I wrote the following piece for Bleed Cubbie Blue - a Chicago Cubs blog. I’ve posted it here in order to keep all of my work in one place.
Paul Maholm is the latest in a long list of acquisitions made by TheoJed. Maholm, now 29, was selected by the Pirates with the 8th overall pick in the 2003 draft.
Since 2006 - his first full big league season - Maholm has averaged 183.7 IP per season, which ranks 22nd of all pitchers that pitched between 2006 and 2011. While he has displayed great durability in the past, his 2011 season was cut short as a result of a shoulder strain. Fortunately, it doesn’t seem like it was much as he was cleared for workouts on October 24th. Maholm’s ability to stay on the field will be a breath of fresh air for a Cubs team that had a variety of injuries plague it’s rotation last year.
Let’s take a look at what Maholm has produced in the past, and what he could produce moving forward.
Over the course of the past six years, (not including the 41.1 innings that he pitched in 2005), Maholm has posted a wide range of pitching lines. His line has been as bad as a 4.76 ERA/4.81 FIP/4.59 xFIP, (his first season), and as good as a 3.66 ERA/3.78 FIP/4.03 xFIP, (his 2011 season). Below is a chart with Maholm’s career numbers.
In general, Maholm has been improving - aside from 2010, Maholm’s FIP has been decreasing year-by-year. Unfortunately, some of this decrease may be a result of his below average HR/FB rates over the past few years. If you take a look at his xFIP, (the same as FIP excepts it uses the league average HR/FB rate instead of the pitcher’s HR/FB rate), you’ll see much more varied results.
That said, let’s look at something Maholm is clearly good at: inducing ground balls. Since being called up to the majors, Maholm has shown a knack for getting ground balls. Below are his ground ball rates by season:
At his best, Maholm induced ground balls at a rate that would put him in the 90th percentile amongst all pitchers. While his ground ball rate has been trending slightly down for the past couple of years, it is still well above average, and ranked close to the 80th percentile amongst all pitchers in 2011.
On the other hand, I do have a couple of concerns with some of Maholm’s peripherals. His SwStr% has fallen every year since his career high of 8.4% in 2008; it stood at 5.7% in 2011, (well below the 8.6% league average). Given his declining SwStr%, Maholm’s Contact% has increased since 2008, rising to a career-high 86.9%, (well above the 80.7% league average). Furthermore, Maholm has had a history of high HR/FB rates. He posted three straight seasons with a HR/FB rate above 12.0%. Fortunately, in the three years since 2008, Maholm’s HR/FB rates have hovered around 7.5%, (well below league average, which was 9.5% in 2010). While some of this may have been a result of luck, Maholm did change his pitch selection after 2008. He cut back on throwing his fastball in 2009 - throwing it 8.1% less than he did in 2008 - and instead chose to throw his CB% (15.9% in 2008 | 17.7% in 2009) and CH% (10.1% in 2008 | 16.2% in 2009) more frequently in 2009. This change in pitch selection as an explanation for the decreased HR/FB rates is somewhat reassuring.
Maholm’s ability to induce ground balls is his most valuable skill. If he can continue to keep his GB% high, Maholm can continue to be a league average starter, (his career FIP- of 100 is the definition of league average).
At $4.75 million for one year with a club option for $6.5 million in 2013, Maholm comes at a bargain. He has averaged 2.1 WAR per year over his career, and 2.5 WAR per year over the past four years. At 2.5 WAR, Maholm has, on average, been worth anywhere between $10 and $12 million dollars a year. Maholm could conceivably be worth his entire contract, (assuming the club option is picked up) in one year.
While Maholm isn’t necessarily the young pitcher that you’d expect a team to build around, signing Maholm opens up a few different possibilities:
1. Maholm could fill a spot in the rotation left by a Garza trade.
2. Maholm could fill a void left by Garza or Dempster in 2013.
3. Maholm could be traded this July, or next off-season, and net a prospect or two.*
4. Maholm could become a long-term fixture at the back-end of the rotation.
Maholm’s another asset in the cupboard, and at the cost that he comes at, is another shrewd move by TheoJed that opens up a bunch of possibilities.
* It might not be in our best interest to consider trading a bunch of the free agents that we have singed, (this matters more with a pitcher like Maholm or a player like DeJesus as opposed to someone like Sonnanstine or Corpas), for the following reason: if we sign people to major league contracts with the intention of flipping them at the deadline, or in the middle of their deal, free agents may be wary of signing with us in the future and may end up choosing a different team to sign with because they’re scared that they’ll just be another asset that we leverage in a trade. I’m not sure how important this effect is - it might be the case that certain players don’t mind being traded - but I think it’s something worth mentioning.
I wrote the following piece for Bleed Cubbie Blue - a Chicago Cubs blog. I’ve posted it here in order to keep all of my work in one place.
Chris Volstad, who just turned 25 at the end of this past season, was drafted out of high school with the 16th pick in the 2005 draft. In 2006, he was named the Marlins’ #1 prospect by Baseball America, and just cracked the Top 100 prospects list - he came in at 97. In 2007, he jumped up the Top 100 prospects list and landed at #40, subsequently falling to #58 in 2008.
I’ll be looking at each year of Volstad’s career, so the following chart should help you follow along.
Volstad was called up to the majors in 2008, and has had mixed results since. In the 84.1 innings that he pitched in 2008, Volstad put up a 2.88 ERA/3.82 FIP/4.55 xFIP line, which was worth 1.5 WAR. As you could probably tell by his line, he was pretty lucky in 2008. His .271 BABIP, (career average .295 BABIP), and 3.9% HR/FB, (career average 12.3% HR/FB), have been the lowest of his career, while his 77.1 LOB%, (career average 70.4% LOB%), has been the highest of his career.
His luck dried up in 2009 - he actually ended up being unlucky - and he posted a somewhat atrocious line: 5.21 ERA/5.29 FIP/4.29 xFIP and a .3 WAR in 159.0 innings pitched. While his BABIP and LOB% came closer to his career averages - and league averages - his HR/FB% spiked to 17.5%. To put that a couple of different ways: his HR/9 increased from .32 to 1.64; he gave up 3 HRs in 2008 and 29 HRs in 2009, (albeit in roughly twice the innings).
In 2010, his line improved to the tune of a 4.58 ERA/4.34 FIP/4.43 xFIP due in large part to his decreased HR/FB rate.
While Volstad’s HR/FB spiked once again in 2011, he had arguably his best year in the majors from a peripherals standpoint. Volstad posted his best K/9 and BB/9 numbers, 6.36 and 2.66 respectively, and he induced ground balls at a very high rate of 52.3%, which according to 2010 league numbers, would rank close to the 90th percentile amongst all qualified pitchers.
While some may be concerned about how his HR/FB and HR/9 numbers will translate to Wrigely Field, here is a collection of statistics, statements, and images that should help quell your fears. HR/FB rates are highly variable from year to year and tend to regress toward league average, so Volstad will likely have a lower HR/FB rate moving forward. In terms of Park Factor, Sun Life Stadium had a .991 runs factor, while Wrigely had a .934 runs factor in 2011. Furthermore, Sun Life Stadium’s HR factor was .941 while Wrigley’s HR factor was .987, not a terribly large difference, and one that could be offset in the aggregate by the aforementioned difference in runs factor. The last reassuring point will come in the form of the following image:
The above image maps all of the hits and outs Chris Volstad recorded at Sun Life Stadium onto Wrigley Field.* Of the seven home runs that Volstad gave up at Sun Life Stadium, five would have been home runs at Wrigely, (I’m counting the one that’s on top of the left field wall as a home run), and only one non-home run in Sun Life Stadium would have been a home run at Wrigley, (the double to deep center field). So, of the hits and outs that Volstad recorded at Sun Life Stadium, he gave up 7 home runs at Sun Life, which would have theoretically been 6 home runs at Wrigley. Volstad’s move to Wrigley should not result in an increase in his HR/FB rate; in fact, Volstad will likely have a lower HR/FB rate going forward.
If Volstad continues to improve upon his K/9 and BB/9 numbers, and if his BABIP and HR/FB rates regress to the average - which isn’t a huge “if” - he could become a very valuable middle of the rotation starter over the course of the next three years. In my opinion, Volstad is a pretty impressive return for Zambrano given the situation. Well done, TheoJed.
* These plots are taken from Gameday hit-location data, which track where the ball was fielded, not where the ball landed.
Thanks to MLB Gameday BIP Location.
As I was flipping through a friend’s copy of Flip Flop Fly Ball, I came across a very interesting infographic titled “World Series Winners: How the Players Were Acquired, 2000 - 2010.” It essentially color coded the players on each of the World Series winning teams according to the way that they were acquired. I found this fascinating, and decided to convert the infographic into numbers. I also added 2011 to the data set. Below is a summary of the numbers, followed by a graph highlighting an interesting trend:
The trend of World Series winning teams acquiring more players from the amateur draft is quite apparent. Aside from the 2002 Angels, none of the World Series winning teams between 2000 and 2005 had more than 3 players that they acquired via the amateur draft. Between 2006 and 2011, every World Series winning team had at least 6 players that they acquired via the amateur draft.
The trends regarding free agents and players who were acquired via trade are not as clear. While trades seemed to be on the decline between 2005 and 2010, the Cardinals acquired 9 of their players via trade. Free agency has been all over the place, and is likely more of a function of a team’s individual situation rather than league-wide trends.
While there is no one way to go about creating a World Series winner, the rising prevalence of player’s from the amateur draft is an interesting trend to note.
At some point, I’d like to further this discussion with (i) a study of the composition of World Series winning teams prior to 2000 in order to see if the rise of the amateur draft is a brand new phenomenon, or something that has already occurred in the past, and (ii) a study of the composition of all 30 teams on an annual basis in order to determine the relationship between specific attributes of team composition and winning percentage.
Nota bene: Any analysis of a dataset with 11 points has to be taken with a grain of salt as it’s quite a small sample size.
I downloaded the line scores all of the games that were played between 2000 and 2010. I removed all of the extra inning games, and games that contained innings of 10 or more runs from consideration because of the difficulty in working with them in Excel. Fortunately, that still left me with 24371 games.
At first, I was interested in quantifying home-field advantage, which is a relatively simple task. According to my results, the home team won 54.5 percent of the nine-inning games between 2000 and 2010. After doing all of the work to set up the data set of games within Excel, I felt it a shame to stop there. So I quantified another advantage of sorts: scoring first. How often do teams that score first win?
According to the data set, teams that scored first won 67.0 percent of the time. What’s more interesting though, is the breakdown of win expectancy based on the number of runs that were scored in the first run-scoring inning - the first inning that a run was scored in. How valuable is scoring one run first versus scoring two runs and so on. Below is a chart that sums up the results.
It’s interesting to see that the second run is the most valuable run from a marginal value standpoint.* A team that scored one run in the first run-scoring inning won 59.2 percent of the time, while a team that scored two runs in the first run-scoring inning won 70.4 percent of the time. The 11.3 percent change in win expectancy is the highest change among all consecutive pairs of runs. The marginal value of the second run is followed very closely by the marginal value of the fourth run, which has a change in win expectancy of 11.0%.
The least valuable run from a marginal value standpoint was the 8th run, which actually had a negative effect on win expectancy; the 8th run had a change in win expectancy of -0.2 percent. Teams that scored run number 8 in the first run-scoring inning had a lower win expectancy than teams that scored 7 runs in the first run-scoring inning. While the value of the 8th run is definitely low, it doesn’t make logical sense for it to be below zero. This is most certainly the result of a small sample size, as there were only 33 games in which 8 runs were scored in the first run-scoring inning. It is much more likely that the change in win expectancy of the 8th run is between 5.9 percent, (marginal value of the 7th run), and 2.9 percent, (marginal value of the 9th run).
The first chart below shows win expectancy as a function of the number of runs scored in the first run-scoring inning, while the second chart shows the diminishing marginal returns of runs scored in the first run-scoring inning.
While the returns to the runs scored in the first run-scoring inning are jumpy, the general trend of diminishing marginal returns of runs scored in the first run-scoring inning is quite apparent, and certainly confirmed once the trendline is considered.
While numbers like these have certainly been derived by many before, deriving these numbers for oneself is always a rewarding experience.
* With the assumption that the baseline for win expectancy is 50.0 percent.
Last week, we took a look at Weighted On-Base Average, wOBA, which represents a player’s total offensive value in the form of a percentage. This week, we will attempt to both index a player’s total offensive value to the league average and adjust it for ballpark factors with Weighted Runs Created plus, or wRC+.
wRC+ was created in response to OPS+, which measures On-Base plus Slugging Percentage, OPS, against league average and adjusts it for ballpark factors. Measuring OPS against league average essentially adjusts for the run-scoring environment in a given year. In 1925, the league average OPS was .765, while the league average OPS in 1967 was .664. Let’s take two hitters, hitter A and hitter B. Hitter A played in 1925, while hitter B played in 1967. Both hitter A and hitter B each had a .765 OPS. However, hitter B did it in a season where the average OPS was .664 as opposed to .765. Hitter A was a league average player, while hitter B was approximately 30% better than league average, according to OPS+, where,
OPS+ = 100 * [(OBP/lgOBP) + (SLG/lgSLG) - 1].
As you can see, adjusting for the run-scoring environment of a given year is important in evaluating a player’s true offensive value. OPS+ also adjusts OPS for ballpark factors - hitter C benefitted from playing in the Ballpark at Arlington, while hitter D was hurt from playing in PETCO park. You can also see that adjusting for ballpark factors is important in evaluating’s a player’s true offensive value.
However, since OPS is aflawed statistic, sabermetricians decided to create a more accurate statistic to evaluate offensive value adjusted for run-scoring environment and ballpark factors. As we saw last week, wOBA is much better at evaluating a player’s offensive value than OPS; thus, we will use wOBA to create a league adjusted and ballpark adjusted statistic that encompasses a player’s offensive value.
Weighted Runs Created, wRC, measures a player’s total offensive value by runs. It uses wOBA to calculate the total runs created as a result of a player’s offense.
wRC = [((wOBA - lgwOBA)/wOBAScale) + (lgR/PA)] * PA.
It essentially takes a player’s wOBA, subtracts the league average wOBA, and then divides the difference by wOBAScale - a multiplier that converts wOBA to runs per plate appearance; it then adds the league average runs per plate appearance, and multiplies the resulting sum by the number of plate appearances the player had.* We now have a player’s wRC.
In order to get wRC+, you simply divide a player’s wRC by the league average wRC, and multiply it by 100. A wRC+ of 100 is average. A wRC+ greater than 100 is above average, and every point above 100 is a percentage point above league average. For example, a 130 wRC+ means a player created 30% more runs than the league average. Likewise, a wRC+ less than 100 is below average, and every point below 100 is a percentage point below league average. For example, a 70 wRC+ means a player created 30% fewer runs than the league average. wRC+ translates wOBA into a run-based measure of a player’s offensive value, while adjusting for both the player’s run-scoring environment and for ballpark factors.
With wRC+, you can now compare Babe Ruth and Albert Pujols, even though they played in different run-environments and different ballparks.
* In other words, wRC converts the player’s excess wOBA into runs per plate appearance above league average, adds league average runs per plate appearance, and multiplies by plate appearances to get the total runs created.