Open to all BP staff, Between the Numbers is about sabermetrics, performance analysis, baseball and data, and anything remotely referring to the subjects of statistical information, its applications and interpretation.
I restricted myself to pitchers who had made their debut in or after 1974. First, I took RA_PLUS and the league average RA during Rivera's career (not ERA - 4.97 was the average) to put everyone on equal footing. Given this, I used a little bit of algebra to figure out what RA would have had to put up to match the normalized RA of each starter, assuming Rivera pitched enough total innings to match that starter's career IP total. Sorted by rest-of-career RA, the top 20:
Are the super-agent's statistics damned lies? And is he any more credible if he's technically telling the truth?
Combing through Scott Boras' statements for inaccuracies is a little like tilting at windmills (or so I assume—it's been years since I've seen a windmill, let alone tilted at one). That’s because Boras' greatest ambition isn't impeccable candor; it's getting the most money for his clients (and by extension, of course, himself). Telling the truth is often a good way to get paid, since no general manager likes to be lied to. Sometimes, though, the best way for an agent to stretch his wallet is to stretch the truth. That’s why every offseason, each team can expect to receive a hefty booklet about the latest big Boras free agent, explaining why Oliver Perez is the second coming of Randy Johnson or how Prince Fielder isn't fat, he's just big-boned. There's nothing wrong with these tall tales, so put down your pitchforks. Boras is just doing his job, and he’s doing it better than anyone else. (Just ask Fielder.)
Still, it doesn't hurt to hold him accountable for some of his more glaring leaps of logic. That’s why alarm bells went off in my head when I read a quote of his from a couple weeks ago:
A PITCHf/x look at Aroldis Chapman's transformation.
If I could take any pitcher to retire any batter at any point in the history of baseball, I might choose Aroldis Chapman at present. If I had to choose the worst pitcher in MLB in late May, I might have chosen Aroldis Chapman. Chapman didn't allow a run in his first dozen innings of the year, but in his subsequent four appearances, he allowed ten runs while recording four total outs. Over that stretch he walked 12 of the 19 batters he faced, bringing his seasonal line to 20 walks in 13 innings. Chapman was sent to the minors, but at some point he figured things out. Since being recalled in late June, Chapman has tallied 41 strikeouts compared to eight walks in 23.1 innings. I compared the PITCHf/x data of Bad Chapman (April/May) to Good Chapman (June-Present).
Picking apart the rest-of-season numbers reveals some interesting flaws.
Yesterday I claimed that the rest-of-season projections at Fangraphs are wrong. Again, I am well aware this is a statement from interest, as I made this claim as I was announcing our own rest-of-season PECOTA projections. Some people were skeptical of my illustrations, so I’ll go into the weeds here and explain why I think the methodology is incorrect, as opposed to simply disagreeable. First, I’ll lay out the methodology in neutral terms, then I will provide my own commentary.
What I’ve done is recreate at least a portion of the methodology used to compute rest-of-season ZiPS, as based on this spreadsheet, particularly, the portion having to do with HR. (If you want to look at the methods yourself, just download that spreadsheet, and copy the batter worksheet into a blank sheet so you can unhide the locked columns in the sheet.) First, you need to take the number of games a player’s team has played, and divide by 162. This is called G% in the original spreadsheet. For clarity I will call this G_RT.
A look at which batters this season have done the least with the most (hits).
Sabermetricians have long talked about the empty batting average. So who so far this season holds onto the emptiest batting average?
We have a stat here at BP, called True Average, that accounts for the totality of a player’s production at the plate, while still being on the familiar scale of batting average. What’s missing from this is a sense of opportunities; the more playing time a player has, the emptier his batting average can really be.
I've got a little puzzler for you - a brain teaser, if you will.
Here is a CSV file containing descriptive measures of a batter's batted ball distribution over his first 100 plate appearances, from two separate sources - Set A and Set B, as they are called. Then you have that batter's results for the rest of the season, in terms of BABIP, BACON (batting average on contact - included largely because I love having a reason to say BACON in a sabermetric context) and home runs on contact. Each player season has been identified by a "hash," in order to provide a unique identifier without giving any information about the player's identity. The reason is that I'm asking you all to participate in a blind taste test of two sources of information about the distribution of a player's batted balls, and how well they predict that player's future results.
When in the season do most valuable players tend to debut, and what can consecutive Opening Day starts reveal?
Armed with a WARP database, I trekked back to my debut article on MLB debuts. Here are the dates on which a group of players worth at least 100 career WARP debuted, as well as the names of the players who contributed the most to those totals:
The dominant story to start this season has been the cold start from the Red Sox, the preseason favorite to crush their enemies, see them driven before them, and to hear the lamentation of their women. Since yesterday’s “no reason to panic” post (I regret nothing), the Red Sox have lost another game–to the Indians, no less.
I’ve mentioned what the cold start doesn’t mean–it’s not a death sentence, as the Sox obviously aren’t an ordinary 0-5 or 0-6 team–but let’s spend some time on what it does mean, with the help of the adjusted standings (given a fresh coat of paint as of today) and the playoff odds report.
Cold starts for the Rays and Red Sox means frustration for fans, but it shouldn't mean panic.
Did you know? Only two teams have begun the season 0-5 and made the playoffs! It's, like, math and stuff!
Okay, let's put this into some context. Going back to 1974, 68% of World Series teams had at least one five-game losing streak. If we look at all playoff teams, that number creeps up to 70 percent. (Counterintuitively enough, if you only look at World Series winners, the percentage goes up to 75%. Ah, the joys of small sample sizes.)
A quick reminder of how teams have fared in the past as compared with their projected records.
I decided to take off work for my day job tomorrow for a religious holiday. Yes, I belong to the Church of Baseball—how can I be asked to work on a day like tomorrow? Tomorrow is a time to focus on the important things in life, and for me, this is a mixture of baseball and its statistics. It’s a time to finally watch nine men stroll out onto green grass and officially move the baseline of the expectations that my colleagues at Baseball Prospectus and I have been zeroing in on since the Giants ran on to the field to celebrate their 2010 World Championship. With the release of our final version of the PECOTA-Projected Standings on the Depth Charts page for 2011, I am reminded of an article that I wrote last September called Sabermetric Teams and Sabermetric Scouting. In that piece, I discussed how teams have done as compared with their projected standings over the past five years.
My most interesting finding was that teams that were perceived by Baseball Prospectus Staff to incorporate more from sabermetrics into their front office’s decision-making tended to under-perform their PECOTA-projected records. This was interesting, because it suggested the importance of contextualizing what statistics suggest, and even Major League Baseball clubs miss the same information that our analysts miss as well. However, these sabermetrically inclined teams also fared better overall relative to clubs with similar payrolls that were less sabermetrically inclined, which also highlights the importance of listening to what analysts like us have to say!