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<\/p>\nYep, first base bites. It really can. First base is like the dog you\u2019ve had for years \u2013 old faithful, your best friend \u2013 who suddenly goes and bites a neighbor kid and then all hell breaks loose.<\/p>\n
First base is like that. \u00a0It\u2019s familiar. Cozy. For the umpire working the field (on a two-man crew, or U1 on a three- or four-man crew), first base is where most of the putouts are made. But, of course, almost all of these putouts are easy to handle. Almost<\/em> all of them. A couple of steps inside the line, then a slow, drawn-out fist to signal the out. Piece of cake.<\/p>\n You have nearly five times the number of plays at first base than at second, and the ratio over putouts at third is over a thousand-to-one. But again, you could easily make almost all of these calls from the cheap seats. That\u2019s your old dog. And the bang-bang play? That\u2019s the neighbor kid. And then, as I said, all hell breaks loose.<\/p>\n We\u2019re going to come back to the bang-bang play in a bit, but first let\u2019s check my claim that first base is where most of the putouts are made. Is it really true that first base is action central for fair batted balls? Common sense, along with familiarity with the game, suggests that it is, but impressions are not facts (except in politics), so let\u2019s ask again, what am I basing my claim on?<\/p>\n Baseball stats are abundant. If you doubt, check out a site like Retrosheet<\/a><\/strong>. And while most published baseball stats track player performance (batting average, RBIs, ERA, and all the rest), the immense reservoir of game data is available for other investigations as well. And the data is available to any creative analyst out there. All you have to do is download it.<\/p>\n One such analyst is Chris Ford, the owner and proprietor of a sporting blog called All My Sports Teams Suck<\/a><\/strong>. While I was researching this post, Googling for data about where putouts are made, I stumbled on Chris\u2019s blog. It was there that I came across Chris\u2019s fascinating blog post entitled A Look At Every Out Made Since 1952<\/a><\/strong>. Wow!<\/p>\n Every single out made in Major League Baseball since 1952. If that\u00a0doesn’t rev your jets, you\u2019re one of those caught snoring in the bleacher seats. So, from Ford’s analysis, we learn that, over the past 64 years, through the 2015 season, Major League Baseball has recorded 6,377,594 outs. Not only that, but we know exactly where each and every one of these outs was made.<\/p>\n Here’s the first really interesting thing we learn (I was flabbergasted): the data captures over a thousand put-out scenarios. If you’d asked me before this how many different defensive scenarios could lead to an out, I’d have answered something like, oh, a couple hundred. Nope. In fact, over these 64 years, the data tracks 1,228 different put-out scenarios.<\/p>\n That said, roughly 96% of these six-million-plus putouts are captured in just the top twenty putout scenarios.\u00a0These twenty also represent all putout scenarios whose frequency is greater than or equal to one half of one percent. So, except for oddities like six-throw rundowns, these twenty putout scenarios pretty much have the action covered.<\/p>\n Here\u2019s a table that tabulates these six million (plus) putouts since 1952. Putouts made at first base are highlighted<\/strong>:<\/p>\n Adding up all of the putouts made at first base (1,754,733), we see that these account for 27.5% of all putouts, nearly a third, outpacing the next most frequent, strikeouts, and the third most frequent, catches in center fielder. Putouts at second base represent just under six percent of all putouts. The ratio of putouts at first over putouts at second is nearly 5:1. You have to go all the way to the 38th most frequent play to get a putout at third base (a 1-5 putout, pitcher to third baseman); the frequency of putouts at third base is just two-hundredths of a percent (0.02%). It\u2019s not even close to making the list.<\/p>\n Okay, so it\u2019s abundantly clear: first base is where most of the putouts take place. But again, almost all of these are easy. But let\u2019s not forget about the old dog.<\/p>\n A bang-bang play is a play that is so close, the events so nearly simultaneously, that only the most astute observer, using both eyes and ears, can make a judgment on whether the runner is safe or out. When you make this call, there are three reference points: Ball in glove (securely), the runner\u2019s footfall on first base, and the fielder\u2019s contact with the bag. And you\u2019re using two senses \u2013 both vision and hearing (seeing the footfall and hearing the pop of the catch), although crowd noise can complicate hearing the pop.<\/p>\n What you\u00a0have, then, are intersecting events that are described by the trajectories of both the runner and the ball; and then we have three reference points; and finally, we have sensory inputs from both vision and hearing. And then, on top of that, we have all of these coming rapidly together at a point in time that\u2019s about the width of a water molecule, and then a lot of crowd noise to boot. Who can possibly judge this?<\/p>\n What we\u2019re talking about here is the play that\u2019s so close, so perfectly simultaneous, that even the term bang-bang doesn\u2019t describe it. What we have, here \u2026 wait for it \u2026 what we have here is the b-bang play. B-bang! And that\u2019s the play that bites. This is your old dog biting the mailman in the ass. This is all hell breaking loose on a warm summer day. This is the unwinnable dilemma, where it\u2019s entirely possible (and not that uncommon) for you, the umpire, to see one thing (correctly), and yet for the base coach (again correctly) to see the same event entirely differently. Both are true, and both are correct. And good luck with that!<\/p>\n (And there\u2019s more where this came from … that is, about the mysterious quantum physics of simultaneity in baseball. If you like mysteries like this, you’re going to like my post, The Theory of Umpire General Relativity<\/a><\/strong>.)<\/p>\n <\/p>\n![\"\"](\"https:\/\/i0.wp.com\/www.umpirebible.com\/ubBlog\/wp-content\/uploads\/2016\/12\/ScoreSheet.png?resize=300%2C224\")
We all know that baseball is the most data-centric sport on the planet. It\u2019s an outcome of the nature of the game, wherein every play (except steals and pick-off attempts) begin with a single, discrete action: a pitch. And upon each pitch, a specific outcome occurs \u2013 a ball or strike, a foul ball or a fair batted ball; a caught fly ball, a base hit, ground-rule double, putout at first base, double-play, a strike out, base on balls \u2026 the list goes on. And each of these events is recorded in its special code on the score sheet \u2013 an ancient, humble page with an array of arcane scribbles that grows, over the life of a nine-inning game, into a vast pool of data. And all of those pools from all of those games drain into a gigantic ocean of data. That\u2019s how we know that, while third base may be the hot corner, first base is action central.<\/p>\n\n\n\n
\n Scoring<\/strong><\/td>\n Description<\/strong><\/td>\n Raw count<\/strong><\/td>\n Frequency<\/strong><\/td>\n<\/tr>\n \n K<\/td>\n Strike out<\/td>\n 1,455,075<\/td>\n 22.80%<\/td>\n<\/tr>\n \n 8<\/td>\n Catch, center fielder<\/td>\n 650,819<\/td>\n 10.20%<\/td>\n<\/tr>\n \n 43<\/td>\n 4-3 putout (first base)<\/strong><\/td>\n 509,106<\/td>\n 8.00%<\/td>\n<\/tr>\n \n 63<\/td>\n 6-3 putout (first base)<\/strong><\/td>\n 502,374<\/td>\n 7.90%<\/td>\n<\/tr>\n \n 9<\/td>\n Catch, right fielder<\/td>\n 499,270<\/td>\n 7.80%<\/td>\n<\/tr>\n \n 7<\/td>\n Catch, left fielder<\/td>\n 488,288<\/td>\n 7.70%<\/td>\n<\/tr>\n \n 53<\/td>\n 5-3 putout (first base)<\/strong><\/td>\n 387,082<\/td>\n 6.10%<\/td>\n<\/tr>\n \n 3<\/td>\n Catch, first baseman<\/td>\n 339,636<\/td>\n 5.30%<\/td>\n<\/tr>\n \n 13<\/td>\n 1-3 putout (first base)<\/strong><\/td>\n 218,715<\/td>\n 3.40%<\/td>\n<\/tr>\n \n 4<\/td>\n Catch, second baseman<\/td>\n 198,778<\/td>\n 3.10%<\/td>\n<\/tr>\n \n 6<\/td>\n Catch, shortstop<\/td>\n 195,575<\/td>\n 3.10%<\/td>\n<\/tr>\n \n 5<\/td>\n Catch, third baseman<\/td>\n 156,198<\/td>\n 2.40%<\/td>\n<\/tr>\n \n 31<\/td>\n 3-1 putout (home plate)<\/td>\n 97,566<\/td>\n 1.50%<\/td>\n<\/tr>\n \n 64<\/td>\n 6-4 putout (second base)<\/td>\n 88,775<\/td>\n 1.40%<\/td>\n<\/tr>\n \n 2<\/td>\n Catch (pop up), catcher<\/td>\n 73,045<\/td>\n 1.10%<\/td>\n<\/tr>\n \n 643<\/td>\n 6-4-3 double play (first base)<\/strong><\/td>\n 58,058<\/td>\n 0.90%<\/td>\n<\/tr>\n \n 54<\/td>\n 5-4 putout (second base)<\/td>\n 56,829<\/td>\n 0.90%<\/td>\n<\/tr>\n \n 463<\/td>\n 4-6-3 double play (first base)<\/strong><\/td>\n 47,213<\/td>\n 0.70%<\/td>\n<\/tr>\n \n 46<\/td>\n 4-6 putout (second base)<\/td>\n 43,315<\/td>\n 0.70%<\/td>\n<\/tr>\n \n 543<\/td>\n 5-4-3 double play (first base)<\/strong><\/td>\n 32,185<\/td>\n 0.50%<\/td>\n<\/tr>\n \n <\/td>\n \u00a0TOTAL<\/strong><\/td>\n 6,097,902<\/strong><\/td>\n 95.6%<\/strong><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n Let’s get back to the bang-bang play
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And then there\u2019s this. There is a play at first base even tighter and closer than a bang-bang play; a play where events are in a range (it\u2019s in the hundredths, maybe thousandths of a second) in which (like Schr\u00f6dinger\u2019s cat) it is impossible to know with certainty whether a runner is safe or out. (You can solve this with super slo-mo cameras, but only the pros have super slo-mo, and we\u2019re not talking to the pros, so just let that drop.)<\/p>\n![\"\"](\"https:\/\/i0.wp.com\/www.umpirebible.com\/ubBlog\/wp-content\/uploads\/2016\/12\/UmpArguing.jpg?resize=300%2C169\")
<\/strong><\/em><\/p>\n
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<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"