Archive for February, 2011

The Elusive Clutch Hitter–Part II

I made some mistakes, some careless, some unknown, with the charts included in my post titled “The Elusive Clutch Hitter,” and I wanted to clear them up.

The first correction shows the batters in my sample who had an increase of at least 10% in batting average with runners in scoring position (RISP) vs. no runners in scoring position (nRISP). It was my intention to do this all along–indeed, I had posted earlier and woke up the next day and realized I had compared RISP batting average with career batting average, which would cause an overlap in the data. I trashed that post prior to it being published, but it appears I made the same mistake, at least with this particular chart. Here’s the corrected chart:
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The Elusive Clutch Hitter

It’s (almost) spring (training), and a young man’s thoughts turns to baseball metrics. I’ll start with two charts:

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Rationalizing the Next Pujols Contract

If you haven’t heard anything about what Albert Pujols’ value will be over the next 7-10 years, I suggest you go here…Or here…Or here…Or take a look at the discussion here. Haven’t had enough? Read on.

Somebody is going to sign Pujols to a massive contract in the next 12 months. That contract will likely be hard to justify in projected on field value alone. If you are a fan of the team that gets him, after you get done saying PUJOLSAWESOMEBASEBALLYAY, you may want to know what his expected value will be and then take some time rationalizing the contract to yourself and justifying it to rival fans.

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Comparing 2010 Pitcher Forecasts

In two previous articles, I considered the ability of freely available forecasts to predict hitter performance (part 1 and part 2), and how forecasts can be used to predict player randomness (here).  In this article, I look at the performance of the same six forecasts as before (ZIPS, Marcel, CHONE, Fangraphs Fans, ESPN, CBS), but instead look at starting pitchers’ wins, strikeouts, ERA, and WHIP.

Results are quite different than for hitters. ESPN is the clear winner here, with the most accurate forecasts and the ones with the most unique and relevant information. Fangraphs Fan projections are highly biased, as with the hitters, yet they add a large amount of distinct information, and thus are quite useful.  Surprisingly, the mechanical forecasts are, for the most part, failures. While ZIPS has the least bias, it is encompassed by other models in every statistic.*  Marcel and CHONE are also poor performers with no useful and unique information, but with higher bias.

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Can You Quantify Disappointment?

After reading an extremely interesting piece by Jeff Passan on the legendary sabermetrics whiz Voros McCracken I have to admit it had me a bit down in the dumps, and depressed.  How could the man who basically redefined the sabermetric movement not be involved in baseball in some form or fashion?  It doesn’t seem right, or fair, that the man who basically founded and created ‘defence independent pitching’ (or DIPS) statistics wasn’t good enough for the game anymore.

Maybe it affected me more on a personal level and it was gut check time, if ‘Voros’ wasn’t accepted and embraced by the baseball world, what chance in hell did I ever have?  Now is the time for you to snicker, or snidely remark ‘fat chance in the first place’ and, to be honest, I would be saying the exact same things.  But I have a confession, and on some level every fellow ‘baseball nerd’ who writes about the game we love was affected in the same manner – we lost a bit of hope.

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Does IFFB% Correlate with HR/FB Rates?

Over the last week or so, various reputable baseball analysis sites have been digging into the relationship between infield fly ball rates (IFFB%) and home run per fly ball rates (HR/FB). The discussion was prompted by a blog post by Rory Paap at Paapfly.com called “Matt Cain ignores xFIP, again and again,” which generated a response from Dave Cameron here at Fangraphs.

Paap suggested FIP and xFIP do Cain a disservice because they don’t give him his due credit for possessing the “unique skill” of inducing harmless fly ball contact, a theory that David Pinto at Baseball Musings attempted to quantify last October. Cameron’s response included some interesting analysis that looked at the best pitchers from 2002-2007 in terms of HR/FB rate and compared their IFFB% over that span to what they posted the next three seasons. His conclusion?

Is there some skill to allowing long fly outs? Maybe. But if you can identify which pitchers are likely to keep their home run rates low while giving up a lot of fly balls before they actually do it, then you could make a lot of money in player forecasting.

Simply out of curiosity, I decided to throw my hat into the ring and see if I could find a trend between IFFB% and HR/FB rate. My theory was that if IFFB% and HR/FB rate showed some sort of correlation, then plotting HR/FB rate as a function of IFFB% would show a clear inverse trend (meaning that a higher IFFB% would more likely generate a lower HR/FB rate, and vice versa).

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Projecting Uncertainty

This article explores the ability to predict the randomness of players’ performance in 5 standard hitting categories: HRs, Runs, RBIs, SBs, and AVG. There have been efforts to do so by forecasters, most notably by Tango’s “reliability score.” (See Matt Klaassen’s article) I also test the idea that variation among forecasts (among ESPN, CHONE, Fangraphs Fans, ZIPS, Marcel, and CBS Sportsline) can predict player randomness as well.

I find that 1) variance among forecasts is a strong predictor of actual forecast error variance for HRs, Runs, RBIs and Steals, but a weak one for batting average, 2) Tango’s reliability score serves as a weak predictor of all 5 stats, and that 3), the forecast variance information dominates Tango’s measures in all categories but AVG.

Now let’s set up the analysis. Say, for example, that three forecasts say that Player A will hit 19, 20, and 21 home runs, respectively, and Player B will hit 10, 20, and 30 home runs. Does the fact that there is agreement in Player A’s forecast and disagreement in Player B’s provide some information about the randomness of Player A’s eventual performance relative to Player B’s?

To answer this, we need to do a few things first. We need a measure of dispersion of the forecasts. To do this, I define the forecast variance as the variance of the six forecasts for each stat, for each player.  If we take the square root of this number, we get the standard deviation of the forecast. So, the standard deviation of the forecasts of Player A’s HRs would be 1, and the standard deviation of the forecasts for Player 2 would be 10.

Next we turn to some regression analysis.* The dependent variable is the absolute error for a particular player’s consensus forecast (defined as the average among the six different forecasts). For both players A and B in the example, this number would be 20. This is my measure for performance randomness. Controlling for the projected counting stats, we can estimate this absolute error as a function of some measure of forecast reliability.

Tango’s reliability score is one such measure, and the forecast standard deviation is another.  What we would predict is that Tango’s score (where 0 means least reliable and 1 means most) would have a negative effect on the error. We would also predict that the forecast standard deviation would have a positive effect on the error. Now let’s see what the data tell us:

Runs:

R absolute error
[1] [2] [3]
R Standard Deviation 0.45 0.44
(0.27) (0.32)
R mean forecast 0.05 0.02 0.03
(0.06) (0.05) (0.06)
Tango’s reliability measure -8.15 -0.59
(9.09) (10.60)
Constant 22.94 14.93 15.36

HRs:

HR absolute error
[1] [2] [3]
HR Standard Deviation 0.82 0.78
(0.30) (0.32)
HR mean forecast 0.20 0.12 0.13
(0.03) (0.04) (0.04)
Tango’s reliability measure -3.26 -0.84
(2.52) (2.69)
Constant 5.32 2.31 2.94

RBIs:

RBI absolute error
[1] [2] [3]
RBI Standard Deviation 0.44 0.34
(0.28) (0.31)
RBI mean forecast 0.09 0.05 0.08
(0.05) (0.05) (0.05)
Tango’s reliability measure -12.52 -7.83
(9.12) (10.08)
Constant 23.78 12.66 18.37

SBs:

SB absolute error
[1] [2] [3]
SB Standard Deviation 0.50 0.41
(0.24) (0.27)
SB mean forecast 0.37 0.30 0.31
(0.03) (0.04) (0.04)
Tango’s reliability measure -3.47 -1.90
(2.19) (2.42)
Constant 3.80 0.75 2.30

AVG:

AVG absolute error
[1] [2] [3]
AVG Standard Deviation 0.567 0.287
(0.689) (0.713)
AVG mean forecast -0.085 -0.107 -0.083
(0.091) (0.090) (0.092)
Tango’s reliability measure -0.023 -0.022
(0.014) (0.015)
Constant 0.069 0.054 0.066

We see that HRs are the statistic for which errors are most easily forecasted, errors for Rs, RBIs, and SBs are moderately forecastable, and errors for AVG are not very forecastable. We see this because of the negative and statistically significant coefficients for Tango’s score and the positive and statistically significant coefficients on the standard deviation measure.  In regressions with both measures, the standard deviation measure encompasses Tango’s measure, except in the AVG equation.

So what does this all mean? If you’re looking at rival forecasts, 80% of the standard deviation between the HR forecasts and about 50% of the standard deviation of the forecasts of the other stats is legitimate randomness. This means that you can tell how random a player’s performance will be by the variation in the forecasts, especially home runs. If you don’t have time to compare different forecasts, then Tango’s reliability score is a rough approximation, but a pretty imprecise measure.

*For those of you unfamiliar with regression analysis, imagine a graph of dots and drawing a line through it. Now imagine the graph is 3 or 4 dimensions and doing the same, and the line is drawn such that the (sum of squares of) the distance between the dots and the line is minimized.


2011 Cleveland Indians Lineup by The Book

Last year, Manny Acta made a splash by dropping Grady Sizemore to second in the batting order. This year, he’s considering moving him back to leadoff. Is either the right move? And how should the rest of the lineup look?

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