Archive for Essential

Mets right-hander and soft-spoken Southern gentleman R.A. Dickey threw his second consecutive one-hitter tonight — in this case, against the Orioles of Baltimore. Nor do his defense-independent numbers suggest that he was anything but excellent on Monday night (box): 9.0 IP, 30 TBF, 13 K, 2 BB, 11 GB on 15 batted-balls (73.3% GB), 1.14 xFIP.

The average knuckleball from Dickey has approximately zero inches of horizontal movement and a single inch of positive vertical movement — or “rise,” a concept the present author discussed briefly earlier on Monday. Of course, the idea of an “average” knuckleball is a bit of a misnomer: given the nature of the pitch, the standard deviation of both sorts of movement is likely quite high. Indeed, this is the strength of the pitch: no one really knows where it’s going, not even Dickey.

As a sort of celebration of Dickey’s last two games — of his entire season, really — I sought out Dickey’s three “movingest” knuckleballs from his Monday start. In this case, I’ve identified the three of Dickey’s knuckleballs with the highest absolute value of total movement (i.e. the sum of the absolute values of both horizontal and vertical movement, in inches).

It’s hard to say if what follows are necessarily Dickey’s three best knuckleballs from Monday. However, each of them really does move quite a bit: indeed, the reader will note that catcher Josh Thole is unable to catch two of the three pitches and has to sort of violently move his glove to catch the other.

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Last week, ESPN conducted their second annual Franchise Player Draft, where they recruited thirty of their contributors to select players to theoretically build a team around. When they did this last year, we thought it was a fun idea, so (with their permission), we did one ourselves. Obviously, our guys have a slightly different perspective than many of the ESPN guys, so while the idea is the same, the theories behind the picks are not. It won’t take very long for you to see where some of the ideological differences begin to display themselves.

The draftees were given fairly simple instructions — everyone is starting a franchise from scratch, so it’s up to you to decide how to value short term versus long term wins. You’re not currently in either rebuilding or win now mode, but what pick you make might determine which path you go down. You will control the player’s rights for 10 years, and the actual contract they are signed to in MLB does not come into play. This is solely based on expected future production, so there is no cost analysis that needs to be done. That’s the game — take the guy that you would most want to build around for the next 10 years.

So, without further ado, let’s get to the picks.

#1 — Mike Axisa: Matt Kemp, OF

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All 30 Top 15 prospects lists are done and I’m wrapping up my off-season prospect coverage with the Top 100 Prospects for 2012, as well as a chat. I’ll let the list speak for itself. I’ll be back at noon eastern to discuss anything and everything prospect related.

Be sure to keep visiting FanGraphs as we approach Opening Day 2012… We’re going to be launching an exciting new prospect feature for 2012 that I think you’re really going to enjoy.

100. Garin Cecchini, 3B, Boston
99. Dillon Howard, RHP, Cleveland
98. Mason Williams, OF, New York AL
97. Alex Meyer, RHP, Washington
96. Sonny Gray, RHP, Oakland
95. Chris Carter, 1B, Oakland
94. Christian Bethancourt, C, Atlanta
93. Andrelton Simmons, SS, Atlanta
92. Tyler Pastornicky, SS, Atlanta
91. Jean Segura, 2B/SS, Los Angeles AL
90. Matt Davidson, 3B/1B, Arizona
89. Jesse Biddle, LHP, Philadelphia
88. Wily Peralta, RHP, Milwaukee
87. Brett Jackson, OF, Chicago NL
86. Sean Gilmartin, LHP, Atlanta
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Introduction and #31
#30 – #21
#20 – #11

Beginning in late November, we’ve spent much of the offseason asking readers to rate the television broadcast teams (on a scale of 1-5 for charisma, analysis, and then overall) for all 30 major-league clubs — with the intention, ultimately, of determining which broadcasts might best reflect the sorts of inquiry and analysis performed here at the site. (Click here for more on this project.)

Below are the 10th- through 1st-ranked television broadcast teams, per the FanGraphs readership.

But first, three notes:
• Teams are ranked in ascending order of Overall rating. Overall ratings are not merely averages of Charisma and Analysis.
• I’ve attempted to choose reader comments that are either (a) illustrative of the team’s place in the rankings or (b) conspicuously amusing.
• A complete table of ratings and ballots cast will appear in these pages Friday.

10. Tampa Bay Rays
Broadcasters: Dewayne Staats and Brian Anderson
Ratings (Charisma/Analysis/Overall): 3.3, 3.2, 3.4

Three Reader Comments
• “Brian Anderson was a huge upgrade over Kevin Kennedy.”
• “Generally good, except for too frequent gigglefits from Brian Anderson.”
• “Dewayne… dominates too much with the same canned pregame notes over and over (Johnny Damon’s hit counts being a big example). By the end of the year, BA was almost openly making fun of him, which was a big part of why we liked him.”

Notes
The comments for the Rays broadcast team don’t match up particularly well with the favorable rating. There are a number of negative — if not particularly expansive — comments not published here. The 35 votes received by the Rays broadcast was among the lowest of any team, and might explain the lack of consistency between the numbers and written responses.

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Base running linear weights or base running runs, or Ultimate Base Running (UBR), is similar to the outfield arm portion of UZR. Whatever credit (positive or negative) is given to an outfielder based on a runner hold, advance, or kill on a batted ball is also given in reverse to the runner (or runners). There are some plays that a runner is given credit (again plus or minus) for that do not involve an outfielder, such as being safe or out going from first to second on a ground ball to the infield, or advancing, remaining, or being thrown out going from second to third on a ground ball to SS or 3B.

Runs are awarded to base runners in the same way they are rewarded to outfielders on “arm” plays. The average run value in terms of the base/out state is subtracted from the actual run value (also in terms of the resultant base/out state) on a particular play where a base runner is involved. The result of the subtraction is the run value awarded to the base runner on that play.

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In the first part of this piece, I established a framework for evaluating streakiness, using David Wright’s consistent performance in 2007 and his streaky performance in 2010 as examples. Now that we have a methodology for assessing the streakiness of players, we can extend it to all players. I repeated the same process I applied to Wright for all 1,545 players with 500 or more PA in every year dating back to 2001. To save computer processing time, I only ran 1,000 simulations for all players, rather than the 10,000 I ran for Wright in the first part of this piece (this is the difference between the calculations taking days and their taking weeks). While this reduces our precision slightly, the distributions are nearly identical:

So, let’s just get right to the red meat. Here are the five most and five least streaky players in every year from 2001 to 2010:

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We are happy to present a two-part guest post by Seth Samuels, who takes an in-depth look at a topic that is often a source of disagreement. Part two will run tomorrow.

Last summer, I was catching up with Fangraphs founder (and my elementary school classmate) David Appelman when he mentioned an interest in being able to identify streakiness in baseball players. Baseball announcers and writers are often criticized for psychoanalyzing a player’s current hot or cold streak, even though those streaks may often be a function of small sample sizes. A full season, however, is a much larger sample than five games. So it certainly seems reasonable that some players might tend to be streakier than others over the course of a full year.

As both a Mets fan and an occasional fantasy player, I’ve repeatedly seen my teams — both real and imaginary — bolstered by surges and short-circuited by cold spells. So being able to identify which players are most or least likely to go on streaks would be a useful tool.

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Index

1. Introduction
2. Fielder Credits and Debits
3. Batted Ball Types
4. GDP and Arm
5. Base Runner and Outs Adjustments
6. Other Infield Positioning
7. Outfield Positioning and “Wall Balls”
8. Infield Line Drives and Pop-ups
9. Park Adjustments
10. The Baselines
11. Recent Revisions
12. Sample Size and Reliability
13. UZR and Aging
14. Consistency from Year to Year
15. Does UZR tell us what actually happened on the field?
16. Conclusions

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The answer to that question is complicated. There is no easy yes or no answer and that is not so much because there are so many variables we don’t know the answer. It has to do with game theory. Oh, in case you didn’t watch the 6th game of the ALCS or you forgot, in the 8th inning with the Yankees up 3-2, Swisher bunted with a runner on first (and no out of course) and when he reached on an ROE, Melky bunted with runners on first and second.

Many people, including those who are sabermetrically inclined, typically decry the sacrifice bunt – why give away outs? The conventional (and lazy) sabermetric wisdom used to be that sac bunt attempts were almost always incorrect – at least ever since The Hidden Game of Baseball told us so and legions of sabermetric fans and even sabermetricians looked at the RE and WE tables and noticed that the game state after an out and base runner advance was worse than before – hence the sac bunt is wrong.

The problem of course is that that is a ridiculously simplistic way to answer the question on two fronts. One, the WE or RE before and after a “successful” sac bunt, using a standard table, is based on an average batter in an average lineup against an average pitcher and defense in an average stadium on an average Spring day. At least some analysts recognized that in different contexts, those numbers would have to be revised. However, most of them also noted that the gap was so large between the “before” and “after” state (in favor of the “before” state – which assumes hitting away most of the time), that it would take an enormously bad hitter -like a pitcher – to make it correct to bunt. They would basically be right.

Now, there is a more important and pertinent reason why looking at RE and WE charts and comparing the “before” and “after” numbers do not help you in answering the question as to whether a sac bunt (by a non-pitcher) is correct in any given situation. And that is because a sac bunt attempt obviously does not lead to an out and a base runner advance 100% of the time (or even close to 100%); in fact the average result from a sac bunt attempt is not even equivalent to an out and a base runner advance. Also, the average result varies a lot with the speed and bunting skill of the batter and whether and by how much the defense is anticipating the bunt or not (among other things).

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