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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

Wednesday, January 11, 2017

2016 Platoon Advantage | Exploring Baseball Data with R

Some interesting research on platoons.

Jim Furtado Posted: January 11, 2017 at 09:14 AM | 0 comment(s)
  Beats: sabermetrics

Nate Karns and Useful Spin | FanGraphs Baseball

Nate Karns under the Enoscope.

Jim Furtado Posted: January 11, 2017 at 06:18 AM | 1 comment(s)
  Beats: nate karns, pitchfx, sabermetrics

Monday, January 09, 2017

Is Home-Field Advantage Becoming Endangered? | FanGraphs Baseball

Ahmed found the minor leaguers he was studying were slightly less effective on the the road after traveling, and in returning home, because they were less likely to be at full recovery.

“There was a direct correlation between the higher your recovery and the faster your exit [batted ball] velocity. We found the same correlation with pitchers and fastballs,” Ahmed said. “I didn’t know for sure we were going to see such strong correlations with travel, recovery and performance. Those were a bit of a eureka [moment] with us and for people in Major League Baseball who saw this study.”

Ahmed said that among the 28 teams that experimented with Whoop, 70% of players participated. Whoop gives a metric before 0% and 100% in regard to recovery. He said habits changed.

“The athletes themselves are self-managing,” Ahmed said. “One thing we found is athletes would talk about drinking less alcohol or going to bed earlier. They are starting to think about their recovery. The higher their recovery the better they play. You go from thinking of yourself as an athlete three hours a day, to thinking of yourself as an athlete 24/7.”

Jim Furtado Posted: January 09, 2017 at 11:18 AM | 1 comment(s)
  Beats: home field advantage, sabermetrics

Monday, December 26, 2016

SB Insights from 2016 Retrosheet Data | Exploring Baseball Data with R

OK, now that Christmas is past, you are ready to play with Retrosheet data. Right?

Jim Furtado Posted: December 26, 2016 at 08:15 AM | 0 comment(s)
  Beats: sabermetrics

Saturday, December 24, 2016

Pitch Framing Was Doomed From the Start – The Hardball Times

Pitch-framing numbers answered questions we didn’t know we had. A new statistical category emerged out of nowhere, and before long, the research proved its own worth. That was the birth of the revolution. Every team now wants good-receiving catchers. Every team, additionally, wants to develop more good-receiving catchers. The market is going to end up flooded with good-receiving catchers. By then we’ll no longer recognize them as good-receiving catchers. Pitch-framing is sufficiently important that baseball teams will prioritize it right into insignificance.

Jim Furtado Posted: December 24, 2016 at 08:29 AM | 9 comment(s)
  Beats: pitch framing, sabermetrics

Thursday, December 22, 2016

On Players’ and Coaches’ Skepticism of Defensive Metrics

When Statcast fixes the missing plays problem, DRS and UZR will go the way of RF and Fielding Pct. Nobody will use them. Regardless, most often it’s a misunderstanding of what the numbers mean and using them improperly that is an actual problem.

“I’ve seen the defensive statistics that say that Hechy (Adeiny Hechavarria) is not a good shortstop, and that’s just stupid,” says Marlins outfielder Christian Yelich. “You can’t say that and have any sort of credibility at the same time. I think a lot of sabermetrics and a lot of the numbers don’t tell the whole story. You’ve got to watch the game, as well. You can’t just look at a sheet of paper, look at what it says, and say, ‘This guy’s good, that guy’s not good,’ just based on looking at paper.”

Jim Furtado Posted: December 22, 2016 at 08:18 AM | 78 comment(s)
  Beats: defense, defensive metrics, drs, sabermetrics, uzr

Tuesday, December 20, 2016

Tangotiger Blog: Pre-Statcast Lab: groupings or single point estimates?

More interesting stuff is coming from Tangotiger.

So, getting back to the above image: it’s likely that the information contained in the barrels will need to get more weight than in the other zones.  In order to understand the hitter, you need to know his profile.  This is true whether you want to know about his future, or simply want to know about what you have on hand as a hitter.  Knowing the profile of the hitter is better than knowing the single end point.  The profile keeps the conversation going, while the single end point ends up being a single data point.

Yes, no reason to choose between the two, and we may as well present it all.  The point remains however that the value exists greater at the component level, whereas at the summary level, it becomes a summary opinion with some evidence.  The fun though is in the evidence.

Jim Furtado Posted: December 20, 2016 at 08:48 AM | 2 comment(s)
  Beats: sabermetrics, statcast

Saturday, December 17, 2016

Embrace the Barrel — Baseball’s Newest Statistic

Although this is an improvement, I’m still not a fan of FIP.

Jim Furtado Posted: December 17, 2016 at 08:16 AM | 4 comment(s)
  Beats: pitching, sabermetrics, statcast

Thursday, December 15, 2016

How catcher arm strength affects stolen bases | MLB.com

I look forward to defensive components being broken down and ranked. Exchange Time, Arm Strength, and Accuracy for catchers seems like a good place to start.

You can see how much more work remains, of course. Once correlations are run against all of the various inputs that go into “safe” or “out,” we’ll have a much better idea of how much credit or blame is due to each party. Instead of just how much lead distance a runner had when the pitcher threw the ball, we’ll be able to push deeper and see just where he was when the catcher caught and released the ball, too.

It’s “good” to have a strong arm for a catcher, and while that perhaps seemed obvious, it’s nice to have some empirical evidence around it. It’s just one of a ton of different things that go into it. You probably already knew not to judge a catcher based on “caught-stealing percentage” alone. Now, you know where and how arm strength starts to make a difference.

Jim Furtado Posted: December 15, 2016 at 07:03 AM | 1 comment(s)
  Beats: billy hamilton, sabermetrics, statcast

Saturday, December 10, 2016

Winning in MLB is harder when everyone is smarter - SweetSpot- ESPN

A lot of good stuff. Read the whole thing.

Teams in the more recent period not only had a lower average win total in the 90-win season, but suffered a bigger decline the following season, were less likely to win more games or to win 90 again and more likely to have a losing season. Winning now is more ephemeral: Winning 92 games and making the playoffs as opposed to winning 85 and falling short is often not just the residue of clever team-building, but to the vagaries of luck, injuries and random career seasons.

Jim Furtado Posted: December 10, 2016 at 07:43 AM | 18 comment(s)
  Beats: dave dombrowski, moneyball, red sox, sabermetrics

Saturday, December 03, 2016

D-Backs hire Mike Fitzgerald as new analytics head

Which direction will Arizona change to when this plan doesn’t produce a World Series championship within two years? Fortune tellers?

“(Analytics) certainly has been an area of focus for us since coming over here; I know it’s been an area of focus for (owner) Ken (Kendrick) and (CEO) Derrick (Hall) when I was hired,” Hazen said. “We want to look to strategically build this department.

“He has a phenomenal reputation, very smart, impressed throughout the interview process. He’s the right guy to help really build our infrastructure in these areas. The game is changing in so many different ways, there are so many new areas of information to explore, and he’s going to help us do that.”

Jim Furtado Posted: December 03, 2016 at 08:17 AM | 1 comment(s)
  Beats: diamondbacks, sabermetrics

Thursday, December 01, 2016

MLB analytics guru who could be the next Nate Silver has a revolutionary new stat - CBSSports.com

“HE FREAKS US OUT.” ~ Harry Pavlidis

Jim Furtado Posted: December 01, 2016 at 04:43 PM | 45 comment(s)
  Beats: sabermetrics

Wednesday, November 30, 2016

The Most Improved Changeup of the Second Half | FanGraphs Baseball

Tyler Chatwood‘s changeup has never been good, but it really looks like he’s tinkering with it, no matter which classification system you’re using. By PITCHf/x, he added nearly two inches of fade and over an inch of drop. Meanwhile, he began throwing the fastball harder and change slower, improving the gap between the two pitches by 2.5 mph, the biggest difference in the sample of 92.

That’s according to the generic PITCHf/x classifications from MLB, at least. According to Brooks Baseball’s data, the improvement was a little more muted, suggesting that some of the difference comes not from any improvement on Chatwood’s part, but simply from mis-diagnosing changeups. He still added an inch of fade there, a half inch of drop, and a half mile per hour of velocity gap. The problem is that he pushed that changeup all the way to… below average with respect to movement. The velocity differential was merely average. Still not a good pitch, even if it was better.

Jim Furtado Posted: November 30, 2016 at 07:04 AM | 0 comment(s)
  Beats: changeup, sabermetrics

Tuesday, November 29, 2016

Sabermetric Research: How should we evaluate Detroit’s defense behind Verlander?

Phil Birnbaum puts Joe Posnanski’s criticism of WAR into his intellectual hopper.

Jim Furtado Posted: November 29, 2016 at 07:08 AM | 7 comment(s)
  Beats: cy young, justin verlander, rick porcello, sabermetrics, war

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