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Wednesday, August 16, 2017

On the risks of categorizing a continuous variable (with an application to baseball data)

Don’t be so quick to discard the bunt into the dustbin of history.

From a statistical perspective, hopefully you can recognize both the dangers of categorizing continuous data, as well as the attractive features offered by a GAM (and if you want to try a GAM yourself – the code is up here).

From a baseball perspective, the hardest hit balls do increase error rates. Additionally, with my intuition being that errors are often discarded from the perspective of analyzing hitter talent, this type of error creation could be worth thinking more about. In addition to a side benefit of someone who hits the ball as hard as Aaron Judge or Giancarlo Stanton, having good, capable bunters could actually be undervalued. Putting down a sacrifice is generally not considered worth it (trading an out for moving up a base), but with error rates as high as 14%, there may be more to the story.

Jim Furtado Posted: August 16, 2017 at 08:50 AM | 17 comment(s)
  Beats: sabermetrics

Applying Asset Pricing Theory to MLB – The Hardball Times

Interesting. She doesn’t quite get what replacement level is, however.

Jim Furtado Posted: August 16, 2017 at 07:54 AM | 4 comment(s)
  Beats: sabermetrics

Monday, August 14, 2017

Benetti: We can learn a lot from baseball’s numbers game

The high point before Jeter returns the numbers back from whence they came.

Jim Furtado Posted: August 14, 2017 at 09:40 AM | 0 comment(s)
  Beats: sabermetrics

Wednesday, July 26, 2017

The Man Who Brought Sabermetrics to Japanese Baseball

An interesting look at Japan’s version of Bill James.

Jim Furtado Posted: July 26, 2017 at 08:24 AM | 1 comment(s)
  Beats: japanese baseball, sabermetrics

Wednesday, July 12, 2017

Midseason 2017 Strike Zone Review – The Hardball Times

Great stuff on the strike zone.

Jim Furtado Posted: July 12, 2017 at 09:18 AM | 0 comment(s)
  Beats: sabermetrics, strike zone, umpiring

Tuesday, July 11, 2017

Baseball Prospectus | Prospectus Feature: Measuring Pitcher Similarity

Some interesting work on pitch similarity.

Jim Furtado Posted: July 11, 2017 at 08:51 AM | 0 comment(s)
  Beats: pitching, sabermetrics

Thursday, July 06, 2017

Sprint Speed Helps Tell Us Who’s Good At Baserunning And Who’s Just Fast

As you might suspect, I have been searching for stuff comparing Sprint Spead to Baserunning Runs. Here’s a pretty interesting look from a week ago.

Jim Furtado Posted: July 06, 2017 at 06:54 AM | 57 comment(s)
  Beats: sabermetrics, statcast

Evaluating the St. Louis Cardinals baserunning with Statcast – THE INTREPID STL

Just stumbled onto this. Here’s an interesting look at the Cardinals baserunning using advanced metrics.

Jim Furtado Posted: July 06, 2017 at 06:33 AM | 2 comment(s)
  Beats: cardinals, sabermetrics, statcast

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


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