A few weeks ago, Sportvision hosted the 3rd Annual Pitchf/x Summit.  Sportvision is the company behind the Pitchf/x system and has initiated Fieldf/x, which I’ll get into in a minute.  The goal of the summit was to share some of the research being done in baseball analysis, while also serving to explain the possibilities that exist with the new system.  Without further ado, here were the presentations:

Using Velocity Components to Evaluate Pitch Effectiveness (Matt Lentzner/Mike Fast): The purpose of this study was to change the reference point by which Pitchf/x data are measured.  Often, fastballs show more movement than breaking balls, but without the proper frame of reference, it means nothing.  Mike and Matt were able to demonstrate how to determine the horizontal and vertical velocities with respect to the batter’s eye and make the Pitchf/x data more meaningful.

Pitchf/x Application in Player Development and Evaluation (Dr. Glenn “Butch” Schoenhals): Dr. Schoenhals has a Pitchf/x system set up at his instructional school, which allows pupils (including some major leaguers) to see the their pitches broken down immediately and make adjustments.  In conjunction with three cameras set up around the pitcher, the Pitchf/x data provide benefit to both pitchers and instructors in learning/teaching how to pitch.

Okajima’s Mystery Pitch (Matt Lentzner): Hideki Okajima throws a pitch roughly 20% of the time that had previously been classified as a curveball, more specifically a “rainbow curveball.”  Actually, it didn’t really fit any of the known pitch types.  Using his research on pitch types and arm slots (“The Pitching Peanut”), we see that this pitch has almost no break, is faster than a curveball but slower than a slider, and falls at the exact center of the peanut.  His explanation: Okajima is the Boston pitcher who is actually throwing the gyroball, not his more famous teammate Daisuke Matsuzaka.

Leaving the No-Spin Zone (Alan Nathan): Dr. Nathan showed his experiments that relate the spin of the baseball just before and just after it is hit. The result? The two are almost totally independent of each other! I couldn’t believe that, but Dr. Nathan made a lot of sense.  This was a high-grade physics lesson, crashed into about 20 minutes.  He explained why balls tend to curve toward the foul lines; he showed that the bat actually “grips” the ball for a few nanoseconds or so before the ball explodes off the bat, which contrasts the earlier model of the ball “rolling” off the bat.  Really, really cool.

Fieldf/x System Overview (Vidya Elangovan): And the main event began.  Fieldf/x is a new tracking system that utilizes cameras attached to the light standards in baseball stadiums (for now, just AT&T Park) to track the movement of every person on the field 15 TIMES A SECOND.  As soon as I heard that, my mind started going crazy and I don’t think I paid attention for about 5 minutes.  The only issue at the time is that the system does not include the ball (but it will).  All ball events currently have to be added by someone watching the video.  The following presentations showed some of the things you can actually do with the data, and it’s fairly obvious that these data, particularly when connected to batted ball data through the Hitf/x database, are about to revolutionize how baseball players are evaluated.

Infield Defense with Fieldf/x (John Walsh): Actually the first presentation, thanks to being in Italy, (tough life), but it really would have been more helpful after the overview.  Either way, a lot of cool stuff.  First thing he said was that in tracking the different players, he noticed that an average centerfielder runs 8 miles per game, which stunned me and kept my attention.  Thanks to these new data, we can also see the effects of shifts and also what players away from the ball are doing while teammates are attempting to make plays.  Other questions John poses: can we see infielders cheating in a certain direction as the pitchers throws the ball? Do infielders lean in a certain direction before the pitch? Based on his initial investigations, he saw that third basemen step toward the line as the pitch is delivered and shortstops step directly at home plate.  Weird, but potentially important, and just a peak into what can be obtained.

From Raw Data to Analytical Database (Peter Jensen): As a baseball nerd and a programming dork, this was really cool.  Peter Jensen took the 400,000 lines of code that results from each game and wrote a macro to display what actually happened in the game in an Excel worksheet.  The simulation relates the position of each player as well as an approximation of where the ball is throughout the play.  His solution with regards to the reorganization of the data was very impressive for a first run, and it is absolutely vital to make the data useful for analysis.

Using Fieldf/x to Assess Fielders’ Routes to Fly Balls (Dave Allen): These next three were absolutely incredible to me (and I’m sure the last three would have joined them had I had the time to stay).  By using the data to reconstruct fielders’ routes to the ball, Allen surmises that the Fieldf/x data can be used to determine the speed of an outfielder as they pursue a ball, the starting points of each fielder at the time of the pitch (and hit), and how efficient each player is in getting to the ball (measuring the distance traveled against the shortest distance to the ball).  To me, this is something that teams can use to help players they already have by addressing alignment issues or noticing what is happening during the different points of pursuit.  Are outfielders getting good reads/jumps on the ball?  Are they running in straight lines or weaving?  Simply put, the data can confirm for us (and also measure exactly and more efficiently) what our eyes (and scouts’ eyes) have seen.

Measuring Base Running with Fieldf/x (Mike Fast): Mike’s presentation examined the different portions of base running and what the data can be used for.  Mike was able to track each base runner’s path around the bases, even what they were doing on pitches that weren’t hit (during which we would typically say “nothing happened”).  Obviously, with all of these data, there’s a lot happening.  Also, by knowing the position of the player at each moment in time, we can track both his speed and acceleration as rounds the bases; very valuable information for measuring “baseball speed.”

Fieldf/x of Probabilities: Converting Time and Distance into Outs (Jeremy Greenhouse): The coolest of the presentations.  As soon as he said the words “probability model,” I was sold.  Jeremy first examined stolen base attempts (in the thirteen games of data released, he only found four) and tried to determine the different component times of the stolen base attempt.  Some things he brought up that were interesting: “Pop” times, or the time it takes a catcher to catch the ball and get it to second base, was between 2.0 and 2.2 seconds for all attempts, which suggests that a lot of stolen bases are taken off pitchers, not catchers.  The ability to get a good lead is now measurable, as well as the jump a runner gets on the pitcher.

Jeremy also developed a model to determine the probability that a player makes a play on a ball hit near him.  The model was based on where the player is, where the ball would come down, and how long it would take the ball to get there.  From there, the player’s probability can change based on his jump, route, speed, and what I called “catching ability,” or the ability to actually make a play on the ball when in the vicinity.  It was shocking to see some of the plays made where players started out with low (less than 10 percent) chances of catching the ball, but by getting a good jump and running (quickly) in a straight line toward the ball, their probability would increase each 1/15 of a second.  He then showed the video of these plays and we were able to see the spectacular catches made by really good outfielders.  This also applies to outfielders who start with a low probability to make the catch, but increase it as they, for example, chase a ball into the gap, close quickly on it, but don’t catch it.  The ability to increase the probability of a catch is very valuable and that knowledge would be immensely valuable to teams.  Lastly, he also showed how bad outfielders can turn outs into hits by reading the ball poorly, getting bad jumps, and being indecisive.  Super cool, and as soon as the presentations are made available online (which hopefully will be soon), I will link to some of them, but especially some of these graphs.

Unfortunately, I missed the following presentations, so I will just show the abstracts presented in the program.

Where Fielders Field: Spatial and Time Considerations (Matt Thomas): Continued application of close-range photogrammetry through high-resolution digital photography to baseball is revealing hitherto unseen patterns of fielding in the game. Matt examines these patterns and where data permit, factors time into this examination. After reviewing general trends he notes specific achievements and then speculates on whether any of this freshly quantified insight tells us what makes for good (and not so good) fielding.

Scoutf/x (Max Marchi): This presentation evaluates players’ tools with Pitchf/x, Hitf/x, and Fieldf/x.

True Defensive Range (TDR): Getting out of the Zone (Greg Rybarczyk): Greg intends to display detailed tracking of the 25 batted balls in the released data that were hit in the air to the outfield. Presented data will include the relative positions of the outfielders and the ball from the time the ball leaves the bat until the time it is retrieved by the fielder. Using the essential elements of this data (fielder starting position, ball hang time and landing point), he outlines the fundamentals of a new outfield defensive metric, called ‘True Defensive Range’ or TDR, which should provide more accurate player defensive ratings with a smaller required sample size than current metrics. Full realization of this metric will require establishment of baseline values using the full data set. TDR for infielders will employ a similar method, but it will not be covered during this presentation.

The Future of Sportvision’s Data Collection (Greg Moore): Greg will talk about several bits of baseball data that Sportvision might collect in the future, and he will discuss how the data can be used in conjunction with Pitchf/x, Hitf/x, and Fieldf/x. Greg will also conclude the 2010 Pitchf/x Summit with closing remarks.

Obviously, there was a lot of cool stuff presented.  As mentioned, only 13 games worth of data were released to the analysts and most of the presentations were about determining what could be done with the data.  But with enough work and research, it will not only change the way teams and analysts evaluate players, but also will give teams another tool with which to teach their players and improve the guys they already have on the roster.  We’ll also know exactly what skills are important in each aspect of the game (base running, fielding, etc.), and as we learn these things we’ll discover other things we want to know.  I’d love to know what you guys think of all this and I’ll try to answer any questions you have about what can and can’t be measured and how we’ll use it in the future.

UPDATE: After I wrote this mess, I discovered this, much cleaner, detailed, mess, written by Baseball Prospectus writer Ben Lindbergh.  I’ll link to it down here because I want you to read what I wrote instead of Ben’s running diary.  Sorry, Ben.

This article was originally published at Knuckleballs, written by Dan Hennessey.

Reading Joe Posnanski’s latest piece about the struggles of the Royals, in which he opines about the fact that they’re a bad offensive team despite leading the league in batting average and showing no desire to promote two of their more intriguing young players (Alex Gordon and Kila Ka’aihue), I kept being struck with two pervading thoughts: (a) it must be terrible to be a fan of the Royals and (b) why would anyone do it?

Posnanski, of course, has become something of a darling in the sabermetric world not only for his excellent and insightful writing but for his acceptance and use of some of the more involved stats that we use to measure ballplayers. As such, he’s able to look past the fact that his Royals are taking pride in leading the league in hitting and have no interest in advancing beyond using batting average to measure players. Walks to them are res non grata, Posnanski argues, and he does so in the sort of tone that suggests that he has come to believe that there is no hope for change on the horizon. Which, you know, as long as Dayton Moore’s in charge, that does seem to be the case. But in any event, that’s really what made me wonder why Posnanski – and the other fans of the (disproportionately well-represented on the internet) Royals – keep following a team that gives them nothing in return.

Now, I suppose that on the surface, it makes sense: you root for the team you root for, and if you don’t root for the team that you grew up near, then you’re destined to be labeled a fair-weather fan. Such is life. But, perhaps because I adopted the Braves as my favorite team when they were on TBS every day and the Cubs were terrible, I don’t understand why that has to the case. See, sports is entertainment; we watch them because we enjoy the athletic splendor and all, but mostly because we are entertained by our favorite players going out there and plying their trade – because it’s fun.

If I had my copy of FreeDarko’s Macrophenomenal Pro Basketball Almanac with me, I’d quote here from their bit about liberated fandom. As it is, though, there is this post (first two paragraphs being of especial relevance) that will have to suffice for the moment; essentially, it argues for the eschewing of The Home Team in favor of the players that we actually enjoy watching. Are you a Cubs fan, but can’t stand watching a team put runners on third base with fewer than two outs in the 9^th 10^th and 11^th innings and not score them even though come on just hit a freaking sacrifice fly or at the very least try a suicide squeeze*? Don’t concern yourself with it; soak in the good times to be had in watching Carl Crawford steal bases or Vladimir Guerrero defy age or…oh, boy…or Albert Pujols beat down the doors to Cooperstown.

*I petitioned for this to happen in each of the three aforementioned innings. People say the triple is the most exciting play in baseball. People are wrong. The brief burst of drama and immediacy that a squeeze provides is unlike anything else in baseball. And it’s not for only that reason that I don’t understand why we don’t see more suicide squeezes, but also because it seems a really easy way to get a run, no? As long as bat hits ball, your chances of scoring a run are far higher than if you’re just letting the guy at the plate hit.

Two things: (1) Outs are almost always the dominant outcome of any game. Teams simply don’t put 27 men on base very often; they will always make enough outs (sometimes as few as 12; far more often 27) to finish a game. (2) Don’t think of batting average relativistically. Viz., think of them as percentages, not in the context of your baseball experience. A .330 hitter sounds like he’s really good at hitting…but ultimately, it means that he’s been getting hits in one-third of his at-bats and making outs nearly 70% of the time. If you’ve a guy on the team hitting .501, then, sure, let him swing away. But hits simply do not happen all that frequently; bunting is a much easier way to put the ball in play.

That’s the kind of suggestion I can make nowadays, what with the availability of MLB.tv and the internet making it easy to follow any team. During the World Cup – and immediately after – people loved to talk about how this would be the World Cup that got people into soccer. Now, they say this after nearly every Cup where the USMNT doesn’t get totally embarrassed, but this one was Different because we have the Internet and can keep track of our Nation’s Heroes as they play for Club Teams over in Foreign Lands. Similarly, though infinitely more effectively, people can follow any baseball team, or any selection of players, that they choose. At the risk of sounding like a shill for MLB, MLB.tv has a feature where you can select any player in the league, and they’ll alert you when he comes up to bat or in to pitch. I myself follow guys like B.J. Upton, Jay Bruce, Ichiro, Matt Wieters, Colby Rasmus and Pablo Sandoval that I wouldn’t be able to see otherwise – guys whose careers I enjoy more than, say, Melky Cabrera’s*.

*This is perhaps the wrong time for me to be writing this article, because this Braves team is probably the most enjoyable one to watch since Andruw Jones and the Baby Braves ruled the roost. Cabrera and Eric Hinske were the only two guys I could think to speak of derisively and I don’t hold any particular grudge against them. I guess Derek Lowe isn’t all that fun, but it doesn’t seem fair to pick on pitchers.

So why shouldn’t I just declare myself a fan of baseball, rather than the Braves? Everyone knows that Seinfeld bit about how having a favorite sports team is like rooting for laundry; what makes the Braves’ laundry so compelling that I should forego the pleasure of watching Jose Reyes and Hanley Ramirez and Chase Utley and Stephen Strasburg? I imagine the answer to this question is ‘it just is,’ or some similarly vague and far-reaching statement. Perhaps the tribal nature of fandom is so engrained in sports fans’ minds that there’s no turning away from it; perhaps people think there is a greater reward to be gleaned from “suffering”* through a team’s ups-and-downs and winning a championship.

*Y’know, for all the good there is in sports, it sure is a bastion of hyperbole. People love to throw around how Cleveland fans have “suffered” because they haven’t had a team in their city win a title in so long, and don’t have any real prospects of doing so in the near future. With the culture of superlatives that dominate sports, is it any wonder that Dwyane Wade dropped his 9/11 line? I’m going to stop there because the only thing more grating than that hyperbole is the moralizing and holier-than-thou attitude inherent in telling people that they’re not feeling real pain, and turn on the news if you want to see tragedy.

I don’t like that view. Sports isn’t life; sports is a diversion from life – it’s a forum for unparalleled conflict resolution (winners, losers, champions, meticulous documentation and quantified performance) and enjoyment of things we don’t see in everyday life (e.g. 450-foot home runs and diving catches and walkoff celebrations*).

*Though I do think this could (and should!) be brought into offices and schools. Say you just gave a really great presentation; you could have the audience cheering throughout your conclusion and hitting a crescendo as you nail the last syllable of ‘thank you,’ and then they vault over their tables and mob you and everyone jumps around. You could even rig the projector to drop company-color confetti. If this were commonplace I’d almost be excited to graduate soon.

So why do we arbitrarily bring unpleasantness into the equation? Why do we self-identify as fans of a team of guys that we may not like? Why do stat-minded guys like Rany Jazayerli and Rob Neyer and Joe Posnanski put themselves through the drudgery of a Royals team that couldn’t be more antithetical to their baseballing values? It is an inherently jingoistic, paleolithic process, a throwback to the days when the United States was a name and not a realized concept. I do think that there is a place for fandom in sports; I would argue that the incredibly fluid player movement in the NFL leaves fans rooting for teams as the only constants in a league of flux, and that watching a game where you don’t care for (or actively dislike) both teams is painful. But, with all due credit to those exceptions, I do not think that we need to subvert our enjoyment of sports and call ourselves ‘fans’ of a team. We don’t need to slog through inning after inning of uninspired baseball that’s not played to our liking when there are so many options out there. We can cast off the shackles of supporting a last place team and enjoy watching whomever fits our fancy; we can, or indeed should, be a fan of the game without being a fan of a team.

It doesn’t take a hardcore sabermetrician to realize that the All-Star vote is a sham. After all, the undeniable best catcher in the game received only the 11th-most votes at his position, and Omar Infante made the cut while MVP candidate Ryan Zimmerman had to sit at home (not the fans’ fault, but still).

But even if it’s impossible to distinguish the game’s best players by looking at the vote totals, I wondered if it would be possible to gather some more unorthodox information from the results: namely, the impact of fans’ biases on their ballots.

I quickly scratched out an equation for a statistic I made up, called “All-Star Score,” to measure how deserving a player is of fans’ votes for the Midsummer Classic:

All-Star Score = (Wins Above Replacement* + 2) ^ 2

*—numbers as of the All-Star Game

I calculated the All-Star Scores for each player listed on the ballot and added them together. I then added up the total All-Star votes cast (Major League Baseball releases the vote totals for only the Top 25 outfielders and Top 8 vote-getters at other positions per league, so I used 300,000 as a baseline for those players whose results were not available) and divided that by the composite All-Star Score to find out what the average All-Star Score Point was worth (just under 74,000 votes).

Finally, I calculated the votes-per-All-Star Score points ratios for each team, then divided that by the league average to get an estimate of what proportion of votes each team’s players got relative to what they deserved. The numbers below show each team’s relative figure as a percentage—a “Bias Score” of 100 would mean the team received exactly the right amount of support (of course, no club came out at 100).

I’m fully aware of the flaws in my experiment: the statistics used were compiled after the voting, not during it; I’m sure my 300,000-vote estimate for the lower-tier players is extremely generous to some and a big low-ball to others; and, of course, there’s no guarantee that my little equation represents the ideal proportion of All-Star votes a candidate should receive.

Nonetheless, I think the results are both somewhat meaningful and interesting:

Tier 1: The Unloved (79 and below)

1	White Sox	47
2	Royals	47
3	Athletics	48
4	Padres	49
5	Giants	50
6	Cubs	56
7	D-Backs	57
8	Blue Jays	59
9	Indians	59
10	Nationals	59
11	Orioles	60
12	Rockies	66

If you look at the vote totals, seeing the Royals and A’s at the top of the list shouldn’t come as a surprise: they’re two of the three miserable teams that didn’t get a single player on the voting leaderboards. Meanwhile, the starting nine for the Orioles—the only other club to be completely neglected—have been so bad that Baltimore landed in the middle third of the Bias Scores despite having the absolute minimum number of votes. Ouch.

It’s no surprise to see struggling teams like the Indians and Diamondbacks fall this low, but I would have expected Padres, Blue Jays, and Nationals fans to show their favorite players a little more love in light of their teams’ expectations-beating early performances. And I’m shocked that the Rockies haven’t been able to generate more excitement, what with their recent string of comeback wins in playoff races.

However, I’d say the biggest upsets here are the teams from Chicago—particularly the Cubs. North Side fans have a reputation of being among the most loyal and passionate in baseball (after more than a century without a championship, they’d have to be). It’s a telling sign that something is very wrong in Wrigleyville.

Tier 2: The Average (80 to 120)

13	Marlins	80
14	Pirates	81
15	Reds	84
16	Red Sox	90
17	Astros	102
18	Mariners	114
19	Rangers	120

The first team that jumps out at you here is Boston: how can Red Sox Nation be classified as a relatively unbiased fanbase? Take a look at the leaderboards and it becomes clear. Adrian Beltre finished behind Michael Young, Kevin Youkilis got barely half the votes of scuffling Mark Teixeira, even local hero David Ortiz fell behind the anemic Hideki Matsui. Derek Jeter has been better than Marco Scutaro, fine, but does he really deserve six times as many votes?

Two teams in this grouping redefine pathetic. A 20th-place finish for Andrew McCutchen is enough to put the Pirates squarely in the middle of the pack because their eight candidates have combined to be of less value than Dan Uggla. Astros fans, meanwhile, turn out to have a positive bias because of Lance Berkman’s eighth-place finish at first base. That’s what happens when your team has a negative composite WAR.

The two AL West teams are both interesting cases. The Mariners don’t have much of a reputation for a strong fan base, but people love Ichiro and the now-retired Ken Griffey Jr. raked in over a million votes. Given that the Rangers have the third-highest team vote total in the game, you might expect them to have a far higher Bias Score. But you might not realize that Texas also has the third-highest composite WAR.

Tier 3: The Coddled (121-150)

20	Tigers	126
21	Angels	129
22	Dodgers	129
23	Cardinals	134
24	Brewers	138
25	Mets	146

Most of these names were pretty predictable. The Brewers are probably the most surprising team to be ranked this far up. Their high score is entirely the fault of Ryan Braun, who led all outfielders with just under 3 million votes despite a significant offensive dropoff and horrific defensive, even by his standards.

Tier 4: The Overindulgent (151-190)

26	Braves	159
27	Rays	163
28	Twins	171
29	Phillies	181

Eight years ago, the Twins were on the verge of falling victim to contraction. Three years ago, the Rays had never finished a season with more than 70 wins. If you’d said then that both teams would soon have some of the most passionate fans in baseball, you would have been laughed out of the room.

Tier 5: The Insane (191 and up)

30

Yankees

199

I’m sure some commenter will accuse me of writing this article for the sole purpose of blasting the Yankees. I’ll say here for the first and only time that, while their coming out on top was somewhat predictable, this is just how it happened.

Just look at the vote totals. A-Rod over Beltre two-to-one, Curtis Granderson over Alex Rios by a nearly three-to-one margin, Teixeira over Paul Konerko almost five-to-one, Jeter over Cliff Pennington by over 10-to-one. Is there any logical explanation for that? And this isn’t even taking into consideration Nick Swisher’s Final Vote victory over Youkilis.

I’ll be the first to admit that this isn’t a definitive study—the rankings would surely be shuffled around if the full, precise vote totals were available (especially towards the lower end), and I don’t think anyone believes for a second that fans in Houston are more loyal than their counterparts in Boston. But I still think the results are somewhat telling, so in the future, fans in Minnesota and Wisconsin might want to think twice before complaining about East Coast bias.

Lewie Pollis is a recent high school graduate from outside of Cleveland, Ohio. He will be attending Brown University starting in Fall 2010. For more of his writing, click here.

I originally wrote this on Amazin’ Avenue, an analytics-friendly (to say the least) Mets blog/community.  It was well received so I am submitting it for cross-posting here.

* * *

A week or so ago, the Mets award-winning television team (well, the Gary and Ron parts) started talking sabermetrics — specifically, BABIP.   They tore it a new one, and for the most part, it’s because they didn’t understand what BABIP meant, or did, or… whatever.  It doesn’t matter.

What matters is that they talked about BABIP.  Which is horrible, because they’re going to botch it 100% of the time.  And that’s our fault, not theirs.  It’s time to stop using it.

By itself, batting average on balls in play means nothing.   It tells us how often a player gets a hit during the at bats when he doesn’t homer or strikeout, which in and of itself is worthless.   We know better.  Gary and Ron know better.  BABIP doesn’t differentiate between lineouts and popouts.  It treats a double in the gap the same as a bloop single.  Gary and Ron know it, and they laugh at our geekiness.  We don’t care how hard a guy hits a ball.  We’re nerds and the numbers don’t tell us that.  Literally:

Gary: Conversely, if a pitcher has a particularly low batting average on balls in play, they like to tell you it’s going to rise eventually. Well, to me that doesn’t make any sense. Certain guys hit the ball harder than other guys hit it. Certain pitchers induce more groundballs or more weakly hit balls than others. That’s part of what you’re trying to do. Am I totally off base with that?

Ron: No I totally agree with you, I think that for the average hitter, to have a high average putting balls in play, it’s probably because they do have some lucky hits. But certain hitters, like [David] Wright, hit the ball hard almost all the time.

Of course, we know it too.  We measure line drive rates and stuff like that.  We have xBABIP!   Yeah, go us!  And no, we don’t differentiate between the bloop single and the gap double — well, not independent of line drive percentage, etc.  But that’s the whole point.  We’re trying to measure how lucky the batter has been.  We want to know what the batter’s expected batting average is.

So let’s just say that.  Stop with the BABIP.  Stop with the esoteric number which only means something in relation to another number (BA) and even then really needs to incorporate other numbers (e.g. LD%) to truly say what we want to say.   Let’s do this instead.

1) Call it “Expected Batting Average.”

Obviously, BABIP isn’t a player’s expected batting average.  BABIP is a tool we use to try and figure out a players xBA (ooh! I acronymifieid it!), but that’s OK.   Let’s figure out the xBA and call it xBA.

2) Explain it in words.

Start with this:

Know what the difference between hitting .250 and .300 is? It’s 25 hits. 25 hits in 500 at bats is 50 points, okay? There’s 6 months in a season, that’s about 25 weeks. That means if you get just one extra flare a week – just one – a gorp… you get a ground ball, you get a ground ball with eyes… you get a dying quail, just one more dying quail a week… and you’re in Yankee Stadium.

That makes a ton of sense.  It has to.  It’s from Bull Durham.

But you know what?  Dying quails are fluky.  They’re luck.  Ground balls with eyes, same thing.  Flares, gorps, whatever.  Luck. That’s what Crash is saying there. The difference between a .250 hitter and a .300 hitter is a little bit of luck each week.

Guys who hit the ball hard, they don’t need as much luck.  Turn those grounders into line drives and those dying quails into warning track doubles and they’re hits — to hell with luck.  Luck is for guys like Alex Cora and Gary Matthews Jr. and that guy Rick Evans or something.

We say, screw that.  Let’s look at each at bat.  If a guy hits a frozen rope that’s caught, we know that’s not his fault.  Over time, that’ll even out, and he’ll get more hits.  If a guy strikes out, that’s an out every time.  Same with a pop up.  That won’t even out.  Homers?  Always a hit.  Grounders with eyes?  Well, that’s usually an out, and that’ll even out over time to.  We look at every single at bat and ask if the guy hit the ball hard enough to “make his own luck.”  That’s xBA.

(And you know what?  At the end of the day, that’s what BABIP turns into, too.  Except that BABIP sucks, because it doesn’t actually start there, in either name or by its equation.)

3) Drop the arrogance of specificity.  Use ranges when possible.

We’re measuring luck.   Luck isn’t exact.   So we’ll never be right on the money.  You’ll never be able to find a season where a significant number of players have an xBA equal to their actual batting average.  That makes us look stupid, when in fact, we’re just being arrogant — by being so exact.

We should use ranges.  xBA should be the 50% confidence interval, not the midpoint thereof.  More made up numbers: If a guy’s xBA is .285, it’s probably better expressed by saying that it’s between .279 and .291, or whatever.  It makes that .290 BA not seem “lucky” (it really isn’t) but tells us that a .274 is really unlucky.   In other words, it does the job — without the excruciatingly nerdy exactitude we are (wrongly) associated with.

It’s our job to communicate this stuff.  It’s not their job to get smarter (they’re not dumb) or to figure it out themselves (they’re busy) or that they don’t respect us (true, but fixable).  The problem is semantic, not logical, and semantic problems can — and indeed, must — be fixed by revising our language.  It’s time to stop using BABIP.

Dan writes a daily email newsletter, “Now I Know,” which shares something interesting to learn each day.

The World Baseball Classic is certainly a noble idea. I mean, what’s not to like about it on paper? You take the best players from each baseball-playing nation and have them battle it out to see which country reigns over the rest of the globe. Can anyone trot out a more thunderous lineup than the USA? Who has the more dynamic pitchers: the Dominican Republic or Venezuela? Does Japan really produce the most fundamentally sound players? Fans all over the world have shown their support for this, as have many players.

All of this would be fine if baseball were like basketball, hockey or soccer; sports where you could wake up, trip over your dog, tumble down the stairs into a pair of cleats, skates or sneakers and play. Those sports employ bio-mechanics the body was designed to handle like running, jumping, kicking and swinging. Baseball, specifically pitching, is not like that. The human arm was not designed to handle the stress and torque put on it by pitching. If you don’t believe me, then I have a few thousand shoulder and elbow scars to show you, including my own.

The lucky few who are able to withstand such actions and be successful are kept on a yearly routine: start throwing in mid-February, build strength and stamina through March before turning up the intensity at the beginning of April. But just like it isn’t wise to turn the ignition on a new Mustang and instantly floor it, it doesn’t seem right to take a pitcher conditioned to ease into a season during Spring Training and tell him to pitch with October-like intensity in March. Unfortunately, this is the case with the WBC.

After looking through the statistics of those who appeared in both WBC tournaments, it is my belief that pitchers who participate in the WBC, especially starters, are far more likely to see a regression in their performance, get hurt or both than pitchers who do not play in the WBC. I reason that the most likely cause is the tournament’s timing disrupts the normal routine of pitchers and their arms are not yet ready to handle the stress and intensity then. With data collected from various sources, I will demonstrate the stark differences between WBC pitchers and their counterparts who did not participate in the tournament, using spreadsheet data and graphs included in this analysis.

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The pitchers who were included in this study had to satisfy a few conditions. First, pitchers in the WBC group had to have pitched primarily in Major League Baseball in 2005, 2006, 2008 and 2009[1]. Players who played in one year but not another (spent one year in the minors or injured; or retired after a WBC) were not included. For the baseline of starters and relievers, a pitcher who made 10 or more starts for the year was counted as a starter while a pitcher who made 25 or more appearances with nine or fewer starts was counted as a reliever. The “all pitchers” category includes every pitcher who made an appearance during the 2005, 2006, 2008 and/or 2009 seasons.

***

At the heart of it, the key to successful pitching is how good you are in preventing runs from scoring, with ERA and component ERA (ERC)[2] being the primary statistics used to measure this aspect. The MLB’s ERA usually falls between 4.25 and 4.45 in most years, with only small differences from season to season. The last four groups saw small-to-moderate increases in their ERA between 2005 and 2006, but WBC starting pitchers saw a dramatic jump, from 3.75 to 4.48 while the ERC inflated from 4.09 to 4.79. WBC relievers also saw a significant jump in their collective ERAs (3.15 to 3.51), but not only is that only roughly half of what starters experienced, WBC relievers saw their ERC drop from 3.86 to 3.41. Compared against the league-wide ERA/ERC jumps of 0.24 (4.29 to 4.53) and 0.25 (4.18 to 4.43), respectively, the WBC starters’ jumps look even more like one of Superman’s single bounds. A major factor for this spike may be the above-average rise in HR/9 ratios. The average MLB starter showed no increase in his HR/9 rates and all other groups had increases of 0.1, but the HR/9 rates of WBC starters rose by 0.2 (0.9 – 1.1).

Home runs aren’t so bad, just as long as there isn’t anyone on base, but WBC starters were putting more and more runners on in 2006. Starting pitchers saw the highest rise in WHIP out of the five groups. The major league-average increase in WHIP between 2005 and 2006 was 0.04 (1.37 to 1.41), but the average WBC-participating starter saw his WHIP rise double that amount (0.08) from 1.29 to 1.37. Part of that increase was fueled by an up-tick in their BB/9 rates, which climbed from 2.9 to 3.1 (0.2). The most startling changes, though, were with the starters’ rising H/9 rates and falling K/9 rates . While all other groups saw a 0.2 increase in their H/9 ratios, WBC-participating starters’ ratios shot up by 0.5, going from 8.7 in 2005 to 9.2 in 2006. This may be attributed to a pitcher’s prematurely tired arm or improper mechanics from being rushed along during what normally is Spring Training. Either way, the pitches became more hittable, which also showed a decrease in these pitchers’ ability to strike batters out.

Every group I collected data on showed an improvement in their K/9 ratios by 0.2…except for WBC-participating starters. Their K/9 ratios actually fell, going from 7.0 in 2005 to 6.7 in 2006—a drop of 0.3 whiffs per nine innings. A good K/9 ratio shows both how good a pitcher is at retiring a batter without the help of his fielders and how dominant his repertoire is. The higher, the better. When I see that one group’s ratio is regressing while all others are improving, that would make me a little curious as to what may be causing such a downturn, especially with a group as valuable as starting pitchers. If I were in a team’s front office, it would make me wonder if this little event that is supposedly good for baseball is actually harming my pitcher and my team’s playoff chances.

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Now, this wouldn’t so much of a concern if the pitchers who saw this decline in performance were just hurlers on the wrong side of 30 and/or at the tail-end of their contract, but that’s not a case. Pitchers like Jake Peavy and Dontrelle Willis saw their performances take a dive after participating in the 2006 WBC, while promising up-and-comers like Francisco Liriano and Gustavo Chacin suffered major injuries that year. Two of the more alarming examples are Peavy and Willis, two National League hurlers from pitcher-friendly ballparks who use complicated or violent deliveries.

Peavy seemed out of sorts during the first half of the 2006 season, posting ERAs of 5.17 or worse in three of the first four months. It was during this time that Peavy was also prone to the long ball, serving up 14 of his 23 home runs in April, May and June. The “gopher-itis” lessened once July hit, but then Peavy had a little more trouble finding the strike zone. After issuing no more than eight free passes in each of the first three months, Peavy walked 12 or more batters in every month during latter half of the season. Peavy eventually straightened himself out in 2007, but the same cannot be said for Willis. After nearly winning the Cy Young in 2005, Willis never could establish any consistency in 2006. His WHIP climbed an astonishing 0.29 points from 1.13 (sixth in the NL) to 1.42 (outside the top 30). At the same time, his HR/9 rate doubled from 0.4 to 0.8 while his opponents’ OPS climbed from .644 to .745. Since then, Willis’ regression went from bad to worse and is now viewed as little more than a reclamation project for the Arizona Diamondbacks.

***

Whereas 2006 saw a decline in WBC pitchers’ performance, the 2009 tournament participants saw an even more disturbing trend: a steep drop in their time on the mound. There were only negligible decreases in innings pitched following the 2006 WBC—10.1 percent for starters, 2.6 percent for relievers—but those figures worsened dramatically following this past tournament. WBC starters pitched, on average, 21.1 percent fewer innings in 2009 than they did in 2008 while relievers saw their innings totals drop by 27.2 percent. Houston ace Roy Oswalt saw his streak of five consecutive 200-inning seasons come to an end due to chronic back problems. Cincinnati’s Edinson Volquez appeared in one WBC game, then made only nine starts during the regular season before undergoing “Tommy John” surgery[3].

A second trend I noticed involved those pitchers who were in the playoffs the previous season. Out of the 11 pitchers who appeared in both the ’08 playoffs and the ’09 WBC, eight of them missed time due to injury (or, in the case of Javier Lopez, demotion) or saw an overall regression in their performance. The pitchers from this group who spent time on the disabled list pitched anywhere from 13.5 percent to 80.3 percent fewer innings than they had in ’08. Some of the more notable examples include Red Sox right-hander Daisuke Matsuzaka, whose 59.1 innings in ’09 were the fewest he’s pitched in either Japan or America, and Angels set-up man Scot Shields, who had never been on the disabled list for his entire nine-year big league career.

***

There are more examples of pitchers seeing their fortunes change for the worse after either of the two WBCs, like Bartolo Colon’s shoulder falling apart after rushing through rehab and Esteban Loaiza’s collapse in Oakland in 2006 or how Volquez’s elbow went kaput in the middle of 2009. I won’t list every pitcher who suffered, but my point is clear: the WBC increases the chances for pitchers to suffer injuries, see an across-the-board decline in performance or both. As I stated earlier, I feel the biggest reason for these unfortunate trends is the timing of the tournament. Holding this tournament in the early spring can only damage the health and careers of the players who wish to represent their countries and, in turn, hurt the player’s team both on the field and their long-term organizational plan. I feel the best possible resolution would be to hold the tournament at two different times: have the preliminary rounds during the week of the All-Star Game—while giving MLB, the Japanese leagues and all other leagues a mid-season break—and the final two rounds shortly after the World Series. This way, not only would the careers and health of the pitchers be better preserved, but it would also be highly beneficial to MLB as a whole.

Under the current scheduling, the WBC and MLB has to battle against the NCAA men’s basketball championship tournament for ratings and coverage. Since all other major professional and collegiate leagues are inactive in July, it would allow MLB a better opportunity to drum up interest in the tournament and give less well-known baseball-playing nations a bigger platform to perform. The week off would also benefit the players who are not in the WBC, as they would have had time to recover from injuries and spend invaluable time with family and friends. Lastly, the buzz over a recently completed World Series could carry over to the final stages of the WBC, with story lines from the first phase being built up prior to the resumption of the tournament. Playoff-participating players could have the option of continuing in the tournament or allow other players, who spent most of October resting and re-energizing, to go in their places. Those fresh bodies would also improve the quality of play seen by the fans.

The bottom line is this: the World Baseball Classic is an excellent idea, but is poorly executed in its current form, with pitchers suffering the most damage. Pitchers are the most valuable and volatile commodity in baseball and MLB should do its very best in order to protect that commodity. Even though there have been only two tournaments to study, the numbers are very clear and the logical decision to change should be made.

Michael Echan is a freelance sports writer from New Jersey. Please contact him if you would like to see the compiled spreadsheet data and graphs. He may be reached at mcechan@hotmail.com

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[1] Francisco Liriano spent most of 2005 in the minors, but was included because he spent most of 2006 with Minnesota before a season-ending elbow injury in August. Luis Ayala was on Washington’s roster in 2006, but injured his elbow during the WBC.

[2] ERC is a statistic created by Bill James. It takes the number of hits, walks, home runs, hit batters and total batters faced by a pitcher to give an “alternate” ERA that better reflects his performance.

[3] Volquez did pitch a career-high 196 innings in 2008, his first full season in the big leagues, but has had his workload gradually increased during his career. His combined innings progression: 140 in ’05, 154 in ’06, 178.2 in ’07, 196 in ’08.

By now, just about everyone knows about how umpire Jim Joyce blew a call during Armando Galarraga’s start against the Cleveland Indians, which cost him the 21st perfect game in major league history. Instantly after Joyce’s error was discovered, fans were calling for Joyce to be fired. However, he certainly isn’t the first umpire fans have wanted removed from the game, and he certainly won’t be the last.

For those that remember, Tim McClelland had his own controversy during the 2009 ALCS between the New York Yankees and the Los Angeles Angels. Long story short, during Game 4, Nick Swisher was at third base when Johnny Damon lined out to center fielder Torii Hunter. Swisher tagged up and presumably scored, until the Angels appealed that Swisher left third too early. McClelland agreed with the Angels and called Swisher out. Here is the video of the play. (Only the first 45 seconds is necessary to watch.)

From personal experience, it seems like we blame an umpire for a bad call without ever attempting to understand why the bad call was made. From the video, it’s clear that McClelland wasn’t directly watching Swisher when Torii Hunter caught Johnny Damon’s fly ball, but no one, not even FOX announcers Tim McCarver or Joe Buck, even bothered to explain why he wasn’t looking at Swisher. Back in October, I found a possibility as to why McClelland blew the call, and I will walk you through my reasoning here.

1. McClelland’s Positioning

Note: I have worked two summers as a baseball umpire for a middle school league. I understand that middle school children and professional athletes are very different, but I still had to learn positioning for certain plays, similar to the MLB umpires. During tag-up plays, I’ve been taught to line up the runner and fielder such that I can see both when the catch is made and when the runner’s foot leaves the base. Basically, this is exactly what you’d expect.

From the video I posted above, if you time when Damon made contact to when Hunter made the catch, it’s about 3.75 seconds. Now, I assumed that McClelland hesitated before making an attempt to get into a position to accurately watch Swisher’s tag because he had to decide on an appropriate reaction to the batted ball (similar to a fielder deciding what he has to do to field a batted ball). This is understandable because a person cannot be expected to know where or if a batted ball is going to be caught the very instant it is hit. Therefore, I assumed that it took McClelland about half a second to decide where the ball was going to land and if it had a possibility of being caught, so he should have had about 3.25 seconds (3.75 seconds of ball flight – 0.5 seconds of hesitation) to react.  However, it’s tough to estimate this time because we don’t know exactly what he was thinking when Damon first made contact.

As for his starting position, it is virtually impossible to know where McClelland started because there isn’t any video I found that showed where he was at the beginning of the play. However, because of the rarity of a pickoff attempt at 3rd base and he was still in motion when Swisher left 3rd, I decided that he wasn’t close to the base when the play started. So, another assumption I made was that he was probably a distance from 3rd base that roughly mirrored the positioning of the 1st base umpire when Damon put the pitch into play.

The fact that McClelland was moving when Swisher left 3rd base poses a question: Why wasn’t he in position yet?

A. He hesitated longer than the 0.5 seconds I assumed and he didn’t move fast enough to compensate.

B. His hesitation was close to my 0.5 second estimate, but he was slow in moving to his position.

Look at the picture above again. The center of the oval where I think McClelland started to where he ended up at the time of Swisher leaving 3rd is roughly 1/4 the distance of the basepaths, or about 22.5 feet. Now, from my quick Google research, I found that the average walking speed is about 3 MPH and the average jogging speed is about 6 MPH. Accounting for McClelland’s age, I decided that his jogging speed was about 4.5 MPH. Doing the math, he should have taken about…

(4.5 mi/hr) x (1 hr / 3600 sec) x (5280 ft / 1 mi) = 6.6 ft/sec

22.5 ft / (6.6 ft / sec) = 3.41 sec

…to move from where he started to where he ended, which is very close to the 3.25 seconds I estimated earlier. So, I’m willing to bet that the correct answer was closer to B than A; he hesitated an appropriate amount of time, but didn’t move fast enough to get into position.

McClelland certainly could have gotten into a better position like I mentioned above, but only if he moved at a faster speed. However, from all of us watching many games, I’m sure we can all agree that umpires are not the fleetest of foot and rarely, if ever, even appear to move at a fast jog. I bet a jog for McClelland is probably a slow jog for the average person. Therefore, I think our answer evolves from B into a C that I didn’t even consider listing:

C. He did nothing wrong. The play just happened too fast, so he was in his best possible position.

2. McClelland’s Vision

No, I’m not recommending that he needed glasses (even after wrongly calling Cano safe at 3rd in the same game). Tim McCarver emphatically stated how McClelland wasn’t directly looking at Swisher, which caused his error in judgment. But what I couldn’t believe was that McCarver, nor Joe Buck, nor anyone else not even related to the FOX broadcast made any mention of peripheral vision in relation to this play. For this analysis, I found that this organization states that normal peripheral vision is about 180 degrees. Examine the following two pictures:

As you can understand, a person’s peripheral vision should decrease as he/she gets older, so I accounted for this by showing McClelland’s as being less than 180 degrees in the picture on the left. I know that I subjectively picked where the two lines are, but they are not intended to be exact nor did I even know what McClelland’s vision was like (neither should you), so I estimated that he saw at least part of Swisher before he left 3rd. Now, I’ve already shown that it was probable that the play in real time occurred too fast for McClelland to get into a good position to make an accurate call, so that was probably why he didn’t line up Swisher with Hunter when the catch was made. The picture on the right shows Swisher leaning forward in anticipation of leaving 3rd base. Once he started leaning, I think McClelland assumed that Swisher was off the base, and thus thought that Swisher had left the base before the catch was made. If the time of ball flight had been longer, McClelland could have gotten into a better position, and he most likely wouldn’t have wrongfully called Swisher out.

In review, I found that the time between when Johnny Damon made contact with Scott Kazmir’s pitch to when Torii Hunter caught Damon’s fly ball occurred too fast for Tim McClelland to properly move into position to line up Nick Swisher at 3rd base with Hunter in center field. With the probability that McClelland had declining peripheral vision, just like many people his age, he saw Swisher lean forward out of the corner of his eye, and thus thought that Swisher left the base much earlier than he actually did. Together, I feel that McClelland did the best that he could in making the correct call, but the play simply happened too fast for him.

Even if you feel that I made too many assumptions here, my main point of this article was for you to learn to understand why an umpire made a certain call before jumping to conclusions that he was out to get a certain player or team, or that he’s incapable of being a good umpire in MLB. You don’t need to go into as much analysis as I did here, but you can at least watch some replays on TV and see if they hint at why an umpire made a mistake. Umpires are not “out to get” particular teams or players. I want you to believe that these guys really are trying the best they can.

This article was originally posted on Off The Mark in October 2009. Portions of the article were rewritten for cohesiveness and relevance to the present.

Thus far, the Year of our Sport 139 — or the Year of our Lord 2000 and 10 — curious whispers have grown to sly murmurs, and in unity they portend: the Year of the Pitcher. Already, fans have indulged in the sight of two perfect games and a third de facto perfect game. By contrast, this time last season, we fans were discussing Albert Pujols’ interminable Power and Joe Mauer’s surprising Pop, all the while swirling the snifter of Slugging; but this year, tales of a Resurgent Carlos Silva and the dazzling Kid Stephen Strasburg have seized our headlines. This, the media assures us, is the Year of the Pitcher.

But is it? Perhaps it is the year of the Pat Burrell — the aging slugger — or perhaps the year of the missing needle? One thing is certain: Our teams are scoring fewer runs. The 2010 MLB’s runs per game has reached pre-Clinton lows:

Of course, taken with a tankard of greater perspective, this recent descent does not appear too outrageous. It in fact puts us closer in line with historical performances:

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Tom Verducci, in a recent article on Bryce Harper, mentions that the Nationals averaged only about 12,000 households viewing each home game last season.

It occurs to me that the Nationals may be the only team in the country where the “fan base” is more likely to go to a game than watch it on TV.  After all, the Nationals to a certain extent positioned their new stadium as a prime location for D.C. power players to have business meetings and discuss the future of our Great Nation.  Of course, the Nationals have not been putting a great product on the field of late, which will diminish any team’s fan base.  But the Nationals’ current path, of increasing respectability borne on the back of several marquee names (Strausberg, Zimmerman, Zimmermann, and now Harper), is precisely the sort of attention-grabbing roster construction that would make an afternoon ballpark business meeting trendy.  Perhaps more than any other city, the Nationals have access to a unique demographic, one with money to spend but questionable rooting interest in the team.

To investigate this, I found stadium attendance and TV ratings from the 2009 season.  The bigger the ratio of game attendance to TV households, the larger the percentage of assumed fan base attends games:

The Nationals were the only team who averaged more fans in the seats than households tuning into the game (the shocking part of this is that their TV ratings were up 67% over 2008).  The Yankees and the Red Sox, as expected, were at the bottom.  The Marlins and Rays both had two different cable networks (FS Florida and SunSports) showing their games, which increased their household viewing numbers.  The Braves’ large number is due to their TBS days and two cable networks (FS South and SportSouth).  The source I used did not have television numbers for the Blue Jays.

There are a couple of factors likely working against the Nationals here.  One is that they are a recently-transplanted franchise which has not had the opportunity to build deep roots in its new city.  The team that arrived in Washington in 2005, and the stadium in which they first played, did them no favors.  Nevertheless, no other team comes even close to the Nationals’ ratio.

I’m no economics major, but these numbers seem to suggest that certain teams are pricing their tickets appropriately.  The Athletics and the Nationals’ average 2009 ticket prices, $24.31 and $30.63 respectively, resulted in the highest ratios of game attendance versus TV audience.  In those cities, it seems, ticket prices are encouraging fans to watch games in person.

There is another interesting aspect to this data.  Much has been written recently about how a new stadium no longer “saves” a team.  Baltimore and Cleveland’s new stadiums in the 1990s ushered in many years of big crowds and increased revenue.  Writers have pointed to Pittsburgh and indeed Washington as examples of how the novelty of a new stadium is wearing off faster these days.

Yet look at Baltimore and Cleveland.  They rank 4th and 9th respectively in ratio of game attendance to television audience.  Their beautiful ballparks are still saving them from an even more precipitous decline in fan base interest.

Sources: 

tv numbers: http://www.sportsbusinessjournal.com/article/63798

attendance numbers: http://espn.go.com/mlb/attendance/_/year/2009

A version of this article first appeared on my blog.

You probably already know why I’m writing this post (but if you’re reading this in 2014, Armondo Gallaraga was robbed of a perfect game in the 9th with 2 outs on 6/2/2010.)

Replay gets discussed a lot these days, and there are those in favor of it and those against it. The main reason for it is that replay is more accurate than an umpire, which has been demonstrated over and over again. There are two reasons against it. One is the tradition of umpires, the other is that it will slow down the game. As far as I can tell, it’s futile to argue with people when they love the tradition enough: if you’re sufficiently committed to tradition, no other value will persuade you to give up your stance. I don’t share that love of tradition, but I won’t enter a futile argument here either.

The lost time due to replay is different. We can count seconds and we can try to balance lost time with increased accuracy. Moreover, as technology improves the amount of time lost rewinding tapes and whatever else they had to do in the NFL in the 1980s goes away. So, theoretically, 100% of time used for review is actually spent making a decision. How much time is worthwhile? We’d have to have a discussion about that, but I’m going to throw out 30 seconds. If we could have 30 second replay, it would be worth it. A controversial call on the field typically takes more than thirty seconds anyway, because umpires huddle (but never change the ruling) and managers come out on the field and argue the call (which never has any effect except to get the manager removed from the game.)

Still, it takes a long to time review the play from every angle to come up with the best judgment that the video evidence supports. You just couldn’t do all the work necessary in 30 seconds, so it looks like we’ll have to settle for a longer review time or sacrifice the accuracy we desire.

That is a mistake. The reason replay takes so long is that we think that the goal is to produce the best judgement that the video evidence supports, using time the way we should in a courtroom, where no minute is more valuable than the freedom of the innocent-but-accused. The reviewer must check the play from all angles. He must double check it. He must confer with other reviewers for their opinion, and then come to a consensus. It’s an inefficient system, which in criminal trials is fair and good, but it’s not good for entertainment.

Replay doesn’t have to be a courtroom. Give five reviewers access to all the available video. Give each 30 seconds to decide what the call should have been. Then the vote. They don’t talk about it, they just vote their own best judgment. No changing the vote once cast. The majority rules the day. Suppose for a moment that there is a .75 probability that each of them makes the right call. Then the probability that the majority is correct is .896. (Binomial distribution probability.) Such reviewers would botch the call (as a group) just 1 in 10 times. Furthermore, in the preponderance of replay cases, video evidence is completely clear cut and it takes less than 30 to make a determination that’s right with a probability of 1.

This 30 second replay system would eliminate the vast majority of all erroneous calls in baseball. It wouldn’t be a fail safe system. It would require that we abandon our standard of having evidence that fully justifies our conclusions to all so that no one could come to a better conclusion on the basis of the evidence. But we shouldn’t let perfection be the enemy of the good. And it’s a good thing to preserve perfection.

Sabermetric pioneer and hero Bill James has suggested that certain teams need to abandon the traditional methods of putting together a baseball team and adopt unorthodox methods. Perhaps no team is better suited for this idea than James’ old favorite Kansas City Royals.

Founding Royals owner Ewing Kauffman was an original thinker which helped him develop his fortune with the Marion Labs pharmaceutical company. Kauffman was not any less creative in setting up the Royals and believed that it was possible to teach great young athletes to play baseball. He tasked his front office with doing just that. The result was the Kansas City Royals Baseball Academy.

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