Archive for Player Analysis

The news that Barry Zito has been called up to start against Tim Hudson, with Mark Mulder in attendance, has rightfully thrown the baseball world into a mini-frenzy. Jonah Keri covered the meat of it spectacularly here. Here’s a dirty secret though: as we evaluate pitchers today, they weren’t great pitchers. An even dirtier secret: I don’t think it matters.

Zito, Hudson, and Mulder were undoubtedly good pitchers, racking up a Cy Young trophy and four more top-10 finishes in their time in Oakland. But were they great pitchers? Let’s take a look at their FIP- during their Oakland careers:

At their peak, they were well above-average pitchers, but, combined, they only had two top-10 finishes in FIP-, with Mulder finishing 10th in 2001 and Hudson 10th in 2004. Good, but not transcendent. If that’s worse than you remember, it’s probably because their ERAs consistently undershot their FIPs:

The Big Three were among the last players before sabermetrics exploded in popularity with casual fans, and our analysis of them reflects that. If they had come up today, would we label them as three guys who are above-average, rather than the cultural phenomenon they became? It’s very likely. That the cultural relevance of the Big Three has carried into the sabermetric era is a delightful reminder of how recently we crossed the frontier.

Does the fact that their accomplishments don’t hold up as well in the FIP era diminish their place in baseball history? I say no. Even though baseball has seen a number of better three-man rotations, the “Big Three” label feels at home in Oakland. In 2008, Dan Haren, Brandon Webb, and Randy Johnson, averaged a 76 FIP- for the Diamondbacks, better than any year of the real Big Three. But would Dan Haren starting against Brandon Webb on Saturday be a headline event (forgetting about the medical miracle required)? I doubt it. Zito, Hudson, and Mulder evoke something in us beyond their raw performance.

I always recited the order as Zito, Hudson, Mulder. Zito always comes first because as a fellow lefty who didn’t throw very hard, but thought he had a big hook, I emulated him both in real life and in MVP baseball, where I spent countless hours dropping his curve in against hapless computer foes. Zito was my guy and Hudson and Mulder fell in line after. Everyone had their own order relative to their personal biases. The combination of youth, talent, and personality made them relatable in a way that other greater pitchers just weren’t.

The Big Three were also the rock on which Moneyball was built. For fans of small-market teams, they represented what was possible. If your team scouted, drafted, and developed well, you too could have your own set of homegrown stars. The 2001 A’s-Yankees ALDS was, in my opinion, the pinnacle of the era. Zito, Hudson, and Mulder combined to throw 28.2 innings and give up just five earned runs, but it wasn’t enough. The 2001 A’s were one of the most likable teams of all time and the Big Three were the dominant reason why.

Although they had their best years in Oakland, when they were forced to move on, there was a sense when that they were headed for greater things. The potential they left behind in Oakland still tantalizes. Although the greatness never materialized in their new homes, it still feels like they left something on the table when they left. We never had the closure of seeing them grow old and decline together which is why finally getting our closure on Saturday feels so comforting.

You will note that Keri’s article does not once mention FIP. It’s a defensible choice because that’s not how we evaluated them at the time so it’s not how we remember them now. None of the reasons why we loved them are because they were the very best pitchers in the game or sabermetric darlings. It was a confluence of harder-to-quantify factors.

Baseball is a funny game. None of the Big Three ever had a season as good as Jake Arrieta’s this season. But ask me who I’m going to remember in 20, 30, 40 years? No contest. Baseball is an analytical nostalgia factory, a game that runs on both numbers and feelings without ever feeling like it contradicts itself. Perhaps no one represents that dichotomy better than the legendary Big Three.

Baltimore Orioles first baseman, Chris Davis, is in the final year of his contract where he is making $12 mil/yr. At age 29, Davis has had a roller-coaster of a career starting in Texas where he burst onto the scene hitting 17 & 21 homers in his first two seasons. After which, he declined dramatically the next season hitting just under .200 for Texas. The following season he was traded to Baltimore where he revived his career and met his long-awaited potential. Today, Davis is one of the biggest power hitters in the game. He hit 53 homers in 2013 and 43 homers thus far in ’15. With Davis on the market we know clubs will be interested in his bat along with some other big FA names such as Yoenis Cespedes, Justin Upton and Alex Gordon.

The problem with Chris Davis is that he’s somewhat inconsistent. As a power hitter we can take a look at his slugging percentage, which will give us a better indication of his extra-base hits and power numbers. In seasons with at least 300+ at-bats he slugged an average of .507 but has a standard deviation of .091. It seems he has struggled to find consistency with his hitting, especially last season when he hit .194, with 23 homers and a slugging mark of .404. Compared to his .286/53 HR/.634 campaign in 2013, it is a huge difference.

If we take a look at a similar power hitter in Nelson Cruz, in his seasons with at least 300 ABs he slugged a similar average of .515 with a SD of only .04. Cruz is more of a model of consistency and has been less risky than Davis. Besides his one season of slugging .460, Cruz was always in the mid-.500s. Which is great for a power hitter. This is a big reason I am not a huge fan of Chris Davis. He just hasn’t shown a high level of consistency.

Another is his strikeout rate, which is extremely high. Since his first full season in Baltimore, Davis struck out 169, 199, 173, 182 times over the last 4 seasons. That’s good for a 31% K-Rate. Easily one of the worst in the league. His 196 strikeouts this season also happen to lead the league! Although he strikes out a ton, he gets the job done by driving in runs. Which at the end of the day could be seen as more important. Davis drove in 138 runs in 2013, 86 in ’14 and so far 110 in ’15. Did I mention he’s also eighth in the league with 118 runs created. A stat used to measure how valuable a player is to contributing runs to his team. So with great power comes great responsibility. Davis may strike out but he can really drive the ball. To me, he’s a high-reward/high-risk guy.

Interested teams:
Chris Davis is so valuable to the Orioles in terms of producing runs it’s hard to imagine him being let go without a fight. I think the Orioles will absolutely make him an offer. He’s already making $12m/yr and the O’s have a lot of money coming off the books this winter (Orioles’ Payroll). Only $41m is committed to next season ($119m payroll this year). So I can see them raising their price tag to about $18m/yr. The O’s are in a position to win now with Machado/Jones and a fairly young team so if they aren’t getting Davis they will no doubt be spending on others.

Other teams I can see having interest in Davis would be Seattle, San Diego, and Houston. Seattle is the kind of team to pay up for hitting; I could see them doing that with Davis as they did with Cano/Cruz. They need offense but they already have Trumbo at first base who has been decent. If they could move Trumbo I could see them making a play for Davis. Having Cano-Cruz-Davis would be quite powerful. They’re losing some money with Rodney & Jackson coming off the books. Seattle could be really interesting to watch.

The Padres I could see showing some interest but only if they lose Justin Upton and keep Wil Myers in CF. I think they’ll try to re-sign Upton who has had a good year playing in Petco Park. They’ve played Myers at 1B occasionally because of his injury concerns. With no DH, it’s harder to maneuver players around. Yet, again, AJ Preller is a magician so no one can really predict what he will do next. I think the Padres’ concern would be as Davis gets older he could regress on the defensive side of the ball and offensively. Petco Park is a pitcher’s palace so if Davis’ power dropped off his value would really take a hit. Putting Davis in as a full-time DH later in his career would help him maintain his power and consistency like it has for David Ortiz and A-Rod.

The Astros are a wild card I think. I said in the Cespedes post, they have a ton of cash to spend but only if they’re willing to spend it. They love guys who hit home runs. That’s basically their back end of the lineup (Carter, Valbuena, Rasmus). Their 1B Chris Carter ($4.5m) is a mini Chris Davis (low avg/high power) and he will be headed to arbitration. But in the offseason I think they will look to upgrade. They’ll obviously want to replace him with someone more improved. But Davis will cost them a lot; as a more analytics front office I’m not sure if they would see the value in paying up for him. Then again, pairing Davis, who hits lefty, with Correa/Altuve would really help them score runs along with mixing and matching their lineup.

Honorable Mentions: 
The last two teams I looked into with 1B trouble were the Cardinals and Pirates. St. Louis has Matt Adams coming off the DL and we’re not sure if he’s 100% just yet. We know Brandon Moss is not a long-term solution. The Pirates’ Pedro Alvarez has been super inconsistent with Pittsburgh. I think they’ll look to upgrade or float around some other names during the offseason. To be honest, I think Adam Lind would be a great addition to the Cardinals or Pirates instead of Davis. Adam Lind has a club option for $8m this offseason; he hits lefty and has had a solid year for Milwaukee. Overall, I don’t think these two teams will end up throwing money at Chris Davis but they may need 1B help next season. Its baseball, anything can happen.

In the end, I believe Davis enjoys playing in Baltimore. Due to his success there, the favorable ballpark and the DH factor I think he should stay. For his long-term career he should seriously consider staying in the AL with the DH factor. But I think another club will come in and make a play to acquire Davis. Power/RBIs come at a premium these days. Dan Duquette, GM of the Orioles, has experience and knows what he’s doing. If the price tag became too high I think he will definitely consider looking elsewhere on the market. Possibly an Adam Lind or Mark Trumbo.

Similar players such as Nelson Cruz signed a 4yr/57m (14m AVV) at age 34 and Albert Pujols signed a 10y/240m (24mAAV) in 2011. This sets a decent basis for Davis. In terms of his contract, I think Davis could get a 5-year deal worth about $18-21 million a year. His WAR for this season is 4.2 which puts him in the ballpark for this. So, it’s near our estimation. Personally, I would not give Davis $20m for 5 years. I think that’s going overboard but some teams are more into his skill than others. Power really comes at a premium in today’s game and Davis has a ton of it. I wouldn’t be surprised if it went to 6 years, but I just don’t see as many teams bidding on Davis right now. Scott Boras is his agent which will probably drive up the asking price. That may turn off the Orioles which could lead to another club coming in and swooping up Crash Davis. I think it’s favored to be the Orioles or Mariners come signing day.

Projection: 5 years, $100 million

The timeless struggle between pitcher and batter is one of dominance — who holds it and how. Both players use a repertoire of techniques to adapt to each other’s strategies in order to gain advantage, thereby winning the at-bat and, ultimately, the game.

These strategies can rely on everything from experience to data. In fact, baseball players rely heavily on data analytics in order to tell them how they’re swinging their bats, how well they’ll do in college, how they’ll perform at Wrigley versus Miller.

Big data has been used in baseball for decades — as early as the 60s. Bill James, however, was the first prominent sabermetrician, writing about the field in his Bill James Baseball Abstracts during the 80s. Sabermetrics are used to measure in-game performance and are often used by teams to prospect players.

Baseball fans familiar with sabermetrics, the A’s, and Brad Pitt have likely seen Moneyball, the Hollywood adaptation of Michael Lewis’ book. The book told the story of As manager Billy Beane’s use of sabermetrics to amass a winning team.

Sabermetrics is one way baseball teams use big data to leverage game theory in baseball — on a team-wide scale. However, by leveraging their data through the concepts of game theory on a smaller scale, baseball teams can help their men on mound out-duel those at the plate.

Game theory studies strategic decision making, not just in sports or games, but in any situation in which a decision must be made against another decision maker. In other words, it is the study of conflict.

Game theory uses mathematical models to analyze decisions. Most sports are zero-sum games, in which the decisions of one player (or team) will have a direct effect on the opposing player (or team). This creates an equilibrium which is known as the Nash equilibrium, named for the mathematician John Forbes Nash. What this means is that if a team scores a run, it is usually at the expense of the opposing team — likely based on an error by a fielder or a hit off a pitcher.

In the case of pitching, game theory — especially the use of the Nash equilibrium — can be used to predict pitch optimization for strategic purposes. Neil Paine of FiveThirtyEight advocates using big data and sabermetrics to analyze each pitch in a hurler’s armory, then cultivating the pitcher’s equilibrium — the perfect blend of pitches that will result in the highest number of strikeouts, etc.

Paine has gone so far as to create his own formula, the Nash Score, to predict which pitcher should throw which pitches in order to outwit batters.

In perfect game theory, the Nash equilibrium states that each game player uses a mix of strategies that is so effective, neither has incentive to change strategies. For pitchers, Paine’s Nash Score uses their data to find the optimal combination of pitches to combat batters, including frequency.

Paine does point out that creating this kind of equilibrium in baseball can be detrimental to a pitcher. He is, after all, playing against another human being who is just as capable of using game theory to adapt strategies to upset the equilibrium.

If a pitcher’s fastball is his best, and his Nash Score shows that he should be using it more often, savvy hitters are going to notice. “ . . . In time, the fastball will lose its effectiveness if it’s not balanced against, say, a change-up — even if the fastball is a far better pitch on paper,” writes Paine.

In this case, a mixed strategy is the best — in game theory, mixed strategies are best used when a player intends to keep his opponent guessing. Though pitch optimization using Paine’s Nash Score could lead to efficiency, allowing pitchers to throw fewer pitches for more innings, it could also lead to batters adapting much quicker to patterns, thus negating all the work.

In his first year of being eligible for arbitration, Matt Harvey will be able to substantially increase his salary for the 2016 season. Since beginning his career with the New York Mets in 2012, he has taken off to become an All-Star pitcher and fan favorite. His agent, Scott Boras, and the front office of the Mets will negotiate a one year salary based off his success in 2015. We’ll cut right to the chase and get into the hard numbers which will help us identify a rough projection of what we would expect Matt Harvey to receive this coming winter.

For more background on arbitration cases, read my previous article which discusses what is allowed/not allowed.

Overall performance:
Since 2012, Matt Harvey at age 26 has a career 2.59 ERA with 24-17 win/loss record. During his 2013 season Harvey was on a tear with a 2.27 ERA and became one of the leading NL Cy Young candidates before his injury. He also started the 2013 All-Star game which happened to be in Citi Field that year. After tearing his UCL and missing the entire 2014 season, Harvey came back strong this year and has pitched in 26 games thus far with a 2.88 ERA through 171 innings (11th best in league). He has a 12-7 win record and gives up less than a hit per inning (which ranks 9th in all of MLB). His WHIP is also one of the top 10 leagues best at 1.03 so he rarely allows runners on base and is averaging 8.6 strikeouts per game.

His W/L record this season does not show his true value, as the Mets started the first half of the season with one of the worst offenses in the league. After acquiring premier Major League hitters such as Yoenis Cespedes and Juan Uribe, the Mets have led the league in runs scored giving Mets starters big run support. Since those acquisitions, Harvey has pitched in 7 games winning 3 and losing 0. But the Mets’ bullpen blew Harvey’s lead in 3 other games in which he had outperformed the other team. Had it not been for a mediocre bullpen, Harvey could have been 6-0 in 7 games since August 1st. Clearly, Harvey is an ace to this team and is the backbone of a staff that has propelled the Mets to first place. He is a consistent pitcher and does not show signs of letting up even after having TJ surgery. Without Harvey, the Mets would lose a dominant, consistent ace which is obviously hard to come by.

Leadership/Public appeal:
As one of the older members on the New York Mets’ young pitching staff, Harvey is one the leaders on this team. After fighting his way back from injury rehab, he has become a consistent stronghold to the Mets’ rotation. Although Dr. Andrews, who performed Tommy John surgery on Harvey, has stated he should not exceed 180 innings due to his injury, Harvey is continuing to pitch on an innings watch to help the Mets win, especially through the postseason. Even if it hurts his chances at re-injuring himself, he is going out there to pitch.

As a leader, you need to show guts and heart; Harvey has definitely displayed that, battling out there everyday. Matt Harvey also is a fan favorite.  He ranks 9th in all of Major League Baseball and 1st with the Mets in 2015 top jersey sales. Many fans across the country are purchasing his jersey, thus showing how popular he is with people. When he returned to the mound this season to pitch, his first game back drew the biggest crowd (39,000 fans) for the second home game of the season since Citi Field opened in 2009.  That was 10,000 more fans in attendance than last year and 20,000 more than two years ago. During the 2013 All-Star game at Citi Field, which Harvey started, the Mets drew their most fans in history at 45,000. When he’s the night’s starting pitcher, fans flock to the ballpark to see Matt Harvey. At the same time he’s able to strikeout hitters, captivate a crowd and draw extra revenue in from ticket sales than if he wouldn’t be pitching. The Mets fans also have a popular nickname for Harvey: The Dark Knight. Symbolizing his leadership skills and journey back from Tommy John surgery, Harvey symbolizes the 2015 Mets team and has dramatically changed the mood of the fan base since his arrival/return. There’s no denying this.

Injury history:
As stated earlier, Matt Harvey missed all of 2014 season undergoing Tommy John surgery to repair his torn UCL. His recovery has been a success thus far but is always a case for concern in the future. But arbitration cases do not quite debate the future; only his previous success. He has shown no discomfort and has spent 0 days on the disabled list this year. To combat future problems the Mets’ pitching staff went to a 6-man rotation, which has caused Harvey (and other Mets pitchers) to skip a couple starts. Harvey has constantly said he feels good and does not show any signs of slowing down unless the Mets management shut him down.

Performance of club:
The Mets are currently in first place by 6 games and it looks like it will stay that way come October. Largely in part due to Harvey’s success on the mound, the Mets would not be in the same situation without him or his 12 wins this season. When the playoff schedule arrives, Harvey will easily be the game 1 or game 2 starter depending on how he finishes the season.

Record of the players past compensation: 
Harvey made MLB’s minimum salary in 2013 at $498,000 and this year at around $510,000. This will be his first eligible year of Arbitration 1. His value to the team over the last couple years has been sky-high but he’s been grossly underpaid.

Comparative salaries:
Tyson Ross was arbitration 1 last year for the San Diego Padres. In his 2014 campaign he pitched to a 2.81 ERA / 1.211 WHIP with 13 wins in 191 innings pitched. He also struck out 9 hitters per inning and was named an All-Star that same season. But Ross pitches in a heavily favored pitcher’s ballpark. His stats at home included a 1.88 ERA with an 8-5 record but his away stats included a 3.79 ERA with a 5-9 record. Clearly, Ross does not pitch better on the road and his starts could have been affected by where he pitched. Compared to Harvey’s career numbers, he pitches more consistently than Ross at home (12-7, 2.15 ERA) and away (12-10, 3.14 ERA). From our previous numbers we know that Harvey has been a better pitcher overall this season in ERA, WHIP, wins and many other pitching statistics than Ross had in his 2014 season. Following Ross’ 2014 year, he was able to negotiate a 1yr/$5.25m deal in January. Ross is not as consistent and skilled as Matt Harvey. Since Harvey surpasses Ross in success we can see he is due much more in salary as well.

Chris Tillman is the next player we can compare to. Although a little less successful, Tillman was able to get a 1yr/$4.3m deal. The season prior to his arbitration, Tillman had a 13-6 record with a 3.34 ERA and struck out only 6.5 K/9 in 207 innings. Tillman is on the lower end of the comparison as he agreed to almost a million dollars less than Tyson Ross.

Summary:
These players give us the best guideline and recent examples in terms of numbers/dollars that can help us estimate what Harvey should be owed for the 2016 season. Harvey is definitely much better than Ross and Tillman. He brings more to the table than just numbers as he is a figurehead in New York, one of the largest markets in baseball. The first-place Mets could not be where they are if it was not for Harvey. His health was a concern earlier this year but he hasn’t had any setbacks this entire season except for skipping a start here or there. We can expect Harvey to easily surpass Tyson Ross and his $5.25m deal.

Due to the pizzazz of the Dark Knight, the revenues generated from his starts/jersey sales and the recent success of the team, Harvey should be able to negotiate himself around a 1yr/$6.3m deal. If we talk about fairness in terms of his contract, I think this is “fair” to both parties. We have to take into account everything that Harvey brings to the table and I think he’s more valuable than Ross and most previous pitchers who went to arbitration 1 and did not sign a multi-year deal. The one factor that could haunt Harvey’s dollar amount is his elbow due to TJ surgery. If that happens to wear out during the last couple of weeks in September and postseason, we can easily make a case that he should be owed less. But as for now he’s been Harvey-esque and back to where he was before the surgery. Next year his innings limit should be lifted or increased dramatically so there won’t be too much of a cause for concern compared to if he spent time on the DL this season. Obviously, he isn’t a sure bet that he will remain healthy but arbitration does not greatly take into consideration future success/problems, only previous. That is why we project him to get approximately $6.3m.

Overall, both sides will negotiate and the Mets will offer less than what I project. I could definitely see the Mets’ offering $5.5 to $6m. But Scott Boras will clearly try to get more for Harvey — I think around $7m. Both arguments will be justified. In the end, I think an arbitrator would agree that 1yr/$6.3m is common ground, a good midpoint and fit for an agreement by both parties. Stay tuned for more…

Projection: 1yr/$6.3m

…because he’s the hero Queens deserves…

Yoenis Cespedes will be one of the most watched upcoming free agents this winter. He has become a coveted player on the market that has earned a huge payday. Cespedes defected from Cuba and signed a 4yr/36m deal (’12-’15) with the Oakland A’s in 2011. He immediately burst onto the scene as an offensive force hitting 20+ home runs, driving in at least 80 runs and slugging .450+ in each of his three seasons. After his success in Oakland, he became a journeyman over the last two seasons being traded to Boston, Detroit and then to the New York Mets this past July.

As a Met, he’s hitting .302 with 17 home runs and 42 RBI in 42 games after a week of hot hitting. Cespedes has totaled 2.9 Wins Above Replacement (WAR) with the Mets and his 6.9 WAR for the 2015 season ranks in the top 10 in the major leagues between both pitchers and hitters. Cespedes is a free agent this off-season but is a special case, as he can’t receive a qualifying offer for a draft pick. When Oakland signed Cespedes as a free agent out of Cuba, they took a significant gamble that he would be major-league ready, inserting him into their lineup right away. Cespedes’ contract requires that he be released after the season without the qualifying offer. The Mets would’ve retained exclusive negotiating rights for the first five days following the conclusion of the 2015 World Series. After that, they couldn’t negotiate with Cespedes until May 15, 2016. But due to the recent success and big gamble of a new payday from New York, Cespedes waived that part of his contract and will now be able to sign with New York at any time in the FA period.

Cespedes’ value with the Mets is astronomical. After acquiring him on July 31st the Mets have scored the most runs (311) in the majors since that time. Before the acquisition the Mets were 28th in the league! They had what was considered a minor-league offense. Although they did acquire Kelly Johnson and Juan Uribe, none have made more of an impact than Cespedes. What Cespedes brings to a team is power and pure run support. He became a catalyst for a struggling team and propelled them into first place. Almost all the hitters in their lineup have boosted numbers since that time as well. Pitchers can’t afford to pitch around batters in front of Cespedes. More guys are getting more pitches to hit resulting in more men on base for Cespedes to drive home. He also plays average defense and has a cannon for an arm.

INTERESTED BUYERS:
I can see Cespedes re-signing with the Mets if they have a successful postseason. The fans along with the media would grill the front office/ownership if they did not get him back. That was the case this past trade deadline when GM Sandy Alderson was scrutinized for not making any moves until the final days leading up to July 31st. Roc Nation has the rights to negotiate his contract and as we saw from Cano’s FA market in 2013, they may try to get the most lucrative deal by waiting it out and reaching out to all interested teams. If the Mets want him back they are going to have to give him a big payday.

Some other clubs I can see having interest in Cespedes could be Baltimore, Houston, Miami, and San Diego (if they lose Justin Upton). I think the ones that just makes the most sense are Houston and Baltimore — they need to have more consistency, especially in their OF positions. Houston has Rasmus, Gomez and Springer. But Rasmus is a FA this offseason and Gomez has struggled. I think Cespedes provides that jolt to an offense that’s hard to find. He produces runs, which any offense needs. They could have Altuve, Correa and Springer/Gomez hit in front of Cespedes. That would be an incredible lineup. Throw in their great young staff and a decent back end of a lineup. That’s a scary team. Plus, they have the payroll to go out and get him. Right now its only about $72,000,000, but we’re not sure if ownership wants to go out and spend on one guy. They may try to find value elsewhere for cheaper which is something that General Manager Jeff Luhnow likes to do.

As for the Baltimore Orioles, they have Adam Jones in CF and could sign Cespedes to play LF. He has better defensive numbers in left and if the Orioles cannot sign 1B Chris Davis back I think they will strongly consider Cespedes. Baltimore has a ton of money coming off the books having only $41 million committed to next year so it seems as if they will have a lot of changes coming their way.

San Diego could be a good fit only if Justin Upton signs elsewhere, otherwise he’d be useless on a team that has three solid outfielders and no DH. We know AJ Preller could wave his magic wand at any moment and make something happen. Lastly, Miami could be a dark horse. They cut back on their spending in the last couple years but could look to make another “Marlin splash” with Yoenis Cespedes. Miami plays in a big Hispanic market and considering they are located not too far from Cuba, that could be influential in their decision-making.  The excitement from the fans and a power-filled lineup would be tremendous. Just imagine facing a lineup with Dee Gordon, Yoenis Cespedes and Giancarlo Stanton…someone call Jack McKeon to manage this squad.

In the end, I think Cespedes does sign back with the Mets, especially if they have a deep postseason run. The Orioles are the second favorite. I think the fans/media will get on the front office/ownership to sign him back. The Wilpons might be cheap but after seeing this postseason run they are going to ask themselves, how could they not? Cespedes seems to like being the top dog on a team; with the Mets he’s exactly that. As Reggie Jackson would say, “the straw that stirs the drink.” Without him, they are very vulnerable, as shown before his acquisition.

In terms of his deal, I think he will get around $26 million average annual value (AAV). The big part is how many years he will be able to get. After seeing Roc Nation get Cano to sign for 10 years, I don’t think we will see another double-digit figure like that. It seems that most teams are trying to shy away from that long-term deal and rather give a 6/7/8yr contract with more AAV. So I believe Cespedes will most likely go for about 7 years but higher AAV. Although I don’t think we will be surprised if a front office came up to 8-9-10 years…it’s not every day a .290, 30+HR, 100+RBIs, 7 WAR guy comes on the market.

Similar players we can compare to: Shin Soo Choo – 7yr/130m (18m/AAV) and Jacoby Ellsbury – 7yr/153m (21m/AAV). Cespedes has better numbers than both these players, especially in the power department. Choo was a guy known more for getting on base and Ellsbury had his average/legs/defense behind him. There’s no doubt in our minds Cespedes will get more than these two. If we also take into consideration that for every 1 WAR, a player usually gets $7-8 million/yr. Cespedes this season already has 6.9 WAR. That’s incredible but he’s more likely not to keep that up and fall back to his career average of about 4-5 WAR per season. With that being said, 4-5 WAR equates to a very high salary. The Mets/other teams will probably go a bit overboard and give on the higher end as usual. I think he will get to 7 years/182m for 26m AAV. I don’t think any of these teams will go to 8 years or longer because of the history of longer contracts not working out. If anything this deal will contain more AAV.

As Jerry Seinfeld tweeted: “A Cespedes for the rest of us.”

PROJECTION: 7 years, $182 million with Mets or Orioles.

Innings pitched (IP) seems to be the standard for judging a player’s workload. Sure it will tell you how deep into a game a pitcher went and it’s often used as a measure of pitcher durability, but it tells you nothing about a pitcher’s effectiveness. A far more useful stat is the pitch count during each particular outing, or even better pitches per innings pitched (P/IP). I think we can all agree that all innings are made differently. A pitcher can throw three pitches or it can take 61 pitches as evidenced by Steve Trachsel (1997 – Chicago Cubs) and still get credit for 1 IP. Actually I think it’s possible to throw zero pitches and get 3 outs, but I don’t have the motivation to look up the rule at this particular moment.

Here are some stats for three players in the 2015 season.

All the players in the table above have very similar peripheral statistics, aside from an IP difference of 12 between players 1 and 2. From looking at these stats it’s a toss-up as to who has had the most successful season — do you choose player 2 since he has the most IP or player 3 since he’s made the most starts? In the table above Chris Heston is player 1, Matt Harvey is player 2 and Yovani Gallardo is player 3. What really separates the players is the pitch counts and P/IP.

Chris Heston – 2461 Pitches and 15.4 P/IP

Matt Harvey – 2533 Pitches and 14.8 P/IP

Yovani Gallardo – 2959 Pitches and 17.5 P/IP

Chris Heston has 12 IP less than Harvey but has thrown 72 fewer pitches this season. Harvey and Gallardo have thrown about the same amount of innings, but Gallardo has thrown 426 more pitches this season. The reason I chose Harvey as one of the pitchers for this comparison is due to the very public feud between the Mets, Boras and Harvey. In case you missed it, there was a disagreement with the innings limit imposed on Harvey in his first season after Tommy John surgery. Boras wants the Mets to stick to 180 IP while the Mets thought it was more of a soft cap. I wanted to look at the relationship between the IP in a season and the total number of pitches thrown. Luckily this data was readily available for download via FanGraphs, but only pitch counts back to 2002 were available. Below is a plot showing all pitchers who threw more than 100 innings in a season compared to their pitch counts. The data has a linear relationship, with the red line showing the mean and the outside black lines are the prediction intervals where we would expect 95% of the observations to fall within.

Now based on the 180 IP limit imposed on Matt Harvey, a linear model predicts that a pitcher would throw 2867 pitches in a season with an upper limit of 3158 and a lower limit of 2576. Now this means that at 180 IP we can reasonably expect a pitcher to throw between 2576 and 3158 pitches. Now for a guy coming off a major surgery, doesn’t a range of 582 pitches seem a bit extreme? It basically amounts to a difference of 5 complete games’ worth of pitches. In the plot below I also highlighted an innings range based on the range of innings where a pitcher throws 2867 pitches in a season. Now most importantly this range extends from 160 to 200 innings.

The medical team could just have easily set a limit anywhere between 160 and 200 IP. This is why an innings limit doesn’t work well in this situation; there is just too much variability in the data. In the future it will probably be a better idea for team officials and the medical staff to discuss a pitch limit over a season instead of an innings cap. Since the main goal of limiting a pitcher’s workload is to reduce stress on his arm I think the plot above does a good job showing that innings limits will have very little effect on actually managing a pitch count. Harvey is obviously thinking about the long term here because I know he doesn’t want to go through another surgery. After a second Tommy John the chances of a pitcher returning to the majors drops to somewhere around 30%, not to mention the drop in potential future earnings.

So I’ve shown you why I don’t think IP is a good indicator and now I’m going to show you why I think pitch counts and P/IP should be more important statistics.  Based on the linear model shown in plot 1 the formula to predict pitches in a season is as follows: Pitches = IP*14.5 + 256.9. Now the intercept for this model is 256.9 which suggests that if you don’t throw a single inning in a season you would still be expected to have thrown 257 pitches. Obviously there is something going on at the lower inning totals, but we are going to ignore that for the purpose of this article. As an added note, the lower prediction interval from plot 1 has an intercept of -33.975, so we are very within range of showing 0 pitches for 0 IP from this model.

Heston and Harvey both rank very high in P/IP among qualified starters while Gallardo is dead last among qualified starters. Efficiency is key here. Should Harvey be directly compared to Gallardo based on IP? No, absolutely not, Harvey is among the most efficient pitchers in the game this year. He has been able to get through innings while keeping his pitch count down and most importantly reducing stress on his arm. An inverse prediction based off pitch counts was used to predict the IP in the table above. Based on their pitch totals from this season Harvey and Heston have “thrown” less than their IP totals suggest and Gallardo has actually thrown quite a bit more. This has a big effect on that innings cap imposed on Harvey for this season. His stats show that he’s thrown 171.2 IP, but based on the number of actual pitches he’s thrown in game situations his number may be closer to 157 IP. Does that mean he should have the equivalent of 23 IP left in the tank for this season? Well that’s not up to me, but IP should less important than total pitches.

One thing I didn’t look at this article was the proportion of pitches thrown throughout the 2015 season. It’s been in the back of my mind, but I don’t have a reference for what the most stressful pitches are on a pitchers arm. I think it’s safe to assume that all pitches are not equal. Let’s think a Dickey knuckleball vs. Chapman fastball. The amount of effort needed for each pitch type is likely highly dependent on the pitch speed and type, but to simplify things here I’ve just assumed that all pitches are equal. We also need to realize that all pitchers are not equal, whether it be mechanics or individual variation in abilities. I was curious to see where Mark Buehrle’s pitch count (leaderboard here) lined up with all other pitcher since 2002 and lo and behold he’s thrown the most pitches since records became available. Obviously he doesn’t throw as hard as many of the other guys in the league, but that hasn’t stopped him from being a workhorse and one of the most effective pitchers over the last decade.

The Orioles are having a rough go of it. After being tied with the Yankees for first place in the AL East on July 2nd with a 42-37 record, the Orioles have gone 26-36 since and, as of September 13, are just a half-game above the last place Red Sox.

However, the standings don’t appear to be having an effect on Chris Davis, the slugging Orioles first baseman who is in the midst of a hot streak that includes 6 HR and a .493 OBP in his last 15 games. Davis’ recent performance continues his resurgence, bringing his average up to .261 and his home run total to 41 on September 13. Davis struggled mightily last year before a suspension for unapproved Adderall use cut short his season, finishing with 26 HR and a miserable .196 BA. A power surge couldn’t come at a better time for Davis, who is looking to make more money in the free-agent market this offseason.

Just how rare is Davis’ comeback, however? Davis was an established major-league player before this season, having played 723 games while averaging 2.0 WAR per 162 Games. His Oriole-record 53 homers in 2013 (which included 7.1 WAR) made him a star while his forgettable 0.8 WAR in 2014 made him just another one-hit wonder.

Examining position players with at least a full season’s worth of games played before their comeback season, we’ll set the following criteria for a comeback:

• At least 2.0 WAR per 162 Games prior to the comeback year
• The WAR for the comeback year is at least 4.0
• The WAR for the previous year is less than 1.0

These baseline cutoffs are very similar to Chris Davis’ 2015/2014 experiences. Noting these, we find 70 comeback seasons since the beginning of the expansion era (1961) that fit the criteria.

Davis’ 2015 is bunched around Coco Crisp’s 2007 with the Red Sox and Victor Martinez’ 2014 with the Tigers. These players all saw their WAR increase by about 4.3 from their previous years.

The most impressive comeback in terms of WAR improvement was Jacoby Ellsbury’s 2011 with the Red Sox, when he put together a 9.4 WAR season after an injury-shortened -0.2 WAR season.

Overall, a comeback like Davis’ isn’t all that rare. In fact, comebacks as or more impressive happen about five times every four years. That shouldn’t deter Davis, however, whose performance is one of the bright spots on a struggling Orioles team.

In the past few years a number of high profile pitchers have gone under the knife for Tommy John surgery (TJS). This surgery involves reconstructing the ulnar collateral ligament (UCL) in the throwing arm to re-stabilize a players elbow. I’ve heard a few stories about TJS — firstly, pitchers who get the surgery are able to throw harder after the procedure and another where college pitchers were voluntarily undergoing the procedure and sacrificing a year of pitching due to the belief that they would be able to throw harder or have more stamina. Whether either of these are actually true I have no idea, and I didn’t do any digging to find the answer. Instead I wanted to take a closer look at some pitchers who’ve undergone the procedure in the last couple of years and compare their performances before and after the surgery. In the table below I’ve included 4 players who missed the entire 2014 season or a significant portion of it. Matt Harvey underwent the procedure in October of 2013 while the other pitchers had the surgery sometime in 2014.

In 2013 all of the pitchers had pretty good years. They all made at least 26 starts and threw at least 150 innings. Fernandez and Harvey were both striking out more than one batter per inning, while Moore and Corbin still posted very respectable numbers. Now Harvey and Corbin didn’t pitch at all in 2014 and the other two suffered their injuries early in the 2014 season. Matt Moore only pitched 10 innings so it is tough to draw any conclusions due to small sample size, while Jose Fernandez threw 51.2 innings before he was shut down. His 2014 season was looking very promising posting very high K/9 numbers with a low ERA and his FIP and xFIP were even more favourable.

Now lets jump ahead to 2015. If you want to check over their 2015 stats they are in the table above. I’m not going to regurgitate them for you, but I will give a quick synopsis of each player. Harvey is having an excellent first year in his recovery, and in limited sample Corbin and Fernandez are also throwing really well. Matt Moore has had a season to forget so far, but he is just about return from a stint in AAA where he posted pretty strong numbers so the jury is still out.

Any time a player is coming off a major injury it is entirely within reason that psychological issues, fitness/conditioning or lack of practice has an effect on their performance. Without any first-hand knowledge of their unique situations fans always want a pitcher to just step right back in and perform at previous levels without any decline in performance. It’s tough to only compare stats from a before and after season and say with confidence whether a pitcher has lost any ability. So I wanted to go a step further and look at some PITCHf/x data and take a look at how their fastball, breaking ball and change-up velocities have changed, as well as any changes in the movement of their breaking balls.

Let’s start off with fastball velocities. As you can see from the table above Matt Moore has data going all the way back to 2011. His fastball velocities have decreased each year which should be a cause for some concern. The remaining 3 pitchers have all shown increased fastball velocities since their rookie years. Whether this is proof that TJS has an effect on increasing pitch speed I’m not sure and I’m not going to speculate, but I would welcome any comments from people who may have some theories. I’ll let you read through the rest of the table, but in general, Moore is showing decreased speed for all of his pitches this year and everybody else is throwing their stuff just a little bit harder.

OK now that’s enough looking at tables, let’s move on to some pretty graphs. Who doesn’t like a nice graph? So the first one from the set of pitch trajectories that I’m going to show you are the mean fastball trajectories from each pitcher with different colours showing a trajectory from different years. Now I’ll admit that I don’t know much about trajectories and how to analyze them, but the interesting part that I found from these was the release point. Matt Harvey has been remarkably consistent with his fastball release point; Fernandez and Corbin haven’t changed all that much either. But look at how Moore’s arm slot has dropped in the last three years. Now again I’m certainly no expert in pitching mechanics but something seems to be going on there that might be related to the drop in velocity that we saw above.

On to the curveballs! There doesn’t seem to be too much going on with arm slot changes here. Fernandez looks like he changed up his arm slot from the 2013 season and his release point has been almost identical in 2014 and 2015. Harvey on the other hand has slightly dropped his arm, but from my standpoint it doesn’t seem too significant.

Lastly we come to the sliders. Look at Harvey and Corbin! If the pitches weren’t different colours it would be very difficult to tell them apart based on the release point. Moore seems to have dropped his arm slot from the 2013 season, but his release point has remained the same the last 2 years. Corbin is definitely targeting the bottom corner of the strike zone with his slider; it looks like he may be trying to get hitters to chase. Moore and Harvey look like they are also doing a good job of keeping those pitches down in the zone.

For those of you who are not too familiar with stats, I’m going to give you a quick lesson about confidence intervals. In the plots below I’ve included the 95% confidence intervals. Basically if the ends don’t overlap from the coloured bars you can consider the differences from year to year to be significantly different statistically (boring!). On to the fun stuff — the year after Fernandez and Harvey had TJS, the spin rates on their curveballs are considerably lower. I know it’s a little tough to tell if the bars are overlapping on Harvey’s curveball, but trust me, the lines aren’t overlapping. Maybe both pitchers are a little worried about their elbows or maybe it’s just advice from the doctor, trainers, coaches, their parents, who knows. Harvey is also showing a decreased spin rate on his slider from 2 years ago. If we ignore 2013 for Moore, then Moore and Corbin have maintained consistent spin rate from their last season.

And finally we get to our last plot; hopefully I’ve kept you all interested up to this point. This is looking at the pitch movement (in inches). The decreased spin rate illustrated above for Fernandez and Harvey’s curveball has also led to less movement. Fernandez has lost just a little over a 1/2 inch from his curveball since last year, but about 1.5 inches from his 2013 curve. That seems like an awful lot, but I don’t know if there has been any change in the effectiveness of his curveball in that time. Oddly enough after TJS the sliders are showing more movement. Maybe that elbow is a little more stabilized, or maybe it has something to do with increases in velocity, but unexpected on my end to see that.

From what I can tell Harvey, Corbin and Fernandez haven’t lost a step. Moore is somewhat of a mystery though. It’s tough to tell if anything has changed, but he only threw 10 innings last year so any direct comparison to last year would be useless. I’m a little alarmed at Moore’s decreasing fastball velocity since 2011. He’s going to need to start relying on his secondary pitches if he’s going to be successful going forward. But the basic conclusion that I’m going to draw from this analysis is that players are able to come back from Tommy John and still be effective. I’m sure there are articles that argue in favour and against my conclusion, but by showing you some information about pitch speed, release point and spin rate you can go ahead and make you own conclusions.

It’s been just over a month since Ian Desmond’s mid-season outlook. Things were not going well for Ian Desmond, playing in his contract year in 2015 he was hoping to set himself up for a massive pay day. After turning down a reported $107 million dollar extension, Desmond was hoping for a productive 2015 season. Things could not have gone much worse in the first half of the season.

Desmond’s monthly splits reveal a roller coaster season for the soon-to-be free agent. March and April started out slowly, his play picked up in May, and then June came. The month of June was simply abysmal, so of course let’s take a more in-depth look at his numbers that month. His performance that month compared to his career averages were all much worse. He walked only 3% of the time while striking out 33.3% of the time (just over 10% higher than his career average). Any time you combine a low walk rate and a high strikeout rate you can expect a really poor OBP. In the month of June his OBP (note: NOT HIS BATTING AVERAGE!) was below the Mendoza line and his wRC+ was 22. That means in the month of June Ian Desmond created 78% less runs than league average. For a player in his walk year and especially someone who turned down over $100 million, it should be concerning to say the least.

Then something strange happened: Ian Desmond started turning his season around after the All-Star break. His stats in the second half have been a complete turnaround. He’s walking more, striking out less but still more than his career average, and generally just performing better. His August BABIP is well above his career average suggesting that we can expect some regression at some point.

While only 35 games into the second half, his performance compared to the first half is night and day. He has already hit more home runs and stolen more bases in less than half the games, and his RBI total is inching closer to his first half mark. Most importantly, in the second half of the season he has been worth 1.1 WAR (Bryce Harper for comparison has been worth 1.5 WAR in the second half). Not only is this good news for Desmond’s free-agent stock, but the Nationals will need all the help they can get while they try to chase down the teams in front of them for a playoff berth. As of right now, the Nationals are 5.5 games back of the Mets for the division lead and 10.0 games back of the Cubs for that second wildcard.

As an added bonus, I thought it might be useful here to show a plot of Ian Desmond’s career trajectory as predicted by his seasonal OPS. This model was created using the methods presented in the book “Analyzing Baseball Data with R” by Max Marchi and Jim Albert, and I’ve excluded Desmond’s age-23 season where he only played 21 games.

Based on the age trajectory graph it looks like Desmond may have already peaked in his career. What this means for his potential earnings this upcoming offseason remains to be seen. Any GM looking to add a top-tier hitting shortstop for the next few seasons will inevitable come calling his agent, but the data tells us that his best days may be behind him.

exOUT%

In the early 21st century, Oakland Athletics’ General Manager Billy Beane revolutionized baseball forever. He was the first general manager in baseball to heavily utilize sabermetrics in his baseball operations. This isn’t a history lesson though, I bring him up because of his idea that outs are precious, and as a hitter your goal is to not make out, thus him prioritizing OBP so heavily. In the following years, baseball statistics have seen phenomenal progress on both offense and for pitchers. While I believe FIP and xFIP are both very useful statistics in really measuring a pitcher’s skill, my problem is that they essentially ignore all the batted ball data that we have (GB%, FB%, LD%). SIERA and tERA have solved some of these problems, but are far from perfect, and I believe the more statistics we have, the better.

As I mentioned with Beane, while we largely focus on a hitter’s ability to not make out, we still don’t have a catch-all statistic to realize how effective pitchers are at getting batters out, because if the batter’s goal is to not make out, the pitcher’s goal is to get the batter out. So I present to you expected out percentage, or exOUT% (the name is certainly a work in progress). exOUT% sets out to answer a simple question: For any plate appearance, what is the likelihood that the pitcher will get the batter out? This can easily be found by just looking at a pitcher’s opponent OBP, but that is rather primitive, and we can get a better estimate by focusing more on pitchers’ skills to strike people out, not walk batters, and the type of contact they are giving up, and also trying to negate the effect of the defense by him, by just using league averages. So to calculate a pitcher’s exOUT%, I used K%, BB%, GB%, LD%, FB%, lFFB%, and 2014 league averages on ground balls, line drives, and fly outs. (HBPs are essentially ignored but can certainly be incorporated in a future version, this is pretty much exOUT% v1.0)

I want to give full disclosure, I am not a statistician or close to it. Math and statistics are an area of interest and I am currently pursuing a degree in math-economics, but I am far from a professional, so I recognize there are going to be errors in my data. This is an extremely rough version; there’s even a combination of data from this year and last year so there will be inconsistencies, as I don’t have the resources to gather all the data I need. If after reading this, you are interested in this and would like to take this further, please feel free to contact me if you have the skills necessary to advance this further (or even if you don’t).

I will first post a simple step-by-step breakdown of how to calculate exOUT%, and then get into more detail and take you through it with Clayton Kershaw, because well, he is awesome.

1- Add K% and BB%, subtract this percentage from 100%, this leaves you with a balls in play%, let’s just say BIP%

2- Multiply the pitcher’s GB% (make the percentage a number less than 1, for example 40% is .4) and BIP% (leave it between 1 and 100, ex 40%), this gives you a GB% for all PAs, not just balls in play, we’ll call this overall GB%, or oGB%… now multiply this percentage (in between 1 and 100) times the league average percentage of ground balls that don’t go for hits (league average is .239 on ground balls in 2014, so out percentage on ground balls is 76.1%, but make it .761…. this will give you a percentage you can leave between 1 and 100, if the number is 20%, that means that there’s a 20% chance that pitcher will induce a ground ball out that PA, assuming league average defense, we can assume this because we’re using the league average for batting average on groundballs… we’ll call this exgbOUT%

3- Now follow the same steps but with LD%, exldOUT%, the percentage chance for any given PA that the pitcher will produce a line drive out. (The league average on line drives last season was .685 (!) so that means there is a 31.5% chance a line drive will result in an out)

4- Same thing with FB%, sort of, because we also want to incorporate IFFB%. So multiply a pitcher’s FB% by their IFFB%, this gives you the percentage of balls in play that the pitcher produces an infield fly ball (bipIFFB%). Multiply this percentage by their BIP% to get his overall percentage of PAs that result in an infield fly, and this will also be their exiffbOUT%, because any infield fly ball should be converted to an out, and if not, it’s to no fault of the pitcher, so we won’t punish him. Next subtract a pitcher’s IFFB% from 1 or 100, whatever, and this is their balls in play percentage of fly balls that are normal fly balls, to the outfield. Multiply this number by their BIP%, this gives you the overall normal FB% for a pitcher, not just balls in play. Multiply this number by .793 (the league average on fly balls in is .207, so there’s a 79.3% that a fly ball will result in an out). This number is the percentage chance that for any given PA, the pitcher will produce a fly ball out to the outfield. Add this exnfbOUT% (n for normal) and his exiffbOUT% and you have his exfbOUT%, the percentage that for any given PA, the pitcher will produce a flyball out, to the infield or outfield.

5- Add K% + exgbOUT + exldOUT + exfbOUT

6- You have your exOUT%

The terms are not that technical or scientific so I don’t confuse anyone — I tried to simplify a very complicated procedure as much as possible. To clarify and give you an example, let’s go through Clayton Kershaw.

Kershaw profiles like this (I compiled this data on 8/21): 32.3 K%, 4.9 BB%, 52.8 GB%, 26 FB%, 11.8 IFFB%, 21.2 LD%.

So let’s look at the balls that don’t go in play, strikeouts and walks. Add the two and balls not in play percentage is 37.2, 4.9% are walks and thus won’t be an out, and 32.3% are strikeouts so will be an out. Thus far, Kershaw’s exOUT% is 32.3 (of a possible 37.2 so far)

Now let’s look at the balls in play. People will usually say that a pitcher can’t control what happens when a ball is in play, but I vehemently disagree, the type of contact the pitcher gives up can’t be ignored and largely effects what will happen to the ball in play. I will quote a FanGraphs article here to explain it, “Generally speaking, line drives go for hits most often, ground balls go for hits more often than fly balls, and fly balls are more productive than ground balls when they do go for hits (i.e. extra base hits). Additionally, infield fly balls are essentially strikeouts and almost never result in hits or runner advancement.” And FanGraphs also gives us this data from 2014.

GB: AVG- .239, ISO- .020, wOBA- .220

LD: AVG- .685, ISO- 190, wOBA- .684

FB: AVG- .207, ISO- .378, wOBA- .335

So this means that fly ball pitchers are most likely to get outs, although they may be less effective because when they don’t get outs, it’s more trouble than for ground ball pitchers. But remember, this statistic is just finding the chance that the pitcher will get a hitter out.

All right, so, let’s calculate Kershaw’s exgbOUT%, exldOUT%, and exfbOUT%; you can follow the numbers along with the steps I listed above.

GB%- 52.8

62.8 x .528 = 33.1584

(33.1584 x .761)=  25.23354424 exgbOUT

LD%- 21.2

62.8 x .212 = 13.3136

(13.3136 x .315) = 4.193784 exldOUT

FB%- 26

26 x .118= 3.068 bipIFFB%

26 x .882= 22.932 (bipFB%)

62.8 x .22932= 14.401296 (onFB%)

14.401296 x .791= 11.3914251 exnfbOUT%

62.8 x .03068= 1.926704 oIFFB% and exiffbOUT%

exnfbOUT% + exiffbOUT% = 13.3469317 exfbOUT%, if you followed my math exactly a decimal may be off, like 13.31 something, but this is the number the excel doc chugged out, so I’m trusting that, my iPhone calculator can’t carry all the decimals sometimes.

Now add them all up

32.3 + 25.23354424 + 4.193784 + 1.926704  + 11.3914251 = 75.07%

K% + exgbOUT% +  exldOUT% + exiffbOUT% + exnfbOUT% = exOUT%

The league average exOUT%, using league average statistics from 2014 for the ones involved, is 69.8%. Scherzer leads the majors (well the 89 pitchers I was able to export data from FanGraphs) with a 76.43 exOUT%. If you want to look at it as a more concise and better version of opponent OBP, his is .236, so, you know, good. Here is a picture of the data for the top 37 — the J column is what you are looking at. Betances is in their because I wanted to calculate one reliever. 

View post on imgur.com

All right, I’ve explained it a bit in the prologue, but now that you’ve seen it, let me explain more why I like this stat. Well first, I created it and calculated, so, well, yeah… but I also like this stat because it answers a very simple question “How good is a pitcher at getting people out?” Pitching in its simplest form, is exactly that, getting people out. The stat recognizes that there’s basically only these outcomes for an at bat: strikeout, walk, ground ball, line drive, and fly out, and looks at the pitcher’s stats in these categories to determine how many people he should be getting out. The stat is more predictive than evaluative in nature, because you can calculate a pitcher’s actual out percentage, but that doesn’t nearly tell the whole story, because a lot of luck is involved with balls in play, and other fluky outcomes.

This operates under the basis that a ground ball will perform the way the average ground ball does, a line drive performs the way an average line drive does, and a fly ball behaves the way a typical fly ball does. There could be guys getting very fortunate with ground balls: having a great infield behind them, balls not squeaking through the holes; with line drives: being hit right at people; and fly balls: staying in the park, having outfielders who cover a lot of ground. And there could be guys who are getting unlucky: the ground balls are getting through the holes, the infielders don’t have range; line drives seem like they are always going for hits, and fly balls are falling in. This says that a pitcher can’t control that, but they can control how much they strike out people, how much they walk people, and how often they give up ground balls, line drives, and fly balls, and if these balls in play behaved the way they should, the pitcher should be getting this percentage of people out.

I will address the flaws I have found with it. As much as getting people out is important, sometimes what happens in the plate appearances that don’t end in outs are almost as important. This only deals in batting average regarding balls in play, but wOBA is very important too. Fly balls are more likely to be outs than ground balls, but the wOBA on fly balls is over 100 points higher. Additionally, I’d prefer instead of ground balls, line drives, fly balls, to use soft contact, medium contact, hard contact, because that is a truer test of pitcher skill, however, I did not have this data at my disposal as far as league averages on what the batting average is for soft contact, medium contact, hard contact (if someone does, please contact me like I said). So what I have for now will do and this batted ball data is still a good measure. I set out to calculate what percentage of batters a pitcher should be getting out, and that is exactly what I found out. So while it’s not perfect, it has its use, and it’s something to build on.

exRP27

And build on I did. While the out percentage is nice, it doesn’t give us a measure like ERA or FIP or xFIP, that tells us how many runs a pitcher should be giving up. So using the data I used to calculate exOUT%, I present to you exRP27 (expected runs per 27 outs, a stupid name for a hopefully not stupid stat).

The basis for this stat is this data from FanGraphs, “Line drives are death to pitchers, while ground balls are the best for a pitcher. In numerical terms, line drives produce 1.26 runs/out, fly balls produce 0.13 R/O, and ground balls produce only 0.05 R/O.” (I don’t know how this was calculated, or when it is accurate for, but this is what I got). We don’t know this for soft contact, medium contact, hard contact, so again I’m sticking with ground balls, line drives, and fly balls. 

All right, so what I am going to do using this stat and the pitcher’s K%, BB%, GB%, LD%, and FB% is see how many runs the pitcher should be allowing over 27 outs, and then adjust it to get it on a scale similar to ERA, FIP, and xFIP.

Keeping Clayton Kershaw as our example, let’s take a look.

Kershaw’s K% is 32.3 — we’re multiplying this by 27 (for outs in a game), and we get 8.721 K’s, so 0 runs so far because a K will never produce a run

Now GB%. His exgbOUT% is 25.23354424, multiply this by 27 and we get 6.8 (ish, final number will be exact via the Excel doc). Multiply this by .05 (the runs per GB out he gets) and we get .34 runs.

LD%- his exldOUT% is 4.193784, multiply by 27 and get 1.13232168, and multiply this by 1.26 for LD runs/out and we get 1.43 runs

His exfbOUT% is 13.3181291, now multiply by 27 get 3.6 and then that by .13 and you get .47 runs

Add up all these exRUNS and Kershaw’s total is 2.24. However, we can’t stop here because the number of outs he’s recorded is only 20.3 (8.7+6.8+1.1+3.6) approximately. 20.3 is the rounded up total. So get this 20.3 (or whatever the pitcher’s exOUTS is) up to 27  by multiplying by whatever it takes, and then multiply his exRUNS by this same number. For Kershaw you end up with 2.97 exRP27. The league average would be 3.78. Last year’s average ERA/FIP/xFIP was 3.74, but when I adjust everything to that, everyone’s exRP27 just goes down slightly (Kershaw’s from 2.97 to 2.94), but I want it to be on a more realistic scale where everyone’s totals are lower and a really good exRP27 is comparable to a really good FIP, like in the low 2s. 

So I don’t know what the statistic’s correct way is, but here is what I did to make it work. I calculated what his “ERA” would be using by multiplying his exRUNS by 9 and then dividing that by his exOUTS. His was .99, the league average was 1.26. I then did .99/1.26 to get .78 or so, I then multiplied that by his exRP27 and got 2.34. I felt like this was more realistic and in line with his ERA/FIP/xFIP. Obviously, can’t be the same because they measure different things, but just got in in the area. And the same is done for all pitchers. Obviously, not everyone gets multiplied by .78 of course. The league average remains 3.78, between last season and this season’s average for ERA/FIP/xFIP.

Here is the leaderboard for that (S column):

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 I really like this stat a lot, and feel like it does what I wanted to accomplish: figure out how many runs a pitcher should allow per 27 outs given his K%, BB%, GB%, LD%, FB%, and the notion that balls in play will behave the way they normally do, as anything else is likely luck and not indicative of the pitcher’s performance.

I look at Sonny Gray as someone this stat is perfect for. His ERA is outstanding at 2.04, but his FIP is 3.00, his xFIP is 3.47 and his SIERA is 3.50. The problem is, at least with FIP and xFIP for sure, is that they ignore what happens when the ball is in play. He doesn’t strike out too many people, he has a good BB% but not spectacular, and he’s given up 10 home runs, a fair amount, so this hurts his FIP and whatnot. However, instead of saying “well he will regress, look at his FIP/xFIP/SIERA” this looks at why he’s having this success, and it has to do with the balls in play, which is getting ignored. Gray’s LD% is just 14.6! That is really good! Second best of the 90 pitchers I did this for. And his GB% is 54%, 9th best, also really good. The pitcher does have control over the type of contact he allows, and the fact that Gray is producing a ton of ground balls, and very few line drives, is why he’s been so successful. His 2.34 exRP27 suggests that he has not been as good as his 2.04 ERA suggests, but he’s not as far off as the other stats suggest. 

Obviously exRP27 is far from perfect, and is in no way supposed to replace FIP/xFIP/SIERA, but it is something to look at with them. I am a big believes in aggregation, so I think that averaging some combination of these 4 stats together or them all, is an even better way to evaluate a pitcher. We’ve got more data than ever, so it makes sense to use it, exRP27 and exOUT% are just more examples of utilizing this data to help better evaluate pitchers.  

I hope you guys enjoyed. Any feedback please comment or contact me. Next I will be looking at exWOBA against for pitchers using similar data, and exWOBA for batters using the data but for hitters.