THE BOOK--Playing The Percentages In Baseball

Thursday, November 05, 2009

Sabermetric moves of the 2010 pre-season

Let the moves begin…

Past seasons:
2009: 2008: 2007: 

Monday, February 08, 2010

How do you spot a lefty masher?

Pat asks:

However, lefty/right analysis has advanced since my adolescence, and I think this post from MGL is a must read for what I’m talking about. In it he says:

IOW, how a batter does against RH pitchers informs us on how he will likely do against LH pitchers and vice versa. Why? Because there is not much of a spread in true platoon splits among ML baseball players yet there is a large spread in overall true hitting talent among ML baseball players. So if we see a large platoon split, like for a player like [Ryan] Howard, it is likely a fluke. If a player does really well versus RH pitchers but terrible against LH pitchers, both the “really well” and the “terrible” numbers are likely fluky and the “truth” is somewhere in between.

Howard has a .719 OPS in the last 4 years versus LHP. How would we estimate his “true” OPS versus LHP? You might be tempted to just use the .719, which is not too good or you might be tempted to use the .719 and then regress that toward the league average for a LH batter of Howard’s physical characteristics, which might be around the same or a little higher – I don’t know. Both of these methods would be wrong. You cannot ignore the fact that he also hit 1.052 in OPS versus RHP over the same time period (last 4 years) and in many more PA. This suggests that he is a very good hitter overall (which he is) and that the .719 is somewhat of a fluke.

And then Pat asks the interesting question: do splits change by age?

I’d also like to see how players do split wise over the course of a career. Obviously the skill of hitters diminishes over time, but it’d be interesting to see if the splits are larger. Ryan Howard certainly is not “old” at 30 years old, but his skill set and physical size certainly have shifted over the years. While he was never slender, Howard is certainly a “bigger” guy than he used to be, and probably a good amount slower. Besides that, as players get older they tend to lose some hand-eye skill, an effect that may be magnified when facing a pitcher of the same handedness. Here are Howard’s wRC+ from 2006-2009, going from overall to versus lefties and then versus righties.

2006: 166, 133, 182
2007: 140, 110, 159
2008: 123, 91, 143
2009: 141, 71, 178

SIERA

Matt and Eric introduce their metric.  A correction:

Nate Silver invented QERA back in 2006 for Baseball Prospectus to adjust for a few issues with FIP and xFIP, and while he referred to the stat as a toy, it represented a big step upward in the methodology of estimators.... QERA has another problem of its own, in that GB% is really GB/Ball in Play (or, GB/BIP), while BB% and K% are measured per batters faced (SO/PA and BB/PA) ...Further, while QERA picks up some of the interaction between walk, strikeout, and ground-ball rates, it does not necessarily weight them correctly

Good for Matt and Eric for noting the two problems with QERA.  I dispute the big/upward claim however.  Also, while FIP has a glossary page, “tRA” is unlinked.  A BPro reader will have no idea what it is.

Anyway, here it is:
SIERA = 6.262 – 18.055*(SO/PA) + 11.292*(BB/PA) – 1.721*((GB-FB-PU)/PA) +10.169*((SO/PA)^2) – 7.069*(((GB-FB-PU)/PA)^2) + 9.561*(SO/PA)*((GB-FB-PU)/PA) – 4.027*(BB/PA)*((GB-FB-PU)/PA)

I’ll need a few hours to test this to see why it works, when it breaks down, and how much of a gain we’re getting over FIP, xFIP and tRA.

Michael Farber’s award-winning article from 1982 on Tim Raines

Here you go.

Glove-slap Kincaid for showing us about google archives.

Publication bias

This appeared on Baseball Prospectus:

For 2003: Jan, 2004:

But for the time being at least, it looks like PECOTA walks the walk.

For 2006: Dec 2006:

Although we’d like to confirm these results independently, Chone Smith, a poster at Baseball Think Factory, has run a comparison of projection systems in 2006 and PECOTA has come out on top, with a huge lead in position player projections and a very narrow second place in pitcher projections.

For 2007 and here: Oct 2007:

{hitters} So, another good year from PECOTA, certainly a good year from ZiPS — Dan does excellent work. I think we can call those two co-champs, but several of the other systems weren’t far behind.
...
{pitchers} The best you could have done last year is to bundle PECOTA and CHONE in about a 4:3 ratio.

***

This did not appear at Baseball Prospectus:

For 2007-08, and written by CURRENT BPro author Matt Swartz in Apr 2009:

--CHONE was the best at projecting most things.
--PECOTA was very close behind but had some systematic biases, specifically for speedy players’ BABIPs, which ZIPS struggled with as well.

Can the Bpro editors approach and pledge to not fire Matt Swartz if he repeats his study for the 2009 season?

This week’s primer: WAR

Good job by VEP to get everyone up to speed.

Bill James being Bill James

The kind of reasearch I love:

Anyway, I was wondering:  does a low walk rate predict a failure to develop as a hitter?  Because I can see it either way.  I can see that a low walk rate for a young player could be an impediment to development, but I can also see how a low walk rate might be predictive of development, in this way:  that the hitter who walks more, as a young player, can be seen as a more finished product, and therefore as a player who has less room to develop.  There’s an extra door open for the undeveloped hitter.

My thoughts exactly.  I was thinking Frank Thomas, who was such a polished hitter at such a young age, and I knew that walk rates for players generally increase all the way until their late 30s, that I figure that polished hitters simply are wise early, and don’t need to make the mistakes that others do to learn to take a walk early.  At the same time, maybe they are so smart that they will draw a walk when they realize they can’t reach that outside pitch in their late 30s. 

Bill then does his magic (behind the pay wall).  And ends with:

Essentially, there is no reason to believe that the walk rate plays any predictable role in the future development of a young player.

Best Stadiums you’ve been to

Chris:

At any rate, if people like stupid lists, that’s perfect for me. My two specialties are making lists and being stupid. This is right up my alley. Besides - it’s the off-season. If it wasn’t for dumb lists what the hell would we have left to talk about?

This week’s dumb list: ranking stadiums I’ve attended. No, it isn’t even a remotely deep or original idea. It’s still a fun, dumb column to scrawl out, though.

I’ve been to very few, so my list is:
Fenway
Yankee
Skydome
Big O
Shea

You really need to cozy atmosphere.  For example, at Le Forum, it was GREAT.  In the new arena (Bell Centre), I went to see the 1996 World Cup, Canada v USA.  One set of friends sat in the lower level, and we sat in the upper level (but right at the first row of the upper level).  In talking to him after the game, it was as if we saw two different games.  We thought the game was good, and he thought it was incredible.  Basically, we got the TV angle, and some of the fan response, while he got the ice-level view with great fan response.

I’ve had the same experience at Giants / Jets stadium, where when we sit in the top level, it’s one feeling, but down in the bottom level is a whole other thing.  I think their new pricing for the new stadium reflects that.  I would say it’s fair that if you have a 75$ ticket for the upper level, that that’s the same as 300$ for the lower level.  I think in all the sports, the price between the worst ticket experience and best ticket experience in the same stadium should be about 3x to 5x.  (Excludes courtside seats, and obstructed view seats.)

Evaluating the 2009 forecasts - Chone/ZiPS + Fantistics win

Professor Jared and his merry band of high school students take on the task.  Those ambitious fellows created the Steamer forecasts, and they submitted it as part of my Forecasters Challenge last year.  They finished 6 out of 22 in the official competition, and number 2 out of 22 in the rules I’ll be using for the 2010 competition.  And they did this while submitting an abbreviated list of players (which I supplemented with the Marcel draft list in the late rounds).  Yes, we should all be impressed. 

Anyway, let’s see what their analysis reveals. They compared these systems: Steamer, Marcel, PECOTA, Chone, ZiPS, Sporting News, Fantistics.

Missing data - 475 hitters had 50 or more PA in 2009.  465 of these hitters had projections from each of the big 3 (chone, pecota and zips).  438 were projected by Marcel.  We looked at these 438 hitters.  Projection systems that projected fewer players (Steamer Projections and Sporting News were the main guilty parties) were given the Marcel projection for that player.  This allowed for a comparison of all 438 hitters across systems.  Sporting News and Steamer only projected about 270 players each.  Systems could beat the monkey so long as the projetions they actually made were better than Marcel.

A technical note: Marcel’s official forecast for anyone not in the downloaded file is:

FAQ: “But, what about a player who’s never played MLB? Where’s his forecast?” That’s simple. His forecast is the league mean over 200 PA, 60 IP (starter) or 25 IP (reliever). If you want to know what the league mean is, just take the average of anyone forecast with a reliability of 0.00. So, Marcel’s official forecast for anyone coming over from Japan is that.

So, to be fair to The Big 3, the 27 missing players should be included with a forecast exactly as I said it should be.  It makes no sense for me to include it explicitly in the download file, if it’s just going to be a line of data that repeats for all players.  But, this is what should be done, because that’s what I said it is.  This is using Fantasy points:

RMSE* System
2.41 Avg Projection
2.43 Fantistics

2.49 Sporting News

2.52 Marcel
2.53 Steamer
2.55 ZiPS
2.56 Chone

2.65 PECOTA
2.67 2008

Holy moley.  First of all, Marcel had a great year.  Secondly, PECOTA did so bad, that it was as accurate as simply taking the previous season’s stats and running with those.  Don’t like RMSE?  How about correlation:

R with actual System
0.729 Avg Projection
0.723 Fantistics

0.707 Sporting News

0.697 Marcel
0.696 Steamer

0.688 ZiPS
0.687 Chone

0.657 PECOTA
0.653 2008

Again here, Marcel did very well, while PECOTA got trounced.  This is pretty shocking actually.  I don’t know what happened to PECOTA, but I’d love to see Baseball Prospectus explain it.  And who the heck is Fantistics anyway?

Anyway, let’s go on:

Also worth noting, each of projection systems has a smaller standard deviation across their projections than the standard deviation of actual results from 2008 or 2009.  This is as it should be.  The projection systems are trying to forecast true talent whereas the variance in actual results is a combination of the variance in true talent and the variance in luck.

Nothing novel there, but it should be noted because lots of people who are new to this aren’t aware.

Continuing:

And, if you want the best linear equation of these systems for projecting actual 2009 SGPs:
Actual = 0.527*Fantistics + 0.409*Chone + 0.243*SportingNews – 0.761 (R2 = 0.547)
While Sporting News projected SGP’s better than Chone, Chone added more information to Fantistcs because Chone was the most unique system while Sporting News was the least unique system.

Very interesting.

And this is using OPS:

RMSE* System
1.55 Chone
1.57 ZiPS
1.57 Avg Projection

1.62 Marcel
1.63 Fantistics
1.65 Sporting News
1.66 Steamer
1.66 PECOTA

1.84 2008

Chone and ZiPS lead the way.  Marcel is where it should be, whil PECOTA once again brings up the rear (just not so obvious this time).  And if you prefer r:

R with actual System
0.638 Chone
0.624 Avg Projection
0.623 ZiPS

0.590 Marcel
0.583 Fantistics

0.568 Sporting News
0.567 Steamer
0.564 PECOTA

0.401 2008

The usual story.  Chone does great, Marcel does average, PECOTA is in last place.

And, if you want the best equation to project 2009 OPS:
ActualOPS = 0.716*Chone + 0.199*ZiPS + 0.081 (R2 = 0.414)
Although this equation doesn’t do much better than simply using Chone.

So, if Fantistics was only in the middle of the pack in projecting OPS, how did it dominate SGP’s?  Ok, so Chone and ZiPS aren’t really trying to project playing time and, despite their excellence in projecting hitter quality (as evidenced by OPS) don’t do well here.  Pecota doesn’t try to project playing time in their weighted mean forecasts but does in their depth charts (used by Steamer).  The community forecasts that Marcel used do reasonably well, but not as well as the fantasy basebal gurus in projecting playing time.  Limiting this to the systems that try to project playing time (and using their proper names this time) we have:

R with actual System
0.721 Fantistics

0.694 Sporting News

0.666 Pecota Depth Charts for Fantasy
0.657 Community Forecasts

It looks to me like we found our secret recipe: Chone/ZiPS forecasts for rate, with Fantistics for playing time.  What I’d like for the professor and his kids to do is to run their study that uses Chone/ZiPS for rates and Fantistics for playing time.

That onside kick to open the 2nd half

I’m the biggest champion of WPA (win probability added) and WE (win expectancy) that there is.  This is why I just love all the work that Brian Burke does.  I couldn’t wait to read what he had to say about it:

Onside kicks are surprisingly successful when they are not expected. Since 2000, slightly over 60% of unexpected onside kicks have been recovered by the kicking team. An analysis based on Expected Points suggests teams should occasionally attempt surprise onside kicks if they believe their chances of recovery exceed 42%. Let’s also take a look at what WP would say.

In this case, the Saints were down by 4 points. A deep kick would typically give the Colts a 1st and 10 near their own 30, worth 0.32 WP to the Saints. A failed onside kick gives the Colts a 1st down at the Saints’ 40 or so, worth 0.26 WP to the Saints. A successful recovery gives the Saints possession at their own 40, worth 0.39 WP. In total, the onside attempt is worth:

0.60 * 0.39 + (1-0.60) * 0.27 = 0.34 WP

The onside attempt was a good gamble according to the numbers, but not by much--0.34 vs 0.32 WP. it paid off, and the Saints capitalized with a TD drive to take the lead for the first time in the game.

Or, another way to say it is: what IS the break-even point for recovery?  Using those numbers, the win expectancy model ALSO says the breakeven point is 42%.  Up until a point, using a point or run model is almost as good as using a win model.  In baseball, you can safely do so all the way through the 7th, even 8th innings.  So, I would suspect in football, you’d be safe up until the last 5 or 6 minutes of the game.

So, I disagree with Brian’s conclusion that it’s “not much”.  .02 wins is very much alot, relative to everything else that goes on.  If the breakeven point is 42% and the observed actual in those situations is 60%, then there is a huge market inefficiency going on here.  A “surprise” onside kick is a great strategy.  Of course, the classification of something being surprise is going to change alot once the receiving team starts to expect an onside kick.

I have the same issue with people who say that .02 wins is not alot in baseball.  A great pitcher would be say a .680 pitcher, or +.180 wins above average per 9 IP.  That’s +.02 wins per IP.  So, is it a big deal if Halladay or an average pitcher pitches one inning?  Well, I think it’s big.  The actual number is +.02 wins.  So, that means that .02 wins IS big.  At the same time, an extra walk in an inning is +.03 wins.  So, if you are upset if Halladay is taken out one inning early, you need to be furious when you have an extra walk in an inning.

Send your scoresheets

Patriot is interested in seeing how you score games.  Send him a scan of your scoresheets.

Fangraphs now has some Splits data

Finally. 

Personally, I’d like to see each split data have its own subtab under the Career/Year tab.  If I’m interested in L/R splits, I’d like to see the STandard / Advanced / Batted Ball all together, not navigate through the other splits I don’t care about (at that moment).  Also, as more splits are added in (like men on base, role, park, etc), it just makes this more cloudy.  Basically, splits should extend horizontally (add more tabs) rather than vertically.

David’s taking suggestions, so post it here or there.

***

I linked to Granderson’s splits, who has a .270 wOBA against LHP (685 PA) and .380 against RHP (2211 PA).  (If David wants to wow us, when he does his leaderboards, give us the “differential”, and also shows us the batter handedness on that table.) Andy said that for LHH, that you would regress the observed split (110 points in this case) 50% toward the mean if you had 1000 PA against LHP.  In this case, with 685 PA, you would regress 1000/(1000+685) or 60% toward the league observed splits for LHH, which I think is like 27 points. 

So, you regress 110 60% toward 27, or 110*.4 + 27*.6 = 60.  So, our estimate of Granderson’s handedness split is .060 in wOBA.  He’s a career .358 wOBA, with 24% PA against LHP.  We can reconfigure his observed split to be:
.358 = .24(x) + .76 (x+.060)

So, his LHP adjusted-observed split is .312 and his RHP split is 60 points higher or .372.  This is his observed baseline.  If he’s a true .350, as opposed to his observed career .358, you bring him down by 8 points on both sides.

In any case, with his excellent fielding, even his poor hitting against LHP means he’s an average player.  When you are an average player against the platoon, this is not really the kind of guy you need to be platooning.  (Unless of course you already have a good platoon partner, so you might as well take advantage when you can.)

***

Here is Andre Ethier in high-leverage situations.  Dave in the THT Annual noted how Ethier had back-to-back years of great high-leverage performance.  We see that this can extend even to 2007.  Here are his wOBA in hi-lev, 2007-09: .403, .451, .454.  (Hi-Lev must be based on LI of at least 2.0, since we have about 10% of Ethier’s PA.  This is different from b-r.com’s hi-lev that uses LI of 1.5 or higher, which is about 20% of PAs.) Anyway, compare that to his overall career wOBA of .363.  His incredibly sucky hi-lev in his rookie year really drags down his career split in hi-lev.

Anyway, love this stuff.

Sunday, February 07, 2010

Would this commercial make it for the Super Bowl?

You decide.

Spitting delaying honor

We know about Alomar.  I did not know about Ted Williams:

August 7, 1956 - In a game against the Yankees at Fenway Park, Williams drop an easy fly ball and is booed lustily by Red Sox fans. At the end of the inning, Williams jogs toward the dugout and launches into one of the worst tantrums of his career by spitting at fans near the dugout. Williams is fined $5,000 but tells the Boston Herald he doesn’t regret his actions. “I’d spit again at those booing bastards,” Williams says.
...
July 23, 1958 - In a game at Kansas City, Williams fails to run all the way to first on a groundout and the crowd loudly boos him. Williams, in turn, spits at the fans and is fined $250.

In 1956, Ted made 125K, so he was fined 4% of his salary.  That’d be like a 25MM$ player being fined one MILLION dollars for spitting on fans.  That’s how much of an afront that incident had.

***

Patrick Roy gave the Montreal fans the one-finger salute in his last game.  We laughed it off by the next day.

Ah, but the Holy Writers, they will always talking about the spitting incident (of Alomar) because it benefits them to act like the gatekeepers of all that is holy about baseball. 

Friday, February 05, 2010

Baseball Jeopardy

Great question being asked:

“Let’s say you’re on ‘Jeopardy!’ and you’re absolutely routing your two opponents. You have $40,000 going into the final round, while one of your opponents has, let’s say, $15,000. You’re guaranteed to move onto the next day, but the final category comes up and it has something to do with baseball, which is your favorite sport. How much — if anything — do you risk?”

He then proceeds to show us 10 real Final Jeopardy baseball questions.  Remember, you have 30 seconds to answer.  I count 4 as gimmes, and the other 6 depend on deep history or geography knowledge, or can recall things that you normally would get in a minute or two, but you need to do it in 30 seconds.  Let’s say you have a 75% success rate because you really know your sh!t.

Here’s how it works.  You bet 9,999$, which is the maximum you can bet, and be guaranteed to win the game, even if you lose on this question.  And you get to come back tomorrow to play.

Let’s say the average winner makes 30,000$ for each game, and you have a 1 in 3 shot of winning.  And if you win, you get to come back the next game.  So, one shot in 3, you pocket 30K and come back again.  Two times in 3, you are left empty-handed.  Your overall expectation therefor is 45,000$.  That’s what playing in a 30,000$ game in Jeopardy is worth, if the winner gets to keep coming back.

Back to where you are in the decision making:
1a. You bet 9,999$, you lose on the question (happens 25% of the time), but win on the game (happens 100% of the time), pocket 30,001$, plus the 45,000$ in future dollars: total earnings = 75,001$
1b. You bet 9,999$, you win on the question (happens 75% of the time), but win on the game (happens 100% of the time), pocket 49,999$, plus the 45,000$ in future dollars: total earnings = 94,999$

Total expected earnings if you bet 9,999$ = 89,999.50$

2a You bet 40,000$, you lose on the question (happens 25% of the time), are out of this game and future games: earnings = 0$
2b. You bet 40,000$, you win on the question (happens 75% of the time), win on the game (happens 100% of the time), pocket 80,000$ dollar, plus the 45,000$ in future dollars: total earnings = 125,000$

Total expected earnings if you bet 40,000$ = 93,750$

Indeed, the breakeven point is 71.4%.  That is, if you are 71.4% sure of your ability to answer the question, then you can go either way.  If you are more sure, you should go for the bigger bet.  And if you DON’T, then you are being risk averse.  And the more sure you are above that 71.4% and that you don’t make the bigger bet, the more risk averse you are.

It seems to me that, given the setup (30 seconds to answer), you should not risk the big payoff today. 

(I’m also ignoring a tie if everyone bets all their money and they all lose.  Presumably, the 2nd place guy isn’t as knowledgeable as you are in baseball and will lose.  And, you would think, the 3rd place guy would bet almost nothing, just to make sure that if two guys bet it all and lose can come out the winner.)

What do you forecast?

Someone sent me an email about how to handle forecasting negative WAR since every year, we see negative WAR being actually generated.  Here is my response:

There is a difference between OBSERVED and TRUE.

If Garret Anderson and Jacque Jones and a host of other players are all TRUE replacement level, and if you give them each 600 PA: guess what happens?  Some will have a 2 WAR, some will have a 1 WAR, some will have a 0 WAR, some will have a -1 WAR and some will have a -2 WAR.

Overall, as a group, these players will have a 0 WAR.  And that’s because we KNOW that they were TRUE replacement level.  So, if you start with the idea that you know someone is a true 0 WAR, then it’s irrelevant what we will observe, any more than you know you have a true fair coin and you observe 60 heads in 100 flips or 30 heads in 100 flips.

And when you forecast players, it would be insane to give PA or IP > 0 for players who are below replacement level.  Therefore, by definition, the lowest (true) WAR you can give someone is 0 WAR.

We are not trying to forecast observations.  We are trying to establish the (unknowable) true rate.  And your forecast must equal the true, just as you would ALWAYS forecast a coin to come up heads 50% of the time, regardless of how many observations you have seen or are about to see.

Vaclav Nedomansky

As you guys know, I think it’s beyond silly that a player’s career totals excludes performance against the toughest competition.  That is, the playoffs.  Why ignore the playoffs and treat it the same as spring training? It’s stupid and lazy frankly.  If you ask a soccer fan how many goals Pele scored, guess what: he’s going to count all his tournaments as well, including the World Cup, the toughest of all competitions of any sport in the world.  North American fans would count such an accomplishment as zero for hockey players, as if it didn’t even happen.

Hawerchuk counted it all:

This revised goal-scoring table includes regular season and playoff goals from the NHL, WHA, Russia, Sweden, Finland, the Czech Republic and Slovakia, as well as all international tournaments of consequence: the Summit Series, the Canada Cup, the World Cup, the Olympics since 1998 and the World Championships.

All the usual suspects are there, with some jockeying around for position.  But, we get one surprise.  And, it’s these kinds of surprises that makes the effort to do this worthwhile.  Great job to Hawerchuk for doing what historians need to be doing.

Thursday, February 04, 2010

BtB Sabermetric Writing Awards Results: Best Researcher or Writer

And the winner is...

1. Tom Tango

I honestly was surprised to see Tango take first place overall in the voting.  But I probably shouldn’t be.  Tom isn’t flashy, but he is one of the few remaining major sabermetric voices still active from the generation that immediately followed Bill James’ more or less solo era.  And he’s also one of the most prolific, playing a significant role in the development or establishment of virtually every major statistic that we used to evaluate players: WAR, wOBA, FIP, WPA, LI, WPA/LI, lwts, Base Runs, Fan Scouting Report, wRC+...the list goes on and on.  Perhaps as a result of his role as a consultant for the Mariners, he didn’t seem to contribute as many major original research projects in 2009 as he has in the past.  Nevertheless, he contributed a large volume of smaller pieces throughout the year, and more importantly played a major role in helping to draw attention to and improve countless individuals’ work at his blog.  He is very deserving of our final saber award for 2009.  Congrats!

You almost made me believe I deserve it!  I’m pretty sure I got some “Al Pacino” type of votes for past services.

I think my vote went for Dave or Max. As I was going down the list from 6 to 2, I was thinking “Uh, well, those should be the top five… who else could be ahead of them?” I was very surprised to see me there.

Well, thank you guys.  It’s a pleasure to contribute what I can, when I can, and to see this community share, develop and grow as it has. And if I played some part in all that, that pleases me tremendously.  There are many bright and dedicated researchers that are out there, and I look forward to seeing their names nominated in the coming years, and more importantly to seeing their research. 

Linear Weights by Strike Zone location

Fantastic job by Jeremy.  How is it possible that a pitch down the middle can generate negative runs for any hitter:

So, Nick Punto, at 191 swings, is -11.3 runs, or -6 runs per 100 swings.  I get the feeling that on swings that are NOT down-the-middle that Punto is better.  Anyway, just great stuff overall.

It’s important to note that while a pitcher will NEVER intend to throw a pitch down the middle (except maybe at 3-0 to weak hitters), they still end up doing it often enough that the big boppers get to swing at such a pitch three HUNDRED times (basically, in half a hitter’s plate appearances, he will get to swing at a pitch down the middle, hence the conventional wisdom to wait a pitch you can drive). 

And this is what MGL is talking about in terms of separating intent of pitcher from result of pitcher. 

Can’t you just refuse search and seizure at schools?

Non-sports post.

First there was that girl that got strip-searched at school.  Now there’s this ruling from the NJ Supreme Court:

School officials can search students’ cars on school property if they suspect them of illegal activity, the state Supreme Court unanimously ruled today in a decision that further broadens administrators’ investigatory rights.

How far can they go?  The kid puts the keys of his car in his pocket.  The school administrator says “give me the keys or I break your window”.  The kid refuses.  Can he break his car window?  Why is it that the police require something more than a school administrator? Indeed, in the strip-search case, how far could the administrator go if the girl refuses?  Could he tie her down, do it in a crowded room? I find it insane that a school administrator has more power than the police and less restrictions.  Indeed, the child, who is not even legally allowed to consent to anything the school administrator asks, is being asked to consent.

Listen, I know my feeble mind is too small to understand what the greatest judges in NJ are thinking.  But, to these guys, tell me why I shouldn’t give them a “Dudes, c’mon.”