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Thursday, April 26, 2007
MGL Archives
For those new around here, you’ll enjoy some fantastic old articles by MGL:
DIPS Revisited
UZR Part 1, Part 2 (includes extensive methodology)
Super LWTS Part 1, Part 2, Part 3
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Pitchers
Tuesday, April 24, 2007
Pitching Measure: call for comments
Someone is asking for more feedback on his work.
Thursday, April 05, 2007
Pitcher release points and target areas
Is this cool or what? It’s by Joe Sheehan, but not THAT Joe Sheehan. If he keeps doing work like this, the BP Sheehan will be introduced as “not THAT Joe Sheehan”.
Thursday, March 29, 2007
Breaking Pitches
Wouldn’t we need to look at the quality of the pitchers in both groups? The cutoff seems to ensure we are at least only looking at starting pitchers. I also don’t know that the “ratio” is what we want: I would rather have seen the before and after totals, instead. The actual list of pitchers might have been interesting as well, as maybe some were clumped in the 70s, a time period when pitchers threw alot more than before or after.
We find that non-breaking ball pitchers throw, on average, 382% more pitches after turning 23 than before, while breaking ball pitchers throw 311% more.
Tuesday, February 20, 2007
More Pitch-by-Pitch
Schmanzy-fancy acronym aside, this is the way to look at it. And, if you include the count, this is exactly how I look at it, which alllows you to generate a Markov for each pitcher and hitter. That is, rather than relying on the 2000 historical PA for a pitcher or batter, you rely on their 7500 pitches to create a much stronger profile as to exactly how they hit and pitch. You remove tons of luck this way. You can further add parameters to your Markov model by also considering base/out and inning/score, as well as two-pitch or three-pitch sequence state, rather than the current count. And if you want to get even more creative, you include the profile, or identity, of the opponent.
The only thing missing from the article was the actual data itself. All we see was one example.
Tuesday, January 23, 2007
Wind Patterns Affecting Stats
Yet another in the category of insightful, easy-to-digest, but heavily-researched articles. Lots of great researchers out there who roll up their sleeves to bring great ideas to the masses. This is what sabermetrics needs.
Friday, January 12, 2007
Solving Barry Zito, or Why Does DIPS Not Work With Him?
When you get data into the hands of smart, resourceful people, you get answers. And boy, did we get answers:
Platoon Advantage
Yet another in my favorite category (straightforward, well-researched, insightful) is how often does a team take the platoon advantage. Andy showed us that teams will take the platoon advantage 78% of the time with the pinch hitter. DanAgonistes adds to that by showing relievers coming in with the platoon advantage 62% of the time. He then further breaks it down by when a manager is consciously trying to take a platoon advantage with the reliever (close game, non-ace reliever) to get a total of 66%. Which got me thinking:
Thursday, January 11, 2007
Quality of Pitching in NL and AL
John Walsh wrote a great piece about OF arms in the Hardball Times Annual. It’s one of those great “well-presented, intensely-researched, easy-to-grasp” articles that I like. You don’t have to get the math-mumbo-jumbo to understand what’s going on. John is one of my favorite sabermetricians, and for that reason, I hold him to a higher standard. In his article:
Tuesday, January 09, 2007
Randy Johnson
I don’t have anything to say at the moment, but I did notice something interesting with him. In the last three years, the umpire has called “ball” on him 1125, 1117, and 1122 last year. How many called, swinging or in-play strikes? 2509, then 2309, then 2137. He’s still got good numbers, going from a Schilling-like 69.0% to 67.4% to 65.6%. I don’t know if it means anything, but I thought that was interesting.
GB plus K equals Great
Rich Lederer gives us a great graphical presentation of pitchers’ GB and K rates. If he could have figured out how to do a three-dimensional chart to include the walk rate, that would have been perfect. Otherwise, I would have suggested K minus BB rate instead (I think Guy first suggested it, and I’m on board with it). It is almost as good as FIP and nice and clean.
Thursday, January 04, 2007
Preselection and postselection
I meant to criticize Jeff Sackmann’s recent article, which selects the top 5 starters on each team, after the fact, but Fifth Outfielder did it clearer and better than I would have. You *must* select before the fact.
Friday, December 15, 2006
Eric Van: Clutch Relieving
Eric Van says:
I calculated the total CluRat for 2002-2005 and compared it to CluRat for 2006, for the 27 guys who pitched 20 or more innings in 2006. Would you expect CluRat to have any predictive value? The correlation of 2002-5 CluRat to 2006 CluRat is .47, hugely significant at p = .01. ... The worse CluRat for 2002-5? Rudy Seanez. The worse for 2006? Rudy Seanez.
His long equation is really this:
(IP * 5.00 / 9 - WPA / pLI * 10.81) / R
where the first part, IP*5/9, simply gives you the league average runs, and the second part, the WPA*11/LI tells you how many runs better than average he was. It’s possible that what we’re seeing though is a bias in the equation. He uses 10.81 as a constant, when in fact it should differ. However, he is likely saved in that a relief pitcher only pitches 1 or 2 innings, and therefore, doesn’t perform long enough to effect his run environment enough to change the runs per win value of 10.81. But, maybe I’m wrong.
Thursday, December 07, 2006
Braves and Mariners
What’s great about the web is seeing instant feedback by people who follow in-depth the workings of their own team. Take for example, Horacio Ramirez and Rafael Soriano.
On one side…
Wednesday, December 06, 2006
How much should relievers get?
The replacement level winning percentages for nonpitchers, and pitchers is .390 and .420. This makes the average nonpitcher +.110 above replacement, and the average pitcher is +.080 above replacement. This means that 58% of the value is in nonpitchers, and 42% in pitchers. In 2005, teams spent 59% of their payroll on nonpitchers. So, teams get it. They know how to split their money between nonpitchers and pitchers.
But, what about between starters and relievers?
Monday, November 20, 2006
Pitcher Scouting Report?
Someone asked me if I would run a pitcher scouting report. A little background first…
Thursday, November 02, 2006
Scratching the Holy Grail
Sabermetrics is the convergence of performance analysis and scouting observations. David scratches the surface. Tracking pitch-by-pitch, including count, pitch type, velocity, location, famigli of batters, pitchers, as well as tracking hit-location, millisecond-by-millisecond, including fielder positioning, is the holy grail. There is a mountain of haystack for us to find that needle. All we need is more researchers.
One interesting question is…
Thursday, October 26, 2006
Scorer Bias in the Minor Leagues
What happens when you take a guy who spends alot of time collecting and presenting data, and another guy who spends alot of time analyzing said data? You get great research, and potential for even more:
http://sonsofsamhorn.net/index.php?showtopic=12439
I will reiterate here what I said there:
Thursday, October 12, 2006
When Did Pitchers Become Bad Hitters?
From the beginning, and the gap has been widening. Here are some numbers to consider:
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