Tag: fun with google spreadsheets

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FIP League Constants for Minor Leagues

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I needed to determine what the FIP league constants were for my ML Splits application (the rebirth of minorleaguesplits.com), so a friend helped me mine this data and put it into a Google Spreadsheet. It's here for you all to view!

Custom wOBA and Linear Weights Through 2010: Baseball Databank Data Dump 2.1

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In response to overwhelming demand (ahem), here are are customized weights from 1871-2010 for calculating hitter value with linear weights.

Return of the Son of the Revisitation of the Myth of the RBI Guy: Careers Overrated by RBI Totals

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Who are the most overrated and underrated RBI Guys of the last 50+ years? A list and discussion of players most overrated by their RBI numbers.

Revisiting the Myth of the RBI Guy (Revisited), Part Two: Situational Hitting and RE24

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Part two (of two) in a series looking at how bad a hitter can be and still accumulate RBI. This installation uses and explains FanGraphs' RE24 stat to discuss the impact of "situational hitting."

Old and New Money(Ball): Walks versus Defense

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What is more valuable, walks or defense? Walks were (supposedly) the favored currency of the old Moneyball, while defense is the currency of the new. This article checks on compares the walk rates...

They should have named it willie O' Bloomquist Average

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I finally published another highly anticipated article on quantifying the "Little Things" at Driveline... Features my usual Mechian efficiency in getting to the point, but if you want to understand this spreadsheet I've linked here, you should at least take a look at it... That's right, Jose Guillen really did do something. That's what he's getting 3/36 for -- not getting on base, or playing defense, or being able to hit right-handed pitching, but doing the "Little Things." Royal #2 is shocking, but maybe not so much when you think about what this stat means. I actually did the Royals calculations a while before I started writing for Driveline, and it turned into this... which ended up a lot longer and more inconclusive than I had hoped, but maybe it will go somewhere. I hope that if you find it interesting, you'll discuss the conceptual side of things over there, although I'm willing to learn here, too. If nothing else, it prominently features a classic example from the late 70s-early 80s Royals dynasty. And just for the record: +0.43 -0.03 +0.06 Figure it out.

God of Little Things 2008

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Can we really quantifythe impact of the "little things" baseball journalists and managers are always babbling about? Whose offensive value was most tied up in "little things" in major league...

2008 Gold Gloves: Designated Hitter

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Is the Designated Hitter's job to prevent runs from being scored? Well, that would be absurd...wouldn't it?

Two Monkeys and a Whole Lot of Money: Marcel, Rally, and the 2009 Free Agent Position Players

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I was going to do a whole FanPost with cute monkey pictures and more detailed analysis, but I have a backlog of stuff to bore you with, and this isn't my blog anyway. I figure people can discuss this on its own, and at the very least it might be a handy reference tool. Unless it sucks, in which case feel free to ignore it. In short, I constructed another Google Spreadsheet to give some estimated market values of free agent position players (with a few of my non-FA favorites mixed in for fun) for the 2008-2009 off-season, just in time for Winter Meetings. These are not my projections. They take the (intentionally) most basic offensive projections available -- Tom Tango's Marcel projections (named after the monkey from the first season of Friends [ugh]) and combine it with the defensive projections done by "Chone" Smith, aka the Rally Monkey. I don't get into the in and outs of Marcels (which is intended less as a "real" projection system than as a baseline for judging others, although it does surprisingly well with established players, considering its simplicity), nor of Chone's defensive projections (which have been discussed on this blog a bit already. Keep in mind that some of this stuff can seem pretty silly (Marcel doesn't understand park effects or injuries, for example, but I still think any monkey that can regress to league average is pretty impressive), but I didn't adjust anything because then I would have to adjust everything. This sort of thing is discussed a great deal on the intertubez, so I thought I'd put a lot of it together with "custom" salary estimation for each player based on $4.84 million per win above replacement (WAR) adding in $400,000 a year replacement cost. I have also included a generic salary chart. I make no great claim to originality here. I'm just taking other people's projections and combining it with stuff I've picked up by reading smart people (Tom Tango, MGL, Dave Cameron, and on and on) -- the story of my life. To borrow one of Tango's lines about this: teams who sign players for their market value (relative to whatever projection you have for the player, of course) aren't particularly smart or particularly dumb. They're just doing what any other team would do in signing for market value. The smart teams are the ones that manage to find players they can sign for less than the market value. So discuss away! Just to reiterate: I am not saying a couple of monkeys (Marcel and Rally) should be running the Royals. Some things can't be projected by simple computer programs. The monkeys would never have been able to see Gil Meche's past two years of performance coming. Then again, Marcel and Rally would never have given Jose Guillen 3/36, so maybe Dayton Moore should bring them on as consultants...

wOBA Positional Averages 2008

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At the request of a party who shall remain anonymous, I have constructed a spreadsheet to give pseudo-Fangraphs-style positional wOBA averages by position for 2008 MLB, AL, and NL. I still think the best general model for such averages is something like this (see my related FanShot here), but it is interesting to see how closely the empirical data from last year matches up. I've even included a silly wOBA-fied version of baseball-reference's OPS+. Since I'm guessing Fangraphs' version of wOBA will become the most frequently cited, I've used a formula that I think they are using -- that is, one that excludes reached base on error (this makes sense for them, since I assume they are using the baseball databank data, which doesn't include that information). They also use custom linear weights for each year, which I don't use here. However, Tom Tango (the creator of wOBA) notes that the weights since 1956 don't stray all that far from the generic formula. I think Fangraphs is great, and is quickly becoming the best source for sabermetric data on the internet -- free or pay. Personally, I prefer that ROE be included in wOBA, since I do think it at least partially reflects player skill. Stat Corner does so (and they also have park-adjusted wOBA*). I would also prefer that both sites includes stolen bases and caught stealings in the formula (I don't think they do -- but correct me if I'm wrong). But they have their reasons. I have a sheet ready with SB/CSs and also one with ROEs if people are interested. Update: Thanks to the coment below, I now realize that Fangraphs does include SB/CS in their wOBA figures, and have corrected the spreadsheet.

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