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Wednesday, June 21, 2017

How the wonks won baseball coverage

“It’s a ball now. It’s my favorite period in the business — by far,” says Tom Boswell of The Washington Post, a great baseball writer who straddles sharply different eras.

“I wish it had always been like this. You have all the old approaches to coverage still available — profiles, human interest, humor, etc. But so much more, too. For people who love to analyze (me), there’s nothing as good as real data, plus tons of unmined data where you can discover patterns that others haven’t spotted. FanGraphs, MLB.com/Statcast and baseball-reference are just an addictive gold mine. You have to restrain yourself.”...

It wasn’t long ago that baseball statisticians like Bill James were a curiosity, with the rise of the species even spotlighted in a Hollywood movie, “Moneyball,” about the Oakland A’s and based on the Michael Lewis book that chronicled Billy Beane, its idiosyncratic general manager.

Now, metrics rule baseball. As put by Keith Law, a baseball expert at ESPN who once worked for the Toronto Blue Jays, the revolution is over. People aren’t slaves to data but it plays a central role, with many basic assumptions of the past undermined. Thus, even the casual fan may view a player’s on-base percentage as more important than his batting average.

RoyalsRetro (AG#1F) Posted: June 21, 2017 at 04:05 PM | 19 comment(s)
  Beats: bill james, reporters, sabermetrics

Monday, June 05, 2017

Regression with Changing Talent Levels: The Effects of Variance – The Hardball Times

Estimating talent level is not for the squeamish. Interesting stuff.

Jim Furtado Posted: June 05, 2017 at 10:54 AM | 0 comment(s)
  Beats: sabermetrics, talent level

Hits on the Diamond | Tufts Now

Tufts’ impact on statistical analysts in MLB.

For the Cubs, the moment marked the end of a century of futility. For Tufts, the World Series represented another step forward in baseball’s statistical revolution. Gone are the days when teams acquired players based solely on the instincts of grizzled scouts. An influx of analysts, coders and game theorists, utilizing numbers you won’t find on the back of a baseball card, has changed the game. Everything these days is worth quantifying—from the angle at which a ball leaves the bat to the speed with which an outfielder races to catch it—and every front office in the game is expanding its analytics department in an attempt to get, or stay, ahead in the game’s statistical arms race.

Jim Furtado Posted: June 05, 2017 at 10:24 AM | 0 comment(s)
  Beats: analytics, front office, sabermetrics

Friday, June 02, 2017

Batted-ball data visualization using an alternative to a heatmap – fivetwentyone

This is some cool work. (Yes, data analysis can be cool.)

Data visualization research suggests that spatial separation and length are the most effective ways of showing quantitative comparisons, and in particular that color is better for categorical variables than quantitative variables. My goal here is to explore an alternative to a heatmap that uses a line graph instead of color to show the quantitative dependance of batting average on launch speed. One complication with that is that the way the batting average changes with launch angle depends on launch speed which gives the data interesting spatial behavior in the launch-angle / launch-speed plane. To try and keep this information, I came up with the idea of using brushing on the launch angle variable to highlight a given value of launch angle but to also highlight the neighboring few values to try and show the gradient in the launch angle direction. The result looks like this,

Jim Furtado Posted: June 02, 2017 at 06:39 AM | 0 comment(s)
  Beats: sabermetrics

Tuesday, May 23, 2017

Curiosity Might Kill the Home-Run Spike | FanGraphs Baseball

Adjust, then adjust to the adjustments.

It appeared at one point as though the most popular counterpunch to the uppercut swing would be an increase of elevated four-seam fastballs. But we’re not seeing more teams follow the Tampa Bay model. In fact, according to Baseball Savant data, we’ve seen fewer four-seam fastballs thrown in the upper third of the zone and higher this year: an 8.8% rate this season compared to 9.9% in 2016 and 9.8% in 2015. Pitches down and below the zone have inched up from 35.7% in 2015 to 39.3% this season, according to Statcast’s detailed search. Jeff Sullivan wrote about the missing elevated fastballs last week.

Jim Furtado Posted: May 23, 2017 at 07:02 AM | 0 comment(s)
  Beats: sabermetrics

Wednesday, May 17, 2017

How to Beat Statcast’s Hitting Metric | FanGraphs Baseball

At first glance, the relationship doesn’t appear to be particularly strong. To receive an r-squared of .27, however, between two stats that wouldn’t seemingly have a lot to do with each other, suggests that there’s something to this idea. Of the 28 batters above who recorded at least five runs on the bases over the last two years, all but one produced a positive xwOBA-wOBA. That one outlier is Gregory Polanco and his xwOBA-wOBA was .001. Those good baserunners, on average, beat their xwOBAs by 22 points per season in 2015 and 2016. The remaining players beat their xwOBA by an average of just two points per season in those years. At the very bottom, the 10 worst baserunners averaged a 15-point surplus in xwOBA compared to wOBA. The effect among slower players appears to be minimal when it comes to determining whether posting high xWOBAs (relative to observed wOBA) is some sort of skill. For the most part, it seems to have little to do with skill. So, in others words, if you see a player underperforming his xwOBA, it would seem that bad luck actually is involved.

On the other hand, if a player is posting an xwOBA lower than his wOBA, we can’t immediately jump to the conclusion that there’s a lot of good luck involved. Speed is a skill which has been stripped out of xwOBA. If a player can run out a lot of infield singles, that’s going to show up in wOBA, but not in xwOBA. If a player can turn a bunch of singles into doubles, that’s going to factor into wOBA, but not xwOBA. This doesn’t really discount xwOBA’s utility: after all, wOBA and wRC+ are fairly comprehensive offensive statistics, and they don’t account for a player’s offense once he reaches base. In addition, it still should be possible to identify players who have had good and bad luck simply by mentally compensating for speed a little bit. There should be a ton of great uses for xwOBA and we will get to more later, but we should keep in mind that players can beat xwOBA with their legs.

Jim Furtado Posted: May 17, 2017 at 10:37 AM | 0 comment(s)
  Beats: sabermetrics, statcast

Tuesday, May 09, 2017

Jedlovec joins MLB Now | MLB.com

Tip: If you can’t always watch MLB Now, here’s a link to a segment archive on MLB.com.

Jim Furtado Posted: May 09, 2017 at 08:33 AM | 1 comment(s)
  Beats: defense, drs, sabermetrics

What Age Do Baseball Players Peak? | Exploring Baseball Data with R

Some people have made some (in my view) unreasonable assumptions to learn about aging.  For example, it doesn’t make sense to assume that each player peaks at age 28.  People have different aging patterns — this means that players have different peak ages and also that players have different paths in maturing and in declining towards retirement.  So one need flexibility in any model to allow for these differences.  If one makes restrictive assumptions, then you’ll get answers which are inconsistent with the data.

Jim Furtado Posted: May 09, 2017 at 08:19 AM | 1 comment(s)
  Beats: aging, sabermetrics

Monday, May 01, 2017


Tuesday, April 25, 2017

Aging Patterns | Articles | Bill James Online

Bill James with a new aging study. HT to Tangotiger.

Jim Furtado Posted: April 25, 2017 at 08:30 AM | 6 comment(s)
  Beats: sabermetrics

Monday, April 24, 2017

2017 UZR Updates! | FanGraphs Baseball

MGL updates UZR.

Jim Furtado Posted: April 24, 2017 at 10:57 AM | 2 comment(s)
  Beats: analytics, defense, sabermetrics, uzr

Wednesday, April 19, 2017

Sac Bunting Summary and Redux | MGL on Baseball

MGL discusses the bunt in his unique style.

Jim Furtado Posted: April 19, 2017 at 08:38 AM | 46 comment(s)
  Beats: analysis, bunting, sabermetrics

Do MLB Teams Undervalue Defense — Or Just Value It Differently? | FiveThirtyEight

Whether because of Statcast or scouting, the Cubs and now the Cardinals have seen something in Fowler’s performance that current fielding valuations don’t seem to capture. And when two of the smartest front offices in baseball appear to be discarding defensive metrics, it makes you stop and wonder whether the metrics might just be wrong.

Jim Furtado Posted: April 19, 2017 at 08:38 AM | 19 comment(s)
  Beats: analytics, defense, sabermetrics, statcast

Wednesday, April 12, 2017

Stats All, Folks: WAR, what it is, what it’s good for and the absolute best WAR Orioles - BaltimoreBaseball.com

fWAR and bWAR, it’s no wonder people get confused.

Jim Furtado Posted: April 12, 2017 at 08:46 AM | 56 comment(s)
  Beats: orioles, sabermetrics, war

Tuesday, March 14, 2017

Let’s Play With New Defensive Data | FanGraphs Baseball

I’m not sure there’s a surprise in the bunch. Which is probably more of a good sign than a bad one — one wouldn’t think we’ve been completely wrong all this time. For as much as people have openly criticized the advanced defensive numbers, I think the bulk of the disagreement has centered on infield play, especially in the age of infielders moving around all over the place. We’ve long had a pretty good grasp on the outfield, I think. Statcast here mostly supports the information we already had. Kevin Kiermaier? Amazing! Billy Hamilton? Amazing! Keon Broxton? You better believe he’s amazing!

Maybe one way of interpreting this is as further evidence that Kiermaier has been better out there than Kevin Pillar. I know that’s been fiercely debated, but Statcast knows more than most of us do. There’s still room for these numbers to be adjusted, so Blue Jays fans can continue to take some heart. Travis Jankowski has apparently got it. Peter Bourjos has apparently still got it.

Jim Furtado Posted: March 14, 2017 at 02:56 PM | 2 comment(s)
  Beats: sabermetrics, statcast

Monday, March 13, 2017

Tangotiger Blog: Weekend at SABR’s (part 1 of 3)

Tangotiger’s experience at the SABR Analytics Conference.

This is the non-technical post.  Part 2 will have the new stuff I did on “What If” and Part 3 will have the new stuff on “Shifts against RHH”.  Both are work-in-progresses.  Which is really what Statcast is, every question answered, uncovers another two.  Our LACK of knowledge will grow exponentially with more Statcast findings!  More accurately, the awareness of our lack of knowledge will do so.

Jim Furtado Posted: March 13, 2017 at 10:24 AM | 0 comment(s)
  Beats: sabermetrics, sabr

Friday, March 10, 2017


Thursday, March 09, 2017

Baseball Prospectus: DRA 2017: The Convergence

I’m just not a fan of attempts at creating a be-all, end-all stat. Doing so just limits your stat because you have to make too many compromises to make the data work. Why not have one metric that looks back and another that looks forward?

On another note, does anyone know if DRA has separate components for pitching in the stretch and in a full wind up? Some old research suggested the difference can have a significant impact on runs allowed.

Two years ago, I wrote the first DRA essay, focusing on the challenge of modeling descriptive versus predictive player performance. At the time, my prognosis for threading that needle was rather grim:

What is it, exactly, that you want to know? For example:

(1) Do you care primarily about a pitcher’s past performance?

(2) Are you more worried about how many runs the pitcher will allow going forward?

(3) Or do you want to know how truly talented the pitcher is, divorced from his results this year or next?

The reader’s likely response is: “I’d like one metric that excels at all three!” Sadly, when it comes to composite pitcher metrics, this might not be possible.

The article reviewed a variety of metrics from plain RA9 to Fielding Independent Pitching (FIP) to SIERA (Skill Independent Earned Run Average) to show that all of them made sacrifices that committed them to one direction or the other.

DRA itself has tried to ride alternate sides of this fence. In its first year (2015), we elected to focus on descriptive performance, and designed DRA to be the best descriptive metric of what had previously happened short of RA9 itself.

Last year, we began to question the value of prioritizing descriptive performance, and switched to focusing on future performance instead. Again, though, this was presented in terms of a choice: decide which direction you care about, and resign yourself to it.

Jim Furtado Posted: March 09, 2017 at 09:47 AM | 3 comment(s)
  Beats: dra, pitching, sabermetrics

Tuesday, March 07, 2017

Fixing Batted-Ball Statistics with Statcast – The Hardball Times

Everything you wanted to know about line drivers but were afraid to ask.

Jim Furtado Posted: March 07, 2017 at 08:32 AM | 4 comment(s)
  Beats: sabermetrics, statcast

Monday, March 06, 2017

Mike Gimbel’s video of RPA vs. WAR plus 2017 predictions.

Mike Gimbel’s video was made on February 28th. It contains criticism of the WAR formula(s), using a comparison of results from the 2016 season and an analysis of all 30 MLB teams going into the 2017 season.

caiman Posted: March 06, 2017 at 09:49 AM | 8 comment(s)
  Beats: mlb, rpa, sabermetrics, war

Tuesday, February 21, 2017

In a world without stats, who would be the best baseball player?

This started with a crush on Starling Marte.

Marte is an outfielder with the Pittsburgh Pirates and my favorite baseball player to watch. He’s fast enough to have stolen 47 bases last year, third-most in the majors. His arm is one of the best in the game, capable of firing 101 mph bullets from the outfield. He is strong enough to have hit a baseball 460 feet, something only 48 other hitters have accomplished outside of Coors Field over the past two years. (Bryce Harper hasn’t. Mike Trout hasn’t.) He might be the most creative base slider in the game. He has demonstrated a talent for getting hit by pitches. Over the past three years, he has been the best defensive left fielder in the game, and this year, he will finally get to be a very good defensive center fielder.

I also know (or think I know) exactly how good Marte is: He was the 28th-best position player last year (by WAR) and the 53rd-best hitter (by OPS+). You might think those advanced stats are junk, but whatever stats you prefer, you have some idea how good he is: The 13th-best hitter (by batting average) or the 91st-best (by runs scored). We’ve all got stats. We all use our stats.

What if we had none? Not just no WAR but no nothin’. What if some ministry of information outlawed the collection of baseball statistics and we were all left to judge players exclusively by what we saw, what we perceived and what we remembered? Who would be perceived as the best player in baseball? Who would be the first player chosen in a franchise draft? Or, the more important question: With how much eye-rolling would actual major league general managers respond to a weird thought experiment on the subject?

Bourbon Samurai is disturbed by bagel developments Posted: February 21, 2017 at 10:23 AM | 64 comment(s)
  Beats: pirates, sabermetrics

Velocity drops in Spring Training can be red flags. | Sports on Earth

Pitching is hard.

Jim Furtado Posted: February 21, 2017 at 09:54 AM | 0 comment(s)
  Beats: pitching, sabermetrics

Tuesday, February 07, 2017

Inside Baseball Teams’ Battle to Keep Their Secrets Safe

A lot of good stuff in this one.

“That is the key: how you maintain … consistency in the face of success, how you remain open-minded enough to change despite your success, how you continually have personnel that leak nothing to the media,” says the former scouting exec. “That requires dedication and commitment that can’t just be thrown on a flash drive or backdoored by someone with knowledge of a system; the real secrets are the ones that can’t be stolen.”

Jim Furtado Posted: February 07, 2017 at 06:27 AM | 8 comment(s)
  Beats: cyber security, sabermetrics

Monday, February 06, 2017

Baseball Prospectus | Rubbing Mud: Command, Framing, and Teamwork

An interesting look at the Cubs catching match-ups.

Jim Furtado Posted: February 06, 2017 at 05:04 PM | 0 comment(s)
  Beats: pitch framing, sabermetrics

Monday, January 23, 2017

Baseball Prospectus | Prospectus Feature: Command and Control

Interesting stuff. Read the whole article.

Command

Now that we’ve established that CS Prob is a proxy for control, we can build on it. After extensive review, we’ve concluded that CSAA substantially reflects a pitcher’s ability to command his pitches. It’s important to make the connection between what CSAA does and the popular definition of command.

Traditionally command is understood as the ability to “hit your spots”—having the ball end up where you intend it to. Over the years this has been studied in numerous ways—most notably by attempting to determine how much the catcher moves his glove to receive a pitch. This is flawed because the catcher’s glove isn’t always the target, and we can’t know where the pitcher is truly intending the pitch to go.

What we can do is come at command from a different angle. A pitcher with good command should be more predictable for the catcher—their pitches often end up in the locations, and with the movement that the catcher expects. This skill results in easier receiving for catchers, and additional called strikes for the pitcher. Once we aggregate the data cross thousands of pitches, CSAA is able to tell us whether a pitcher is reliably hitting his spots.

CS Prob is actually covariate in the model for CSAA, which is a fancy way of saying that CSAA measures the extent to which a participant tends to affect the likelihood of a strike being called, notwithstanding its final location. As such, CSAA controls for all of the same things as CS Prob and adds in the umpire and catcher for good measure.

So what does accumulating CSAA look like? It’s not as easy as it sounds. Sure, you could throw a ton of pitches in the middle of the zone and basically guarantee that you’ll wrack up called strikes on the pitches hitters don’t offer at. The downside to that approach is that pitches in the center of the plate get crushed.

Jim Furtado Posted: January 23, 2017 at 10:18 AM | 1 comment(s)
  Beats: sabermetrics

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