General question for the members of Btb:  When using Sky Kalkman's Trade Value Calculator, during the offseason, do you include '09 numbers to determine the players value?

Josh Johnson for example, if you input his 5.5 WAR, and $1.4 million salary, and then input 60 and 80% for his '10 and '11 salary, with a 4.7 and 4.3 WAR for '10 and '11 respecitvely, his surplus value comes out to be a little over $41 million.  But this just doesnt seem right to me.  Why would a team pay for past performance?

If you eliminate the '09 information altogether, but leave the '10 and '11 the same, the surplus value comes out a little over $17 million.

Should you just split the difference to determine his value?  Or is the $17 mil number correct, and if so, it seems a little low.  Any advice?

It is well-established that pitchers have control over their ground ball and fly ball rates--some pitchers, like Roy Halladay, are known for their extreme ground ball tendencies.  But what allows these pitchers to achieve a markedly different batted ball profile from the average pitcher?  I decided to use Pitch f/x data to determine whether batted ball rates depend on pitch type (as classified by Gameday) and location.

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I've been a big fan of the hardball times xBABIP calculator over the last 6 months or so, but there were a couple of things that I didn't like about it.  The first thing I didn't like, was having to stick in exact numbers for AB's, HR's, etc.  When dealing with projections, I much prefer to work in percentages.  With percentages you can see what their BABIP for a partial season, or even a span of several years, or a career much easier.  I also am not so sure about the inclusion of stolen bases as a statistic.

I'm a big fan of the fangraphs website, and they provide a wide array of batted ball data for each player.  I determined that BABIP is very strongly determined by a combination of LD%, GB%, FB%, IFFB%, HR/FB%, and IFH%.  That is to say, as much as BABIP can be.  This is right along with what the hardball times uses, except in my case, I'm dealing strictly with percentages, and I've substituted in IFH% as opposed to SB's.  It's worth noting, that I'm not taking into account ballpark factors (which surely have some kind of effect on BABIP as well).

I came up with my numbers, plotting a large amount of data (3 years worth of individual player statistics), and doing a multi-variable regression analasys on it (I'm not sure if that's the right wording or not, I have no formal training in statistical analsys, just some stuff I've picked up).

Here's the equation I came up with:

xBABIP =0.391597252 + (LD% x 0.287709436 ) + ((GB% - (GB% * IFH%) ) x -0.151969035 ) + ((FB% - (FB% x HR/FB%) - (FB% x IFFB%)) x -0.187532776) + ((IFFB% * FB%) x -0.834512464) + ((IFH% * GB%) x 0.4997192 )

Here's a published view of a spreadsheet showing it in action:

http://spreadsheets.google.com/ccc?key=0AuaVTUnZda7fdFVpY2NoRC1zS1p0UlNPaDlVdlRhN1E&hl=en

Here's a download of the spreadsheet in open office (Forgive the lame hosting service, I wasn't sure where to upload):

http://www.filefactory.com/file/a1a2d5a/n/public_xBABIP_Calculator_ods

I've been using the following calculator (along with a number of other equations) to build my own projections for 2010, and here are a few of the interesting things I've noticed.

First off, LD% has a very strong correlation to BABIP (not exactly a revolutionary statement), but it's also very hard to project it seems.  There seems to be a lot of luck built into it, so even taking career LD% rates is still factoring in some luck, so I tend to trend them closer towards the league average (19.5).  

GB% is a little easier to predict  Higher GB% tend to yield higher BABIP's, but that's based on your IFH% as well.  A player who can post high IFH% with a lot of ground balls will greatly increase their BABIP, while a slow player with a terrible IFH% with a lot of GB% won't increase their BABIP nearly as much (makes sense).

FB% is again easier to predict then LD% typically, and high FB% tend to yield lower BABIP's, as they are more likely to record outs.  But you've got to look at HR/FB, and IFFB% as well to get an accurate picture.  A player who hits a ton of fly balls, but has a very high HR/FB rate, with a very low IFFB% (ryan howard), can post more respectable BABIP's (they have a better shot of landing if they are getting out of the in field)

HR/FB is also a little easier to predict, and doesn't directly effect your BABIP, it's only used to take the home runs out of your fly balls (which in turn helps your BABIP).  One thing that strikes me as problematic here, is line drive home runs.

IFFB% seems somewhat player controlled, but also has a large luck component to it  from year to year (probably largely due to sample size).  This has a definite impact on your BABIP, as fly balls on the infield are automatic outs.

IFH% seems very speed dependant.  The more in field hits you have, the higher your BABIP as well.  This can vary from year to year with luck, but generally speedy players will post better (there are a few notable exceptions, like jason bay's abnormally high IFH%, which I chalk up to some luck) numbers.  Ballpark factors play a role here I'm sure as well (which I'm not accounting for).

So in the end, what we get, is a way to take numbers directly from fangraph (over the course of a career, full season, or even partial season), and get a descent idea of what their BABIP should be like, and how lucky they have been.  As always, this will still vary a lot from year to year (and the BA, OBP, and SLG along with it), but this is an attempt at trying to get an idea of what that middle number, that the BABIP will fluctuate around is for a given player.  Outside of using a calculator like this one, or the hardball times, the next best way to evaluate BABIP is probably to look at a players career numbers, but even those are prone heavily to be skewed by some lucky streaks.

I'm very interested in any feedback/critique that anyone has to offer, or any ideas on improving it.  I've also got a number of other calculators (one that does batting average, xHR, xR, xRBI, xSB, xAvg, xOBP, xSLG, that I'd be willing to throw out there as well, but I figured before I went through the trouble, I'd see what kind of buzz I get from this one.

Greetings fellow Beyond the Box Score readers,

I write about the MLB Draft for Examiner.com and am hoping to direct some traffic to my first post on the top 15 high school MLB draft prospects.  Please feel free to comment (preferably on my site, but I'm happy to respond here as well).

Here is some information about me:

Jesse Burkhart, 23, is a North Carolina resident and a former correspondent for Baseball America.  He attended USA Baseball's Tournament of Stars in Cary, N.C. and the East Coast Pro Showcase in Lakeland, FL in 2009, and he regularly attends other high school and college baseball games along the east coast.  He recently graduated from the University of North Carolina at Chapel Hill with a degree in Journalism & Mass Communication.  You can contact him at jesse.burkhart@gmail.com.

Thanks for reading,

Jesse

                                                                                                                                                                                                        twitter | jesseburkhart     AIM | jb02186

When we use a DIPS-based pitching statistic like FIP to calculate win values, our intention is to isolate the pitcher's contributions from those of his defense.  FIP, however, is only a crude approximation of the truth: it assumes that all pitchers should have a league-average BABIP, it fails to correct for double plays and pitchers who struggle out of the stretch, and as a linear run estimator, it underestimates good pitchers and overestimates bad pitchers.  PZR does a much better job of isolating pitching from defense without removing the effects of timing or hit quality.  PZR is UZR, but from a pitcher's perspective.  The pitcher is credited the run value of each batted ball, based on its trajectory, location, and speed, regardless of whether or not the ball fell for a hit.  When these run values are totaled, we obtain a measure of a pitcher's "defensive support;" we can estimate his true performance by adding his defensive support to his actual runs allowed.  As a result, PZR is perfect for Win Values, since it separates defense from pitching without removing anything else.

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The World Series is over.  You're looking at another off-season of trying to figure out if your team should sign some middle-of-the-road middle infielder who won't bring the pennant next year either.  And because you can only do that for about 15 minutes before crying, you need something to do that can keep your mind on baseball, so that you don't have to resort to *gasp!* watching the NFL, NBA, NHL, EPL (go Everton!), WWE, or SCOTUS.  (Tonight on Monday Night Litigation, it's Sotomayor vs. Alito.  Justice will prevail!)

No, let's do something baseball-related.  Let's plan a baseball road-trip, the best baseball road trip ever.  And let's do it on a budget.

Your mission is simple:  In 2010, you will (in your imagination) travel to all 30 MLB parks, see 30 games, and you will figure out how you would do it at the lowest possible cost.  At least in theory.  (It's a free country, so if you actually want to do this in real life, you can... but the point is that it's a fun mental exercise.)  Using the real 2010 MLB schedule, and some other web-based resources, you will put together an itinerary that would allow you to meet your goal.  The person who can do it in the lowest amount of cash will win a cookie and bragging rights.  (Note: no actual cookie will be awarded.)

I will tell you, this "game" is horribly addictive.  Worse than Farmville.  You won't think so at first, but it is.

Intrigued?  The rules are below...

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I used my simulator to simulate 100000 baseball games between the Phillies and Yankees using what to the best of my knowledge will be close to today's starting lineups.  The simulator outputs a win probability for each team, along with the average total runs scored, a distribution matrix of all the final scores and how often they occurred, and an averaged box score tally of all games combined.  You may find it strange that there are so many one run games listed as the most likely score, and that the home team is always favored to win in the top couple of scores.  This is due to basic math and the way that baseball rules play out of having the home team bat last.  Here is a great article explaining this phenomenon.

Today's Results... (Last simulation ran on Monday at 930PM)

 Skinny:  Simulator Fun Facts... Vegas and my simulator are in almost perfect agreement on the Yankees win probability.  Once again the key for the Phillies will be to silence the heart of the Yankees order as much as possible.  Not an easy thing to do.  AccuScore has come in with the lowest win probability of the three for the Yankees.  Meanwhile, the Vegas line has moved up to 65.87% since the opening lines came out.

Note: Keep in mind I did this simulation before knowing the actual starting lineups, so some of the players I used may not be starting.   Many of the "Players Most Likely To" stats depend on having the correct lineup.  The lineups  I used are listed below.  Chances are you won't see a big difference in win probability from having the lineup order slightly off.  Picking the correct starters is more important, especially if one of the better players is taking the day off.  But you still won't see a big change in win probability if the starter and backup are interchangeable.

I have started started a free fun dynasty league in which each person takes control of a MLB roster & minor league system, drafts, signing free agents....

If this sounds interesting to you, go check out the league message board & league rules and sign up. There is no prize for the winner for each season, it is all just for fun, so join!

http://jt20.proboards.com/

thought people here would be interested sign up soon!

As part of SB Nation's visual refresh, Beyond the Box Score has a new look. Some widgets have moved around, but nothing should be too hard to find.

Like all upgrades, things may not work perfectly at first. If you encounter any issues, please let us know so the coders can address the problem. Thanks for reading.

This summer, Major League Baseball revealed its newest statistical plaything, hit f/x, which records the speed of batted balls. This technological toy may not exactly revolutionize the way the game is played, but it does add a critical variable to a hitter’s value – how hard he can hit. Now teams can determine which batters truly hit the stuffing out of the ball.

Strangely, this hit-tracker data seems to go against conventional baseball wisdom. Hit-tracker results have recently revealed that players with lower batting averages actually hit the ball harder than those with high batting averages. Does that mean that to achieve a high degree of success, a hitter needs to hit the ball softer that he otherwise would? Looking beyond the numbers, we discover that this conundrum is an issue of logic.

It is generally accepted in the literary world that plot and character vary inversely; rarely do we find a book with an in-depth plot and intricately developed characters. Similarly, in the world of baseball, speed varies inversely with power; there are exceptions, but it is unusual for a power-hitter to steal 20 bases in a season, or for a lanky speed-demon to hit 30 homers.

A substantial percentage of the batters with batting averages over .300 in the 2009 season had around 15-18 infield hits. There were outliers, of course, but the trend stuck for the most part.  Let’s take a look at how some of baseball’s top hitters (in terms of batting average) might have fared without that extra bit of speed.  Obviously this is not completely indicative of how they actually would have done, but it’s an interesting experiment.

A table:

Notice that this trend only serves to prove the importance of speed in the batting averages of players who have an abundance of it.  David Wright and Pablo Sandoval still retain high batting averages despite when we subtract a portion of their speed.

Because power-hitters tend to be physically larger and thus slower than other hitters, they don’t beat out as many groundballs as quicker hitters, decreasing their batting average by approximately 17/550, or almost .031. For example, Wladimir Balentien, formerly of the Seattle Mariners, had the fastest average batted ball speed during April 2009 (according to Matthew Carruth of Lookout Landing http://www.lookoutlanding.com/2009/6/6/901100/mariner-hitters-batted-ball-speeds), but a paltry average. Ichiro, on the other hand, had the lowest average batted ball speed but his usual high average during that stretch.

In reality, batters that hit the ball hard often aren’t quick enough to reach first base on a weakly hit ground ball, knocking a significant amount off their average.

Contact rates actually player a larger role in this issue than does speed.  Players, like the aforementioned Balentien, who swing for the fences all the time usually have low contact rates.  And you’re certainly not going to get a hit if the ball doesn’t leave the catcher’s mitt.  Here I generalize quite a bit – not all power hitters are free-swingers, but those that do see that batting averages drop quite a bit despite hitting for power and a high hit f/x value.

What does this mean?  Honestly, I don’t know.  Infield hits are flawed because in using them to make a point, one must assume that the official made the right call (error vs. hit).  Plenty of power hitters are disciplined.

Any thoughts?  I'm really not sure what to take from this.