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:
Read the rest of this entry »

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

Read the rest of this entry »

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.

Read the rest of this entry »

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).

Read the rest of this entry »

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:

HRs:

RBIs:

SBs:

AVG:

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.

In Part 1 of this article, I looked at the ability of individual projection systems to forecast hitter performance. The six different projection systems considered are Zips, CHONE, Marcel, CBS Sportsline, ESPN, and Fangraphs Fans, and each is freely available online.  It turns out that when we control for bias in the forecasts, each of the forecasting systems is, on average, pretty much the same.  In what follows here, I show that the Fangraphs Fan projections and the Marcel projections contain the most unique, useful information. Also, I show that a weighted average of the six forecasts predicts hitter performance much better than any individual projection.

Forecast encompassing tests can be used to determine which of a set of individual projections contain the most valuable information. Based on the forecast encompassing test results, we can calculate a forecast that is a weighted average of the six forecasts that will outperform any individual forecast.

Read the rest of this entry »

We hear about position scarcity all the time, but category scarcity also plays a role in valuing players. In 2000, 47 players hit at least 30 HR (hmm, wonder why?) as compared to just 18 players in 2010. Mark Reynolds hit 32 HR last year and tied for 10th in baseball. Many fantasy owners continued to start Reynolds every day despite his sub-Mendoza .198 average because his power was so valuable. Had Reynolds hit 32 HR with a .198 average back in 2000, he would have been riding the digital pine. Power wasn’t at a premium back then.

And that’s category scarcity in a nutshell. In fact, position scarcity is really just a function of category scarcity. Shortstop is only considered shallow because there are so few players who can contribute across the board. A quick look at any shortstop rankings shows how rapidly talent plummets at the position.

Read the rest of this entry »

ERA can be a weird thing at times. I love it, but it doesn’t always reveal the full story. Jason Hammel is the perfect subject. After six years in the Rays minor league system, and three bad stints with the Rays Major League club, he found himself looking up at a logjam of starting pitchers in Tampa Bay. The Rays traded him to the Rockies after the 2008 season in exchange for Aneury Rodriguez.

With the trade to Colorado, Hammel was given a great opportunity to start in the Majors for a full season. Since his arrival in Colorado two seasons ago, Hammel has been nothing but consistent. Take a look at his stats:

Read the rest of this entry »

This post originally appeared on Yankeeist.

It’s no secret that Alex Rodriguez produced the lowest full-season wOBA of his career in 2010 — his .363 mark was fueled by career-lows in batting average (.270), on-base percentage (.341) and the second-lowest full-season SLG of his career (.506). That these numbers were not only dramatically off from his superb 2009 (.286/.402/.532; .405 wOBA) but his majestic career triple slash (.303/.387/.571) suggests to me that he should be due for a reasonable bounceback. While it’s not impossible Alex has reached an irreversible decline, he’s been too historically good for me to be willing to write him off just yet. I won’t go so far as to proclaim that the Yankees are going to be getting .400-plus-wOBA A-Rod back, but as I’ve noted on at least one occasion this offseason, all A-Rod needs to do is exercise just a tad more patience and a wOBA in the .380s should be more than doable.

Read the rest of this entry »

This was originally posted on WahooBlues.com

When the Baseball Writers Association of America announced Wednesday that Roberto Alomar and Bert Blyleven had been elected to the Baseball Hall of Fame, two worthy inductees who had waited too long were granted entrance to Cooperstown. But to judge the voting process solely by the selections of two worthy candidates would be to ignore the massive problems with the way the BBWAA does business.

Read the rest of this entry »