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Jim Lefebvre

February 14, 2009 in Sabermetrics, San Diego Padres, scouting with 1 Comment

by Myron Logan

Check out this nugget on Jim Lefebvre, Padres new hitting coach, from Corey Brock:

In fact, Lefebvre has fully embraced statistical analysis and is using that information to his benefit as he works with hitters. He’s worked closely during the off-season with Josh Stein, the Padres coordinator of baseball research and advance scouting. Lefebvre carries all this information in a blue binder and consults it frequently.

Lefebvre likes to talk about on-base percentage but is more concerned with ball flight and measuring balls in plays, run expectancy and non-productive outs. The information Stein provides shows Lefebvre what areas his individual hitters need to work on.

That is interesting, and I think, very good. You surely want a hitting coach that knows the basics of hitting, from a mechanics perspective, but let’s face it, most hitters have heard about all there is to hear on mechanics by the time they reach the majors. A hitting coach willing to embrace the numbers, especially ones more meaningful than batter-pitcher matchups and lineup order splits, can really provide an edge.

The stuff mentioned in the above passage, ball flight, measuring balls in play, run expectancy, all sounds pretty ‘cutting edge.’ I wonder how ball flight is measured. I don’t think it could be with HITf/x, because I don’t think that debuts until this year. Maybe it’s done by using a model similar to Greg R’s at Hit Tracker or maybe just by watching video. I don’t know, but either way, it sounds promising. File this under the ‘interesting to things to follow in ’09’ category.

And by the way, it’s good to see Corey is blogging again.

UZR Updates

February 11, 2009 in contracts, fielding, Mike Rogers, player evaluation, projections, Sabermetrics with 1 Comment

by Mike Rogers

Fangraphs just keep getting better. They now have updated the UZR Defensive numbers to include outfield arms and double play runs. Back when The Hardball Times updated their 2008 outfield arms data, Myron looked at it and helped bolster his idea that Brian Giles should be moved off of the right field postion and switch to the oppostie corner. So, lets take a look at Brian Giles now with the UZR outfield arms update.

Brian Giles’ arm is bad. Like, on the extreme end of the worst bad. I’m talkin’ -19.5 runs bad over the last three years.  When the talk about outfield arms was being bandied about as being incorporated into UZR, it has been said that it only really effects the guys on the ends of the category — the very good (Jeff Francoeur) and the very bad (Brian Giles). Giles’ arm is averaging -6.5 runs off of his defensive value on average from 2006-08, and that’s not weighting it at all which would change that since he’s declined each of the last three years: -4.2 in 2006, -5.8 in 2007 and -9.4(!) last year. So, let’s just call it -6.5 runs, over his average of 140 games played in those three years. That would then become about -6.9 runs or we’ll just call it -7.

Defensively, as I noted in the comments of Myron’s post I linked to earlier, the arms ratings really puts a dent in Giles overall value. My comment noted that without arms ratings his defense is +4.42 over the last 4 years. Run that to a Wins Above Replacement conversion using CHONE’s projected .346 wOBA (and a league average of .332), and I get +8.52 offense, +4.4 defensively, +20 for replacement level and -7.5 for positional adjustment, converted to wins above replacement I get 2.4 WAR. Multiply by 0.85 to account for playing time and that’s 2.06 WAR — a bit above-average.

However, if you account for his arms ratings, and to keep it on the 4 year average like I used in my comment, his 4 year arms ratings comes out to -19 (2005 was +0.5 for him in RF). Averaged out, that’s -4.75 runs per year with his arm. Run this into a WAR conversion and his WAR drops to +1.97 WAR. A one-year deal on the open market for a 1.97 WAR player is $9.07 million. Value for a 2.4 WAR player for a one-year deal on the open market? $10.96. Basically, his bad arm is worth about $1.89 million to the bad in terms of his value.

Hit Tracker projection system

February 6, 2009 in projections, Sabermetrics with 2 Comments

by Myron Logan

I think this projection system, created by Greg Rybarczyk, has a chance to be the best one out there. Why? Because it’s working with a different, more accurate dataset than the other systems, which are all of course developed by very smart people. There is, however, only so much you can get out of the traditional data.

If you’ve been around here for a while, you may remember me talking about what Greg’s model is trying to take into account: the idea that a double isn’t necessarily a double, a homer isn’t a homer. All hits aren’t created equally, especially when you’re trying to predict the future. Why count a bloop double the same as a rope off the wall, when you want to know how a player is going to perform in the future?  We’d all take the rope, right?

For the most part, these things are supposed to even out over time, and that’s what the other projection systems assume. They also regress data back toward some mean, to try to account for the noise. Well, the Hit Tracker model takes it one step further, and actually attempts to correct for this problem using weather information, batted ball speed off the bat, spin off bat, etc. The article I linked to, if you’re into projections, is a must-read.

By the way, my thoughts regarding this were certainly not original. I remember reading an interview of Mark Shapiro where he stated that he thought there was a lot of unexplored work to be done with offensive stats. Surely, they were talking about it in the Spalding Baseball Guides in, like, 1912* : ) The thinking isn’t original, but as far as I know, nobody has actually gathered the data or put the model together, like Greg has. I’m also looking forward to HITf/x, which will apparently debut this year, and could also advance hitting projections (among other things, like fielding analysis). Good time to be a baseball geek!

*And, no, I’m really not kidding. There were some super-advanced articles in those things.

Breaking Down the 2008 Draft Part 3

February 4, 2009 in college baseball, Mike Rogers, player evaluation, prospects, Sabermetrics, San Diego Padres, scouting with 6 Comments

by Mike Rogers

Here’s the final part in my three-part breakdown on the Padres 2008 draft in which we looked at the college bats that San Diego drafted last June. So far in Part 1, we broke down the first five and then did the same for the next five in Part 2. Here in part 3, we’ve got the final 3 bats that they took from the college ranks that are in my college hitters study; Robert Lara of Central Florida, Aaron Murphree of Arkansas, and Dan Robertson of Oregon State, right after the jump…

Continue Reading…

Breaking Down the Draft

January 20, 2009 in college baseball, draft, Mike Rogers, park effects, player evaluation, projections, prospects, Sabermetrics, San Diego Padres with 3 Comments

by Mike Rogers

Over the past couple of months, I’ve been filling any down time I’ve had (within reason) to importing and quantitatively evaluating college hitters. After about 3-4 weeks of constant tweaking, deciding what works and what doesn’t, I finally started to settle in to a system that analyzed what I felt were the key points to hit on – however, I’m still not 100% satisfied with the results.

Thus far, I’ve got the 2007 and 2008 numbers inputted into an excel spreadsheet (that’s too big to upload to google docs as-is, so I’ll have to do some more copy/pasting and get it up on google docs or edit grid). The things that I’m tracking are:

Avg/OBP/SLG
Isolated Power (IsoP)
Strikeout and Walk percentages (K/PA, BB/PA  — Note: PA’s are estimated since I don’t have Reached On Error results)
A Speed Score that resembles the one Bill James invented many years ago (before I was born).
Stolen Base Runs (SB*.22)-(CS*.33)
Weighted On Base Average

In addition to this, I’m using the Park Factor numbers from Boyd’s World (invaluable tool in my analysis). This allows me to use the Total Park Factor (TPF), which is the park factor of all the stadiums in which a college team played in over a 3 year stretch (much more reliable to use the 3 year stretch than the single year park factors due to their vast fluctuations year-to-year — especially in college baseball). In turn, I’ve used this to park-adjust all of the hitting statistics to give me park adjusted Average, OBP, Slugging %, Isolated Power, and wOBA. I also got the average wOBA for each conference and then averaged the park factors for each conference and got an Average Park Adjusted wOBA (APAwOBA?) for each of the conferences I tracked. Using this and a players Park Adjusted wOBA, I’ve calculated a Runs Above Average total versus their conference peers.

What I plan I doing in my next post (maybe two posts), is looking at the Padres 2008 draft. The Pads took 21 college players, 16 of which are among the 1988 entries into my system thus far. Of the 5 I don’t have, one was a Division 2 college player and the other 4 were from smaller schools who weren’t in conferences I tracked. The ones that I did track, and have 2 seasons worth of data for, are the SEC, ACC, Big East, Big West, Big 10, Big 12, Pac 10, Mountain West, and Conference USA. I would like to add in the other conferences (like the Mid American, for instance), but some of the data is missing and that’s not as straight-forward of a process, but it’s not impossible either.

However, having 2 years of data and 16 Padres 2008 draft picks, I think is a decent starting point for a possible two-part breakdown of the hitters they’ve selected. I’ll hope to have the first 8 college hitters the Padres selected up Thursday night or Friday afternoon.

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