Earlier this year, Alex Chamberlain of FanGraphs took a look at the top NL outfield prospects for the 2015 season. On this list of five outfielders, three of them have had significant time in the big leagues: Joc Pederson, Jorge Soler, and Randal Grichuk. They were ranked 1, 2, and 5 respectively prior to the season (Andrew Lambo and Eury Perez were ranked 3 and 4 but due to both playing less than 25 games, I did not include them in my analysis). The purpose of my writing today is to analyze these three outfielders and see if the preseason rankings has translated to the regular season. To understand if the rankings hold true, I will compare how each player has hit to this point in the season as well as their ability to help their team on defense.

Offensive Analysis

Using simply batting average as a definitive metric for determining which player is succeeding is especially tough considering the snapshot is only the first half of the 2015 season. That being said I think it is important to look at simple batting average to see if there is any outliers when comparing it to BABIP. In terms of batting average, Randal Grichuk is leading the way with a .277 average, followed by Soler and Pederson at .260 and .230 respectively. While this is a good baseline, comparing it to BABIP and % of hard hit balls identify if these averages are sustainable or unsustainable over the course of a season.

Pederson has a BABIP below the league average at .282, while Soler and Grichuk have BABIP almost 80 points higher than the league average. One of the factors that could be contributing to Pederson’s significantly lower average is simply being unlucky. In terms of Hard Hit%, Pederson is tops of the three with 41.5% of the balls he makes contact with being classified as hard hit. Soler and Grichuk have a 38.6% and 39.2% Hard Hit%. Another factor to consider is K%, but this is somewhat of a moot point considering that each outfielder has a K% within one point of 30%, nearly 12% above league average. This is to be expected of young players who are adjusting to big-league pitching and have a large amount of raw power. These percentages indicate that Pederson’s average should not be nearly as low as compared to the other two and would be considered an outlier and his .230 average is not indicative of how he is hitting overall. But it stands to reason that both Soler and Grichuk could see a regression in average if their BABIP falls towards league average.

Another factor that is important to look at when ranking these three budding All-Stars is their raw power. Hitting for average is important but in today’s game it is much harder to string together multiple hits, so being able to drive the ball in gaps or over the walls is a premium quality in a young player. Isolated Power is a great metric to look at when evaluating the raw power a player is displaying because it takes out the batting average variable out of Slugging %. Grichuk and Pederson have the exact same ISO at .257 while Soler’s is much lower at .128, just below the league average of .135. What this metric means is that Soler’s actual slugging% is somewhat inflated by his average. If his average falls, which would be due to a decline BABIP, his slugging% will suffer. It seems that even if Grichuk’s BABIP slumps he will continue to make an impact at the plate in terms of slugging% since his ISO is over 120 points higher than Soler.

To wrap up the offensive portion of my analysis, I would rank the three rookie outfielders 1) Pederson. This is based on the fact he has made a huge impact on the Dodger’s lineup (20 HR, 40 RBIs) although he has been hampered by an unlucky BABIP.  2) Grichuk. Due to the higher ISO than Soler against major league pitching. Grichuk has not only produced more this year than Soler but he projects to continue to have an impact on his team through his higher ISO even if his batting average drops. 3) Soler, is ranked third because the high BABIP and lower ISO make him less appealing throughout the course of the long MLB season.

Defensive Analysis

Defensive metrics also help determine which outfielder is helping save runs for their team which in turn improves the chance of winning a ball game. Revised Zone Rating or RZR is a good indicator of how well a player has been able to make plays in their fielding zone. In terms of RZR, Grichuk is the low man on the totem pole with .902 RZR. Pederson is tops in the group with .926 and Soler in the middle with .916. While RZR is a helpful stat it does not tell the full story of which outfielder is saving the most runs for his team.

DRS or the total defensive run saved helps give an idea of how many runs a fielder has saved above the average player. In terms of DRS, Grichuk has saved 6 runs above average even though he had the lowest RZR. Whereas Pederson has saved 1 run and Soler has saved 0 runs even though both have more outfield assist with than Grichuk (4 each vs. 1). Soler’s low DRS can be attributed to the fact on balls where Soler has between a 60-90% chance of making a play, he has only made the play 66.7% of the time compared to Grichuk and Pederson who have made those same plays 100% of the time.

In terms of defense, I would rank Grichuk number one due to his high DRS. Since saving runs is vital to a team’s success. Pederson would rank second and Soler would be third.

Conclusion

To wrap up my analysis, I think it is important to look at total WAR when evaluating players. Especially when breaking down these three young outfielders. I think it is clear that to this point in their 2015 seasons, Jorge Soler would be ranked third of the three due to the fact that his WAR is only .4 and he lags Grichuk and Pederson in almost every metric I have discussed. Grichuk’s WAR of 1.9 places him second. Even though he saves more runs than Pederson with his glove, the offensive impact Pederson has made even with his .230 batting average is extremely impressive. With 20 home runs at the All-Star break and a WAR of 3.3 Pederson would have to be ranked as still the top NL rookie outfielder that was ranked in the preseason.

All stats for this article were taken from FanGraphs.com

In 2015, MLB implemented changes to shorten the length of games. As has been widely reported, game times have been reduced. Less widely reported is that the majority of the reduction is due to shorter breaks between innings, and the time between pitches has not decreased.

There is concern that rule changes to directly address the time between pitches will impact the game negatively. There are concerns for the logistics of a pitch clock and tasking umpires to somehow legislate/manage situations requiring exceptions. I am a wholehearted proponent of reducing time between pitches, but I have a hard time envisioning how a pitch clock would work with a fast runner on 1B or when the pitcher has mud in his cleats or when the batter gets dust in his eyes.

I propose an effective and non-invasive method for reducing the time between pitches: focus on player averages. A pitcher (or hitter) can be judged over a rolling sample of pitches and with escalating fines/penalties administered to the player and/or team. This method would not dictate any specific in-game action/penalty. It would not require involvement by umpires. It would be transparent to fans, other than less yawning and urges to check email.

While a simple rolling average would be…simple, improvements to the methodology can easily be envisioned. A player’s time score could be adjusted based upon the batter/pitcher faced, foul balls, stolen base opportunity, etc.

I’m surprised this type of method has not gotten much discussion in the media. I think it would allow MLB to steer behavior change without the negative impact of trying to take action in-game on a per-pitch basis.

If you’ve ever attended a Nationals’ home game, you’ve probably seen the best promotional event held in the Washington D.C. area– the Presidents Race. Beginning as a cartoon race featured on the video board of old RFK Stadium in 2005, the first-ever live race was held on July 21, 2006. The 10-foot tall presidents run the length of the field — across the warning track, down the foul lines, around the diamond — while often avoiding obstacles such as traffic cones and competing teams’ mascots. The race reached a fever pitch in the community and media in 2012 when Teddy Roosevelt finally broke his humiliating 500+ race-losing streak. The original competitors — Teddy, George Washington, Abraham Lincoln, and Thomas Jefferson — were joined by William Howard Taft in 2013 and Calvin Coolidge earlier this month.

As we approach the 9th year anniversary of the Presidents Race, I thought it would be interesting to look for correlation between the Presidents Race winners and the Washington Nationals’ on-field performance. Let’s begin with a few caveats. I’ll be looking at data from the beginning of 2013 to July 2, 2015. I chose 2013 as a starting point because it marked the end of Teddy’s losing streak and the beginning of William’s running career. I did not include any data from Calvin’s career because of small-sample-size issues. Also, regarding the racing record in relation to the Nats’ performance, I include data from 4th-inning races, extra-inning races, both races in a double-header, and all playoff races. Finally, I want to give a big thanks to Let Teddy Win! which is a tremendous wealth of Presidents Race knowledge, data, and video.

Abraham is the easy race champion over this time period, finishing 2nd in the final standings in 2013 and 2014. Teddy was carried by his impressive 29-win campaign in 2014, while let’s just say that Thomas is better at writing declarations than at running races. It should be noted that Teddy has been disqualified many times in his racing career because of infractions like unnecessary roughness and cutting the outfield corner.

From 2013-2015, the Nationals were 123-80 (.606) at home, the 3rd best home record in MLB, trailing only St. Louis (.667) and Pittsburgh (.632) over the same time period. To fully appreciate the influence (for better or worse) that the Presidents Race winners had over the Nationals’ on-field performance, we need to look for the winning percentages farthest from .606.

Unsurprisingly, the father of our nation has the biggest positive influence over the Nationals ballclub, leading the squad to a crushing .697 winning percentage. The newcomer, William, also inspired the Nats to play well, despite their mediocre run differential after his race victories. And while Nats fans and opponents may love Teddy (first as a lovable loser and now as a legit competitor), Nats players have not been inspired on the nights he crosses the finish line first. (Teddy went undefeated in the 2014 playoffs, and the Nats went winless in those games.)

The front-runner for this year’s National League Most Valuable Player is clearly inspired by the nation’s front-runner for Most Valuable President. At the plate after a George victory, Harper mashes to the tune of .325, while on-pace for a 50+ HR season. Teddy and Abraham again bring up the rear, and William has another strong showing, reinforcing the idea that “as Harper goes, so go the Nationals.”

Not only does Zimmermann pitch more often on George-victory days than on other days, but he also puts up his best numbers after George pulls out a win. Teddy upsets the pattern by inspiring Zimmerman to a 2.35 ERA and a 9 K/9 mark, the best of the five.

Taft famously threw the first-ever presidential first-pitch, yet both Nats pitchers remain uninspired on William’s victory days. Thomas remains the least influential president (perhaps due to the rarity of his victories), inspiring the team, Harper, and both pitchers to average winning percentages and average career numbers. Lincoln inspires Storen’s lowest ERA and 2nd best K/9.

The results of this data crunch are clear: while Teddy may be a lovable loser, some of that losing might be rubbing off on the Nationals. And if you’re a Nats fan, you probably want to root for George. Bryce Harper and Jordan Zimmermann clearly do.

If you are like me, and you are in a custom, home-run-only fantasy baseball league, you might lie in bed around midnight and look through box scores on your phone. You also might look through box scores for a number of other reasons. Looking through them, I’ve noticed what I believe to be trend. Managers are shifting their lineups to put much more productive players second in the order. That is what this post is about. Another in a long line of posts about something that at the end of the day doesn’t really matter. As long as a manager puts the right names on the card, he is unlikely to screw this up too much.

As a longtime baseball fan, I had a feeling this was a trend that has happened during the course of this year. It was widely publicized that the Reds were going to hit Joey Votto second in the order. 2014 Votto is not exactly a controversial choice for that spot in the lineup. While his power has had a bit of a resurgence, the Reds have still left him in the two hole. So, in what is definitely a very unscientific study, I looked at who each team batted second on July 7^th (I started writing this on July 8^th. I have a job, so it has taken me a few days. I promise this was random) and I compared that to opening day (and opening night the day before in the case of the Cardinals and Cubs). What follows is a list of the same (two players listed denote a double-header on July 7):

Team                               Opening Day                            July                                                                 

Anaheim                           Mike Trout                                     Kole Calhoun

Atlanta                              Jace Peterson                                Cameron Maybin

Arizona                             Ender Inciarte                               David Peralta

Baltimore                         Manny Machado                           Jimmy Paredes

Boston                              Dustin Pedroia                               Brock Holt

Chicago (AL)                   Melky Cabrera                               Jose Abreu

Chicago (NL)                   Jorge Soler                                     Anthony Rizzo, Rizzo

Cincinnati                        Joey Votto                                      Joey Votto

Cleveland                         Jason Kipnis                                  Francisco Lindor

Colorado                          Carlos Gonzalez                             DJ LeMahieu

Detroit                              Ian Kinsler                                      Yoenis Cespedes

Houston                           George Springer                            Preston Tucker

Kansas City                     Mike Moustakas                            Alex Gordon, Gordon

Los Angeles                     Yasiel Puig                                     Howie Kendrick

Miami                              Christian Yelich                             Christian Yelich

Milwaukee                      Jonathan Lucroy                           Jonathan Lucroy

Minnesota                      Brian Dozier                                   Joe Mauer

New York (AL)              Brett Gardner                                Chase Headley

New York (NL)              David Wright                                 Ruben Tejada

Oakland                          Sam Fuld                                        Stephen Vogt

Philadelphia                  Obudel Herrera                             Ben Revere

Pittsburgh                      Gregory Polanco                           Neil Walker

San Diego                       Derek Norris                                 Yonder Alonso

San Francisco                Joe Panik                                       Joe Panik

Seattle                             Seth Smith                                     Franklin Gutierrez

St. Louis                         Jason Heyward                             Kolten Wong, Matt Carpenter

Tampa                            Steven Souza                                 Joey Butler, Grady Sizemore

Texas                              Elvis Andrus                                  Rougned Odor

Toronto                          Russell Martin                              Josh Donaldson

Washington                   Yunel Escobar                              Danny Espinosa

What follows is a categorization of the difference between then and now. I’ve categorized each as either the same; functionally the same (old-school); functionally the same (new-school); shifting old-school; shifting new-school; and wildcard. It’s tough to define exactly what is old-school versus new-school. Some attributes of old-school second-hole hitters are: bad hitters, no power, middle infielder, speed, and younger players. While only the first two of these are actually bad attributes, a new-school thought would be to put a good power hitter second even though he is slow and plays a corner. Anyway, when looking at the choices, sometimes it is harder than you might think, and you may disagree with a few of these. You can read my brief analysis for the 26 teams that had different players, or skip to the bottom for the anticlimactic conclusion.

Same – Miami, San Francisco, Milwaukee, Cincinnati

My article is about the shift in attitude from the beginning of the season to now, but I will note that this seems to be a group that is not behind the times, with the exception of San Francisco. Despite the fact that Joe Panik has wildly exceeded expectations, it’s hard to argue that this isn’t the classic no-bat middle infielder that should not be hitting second. Process, bad. Results, good!

Functionally the same (old-school) – Washington, Arizona, Cleveland, Philadelphia, Texas

Perhaps Peralta has shown himself to be just a little too good of a hitter to waste in the two-hole, so Inciarte took his place. You could argue this is shifting backwards, but Peralta is a young guy who was not considered one of Arizona’s best hitters. Cleveland had previously lucked into the fact that the low-power middle infielder they hit second was actually not bad offensively. But now there is a new middle infielder who is a rookie and can’t hit, so he should hit second! It is possible that Washington simply bats whoever is playing third base second in the order. You can’t prove they don’t. Well, you could, but please don’t. Philadelphia is a team that has slotted FanGraphs whipping boy Jeff Francoeur either 4^th or 5^th in about one third of its games, so why be surprised that both then and now a below average hitter, even for this team, is hitting second? You might think that Texas could do better than hitting Andrus second. They could. But instead they have inserted another below average middle infielder, who I assume gets this honor because he’s the less experienced player.

Functionally the same (new-school) – Boston

Boston is a tough one as could see Brock hitting here because he does so much to help the team win and Pedroia, while a great hitter, is also a tiny middle infielder. I’m giving them the benefit of the doubt as Pedroia is also a star player and Holt is their only All-star and has been perhaps the best hitter on the team in 2015.

Shifting towards old-school – Houston, Anaheim, Los Angeles, New York (NL), Baltimore, Tampa, Colorado, New York (AL)

Houston went from one of its best hitters, who is also a high OBP/speed guy, to the rookie, because I guess it’s embarrassing to hit in the same spot in the order as Matt Rizzo or Jose Abreu. This might be bad luck as Trout has batted second in the vast majority of Angels games, but this is a huge drop off in production (though, to be fair, almost anyone alive is a huge drop off in production from Trout). On the plus side, Calhoun is actually one of the better offensive players for the top-heavy Angels. Yes, Puig is young and fast, but he is also big, strong, and a great hitter. Kendrick is your grandpa’s choice to bat here. Wright is hurt. With the Mets batting Tejada second, it’s hard to know if good lineup construction is just a matter of luck for this team.

Baltimore went from one of its best hitters (and one of the best players in baseball) to a guy, Jimmy Paredes, that I definitely had to look up to know who he was. Souza may be striking out at an incredible rate lately, but that is no excuse to bat the walking corpse of a once great player second. Tampa played two games, and batting Butler second in one of them is excusable. If Sizemore plays at all, he should be hitting on the other side of lead off. Perhaps the biggest shift of all is in Colorado (which I guess shouldn’t surprise anyone as this is the team that might be the hardest to understand). They went from a star player who does not fit the traditional mold to a below average middle infielder who screams 1980s bunting-the-runner-over. For New York, Gardner was a guy that both fit the old-school model and the new-school model. On the other hand, the current version of Headley is a baffling choice to hit second.

Shifting towards new-school – Toronto, Atlanta, Chicago (NL), Seattle, San Diego, Pittsburgh, Kansas City, Detroit, Minnesota, Chicago (AL), Oakland

While the Blue Jays were already ahead of the curve with Martin, they now have their MVP candidate and a guy that would be the best hitter on many teams hitting second. It may be luck that Atlanta is here as Maybin has not hit second frequently for this team. Sadly (for the Braves), Maybin is arguably the best healthy bat in the Braves lineup, a huge improvement over sticking Peterson in the two-hole, a move that could have looked a lot worse if not for a surprising start to his season. Chicago was one of my inspirations for this piece and it’s easy to see why. Rizzo is clearly one of the two best hitters on this team, and he’s a power hitting first baseman to boot. These guys never, ever, ever hit second even 10 years ago. Soler wasn’t a terrible choice, but this is clearly a shift.

Seager fits some of the old-school bill, but compared to trotting out Gutierrez, this is clearly higher-level thinking. I’m giving San Diego credit because Alonso is a pretty good choice for this team, especially considering he’s having a good year, and also because he’s a first baseman. While he’s not a first baseman like Rizzo, it was still rare to see lineup with a “3” next to the two spot a decade ago. Perhaps Pittsburgh thought the previously highly touted Polanco would be better, but there is no doubt that Walker is one of the best hitters on this team. Unlike some other teams, such as Boston and Cleveland, Pittsburgh has not allowed him to “graduate” out of hitting in one of the most important spots in the lineup.

Kansas City was not going with the prototypical guy beforehand, but by inserting Gordon, who hit second in both of Kansas City’s games on Tuesday, they have gone with the best hitter and best player on their team. Detroit is another team that has gone from a mediocre offensive middle infielder to a power hitting outfielder. You could easily argue that Victor Martinez would be a much better choice, but I guess hitting perhaps the slowest player in baseball second is a bit too far for now. Dozier is a nice little player for Minnesota. Their place on this list is more about who took his place, face-of-the-franchise Joe Mauer. He also happens to be easily the best OBP guy on this team. There must be something in the water in Chicago, because the two best examples come from the south- and north-side. Chicago flipped Cabrera, a fairly classic two-hole guy, and Abreu, clearly not in that category. This is the last one I’m doing, so I’ll just say this. Fuld is not good. Vogt is good.

Wildcard –St. Louis

The Cardinals are two as they played a double-header with two different players batting second. You could argue that Heyward is a new-school choice. Carpenter definitely is as a guy with a .377 OBP and no speed. However, they also used Wong, who is definitely in the old-school camp.

Final Tally: Same – 4; Stayed Old-School – 5; Stayed New-School – 1; Shifted Old-School – 8; Shifted New-School – 11; Wildcard – 1

I wanted to find something. I didn’t. That makes me comfortable with my conclusion. A few teams are definitely bucking the old-school ways, at least for now. But just as many teams seem to have gone backwards since opening day. But overall you do see a much more productive player, on average, hitting second. Both the Chicago teams are the clearest examples, as they have put large first basemen/DH with elite power who happen to be their best hitters second in their lineups. You might think that Kansas City or Toronto have permanently turned over a new leaf. But when you see Colorado go from Carlos Gonzalez on opening day to DJ LeMahieu in July, it makes it hard not to discount the possibility that any shift by any team is merely temporary. And now I’ve written 2,000 words on nothing, except perhaps a warning that if your favorite team does something you like because it seems forward thinking and helpful, don’t get too excited because there is a good chance it a blip and Howie Kendrick will be hitting second before you know it.

Most of the all-time great hitters, since I’ve been watching, are terrible fielders.  Sorted by wOBA, here are the top 15 hitters since the year 2000 and their cumulative career defensive scores.

 Here’s the top 15 cumulative defensive scorers, along with their ISO scores. Jim Thome had an ISO of .288, for reference.

The guy who can knock the crap out of the ball and the guy who can make the SportsCenter highlight defensive plays are usually different people.  Unfortunately, this tradeoff only really hurts fans.  The ‘defensive replacement’ comes in late in games and is rarely noticed.  We like web gems, and we like bombs, why not have both?

We’ve probably never seen the world’s best defensive player.

Hitting an MLB fastball is hard.  It’s a very specific, rare skill set.  Playing outfield, however, is something to which athletes from other sports could adapt.  I bet Cam Newton could play right field, for example.  There are some soccer goalies who could probably play shortstop.  There are guys at every position that are wasting away in the minors or worse because they can’t hit the elite pitching.  If there’s a freak athlete that can jump and catch balls three feet over the wall, I want to see it.

The designated fielder prevents injuries and keeps the stars in the game.

I’m having trouble finding data, but my guess is that a fair portion of playing injuries happen on defense.  Especially for outfielders running into each other and walls.  A designated fielder takes guys prone to aches and pains off the field but lets them contribute on offense, even in the National League.  It also makes big contracts less risky in the National League, which might lose out on an aging slugger like Albert Pujols.

It adds an element of strategy.

There will be tremendous temptation to play a catcher as your designated fielder.  They make your pitchers better and prevent stolen bases.  That said, what if you had a second-best catcher who could hit but an excellent outfielder who can’t hit?  The decision gets cloudy.  Teams might strategize based on the potential base-stealing skills of the opponent versus their expectations of fly balls.

A designated baserunner too?

My vision of the designated baserunner is more like a once-a-game power up you can use rather than a permanent fixture.  I’ve always though it was sort of lame that you had to take a guy completely out of the game to get somebody to run for him.  Currently, that dooms pinch-runners to the eighth or ninth inning.  Well, I and most fans enjoy stolen-base attempts and guys stretching a hit for an extra base.  It’s one of the more exciting parts of the game.  We know Albert Pujols isn’t going to steal too often.  Most catchers aren’t exactly speed demons either.  So, I propose, once a game, managers will be able to pinch-run without making the guy leave the game.  Could you imagine Usain Bolt on the base pads?  If teams wanted speed bad enough, it’s possible.

The right equilibrium.

I like to see baseball with a constant ebb and flow of teams threatening to overtake each other.  The designated fielder adds one more guy who can hit to the lineup.  Defensive shortstops and catchers won’t be weakly grounding out and popping up quite so often.  The ball will be in play more often and will sometimes be negated by amazing plays by the designated fielder.  Catchers with rocket arms will be behind the plate more often.  But, they’ll face more elite baserunners.  Would you pitch around Giancarlo Stanton, if you knew an elite baserunner would run for him?   Do you bring on a lefty to hold the runner on?

I favor letting pitchers hit, however.  I see this as a National League first experiment.  The sacrifice bunt attempt is a pretty exciting part of play, and the shock of watching a hurler rope a hit to left-center is worth it.  I don’t want to be inundated with offense; just enough to spice things up a little.

Any thoughts?

Now that Chris Sale’s historic strikeout streak has ended, it seems an appropriate time to marvel at the dominance Sale has shown. Over an eight-game stretch from May 23 through June 30, Sale had the following line:

I’ve included BABIP to show that Sale was not on some incredibly low BABIP streak. League BABIP this season currently sits at .297. Sale’s career BABIP is .286. Without showing the normal indications for extreme luck, Chris Sale turned opposing lineups into a bunch of light-hitting middle infielders. For comparison, below is the season-to-date line for Indians SS Jose Ramirez.

The fact that the White Sox were only 4-4 in those eight games speaks to how badly the White Sox have played during 2015. Of those eight starts, six occurred during the month of June. It is these starts I’d like to focus on through the lens of pitch values relative to the rest of the league. I’ve posted some previous work on pitch values. The framework for the calculations can be found here. I’ve made some tweaks to the calculations, mainly to allow for player specific Balls/BB and Strikes/K to be calculated and league specific adjustments rather than MLB-wide constants.

According to Brooks Baseball, Sale threw 694 pitches during the month of June with a pitch mix of 45% Four-seam Fastball, 25% Changeup, 22% Slider, and 8% Sinker. The sinker was clearly Sale’s worst pitch in June. With the 55 sinkers thrown, Sale managed to give up six hits (11%). With the other 639 pitches, he gave up 21 hits (3%). So just how good were his other pitches? Chris Sale accumulated 2.9 WAR by my calculations over the month of June. He accumulated 0.0 of that from his sinker. For reference, here is the top ten in WAR from the month of June.

Sale lapped the field no matter which calculation you look at. Now, let’s take a look at a slightly adjusted version of that top-ten list.

We can split Sale’s June into two separate pitchers, and both “Sales” were top-ten in Pitch Value WAR accumulated. The Chris Sale that threw nothing but fastballs (Four-seam Fastballs and Sinkers) was the best pitcher in baseball in June. The Chris Sale without a fastball (Sliders and Changeups) was the ninth-best pitcher in baseball. With that said, we can dig a little deeper into the value of each of his pitches. First, let’s look at Sale compared to other four-seam fastballs. The table below gives the top five most valuable four-seam fastballs as well as a pitch rating based on June data set to a 20-80 scale.

Since WAR is a counting stat, there are two components to accumulating a high total. First, you have to throw a lot of the specified pitch type. Pitchers that threw 300 four-seam fastballs almost always accumulate more PV-WAR than those who only threw 30. Secondly, the pitch has to be of certain quality. Throwing 1000 of the world’s worst four-seam fastballs isn’t nearly as valuable as throwing 100 of the world’s best four-seam fastballs. In June, nine pitchers threw more four-seam fastballs than Chris Sale. No one that threw at least 90 total four-seam fastballs threw a better four-seam fastball than Sale. In fact, Sale’s four-seam fastball was the third highest rated qualifying pitch in June. To qualify, the pitch had to be thrown more than average for that pitch type. For example, if there were 4,000 curveballs thrown in June by 40 total pitchers, anyone who threw more than 100 curveballs would qualify. Moving on, Sale’s second most used pitch was his changeup. Here’s the table for changeups.

Once again, Sale tops the PV-WAR rankings. He was “slacking” on his quality on his changeup though. His changeup only ranked 14^th out of 126 qualifying changeups, so I guess there’s always room for improvement. The last pitch of interest for Sale is the slider. It’s the pitch I most associate with Sale, but it’s only his third most used pitch. Below is the corresponding table for sliders.

I think a trend is developing. Chris Sale threw the most valuable slider in June as well. By rating, Sale finished fourth out of 129 qualifying sliders. The three pitchers above him in rating (Andrew Miller, Mark Lowe, Darren O’Day) are all relievers. To sum up, in June, Chris Sale had three pitches make up over 90% of his pitch mix. All three of those pitches were the most valuable pitch in their respective pitch types in June. He had the overall best rated four-seam fastball and the best slider thrown by a starter. His changeup was the “worst” of his three pitches and was still a top 15 rated changeup. Chris Sale was completely dominant in June. His nearest competitor for most valuable pitcher in June only accumulated a little more than half of Sale’s value. Who knows when we may see a pitcher in this much of a groove again? For curiosity’s sake, I’ve included a table with the most valuable and highest rated of each pitch type for your perusal. The overall highest rated pitch was Andrew Miller’s slider.

Position: Analyst
Reports to: Director of Baseball Operations

The Analyst position will provide systems analyst functions for the Baseball Operations department’s statistical & technology-related initiatives.

Responsibilities include but are not limited to the following:

• Oversee the in-house technological efforts associated with the development of Braves’ new internal player evaluation application
• Daily maintenance and continued development of internal applications, including a robust Microsoft SQL Server database
• Development and on-going adjustment of proprietary statistics and systems
•  Understand internal baseball processes in order to develop functional requirements (specifications) for outside vendors and application developers—includes requirements, system impact, data flow diagrams special considerations, etc.
• Must have a good understanding of the back-end data structures in order to make sure any front-end Tool/Application changes will meet the needs of Baseball Operations
• Identify and diagnose data quality issues and provide recommendations
• Translate unstructured baseball data into valuable analytical information
• Maintain and expand upon the current Baseball Operations analytical strategy including developing and supporting new reports, dashboards, and data integrations
• Develop and support ETL mappings, procedures, schedules etc. This includes the integration of multiple data sources (internal and key 3rd party external data sources) into a single data repository
• Provide data management expertise to the Baseball Operations team by evaluating requirements and developing solution design proposal
• Troubleshoot and resolve data/ system/application issues
• Discuss technical issues with the software and hardware vendors
• Continue to advance the department’s use of technology and analytics
• Additional duties as assigned by Director of Baseball Operations

The ideal candidate will possess the following:

• BA or BS degree in CS, IS, or an Engineering/Technical Major
• A minimum of 5 years professional experience in a technical role
• Strong knowledge of Data Warehousing, Data Modeling and Reporting
• Experience with creating and understanding SQL concepts
• ETL knowledge is a plus
• Previous experience working with Microsoft products preferred
• Database system knowledge and proficient SQL skills.
• Experience in the use of the Microsoft SQL Server Platform tools (Integration Services, Analysis Services, and Reporting Services) is a plus.
• Logical and Physical Data modeling experience using data modeling tools.
• Relational and Data Warehousing design and development.
• SQL Server Database Administration skills, including performance and query tuning, debugging.
• Candidate should have strong communication, organization, project management, and problem solving skills.
• Experience architecting KPI’s and Performance Management solutions for Baseball Operations users.
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• Must successfully pass a criminal and credit background check

Qualified candidates can should submit an application and salary expectations online at www.braves.com/employment.
Please note that no phone calls or emails will be accepted regarding application status.

ANLBC, Inc. is an Equal Opportunity Employer

Orioles fans have been pining for Kevin Gausman ever since 2015 Opening Day at Camden Yards when Bud Norris got shelled by the Blue Jays. The now-24-year-old started in 20 games last season as he was yanked back and forth between the majors and minors and posted a 107 ERA+ in 2014. A good number — not excellent, but good. He stabilized the rotation after Ubaldo Jimenez struggled mightily in his first year as an Oriole. In 2015, he will be asked to do the same as Norris has scuffled in the rotation so far. Two starts in, the results are promising, even better than 2014. One key change so far for Gausman is his willingness and ability to elevate his plus fastball.

The community’s own Jeff Sullivan has written about this as well. Both, before the season and after Gausman’s first couple of relief appearances in 2015. The idea being that some teams have emphasized throwing high fastballs and that not every pitcher is equipped to do so. But, Gausman has a heavy fastball that could stand to work more up in the zone; in fact in 2014 he was at the bottom of the league only throwing that pitch in the upper half 33% of the time. Jeff Sullivan also noted astutely that Gausman and the Orioles have talked about him throwing up more and that he had been doing so coming out of the bullpen.

Well, after two starts, Gausman continues to work up in the zone with the fastball and he is getting some great results. In 2015 his strikeout rate is up from 18.5 percent to 21.7 percent while his walk rate is around the same. His hard contact rate is down from 28.7 percent to 26.9 percent and his soft contact rate is up from 17.2 percent to 25.4 percent. Meanwhile, his fly-ball and groundball rates are up as well while his line-drive rate is at only 10.4 percent. This helps to explain how he has only given up eight hits in his last 11.1 innings pitched.

First, below is a graph of the average vertical location of Gausman’s fastballs over the past three seasons.  

As you can see, Gausman has worked more up in the zone. Not by a lot, but enough to call it a trend so far this year. Working mostly down is always going to be more friendly for a pitcher, but the ability to throw high strikes and for the batter to know that you can, is a very effective weapon. For instance, below is a chart of the whiffs Gausman has gotten from batters so far in 2015.

Notice the upper left portion of the zone. Gausman so far has gotten 11 swings and misses in this area in 2015; he had 29 whiffs all of last year in the same area. Every one of those 11 whiffs in 2015 has come on a four-seam fastball. Again, the bread and butter for Gausman will be dotting his fastball on the outside corner and working his splitter away, but the added weapon of a high fastball has produced some great in game results thus far in 2015.

I paid close attention to the game against the Rangers on July 2nd, in which Gausman pitched 6.1 innings striking out 7 walking 2 and allowing only 4 hits. Gausman utilized his fastball in this upper area of the zone to great effect during this start. Below is the strike-zone plot for Gausman from this game. 

Here you can see Gausman was able to work mostly arm-side low, but he also worked up in the zone and got five swinging strikes on balls at or above three feet off the ground. I also wanted to point out some specific at bats from that game. Below is the strike-zone plot against Robinson Chirinos in the third inning. 

Gausman starts him out with a pretty bad fastball that Chirinos simply swings through, although to be fair it was 97 mph. He then throws two more low fastballs and get Chirinos to foul off the second one. Now with the count 2-1 Gausman throws his splitter which Chirinos is again able to foul off. Now, the key pitch in the at bat, Gausman climbs the ladder for the fifth and final pitch with another 97 mph fastball to get the swinging strikeout. Here is another at-bat where Gausman used the elevated fastball, this one is to Adrian Beltre in the fourth inning. 

This at-bat is again five pitches. Gausman first throws a fastball low and away for a ball. Then, Beltre fouls off an inside and low fastball. Gausman then throws the splitter inside for a swinging strike and follows that up with a low and inside fastball for a ball. Now with the count 2-2 and every pitch in the lower half or below the strike zone so far in the at bat, Gausman elevates a 97 mph fastball high and tight to Beltre who swings through to get another strikeout for Gausman. All right, last one, I swear.

This at-bat is against Shin-Soo Choo in the second inning. Gausman starts him off with one called strike fastball up and away at 96 mph and follows that up with another elevated fastball at 97 mph that Choo swings and misses on. So now Choo has seen two high 90s fastballs three feet off the ground. Next, Gausman drops an 88 mph splitter a foot lower and Choo rolled over on it to second base. Not only can the elevated fastball get strikeouts, it can also setup his other pitches.

Here are the video highlights for that Rangers game where you can see the end of the two strikeouts above. Also, here are the video highlights of his start in Toronto wherein he gets two infield flies and a strikeout on elevated fastballs. Watch both for some more context.

The Orioles are now looking for Gausman to become a rotation stabilizer. Gausman has struggled somewhat in his career thus far because of a lack of true third pitch; he has gone back to his curveball this year which has shown some early promise. However, he has also struggled because for the most part he worked everything down in the zone. With the added focus of throwing elevated fastballs in 2015 it changes the hitter’s eye level and lets them know that Gausman can throw to all parts of the zone, so all parts of the zone are in play. So far, in 2015 that pitch has achieved great results. Now, we’ll have to see if Kevin Gausman can keep replicating those results.

The original version of this article was posted on www.camdenchat.com on 7/6/2015

Amidst a disappointing first half for the Boston Red Sox, one of the few bright spots has been the steady offensive improvement of Xander Bogaerts. The 22-year-old shortstop is beginning to live up to hype that has seemingly plagued the former 6^th overall prospect during his first full season in Boston. Bogaerts maintains a .302/.339/.414 clip through July 6, which equates to a 2.2 WAR, second to only Brandon Crawford’s 2.9 for shortstops in the MLB.

First off, its important to point out to all who thought Bogaerts was a bust after his performance a year ago, that he is still only 22 years old. To put it into perspective, consider this: Francisco Lindor was the #3 overall prospect coming into this year. The Indians called him up from AAA on June 14^th to, like Bogaerts, begin his career as an every-day shortstop at age 21. And similar to Bogaerts, Lindor is enduring his share of rookie struggles, batting .215 through his first 79 at-bats. It’s not fair to write off Lindor, or Bogaerts, as busts after their 21-year old seasons. Most players, especially those that young, need time to adjust to major-league pitching.

Bogaerts is walking about the same as a year ago, but has significantly improved his strikeout percentage, which has fallen from around 23% to 14%. His BABIP has risen almost 50 percentage points from a year ago (up to .347) which would help explain the improvements in batting average.

Another explanation for improvement has been Bogaerts new-found use of the ground and opposite field in 2015. Two-thirds of his balls in play are traveling to center and right fields this year, compared to around 40% last year. And his percentage of balls hit to the opposite field has increased from 19% to 31%. While the Monster may bait right-handed hitters into becoming pull-happy, Xander has found better success driving the ball the other way.

Bogaerts has also been putting the ball on the ground more this season. His GB% has risen 12% (to 50%) and FB% has dropped the same amount (his line-drive percentage has stayed roughly the same). Xander isn’t a burner on the base paths (only four steals) but he can put his athleticism to good use when he hits on the ground.

Xander’s improvement may even result in his first All-Star appearance. Alcides Escobar and former Red Sox, Jose Iglesias, are the two American League shortstop representatives for now. Even if Bogaerts is left off the team, his first-half play has been refreshing enough in an otherwise frustrating year for many Red Sox players. The young shortstop is taking some nice steps towards proving he is the player the Boston media, and fans alike, thought he was going to be.

(This is Part 4 of a four-part series answering common questions regarding starting pitchers by use of discrete probability models. In Part 1 we explored perfect game and no-hitter probabilities, in Part 2 we further investigated other hit probabilities in a complete game, and in Part 3 we predicted the winner of pitchers’ duels. Here we project the probability of scoring at least one run in various base runner and out scenarios.)

V.  I Don’t Know’s on Third!

Still far from a distant memory, the final out of the 2014 World Series was preceded by an unexpected single and a nerve-racking error that brought Alex Gordon to 3^rd base with two outs. Closer Madison Bumgarner, who was on fire throughout the playoffs as a starter, allowed the hit but would be left in the game to finish the job. There is some debate as to whether Gordon should have been sent home rather than stopped at 3^rd base , but it would have taken another error overshadowing Bill Buckner’s to get him home; also, next up to bat was Salvador Perez, the only player to ever ding a run off Bumgarner in three World Series. So even though the Royals’ 3^rd Base Coach Mike Jirschele had to make a spur of the moment critical decision to stop Gordon as he approached 3^rd base, it was a decision validated by both statistics and common sense. We will show our own evidence, by use of negative multinomial probabilities, of how unlikely the Royals would have scored the tying run off of Bumgarner with a runner on 3^rd with two outs and we will also consider other potential game-tying or winning situations.

Runs are generally strung together from sequences of hits, walks, and outs; in the situations we will consider, we will only focus on those sequences that lead to at least one run scoring and those that do not. Events not controlled by the batter in the box, such as steals and errors, could also potentially reshape the situation and lead to runs, but we’ll take a very conservative approach and assume a cautious situation where steals are discouraged and errors are extremely unlikely.

Let A and B be random variables for hits and walks and let P(H) and P(BB) be their respective probabilities for a specific pitcher, such that OBP = P(H) + P(BB) + P(HBP) and (1-OBP) is the probability of an out; we combine the hit-by-pitch probability into the walk probability, such that P(BB) is really P(BB) + P(HBP) because we excluded hit-by-pitches from our models, P(HBP) > 0 against Bumgarner in the 2014 World Series, and the result on the base paths is the same as a walk. The first negative multinomial probability formula we’ll introduce considers the sequences of hits, walks, and an out that can occur after two outs have been accumulated, setting the hypothetical stage for the last play in Game 7 of the 2014 World Series.

In the 2014 World Series, Bumgarner’s dominantly low P(H) and P(BB) were respectively 0.123 and 0.027 and his (1-OBP) was 0.849; by applying these values to the formula above we can generate the probabilities of various hit and walk combinations shown in Table 5.1. The yellow highlighted cells in the table represent the combination of hits and walks that would let Bumgarner escape the inning without allowing the tying run (given a runner on 3^rd with two outs and a one run lead). By combining these yellow cells, we see that the odds were overwhelmingly in in Bumgarner’s favor (0.873); all he had to do was get Perez out, walk Perez and get the next batter out, or walk two batters and get the third out.

Table 5.1: Probability of Hit and Walk Combinations after 2 Outs

The Royals could have contrarily tied the game with a simple hit from Perez given the runner on 3^rd and two outs, yet this wasn’t the only sequence that would have kept the Royals hopes alive. Three consecutive walks, one walk and one hit, or any combination of walks and one hit could have also done the job; examples of these sequences are shown in the graphics below:

Generally, any combination of walks and hits not highlighted yellow in Table 5.1 would have tied or won the World Series for the Royals. This glimmer of hope was a quantifiable 0.127 probability for Kansas City, so it was justified that Gordon was kept at 3^rd rather than sent home after shortstop Brandon Crawford just received the ball. It would have taken an error from Crawford or Buster Posey, with respective 0.033 and 0.006 2014 error rates, to get Gordon home safely. The probability 0.127 of winning the game from the batter’s box is noticeably three times greater than the probability of winning it from the base paths (where Crawford and Posey’s joint error probability was 0.039).

We should note that the layout in Table 5.1 is a simplification of what could occur with a runner on 3^rd, two outs, and a one run lead, because it only applies to innings where a walk off is not possible. In innings where a walkoff can occur, such as the bottom of the 9th, the combinations of walks and hits captured in the red highlighted cells are not possible because they would occur after the winning run has scored and the game has ended. However, Bumgarner was so dominant in the World Series that these probabilities are almost non-existent, thereby making our model is still applicable; we would otherwise exclude these red-celled probabilities for less successful pitchers.

The next probability formula considers the sequences of walks, hits, and outs that can occur after one out has been accumulated, which is situation definitely worth examining if there is a lone runner on 2^nd base.

Once again we’ll use Bumgarner’s 2014 World Series statistics to evaluate this formula and insert the probabilities into Table 5.2. According to the sum of the yellow cells, Bumgarner would be able to prevent the tying run from scoring (from 2^nd base with one out) with a probability of 0.762 and would otherwise allow the tying run with a probability of 0.238.

Table 5.2: Probability of Hit and Walk Combinations after 1 Out

To get out of the inning unscathed, Bumgarner would need to prevent any further hits or allow fewer than 3 walks given a runner on 2^nd with 1 out; it would be possible to advance the runner to on 3^rd with 2 walks and then sacrifice him home in this situation (with no hits), but this probability is insignificantly tiny especially for a dominant pitcher like Bumgarner. Once again we depict these sequences that could get the tying run home from 2^nd with 1 out, with the second out inserted randomly.

A runner on 2^nd base with one out is a scenario commonly manufactured in an attempt to tie the game from a runner on 1^st with no outs situation. The logic is that if the hitting team is down by one run and the first batter leads off the inning with a single or walk, the next batter can control getting him into scoring position and hope that either of the next two batters knocks the run in with a hit. However, this method of control, a bunt, sacrifices an out to move the runner from 1^st to 2^nd. The defense will usually allow the hitting team to move the runner into scoring position for an out, but the out wasn’t the only sacrifice made. The inning is truncated for the hitting team with one less batter and the potential to have more hitters bat and drive in runs is reduced. Indeed, against a pitcher like Bumgarner, the out is likely not worth the meager 0.238 probability of getting that runner home.  We’ll see in the next section what exactly gets sacrificed for this chance at tying the game.

We should note that in this “runner on 2^nd with 1 out” model we added few more assumptions to those we made in the prior “runner on 3^rd with 2 outs” model, neither of which should be farfetched. The first assumption is that with the game close and the manager intent on tying the game rather than piling on runs, he should have a runner on 2^nd base fast enough to score on a single. Another assumption is that the base runners will be precautious enough not to cause an out on the base paths, yet aggressive enough not to get doubled up or have the lead runner sacrificed in a fielder’s choice play. Lastly, we assume that the combinations of hits, walks, and outs are random, even though we know the current state of base runners and outs can have a predictive effect on the next outcome and the defensive strategy used. By using these assumptions we simplify the factors and outcomes accounted for in these models and reduce the variability between each model.

The final probability formula considers the sequences of walks, hits, and outs that can occur when we start with no outs accumulated; this allows to forge situation will allow us to forge the outcomes from a runner on 1^st with no outs scenario and compare them to a runner on 2^nd with 1 out scenario.

Table 5.3 below uses Bumgarner’s 2014 World Series statistics, the same as before, although in this model we deal with more uncertainty because the sequences captured in each box are not as clear cut between run scoring or not given a runner on 1^st with no outs. The yellow and non-highlighted cells are still the respective probabilities of not allowing and allowing the tying run to score, however, we now introduce the green probabilities to represent the hit and walk combinations that could potentially score a run but are dependent on the hit types, sequences of events, and the use of productive outs. These factors were unnecessary in the prior two models because in those models any hit would have scored the run, the sequence of events was inconsequential, and the use of productive outs was unnecessary with the runner is already on 2^nd or 3^rd base (except when there is a runner on 3^rd and a sacrifice fly or fielder’s choice could bring him home).

Table 5.3: Probability of Hit and Walk Combinations after 0 Outs

We must break down each green probability into subsets of yellow probabilities representing the specific sequences that would not score the tying run from 1^st base with no outs; we depict these sequences below, but for simplicity, not all are depicted.

Now that we know the conditions when a run would not score, we take the probabilities from the green cells in Table 5.3, narrow them down according to the proportion of sequences and the proportion of hit types that would not score the run, and separate them based on the usage of productive and unproductive outs; the results are displayed in Table 5.4. For example, there are 6 possible combinations for 1 hit, 1 walk, and 3 outs and 3 of these 6 combinations would not score the tying run on a single, where P(1B | H) = 0.755, with unproductive outs; yet, the run would score with productive outs, with unproductive outs on a double or better, or with unproductive outs and the other 3 combinations. When we finally sum these yellow cells, they tell us that an aggressive manager would score the tying run against Bumgarner with a 0.370 probability and Bumgarner would escape the inning with a 0.630 probability. Otherwise, a less aggressive manager would score the tying run with a mere 0.154 probability and Bumgarner would leave unscathed with a significant 0.846 probability.

Table 5.4: Probability of No Runs Scoring after 0 Outs

We summarize the results from Tables 5.1-5.4 into Table 5.5 from the perspective of the hitting team.  We compare their chances of success not only against Madison Bumgarner from the 2014 World Series but also against Tim Lincecum, Matt Cain, and Jonathan Sanchez from the 2010 World Series.

Table 5.5: Probability of Allowing at least One Run to Score

Let’s return to the scenario that is the launching point for this study… The hitting team is down by one run and there is a runner on 1^st base with no outs. If the game is in its early innings, where it is not mandatory that this runner at 1^st gets home, the manager will likely decide against being aggressive and avoid sacrificing outs in order to increase his chances of extending the inning to score more runs; there are several studies supporting this logic. Yet, if the game is in the latter innings and base runners are hard to come by, the manager should lean towards utilizing productive outs and intentionally sacrifice the runner from 1^st to 2^nd base. His shortsighted goal should only be to tie the game.  By forcing productive outs rather than being conservative on the base paths, his chances of tying the game increase significantly (between 0.216 and 0.271) against our four pitchers given a runner on 1^st and no outs scenario.

However, the if the manager does successfully orchestrate the runner from 1^st to 2^nd base with a productive out, he does still lose a little bit of probability of tying the game; between 0.132 and 0.215 of probability is lost against our pitchers. And if he decides to sacrifice the runner further from 2^nd to 3^rd base with another out, his team’s chances would decrease again by a comparable amount; this decision is ill-advised because a hit is likely going to be needed to tie the game and the hitting team would be sacrificing one of two guaranteed chances to hit in this situation. In general, the probability of scoring at least one run decreases as more outs are accumulated, regardless of the base runners advancing with each out. The manager could contrarily decide against sacrificing his batter if he has confidence that his batter can hit the pitcher or draw a walk, yet the imperative goal is still to tie the game. The odds of tying the game actually favor an aggressive hitting team that is able to get the runner to 2^nd base with one out, by an improvement ranging from 0.012 to 0.084, over a less aggressive team with a runner at 1^st with no outs. Thus, even though sacrificing the runner from 1^st to 2^nd base does decrease the chances of tying the game, it would be worse to approach the game lifelessly when the situation demands otherwise.