Archive for Outside the Box

More on Changing Hitter Aging Curves

A few days ago, I looked at the possibility of major league hitters no longer showing any hitting improvement, on average, once they debut in the majors. I believe both the banning of PEDs and teams being able to evaluate MLB ready talent are the keys to this change.

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The One-Year Effect of the New Balk Rule

I wish I could remember the date. One of my favorite pastimes is looking at the box scores of games I attended that were meaningful for some reason. I was there when Johan Santana struck out 17. I saw Carlos Gomez score from second base to win Game 163 of the 2009 Twins season. But, for the life of me, I can’t remember the date of this game. It was at Target Field — I know that. I was with my wife and two family friends, Abbey and Andrew. We were in the upper deck overlooking left field. Right next to me, a man — a Twins fan, I discerned from his hat — was watching with a companion from England.

From what I could overhear, this companion had never seen a baseball game before, and the other man was trying to explain the basic goings on of the on-field action. He was teaching her how baseball was played, ostensibly. And he was doing a fine job, I remember. He would slowly and assuredly explain how the runners moved, the idea of balls and strikes, tagging up, foul balls, etc. Basically, everything a newcomer to the game would need to know. I don’t even remember who the Twins were playing — the Royals? This is bothering me. But sometime later in the game, just as the English spectator was starting to recite what happened back to her friend in a way that signified that she was beginning to understand, it happened. Just when the traveled fan must have felt pretty good about his lesson, he was shouldered with the unenviable task of explaining just what the hell a balk was. That poor so-and-so.

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More Fun with Markov: Custom Run Expectancies

Before the season, I put up a three-part series (1, 2, and 3) that explained how linearly-weighted stats like wOBA, while useful for comparing players to each other, don’t necessarily reflect each player’s true contribution to their team’s run scoring.  You see, the weights used to calculate wOBA are based on league averages.  So, for a team with league average breakdowns in walk rate, singles rate, home run rate, etc., wOBA (and its offspring, wRC+) ought to work very well in figuring out how valuable a player is (or would be) to an offense.  However, when it comes to particularly bad or good offenses, or to those with unusual breakdowns, wOBA will lose some of its efficacy.

Why?  There are synergistic effects in offenses to consider.  First of all, if a team gets on base a lot, there will be more team plate appearances to go around, which of course gives its batters more chances to contribute.  Second of all, if the team gets on base a lot, a batter’s hits are generally worth more, because they’ll tend to drive in more runs.  And, of course, once the batter gets on base in such a team, it will be likelier that there will be a hit (or series of hits) to drive him in.  The reverse of all three points is true in a team that rarely gets on base.

But it goes even beyond that.  Let’s say Team A gets on base 40% of the time, and Team B gets on only 20%, but their balances of the ways they get on base are equal (e.g. each hits 7x as many singles as they do HRs) .  A home run is going to be worth something like 14% more to Team A, due to more runners being on base.  However, to Team B, a home run is worth over ten times as much as a walk, whereas to Team A, it’s worth only about 5 times as much.  That’s because Team A has a much better chance of sustaining a rally that will eventually drive in that walked batter.  Team B will be much more reliant on home runs for scoring runs.

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Why Strikeouts Secretly Matter for Batters

I got my start at FanGraphs by writing Community Research articles. As you may have noticed, community authors have been very busy this season, cranking out a lot of interesting articles. One that caught my eye the other day was triple_r’s piece on the importance of strikeouts for hitters. The piece correctly pointed out, as other studies have, that there’s basically no correlation between a hitter’s strikeout rate and his overall offensive production. Strikeouts don’t matter; case closed, right? Well, not exactly.

Let me present a hypothetical situation. Say there’s a group of players who go to an “anti-aging” clinic in Florida and pick up some anabolic steroids. Let’s say these hypothetical players are named Bryan Raun, Ralex Odriguez, Tiguel Mejada, Phonny Jeralta, Celson Nruz, and Barry Bon… nevermind. Yet, after using the steroids, it appears that the group of them, on average, has not improved. The steroids didn’t improve their performance, right? But, wait — let’s also say that while visiting Florida, some of them contracted syphilis, which spread to their brains, causing delusions and severely impacting their judgment, strike-zone and otherwise. The players whose brains aren’t syphilis-addled have actually improved quite a bit, but their gains are completely offset by the losses suffered by those whose central nervous systems are raging with syphilis. So, the fact that the steroids actually do improve performance has been completely obscured by another factor that is somewhat — but not necessarily — associated with the steroids.

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Batter-Pitcher Matchups Part 2: Expected Matchup K%

In last episode’s thrilling cliffhanger, I left you with a formula that I brashly proclaimed “does a great job of explaining the trends” in strikeout rates for meetings between specific groups of batters and pitchers.  Coming up with a formula to explain what was going on wasn’t pure nerdiness — making formulas to predict these results is the point of this research project.  You see, the goal of my FanGraphs masters is to come up with a system by which we can look at a batter and a pitcher, and tell you, our loyal followers, some educated guesses of the chances of pretty much every conceivable outcome that could result from these two facing off against each other.  Getting a sense of the expected strikeout rate is merely the first step in what will likely be a long process of continuous improvement.

The idea of this matchup system is to not only give you estimates that are more free from the whims of randomness than “Batter A is 8-for-20 with 5 Ks and 1 HR in his career against Pitcher B,” but also to provide some evidence-based projections for matchups that have never even happened.  How do we propose this can be done?  By looking at the overall trends and seeing how players fit within them.  Can it really be done?  It definitely looks that way to me.  Today’s installment will be about attempting to convince you of that.

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Better Match-Up Data: Forecasting Strikeout Rate

“Riddle me this,” wrote editor Dave Cameron to me some time ago, “what happens when an unstoppable force meets an immovable object?”  OK, that’s not exactly how it went down.  What he actually did was to present me with the challenge of research, with the goal being to develop a model that would forecast the expected odds of an outcome of each match-up between a specific batter and a specific pitcher. Rather than talking about how players have done in small samples, can we use our understanding of player skillsets to develop an expected outcome matrix for each at-bat?

For example, such a tool might tell you that Adam Dunn has a 40% chance of striking out against Stephen Strasburg, a 10% chance of drawing a walk, a 5% chance of hitting a ground ball, etc… Forget I said those particular numbers — I completely made them up in my head just now.  You may be thinking “well, why should I care about that?  Rather than just being inundated with match-up data that is little more than randomness, such a tool might give you some idea of how much of a gain in expected strikeout rate a team would get by switching relief pitchers with a man on third base and less than two out. Or what the probability of getting a ground ball is in a double play situation, which might influence the decision of whether or not to bunt. Knowing the odds of potential outcomes could be quite beneficial in understanding the risks and rewards of various in-game decisions.

This project has been — and will continue to be — a major undertaking, as you can imagine.  This isn’t the kind of thing that can just be thrown together, but I really think the results could be great. Today, I’ll be sharing with you the findings of my research into perhaps the most important aspect of these matchups — K%, or strikeouts per plate appearance.  This will introduce the sort of process that will be involved in figuring out all of the other elements of the matchup tool. Read the rest of this entry »


Randomness, Stabilization, & Regression

“Stabilization” plate appearance levels of different statistics have been popular around these parts in recent years, thanks to the great work of “Pizza Cutter,” a.k.a. Russell Carleton.  Each stat is given a PA cutoff, each of which is supposed to be a guideline for the minimum number PAs a player needs before you can start to take their results in that stat seriously.  Today I’ll be looking at the issue of stabilization from a few different angles.  At the heart of the issue are mathy concepts like separating out a player’s “true skill level” from variation due to randomness.  I’ll do my best to keep the math as easily digestible as I can.

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An Unsolicited Follow-Up Study of Pull%

I’m always looking for new angles to unlock the mysteries of BABIP, so I was intrigued by Jeff Sullivan’s exploration of pull rates against pitchers.  So I grabbed the data from baseball-reference.com, and set to work subjecting it to my usual rigmarole of correlations and multiple regressions.  You know how they say if your only tool is a hammer, everything looks like a nail to you?  Well, plug your ears — there’s about to be a lot of wild, uncontrolled pounding going on in here…

I’ll cut right to the chase — did I find anything interesting relating to pitchers’ overall effectiveness when it comes to their Pull%, Middle%, and Opposite%, as I’m calling them?  Well, I found one decent connection that will seem obvious and stupid after you think about it, and a slight but kind of interesting connection.  I’ll provide you with some correlation tables that have left few stones unturned.  But, mainly, the research might help to set some things straight about how important this stuff actually is for pitchers.

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BABIP Park Factors and the Batted Ball Connection

Some of you may recall that before being promoted from a FanGraphs Community Research writer to an actual FanGraphs writer, my primary focus was on the relationship between batted ball types (infield fly balls, in particular) and BABIP for pitchers.  At the time, I’d been leaving park factors out of the equation in a [vain] attempt to keep things simple, but now I want to give them a bit of attention.

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Team-Specific Hitter Values by Markov

In my first article, I wrote about the limitations of the linear weights system that wOBA is based on when it comes to the context of unusual team offenses. In my second, I explained how Tom Tango, wOBA’s creator, also came up with a way of addressing some of these limitations by deriving a new set of linear weights for different run environments, thanks to BaseRuns. Today, I will tell you about the next step in the evolution of run estimators — the Markov model. Tom Tango created such a model that can be accessed through his website, and I’ve turned that model into a spreadsheet that I’ll share with you here.

I’ve told you that the problem with the standard run estimator formulas is that they make assumptions about what a hit is going to be worth, run-wise, based on what it was worth to an average team. That means it’s not going to apply very well to an unusual team. What’s so great about the Markov is that it makes no such assumptions — it figures all of that out itself, specific to each team. And when I say it figures it out, I mean it basically calculates out a typical game for that team, given the proportion of singles, walks, home runs, etc. the team gets in its plate appearances. It therefore estimates the run-scoring of typical teams better than just about anything, but it also theoretically should apply much, much better to very unusual or even made-up teams.
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1960 Salina Blue Jays: The Year Satchel Paige Came to Town

A small a bigger story sometimes hides behind a bit of information. That bit came in this line I read a few years ago in Larry Tye’s book, Satchel:

In 1960 he [Satchel Paige] threw for the Salina [Kan.] Blue Jays ….

I had no idea. Leroy Robert “Satchel” Paige was arguable one of the best 10-or-so pitchers who played baseball. He was a Hall of Famer on the field, but he was an even better showman. What was one of the greatest players doing playing on a team in Kansas?

I’m a Kansas native. Throughout my life, I’ve had a deep connection with Salina. I lived less than an hour away from the city when I was growing up. Some of my family members still live there. Heck, I was even married there. Because of that, I needed to know what brought Paige to the middle of nowhere to play baseball one summer so long ago.

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Tim Lincecum Needs to Learn How to Pitch, Not Throw

Tim Lincecum‘s resume contains the following items: 2 time Cy Young award winner, 4 time All-Star and twice World Series Champion. With all the achievements over the last 5 seasons, he was relegated to a long relief once the Giants made the playoffs because he was no longer effective as a starter. Lincecum’s problem is he can no longer just throw the ball across the plate and hope a batter just swings and misses. If he wants any hope of returning to be the starter he once was, he now needs to learn how to pitch.

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Andruw Jones: All-Star to Replacement-Level Player

Andruw Jones was having a brilliant career, that is, until he turned 31 years old. Since that point, he’s barely been a league-average player. He went from an all-time great player, to an iffy hall-of-fame candidate.

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Breakout Impossible: Don’t Compare Jose Bautista to Others

Jose Bautista came out of nowhere two-and-a-half seasons ago and hit 54 home runs at the age of 29. At a time when most players’ careers are declining, Bautista’s taking off. In fact, his  breakout has been completely unprecedented for someone his age.

Since the start of the 2010 season, Bautista has accumulated more than 18 WAR. In the history of baseball, only 38 hitters* have reached that kind of production during their age-29 to age-31 seasons. The most amazing part of Bautista’s statistical climb is how it was totally unpredicted.

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Replacing the HR Derby with a Skills Competition

We’re only three weeks into the season and MLB has already released the All-Star ballot for the 2012 mid-summer classic. That means speculation about who will participate in the Home Run Derby.

Too bad, really. I’m tired of the Home Run Derby. I’m tired of the complaints about who’s in and who’s out. I’m tired of the talk about whether participants change their swings to win the Derby. I’m tired of “back, back, back, back, back.” I’m tired of the only non-game activities during the All-Star festivities being about home runs. Because baseball is so much more than home runs.

In that spirit, I propose that the Home Run Derby be replaced with a baseball skills competition. The NBA and the NHL put on skills competitions during their all-star weekends. Sure, they’re a bit goofy, but they do a pretty good job of highlighting the different aspects of the game. Here, take a peak. First, the highlights from NBA’s 2012 Skills Competition:

Dribbling, passing, shooting. Yeah, there’s not a lot of defense involved — unless you count the human-shaped pylons. But it’s better than watching guys shoot bombs from half court.

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Looking into the Crystal Ball: MLB’s Social Media Future

This is the last of four stories on Major League Baseball and social media. You can read the first three parts here, here and here. Full disclosure: Major League Baseball Advanced Media employs FanGraphs contributor Paul Swydan, who wrote this series.

Major League Baseball and its Internet arm — Major League Baseball Advanced Media — started slowly in social media, but the pair has made incremental progress. Technologically, things are running smoothly, and last season the league had lots of success with its Fan Cave, among other initiatives. But what’s in the league’s future?

Certainly the best way for MLB to push the online envelope is to offer good content. But as we’ve seen with countless reality TV shows, what seems fun and exciting one year can soon becomes stale. MLB understands this. “We want the Fan Cave to continue to evolve, so that it’s fresh and unique,” MLB spokesperson Matthew Bourne says. This season, instead of MLB picking Cave finalists on its own, the league is giving fans their say. The league recently concluded a voting period that saw the initial 50 finalists culled down to 30. So far, the results have been promising: MLB’s public relations team said they received more than 1.2 million votes in roughly one month.

All 30 finalists headed down to Spring Training in Arizona this past week, and the league now is deliberating on who will make the final cut heading into the regular season. Once the group — which MLB has promised will include at least one woman — is chosen, fans will once again have the chance to vote off contestants until only two remain in October. “This is an engagement with our fans through social media, and what they say is very important,” Bourne says.

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Social Media Expansion: Teams Get in the Game

This is the third of four stories on Major League Baseball and social media. You can read the first two stories here and here. Full disclosure: Major League Baseball Advanced Media employs FanGraphs contributor Paul Swydan, who wrote this series.

As the social-media revolution began, few major league franchises were fortunate enough to have a championship-caliber team. And perhaps only one was down the street from a company leading that charge. In 2010, the San Francisco Giants went on a historic World Series run while its neighbor was going on a run of its own. That company was called Twitter.

The close proximity between the baseball Giants and the social-media giant gave the team the online head start that perhaps no other team enjoyed — though several teams have now been able to replicate. And the rewards are still rolling in for those franchises.

Case in point: one of the first Tweetups organized by a club was one that the Giants hosted with Twitter founders Biz Stone and Jack Dorsey, “They have been instrumental in helping us understand how to use Twitter to communicate and engage with fans,” says Bryan Srabian, the Giants’ social media director. Twitter, too, most certainly understood the value of a live baseball game.

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MLB Expands Its Social Media Footprint

This is the second of four stories on Major League Baseball and social media. You can read the first story here. Full disclosure: Major League Baseball Advanced Media employs FanGraphs contributor Paul Swydan, who wrote this series.

While other leagues have seen attendance dips in the past few years, Major League Baseball has held strong. And though that success initially didn’t translate online quite as well — as the first part of this series indicated — baseball has begun pumping social media fastballs. Among its best decisions was allowing fans to share video.

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Socially Awkward to Socially Active: MLB Online

This is the first of four stories on Major League Baseball and social media. Full disclosure: Major League Baseball Advanced Media employs FanGraphs contributor Paul Swydan, who wrote this series.

The evening of Nov. 11, 2010, turned into a pretty frustrating one for Kyle Scott. On that night, Scott, who runs the popular Philadelphia sports blog Crossing Broad, got an email from YouTube telling him that several baseball videos he’d posted were being removed from the site. While the videos were short — none exceeded 30 seconds — and contained scant game footage, they’d apparently gotten the attention of Major League Baseball Advanced Media. It wasn’t the first time that Scott had run afoul of MLBAM, but he was frustrated enough by the situation to write about it the next day. “They were short clips that we used for a quick laugh,” Scott says now. The Internet site The Big Lead picked up Scott’s story, and Scott says most readers “sympathized with our frustrations.” That MLBAM put the kabosh on Scott’s videos seems counterintuitive for a sport that’s constantly trying to expand its brand — and 15 months after getting the YouTube email, Crossing Broad averages nearly 1 million page views a month.

So is MLB a big-league bully — or is it simply protecting itself? And how does the league stack up against its peers on the American sports landscape? To figure that out, you first have to take a look at Scott’s case — or more specifically, to YouTube, where the league’s social-media firestorm began. Not only did MLB not post their own videos on YouTube, they actively sought to remove videos that fans had posted — a decision that ran counter to other sports leagues, which never took such heavy handed measures. Sometimes, as in Scott’s case, the deletions left a very public trail — and that critical fallout can have a lasting effect. But while MLBAM could have been more diplomatic about its position, the league’s online media arm had a practical business reason for taking such a hard line: the moneymaker called MLB.tv.

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10 Year Disabled List Trends

With disabled list information available going back 10 years, I have decided to examine some league wide and team trends.

League Trends

To begin with, here are the league values for trips, days and average days lost to the DL over the past 10 years.


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