Since this is the time that everyone is coming up with their season recaps, I decided to recap the season had in 2010 by the Rangers' primary infielders for 2011.  Also, you will notice that I left out catcher, and that is because analyzing Matt Treanor and Bengie Molina's stats really is as boring as it sounds.  For the rest of the infield, however, I tried to find certain aspects of their performances that might not show up in the usual reports that you would read at ESPN or the Dallas Morning News.  As far as statistical jargon for those who aren't up to speed, I've posted a quick-and-dirty glossary below the content of this post.  And here we go:

SHORTSTOP:

Elvis Andrus:  You may remember my previous post entitled "The Eye of the Elvis", which noted the improved plate patience of Elvis set against his less-than-desirable slugging abilities.  The main point of the article is epitomized by the fact that Elvis ended the season with the highest SLG - OBP disparity in baseball this year of -.041 (OBP=.342, SLG=.301).  In fact, -.041 was also the league leader last year, set by Luis Castillo.  This mark is good for third overall in the past three years, the two preceding it being in 2008 when Gregor Blano posted a -.057 and Chone Figgins posted a -.049. 

The previous paragraph brings to light the paradoxical changes in Elvis' numbers from 2009 to 2010.  He improved in OBP and plate discipline, but regressed significantly in terms of slugging (he had only 18 extra base hits all year).  This, coupled with Elvis' registering as an average defensive shortstop this season by most advanced defensive metrics, make it hard to not consider 2010 a bit of a sophomore slump.  And it doesn't just "seem" that way, either:  Elvis was worth 3.1 WAR in 2009, and 1.5 WAR in 2010.  Here's to hoping Elvis can bring the best of both worlds to the diamond in 2011.

SECOND BASE:

Ian Kinsler:  Ian Kinsler is perhaps the most enigmatic of all the Rangers hitters.  He's been consistently above average, but his consistency ends there.  In fact, looking only at his slashlines from the past four years it would be difficult or impossible to conclude that they were produced by the same hitter:

2007:  .263/.355/.441

2008:  .319/.375/.517

2009:  .253/.327/.488

2010:  .286/.382/.412

See what I mean?  There is some reason to think that Kinsler has finally found an approach that has worked, though:  he posted the highest BB/K ratio (.98) of any second baseman in the AL (third second baseman in MLB behind Keppinger and Utley).  It also ranked 8th overall in baseball, 4th overall in the AL.  This is encouraging because it is noticeably different than his previous career norm of .75.  He may pop out more than we care for, not run out ground balls, and put up wildly different kinds of numbers from year to year, but the fact that he ranks so highly in BB/K ratio in 2010 suggests that he has found an extremely effective approach overall.  Hopefully he sticks to it.

THIRD BASE:

Michael Young:  Michael was 1st among AL third basemen with 481 total outs made at the plate.  For those scoring at home, that's 17.8 full games worth of outs.  It's also good for 3rd overall in MLB behind Derek Jeter (488 outs) and Juan Pierre (484 outs).  Yes, much of this is due to his position at the top of the lineup -- more plate appearances do mean more outs.  But it also has just as much to do with his dead-average OBP of .330, which leads us to question whether he belongs in the #2 spot.  Traditionally, average OBP hitters with decent pop are perfect for the #6 slot in the lineup (perhaps he needs to be switched with the more-OBP minded Ian Kinsler?). 

Additionally, I would wager that Young had some of the biggest disparities among two of the major splits (Home/Road, 1st Half/ 2nd Half) : 

Home vs. Road:  .307/.361/.509 vs. .260/.299/.380

1st Half vs. 2nd Half: .301/.353/.478 vs. .262/.302/.401

Good lord those are some horrible splits.  Young was worse than replacement level on the road, and his second half stats are even worse than Vlad's. 

FIRST BASE:

Mitch Moreland:  Mitch posted a more-than-respectable .255/.364/.469 line in 173 PA in his rookie season.  Perhaps more impressive is that he only hit .275 on balls that he put into play.  I've written before that it is noteworthy when a rookie's BABIP significantly deviates from the league average of .300, and usually it means that the rookie is overperforming (see: Francouer, Jeff -- 2005).  In this case, however, it means the exact opposite.  Is it difficult to get too serious about Moreland because of the small sample size?  Maybe.  But more importantly, it means that he posted those numbers in spite of bad luck, meaning that those numbers are largely sustainable and repeatable (with a likely improvement when his BABIP regresses to the mean). 

Eric Nadel commented late in the season that "Moreland doesn't swing before there are two strikes unless he gets the pitch he's looking for."  This approach was certainly reflected in his .69 BB/K ratio.  Not too shabby, considering that the only other first basemen ahead of him are veterans with names like Teixeira, Morneau, Cabrera, Youkilis, Butler, etc.  Of the notable first basemen behind him in this category are Konerko, Lowell, and Cuddyer.  This characterization typifies the player Moreland projects to be:  solidly above average, but below superstar.  Moreland won't win any awards, but his approach is one that will make him a solid-OBP first baseman with some pop for years to come.  In fact, his performance makes me feel much better about losing Justin Smoak.   If Moreland posts a .255/.364/.469 line every year (and we have good reason to believe that he is likely to exceed these numbers), we as Rangers fans should be very comfortable with that.

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On a somewhat unrelated note, I'd like to read what you think about posts like this (as this article is consistent with my overall style).  Also, if you have an idea for a post you'd like to read that seems to be related to these kinds of articles, let me know in the comments.  Thanks for reading!  --JP

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SABERMETRIC GLOSSARY:  Here are the basics you need to know to make sense of the statistics used above:

**The triple slashline: a player's batting average, on base percentage, and slugging percentage separated by slashes as AVG/OBP/SLG.  Though it varies by defensive position, the slashline for the average major leaguer is usually around .265/.330/.445. 

**Strikeout to walk ratio (BB/K):  The number of walks divided by the number of strikeouts recorded by a hitter (there is an analog for pitchers, of course, but it does not appear in this post).  The MLB average this year was .46.

**Wins Above Replacement (WAR):  a statistic that is difficult to compute but easy to understand: it's the number of wins that the player contributed to the team more than an average replacement would have.  For hitters, it takes into account both offensive and defensive contributions.  A negative WAR means the player was worse than replacement level, 0 means the player was replacement level, positive means the player was better than replacement.  For more information, visit the Sabermetrics Library.

The year was 1997.  The Rangers were playing the Angels in a meaningless late September game.  The Angels swept the Rangers in that three game set, winning 7-1, 7-6, and 4-1.  Adding to the misery was that the heavyweight Gary Disarcina, who posted a .246/.271/.326 line on the year, went 6 for 15 (.400/.400/.467) in the series.  It was frustrating.

Unfortunately, this seemed to be a routine occurence; at the time I posited that it was a product of the middling bastion of pitchers the Rangers hurled at opposing teams during the 90s.  My Dad and I had a special name for hitters like Disarcina who played like All Stars against the Rangers but like Little League All Stars against the rest of the league.  We coined the term "Designated Ranger Killers" for these types of players.  This season, amidst a two game stretch against the Royals (from August 31st to September 1st) that saw the almighty Yuniesky Betancourt carve out a 3 for 7 showing with two home runs and two walks, I realized that even with this vastly improved pitching staff, that there were still Designated Ranger Killers lurking in mediocrity, waiting for their chance to strike.   

For the record, a "Designated Ranger Killer" shall be defined as "a mediocre hitter who wreaks havoc on the Rangers".  I don't feel the need to be too precise about this; everyone knows how they felt when Betancourt walked twice in that game on August 31st.  And just as a disclaimer to any strict sabermetricians:  these abominations below are indeed due to a small sample size (and no conclusions are being drawn here as to ability).  But the results are still maddening as hell (see, for example: Punto, Nick).

Just so everyone can share my frustration, I've devised a list at least one player from each team who qualifies in my opinion as a Designated Ranger Killer.  This list was agonizingly compiled from memories of games poisoned by said Ranger Killers.  The players and teams are listed in no particular order.  I'm sure that you will notice that some offenders are worse than others (for example, Nick Punto is a worse offender than Darnell McDonald because Punto is, well, crappier).  Additionally, the stats are taken from the 2010 regular season.  On a separate note, it feels good to have to clarify that it was during the regular season, doesn't it?

Kansas City Royals:  Yuniesky Betancourt

Overall:  .259/.288/.405                Against the Rangers:  .308/.400/.654

New York Yankees:  Francisco Cervelli

Overall:  .271/.359/.335                Against the Rangers:  .556/.733/.556

Oakland Athletics:  Gabe Gross

Overall:  .239/.290/.311                Against the Rangers:  .421/.522/.474

Oakland Atheltics:  Eric Patterson

Overall:  .204/.255/.408                Against the Rangers:  .313./.353/.875

Los Angeles Angels of Anaheim:  Erick Aybar

Overall:  .253/.306/.330                Against the Rangers:  .304/.344/.446

Baltimore Orioles:  Corey Patterson

Overall:  .269/.315/.406                Against the Rangers:  .306/.342/.528

Detroit Tigers:  No Designated Ranger Killer Found (open for nominations, though!)

Cleveland Indians:  Jayson Nix

Overall:  .224/.281/.396                Against the Rangers:  .313/.421/1.063

Chicago White Sox:  Brent Lillibridge

Overall:  .224/.248/.378                Against the Rangers:  .600/.600/.800

Seattle Mariners:  Justin Smoak

Overall:  .218/.307/.371                Against the Rangers:  .368/.429/.842

Tampa Bay Rays:  Jason Bartlett

Overall:  .254/.324/.350                Against the Rangers:  .385/.429/.385

Toronto Blue Jays:  Jose Molina

Overall:  .246/.304/.377                Against the Rangers:  .300/.300/.600

Boston Red Sox:  Darnell McDonald

Overall:  .270/.336/.429                Against the Rangers:  .316/.435/.842

Minnesota Twins:  Nick Punto

Overall:  .238/.313/.302                Against the Rangers:  .400/.625/.400

Honorable Mentions:

Los Angeles Angels of Anaheim:  Peter Bourjos

Overall:  .204/.287/.381               Against the Rangers:  .296/.286/.630

Baltimore Orioles:  Josh Bell

Overall:  .214/.224/.302                Against the Rangers:  .273/.273/.818

Since this is not a rigorous sabermetric piece, I am sure to have left out several who belong on this list -- please feel free to add any players I have overlooked in the comments section.  You are also welcome to share your own Gary Disarcina stories, from this season or any season.  Misery loves company, after all.

Thanks for reading!  --JP

Some folks may remember these posts (see here, for example) from last year, where I do a quick scan of opposing team's blogs to get the feel from the other side of things after a Rangers win.  A quick scour of the Rays blogosphere yielded these results (below), but if you find something particularly informative or hilarious, post it in the comments and I'll update the post with them.  Enjoy!  -JP

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GAME 1: Rangers 5, Rays 1

Price Roughed Up, Rays Lose to Rangers 5-1 from DRays Bay

This opening game of the ALDS was billed as the proverbial pitchers' duel between David Price and Cliff Lee, however, today, David Price failed to live up to that billing.

…

Heck, now that the heat of the moment has passed, I can admit that Price threw a decent game, and really only made three mistakes on the day.

Those three mistakes were all four-seam fastballs that Price left up in the zone to a group of hitters that crush the fastball.  All three resulted in Rangers runs.  The first was a 93.7 MPH pipeline heater that Jeff Francoeur laser-beamed off the wall in dead-center to plate the Rangers first run.  The second was a 3-0 'cookie' that Nelson Cruz hit about 9,000 feet off the restaurant in center, and the last mistake was another 3-0 fastball that Vladimir Guerrerro beasted off the wall in center.

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The Great Regression, and Other Topics of Interest from DRays Bay

Here at DRaysBay, we believe each game is only a glimpse of reality -- a tiny sliver of the talent of everyone involved. When something absurd happens -- say Bengie Molina, I don't know, hits a home run -- we know his homer does not symbolize a permanent and accurate presentation of his daily hitting ability. More than likely, Molina will continue to excel at hitting poorly the following game.

This is regression. This is a concept we must believe, or our veins will split and our heads will pop like an over-filled red balloon every time Kelly Shoppach swings at ball 4 or Jason Bartlett tosses the 3rd out to a ginger kid in the 5th row.

The regression concept gives me hope for James Shields -- and moreover, the Rays as a whole in game 2.

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Rays’ Local TV Ratings Nearly Doubled The Rangers’ TV Ratings

All the talking heads say the Rays have no fans and that we don’t deserve a team. But how do we know the talking heads aren’t crazy glue sniffers that sneak into your house, take money off the dresser and knock your daughter up? Because clearly, the talking heads never do any, you know, actual research.

Take yesterday’s local TV ratings for the ALDS. Game 1 drew a 5.9 rating in Dallas-Ft. Worth. Meanwhile, the same game had a 9.6 rating in the Tampa-St. Pete market.

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GAME 2:  Rangers 6, Rays 0

Rolling Snake Eyes:  Rays Stink it Up, Lose 6-0 to Rangers in Game 2 of ALDS from DRays Bay

The playoffs are a crapshoot, and so far the Rays are rolling snake eyes.

…

We just won the AL East again - I mean, this is a team that we should be proud of right now.

And yet....after watching that game, all I can feel is disappointment. The season isn't over yet, but it felt like it watching the Rays yesterday. James Shields was not the scapegoat of the game and his final lie belies how well he really pitched - Chad Qualls and the umpires letting multiple inherited runners score didn't help that - but he exemplified the Rays yesterday. He looked good in the beginning, striking out multiple batters in the first inning, but he also was a mess at times. Two hit batsmen. Falling off the mound. Looking bug-eyed and crazy on the mound, unsure of himself and caving in to the pressure. Freaking out about baserunners. I know the underlying statistics suggest Shields is better than his 5+ ERA - and I still believe he is - but if he doesn't believe that...well, you get a performance like last night.

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[THE HANGOVER] from Rays Index

THE BAD: Derek Shelton. One of the most overrated coaching jobs in sports is that of hitting coach. But this has to change. And it must start with Derek Shelton. Period…James Shields. Who would have guessed that Shields would be undone by pickoff attempts and a home run. OK, we knew the home run would happen. But Shields may have missed two pickoff opportunities in the third when he held the ball. And when he did throw the ball, it got past Ben Zobrist leading to the first run for the Rangers…Check Swings. The way things are going for the Rays, you just knew that after the umpires missed the check-swing on Michael Young, he was going to get a big hit. That big hit was a home run, figuratively ending the Rays chances and literally ending Joe Maddon’s day (he was ejected).

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Introduction

I was browsing over at Bill James Online the other day, and saw that Josh Hamilton has 46 RBIs on his 31 home runs.  My first thought was, "That's a lot of solo home runs."  This leads to an interesting question in my opinion:  who hits the most "productive" home runs?

Definition of RBIs/HR:

We can measure this by taking the number of RBIs a player has as a result of home runs, and dividing it by the number of home runs (which gives us an RBIs on HRs per HR, or RBIs/HR for short).  

The first set of data I looked at was for Rangers players with at least 10 home runs (to limit random variation due to a small sample size of HRs) , sorted HRs:

Rangers RBIs/HR (minimum 10 HRs): 

From this small sample, it seems that a reasonable average is approximately 1.6 RBIs/HR.  The next obvious step is to examine more data.  However, since this data isn't in a place where I can find it sorted very neatly,  I'm going to select my sample by choosing hitters for which there is not a small sample size (in other words, the top 22 home run leaders in MLB), sorted by HRs:

Top 22 HR Hitters in MLB RBIs/HR

And the winners are:

The most productive home run hitters on this list are (1) Vladimir Guerrero (1.84 RBIs/HR), and (2) Jose Bautista (1.74 RBIs/HR).  Instead of suggesting that someone like Vlad is more "clutch" with his home runs, a more likely explanation is probably that the best hitter in the league, Josh Hamilton, hits directly in front of him and has gotten on base at a rate of over 40%.  In general, a player's RBIs/HR seem to be directly correlated with the ability to get on base of the hitters directly ahead of them in the lineup (but that's another fanpost for another time).  Bautista seems to be a quick exception to this, however, as a quick scan of Toronto's lineups (or their roster in general) reveal a consistence absence of high-OBP guys in their lineup.

The least productive home run hitters on this list both play for the same team: they are (1) Prince Fielder (1.18 RBIs/HR), and (2) Rickie Weeks (1.32 RBIs/HR).  Rickie Weeks, of course, hits leadoff, so he is preceded by the pitcher and the #8 hitter, so his low  RBIs/HR comes as no surprise.  Prince Fielder's is fairly surprising, however, given that he is preceded by Ryan Braun (.303/.361/.484).  Braun reaches base at a respectable rate;  a possible reason may lie in the number of times Braun has cleared the bases ahead of Fielder:  he has 17 HRs and 15 GIDPs, meaning that Braun has voided would-be RBI opportunities for Fielder 32 times.

INTRODUCTION:

I was intrigued by the discussion over the front page post about the Monty Hall Problem yesterday, and I thought that a simulation of the "game show" in question might help to demonstrate what is going on in the problem.  I constructed the simulation (using Excel and the Analysis toolpak plugin) to test whether changing your choice of doors after the host reveals an incorrect door is a good idea.  We will later perform the simulation with 50 iterations, and then 1,000 and 10,000.  The steps are as follows:

SIMLUATION STEPS:

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STEP #1:  Use a random number simulator to randomly select door #1, 2, or 3 as the door concealing the prize.

STEP #2:  Use a random number simulator to randomly select door #1, 2, or 3 as the door the contestant thinks conceals the prize.

STEP #3:  Use a random number simulator to randomly select one of the remaining doors as one of the doors the host "eliminates".  This number/door can not be the same number selected in either of the previous two steps.

STEP #4:  Change the contestant's choice to the door different than his initial choice and different than the one chosen by the host.

STEP #5:  Test whether the new door is the same as the prize door selected in Step #1.

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EXAMPLE (ONE ITERATION OF SIMULATION)

For example, an iteration of this simulation might go as follows:

To preserve people's sanity, I won't write the Excel program codes used to ensure that the steps are effectively random and accurate.  I was able to check that all of the above conditions were fulfilled (i.e. making sure that choices that can't overlap don't in the simulation).  However, if you end up looking at the Excel file and have questions, I'd be happy to answer them.   

TRIAL RUN OF 50 ITERATIONS

The good news about using Excel is that, once the code has been written and verified, it is extremely easy to produce iterations.  Because we are limited by space, all that I will show here is a trial run of 50 iterations, which produced 33 successes (66%).  I give the results from runs with more iterations below, as well as a link to the simulation file:

Our trial run produced a 66% success rate, which seems to support the theory that changing rooms gives you a 2/3 probability of winning.  However, 50 trials still allows for a high amount of random fluctuation.  We need to run more trials to ensure that random fluctuation is negligible (the Law of Large Numbers ensures that, in any experiment, the relative success/frequency in an experiment approaches the true probability as the number of iterations increase).
 

MORE ITERATIONS

Note: due to the randomness of these situations, each time the excel file is edited, the random numbers change, which in turn yields different (though similar results); the fluctuations will be greater for any runs with a small number of iterations (such as 50), and will shrink to a negligible amount for runs with sufficiently many iterations (such as 10,000).  Keep this in mind if you download the simulation .xls file (see below).

50 iterations yield 33 successes -- 66% success rate

1,000 iterations yield 673 successes -- 67.3% success rate

10,000 iterations yield 6,675 success -- 66.75% success rate

SIMULATION FILE

Download the simulation .xls file by clicking here.  You will need Excel with the Analysis Toolpak to read the file.  Feel free to use it and make changes.  I'd be happy to explain any of the techniques used to construct the simulation.

CONCLUSION

These numbers certainly suggest that 2/3 is indeed the correct probability, and that switching your choice is indeed the prudent decision.  Simulations, however, are not proof, no matter how many iterations, so this simulation should merely be viewed as evidence and support for the "switching doors" conjecture (solid evidence, no doubt, but still lacking in the sense of an actual proof).  (There is a theoretical proof, which several posters alluded to in the comments section of the original front page post.) 

This is one of my fanposts from LoneStarBall (another one being The Eye of The Elvis, which philkid3 linked to a couple of days ago here on his AL West report), but since it's equally relevant to teams other than Rangers, I thought I'd post it here as well.   Enjoy!  --JP

Introduction/Statement of Question:

As anyone who has watched a Rangers home game on national television knows, the commentators love to jabber about how the Rangers hitters enjoy a significant advantage when playing at home.  This isn't the exact topic I set out to examine, but instead it provoked me to answer a different question:  which team this year has played better at home, compared to their performance on the road?

 It's worthwhile to clarify the question here:  I don't want to study the best home teams here;  that would hardly be worth the trouble of writing a FanPost, since I could just glance at the home/road (or, if I wanted to take "luck" out of it and be a bit more precise, use the Pythagorean W-L formula for the home and road records).  Instead,  I want to find who plays the best at home, relative to how they play on the road. 

There seem to be a couple of ways to study this question.  The most simplistic (and perhaps least exact) method would be to subtract a teams home winning percentage from their road winning percentage.  This method would certainly give us a vague answer to our question.  A second would be the teams home/road split slashlines (AVG/OBP/SLG), yet this would make it difficuly to quantify our results.   I'd like to look at it from a more exact point of view:  in terms of runs scored/game and runs allowed/game:

Here are the runs scored and runs allowed per game stats for the American League at home, as of August 9th at the end of game play (sorry if it's a bit difficult to read, I had some formatting issues):

The Home Data:

The key stat here is the Differential/Game (given by RS/G - RA/G), which tells us the average scoring margin of the team's home games.  For instance, the Rangers score 1.37 more runs on average at home than their opponents.  The next table shows similar statistics for AL teams on the road:

The Road Data:

These two tables in themselves are enough to study individual teams on their own.  For instance, we can see that the Rangers are indeed a much better team at home:  they outscore their opponents by an average of 1.37 runs/game at home, contrasted with 0.39 runs/game on the road. 

We can further elucidate the differences and allow for easier comparison, however, by combining the results:  substracting the Road Differential from the Home Differential (call this "new" stat Total Home Advantage).  For example, the Total Home Advantage (THA) for the Rangers is .98, meaning that the Rangers' average run differential is .98 runs per game greater at home than on the road.  (**By "new", I mean that I created it based on the data I was using for this post; it's very possible that this or a very similar stat has already been devised.)  The THAs for the rest of the league are listed in the following table:

Combined Data (Total Home Advantage = Home Differential - Road Differential)

Tips for using/how to read the Total Home Advantage (THA) statistic:

Before going any further, here are some important features of this statistic, as a result of it's construction:

**the THA means that the team's average run differential is _____ more runs per game at home than on the road

**a negative THA means that the team is worse at home than on the road (in other words, better on the road)

**A THA near 0 indicates that the team's home/road differentials are virtually indistinguishable

**A higher Total Home Advantage does not necessarily mean that the team has been good at home or even a good team at all (see Seattle and their 1.43 THA, for example), merely that they have been that much better at home than on the road

 **A lower Total Home Advantage does not necessarily mean that the team has been bad at home or even a bad team at all (see Tampa Bay and their -.74 THA), merely that they have not been as good at home as on the road

  **THA is a measure of how the team has played in their home ballpark relative to how they have played elsewhere, not a measure of the ballpark itself (in other words, it's not a park factor)

Summary of results:

We can see that the top 4 teams are:  (1) Oakland, 1.97    (2) Detriot, 1.77    (3) Toronto, 1.61, (4) Seattle, 1.43.  This is interesting to me because all four of these teams play in pitcher's parks, and additionally none of these teams look to be serious contenders this year (so the teams that have played much better at home aren't even the best teams in the league, and in Seattle's case, one of the worst).

On the other hand, the bottom 4 teams are:  (1) Tampa Bay, -0.74    (2) Boston, -0.10   (3) Baltimore, -.01,   (4) LA Angels, 0.22.  This list was quite surprising, due to the fact that Tampa Bay over the last few years has played exceptionally well at home, and Boston is always touted for their homefield advantage at Fenway.  Of course, since THA measures performance at home relative to performance on the road, the home field advantage could very well still be there, and this could just be due to the fact that these two teams are playing superior baseball on the road as well.  It is also noteworthy that one of the worst teams in the AL makes the Top 4 list (Seattle), while one of the best teams in the league makes the Bottom 4 (Tampa Bay). 

Source used for data:  Baseball Reference

So here goes a second FanPost (the first one being The Eye of The Elvis).  I've taken a slightly more difficult topic, which neccessitated a slightly more technical approach, as well as slightly more tables and words to explain and answer the question (that having been said, I attempted to keep everything as organized and easy to read as I could).  Enjoy!  --JP

Introduction/Statement of Question:

As anyone who has watched a Rangers home game on national television knows, the commentators love to jabber about how the Rangers hitters enjoy a significant advantage when playing at home.  This isn't the exact topic I set out to examine, but instead it provoked me to answer a different question:  which team this year has played better at home, compared to their performance on the road?

 It's worthwhile to clarify the question here:  I don't want to study the best home teams here;  that would hardly be worth the trouble of writing a FanPost, since I could just glance at the home/road (or, if I wanted to take "luck" out of it and be a bit more precise, use the Pythagorean W-L formula for the home and road records).  Instead,  I want to find who plays the best at home, relative to how they play on the road. 

There seem to be a couple of ways to study this question.  The most simplistic (and perhaps least exact) method would be to subtract a teams home winning percentage from their road winning percentage.  This method would certainly give us a vague answer to our question.  A second would be the teams home/road split slashlines (AVG/OBP/SLG), yet this would make it difficuly to quantify our results.   I'd like to look at it from a more exact point of view:  in terms of runs scored/game and runs allowed/game:

Here are the runs scored and runs allowed per game stats for the American League at home, as of August 9th at the end of game play (sorry if it's a bit difficult to read, I had some formatting issues):

The Home Data:

The key stat here is the Differential/Game (given by RS/G - RA/G), which tells us the average scoring margin of the team's home games.  For instance, the Rangers score 1.37 more runs on average at home than their opponents.  The next table shows similar statistics for AL teams on the road:

The Road Data:

These two tables in themselves are enough to study individual teams on their own.  For instance, we can see that the Rangers are indeed a much better team at home:  they outscore their opponents by an average of 1.37 runs/game at home, contrasted with 0.39 runs/game on the road. 

We can further elucidate the differences and allow for easier comparison, however, by combining the results:  substracting the Road Differential from the Home Differential (call this "new" stat Total Home Advantage).  For example, the Total Home Advantage (THA) for the Rangers is .98, meaning that the Rangers' average run differential is .98 runs per game greater at home than on the road.  (**By "new", I mean that I created it based on the data I was using for this post; it's very possible that this or a very similar stat has already been devised.)  The THAs for the rest of the league are listed in the following table:

Combined Data (Total Home Advantage = Home Differential - Road Differential)

Tips for using/how to read the Total Home Advantage (THA) statistic:

Before going any further, here are some important features of this statistic, as a result of it's construction:

**the THA means that the team's average run differential is _____ more runs per game at home than on the road

**a negative THA means that the team is worse at home than on the road (in other words, better on the road)

**A THA near 0 indicates that the team's home/road differentials are virtually indistinguishable

**A higher Total Home Advantage does not necessarily mean that the team has been good at home or even a good team at all (see Seattle and their 1.43 THA, for example), merely that they have been that much better at home than on the road

 **A lower Total Home Advantage does not necessarily mean that the team has been bad at home or even a bad team at all (see Tampa Bay and their -.74 THA), merely that they have not been as good at home as on the road

 **THA is a measure of how the team has played in their home ballpark relative to how they have played elsewhere, not a measure of the ballpark itself (in other words, it's not a park factor)

Summary of results:

We can see that the top 4 teams are:  (1) Oakland, 1.97    (2) Detriot, 1.77    (3) Toronto, 1.61, (4) Seattle, 1.43.  This is interesting to me because all four of these teams play in pitcher's parks, and additionally none of these teams look to be serious contenders this year (so the teams that have played much better at home aren't even the best teams in the league, and in Seattle's case, one of the worst).

On the other hand, the bottom 4 teams are:  (1) Tampa Bay, -0.74    (2) Boston, -0.10   (3) Baltimore, -.01,   (4) LA Angels, 0.22.  This list was quite surprising, due to the fact that Tampa Bay over the last few years has played exceptionally well at home, and Boston is always touted for their homefield advantage at Fenway.  Of course, since THA measures performance at home relative to performance on the road, the home field advantage could very well still be there, and this could just be due to the fact that these two teams are playing superior baseball on the road as well.  It is also noteworthy that one of the worst teams in the AL makes the Top 4 list (Seattle), while one of the best teams in the league makes the Bottom 4 (Tampa Bay). 

Source used for data:  Baseball Reference

Better than last year

It's no secret -- the improved eye of Elvis Andrus this year has gained much notice and praise (despite his recent struggles).  In case this is news to you, you need only look at last years walk rate at 7.4% compared with this year's 10.8% (roughly 20-25 more walks over the course of the season -- an encouraging one-year improvement for such a young player).

What makes Elvis different?

While walking 10.8% of the time is certainly above average and a desirable quality in a leadoff man, that in itself is hardly remarkable (and hardly worth wasting my time writing and your time reading a fan post over); indeed, 25.0% of all qualifying major league hitters have a higher walk rate than Elvis.

Based on my tone in the previous paragraph, you undoubtedly guessed that there is something remarkable about Elvis in this regard.  To see what sets Elvis apart, let's take a look at some other hitters with a similar walk rate:

Directly above Elvis at 11.0%, we have:  Magglio Ordonez, David Wright, Jason Bay, Derrek Lee, Andres Torres, Ian Stewart, Adam Dunn

Without glancing at the stat sheet, we can readily identify the above as players who can be projected to hit 20+ homers in a season (in general; Torres is having an up year and Lee and Bay are having down years to be sure).  And in case you haven't noticed, Elvis only has 6 homers in 1022 big league plate appearances, none of those six coming in 2010.   And therein lies the difference:  slugging.

How unique does that make Elvis?

Let's not beat around the bush:  Elvis doesn't slug, in any sense of the word.  He only has 15 extra base hits so far this year.  In fact, he's 3rd lowest in the majors in ISO (isolated slugging:  slugging avg - batting avg) with a wallopping .041, kept out of the cellar only by the mighty Juan Pierre and Cesar Izturis.  However, this is perhaps what makes him so unique: out of the lowest 20% of qualifiers in ISO, only four have a higher walk rate:

Ben Zobrist (13.2%)

Brett Gardner (12.7%)

Russell Martin (12.4%)

Chone Figgins (12.0%)

To me this is remarkable;  traditionally the highest walk rate guys are mashers, and their good eye is exaggerated by the fact that pitchers are unwilling to catch too much of the plate (which leads to more pitches around the corners).  However, pitchers shouldn't have to fear that too much damage will be done by attacking the strike zone with Elvis (unless there are runners on base), since the odds say that the worst thing he can do with the bat is end up on first anyways.  Just as power can exaggerate the eye of a slugger, I believe a lack of power understates the eye of a, well, non-slugger. 

In summary:

If you grew tired of my semantics above, this should give you the gist of the entire post (the last two being the main points):

**Elvis' walk rate has increased impressively from last season, given his age

**Elvis has the fifth best walk rate for players who slug as little as he does

**Elvis' walk rate is all the more remarkable because pitchers aren't as likely to pitch to him carefully

*************************************************

By the way, I hope you found this an acceptable use of a FanPost -- I'm a longtime reader and lurker (I don't comment much).  I used to write over at the "Hello Win Column" blog at http://hellowincolumn.blogspot.com (with lonestarJon), but time constraints forced me to take a step back (at least from writing everyday). I haven't completely given up on the blog, but I'll settle for writing occasional articles like this as FanPosts if you guys find these articles at least halfway interesting.  Thanks for reading!  --John Paul

So, after a little bit of a break (i.e. missing wins 34 through 67) due to pesky little life-changing events like getting married, the "losing locker room" editions of Rangers wins are back.  Here were the highlights from the latest installment (wins 68 through 79):

From Win #71:

My buddy texted me "Why isn't Texas better? They're [expletive] good. They should be running away with the division." Baseball's a funny game.

 

From Win #72

That was frustrating.

It was an early strike by the Rangers that did all the necessary damage on Saturday night. I'm glad I fell asleep during the fifth inning, because I'm pretty sure my frustration wouldn't have let me sleep . . .

 

From Win #76

Ahh, who to pull for? I can't actively root against the Orioles—it's just not in my blood—but given that the Rangers are the only team standing between the [Red Sox] and a wildcard berth, I will have no problems, no problems whatsoever, if we get swept by the Rangers.

 

From Win #79

It took the Texas Rangers less than 24 hours to sweep the Indians . . .

The only upside here is that the Rangers are only 1.5 back of Boston now.

 To read the entire collection, go here.

Hello again.  You might remember this fanpost from roughly two weeks ago, which chronicled the Rangers first 20 wins.  That went over so well that I've decided to keep everyone updated every ten wins (instead of 20). 

For those who didn't read the first post, after each Rangers win we at Hello Win Column scour the opponents' fan blogs and pick snippets from each one that are representative of the game.  Sometimes the snippets aren't representative but instead are pretty darn funny. 

Here's an excerpt (click the link below to read them all):

From Win #21 vs. Angels

"Angels fans will tell themselves that It was just one of those games where nothing went right, and they better hope this is the case, because if the Texas Rangers are as dominant as they were Friday night at home, the division will be all theirs . . ."

 

From Win #22 vs. Angels

"May the farce be with you, MLB, the Rangers are 6 games over .500 for the first time since steroids were banned by baseball and are one urine specimen and a Tim Donaghy-style investigation away from their rightful place... and that would be fighting for third with Seattle.

Lackey was ejected after hitting Ian Kinsler. The plate-crowding cheat ended up scoring that inning to tie the game 1-1"

From Win #26 vs. Astros

"Ugh. I planned my afternoon around this game… And what happens?

Blanked by the Rangers.

Let me put that in context. Swept by the Rangers.

It was one of those games when we didn’t hit and we didn’t make any miraculous fielding plays either. It was one of those games when our starting pitcher gave up hit after hit and then our hitters countered with one pitch pop fly outs. "

 

From Win #27 vs. Yankees

This was the highlight of my research for today's HWC post: a contrast between Lone Star Ball's game report and Pinstripe Alley's game report:

Adam Morris (of LSB): "That was worth the wait."

Travis G (of PSA): "I can't believe I stayed up for this."

 

From Win #28 vs. A's

"Josh Outman pitched great in Game 1, striking out a career high 9 in 6.2 IP and allowing just 3 hits, but was pulled after 101 pitches because Bob Geren saw a game that was becoming dangerously close to being an A's win and took swift and decisive action."

 

Check out the full transcript at Hello Win Column.

First of all, let me introduce myself.  I'm John Paul.  Some of you may recognize my name if you have visited my somewhat new Rangers blog, Hello Win Column.  Though I've been reading Lone Star Ball almost religiously for the past 2 years, I, regrettably, have never posted.  That's right, I'm a lurker.  But let's move on . . .

A tradition that we have over at Hello Win Column after each Rangers victory involves scouring the opponents blogosphere to provide a different point of view of the game.  This is interesting for a number of reasons:  1) the remarks are often funny, and 2) fans of the opponent often see the games from perspectives that you won't find on Rangers blogs. 

Given the Rangers current play, and the fact that 20 wins is as good a landmark as any, I thought this was a great opportunity for everyone to read and enjoy a different sort of game recap for the Rangers first 20 wins.  I hope you guys like it.

There's quite a lot to read, so instead of posting them all, here are some of the "greatest hits" from the first part of the season.  If you follow the link at the bottom of the blockquote, you can read quotes from all 20 wins so far.  Have fun, and go Rangers!

From Win #1

Game One: Oh, Hamburgers from Lets Go Tribe

"The Indians have produced two Cy Young winners in a row; pride of the organization. And now, two years in a row, our reigning Cy Young has totally soiled the sheets on Opening Day."

 

From Win #5

A breakdown of Treydaddy's Decisions: April 19th Edition from Royals Review

". . . Did a lot of first-guessing of Trey's managerial decisions as the Royals' chances to win slowly spiraled down the drainage pipe of doom . . . What more needs to be said at this point? Of course, Trey did the "conventional" move, saving his closer Soria until the Royals had taken an imaginary lead against the Texas bullpen's next imaginary pitcher."

From Win #8

Rangers 6, Orioles 5: I am SO over Ian Kinsler from Camden Chat

"What are the odds you'll get good starting pitching from Adam Eaton and Mark Hendrickson in the same week? Sucky, that's what they are."

From Win #9

Texas Takes Three from The Baltimore Sports Report

"All I’m saying is “thank goodness Texas is leaving.” The Orioles play horribly against the Rangers, I’m just glad it’s over . . ."

 

From Win #14

Game 27: Rangers at Mariners from USS Mariner

"You know how I keep harping on the roster being too right-handed and how it’s costing us wins?

Padilla, career vs RHB: .241/.300/.370
Padilla, career vs LHB: .299/.381/.484

The Mariners are starting seven right-handed bats and two left-handed bats today. Yea."

 

From Win #16

Jose Contreras is broken from Sox Machine

"I can pinpoint the exact moment where I threw in the towel for Jose Contreras' near future -- and nothing even happened on the play.

In the second inning of Friday night's 6-0 loss to the Texas Rangers, two outs, Omar Vizquel at the plate, and runners on first and third, Contreras screamed:

"I AM AFRAID OF PITCHING!"

...by trying one of those fake-to-third, throw-to-first deals.

Then, add in these two factors:

Two outs!

Two outs and Omar Vizquel!

He stalled despite that hitter being Vizquel, the one guy on the field who might be older than Contreras, and the guy who slugged .267 last year.

 

From Win #20

Oh Baseball, You So Crazy! from Section 331

"I dated this guy for like 3 years. When we first met, it was all whirlwind and fun . . . Eventually, I found out that he had a HUGE meth addiction all along, and that was where all our money was starting to disappear to.

That was basically today’s game, in a nutshell."

16-19, Game Notes from Lookout Landing

"Today's hockey games were good . . . .

Chris Davis, by the way, has the lowest contact rate of any regular in baseball. If ever there were a good time for Morrow to take something off of his heater, that was it. Instead he played right into Davis' hands, going to the same spot with the same pitch four consecutive times . . ."

Click here to read the rest of how the victims of the Rangers first 20 wins reacted.