Home Run Rates and Fastball Movement

In the offseason, there was a lively debate about Matt Cain and his ability to keep the ball in the park. There was one hypothesis that suggested that pitchers who throw a hard, rising fastball* give up less home runs per fly ball than would be expected.

I did some initial research into this and found that there is a fairly strong negative correlation between vertical movement and home run rate. My initial research looked just at individual starting pitchers and was not able to isolate just fastballs, giving my concern that there might be a lot of noise that could have distorted the true effects. Well, now I have much better data that gives me every fastball tracked by Pitch F/X from 2008 through last night's games to study from.

This new sample is huge (54,396 pitches) and should hopefully give me some robust answers as I work with it. My first exercise was to break the pitches into buckets grouped by vertical movement and look at the corresponding home run rate.

The correlation here is pretty astounding (R² of 97). In my previous study, there was some correlation but nothing close to the magnitude here. This seems to lend credence to my theory that fastballs that drop less than expected induce more "lazy" fly balls than hard hit fly balls.

To me, this seems like common sense reasoning as well. Most would tend to agree that if a pitch has more sink, it would have more gravity that would cause batters to hit the top portion of the ball and induce ground balls.The opposite should be true as well. If a pitch fails to drop as much as gravity suggests, batters would hit the bottom portion of the ball and induce more pop ups and fly balls.

The next step will be to create a regression estimate including everything that goes into explaining a pitcher's home run per fly ball rate. At least this gives me an idea that I am on the right path.

*The fastball is not literally rising but would be in a gravity neutral environment. The vertical movement described here means that the pitch is dropping less than expected.

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'Best Shape of My Life' Not Just a Cliche

At the always interesting Baseball Prospectus they have a guest post that investigates the "Best Shape" phenomenon that ripples through Florida and Arizona this time of year. Rob Pettapiece bravely wades through all the fluff pieces about players and finds some interesting nuggets.

It appears that BSOMLers (Best Shape of My Life) may, in fact, outperform their projections more than non-BSOMLers outperform theirs. More accurately, the best-shapers held steady (as indicated by the +6 runs we noted earlier or a huge 0.7% above projected numbers) and the non-best-shapers failed to meet expectations (-27). Of course, the non-BSOMLers who had poor seasons and received less playing time as a result don't affectnumbers, so the true drop-off by the control group should actually be larger. the actual numbers as much as they would have had they kept putting up poor numbers, so the true drop-off by the control group should actually be larger.

To simplify the results here players in the best shape of their lives tend to meet their projections (PETCOA in this case) then players who don't utter the phrase.

Rob speculates that this could be because that players who work out address their problem areas and the ones that don't may loose a step in the ultra competitive atmosphere. The results meld with common sense and this gives hope for those lofty projections that the computers spit out for Pablo Sandoval.

(h/t to Julian Levine at Beyond the Boxscore)

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Dave Righetti: The $110 million man

The discussion surrounding home runs per fly ball has been interesting to say the least. It has spawned some deep discussion and a bunch of new research into determining if it is a skill, including some research done by yours truly that you can find here.

Today, there is a new advancement on the subject. Jesse Wolfersberger of Fangraphs first created a regression model that gave the expected home run rate and has now used that to try to estimate the effect of a pitching coach in preventing home runs.

He looked at the data from 2002 to 2010 of the four pitching coaches who have been with the same teams and found some very interesting results with regards to Giants' pitching coach Dave Righetti.

For the most part, the model splits each coach’s qualified pitchers in half between those who outperformed their expected HR/FB rate and those who underperformed their projection. Righetti is the only exception. Of the 44 qualifying pitcher-years in Righetti’s reign with the Giants, 38 had a lower HR/FB rate than the model predicted. Considering none of these other coaches had even 60-percent of their pitchers fall on one side or the other, Righetti’s 86-percent rate of starters outperforming their expected HR/FB is simply stunning.

I think it's pretty safe to say with a nine year sample, this is beyond simple luck and something interesting is happening to produce consistently low home run rates.

The results stay the same when you look at total innings of the Giants pitchers under Righetti:

Righetti is the only pitching coach who seems to have any significant effect on HR/FB rate, and it’s decidedly negative. His starters have a HR/FB almost two standard deviations away from the model’s expectations, while none of the other coaches are over one standard deviation in either direction. Also note that this looks at more than 8,700 innings under each coach’s tutelage. To outperform the model’s expected rate, which predicts the Giants to have a below-average HR/FB already, seems beyond the bounds of luck.

Check out the full article because there is too much good stuff to excerpt and there are some nice tables that summarize things.

The Giants pitchers have suppressed on average 25 home runs a season. When that is converted into runs, it translates into about 35.5 runs prevented a year, or a little over three wins. With the marginal cost of a win in the $4-5 million range, Righetti has given the Giants a value in the range of $100 million on his teachings on home run prevention.

Someone give that man a raise.

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Researching the effect of the rising fastball on home run rates

Yesterday I started a thought suggesting that a rising fastball acts as an "anti-sinker" and that the rising action of a pitcher like Matt Cain's fast ball could be the explanation for why he is able to induce a lot of fly balls while keeping them in the park.

If a pitch has more sink then it would from gravity alone that this would cause batters to hit the top portion of the ball and induce ground balls. Common sense suggests that the opposite would be true as well, that if a pitch fails to drop as much as gravity suggests that batters would hit the bottom portion of the ball and induce more pop ups and fly balls.

So I set out to test this theory and found evidence that this is a plausible explanation for the lower home run rates.

I pulled data from the 2008 to 2010 seasons for pitchers who had logged at least 400 innings over that span. I then used the pitch f/x data for these pitchers vertical movement of fastballs from Texas leaguers for the same period.

My initial sample included 87 pitchers but I then limited it to pitchers who threw a fastball at least 50% of the time because I was interested in the effect of a rising fastball on a pitchers home run rate so I wanted to focus as much as I could on pitchers who primarily threw a fastball. (In the future I would like to look at just the home run rate of fastballs but I was unable to locate that data.) So my actual sample contains 54 pitchers.

Below is a scatter plot for the vertical movement of a fastball versus that pitchers home run rate. Vertical movement is measured in inches.

There is a fairly strong negative correlation between between the two. In the plot you will see some outliers the two pitchers with the lowest vertical movement are Tim Hudson and Fausto Carmona. The pitchers with the high rates of home runs and vertical movement in the upper right are Cole Hamels, Aaron Harang and Manny Parra.

My next level of analysis included multiple regressions, initially I included K/9, BB/9, park effects, fastball velocity, and vertical movement. I ended up removing K/9, BB/9 and park effects because they were not statistically significant and did not help to explain the variance.

The final regression has a p-value of 0.0018, an r-squared of 0.219 meaning that roughly 22% of the variance is accounted for by velocity and vertical movement. Both of the independent variables are significant at the 95% level with p-values of 0.018 for vertical fastball movement and a p-value of 0.006 for velocity.

The regression equation for home run rate per fly ball is  HR/Fly ball= 0.45 - (vertical movement*0.0035) - (Velocity*0.0035)

So for example a pitcher who throws 95 mph with 10 inches of vertical movement on their fastball would have a predicted fly ball rate of 8.25% compared to the league average home run rate 10.52% the last 3 seasons.

I think that this is something that could need further research with more data and perhaps a finer look at the effects of a rising fastball on the home run rate per fly ball for just fastballs instead of just the overall home run rate that includes other pitches as well. The other pitches in the sample could be clouding the magnitude or giving a false effect as pitches other then fastballs make up anywhere from 50% to 30% of each pitchers sample.

In the meantime it may be time look more skeptically at the assumption that every pitcher regresses toward the league average home run rate.

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