One of the perks of traveling for work–I’ve been doing a lot of that lately–is the USA Today planted in front of your hotel room door. Sure, for the most part, McPaper’s articles are about as substantive as the “continental breakfast” you’re likely to eat while reading it–but now and then, in its own glossy, Technicolor way, USA Today stumbles across something significant.
Last Wednesday’s sports page featured a headline on leadoff hitters–it seems that there aren’t very many good ones these days. As the article pointed out, none of the league’s leadoff hitters are among the top 30 players in OBP. Among qualified players, the highest-ranking leadoff hitter is Ichiro Suzuki, 39th as of this writing (Jason Kendall, who has occupied the leadoff spot in Pittsburgh since the departure of Kenny Lofton, ranks 31st). And it’s not as if Suzuki or Kendall are walking machines in the mold of Rickey Henderson–Ichiro is a fine player who can hit .340 consistently, but his walk rate is well below league average, while Kendall’s OBP is boosted in part by his fearless desire to lean into pitches.
Then again, players of the Rickey/Tim Raines profile have never been terribly common. It also doesn’t help when teams insist on placing mediocrities like Eric Young or Endy Chavez in the one-hole. Is anything going on here, apart from a one-year fluke?
OK, so it might not have been the most controversial thing he’s said this month–even our intrepid Derek Zumsteg didn’t dare sweat out this Dusty Baker gem. But the Cubbie manager also made the claim that older players fare better in the second half.
Dusty’s claim has at least some grounding in his own experience–under his management, the veteran-laden Giants were markedly better in the second half in both 2002 and 2000, and marginally better in 2001. (Over the course of his entire tenure, the record is far more ambiguous: in Dusty’s 10 seasons at the helm, the Giants played .535 ball before the first of July, and .546 after it). While the Cubs’ second half didn’t get off to a great start with the injuries to Corey Patterson and Mark Prior, it’d sure be nice to see them still in the race come September. The acquisitions of Aramis Ramirez and Kenny Lofton have the Wrigley faithful in a frenzy; will Baker prove to be a sage or a charlatan?
Not to ruin the fun or anything, but this is a testable claim. By comparing the first and second half performances of players of various ages, we can see which ones really perform best down the stretch.
Watch SportsCenter this time of year, or read the Sunday baseball page–that’s the one with the long list of players sorted by their batting averages–and you’re sure to see plenty of stories about what a wonderful, surprising baseball season this has been. Why, who would have thought that Dontrelle Willis would have been drawing Mark Fidrych comparisons, that the Royals would be 10 games over sea level at the Break, that Melvin Mora would be an MVP candidate, that Esteban Loaiza would be the best arm in the American League? Perhaps there’s some Joe Namath among you, some Nostradamus, some Miss Cleo, but we certainly didn’t.
Nate Silver plays cartographer in this edition of Lies, Damned Lies, in search of untapped sources of amateur talent in the U.S.
Through Sunday night’s game in Anaheim, the Dodgers had scored an average of 3.46 runs per game, the lowest total in the league. Thing is, they’re allowing even fewer runs–only 3.03 per game. It’s an odd formula, as if concocted from the lovechild of Whitey Herzog and Hal Lanier, but for the most part, it’s been working.
Has the Dodgers performance thus far been historically significant? You bet your Lasorda. Since the end of the deadball era, no team has turned in a performance so out of line with the rest of the league. In the table below, I’ve listed those teams since 1920 whose runs scored plus runs allowed represented the lowest percentage of league average…
Nate Silver takes a closer look at replacement level in search of a better, zestier approach.
Baseball is full of bounces, and not just the path of a Jacque Jones double as it skips across the Metrodome turf (or a Carlos Martinez homer as it skips off Jose Canseco’s head). Rather, teams can expect a bounce in attendance when they move into a new facility, facilitating a higher payroll, a more competitive club, and ultimately, it is hoped, a couple of pennants to hang on the outfield wall.
Or at least, once upon a time, they could have. The standing-room-only precedent established in places like Toronto and Baltimore and Cleveland no longer seems to hold. Attendance in Detroit, Milwaukee, and Pittsburgh has already regressed to the levels those teams had grown accustomed to prior to the opening of their new stadiums. Attendance in Cincinnati is up, but only barely–and this with reasonable ticket prices and a fun team on the field. Nobody expects the honeymoon to last forever, but the reinvigorated relationships between ballpark and city that the new stadiums were supposed to engender have lasted shorter than a Liz Taylor nuptial.
Since the debut of SkyDome in 1989, 13 of the 26 teams in existence at that time have opened new parks. Two more will open new facilities next year. It has been the longest sustained period of new stadium construction in baseball history. Call them mallparks or, as I prefer, Retroplexes. Either way, there’s plenty of evidence that the ball isn’t bouncing quite as highly these days.
Don’t tell anyone, but I really enjoy watching Randall Simon hit. The loose, goofy motion in his stance as the ball approaches the plate; the flyswatter swing; the big-stepping follow through, his blubber, after half a second in gelatin-like suspension, mimicking the motion of his bat. It’s a lot of fun to watch, especially when Simon manages to make contact, which happens more often than you’d ever expect.
I’ve had the occasion, however, to watch Simon against Kerry Wood a couple of times this year, and from Randall’s point of view, the results have been disastrous: zero-for-six with four strikeouts. Not just any kind of strikeouts, mind you, but ugly, pirouetting, breeze-generating, no-chance-in-hell strikeouts, the sort that make you think that Simon could face Wood 500 times and go oh-fer.
I didn’t mind this, really; Wood is one of my favorite pitchers. But this particular matchup was interesting to watch because Simon and Wood are such an odd couple: Simon swings at everything, and never draws any walks, but by virtue of his superior hand-eye coordination, manages to keep his strikeout rate very low. Wood, on the other hand, is one of the toughest pitchers in the league to make contact against–though sometimes that’s because he isn’t throwing the ball anywhere near the strike zone. In any event, Simon’s performance against Wood looked so bad than I began to wonder whether the batter isn’t at some sort of systematic disadvantage in pairings of these types of players.
To study the question, I’ll leverage from a technique that Gary Huckabay and I introduced last month in a 6-4-3 column, comparing the actual performance observed when certain types of batter-pitcher pairings occur against the results predicted by Bill James’ log5 formula. Instead of dividing players up based on groundball and flyball rates, this time we’ll look at a quick-and-dirty index of plate discipline.
Statheads…often lament the intentional walk with an argument that usually goes like this: With a runner on third and one out, the expected runs scored for the inning are X. With the bases loaded and one out, that number is Y (higher than X). This argument normally makes sense, but in a situation where one run is all that matters, the manager should instead try to maximize the probability that no runs will score…Does walking the bases loaded with one out make sense on this basis?
As D.H. points out, the only thing that each manager need concern himself with is whether that one essential run scores. All the strategic elements of the game–hitting, baserunning, pitching, defense–are profoundly different under these conditions. What’s a manager to do?
In last week’s Lies, Damned Lies, I reviewed Adam Dunn’s major league career one plate appearance at a time, in order to determine how his performance changed when facing the same pitcher multiple times. For those of you who, like me, did some damage to your short-term memory over the long weekend, the idea was to discover whether, per Michael Lewis’ discussion in Moneyball, Dunn is a hitter with a hole in his swing that gets continually more exploited in repeated trials.
In Dunn’s case, the answer was a tentative “no”, but a lot of people mailed me to ask that I broaden the scope of the analysis. As D.H. writes:
“I like your research, but my problem is that you’ve presented no baseline. It
reminded me of a STATS Baseball Scoreboard article on whether Greg Maddux did
better the more times he faced a particular batter because he’s so “smart.” The
data showed that the hitters improved as time went on. But, like in your study,
there was no baseline to compare against. Adam Dunn may show a drop-off the
more he faces a particular pitcher, but maybe all players exhibit identical
drops. Or, maybe all players exhibit more precipitous drops, and only the good
ones (like Dunn) stick around because they only lose 20% of their value.”
In other words, is there any systematic advantage to the pitcher or the hitter given repeated trials? Doesn’t seem likely, I wrote back, not if the league is going to remain at some kind of equilibrium for very long. But D.H. is correct that it’s a question that deserves further study, much like why on Earth I didn’t wear sunscreen to the ballgame on Sunday.
As I mentioned in the Dunn piece, there is publicly available play-by-play data for each season from 2000-2002. In order to make sure that the players we’re working with formed a closed system, I limited the analysis to players who made their major league debuts in 2000 or later. It was then possible to look at all possible ‘pairings’ of the batters and pitchers within this group–what happens when Billy Batter faces Pete Pitcher for the first time? For the fifth time? For the 20th time, after Bill Batter has dropped the -y from his name and grown a mustache, and Pete Pitcher is discovered to be three years older than listed and actually named Pedro Pichardo?
There’s an awful lot of stuff in baseball analysis that’s just a complete waste of time. Some people love doing studies that take a look at something either esoteric, rare, or with no potential practical application when it comes to the actual game of baseball. That’s great; there’s nothing wrong with those kinds of diversions. We’ve all got those kinds of activities in our lives. But in terms of practical application on a real life baseball team, a “sabermetric” biography of the 1952 Yankees isn’t particularly useful. That sort of stuff has never spun my wheels, and it’s one reason I tend to yell and scream at BP writers who mention ballplayers from before Kristy Swanson was born.
Historians and fans of sepia tones will undoubtedly pipe in with: “Of course you can learn something from history!” (Derisively insert sound of adults in Charlie Brown cartoons here.) No one’s saying that’s not the case. But we prefer to focus on ideas that actually have practical applications on the field, and can directly and visibly translate into more wins, which means more championships, more money, etc. We’ve taken a fair amount of flak over the years for not making more things public, and not fully embracing an academic model for the serious study of baseball. Some of the criticism is well-deserved, some of it’s simply a disagreement over what people in the field are really doing. We like the idea of innovating to gain a competitive advantage and beat the snot out of opponents, rather than having the material published in some peer-reviewed journal.
When Rany Jazayerli came back from a Pizza Feed a few weeks back and mentioned that he had talked to a couple of front office guys about a different kind of platoon, my chin hit the virtual floor. The idea he had mentioned, and which was apparently perceived as novel, was at least 20 years old, and Gary Huckabay had been approached about studying the idea by a major league club back in 1998. (Even more surprising is that the club that wanted this issue studied is not largely perceived as a progressive organization.) This supposedly novel idea had also been mentioned in one of the old Elias Analysts, but was never really fleshed out in those pages.
What kind of platoon are we talking about? Using the groundball/flyball tendencies of pitchers and hitters to determine and acquire the most favorable possible matchups.
Holes isn’t just the movie you see begrudgingly upon discovering that The Matrix Reloaded is sold out on all 17 screens at the Springfield GooglePlex. No, “holes” are also one of the big concepts in Michael Lewis’ Moneyball, and not just as a part of Billy Beane’s vernacular. Rather, Lewis contends that every hitter (excepting Scott Hatteberg, Pickin’ Machine) has a hole in his swing, and that the hole will inevitably be discovered and exploited in repeated trials. Unless the hitter is able to make adaptations of his own–retooling his swing, standing in a different place in the batter’s box, taking more pitches–the hitter will not be able to survive in the big leagues for long, and will join Kevin Maas and Joe Charboneau in baseball purgatory.
It’s a nice concept. Game theory hasn’t been this sexy since Russell Crowe played the genius/lunatic somewhat resembling Princeton scholar John Nash in A Beautiful Mind. But is it real? Can it be tested? Does it hold its sabermetric water?
Let’s use Reds slugger Adam Dunn as a test case.
Your favorite player hit .360 last season. If you know nothing else, what can you expect him to hit this season? This isn’t meant to be a trick question; let’s assume the guy had at least 500 at bats in the previous season. Gates Brown and Shane Spencer need not apply. What’s your best guess? .350? .340? Not likely. The evidence is overwhelming. Let’s look at all hitters since WWII who hit .350 or better in at least 500 at bats; the only other requirement is that they had at least 250 at bats in the year following.
It doesn’t matter whether your game is roto, Strat, Scoresheet, or fantasy NASCAR: Drafting for value is the right way to go. Cute little strategies might help to break a tie, and a mastery of bidding psychology can matter at the margins, but sound player evaluation is the name of the game. Between the PECOTA projections and the Will Carroll Walking Injury Database, we felt that Team BP was in an in ideal situation to leverage our edge in information into success in Tout Wars. The results so far have been affirming: in spite of some disappointing individual performances, we’re in first place by a healthy margin.
It’s too soon, of course, to come to any conclusions about how the standings will end up–hell, it’s early enough in the season that Carl Everett hasn’t even been suspended yet. Still, there are a few take-home lessons from the season thus far, as embodied by some of our more successful acquisitions and strategies.
The baseball season has reached its adolescence. Oh sure, there are the still the occasional temper tantrums, the delusions of grandeur, the fashion faux pas. But the season has been around for long enough that we can’t totally dismiss it, even when it mouths off without reason or, convinced of its own invincibility, it pushes its limits a bit too far.
The PECOTA system wasn’t originally designed to update its forecasts in real time, but through some creative mathematics we can adapt it to that purpose. In particular, we can evaluate its projections by means of a something called a binomial distribution (geek alert: if you’re uninterested in the math here, the proper sequence of keystrokes is Alt+E+F+”Blalock”). The binomial distribution is a way to test the probability that a particular outcome will result in a particular number of trials when we know the underlying probability of an event. For example, the probability of a “true” .300 hitter getting six or more hits in a sequence of 15 at bats is around 27.8 percent. (The binomial distribution’s cousin, the Poisson distribution, has a cooler name but is less mathematically robust).
A couple of important objections are going to be raised here. First, the binomial distribution is designed to test outcomes in cases in which there are mutually exclusive definitions of success and failure–for example, “hit” and “out,” or “Emmy Nomination” and “WB Network.” The measures of offensive performance that we tend to favor don’t readily meet that criterion. Second, the binomial distribution assumes that we know the intrinsic probability of an event occurring, as we would with a dice roll or coin flip. But we never really know what a baseball player’s underlying ability is–we’re left to make a best guess based on his results, presumably coming closer to the mark as the sample size increases.
The first problem has an intriguing, if mathematically sketchy solution in the form of Equivalent Average, which is scaled to take on roughly the same distribution as batting average, even though it accounts for all major components of offensive performance. So, we could test the probability of a “true” .300 EqA hitter putting up an EqA of .400 in 15 plate appearances by assuming that this is equivalent to six successes (40%) in 15 trials. Since I haven’t heard any objections, let’s roll with it.
You’ve been hanging ’round these parts long enough. You’ve heard the party line, once or twice or 20 times: Higher payrolls don’t result in higher ticket prices. Correlation is not causation. Salaries don’t shift demand curves. It’s Economics 101. Simple, textbook stuff.
The problem with this line of argument–the problem with a lot of economically-based arguments–is that it’s easy to let the theory get ahead of the data. Well, I should state that more precisely: It’s easy to let an oversimplified theory get ahead of the data. A lot of what you learn in Economics 102, and Economics 201, and graduate-level classes that I was too busy drinking Boone’s Farm to take advantage of, is that much of the theory you master in an intro-level class is based on a particular set of assumptions that can prove to be quite robust in certain cases, and utterly misleading in others.
A lot of people shun economics for this very reason–we’ve all had coffee shop conversations with the scruffy, Skynard-mangling philosophy major who is fond of spewing out faux-profundities about the irrationality of human nature. He’s missing the point, of course, but so too is the Ayn Rand-spouting prepster from down the residence hall who conflates assumptions with hard rules.
In either case, a little bit of knowledge is a dangerous thing. Economics, though it sometimes harbors pretensions to the contrary, is above all else a behavioral science, and an empirical science. If the theory doesn’t match the data–well, it’s not the data’s fault. This is especially important to keep in mind when evaluating something like ticket prices to baseball games, a commodity that is unusual in many ways. As we’ve stressed frequently, ticket prices ought to be almost wholly determined by demand-side behavior–the marginal cost of allowing another butt in the seats is negligible. But baseball tickets are unusual in other ways, too: They’re very much a luxury good, and their prices are determined by a finite number of decision-makers who may be subject to conflicts of interest. It’s certainly worth evaluating the available data to see whether we can put our money where our mouth is.