Remember lichens? Those organisms you learned about in biology that form as a partnership between fungi and algae in which both benefit? I would argue that a new such mutualistic symbiotic relationship has formed: the one between the Atlanta Braves and Waffle House. Long a revered Southern institution, Waffle House was installed at Turner Field last Friday (though it’s rumored after last week’s ankle injury that Tim Hudson prefers IHOP). Since then, the Braves have been on a tear, winning six straight, including a sweep of the (at the time) red-hot St. Louis Cardinals. Coincidence? I think not!  As always, let’s take a look at the numbers.

The following charts will be broken up into two distinct periods: the first 102 games (“pre-WaHo”) and the past six games (“post-WaHo”). We’ll start with the bottom line.

If the numbers are to be believed (and Numbers Never Lie, right ESPN?) then the Braves, a strong team already pre-Waho, are now unbeatable post-WaHo. It remains to be seen if this is merely a home game effect, however. It’s also possible that the Braves are playing similar baseball post-WaHo compared to pre-WaHo and are fortunately getting better results. To investigate, let’s dig deeper into the components that drive wins: run scoring and run prevention.

In fact, the Brewers were downright putrid, winning only six games the entire month.  Their record in May was so bad (6-22) that it tied the worst month in franchise history: the August turned out by the 1969 Seattle Pilots, who ended the following season in bankruptcy, followed by a permanent road trip to become the Milwaukee Brewers.

The Brewers ended the month of April only a half game out of first place.  The Brewers ended the month of May 15 games behind the St. Louis Cardinals, managing the impressive feat of losing 14.5 games in the standings in one month.  Now that is a tailspin.

CoolStandings.Com currently gives the Brewers a 1 in 250 chance of making even the wild-card play-in game.  GM Doug Melvin admitted there is no chance the Brewers will be buyers this year at the trade deadline.  Rather, they will either be in a sell mode, seeking high-ceiling prospects a few years away, or keeping the assets they have, presumably only if they cannot get anything in return.  In short, the Brewers are suddenly rebuilding, and are focusing on  stocking up their farm system and developing controllable rotation talent.

But, rebuilding is a complicated topic in small markets like Milwaukee.  As Wendy Thurm has noted, the Brewers, with their limited geographic reach, have one of the smallest television contracts in the league.  Thus, the Brewers rely upon strong attendance to deliver profits for Mark Attanasio and his ownership group.  In recent years, the Brewers’ attendance fortunately has been some of the most impressive in baseball, particularly in comparison to the size of the Milwaukee metropolitan area.  Over the last five years, the Brewers have consistently approached or exceeded three million fans, despite challenging economic times.  So, one thing the Brewers cannot afford is a collapse akin to the mere 1.7 million fans they drew in 2003 during a terrible season — not if they want to make the investments in future talent required to make the franchise a perennial contender.

So, the Brewers face an obvious challenge: the team needs to lose enough games to obtain a prime draft position, and thereby maximize its chances to draft a top-ceiling player with minimum bust potential.  At the same time, the Brewers need to avoid losing in any drawn-out fashion, because a corresponding and sustained decline in attendance could hemorrhage desperately-needed cash from their balance sheet.  As Ryan Topp and others have argued, this need to maintain attendance in the short term seems to be one reason why the Brewers have systematically traded away what previously was an excellent farm system, with the apparent goal of maintaining the aura of a competitive team.

How does one navigate this problem?  Well, the best solution could be to experience a May like the Brewers just suffered.  Doing so addresses two problems: (1) it abruptly puts the team on course to get a top 5 draft pick, and (2) it achieves this result so abruptly, and in this case so early in the season, that the fan base can still — at least in theory —enjoy much-improved baseball for the remainder of the season without jeopardizing that draft slot.  In short, when you can take your medicine over the course of one month, instead of over an entire season, you really ought to do it.

As to the draft:

Thanks to May, the Brewers currently have the fifth-worst record in baseball at 23–37.  As of the morning of June 8, 2013, FanGraphs predicted that the Brewers will end the season tied for baseball’s fourth-worst record with the New York Mets at 73–89.  Provided that 2013’s top five draft picks all reach agreement with their teams, the Brewers are on pace for a top-5 draft slot in 2014.

The Brewers have not had a top-5 pick in the Rule 4 draft since 2005, when they picked some guy named Ryan Braun.  Before 2013, the top five slots in the draft provided, among others, Buster Posey (#5, 2008), Stephen Strasburg (#1, 2009), Manny Machado (#3, 2010), Dylan Bundy (#4, 2011), and Byron Buxton (#2, 2012) — the types of superstar prospects the Brewers have been denied for years, and which they need to anchor their next generation of players.  At the end of April, and before May occurred, the Brewers were on track for yet another mid-round pick slot.

As to the rest of the season:

It is unlikely that the Brewers will continue to suffer the combination of injuries and dreadful rotation pitching that helped ruin their May.  FanGraphs seems to agree, predicting that the current Brewers roster (or something like it) will essentially play .500 baseball for the rest of the season, even while maintaining one of the five worst records in the game.

Average baseball is not contending baseball, but average baseball at least would offer Brewers fans — already pleased with Miller Park’s immunity from rain delays — a reasonable likelihood of seeing a win on any given day.  In 2009, the Brewers were able to bring in over three million fans, despite finishing under .500 overall.  In 2010, the Brewers ended up eight games under .500, but still brought in 2.7 million fans.  It remains to be seen whether playing .500 baseball for the rest of the 2013 season would be sufficient to keep fans coming through the Miller Park turnstiles, but if so, the increasing remoteness of May could be a significant factor, particularly if the team can convince fans that “one bad month” does not represent the current Miller Park experience or true caliber of the team.

Of course, it is also possible that the Brewers will be able to trade significant assets at the deadline in exchange for the prospects Doug Melvin wants.  If so, their projected record could, and probably would decline.  (This is necessarily not a bad thing, given that 68.5 wins is the average cut-off to secure a top 5 draft spot from 2003 through 2012).  If that happens, the Brewers will have a further challenge on their hands in trying to provide even average baseball for their fans, and maintain the attendance they need.

That said, the Brewers’ remarkable close to 2012 — an incredible .610 winning percentage from August through October — was accomplished after trading away Zack Greinke and calling up minor league talent to plug gaps in the rotation left by Greinke’s trade and Shaun Marcum’s injuries.  If the Brewers are once again able to make advantageous trades at the deadline, and also able to play even .500 ball for the rest of the year, they are still in a position to do so without hurting their chances to get the impact player they need in the 2014 Rule 4 draft.

If they can pull both of these things off, much of the thanks should be given to the horrible month of May.

A lot of ink (and pixels) have been spilled about Chris Davis’ great season.  It’s hard to overstate just how great a .337/.432/.721 start through roughly one-third of the season is, especially in this renewed era of depressed offense.  MLB’s .722 OPS this year so far ranks it as Baseball’s second-lowest since 1992’s .700.  (2011 = .720)  Quite straight, Davis is having the best offensive season in the American League of any player whose first name is not some variation of “Michael”.

Here’s yet another data point for you to chew on: Chris Davis is on track to have one of the highest extra-base hit (XBH) to plate appearance (PA) ratios in history.

As of the morning of Memorial Day 2013, Davis has hit an XBH in 16.5% of his PAs.  In conversational terms, he hits an XBH about every six times he steps to the plate.

If Davis were to end the season with this ratio and qualify for a batting championship, it would rank second in history behind this other guy’s pretty good season.

In fact, only nine qualified players in modern history have ever had an XBH-PA ratio of greater than 15% over the course of an entire season.  Here is the list, with Davis’s 2013 added for context:

You may have noticed that 30% of the players on this list are named either Al or Albert, but none of them are named Pujols.  None of them are named Miguel, either.  In fact, the closest the reigning American League Triple Crown winner has come to cracking this list was in 2010 with a 13.0% XBH-PA ratio, and as of this morning he sits well out of range in 2013 at 12.5%, despite his own empirically otherworldly start.

This is, without a doubt, a most exclusive list of a most consistently slugging nature.  It’s enough to send pitchers into grand mal seizures at the very contemplation of this.  Or perhaps more exactly, it might if they were even aware of it.  This data point has probably not yet been illuminated in quite this way—this here article is the closest I myself have found so far, and Davis is not even the star of the piece.  But that does not mitigate the impressiveness of this feat of his so far.

This is not to say that Chris Davis is a better hitter than Miguel Cabrera, or Albert Pujols or Joey Votto or even Shin Soo Choo, for that matter.  But even if this does turn out to be a world class-level fluke season for him, Davis has a chance to crack an elite list inhabited only by the greatest of the great, even if he never knows it.

As we start a new baseball season, I start a new season of my own. This is my first – of many I hope – analysis and write-up on baseball that I am submitting. I am an avid fan, a numbers geek, an aspiring writer and lastly a bored software engineer. I am also very fortunate. I have a close connection with a former major league player and the ability to leverage his vast experience and knowledge of the game. Hopefully, I can parlay the knowledge I have learned from many years of observation along with the knowledge I have gleaned from my connection to realize my goal as a contributor to the sabermetric community and to the enjoyment of baseball fans everywhere. Here we go!

Question

Is the effectiveness of a sinker dependent on from which side of the rubber the pitcher throws?

I was in Florida in mid March for spring training, talking with a minor league coach when he mentioned that he and a former all star pitcher were in a disagreement about how to throw a sinker. Their debate centers on where a pitcher should stand on the rubber to throw a sinker most effectively. We all understand that a pitcher should not move all over the rubber to become more effective on a single pitch. This would obviously tip off the hitters as to what type of pitch might be coming. But for argument’s sake, a team might have some newly transformed position players learning to throw different pitches. Wouldn’t a team want to know if, for some pitches, it was more beneficial to stand on one side of the rubber than another?

I consider myself a pretty observant guy, but I will have to admit that I never really paid much attention to where a pitcher stood on the rubber. To me the juicy part is watching the ball just after it is released. The dance, dip, duck and dive a pitcher is able to command of the ball is where the action is as far as I am concerned. So watching what a pitcher does before he even starts his motion was asking a little much. Nonetheless, I was certain that with so many pitchers in the majors, that a breakdown of data would show that there was not a singular starting point on the rubber. Every pitcher is different, right?

Setup

I started my analysis by downloading the last 4 years (2009-2012) of PitchFx data. Most of us know this already but by using PitchFx data there are some limitations to analysis. Unlike Trackman, PitchFx initially records each pitch at 50’ from home plate, not the actual release point of the pitch. For PitchFx this data point is called “x0”, and for all intents and purposes this is pretty good data, as for most pitchers their strides are approximately 5 to 6’ from the rubber, and with arms length added in we are talking about a difference of a couple of percentage points from being the same as the release point metric from Trackman. But full disclosure, it is not exactly the release point. Another factor that I didn’t measure is a pitcher’s motion to the plate. Some pitchers throw “across” their bodies and not down a straight line, and even fewer open up their body to the batter (stepping to stride leg’s baseline). Also, there is probably a bit to glean from going between the stretch and wind-up, but again without doing a very in-depth study I assume no factor in the analysis. Lastly, arm length is an unmeasured factor. For example, I didn’t check to see if there were any right-handed pitchers with extra long arms standing on the first-base side of the rubber distorting the data.

I started by combining the PitchFx Sinker (SI) and Two-seam fastball (FT) data into a single database. The reason to combine the data is due to the fact that the grips for each pitch are the same, combine this with a two-seam fastball can and a sinker break the same way (down and in to a RH batter from a RH pitcher), and lastly they are also somewhat synonymous in major league vernacular. Maybe somewhere along the line the pitch was invented twice (north or south), the name given is based on region like when asking for a Coke… it’s a “soda”, a “pop”, or a “tonic” depending on where you are in the states. Maybe in the South it was labeled a sinker and the North it was taught as a “two-seamer”? Either way it’s the same pitch as far as I am concerned, and the etymology of pitch naming is a different topic for a different time.

Back to the question above about every pitcher being different, I was wrong. Using the 2012 data I created a frequency distribution for right-handed pitchers (figure 1), and as you can see there is definite focal area at around -2’ point from the centerline of the pitching rubber (and home plate).

Figure 1 – Right-handed pitchers in 2012

This shows that most pitchers start from about the same side; which I determined to be the right side of the rubber (3^rd base side). I determined this by adding 9” to one-half the length of the pitching rubber (24”) which comes to 21” (9”+12”). Add in arm length and you can see that using an x0 that is less than or equal to 2’ (remember we are using negatives here) should prove that the pitcher is throwing from the right side.  I would like to add that the 9” used above is based on the shoulder width of an average man, which is around 18”. This metric is based on studies on the “biacromial diameter” of male shoulders in 1970 (pg. 28 Vital and Health Statistics – Data from the National Health Survey). I think we can all agree that the 18” is probably conservative by today’s growth standards. I mentioned in the limitations of the analysis written above, I don’t account for arm length or pitcher motion. Therefore I needed to make sure that there are right-handed pitchers who are throwing from the left hand side of the rubber; just not a bunch of super long-armed, cross bodied throwers.  With the data in hand I was able to identify which pitchers had thrown the ball closer to centerline of the rubber and therefore would be good candidates for standing on the left side of the rubber. The first pitcher who had a higher (>-2) x0 value was Yovani Gallardo of the Milwaukee Brewers. Without knowing Gallardo’s motion I needed to go to the video. From the video, you can clearly see that Gallardo starts on the left side of the rubber and throws fairly conventionally, straight down the line to the batter.

I wanted to keep this as simple as possible, breaking up the pitchers in two categories – Left side or Right side. Without looking at video for each pitcher I had to come up with a tipping point for classifying the side based on the x0 data I had available. If we simply take what we determined above and correlate it to the left hand side we will come up with 1 (starting on left side of rubber) and an x0 of 0. But it isn’t quite that simple. The frequency chart shows that there are less than 1000 balls thrown in 2012 with an x0 greater than or equal to 0. Gallardo threw 504 pitches himself in 2012. So we have to increase the scope a bit. By arranging the x0 data into quartiles we see that upper or lower quartile – depending on handedness – is around -1 or 1 (remember we are using negatives) so for a right handed pitcher the x0 splits are:

For left handers:

As I am trying to stay conservative, and the fact that these are not release point numbers I use 1 and -1 as the cut off for classification based on the handedness of the pitcher. Using these numbers provided a pretty clean break in the distributions (90-10%).

Findings

So who was right, the all star pitcher or the minor league pitching coach? Is there an advantage depending on where the pitcher stands on the rubber? Neither – both of them. It’s a tie.

What can I say; my initial analysis is a bit anticlimactic, but not because of lack of effort.  To denote the labels below:

• LH or RH (Handedness)
• RR or LR (Right or Left Rubber)
• B – Balls
• K – Strikes
• P – In play (No Outs)
• O – In play (Outs)
• BackK – Called Strikes
• FT – Two seam fastballs
• SI – Sinkers
• Efficiency – O/(P+O)
• XSide – Cross Side (i.e. RH-LR or LH-RR)
• Same side – LH-LR or RH-RR

The efficiency is so very close. Twelve-hundredths (.12) of a percent is not a lot – 169 outs out of 140678 – but give any Chicago Cub fan five of those outs in 2003 and Mr. Bartman would be an afterthought. Which, I am sure is the way he and all Cub fans around the world would like it. The efficiency is the same, no other way to put it which is the beauty of statistics and sabermetrics. Numbers can say so much, even when they are the equal.

But the analysis wasn’t all for naught, there are some nuggets to glean from the numbers above. As a segue, I am currently watching Derek Lowe of the Texas Rangers pitch on opening night and from the left side of the rubber he throws a sinker and it dips back over the rear part of the plate for a called strike. With all of the similarities within my analysis the most striking observation is the difference in called strikes depending on the side of the rubber. If a pitcher, coach or manager could get a strike or a strike out without the fear of having a batter get a hit or moving a runner forward they would do it every time. With a five percent difference in getting a strike and not having the worry of the ball being put into play would be an interesting thing to know in some tight situations with runners on base. My thought on the difference revolves around the back door being open a little wider when it comes to getting called strikes. With a pitcher throwing X-side you can definitely see a pattern of called strikes on the same side of the plate from which the pitcher throws from. Positive numbers in figures below indicate right side of plate (1^st base side)

With today’s specialization where pitchers are matched up to batters based on handedness, the ability for a pitcher to throw a strike as it tails back over the plate or close to the plate (or maybe not even close for some of the pitches above ) is essential. It appears that umpires are a little more flexible with their perception of the strike zone for these pitchers as well.

Closing

I didn’t get the results that I anticipated when I started this analysis, and that is great! As a society we are determined to have a winner! Just as there is “no crying in baseball”, there are no ties in baseball. Even when there is a tie; like on a close play at first – it proverbially goes to the runner. We can’t settle for a tie…. hockey reduced ties by adding a shootout after overtime.  College football removed the tie by introducing sudden death (hopefully the bowl playoff with help eliminate the subjective BCS tie). With no clear cut advantage (read – TIE) identified in my analysis means that a more in depth analysis could/should be performed to validate. Maybe expanding the percentage of X-side pitchers to 15-20, or identifying when pitchers are throwing from the stretch and removing those instances would alter the results and provide a much needed winner? If after all analytical statistical avenues have been exhausted there’s still not a proven advantage, we can always resort to having the coach and player settle it with a coin flip?

I was sitting on a park bench reading Veeck as in Wreck, the memoir of legendary ballclub owner Bill Veeck, when I came across this passage:

Ken Keltner, our third baseman and one-time power hitter, had a miserable season in 1946. There seemed little doubt that he was on the downgrade. Still, when I signed him for the next year, I gave him the same amount of money and told him that if he had what I considered a good year I’d give him a bonus of $5,000.

The next year, Kenny hit the ball better than anybody on our club, with less luck than anybody in the league. If you walked into the park late and saw somebody making a sensational leaping, diving backhanded catch, you could bet that Keltner had hit the ball.

On the last day of the season, he was hitting under .260 and had driven in around 75 runs. I called down to the locker room, got him on the phone, and said, “Hey, where have you been? Weren’t you supposed to come up and see me at the end of the season?”

“I didn’t win anything,” he said. “I’m having a lousy season.”

I suggested that he wander up anyway. As he came through the door I said, “I’ve got $5,000 for you.”

And he said, “I didn’t earn it, Bill.” And he started to weep.

“You hit the ball better than anybody else on this club,” I told him. “It wasn’t your fault they kept catching it.”

As a loyal FanGraphs reader, I immediately thought: BABIP! For those who need a quick reminder, batting average on balls in play (BABIP) measures just that: batting average on balls hit somewhere the defense can get to them. It’s expected that BABIP will generally hover around .300, modified by such factors as the enemy defense (this averages out over a season), whether the balls you hit go over outfield fences, and, most of all, luck.

Now, Veeck’s comment that Keltner “hit the ball better than anybody else” was probably a kindness rather than a hypothesis. But his observation that “they kept catching it” checks out. I looked at the leaderboard for the BABIPs of every qualifying hitter in 1947. Sure enough, Ken Keltner’s down near the bottom, ranking 68th of 86 with a BABIP of .264. The median that year was almost thirty points higher: .292.

Ken Keltner had lousy luck, but was still an average hitter (102 wRC+). And the next year was the best of his career (7.9 WAR), so it looks like Bill Veeck saw the Keltner case exactly right. Only there’s a twist. One of Veeck’s 1947 Indians had it even worse. Down there at 74th is the .256 BABIP of Joe Gordon. Joe Gordon slugged 27 doubles, 6 triples, and 29 home runs, so things turned out well for him, but if Veeck’s latecomer had bet that “a sensational leaping, diving backhanded catch” was on a ball hit by Ken Keltner, you’d want to bet against him. Joe Gordon’s luck was worse; he compensated by putting more balls in the outfield bleachers.

There’s weirder to come. Dead last, 86th of 86, is Roy Cullenbine, Tigers first baseman, who paired a grotesque .206 BABIP and .224 average (83rd of 86) with the second-highest walk rate in baseball. His 22.6% walk rate was topped only by Triple Crown winner Ted Williams. (By the way, in the previous year, Williams had been introduced to the defensive shift, as pioneered by, yes, Bill Veeck’s Indians.)

No player in 2012 came close to matching Cullenbine’s bizarre season. The lowest BABIP of any qualifying hitter in 2012 was .242 (Justin Smoak); of all hitters with BABIPs below .256 (fifty points higher than Roy Cullenbine’s), none came within fifty points of Cullenbine’s .401 OBP. The best analogy is this: Cullenbine hit for average like Dan Uggla, had Justin Smoak’s luck, and still drew walks at the rate of Barry Bonds.

Roy Cullenbine was only 33 in 1947, and in past years his offensive numbers were impressive. Had he been on Bill Veeck’s Indians instead of playing for the Tigers, his unlucky 1947 might have ended as Ken Keltner’s did,with a $5,000 bonus. The Tigers, not valuing Cullenbine’s patience, released him, and he never played a major-league game again.

There’s another interesting name among the ten unluckiest batters of 1947. Coming in at sixth-worst, with a BABIP of .247, is a patient slugger who got on base even more than Cullenbine did, with four more walks than he had hits. He too retired after the season. His name was Hank Greenberg, and that winter he accepted a job in a major-league front office, where he was groomed to be the team’s next general manager. The team was the Cleveland Indians. His new boss was Bill Veeck.

Most people that try to analyze this Dickey effect tend to group all the pitchers that follow in to one grouping with one ERA and compare to the total ERA of the bullpen or rotation. This is a simplistic and non-descriptive way of analyzing the effect and does not look at the how often the pitchers are pitching not after Dickey.

I decided to determine if there truly is an effect on pitchers’ statistics (ERA, WHIP, K%, BB%) who follow Dickey in relief and the starters of the next game against the same team. I went through every game that Dickey has pitched and recorded the stats (IP, TBF, H, ER, BB, K) of each reliever individually and the stats of the next starting pitcher if the next game was against the same team. I did this for each season. I then took the pitchers’ stats for the whole year and subtracted their stats from their following Dickey stats to have their stats when they did not follow Dickey. I summed the stats for following Dickey and weighted each pitcher based on the batters he faced over the total batters faced after Dickey. I then calculated the rate stats from the total. This weight was then applied to the not after Dickey stats. So for example if Francisco faced 19.11% of batters after Dickey, it was adjusted so that he also faced 19.11% of the batters not after Dickey. This gives an effective way of comparing the statistics and an accurate relationship can be determined. The not after Dickey stats were then summed and the rate stats were calculated as well. The two rate stats after Dickey and not after Dickey were compared using this formula (afterDickeySTAT-notafterDickeySTAT)/notafterDickeySTAT. This tells me how much better or worse relievers or starters did when following Dickey in the form of a percentage.

I then added the stats after Dickey for starters and relievers from all three years and the stats not after Dickey and I applied the same technique of weighting the sample so that if Niese’12 faced 10.9% of all starter batters faced following a Dickey start against the same team, it was adjusted so that he faced 10.9% of the batters faced by starters not after Dickey (only the starters that pitched after Dickey that season). The same technique was used from the year to year technique and a total % for each stat was calculated.

Here is the weighted year by year breakdown of the starters’ statistics following Dickey and a total (- indicates a decrease which is desired for all stats except K%):

2012:
ERA: -46.94%  with 5/5 starters seeing a decrease
WHIP: -16.16% with 4/5 seeing a decrease
K%: 47.04% with 4/5 seeing an increase
BB%: 6.50% with 3/5 seeing a decrease
HR%: -50.53% with 5/5 seeing a decrease
BABIP: -14.08% with 4/5 seeing a decrease
FIP: -25.17% with 5/5 seeing a decrease

2011:
ERA: 17.92%  with 0/3 seeing a decrease
WHIP: -9.63% with 2/3 seeing a decrease
K%: -2.64% with 2/3 seeing an increase
BB%: -15.94% with 2/3 seeing a decrease
HR%: -9.21% with 2/3 seeing a decrease
BABIP: -15.14% with 2/3 seeing a decrease
FIP: -5.58% with 2/3 seeing a decrease

2010:
ERA: -23.82%  with 5/7 seeing a decrease
WHIP: 1.68% with 5/7 seeing a decrease
K%: -22.91% with 1/7 seeing an increase
BB%: -2.34% with 5/7 seeing a decrease
HR%: -43.61% with 5/7 seeing a decrease
BABIP: -3.61% with 4/7 seeing a decrease
FIP: -10.61% with 5/7 seeing a decrease

Total:
ERA: -17.21%  with 10/15 seeing a decrease
WHIP: -8.10% with 11/15 seeing a decrease
K%: -3.38% with 7/15 seeing an increase
BB%: -5.17% with 10/15 seeing a decrease
HR%: -32.96% with 12/15 seeing a decrease
BABIP: -11.04% with 10/15 seeing a decrease
FIP: -13.34% with 12/15 seeing a decrease

So for starters that pitch in games following Dickey against the same team, it can be concluded that there is an effect on ERA, WHIP, BABIP, and FIP and a slight effect on BB% and on K%. There is also a large effect on HR rates which we can attribute the ERA effect to. This also tells us that batters are making worse contact the day after Dickey.

So a starter (like Morrow) who follows Dickey against the same team can expect to see around a 17.2% reduction in his ERA that game compared to if he was not following Dickey against the same opponent. For example if Morrow had a 3.00 ERA in games not after Dickey he can expect a 2.48 ERA in games after Dickey.

So if in a full season where Morrow follows Dickey against the same team 66% of the time (games 2 and 3 of a series) in which he normally would have a 3.00 ERA without Dickey ahead of him, he could expect a 2.66 ERA for the season. This seams to be a significant improvement and would equate to a 7.6 run difference (or 0.8 WAR) over 200 innings.

Here is a year by year breakdown of relievers after Dickey (these are smaller sample sizes so I will not include how many relievers saw an increase or decrease):

2012:
ERA: -25.51%
WHIP: -1.57%
K%: 27.04%
BB%: -49.25%
HR%: -34.66%
BABIP: 30.23%
FIP: -38.34%

2011:
ERA: -17.43%
WHIP: 8.45%
K%: 6.74%
BB%: -5.14%
HR%: 7.34%
BABIP: 9.75%
FIP: -2.05%

2010:
ERA: -2.55%
WHIP: 7.69%
K%: -9.28%
BB%: 10.84%
HR%: 2.11%
BABIP: 4.23%
FIP: 9.43%

Total:
ERA: -16.61%
WHIP: 5.38%
K%: 7.50%
BB%: -12.65%
HR%: -8.53%
BABIP: 13.38%
FIP: -10.40%

As expected there was a good effect on the relievers’ ERA, FIP, K%, and BB%, but the WHIP and BABIP were affected negatively. This tells me that the batters were more free swinging after just seeing Dickey (more hits, less walks, more strikeouts).

So in a season where there are 55 IP after Dickey in games (like in 2012) there would be a 16.6% reduction in runs given up in those 55 innings. If the bullpen’s ERA is 4.20 without Dickey it can be expected to be 3.50 after Dickey. Over 55 IP this difference would save 4.3 runs (or 0.4 WAR).

Combine this with the saved starter runs and you get 11.9 runs saved or (1.2 WAR). This is Dickey’s underlying value with the team that he creates by baffling hitters. This 1.2 WAR is if Morrow has a 3.00 ERA normally and the bullpen has a 4.00 ERA. If Morrow normally had a 4.00 ERA than his ERA would reduce to 3.54 over the season with 10.2 runs saved for 200 innings (1.0 WAR) and if the bullpen has a 4.00 ERA normally as well, 4.1 runs would be saved there, equating to 14.3 runs saved or a 1.4 WAR over a season.

Coming up with BERA… like its [almost] namesake might say, it was 90% mental, and the other half was physical.  OK, maybe he’d say something more along the lines of “what the hell is this…” but that’s beside the point.    By BERA, I mean BABIP-estimating ERA (or something like that… maybe one of you can come up with something fancier).  It’s an ERA estimator that’s along the lines of SIERA, only it’s simpler, and—dare I say—better.

You know, I started out not knowing where I was going, so I was worried I might not get there.  As you may recall, I’ve been pondering pitcher BABIPs for a little while here (see article 1 and article 2), and whereas my focus thus far had been on explaining big-picture, long-term BABIP stuff in terms of batted ball data, one question that remained was how well this info could be used to predict future BABIPs.  After monkeying around with answering that question, though, I saw that SIERA’s BABIP component could be improved upon, so I set to work in coming up with BERA.  In doing so, I definitely piggybacked off of FIP and a little of what SIERA had already done.  You can observe a lot just by watching, you know.   I’m also a believer in “less is more” (except for when it comes to the size of my articles, obviously), so I tried to go for the best compromise of simplicity and accuracy that I could.

Read the rest of this entry »

In my last post on explaining pitchers’ BABIPs by way of their batted ball rates, I was very careful to say that it was applicable in the long run, as it’s hard to be accurate over a short number of innings pitched, due to all the “noise” in BABIP (Batting Average on Balls In Play).  I only used pitchers with a qualifying number of innings pitched (IP) in the calculations, for that reason.  After writing the post, I did some messing around with the data, to find out just how much of an effect IP had on the predictability of BABIP.

Hold on to your propeller beanies, fellow stat geeks: the correlation between xBABIP and BABIP went from 0.805 when the minimum IP was set to 1500, to 0.632 at a 200 IP minimum, down to 0.518 at 50 IP.  OK, maybe it’s not that surprising.  Still, I thought I’d better show you how confident you can be in my xBABIP formula’s accuracy when you take the pitcher’s innings pitched into account.

The formula, again: xBABIP = 0.4*LD% – 0.6*FB%*IFFB% + 0.235

And remember, that formula is primarily meant to be a backwards-looking estimator of “true,” defense-neutral BABIP.  My next article will (probably) discuss another formula I’ve come up with that’s more forward-looking.

Read the rest of this entry »

Hi everybody, this is my first post here. Today, I’ll be sharing some of my BABIP research with you. There will probably be several more in the near future.

Now, I don’t know about you, but Voros McCracken’s famous thesis stating that pitchers have practically no control over their batting average on balls in play (BABIP) always seemed counterintuitive to me, ever since I heard it about 10 years ago. Basically, my thought this whole time was that if an Average Joe were pitching to an MLB lineup, the hitters would rarely be fooled by the pitches, and would be crushing most of them, making it very tough on the fielders. Think Home Run Derby (only with a lot more walks). Now, the worst MLB pitcher is a lot closer in ability to the best pitcher than he is to an Average Joe, but there still must be a spectrum amongst MLB pitchers relating to their BABIP, I figured. After crunching some numbers, I have to say that intuition hasn’t completely failed me.

This is going to be a long article, so if you want the main point right here, right now, it’s this: in the long run, about 40% or more of the difference in pitchers’ BABIPs can be explained by two factors that are independent of their team’s defense: how often batters hit infield fly balls and line drives off of them. It is more difficult to predict on a yearly basis, where I can only say that those factors can predict over 22% of the difference. Line drive rates are fairly inconsistent, but pop fly rates are among the more predictable pitching stats (about as much as K/BB). I’ll explain the formula at the very end of the article.

Read the rest of this entry »