB- from highest to lowest (i.e. worst to best); the top 10 are listed below.

Year NL K/BB lgK/BB K/BB-
2013 Carlos Gomez* 4.24 2.51 169
1941 Pete Reiser 1.54 0.99 156
1988 Andy Van Slyke 2.21 1.8 123
1984 Ryne Sandberg 1.94 1.69 115
1937 Medwick 1.22 1.07 114
1971 Willie Stargell 1.86 1.67 111
1978 Dave Parker 1.61 1.48 109
1970 Tony Perez 1.61 1.63 99
1926 Hack Wilson 0.88 0.89 99
2011 Matt Kemp 2.15 2.3 93
*ZiPS Projection

The average K/BB- was 58, meaning Gomez’s was almost 200% worse.

The closest match to Gomez’s season (at least in terms of plate discipline) was Pete Reiser in 1941. That year, his K/BB was a very solid (by our standards) 1.54, but the league-average was below 1, meaning he was actually pretty bad by league-adjusted standards.⁷

Even when we adjust for the era, Gomez’s plate discipline is historically bad. People may argue about the value of plate discipline to a hitter, but you can’t dispute the facts: the average K/BB for a WAR leader is 42% better than league-average, and Gomez’s is 69% worse than league average, and yet he is contending for the WAR lead.

So, what does this mean? Obviously, as I mentioned in the introduction, a large part of Gomez’s value comes from his defense, and thus his offense is probably behind that of many others on this list. Gomez’s season has come out of nowhere, at least to some degree, meaning that it may be a fluke; for that to be determined, we’ll just have to wait and see. Though Brewers fan may be discouraged to hear it, history suggests it probably is.


¹You know, from here, and here, and here, and here, and here, and here, and here. Also, I’ve now started doing footnotes a la Grantland, although there isn’t any linking yet.

²All stats are as of Tuesday, August 13th, in case this takes some time to get published.

³I’m really getting sick of people using “the league” to refer to MLB as a whole; it’s misleading and it’s wrong. This isn’t the NFL–there are two leagues, not one. When you’re referring to MLB, say “the majors”, not “the league”.

⁴Strikeouts for pitchers go back all the way to 1876 (i.e. when all pitcher stats go back to). Why’d it take 34 years to record strikeouts for batters?

⁵I’ve always hated BB/K–it returns numbers that are much too minuscule. I prefer the larger form of K/BB.

⁶Is that correct, or should there be no “s”?

⁷Reiser’s success that year–166 wRC+–was mainly motivated by a .377 BABIP, 97 points higher than the MLB average that year, and by far the highest of his career.

The Most Predictable Hitters of 2013

I was watching the Twins game a few weeks ago when veteran Jamey Carroll effortlessly took an outside pitch to right field, as one might hope he would. The announcers were quick to praise his ability to “go with the pitch”. I’ve seen this play out time after time, often followed by praise for “going with the pitch” and “not trying to do too much”. That got me thinking, do some hitters go with the pitch better than others? Is this a desirable skill or does it leave the hitter vulnerable? Can a defense exploit this trait with a defensive shift much like we see shifts on straight pull hitters?

To dive into this I captured the angle of each hit ball since 2010 and displaced that against the angle that I expected the pitch to be hit. For example, an inside pitch on a right-handed batter could be expected to be hit near the left field line, while an outside pitch could be expected to be hit near the right field line. Everything in between would be evenly spread across the field, relative to the pitch’s location across the plate.

To make it a little more accurate for right-handed hitters vs left-handed hitters, I analyzed the actual pitch placement for pitches that become hit balls. As you can see below, all hitters prefer the ball just a touch on the outside part of the plate. I took two standard deviations of the hit pitches and considered that the spectrum that we’ll map to the field, with unique values for right or left handed hitters. We’ll call this our hit zone.

The players that made it to the top of the data below are the ones that tend to go with the pitch. That is, they take the outside pitch to the opposite field, they pull an inside pitch, and they take a pitch down the middle of the plate straight through center field. They are less random and more predictable.

With that, here are the most predictable hitters of 2013 through August 10th.


Average Absolute Angle Difference

Mean Angle Difference (Pull Tendency)

Standard Deviation

Hit Balls

Melky Cabrera





Pete Kozma





Marco Scutaro





Everth Cabrera





Chris Stewart





Jamey Carroll





Martin Prado





Elvis Andrus





Lorenzo Cain






For comparison sake, here are the 10 least predictable hitters.


Average Absolute Angle Difference

Mean Angle Difference (Pull Tendency)

Standard Deviation

Hit Balls

Carlos Santana





Howie Kendrick





Juan Francisco





Yasiel Puig





Jimmy Rollins





Ryan Flaherty





Pedro Alvarez





Ryan Howard





Chris Young





Chris Heisey





Let’s explain this data before we go any further.

First off, the field is 90 degrees and thus, the values are all in degrees.

  • Average Absolute Angle Difference: If a pitch was on the inside of the plate on a right-handed hitter, and was determined it would be “properly” hit somewhere near the left field line, but was actually hit 20 degrees to the right of that expected spot, this number shows that difference, averaged across all hit balls.
  • Mean Angle Difference: Some balls are pulled against their expected spot, others are not. Pulled balls show up as a positive angle (for both L or R hitters), while negative angles indicate the batter was behind the pitch. The Average Absolute Angle Difference does care either way, while this metric does. A higher positive value here indicates a pull tendency while a negative value indicates that a batter is more often than not behind the pitch. Those batters with a higher value from 0 indicate they could be a little more predictable to pull or push.
  • Standard Deviation: This should give you an indicator as to what kind of angle you could expect ⅔ of a batter’s hit balls to be where you expect them to be. For example, Chris Stewart has a standard deviation of 21.97 degrees. Given a very outside pitch that you’d expect to be hit down the right field line, you can expect that Stewart will usually hit that ball down the line or at most 20 degrees to the left or foul.

Looking at the data back to 2010 I found these players continually near the top. It seems for them, they have always hit this way, and can be expected to continue to hit this way.

  • Marco Scutaro
  • Ryan Hanigan
  • Jamey Carroll
  • Denard Span
  • Elvis Andrus

Now, what can we do with this knowledge? Can a defense use the left-handed shift on a right-handed hitter? To look at this we’ll look at spray charts, but with a very important distinction from a standard spray chart – we’ll limit the hit balls to those hit on pitches on the outside of the hit zone.

I’ll start you off with a spray chart for someone not on our list – Jose Bautista. This chart shows where he hits outside pitches. He looks like a good spray hitter when you look at only the outside pitches. As a defense, you wouldn’t shift on Bautista AND pitch him outside.

Let’s move on to someone who was continually at the top of our list, Marco Scutaro. You’ll see Scutaro reliably hits balls on the outer third of the hit zone to the right side. He still hits a fair number of ground balls across the infield, so an infield shift wouldn’t be advised. But liners and fly balls in the outfield are heavily weighted to the right. Using a control pitcher, pitching on the outside ⅓ of the hit zone, you could reliably shade the outfield to right field.

The same applies for Jamey Carroll, another player who, like Scutaro, shows up on our list year after year.

I’ve found that the tendency of pushing the ball on outside pitches to be much more predictable with our leaders than pulling the ball on an inside pitch. There’s surely more to be gleaned from this data, but the outfield shift on these predictable push hitters is definitely the most interesting.

Data Collection & Mining Techniques
The metrics for all hitters, year-by-year back to 2010 can be found here:

All of the data used in this post was loaded from MLB’s gameday servers into a MongoDB database using my atbat-mongodb project. This project is open source code that anybody can use, modify, contribute to, etc. Fork me please!

The following programs were used to mine and plot the data from the mlbatbat MongoDB database.

The Tale of Two Drews

The Red Sox employed outfielder J.D. Drew from 2007-2011 and signed his brother, Stephen, to a one-year contract prior to the 2013 campaign. The Drews are ballplayers who go about their business in similar ways — they’d prefer to avoid the limelight and just hit the baseball. It’s an admirable quality, but not one that’s so cooperative with the Boston media or fans. For some inexplicable reason, Boston is enamored with players whose highs are raucous and whose lows are dismal. This was never the case with J.D., and doesn’t appear to be the case with Stephen, but the numbers say that they’re some of the best Sox contributors in recent history.

The Background

J.D. and Stephen were high profile prospects in their respective draft classes and both went to Florida State University.* Prior to signing with the Sox, the two had established themselves in the National League. Both brothers, however, followed completely different paths to their contracts with the Boston Red Sox. In 2007, the Sox signed J.D. at the pinnacle of his career to a 5-year, $70 million contract. Stephen signed a low-risk, high-reward deal with the Sox for 1-year at $10 million prior to 2013. He’s the shortstop for now — Xander Bogaerts is the future. Boston fans can’t help but notice the similarities between the two brothers, which extends beyond the striking resemblance to one another and the shared uniform number (#7). Stephen plays the game much same way as J.D. did, with a smooth and dispassionate style that makes hitting and fielding a baseball seem as simple as driving a tractor (because this is all I like to imagine J.D. does now that he’s stepped away from the game). The two have nearly identical left-handed swings and are known around baseball to share one elite quality: their approach to an at-bat and their knowledge of the strike-zone.

Batter’s Eye

J.D. Drew was heralded as one of the most disciplined hitters in baseball when he signed with the Red Sox in 2007. This means he had an excellent understanding of the strike-zone and had the ability to take close pitches for balls to reach base. Less was known about Stephen when he arrived in Boston, as he was a lower-profile signing. But after his first 84 games, it’s clear that he possesses the same skill. The skill can be quantified by using a PITCHf/x statistic called O-Swing%. The stat measures the percentage of pitches a batter swings at outside the strike-zone. If you need more info on O-Swing%, FanGraphs has a good summary. But suffice it to say that the lower a hitter’s O-Swing%, the better handle he has on the strike-zone (there are a few exceptions; for example, Miguel Cabrera does not see very many pitches in the zone, but is still skilled enough to square up balls that are off the plate. He has one of the highest O-Swing% in the MLB). I’ve plotted BB% (a hitter’s rate of drawing walks) vs. O-Swing% for each hitter with at least 300 plate appearances this season and super-imposed J.D.’s numbers he racked up with the Sox (2007-2011):


We can make a couple of observations. First off, BB% clearly trends with O-Swing% — this makes sense: those who swing less often at pitches outside the zone are more likely to walk. Second, we see that Stephen possesses the same plate discipline as J.D., ranking around the 15th percentile in O-Swing%. In fact, both brothers’ BB% is slightly higher than we might expect based on the linear regression (i.e. the data points lie above the trend line). Finally, we notice that if J.D. played in 2013, he would lead the league in O-Swing%. That’s right: J.D. Drew would have the best eye in Major League Baseball if he strapped on the spikes and decided to have another go. Players who are more likely to walk (i.e. who have a high BB%) are more likely to have a higher OBP, one of the fundamental stats for determining a player’s value. It’s not difficult to see why the Drews got the big bucks from Boston.

Fans (including myself) were under the assumption that if you have a great eye, you strike out less. This is not such a ridiculous proposition: if you have an elite knowledge of the strike-zone, then surely you should utilize it with two strikes. But a simple plot of K% (the rate at which a hitter strikes out) vs. O-Swing% demonstrates otherwise:


A blob. The two statistics are not correlated in the slightest. To Sox fans, it seemed that J.D. Drew often took the third strike with the bat on his shoulder — the “Master of the Backwards K”. Since Sox fans knew he had a great eye, it seemed as though this happened at an alarming rate, as the expectation was that a lower O-Swing% should also lead to a lower K%. The two stats are not correlated and Drew did not strike out at an alarming rate at all — if he decided to step into the batter’s box in 2013 he’d be right around the league average in K%. Because J.D.’s eye was touted (for good reason) as one of the best in the league, many fans unfairly jumped to conclusions about how often he should strike out. Also, if we take a look at where Stephen lies in the data spread, we see that he strikes out at a much greater rate than his brother, but seems to take less heat from Red Sox Nation. This might be because Sox fans love players with a flair for the dramatic — something Stephen has shown he possesses whereas J.D. never did.

The “Anti-Clutch”

The biggest hit I remember from J.D. Drew was a grand slam in Game 6 of the 2007 ALCS, which turned the tide of the series. As for walk-offs, I remember one biggie: a line-drive over the head of the right-fielder in Game 5 of the 2008 ALCS against the Rays to cap a massive Sox comeback. Gordon Edes of ESPNBoston reminds us that there was, in fact, one more, but goes on to summarize J.D.’s reputation brilliantly: “Mr. Excitement, he was not.”

“The Anti-Clutch” was the nickname bestowed to J.D. Drew by my dad, who was often frustrated with his performance in tight spots. But my dad’s a stubborn guy and may have been swayed by one strikeout (he also championed the nickname “Master of the Backwards-K”). Certainly he hasn’t done a fair analysis of the relevant statistics, so I’ll do it here. CLUTCH is a complicated statistic that attempts to quantify a player’s performance in high-pressure situations. It utilizes WPA (win probability added) and LI (leverage index, a measure of just how “high pressure” the situation truly is) and normalizes the league-average player to zero. You can read more about CLUTCH here, but the number generally ranges from -1 to 1. Thus, a player with a positive CLUTCH can be considered just that (clutch) but a player with a negative CLUTCH often chokes in the tight spots. So how did J.D.’s numbers look during his time in Boston?


Yikes. That’s all there really is to say about that, except for it likely validates the opinion of Dr. D’Andrea. For reference, Stephen Drew’s CLUTCH is 0.64 during his first season in Boston, which checks in at well-above average. Nonetheless, J.D. Drew has had a tremendous, all-star career, similar to the likes of Eric Davis, Raul Mondesi, and Kirk Gibson.

Stephen’s Trend

Jose Iglesias started the season as the Red Sox shortstop when Drew missed much of spring training due to a concussion. When Drew returned, Iglesias was optioned to Pawtucket, but was recalled when Stephen missed time in July with a hamstring injury. Iglesias was traded to the Tigers in the deal that brought Jake Peavy to the Sox, clearing the way for Drew to re-assume the everyday job on the left side of second base. Drew’s season trend, especially as it pertains to his batting average, was likely a main reason why GM Ben Cherington felt comfortable giving up Iglesias, a defensive wizard:

Stephen Drew

While Drew’s not even half the fielder that Iglesias is, he has the potential to carry a team for weeks at a time with his bat. Fitting his season trend to a third-degree polynomial (this is not a “random” choice — he has clearly had two critical points over the course of the year), we can see that Drew is heating up as the season turns to August. In the best-case scenario (the one in which Drew continues or surpasses his current surge), he could be hitting .300 by September 1st. In a more realistic scenario, Drew will continue his current hot streak, and then regress to his career average of .264 by the time September rolls around. In any case, the remainder of the season is looking promising for the Red Sox shortstop, which is a good sign for a team that’s in desperate need of production from the position. In the wake of the Peavy deal, my favorite Globe writer Chad Finn had this to say about the brothers: “And yes, I’m kind of chuckling at the thought that the unfairly maligned Stephen Drew is still here while Iglesias has moved on. The Drews, they’re survivors, man.”

*J.D. Drew was drafted by the Phillies second overall in 1997, but failed to sign a contract. He and agent Scott Boras demanded $10 million whereas the Phils were only willing to offer $2.6 million. He played with an independent league team for one year, then was drafted fifth overall by the Cardinals in 1998, signing for $7 million. Phillies fans booed him for the entirety of his career.

Vince D’Andrea is a rising senior at the Massachusetts Institute of Technology. He is an avid Red Sox fan and his blog, Dave Roberts’ Dive, can be found here.

Yasiel Puig’s Batting Title

I think one of the most fun parts of baseball is this part of the year; as we wind down, you can start to root for unlikely things to happen. For example, I’m kind of hoping the Pirates manage to lose at an .800+ clip and keep their sub-.500 streak alive. I’d love to see the Royals make the playoffs. Finally, I’d love to see Yasiel Puig win the NL batting title.

The rules of the game are that you have to have 502 plate appearances to win a batting title. If you’re short, you’re given an 0-fer for the rest. So if Puig finished with 492 PAs, he’d take an 0-for-10 for the purposes of the batting title. Right now, Puig is projected by STEAMER to finish the year with 435 PAs. We’ll accept that number for now, but given that number, let’s think about how likely it is that he has a high enough batting average to win the title.

The first step is to figure out the mark he needs. Let’s go with STEAMER again, and we see Michael Cuddyer, Joey Votto, Yadier Molina, and Chris Johnson all projected to finish at about .320. Let’s assume that one of those four players finishes right at his 87.5% projection (the middle of the highest quartile)…I’ll say Joey Votto, who is projected to go .302 for the rest of the year (the highest of the bunch). Using the binomial distribution, there’s a 16.2% chance Votto finishes 51/149 or better given his “true” .302 batting average. We’ll say that that is the target Puig has to reach: Votto (or one of the others) adds something like 51/149 to his current stats, for a .329 batting average.

What are the chances Puig reaches that clip? To keep it simple, let’s assume STEAMER is right on the number of PAs, ABs, and Puig’s true chance of getting a hit, and then figure out Puig’s chance of getting enough hits to finish at .329 or better. He’s going to end the year with 435 PAs and 390 ABs, if he keeps up his current pace. To that, add an 0-for-67 to get him up to 502 PAs. So he needs enough hits to have a .329 batting average in 457 ABs. That number is 150. He currently has 85 hits in 224 ABs, so for the rest of the year he needs 65 hits in 166 ABs.

Given that STEAMER projects a .293 batting average for the rest of the year, it’s pretty unlikely that he’ll hit at a .392 clip. In fact, his chances of doing so are only about 0.4%, using the binomial model.

What could help his chances? First, there’s no guarantee Votto/Johnson/Molina will get hot enough to make the mark .329. If we drop the required average to .320, using the same method as above, he’d only need 146 hits, which raises his chance to about 2.3%.

Another possibility is that he’s a better hitter than STEAMER projects. If he only regresses to .310, which would make him one of the better hitters in the league admittedly, he has about a 1.6% chance of winning the batting title. And if he is truly a .310 hitter, AND none of the other players near the top of the leaderboard stay hot enough to beat .320, Puig has a whopping 6.6% chance of winning the batting title.

Yeah, I know batting average is stupid. And I know this is a minuscule chance. But isn’t it amazing that Puig has a chance to do something like this at all, after making his debut in June? Baseball!