THE BOOK--Playing The Percentages In Baseball

Monday, February 06, 2012

Position-switchers

ESPN has the list of story lines.

Friday, February 03, 2012

Werth: How long can a non-CF stay in CF?

Good job trying to identify the Jayson Werth comps.  It looks like Ichiro is the most comparable case for Werth.

Sunday, January 29, 2012

Did Reds SS shift their positioning with Cordero on the mound?

Or was it just random variation?

Thursday, December 29, 2011

Worst-fielders since 2002

Using UZR and applying positional adjustments. 

Tuesday, December 06, 2011

Jayson Werth, CF

Neyer brings us news.

Werth in his MLB career has 877 innings in CF (almost 100 games), where he was +4 runs (or +6 runs per 162 games).

UZR loves him in the corners, with a UZR of +41 runs in 6414 innings, or an average of +9 runs per 162 games.  A standard conversion would presume he’d be -1 run in CF. 

Put the two together, and his translated CF performance was exactly 0 runs.  Of course he’s 33 years old, and not an average of around 30 years old when we have those observations.  That’s going to knock out probably 5 runs.

So, probably he’ll be -5 runs in 2012 in CF (and +5 in RF).  Something like that.  He’s a good enough fielder to be able to hold down CF for maybe the next two years, but after that, he won’t be able to handle it any longer.

A parallel is probably Alex Rios.  Both around the same age, both excellent in RF for most of their careers, both tried in CF with a certain amount of success, and both destined to remain a fixture in the corner OF in their early to mid 30’s.

Monday, December 05, 2011

Are Mets’ Nick Evans and Ruben Tejada “all that” as fielders?

The Fans see Tejada as an average-fielding SS and a bit above average at 2B (something Dewan and UZR would also suggest).  Mark lays out the case that his “extreme” plays, his out of zone plays, or otherwise stand-out kind of plays, elevates him into a higher category.

Similarly, Fans are not impressed with Evans, but Mark suggests otherwise.

Thursday, November 03, 2011

Does Eric Hossmer play too close to the bag?

The stringer-collected data suggests so.

Zones R,S,T,U are those closer to 2B.  On balls hit there (or more precisely, on balls RECORDED to have been hit there), Hossmer started 21 outs, while an average 1B would have started 45 outs.

On the plus side, he makes more plays closer to the bag than the average 1B.  The problem is that the balls he’s giving up on the 2B side is far greater than the balls he’s getting to on the line side.

This is why we want to record positioning, much like we’d record HR and walks for batters: by breaking it down into components, you can see where the strengths and weaknessed are, and you can get a more complete picture of why two different kinds of players can end up with the same wOBA or UZR.  Fans think he’s got the tools to be a good fielder.  Maybe what’s missing is his positioning.

Wednesday, November 02, 2011

What did the Fans think of the Gold Glove winners?

C: Wieters and Molina?  They were 1-2 with the Fans.

1B: Adrian/Votto?  Great on Adrian, and decent (not great) on Votto.

2B: Pedroia/Phillips? They were 1-2 with the Fans.

SS: Aybar/Tulo? Great on Tulo, and decent (not great) on Aybar.

3B: Beltre/Polanco? Great on Beltre, and either decent or great on Polanco.  The better fielders (Zimm, Rolen) missed more playing time than Polanco.  So, Polanco rises to #1 with Fans in NL.

LF: Gordon/Parra? Great on Parra, decent (not great) on Gordon.

CF: Ellsbury/Kemp? FAIL.  With such a huge group of great-fielding CF to choose from, it looks like there was alot of split-voting, and the tie-breaker was the best hitters?  Or something.

RF: Markakis/Ethier: Good to great on Markakis, FAIL on Ethier.

Overall, 13 of the 16 are defensible to uncontroversial.  The other 3 (Ellsburg, Kemp, Ethier) are not fan-favorites.  Ethier did finish #4 among Fielding Bible voters for NL RF, so that might be defensible.  Ellsbury finished #5 among the Fielding Bible voters for AL CF.  So, you can add those two to being defensible if you want.

Kemp though received 0 top 10 votes from any of the 10 voters for the Fielding Bible.  Fans had him at barely above league average.  DRS had him at barely above league average.  UZR had him at below league average.  So, that one is the only clear indefensible choice.  Kemp won the NL CF Gold Glove on the strength of his hitting.  He won the Rawlings Golden Bat.

Tuesday, November 01, 2011

Fielding Bible 2011 - Complete Vote Tally

Here you go.

Looking quickly at the results by position, and comparing the Fans Scouting Report to the consensus:
1B: No big surprises, with Fans wanting Pujols, and him winning.  The top 6 votegetters were in the top 9 for the Fans.

2B: Even more of a match to consensus, including #1 Pedroia.  The only ones to have Espinoza ranked highly are the Fans, Strat-O-Matic, and Doug Glanville.

SS: Good match overall, including #1 Tulo.

3B: Good match overall, including #1 Beltre.

LF: Solid match overall, with Fans’ #2 Gardner being consensus #1.  CarGo ranked #1 by Fans, and #2 by Strat-O-Matic, and #7 overall.

CF: This one is really a tough one, because there’s so little separating all the top CFers.

RF: Very little agreement.

C: Fairly strong agreement at the top, including #1 Matt Wieters.

So, Fans matched the #1 on all the infield positions, and catcher, and had the consensus #1 as #2 in LF.  CF and RF had much less agreement.

***

As for pitcher, I used my own quick algorithm to get the rankings.  I expect little match. 

Friday, October 28, 2011

Was Cruz not deep enough in the 9th inning?

In this article, Tom Verducci, not an intellectual giant when it comes to sabermetrics, said this:

There is a universal rule in baseball about playing the outfield with a lead, especially a two-run lead, and three outs or fewer from victory. Under no circumstance can the ball be hit over an outfielder’s head—not unless it’s flying all the way out of the ballpark. It’s called no-doubles defense. The outfielders have to station themselves deep enough to make sure the ball cannot get over their head.
This is how center fielder Josh Hamilton and left fielder David Murphy played the ninth inning. I saw Cruz early in the ninth inning playing too far in and said aloud, “He’s not back far enough. A ball can get over his head.”

There are so many things wrong with that segment, I don’t know where to begin.  I won’t actually.  Except to say that the article thoroughly evinces the “either/or”, “black/white,” digital rather than analog approach that managers and even journalists apply to baseball decision-making.

Oh, and the ridculous title of Verducci’s article is:

Cruz’s unforgiveable defensive gaffe proves costly to Rangers

In this case, according to Verducci, you simply play so deep that no ball can ever be over your head and stay in the park. As if a single in front of you is tantamount to an out.  And as if by playing deep you are not forgoing some catches on short fly balls.

BTW, if you simply watch the replay of that non-catch, it is obvious that Cruz WAS playing rather deep, and of course it was an eminently catchable ball, not that is HAS to be catchable in order for his positioning to have been correct…

Tuesday, October 18, 2011

Landing/crossing spot of uncaught pitches

Great stuff from Bojan, who models the wild pitch / passed ball scenario into two types: those that actually land in front of the catcher, and those that don’t.  It adds a level of complexity, but it represents reality, so I’m very happy he went the extra mile here:

Now that we see how he models it, we get the payoff.  If you can mentally “fold” it at the line, you can do so if that helps:

Then he has a ton more good stuff.  And the payoff to see the impact by catcher, where you want to focus on the last column that shows that we’re talking about 4 runs of value:

We can compare to data I produced here for 1978-1990, and see that, other than Bruce Benedict, the best catchers saved 15 “passed pitches”, which converts to around 4 runs.

If we both end up in the same place, then why go to the lengths Bojan did?  Well, two good reasons.  Number one is we learn, and for that Bojan does a fantastic job.  Number two is that his model can pinpoint things with a much smaller sample size than what I would need.

Remember the thread I had yesterday about fielding opps not created equal?  The same applies here.  Whereas after a few years, we’d expect all catchers to have the same kind of catching opps (after adjusting for the identity of the pitcher), a CATCHERf/x type of system would require a far smaller sample.

Here’s his list for worst catchers at blocking:

He also shows the correlation, and from there, we can actually figure out how much to regress the observed sample.  The average sample size was over 6000 pitches for each catcher in each bucket.  To figure out how much to regress, you do (1-r)/r * N.  Since r=.68, you add about 3000 pitches of league average performance.  It looks like there’s around 40 pitches per game in his sample, so we’re talking about adding around 75 games of league average performance to get from observed rate into a true talent rate.  That is, r=.50 when G=75.

Anyway, this is in the running for my favorite research piece of the year.

Monday, October 17, 2011

Not all fielding opportunities are created the same

I said this in another thread several weeks back, but I’ll repeat it here anyway.

Batting Opps

A batting opportunity gives you a certain expectation of getting on base, dependent on the context.  That context is the pitcher primarily, but includes the park and the fielders, the base-out state, and other minor things.  So, the chance of reaching on base context for the average batter will stretch from say .250 to .400, or, to put it in clearer numbers: 25% to 40%.  That range is actually fairly tight, if we think of that range as encompassing 99% of the data points presented.  Basically, one SD = 2% to 3%.  So, the OBP would be 33% +/- 1SD=2.5%, or something like that.

If you come to bat 625 times, then the SD goes from 2.5% for 1 PA down to 0.1% for 625 PA.  Therefore, a player coming to bat over a season would have the chance of reaching base context being 33% +/- 1SD=0.1%.  This is why we can say that if Pujols comes to bat 625 times and Ryan Braun comes to bat 625 times, that they’ve faced similar enough opportunities over a season to reach base.

Of course, there are systematic biases.  You will see a starting pitcher 3 times in a game, not once.  You are stuck to your home park half the time, etc.  Those biases add up, and so the “strength of schedule” is probably more like 33% +/- 1SD = 0.3%.  Just a guess, but something like that.

Fielding Opps

A fielding opportunity is far different.  Any baseball fan will know there are many gimmes for a fielder (out rate = almost 100%) to impossible to get (out rate = 1%).  (It also depends how you want to handle positioning as a skill to the player or the manager.) So, we end up with say one SD = 30%, with a mean of 70% out rate.

In addition, fielders get fewer opportunities than batters (strip out the BB, HB, HR, SO, and depending how you want to handle bunts). More like 450 let’s say.  So, the 1SD = 30% for one ball in play becomes 1SD = 1.5% for a whole season of balls in play for a given fielder. Add in a bit of systematic bias, and now we have 1SD = 2%.  (As an illustration.)

That’s the kind of distribution a fielder is going to face in terms of “strength of schedule” thinking.  One SD being 2% on 450 plays is 9 outs (or 7 runs)!  That’s for one standard deviation.  Hence, the reason we don’t like just having a straight “range factor” (i.e., outs per ball in play).

Fielding Opps Classification

That’s why it’s critical to try to qualify each opportunity specifically.  You do that by adding more knowledge to each BIP: its vector, its hang time, etc.  You try to establish some sort of out rate for each fielder for each ball in play: that is, his opportunity to make an out.

This is the idea behind most of the advanced fielding metrics.  The idea is on the right path.  The question is how much tighter can we make that range in opportunity that I mentioned (one SD = 2%).  The better you can classify each batted ball, then the more you know about the quality of the opportunity, and the tighter the range.

You have to be careful that by classifying each ball that you don’t introduce a systematic bias, because that becomes a killer.  In this case, the larger the sample size, the worse off you are!  That’s because a systematic bias becomes persistent across years.

Suppose that we don’t know any extra classification, and we just accept that one SD = 2% for a season of batted ball data.  If you have 4 years of data, similarly unadjusted, then one SD = 1%.  This is why sample size is our friend here: instead of relying on classifying batted balls, you just increase your sample size.  If you have 16 years of data, one SD = 0.5%, and now we’re happy.  This is why for a career’s worth of fielding stats, we don’t need to worry about adjustments too much.  This is why you get Ozzie Smith as #1 in any fielding system: all those annual biases that come into play (who’s his pitcher, where did all those batted ball go) comes out in the wash of a full career.  Of course, this doesn’t help us in evaluating in real-time.

Alternatives

What are the alternatives if you don’t like the unadjusted huge spread in quality of opportunities, or the adjusted (but possibly biased) smaller spread in quality of opportunities? 

. Well, you can just throw your hands up in the air and “look at all of them”.  Life is short, and you don’t want to waste your time evaluating each one. 

. You can “go to the eye test”, though, that certainly has its own inherent biases, not to mention the extremely small sample size (how many of the 130,000 batted balls did you watch?). 

. You can crowdsource, though that has its own bias issues as well.

. You can be a politician, and point out why the method you have chosen is the best by pointing out the good parts of it, and pointing out the bad parts of the methods you have rejected.  For some people, politics is a way of life.

The worst alternative is to laugh or mock.  You simply aren’t offering anything of value by being an a$$hole.  If you go this route, at least be funny.  But being a funny jerk still doesn’t mean that you are providing facts.  So, be funny, and then stand aside.  Friendly hint: chances are, you are not being funny.

Sunday, October 16, 2011

Freeman v Pena, via UZR

Freeman initiated 138 plays at first base that led to at least one out (as best as I can understand ”plays” ).  Since he had 92 assists, he must have had about 46 unassisted putouts.

At 0.9 plays made per game, it’s behind only Carlos Pena for fewest in MLB. According to BIS data, he had 188 opportunities to make a play.  That is one of the fewest opportunities to make a play.  So, is Freeman not making plays because he has few opportunities, or he’s out of position?  Or is he not that good?

138 plays made out of 188 opps is 2nd lowest after Carlos Pena.

Fans see Freeman (and Carlos Pena) as above-average fielding 1B.

So, how does UZR see Freeman as -13 runs, worst in MLB, and Carlos Pena at right around 0?

What is it about the quality of Pena’s opportunities that UZR considers his league-low plays made per opp (111/152=0.726) to be very biased against Pena such that his UZR is +1 (in-line with Fans), but Freeman’s opportunities to be a fair representation and unbiased?

I’d like to see that distribution of balls in play for Pena and Freeman.  Is the distribution that different? Or is UZR that sensitive to their differing opportunities that it could cause a 13 run difference?

Thursday, September 29, 2011

Do teams put the better fielder at 2B or 3B?

A few years ago, MLB was filled with great fielding 3B.  Lately, however, things have started to change back.  I looked at the Fans Scouting Report, to see how the fans evaluated their team’s 2B and 3B.  With only three teams did the fans strongly prefer the fielding talents of their 3B over their 2B:
Cleveland Indians
St. Louis Cardinals
Atlanta Braves

On the flip side however, there were 9 teams that strongly preferred their 2B to their 3B.

And how about on offense?  Well, the shift has been so dramatic that the average 2B is a slightly better hitter than the average 3B!  Just a few years back, MLB not only had the better fielder at 3B, but also the better hitter at 3B.  Now, in 2011?  A better fielder at 2B and just a shade of a better hitter at 2B, too!

***

How about LF/RF?  I noted in the past that the better fielder was by far in RF than LF.  Is this still true in 2011?  Only 1 team had the clearly better fielder in LF than RF (Yankees).  On the flip side, there were 13 teams with the better fielder in RF than LF (with 8 of them being in the NL, a league that doesn’t have a DH, and so, may use LF as a DH-like spot).

How about hitting?  Not only is the RF a far far far better hitter than the LF (average RF is a slightly worse hitter than the 1B), but EVEN THE CF is a (slightly) better hitter than the LF.  The LF in 2011 is clearly the spot where teams “hide” or otherwise “play with” their players.

Never do what others have done in the past, and that is to “zero out” the stats such that the average LF (offense + defense) is considered equal to the average CF or average RF.  This is clearly the wrong thing to do.

Tuesday, September 27, 2011

Catcher Framing Skill, part WOWY

Mike Fast generously posted his file on Catcher Framing in his tremendous article last week.  That file is the data source of everything I’m about to post here.

Also note that when I’m going to present a rate stat, it’ll be “per 75 called pitches”, because there’s around 75 called pitches per 9 innings.  Since runs allowed per 9 innings uses the notation RA9, I’ll use CP9 for called pitches per 9 innings.  (That it bears some resemblance to CP30 is either an unfortunate blemish, or a cool byproduct.)

Another note is that turning a called ball into a called strike is worth roughly +.12 runs.  However, the way that Mike has identified the pitches, it’s more like a “probably would have been called ball” turning into a called strike.  As a result, the run value, using this data source, is going to be more like .08 runs per extra call.

On to the data.

Read More

Tuesday, September 20, 2011

UZR yapfest

Fangraphs posted an article on UZR, which they subsequently pulled, due to inaccuracies in statements.  That led to a post explaining why they did it.  Anyway, I put in my two cents (which I’ll repost below the fold).  And there’s also alot of UZR discussion that is now taking place at this new thread.  Some good stuff, if somewhat disjointed.

My UZR contributions:

***

“- not assessing the starting position of a player”

UZR takes the position that the starting spot of a player is a skill. And it assigns it to the player.

You can agree or disagree. But, it’s not a bias. It’s a feature, insofar as UZR sees it.

And yes, there are random biases (which are solved by larger sample size) and systematic biases (which are exacerbated by larger sample size) to recording data.

There’s also random and systematic biases to the “eye test”.

Pick your poison.

***

Otherwise, what’s the alternative? Your own personal smushing system? Is that somehow going to give you more confidence than fWAR?

And if you don’t like UZR, just replace it with whatever you’d use in your smushing system.

***

(The measurement error for those not following is that we don’t know how many opportunities Tulo actually has in any given year, nor the “quality” of those opportunities. While we can figure out on the batting side what kind of opps he has, facing Halladay, facing Lee, etc, we have a problem figuring out what kind of chance Tulo has on any ball in play. So, we ESTIMATE each and every single ball in play and say, “ok, that one had a 90% chance of making an out, and that one had a 20% chance”. But, we don’t REALLY know that it was 90%. It could have been 80% or 95%. It’s a huge difference. Unlike batter v pitcher where if you face Halladay, you know, plus or minus 1%, the chance of getting on base against Halladay. Measurement error, if random, is of course “solved” by sample size. If systematic, it’s actually made worse by sample size.)

***

My yapfest contribution:

Read More

Monday, September 19, 2011

Fans Scouting Report and the position-neutral aspect

While I have no qualms with his basic point, his conclusion misses the larger point.  If it is impossible for Jason to evaluate Mark Ellis and Luke Scott’s throwing, then this only invalidates the results of the data if you end up comparing Mark Ellis to Luke Scott.

HOWEVER, and this is important, this does NOT invalidate comparing Ellis to Brandon Phillips.  If let’s say everyone is having a hard time following the instructions, then this bias applies to all secondbasemen, to some similar degree.  Which is why getting 20 or 30 evaluators for each player is important: if my instructions to insist on position neutral is too complicated, then those instructions are NOISE.  (Random noise, within each position.) And how do your counter random noise?  With sample size.

So, it works on two levels:
1. If you believe that fans can go the job on a position-neutral sense, then you get great results.
2. If you don’t believe they can do that job on a position-neutral sense, then random noise of the instructions is reduced by sample size, and you get great results (if you stay within position).

Jason ignores the FSR for whatever reason.  But, if you actually look at the results, are you left scratching your head thinking “that’s totally off”?  No, you don’t.  Well, you shouldn’t in most cases.  Indeed, I’ve asked a few teams in the past to evaluate their players (and they do follow a position-neutral aspect to it, as they should).  And guess what?  They always say the same thing: most of it looks really good.

Is where you play on the field conducive to better hitting performance?

I’ve ALSO been meaning to do this study for a long time.  All data from 1993-2010.  I also limit the data to players aged 25-29.  I ensure that a player is equally weighted in the two pools being compared (and that weight is the lesser of the plate appearances in the two fielding positions being compared).

(Imagine there’s three or seven paragraphs of a yapfest going on here.  I’m not going to do it.)

Ok, now, thanks for your patience in wading through all that.  Now onto the data.

Unlike the batting lineup, where I have enough data to make all possible combinations permutations (72… stay in school kids), I don’t have this luxury on the fielding side.  On the lineup side, the least matching pair was cleanup hitter and last hitter, where I had “only” 3740 plate appearances (while I had 94,024 matching PA for sixth and seventh hitter).

On the fielding side?  I have only 54 combinations for the nine fielding positions (excluding pitcher, including DH), with a minimum of 2000 plate appearances.

The biggest gap, by far, involved the DH.  Remember, I’m only looking at players aged 25-29.  I look at how they did at DH and at each of the other fielding positions.  The same players involved, and for each pair of positions, the player is equally weighted (proportionate to the lesser of his plate appearances at the two positions).  It is quite a striking difference:

Across the board, a player hits worse at DH than at each of the other fielding positions.  (You’ll note I didn’t have enough DH/SS in there.) The average difference was 16 points of wOBA.  Each individual position shows enough statistical significance on its own to jump out.  Taken together, and it’s overwhelming.

16 points of wOBA is 9 runs over the course of a season.

Now, I did not try to distinguish between “lifers” at DH, and just those who are transient.  It’s possible the reason some players were DHing was because they had some injury that was hobbling them on the field, but was hoped to be masked while hitting.  Could be many reasons, really.

***

How about the other positions?  If we look at just LF, CF, RF (that is, players who played LF and CF, or CF and RF, or LF and RF), and we see almost no difference in performance.  As an example, of the 45,030 matching PA for players who played both LF and RF, they batted .344 while playing in left and .345 while playing in right.  At 0.3 standard deviations, it’s a difference not even worth pointing out.  (Which I did, which is weird.)

How about in the infield (2B, SS, 3B)?  In that case, there was one HUGE standout: .320 while playing 3B, but .331 while playing 2B (3.5 standard deviations apart).  3B to SS also had a bit of difference: .321 at 3B, .326 at SS (only 1.0 standard deviations).  It’s possible that playing 3B takes a bit of toll on the hitting side.

How about infield/outfield (the above positions)?  When playing 2B, players hit .320, but playing in the outfield was .329.  That difference was 2.0 standard deviations, so it’s possible that it’s a bit easier to hit while playing in the outfield.  Then again, playing 3B, players hit .334, and .336 in the outfield.  We had just figured that playing at 3B is harder than playing at 2B, when it comes to hitting.  But now, we see that 3B/OF is fairly close, but 2B/OF is far apart.

If we just look at the IF/OF comparison as a group, we get: .323 in the infield, .328 in the outfield.  That gap is 1.9 standard deviations.  So, I think we can say that it’s easier to hit while in the outfield, but it’s not that big a difference.

Now, how about 1B?  Surprisingly, it’s harder to hit at 1B!  It was .347 at 1B and .352 at the other six positions I noted above. 

***

Finally, catcher.  Unfortunately, there’s not many players that play catcher and some other position, other than 1B and DH, at the ages of 25-29.  But, of those that did, oh boy!  Get ready for this.  If you played catcher and 1B, you hit .334 while catching and .351 while at 1B.  That’s a 18 (rounded) point difference and is 2.0 standard deviations from the mean.  And if you played catcher and DH, you hit .341 at catcher and .321 at DH, which is 2.2 SD from the mean, going the other way.

In short, the data is showing this:
.348 at 1B
.330 at C
.310 at DH

Yowza.

Now, before we get too far here, it’s worth pointing out we have tons of 1B/DH comparison, without needing to go through the catcher.  Those guys hit .364 at 1B and .349 at DH, a 14 (rounded) point difference (and 3.7 standard deviations).

Let’s go back to catcher.  If I relax the standard, and only look for 1000 matching PA, I also get 3B, LF, and RF.  Including 1B, then this is the comparison:
.323 at catcher
.342 at 1B, 3B, LF, RF

That 19 point difference is 3.3 standard deviations from the mean.  So, we can safely say that it’s harder to hit as catcher than hit at any other position.  Is it really a 19 point difference?  Well, the only thing we can say is that we are almost positive it’s greater than 0.  This is where Bayes would help.  If we had catchers tell us that their body can’t really handle it as easily when behind the plate than when they play the field, then we would use a better prior.  Heck, our prior could have been even 30 points, and seeing only 19 would understate the difference.  The problem is that we have no way of quantifying a good prior.

I’m inclined to give at least a 10 point difference as the true difference, which is 6 runs of impact.

***

Anyway, lots more work to be done.  But, without question, we need to apply a huge bonus to guys who DH, and to guys who play catcher.

Monday, September 12, 2011

On Defense: Subjective Data, Objectively Considered

Great piece by Keith Isley.

Sabermetrics evaluates a player’s “true” abilities as statistical regularities found in large samples. Defensive evaluation is the one aspect of the game that has been resistant to the statistical approach, at least so far. The customary sabermetric solution to this problem is to get more data and treat it with stronger mathematics, which is exactly what the advanced PBP-based defensive metrics do. Sometimes, though, progress requires not simply more data and better processing, but new methods that produce new observations that lead to new abstractions.

Fortunately, there is an established (if not well known) framework for the objective and rigorous analysis of subjective data. It’s called Q methodology, and it’s one of my favorite tools for understanding a phenomenon from a different perspective. Q provides instrumentation (the “Q sort”) for the quantification of subjectivity and a technology (factor analysis) for data reduction and interpretation.

A Q sort ranks orders subjects by a series of relatively subjective variables (such as those typically provided by baseball scouts) and the factor analysis uncovers the commonalities among those subjective elements. The result is a better understanding of what it is that makes a fielder great. Let me run through an example, and I’ll put the more technical details in a footnote.

Note: that article was from Feb, 2006.  I took a two year blogging hiatus between Apr, 2004 (my old blog at Primer) and Jun 2006 (when I started blogging here), between which, I worked on The Book.  I confined my comments to threads at the old Fanhome/Scout otherwise (for all intents and purposes, inaccessible now). 

So, I happen to run into the above article, and, since there’s no active thread for it, I figured it would be a good one to link to.  So, if you happen to see me link to more old pieces between those two dates, will, just treat it as something new.

Thursday, September 08, 2011

Fans Scouting Report 2011: hotseat: Padres

I’m going to make one post like this a day, focusing on a team that requires more participation.

Today, it’s the Padres.  If you follow the Padres, have a Padres forum you frequent, know where the Padres fans congregate, then send them my way.  Or post a link below, and I’ll post in that forum.

http://www.tangotiger.net/scout/