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Sunday, September 25, 2016

Fire Joe Morgan and the Moneyball revolution.

The scouts vs. statheads war is, and was, overblown.

Jim Furtado Posted: September 25, 2016 at 08:34 AM | 15 comment(s)
  Beats: sabermetrics

Monday, September 19, 2016

Constructing Heat Maps for AVG | Exploring Baseball Data with R

Heat maps for all my friends.

Jim Furtado Posted: September 19, 2016 at 08:14 AM | 0 comment(s)
  Beats: sabermetrics, using r

Friday, September 16, 2016

20 wins does tell us something about a starter. | Sports on Earth

I’m sure you want Brian Kenny’s reaction.

So the win, and the 20-win season in particular, continues to hold sway in the Cy Young Award voting despite a brief glimmer of progress a half decade ago. Indeed, while Porcello is having a fine season, as his 20 wins do indicate, he has not been the AL’s best pitcher to this point in the season. Consider this comparison between Porcello and Corey Kluber, who is 16-9:

Pitcher W-L IP ERA+ WHIP K/BB FIP DRA bWAR
Kluber 16-9 197 2/3 157 1.04 4.08 3.14 2.77 6.3
Porcello 20-3 193 2/3 142 1.02 5.55 3.46 3.36 4.4

(DRA is deserved run average, a Baseball Prospectus statistic that attempts to correct a pitcher’s runs allowed per nine innings for all outside influences, from ballparks and opposing lineups to his catcher’s pitch framing, the umpires calling his games and even the weather.)

The key difference between those two when it comes to wins is that Porcello has received more run support than any other qualified starter in the Majors this year by nearly half a run per game. The league’s best offense has averaged 6.97 runs per game over his 29 starts this season. Cleveland, meanwhile, has scored 4.93 runs per game for Kluber, still a big number, but more than two full runs shy of what Porcello has gotten from the Red Sox’s lineup.

That’s a good reminder that wins tell us as much or more about run and bullpen support as they do about a starting pitcher’s actual performance and are thus useless in comparative analysis. Any voter using wins as a measure to fill out an All-Star, Cy Young Award or Hall of Fame ballot is committing malpractice. Still, it’s inaccurate to say that a 20-win season tells us nothing about a pitcher’s performance. Now more than ever, 20 wins are an indicator of an above-average starting pitcher. To be any more specific than that, however, one must look beyond the wins column.

Jim Furtado Posted: September 16, 2016 at 09:05 AM | 61 comment(s)
  Beats: corey kluber, rick porcello, sabermetrics

Thursday, September 15, 2016

Wrigleyville - Baseball Prospectus

Statcasting the Cubs.

Jim Furtado Posted: September 15, 2016 at 09:52 AM | 8 comment(s)
  Beats: cubs, sabermetrics, statcast

Monday, September 12, 2016

Ye Gods of BABIP, Let My Fly Balls Go! – The Hardball Times

So, pitchers can impact BIP. Who knew?

Jim Furtado Posted: September 12, 2016 at 08:09 AM | 0 comment(s)
  Beats: sabermetrics

Wednesday, September 07, 2016

Statcast Lab: How much impact does a great-fielding outfielder have?

This is absolutely fantastic stuff. I cannot wait to see Statcast defensive stats readily available.

Jim Furtado Posted: September 07, 2016 at 08:06 AM | 16 comment(s)
  Beats: sabermetrics, statcast

Friday, September 02, 2016

How Managers Were Fooled by the Home-Run Spike | FanGraphs Baseball

Some interesting breakdowns. Read the whole thing.

What’s fascinating about this is that managers are responding to the home-run spike. If you look at earlier seasons, there’s not a clear relationship between scoring and pitches per start or innings per start. And you wouldn’t expect there to be in the aggregate. The decision to pull the starter should be based on his effectiveness relative to the other pitching options, not his un-adjusted in-game performance. But that’s not how it happened. Managers didn’t realize the league was scoring more runs; they all acted as if their starters in particular were losing effectiveness.

Put another way, the home-run increase disrupted managers’ ability to evaluate their own starting pitchers. Instead of recognizing that run-scoring was up and that expectations should be adjusted as a result, managers started pulling their starters in response to the runs they were allowing.

Jim Furtado Posted: September 02, 2016 at 10:43 AM | 3 comment(s)
  Beats: sabermetrics

Monday, August 29, 2016

Lasers in the outfield—Infield isn’t only place where defense is shifting

When it comes to positioning their outfielders, the Yankees literally keep the information under their hats. Before each game, bench coach Rob Thomson, responsible for the outfield defense, gives Gardner, center fielder Ellsbury, right fielder Hicks and rookie outfielder Aaron Judge an index card with precise locations for each opposing hitter.

Those locations are determined by a proprietary computer program developed by the Yankees’ analytic squad, headed by David Grabiner. It takes a multitude of factors—among them the hitter’s power, his tendency to pull or not pull the ball, and his career history against the Yankees’ pitcher that night—and spits out a spray chart which places the outfielder in the optimal position to make a play.

“We have analytical assessments that show specifically where guys hit the ball,” a Yankees staffer told ESPN.com. “I mean, it shows us exactly where guys hit the ball just about every time. And it’s hitter/pitcher specific, based on pitch velocity and location. Positioning is based on a lot of factors, including the speed of the defender.”

Jim Furtado Posted: August 29, 2016 at 10:29 AM | 1 comment(s)
  Beats: defense, sabermetrics, technology

Sunday, August 28, 2016

MLB’s Statcast creates new era of data and competition | SI.com

Following up on earlier article from this week, one way for a team to differentiate itself right now is finding the best way to handle missing data.

If you are interested in sabermetrics, read this article.

Every MLB organization now has an analytics team in place to try to figure out what to do with all the data that comes from 2,430 games—roughly 750,000 pitches—a season. The 30 teams are using the data in 30 different ways, but they do all share this: an unwillingness to talk about what it is they are doing with it.

“It’s an arms race, with all the different areas to explore. As teams find benefits they’re gaining a competitive advantage that they want to hold very close,” says Greg Cain, BAM’s senior director of sports data. “It even colors how we receive requests for information from clubs. A lot of times we’ll get a long list to kind of hide what they’re looking for.”

The challenge is to know what to look for. “It’s so massive, it’s just about asking the right questions,” says Willman. “As far as the answers: The answers are all there.”

Jim Furtado Posted: August 28, 2016 at 09:19 AM | 0 comment(s)
  Beats: sabermetrics, statcast

Thursday, August 25, 2016

MLB’s Hit-Tracking Tool Misses A Lot Of Hits | FiveThirtyEight

That’s still a lot of missed plays.

But that data isn’t always easy to analyze. Front office analysts I spoke with said that Statcast’s radars frequently lose track of batted balls on atypical trajectories — for example, with extremely high (popup) or low (chopper) angles. In 2015, Statcast failed to provide data on 13.4 percent of all batted balls; it’s gotten a bit better as time has progressed, dropping to 12.5 percent in the first half of 2016 to only 11.2 percent since July.

Without a complete track of the batted ball, the computers must extrapolate, and sometimes they fail to report any data on the trajectory or give implausible readings (exit velocities of zero, or improbable home run distances). They can also spit out velocity readings that are just plain inaccurate. These kinds of errors require extensive manual checking and correction for use by front offices, but for public use, such ambiguous batted balls are sometimes discarded.

Jim Furtado Posted: August 25, 2016 at 07:39 PM | 0 comment(s)
  Beats: sabermetrics, statcast

Wednesday, August 17, 2016

Let’s look at the best and worst baserunning teams - SweetSpot- ESPN

This is like talking about who has the prettiest arms in the Miss America contest.

1. San Diego Padres (+21.6 runs)

Key stats: The Padres are third in the majors in steals with an excellent 78 percent rate and lead the majors in taking the extra base 51 percent of the time (the MLB average is 40 percent).

Key contributors: Wil Myers (+6.5 runs), Travis Jankowski (+4.1 runs), Melvin Upton Jr. (+3.8 runs). Jankowski is one of the fastest players in the majors, but Myers has been the big revelation here with 21 steals (and just four caught stealings) while taking the extra base 64 percent of the time compared to a career rate of 50 percent entering the season. Kudos to Andy Green for pushing aggressiveness with the right guys.

Jim Furtado Posted: August 17, 2016 at 06:50 AM | 22 comment(s)
  Beats: sabermetrics

Monday, August 15, 2016

Exploring Statcast Data from Baseball Savant | Exploring Baseball Data with R

The future of batted ball data.

There seems to be active research of making sense of this type of data. For example, there is an interesting post by Rob Arthur at fivethirtyeight.com which looks at the relationship between launch angle, exit velocity and a linear weights measure of BIP value. There is a more recent article by Bill Petti on the Hardball Times. I agree with Rob Arthur that these findings are currently preliminary — we have a lot to learn about success in hitting balls in play.

Jim Furtado Posted: August 15, 2016 at 07:59 AM | 0 comment(s)
  Beats: sabermetrics, statcast

Thursday, August 11, 2016

Baseball Prospectus | Prospectus Feature: Nothin’ to Do With Groundball Pitchers? DRA Weighs In

So what does this tell us? Does DRA confirm or negate the previous findings suggesting that groundball pitchers are more effective at run prevention? There appear to be three conclusions:

1. In modern baseball, using the most sophisticated measure of run prevention, higher groundball rates are well correlated to fewer runs allowed. It’s not an ironclad relationship—Scherzer and Verlander don’t get a lot of grounders—but inducing groundballs is a positive attribute.

2. To James’ point, if we look over the arc of baseball history, the conclusion above decays with time. Getting groundballs is really good today. It wasn’t necessarily good 40-60 years ago. So while we can say “groundball pitchers are generally better” today, that statement’s time-limited.

3. This illustrates the limitation of single-variable analysis in baseball. Inducing groundballs is positively correlated with run prevention. So is getting strikeouts. But grounders and whiffs are negatively correlated—groundball pitchers get fewer strikeouts than flyball pitchers. So getting a lot of grounders, just like getting a lot of strikeouts, isn’t enough to guarantee success. Among ERA qualifiers this year, Edinson Volquez has the seventh-highest groundball rate, and Ian Kennedy’s no. 18 in strikeout rate. Their ERA/FIP/DRAs are 4.99/4.38/4.85 and 4.03/4.92/3.95, respectively; the American League average is 4.21.

I’ll concede that James has a point about durable pitchers over the past 60 years. But I’ll stick with what I wrote in June, “I’m not backing away from the view that in contemporary baseball, groundball pitchers, in aggregate, are more valuable the flyball pitchers, in aggregate,” and DRA’s got my back.

 

Jim Furtado Posted: August 11, 2016 at 09:21 AM | 7 comment(s)
  Beats: groundballs, pitching, sabermetrics

Monday, August 08, 2016

Rajai Davis is still an elite baserunner - Beyond the Box Score

Davis still isn’t much of a hitter, or a defender. But what kind of fan comes to the ballpark to watch guys hit home runs or make slick catches? For the true baseball lovers, baserunning – be it a move ahead to third or a swipe of second – stands out as the true attraction. Throughout his 11-year career in the Show, Davis has catered to these fans’ esoteric interests, and he doesn’t plan on stopping anytime soon. The man who’s described himself as “always [being] faster than everyone else” has backed up, and should continue to back up, that proclamation.

Jim Furtado Posted: August 08, 2016 at 11:19 AM | 5 comment(s)
  Beats: indians, rajai davis, sabermetrics

Saturday, August 06, 2016

Sabermetric Research: Why log5 is biased against favorites

Phil Birnbaum goes under the hood with Log5.

Jim Furtado Posted: August 06, 2016 at 07:13 AM | 0 comment(s)
  Beats: sabermetrics

Monday, August 01, 2016

Fixing Batted-Ball Statistics with Statcast – The Hardball Times

I would think that arc angle would be very useful for defense as well. Interesting stuff.

Jim Furtado Posted: August 01, 2016 at 10:51 AM | 0 comment(s)
  Beats: sabermetrics, statcast

Friday, July 29, 2016

Tangotiger: StatCast Lab: Once we hit 88mph exit speed, we’re going to see some serious…

You can read about some of the things Tangotiger is working on over on his blog.

Jim Furtado Posted: July 29, 2016 at 10:50 AM | 11 comment(s)
  Beats: sabermetrics, statcast

Monday, July 25, 2016

Solving the Yankee Equation, One Number at a Time - The New York Times

The Yankees analytical team comes out of the basement.

For all you old RSBB guys, there has been a David Grabiner sighting.

Jim Furtado Posted: July 25, 2016 at 06:46 AM | 1 comment(s)
  Beats: sabermetrics, yankees

Saturday, July 23, 2016

Baseball Prospectus | DRA 2016: Challenging the Citadel of DIPS

Baseball Prospectus updates its pitching metric.

Jim Furtado Posted: July 23, 2016 at 08:23 AM | 0 comment(s)
  Beats: sabermetrics

Tuesday, July 19, 2016

Exit Speed and Home Runs – The Hardball Times

I divided up the launch angle into 50 buckets, then found the mean exit speed for each of those buckets, for both 2015 and 2016, pre ASG. And I have to admit that I am very puzzled by this plot. While there is a clear increase in mean exit speed for launch angles in the home run sweet spot, 200-350, the mean exit speeds are essentially identical for line-drive type angles, 00 to 100. This is not what I would expect to find if the ball were indeed juiced. If anything, I would expect the coefficient of restitution to have a greater effect on the line drives, which are generally more “squared up” than fly balls, as one can see from the higher average exit speed. This does not bode well for the juiced ball theory.

My conclusion that the higher exit speeds account for most of the increase in home runs still stands. However, as much as I hate to admit it, the exit speed versus launch angle plot really puzzles me. Sometimes it happens that the answers are not as clean and crisp as we would like; this seems to be one of those times. Looks like I’ll be spending more time on this one.

Jim Furtado Posted: July 19, 2016 at 02:07 PM | 1 comment(s)
  Beats: sabermetrics, statcast

Saturday, July 09, 2016

Quantifying Pitcher Command – The Hardball Times

We begin today’s story with MARS.

Not the delicious candy bar that sticks to your teeth; nor the planet that can grow Matt Damon’s potatoes. Rather, it is Multi-variate Adaptive Regression Splines, or MARS for short. MARS is a trademarked term, so the R or Python implementation is usually referred to as “earth.” Essentially, the MARS approach to regression improves upon basic multiple linear regression in three ways:

It breaks apart each regression line into multiple formulae (for example, incremental fastball velocity below 94 mph has a different value curve than velocity above 94 mph).

It prunes terms that aren’t beneficial to the model and pares it down to the important factors.

It can uncover relationships between variables (say, location and velocity).

I’ll step away here and encourage you to read the Wikipedia article linked above for a more thorough explanation.

Jim Furtado Posted: July 09, 2016 at 11:31 AM | 0 comment(s)
  Beats: pitching, sabermetrics

Wednesday, July 06, 2016

Digging Deep Into Inside Edge’s Fielding Data – The Hardball Times

If you are interested in defensive metrics, this is a must read. Great work!

I originally sought to better understand Inside Edge’s defensive data on my accord and articulate the context of the data here. But my exploration evolved when anecdotal evidence seemed to turn into something more. En route, I offered here limited but still quantitative evidence that WAR, as we now calculate it, fails to properly account for the difficulty of defensive seasons, at least in the tail ends of the distribution of difficulties. Further analysis may further illuminate my findings or invalidate them completely. Such is life. But I am curious to know how much farther we can take this research.

Jim Furtado Posted: July 06, 2016 at 11:13 AM | 2 comment(s)
  Beats: fielding, inside edge, sabermetrics

Revolution in sports doesn’t happen overnight. | Sports on Earth

I guess this post officially makes this Brian Kenny Day at BBTF.org.

Jim Furtado Posted: July 06, 2016 at 08:49 AM | 0 comment(s)
  Beats: books, brian kenny, sabermetrics

Thursday, June 30, 2016

It’s Time for the Sabermetric Revolution to be Televised – The Hardball Times

As long as most sabermetricians aren’t on camera we should be good. Otherwise, things might get ugly.

Ahh, no offense. :P

Jim Furtado Posted: June 30, 2016 at 10:03 AM | 21 comment(s)
  Beats: sabermetrics

Tuesday, June 28, 2016

A New Way to Look at College Players’ Stats – The Hardball Times

Some interesting work.

Jim Furtado Posted: June 28, 2016 at 07:01 AM | 0 comment(s)
  Beats: college, sabermetrics

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