Community Blog

While this year’s iteration of the World Baseball Classic has certainly experienced success, it does not have the juggernaut status that the Football World Cup or the Olympics currently hold. While the Classic will probably never approach the success these two international tournaments have, it does have the potential to spread baseball interest and expand the game around the world, particularly in places like Europe or China. In order for baseball to grow, it has to reach new fan bases outside of the United States, which appears to be at the max of its potential. The WBC is a nice touch to baseball’s international growth, but it needs a few modifications to truly reach its potential.

The problem with the current round-robin format is the attendance figures and interest level with the games involving two lesser-known countries. In pool A, three out of the six games drew less than 5,000 fans, while the other three had more than 10,000 fans each, and two drew more than 25,000. The attendance figures in pool B were even more extreme. three of the games drew less than 2,000 fans, while the other three drew more than 20,000 each. To combat this problem, there have been numerous suggestions about modifying the tournament to turn it into a single elimination format, as Dave Cameron suggested in his post “Fixing the WBC”. This format is definitely the best option for the tournament, as it would increase the interest and attendance in each game given the win or go home nature atmosphere. Hopefully, since all the games would pit a high-seeded team against a low-seeded team, the low-interest games of less than 5,000 fans would be eliminated.

The other advantage to the single-elimination tournament is the elimination of the silly WBC rules and tiebreaking procedures. Run differential would no longer be the difference between advancing out of a pool and going home. The pointless games to determine seeding at the end of the second round would also be eliminated. Perhaps the pitch limits would go away as well because teams would play fewer games. The tournament would no doubt gain some relevancy if the silly rules and restrictions were eliminated.

Most of the potential changes to the WBC involve shortening it to a week or so. While most would agree that the current format is too long, MLB might not bite on a change that shortens the tournament to a mere week. The solution: why not expand the number of teams to 32? The current 16 teams would stay, and all the teams that participated in the qualifier would be added as well. That adds up to 28 teams. I wasn’t really sure what the four other teams could be, so I came up with Pakistan, Russia, Belgium, and Austria. I’m sure there might be better teams out there, but let’s proceed with these four teams to make it easy. To determine the format, I divided the tournament into four conferences: Northwest, Euro, East, and South:

East:                                                    South

1. Japan                                    1. Venezuela
2. South Korea                        2. Australia
3. Taiwan                                 3. Brazil
4. China                                    4. Colombia
5. Israel                                     5. South Africa
6. Czech Republic                   6. New Zealand
7. Pakistan                               7. Philippines
8. Russia                                   8. Thailand

Euro:                             Northwest:

1. Netherlands                     1. Dominican Republic
2. Italy                                    2. United States
3. Spain                                  3. Puerto Rico
4. Germany                            4. Cuba
5. United Kingdom              5. Canada
6. France                                6. Mexico
7. Belgium                              7. Panama
8. Austria                               8. Nicaragua

The current March timing for the WBC works OK, but it’s not perfect. The All-Star break doesn’t work either because MLB would never agree to nix the “beloved” event. That leaves the winter. I’m not sure the middle of the winter makes sense because the offseason is in full swing and free agents wouldn’t want to do it in fear of getting injured. That leaves November and February. Both of these times make sense to me, but I think the players would be less than thrilled to participate right after the postseason. That leaves February. The absence of football is a plus, and players wouldn’t have the excuse of spring training to avoid participation. Assuming that Spring Training starts March 1, here are some potential dates:

February 14: 4 East First Round Games

February 15: 4 South First Round Games

February 16: 4 Euro First Round Games

February 17: 4 Northwest First Round Games

February 19: 2 East Semifinal Games

2 South Semifinal Games

February 20: 2 Euro Semifinal Games

2 Northwest Semifinal Games

February 22: East Final Game

South Final Game

February 23: Euro Final Game

Northwest Final Game

February 25: East Winner vs. South Winner

February 26: Euro Winner vs Northwest Winner

February 28: Final Game

The close proximity of these games might require them to be played in a single country as opposed to the international format used now. I’m not really sure how many countries could host the two-week tournament besides Japan and the United States. Perhaps Japan and the US could alternate until other countries become viable alternative solutions. Or the regional tournament games could be held in that specific region and the winners could meet up for the semis somewhere else, like the current format. It would be great if European countries or other big countries like India could host the WBC, but currently it doesn’t seem likely.

Overall, this format offers some significant advantages to the current one. This version of the classic would have 31 games, only eight less than the current format, which would appeal to MLB because the new version could generate a comparable amount of revenue. However, individual teams would play fewer games, potentially attracting the big stars currently holding out. Already, we have seen players like Chase Headley, Jurickson Profar, Gio Gonzalez, and Kenley Jansen join the Classic in the later rounds when there are fewer games to play. Additionally, players competing for a job in spring training would be more enticed to join the classic because it provides another opportunity to showcase their talent to teams. The injury risk would be less because 1. there are fewer games to play and 2. players would have a longer period of time to recover from injury. Yes, baseball would start earlier, but hopefully this format would attract players the same way the World Cup does for soccer. With increased player participation, more exciting games, more teams involved, and a time frame that doesn’t compete with baseball’s own spring training, these changes make sense for MLB, the players, and most importantly, the fans.

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.

Controlling the run game, pitcher fielding and ERA

Run & Glove

Johnny Cueto has been mocking his peripherals ever since his big league debut.  For the most part FIP serves as a terrific gauge for pitcher performance, but in 2011 Cueto made FIP look like a heart monitor trying to explain the weather.  On what most consider a separate note, base runners have a healthy and robust fear of Cueto’s pickoff move, which is one of the best in the show.

FIP measures outcomes a pitcher can control (home runs, walks and strikeouts) and chalks the rest up to random variation.  Studies have shown that stolen bases contribute relatively little to run creation and perhaps on that basis the ability to control the run game has generally been ignored or deemed overrated.

It is difficult, however, to ignore the six runs Cueto saved the Reds via his contributions to controlling the run game in 2012.  By contrast, A.J. Burnett’s inability to control runners cost the Pirates four runs. The typical scale is that 10 runs amount to one team win – and teams will pay about $5 million per win.

Acknowledging run game control cannot fully explain how Cueto has routinely outperformed his peripherals, just as it cannot wholly explain Pittsburgh’s inability to keep pace with Cincinnati in the NL Central last season.  It does, however, get us closer.

Incorporating a pitcher’s fielding ability proved of comparative importance in explaining and predicting performance.  Here we’ll turn to Mark Buehrle, whose glove has saved four runs per year since 2004, and among fellow hurlers the fast-working lefty has been one of the decade’s most steadily superb fielders.  FIP underestimated Buehrle in eight of the past nine seasons, slighting his ERA by an average of .30 per year over that span.

Numbers

 The numbers indicate that a pitcher’s defense and ability to control the run game should both be considered in assessing and forecasting the pitcher’s value.

Focusing on seasons in which pitchers hurled 100 same-league (AL or NL) innings from 2003-2012 (n=1400), I ran a multiple linear regression to create a formula (“MBRA”) incorporating run control (rSB) and pitcher fielding (rPM) on top of line drive and infield fly ball percentages (credit to BABIP guru Steve Staude) and a regressed take on FIP.

MBRA = (55.25*HR + 14.05*BB – 8.57*K)/TBF – .041*rPM – .056*rSB + (5.71*LD – 8.27*IFFB)/(LD+GB+FB) + 2.34 

MBRA is engineered to properly credit pitchers who can field and control the run game.  When I subtracted MBRA from FIP to locate the pitcher-seasons that benifitted most from my formula, I was encouraged seeing Buehrle show up twice in the top ten, and five times in the top 100 (again, that is out of 1400).

Next, I looked at seasons in which pitchers threw 100 same-league innings in consecutive seasons from 2003 to 2012 (n=791).  This time I ran a regression to create a model suited to predict a pitcher’s ERA based on his previous year’s statistics.

MBRAT = (20.12*HR + 7.13*BB – 6.7*K)/TBF -.025*rPM -.034*rSB + 2.37*ZC% + 2.22

MBRAT stands tall on the lofty pinnacle of public forward-looking ERA estimators, and if you factor in the percentage at which pitchers throw over the edge of the plate (EDGE%) its correlation jumps even higher (.4621).  Unfortunately, I only have Edge% data from 2008 to 2012 (n=362) and cannot yet justify its inclusion.

On Deck

I will create expectations for pitchers with fewer innings pitched and convert my findings to a WAR measure that may serve as a middle-ground between fWAR and rWAR.  I also stumbled on a potentially significant relationship between pick-off attempts and strand rates that may work its way into future formulas.

Evaluating 2012 Projections

Hello loyal readers.  It’s time for the annual evaluation of last year’s player projections.  Last year saw Gore, Snapp, and Highly’s Aggpro forecasts win among hitter projections (http://www.fangraphs.com/community/comparing-2011-hitter-forecasts/) and Baseball Dope win among pitchers http://www.fangraphs.com/community/comparing-2011-pitcher-forecasts/ .  In general, projections computed using averages or weighted averages tended to perform best among hitters, while for pitchers, structural models computed using “deep” statistics (k/9, hr/fb%, etc.) did better.

2012 Summary

In 2012, there were 12 projections submitted for hitters and 12 for pitchers (11 submitted projections for both).  The evaluation only considers players where every projection system has a projection.

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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.

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Every year the least competitive MLB teams decide whether they will commit to “going for it” the next season, or take a step back and wait for some of their cost-controlled young players to develop into big league contributors, then invest money in the team at that time a year or two down the road.  If the situation is dire, the media and baseball executives alike will start kicking the tires on an organization needing an all-out rebuild.  In this case, teams trade away every expensive, though often productive, veteran for young prospects that can hopefully help form a more competitive and sustainable team in a few years in part due to a higher production to salary ratio.  A judgment is made that investing money into the major league portion of the organization will not yield worthwhile results in the upcoming seasons, leading to declining attendance and television ratings.  That money would be better spent on the draft and developing the players acquired through trades of the more expensive players on the team.  These often publicly announced plans usually have estimated times to completion ranging from 3-5 years, often coinciding with a new baseball executive’s contract length within a year or two.  I set out to measure the results of this strategy as it applies to total revenue, as well as how it works out in terms of return on investment.

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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.

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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.

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Stated in as simplest terms as possible, the goal of pitching is to get batters out without allowing runs to score. There are three ways any given pitch can get a batter out. A pitch can either be swung on and missed, taken for a called strike, or batted in such a way that the batted ball does not result in the runner reaching base. Batted balls involve the defence and are therefore less directly related to the pitch’s effectiveness at getting outs. That leaves us with swinging strikes and called strikes as the two best ways to measure a pitch’s effectiveness.

In Part I of my research on curveballs, I looked at what makes a curveball effective from a swinging strike perspective. I used an outcome variable that I like to call: ratio of effectiveness. Ratio of effectiveness is simply a ratio between swinging strikes and home runs hit. In Part II of my research, I will look at the effectiveness of curveballs from a called strike perspective. This work will aim to answer two basic questions: 1) are curveballs taken for strikes more often than fastballs? And 2) what are the characteristics of curveballs most often taken for strikes?

Are curveballs taken for strikes more often than fastballs?

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117 years ago, in response to an epidemic of infielders intentionally dropping popups to attempt double plays instead, the National League adopted the infield fly rule, and with some minor adjustments, the rule has survived to the present. Like many remedies from the 1800s, the intent- protecting the offense from chicanery- was good, but the implementation- calling their batter automatically out- was fraught with problems.

First, and most obviously in light of recent events, even when the defense can’t make the play, the rule intended to protect the offense punishes them by giving the defense the out anyway. Second, any time a fly ball can be intentionally dropped for a good shot at a double play, the offense should be protected from that, but because the play requires calling the batter automatically out, the rule as written can’t be invoked liberally. Third, and related to the second, the umpires have to make a judgment call based on the trajectory of the ball, the position of the fielder, environmental factors, and anything else they consider relevant to determining “ordinary effort”. That leads to late calls and inconsistent application.

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