Archive for January, 2018

Jim Thome: First and Last Three Outcomes Hall of Famer

Jim Thome was elected to the Hall of Fame on January 24th.  Given my recent obsession with the three true outcomes, I immediately recognized the significance of this event.  I believe Jim Thome is the first, and likely the last three true outcomes Hall of Famer.

Table 1 shows Thome’s home run, walk, and strikeout rates along with his three true outcomes rate for each season.  The final column is the MLB average three true outcomes rate for the season.  Thome was a three true outcomes machine from 1996 until his retirement in 2012.

Table 1. Jim Thome, Three Outcomes Hall of Famer

1991 Indians 104 1% 5% 15% 21% 26%
1992 Indians 131 2% 8% 26% 35% 25%
1993 Indians 192 4% 15% 19% 38% 26%
1994 Indians 369 5% 12% 23% 41% 27%
1995 Indians 557 4% 17% 20% 42% 28%
1996 Indians 636 6% 19% 22% 47% 28%
1997 Indians 627 6% 19% 23% 49% 28%
1998 Indians 537 6% 17% 26% 48% 28%
1999 Indians 629 5% 20% 27% 53% 28%
2000 Indians 684 5% 17% 25% 48% 29%
2001 Indians 644 8% 17% 29% 54% 28%
2002 Indians 613 8% 20% 23% 51% 28%
2003 Phillies 698 7% 16% 26% 49% 28%
2004 Phillies 618 7% 17% 23% 47% 28%
2005 Phillies 242 3% 19% 24% 46% 27%
2006 White Sox 610 7% 18% 24% 49% 28%
2007 White Sox 536 7% 18% 25% 49% 28%
2008 White Sox 602 6% 15% 24% 45% 28%
2009 2 teams 434 5% 16% 28% 50% 29%
2010 Twins 340 7% 18% 24% 49% 29%
2011 2 teams 324 5% 14% 28% 47% 29%
2012 2 teams 186 4% 12% 33% 49% 30%

Thome was part of a small group of specialists with multiple dominant three true outcomes seasons.  Table 2 provides a list of players with 4 or more of these dominant seasons.  I consider a season with at least 170 plate appearances and a 49% three true outcome rate as a dominant season.  The casual three true outcomes observer will recognize the players on this list as notable specialists.  Rob Deer, of course, is the iconic three true outcomes hitter.  I used Deer’s career three true outcomes rate of 49% and 4 dominant season to construct the table.

Table 2. Dominant Three True Outcomes Specialists

Player Career Seasons
Jim Thome 1991-2012 10
Adam Dunn 2001-2014 9
Russell Branyan 1998-2011 8
Mark McGwire 1986-2001 6
Jack Cust 2001-2011 5
Chris Carter 2010-2017 5
Rob Deer 1984-1996 4
Chris Davis 2008-2017 4
Alex Avila 2009-2017 4

Thome’s 10 dominant seasons are more than any other player.  He is also the only Hall of Famer on the list.

Maybe Mark McGwire should be in the Hall of Fame (depending on your PED era position).  Already past eligibility to be inducted by the Baseball Writers Association of America (BBWAA), perhaps he will have a chance in the future with the Veterans Committee.

Adam Dunn will be on the 2020 ballot.  He was a consistent three true outcomes specialist, but we will see if the BBWAA consider him a dominant player over the course of his career.

Russel Branyan and Jack Cust are interesting players to see on this list.  Branyan makes the list because of my 170 plate appearance requirement.  Cust was a dominant three true outcomes hitter for five straight years, 2007-2011.  Neither are on the Hall of Fame ballot.

Carter and Avila do not have contracts for 2018, but could land somewhere.  Davis is signed with Baltimore through 2022.  Joey Gallo and Aaron Judge are two young hitters in the three true outcomes mold not yet on the list.  So maybe it is too soon to make a judgement on the Hall of Fame potential of three true outcomes hitters in the future?

But I am going out on a limb to say that despite the trend towards three true outcomes baseball, we have seen our first and last three true outcomes Hall of Famer in Jim Thome.

Maybe the Brewers Are Stepping Up Because of Zach Davies?

Not entirely, no. Before acquiring Christian Yelich and signing Lorenzo Cain, the Brewers still had a host of other talent scattered throughout their lineup that made them interesting, if not necessarily intimidating. But Davies occupies a particular space that their more established players have surpassed and that their other younger players have yet to enter.

He’s going into his third full season and has provided nearly six wins for the team, and his approach could make you believe he could keep doing that in the middle of the rotation. He’ll play 2018 at just 25 years old.

Travis Sawchik detailed why Davies is an outlier toward the end of last season. At 6’0, 155, he creates his success by combining guts, guile, and execution. Of those three characteristics, guile may pique the most interest.

Davies Sequencing

From his start on July 19 and after, Davies was a different pitcher. He made a big adjustment to his sequencing as he adapted the usage of his secondary pitches to better complement his two-seamer.

Davies’s two-seamer and changeup fade to the same quadrant of the strike zone. If he was working off the fastball, it may have been giving batters a better opportunity to tee up a changeup because of how tunneling works. They would have looked the same to batters by the time they had to decide to swing, generating a similar bat path.

Meanwhile, his cutter and curve could have come out of the same tunnel as his two-seamer, but ended up working the opposite side of the plate. Batters would have had a much more difficult time picking up what was coming next.

Davies heatmaps

Given how Davies’s two-seamer and changeup work the same area of the plate, it’s interesting that their drop in wOBA was identical after July 19, and equally interesting that the cutter and curveball became so much more effective.

These differences after Davies’s change in sequencing speak to the possible impact a tunneling effect could have had on his overall game. While tunneling isn’t necessary for pitchers to have success it may be especially productive for a guy who throws in the low 90s and lives on the edges of the zone.

Sequencing better in any count is bound to help performance, but it appears to have helped Davies in one particular situation. When he was behind 3-1 before July 19 — 38 times in 19 starts — hitters were smacking the ball into play at over 100 miles an hour. After that date — 26 times in 14 starts — he coaxed hitters’ exit velocity down to 87.3 mph. Once or twice a game, Davies turned absolute screamers into much more average balls in play. That’s critical when a pitcher isn’t going to generate outs on his own with whiffs, as Davies won’t with his career 6.55 K/9.

We can probably fairly consider this adjustment as deliberate, based on another quote from him in Travis’s article:  

“I think it just comes down to the other side of the game that not a lot of people pay attention to…[t]he thinking part of the game…[smaller guys]  have to rely upon smaller details about their game that can give them an edge.”

What if that also explains, at least partially, how the Brewers decided to go big by trading for Yelich and signing Cain? Teams have billions of data points to measure player performance. Davies’ subpar raw skills apparently haven’t kept him from being able to make adjustments and providing tangible value, despite falling outside what those data points might influence teams to prioritize. In this respect he’s a player who gives his team a unique edge.

We’ll have to wait to see an encore from Milwaukee’s holdovers from 2017, as well as the impact their newcomers make. But Zach Davies finds himself at the heart of a team looking to make some noise in a challenging NL Central.

All data from Statcast.

Who Obtains the Most Assistance in Pitcher Welfare?

Nobody’s perfect, especially umpires. This is the case at any level of the game. Be it softball, tee ball, or baseball, from Little League to the Big Leagues, you will have undeniably disagreed with a call that an ump has made.

Given the movement, velocity, and the newly anointed skill of pitch framing, it’s becoming more difficult for umpires to get the calls right. The robo ump has been discussed quite a bit but I’m not sure how I feel about a machine making decisions in lieu of accepting the concept of human error. We did it for decades before instant replay was instituted.

Umpires get balls and strikes wrong a lot. It’s the way it goes. Given that understanding, I wanted to know which pitcher has in recent years been the beneficiary of favorable calls.

And, like the umpires, not all (strike zone) charts are 100% accurate; leave a little room for error here.

I’ve parsed data on which pitchers have had the most declared strikes that were actually out of the zone. I decided to stop at 2014 because I felt that four years of information was sufficient for the study.

First, the accumulated data.

From 2014 to 2017, the amount of pitchers with phantom strikes has been increasing at fairly high rate; the biggest leap was from 2014 to 2015 (36 pitchers).

chart (4)

Interestingly, the pitchers with at least 100 ‘phantom strike’ calls has actually decreased.

chart (6)

And, despite the jump in total pitchers involved from ’14 to ’15, the pitchers with <=100 strikes called decreased at the highest rate.

Should we go tin foil hat and infer that umps are no longer favoring certain pitchers as much as they used to? Doubtful, but I’m not investigating integrity here.

So who is getting the most benefit from the perceptively visually impaired? First, I took the last four years of pitching data for our parameters. Then, I cut final the list down to a minimum of 10,000 pitches thrown. Lastly, I included only the top 20 pitchers in the group.


As we can see, Jon Lester of the Chicago Cubs has been the most aided overall; 562 non-strikes in four years.

For the optically minded, here is the pitch chart of Lester’s data.

Jon Lester

That’s A LOT of Trix!

Now, lets see if the percent of pitches has any impact on our leader(s).


Not a whole lot of variance, at least near the top. Lester clearly wins The MLB Umpires’ “Benefit of the Doubt Award”.

OK, so now we’ve got our man. Case closed, right?

Oh…that little caveat of ‘pitch framing’. Perhaps its that Lester has had great framing from his catchers. Let’s look into that.

For the moment, we are going to focus on Lester and his primary catcher from 2014-2016, David Ross.


Clearly 2014 was Lester’s most favorable year with Ross. That year, Lester ranked third in total pitches called favorably out of the zone (156) and 11th in ratio of calls (4.47).

The subsequent years with Ross are as follows:

2015- 6th (141), 10th (4.43)
2016- 5th (125), 7th (3.95)

Here’s where things get a bit intriguing. Recapping 2017, things appear to fall apart completely for the Cubs in the context of pitch framing.


The only catcher who was able to garner a positive framing rating was Kyle Schwarber, who caught just seven innings that year. But even his stats are far from impressive.

And how did Lester fair in terms of ‘phantom strikes’ that year? He ranked first in overall strikes called out of the zone (150) and fourth in total call ratio to pitches thrown (4.46).

He wasn’t all that far from the top under Ross, but was basically the frontman of the metrics in 2017.

Some things are hopelessly lost in the sphere of the unexplained. But, the research didn’t set out to find reasoning. In this case its more fun to be left with subjective theories. However, it’s a bit silly to think that there is actually an umpire conspiracy allowing Lester to succeed when he apparently shouldn’t.

My best guess is maybe they feel sorry for him since he can’t accurately throw the ball in the infield anywhere other than to the catcher (which did changed a bit in 2017)?

Regardless, Lester is our guy, here; receiving a sizable edge in terms of missed calls. It will be interesting to see if this trend continues this season.

The MLB Prize Money Solution

First things first, I’m an Englishman and baseball is my favourite sport [I’m English, so Americans, you’re getting an extra “u” there]. This makes me rather unusual in my home country. Most popular in English sport is soccer, a ball game you play with your foot [let’s not go there]. I’m guessing there may be a few Premier League fans here who read Fangraphs, but with the assumption that most of you won’t have much interest it, I have a suggestion to fix some of MLB’s financial problems by importing an EPL solution. MLB needs proper prize money.

I’ll address the elephant in the room. It might sound a bit mad to try and fix MLB’s financial problems by importing strategies from the Premier League, where free-market capitalism is stronger than in MLB and player contracts on small teams can be bought out by rich teams in player transfers. It made it hard to compete if you weren’t a rich team, until recently. In 2016, Leicester City became the only team in 20 years to break the strangle-hold the four richest teams have had on the Premier League title. One reason they managed it might be the way the EPL shares its money out.

Most money made in sport today comes from the TV rights deals made so fans can watch their teams play without needing to go to the stadium. The EPL does this better than any other sport, so despite coming from one of the world’s middle nations in terms of population, it brings in more money per team than almost any other. It does this by the league selling all the commercial rights collectively. This income gets shared out as prize money between the teams based on their records at the end of each season, with a good baseline for all. This meant that last year, Sunderland, finishing as the worst team in the league, got £93.5m ($132m – I’ll convert all figures to dollars from now on) and Chelsea, who won the league, got $212m in prize money. The worst team received around 62% of the prize money the best team did.

Baseball’s TV rights money is (mostly) negotiated separately team-by-team and is one major cause of the variance between incomes of the rich teams and the small teams. One reason the Yankees and Dodgers have the highest payrolls is because of the huge chunk of TV money they collect. The reason the Rays, Pirates and Marlins are at such a disadvantage is because they can’t collect anything like as much. The result is, in order to be competitive at all, they might need to consider losing more, draft well and return to competitiveness in a few years with good, cheap, young players. Financially, they don’t really lose out much by doing so and there are great risks in spending beyond their means in free agency to try and improve (both in results and financially).

It might also lead small and mid-market teams to be conservative in the free agent market. A lower income team might not risk signing a player to a big contract as one big mistake prevents them having the money to fund a good team with their financial burden. A common belief amongst Braves fans was their rebuild in 2014 was in part precipitated by the two big contracts that started producing little on-field value in Melvin Upton and Dan Uggla (together $25m for around zero WAR in 2013 and 6 years left on their contracts). With their good performance, the Braves might have made the playoffs. Without it, they didn’t have the money to replace them, leading to mediocrity or a rebuild. The risk of a big contract failing to provide value dissuades small-market teams from signing free agents much more than big market teams. This off-season’s cold stove has also seen high-income teams saving rather than spending. But there may be a way to encourage the lower-income teams to spend more:

Distribute significant prize money based on winning percentage at the end of each season, funded by collectivizing the TV rights money for all MLB teams. [You could also add a bonus pool for playoffs]

This would do a number of things. Assuming a breakdown of prize money like in the EPL, outlined above (worst team gets 62% prize money of the best team), it would give a higher baseline income to the very low-income teams like the Marlins/Rays/Pirates/A’s. The Marlins new management claimed the club’s losses required them to lay off their expensive contracts this winter. They flooded the market with good players which helped suppress the FA market. The Rays wouldn’t need to trade Longoria for money reasons. The A’s wouldn’t need to trade any good player they have who gets too expensive in arbitration.

The prize money would also provide better financial incentives for teams to try to win more games. The Royals made some significant payroll increases when they started winning in 2014-15 because of the extra revenue it generated. But the majority of that revenue came from their playoff appearances. This method would also provide some incentive to the mediocre teams. The last few years have seen mediocre teams like the Braves, Padres, White Sox and now the Marlins and Pirates trade away some decent core players through having little incentive to remain mediocre and aim upwards. Having good players on the trade market every offseason has (in part) led to the “super-teams” and “rebuilding-teams” dynamic and is likely helping suppress free agent contracts. Let’s say that being mediocre led to $30m more in prize money than being awful. That money might motivate some middling teams to spend and push upwards to the playoffs rather than trade downwards to a rebuild. Middling teams spending more might also threaten the positions of the “super-teams”, pushing them into spending more in the free agent market.


The EPL still hasn’t fully overcome its problem with rich teams winning the league every year and poor teams having no chance. Manchester City is amongst the league’s richest teams and are top of the league by a long way this season. Chelsea (another rich team) won last year. The original winning/financial inequality came about because the top four teams (Man U, Arsenal, Liverpool, Chelsea) qualified for the Champions League every year so got significantly more income (through its TV rights) than those who didn’t qualify. This led to the same four rich teams filling the top four positions in the league from 2003 to 2009, often because they could afford to buy other team’s best players. That aspect hasn’t really changed; only five teams have won the title in 20 years. However, as the prize money in recent years went above $80m for the bottom placed team, the mid- to lower-income teams were able to afford really great players too. If you watch every week you can’t help but notice how much more competitive every game is now. Rich teams are often beaten by poorer teams; Manchester United dropped out of the top four, so did Liverpool, replaced by Spurs and Manchester City; Leicester won the title. It made the league more competitive and better to watch. Perhaps until this year when the transfer market went bananas and suddenly $132m might not buy much anymore. This year’s Man City team could be like this year’s Yankees team. Rich and dominant. I’m sure fixes will be needed in future to stop the dominance of money. But baseball could still learn a thing or two from the Premier League.


Is ‘Tanking’ in Baseball Worth It?

With all the blabbing about the fire sale of the Miami Marlins, and less so with the Pittsburgh Pirates, does the philosophy of ‘tanking‘ in Major League Baseball work? Can it come to fruition the same way it does in the National Football League or the National Basketball Association?

The biggest and most obvious difference in those sports is the vast majority of players you’ll draft in the NFL or NBA are ready to play (even start) the following season. Not only that, players are more of a ‘sure thing’ in those leagues; you’re more likely to hit on a player since the pool is much more shallow than it is in baseball.

While in MLB, there are several levels to break through before you’re actually ready to play in the top-level.

Now, I understand the angle of ‘tanking’ to accumulate funds and eventually splurge on some free agents or wanting to make your team younger. I can follow that train of thought (sort of) but we are going to go on the premise that teams are doing it to grab top-level draft picks through each round.

Yes, the Houston Astros and Chicago Cubs, after many seasons of horrid baseball, are now World Champions thanks to patience and a great analytics department. Let’s just break even and apply this to your average front office.

According to research done by Cork Gains of Business Insider back in 2013:

“After three years, we will probably only see about 15% of this year’s draftees in the big leagues. And for most players, it will take 4-6 years to make it to the highest level”.

Let’s take a more recent peek at draft success within the first five rounds of the MLB draft. We only go that deep because, honestly, after that point (and even five rounds is a reach) it’s mostly a crap shoot and I’d venture to guess no team has any sort of advantage over the other.

I’m using five years as its reasonable to expect, even with a high schooler, to reach the big leagues within that amount of time.  Of players drafted in 2017, none have reached the majors; no surprise there. In 2016 just one player drafted, third-round pick Austin Hays of the Baltimore Orioles, has made it to the majors. We ought not to reference that year, either.

The 2015 draft is when we start seeing results.


Of first rounders in the 2015 draft, first overall pick Dansby Swanson debuted in 2017. Alex BregmanAndrew Benintendi, and Carson Fulmer all came up in 2016.

In the 2015 draft, out of 165 players picked in the first five rounds, 7% have made it to the big leagues.

It goes without saying the list grew considerably in 2014 and it follows that it would in subsequent drafts. Out of rounds one through five in 2014, we have 19% currently in the majors.


Let’s investigate the success rate by round, referencing research done by Mike Rosenbaumof Bleacher Report.



So the first round, you’re likely to get two out of every three players in the majors, then 50/50 in the second round. However, this chart is no reference for success or failure for the player once they do reach MLB.

The standard deviation of success rate drop-off is 4.96%, with a variance of 24.6%

Here, we’ll observe the first overall picks since 2006 and their yearly average WAR from all MLB seasons.


The first-overall pick in this era yielded about 50% averaging WAR over 3.2 (we’ll get to the context in a bit).

Lastly, let’s look at the cumulative WAR of rounds one through five, starting at 2011 to 2015. As mentioned before, there was just one player who reached MLB that was selected in the first five rounds during the last two years.


*Mookie Betts 24.1 WAR

So the chart lends itself to logic; the older the year, the higher the cumulative WAR. But, there is still a lot we don’t know yet as there are still players in the 2011 draft toiling in the minors, yet could break into the big leagues in the next year or so.

Yet, something funny happens. There is a spike in WAR once we get to round 5. As noted, the majority of WAR from that round in 2011 comes from Mookie Betts. Could we infer that later rounds will increase as well? Probably not, as the random variation would likely be all over the place player to player. But it’s not a stretch to assume that you can find just as much value in later rounds as you can in the first couple.

Obviously, the bigger success stories come from the first round. But, keep in mind that’s just one player. On a team of 25 guys, it’s less likely that this player can turn an entire franchise around by themselves. In the NBA its possible, or the NFL where a quarterback can pull a team out of mediocrity within a year or so.

I’ll average the first round pick WAR to get an idea of what a team who continually ‘tanks’, could expect to get out of first rounders for the next several years.

2011- 2.8
2012- 1.7
2013- 1.2

Again, this isn’t concrete information but it’s enough data to get a rough inference. If you ‘tank’ for several years, and get a high first round pick, you can expect to get a players who will average a WAR of about 3 (after about five years); the average WAR of a number one pick (using the data in the ’06-’16 chart) is a little better than 2.

So you’ve got a good shot to get a player considered decent or, at best, above average.

The following chart give some context on WAR for those unfamiliar.


Is it really worth ‘tanking’ in baseball? In, say, five years of mediocrity, how often can you expect to hit on a player in the early rounds (average 2.5+ WAR)? Again, in the first round (’06-’16) chart above, you’ve got a 50/50 shot. Is it worth driving your franchise into the ground with those kinds of odds?

Like I’ve said before, we are using a small amount of data that is on a sliding scale (the older the draft, the higher the WAR). Since it would take roughly 3-5 years for an organization to acquire draft picks that could break into the league and help push the team into championship contention, it’s not too far of a reach. Meaning you can expect your first couple of picks per year to start normalizing WAR after a couple of seasons…if they reach the majors at all at all.

So is ‘tanking’ worth it? Allegedly to the Marlins and Pirates, it seems to be. They have highly paid analysts and I’m a lowly blogger, so they know better than I do. But, with the information I’ve been able to acquire, it doesn’t seem as though stockpiling high picks will benefit an organization enough to risk losing fans, revenue, and respect in MLB in the short term.

Lorenzo Cain Follow-Up: Market Value and 2018 Projections

As one of the remaining top free agents, Cain agreed to a 5yr/$80M deal with the Milwaukee Brewers. Reports indicate that Cain will earn $75M from 2018 to 2022 ($13M/$14M/$15M/16M/$17M) plus incentives, in addition to a deferred yearly payment of $1M for years 2023-2027 ($5M total). Based on Cain’s projected Market Value, he should be worth close to 9.6 sWAR for the next 3 years ($84M approx. Assuming 5% inflation per year). Understandably, Cain’s history regarding injuries and durability is a considerable factor moving forward.

A new home should benefit Cain’s offensive output. For the past 3 years, Miller Park has averaged a 106 HR Factor for RHB; which is significantly (P = 0.03) higher than Kauffman Stadium’s 88 (RHB) in the same period. Moreover, Cain has not “slowed” down, his sprint speed for last year (29.1 ft./sec) ranked him in the top 20 overall, in addition to being ranked in the top 10 among Center-Fielders as well.

Based on the aforementioned factors, Cain’s 2018 updated projections represent an increase in both OPS and ISO from last season. He should be able to get on-base at an above-average rate (0.358 OBP) in addition to an increase in SLG from last year. Although his wOBA is projected to regress marginally, Cain’s updated projections still aim toward a 3.7 sWAR (as a CF) for this upcoming season. Please find Cain’s updated 2018 projections in the chart below.

2018 Projections: Lorenzo Cain

2015 29 5.5 0.360 0.361 0.477 0.838 0.170 0.307 16.2% 6.1%
2016 30 2.7 0.322 0.339 0.408 0.747 0.121 0.287 19.4% 7.1%
2017 31 4.1 0.347 0.363 0.440 0.803 0.140 0.300 15.5% 8.4%
2018 32 3.7 0.336 0.358 0.457 0.815 0.157 0.300 16.9%


———————————————————————————————————————————————————Disclaimer: SEG projections are computer-based projections of performance based on the “SEG Projections System” framework. Regarding Wins Above Replacement (WAR), sWAR is the “SEG Projection System” calculation of WAR. sWAR will always stand for WINS ABOVE REPLACEMENT (“WAR”), unless noted otherwise.


Michael Conforto Had a Unique 2017

A few days ago, I decided to start my 2018 Fantasy Baseball List. My process this year is about categorizing all players into groups that defines each player’s positive and negative traits, all based on league average stats. In this process, while linking players with similar traits, I found something interesting about Michael Conforto‘s 2017 season.

So, without further ado, here is the process and the math disclaimer:

First, I took all the players who had at least 900 PA between 2015-2017 (Averaging 300 PA per season), and while I evaluated a lot of the stats, the one who I’m talking about right now is Hard Contact %. Then, I found out the league average Hard Contact % of those three seasons altogether. After that, I took all the players who were league average and ran another average to know who were the ones on top. (I wanted to make this benchmarking process as easy as possible). I used the trait ”Ball Murderer” on those players.

After that, I evaluated a lot of other stats, but the focus right now is on FB%, LD%, GB% and wOBA. I took all players with at least 200 PA on both 2016 and 2017, and found out the players who made the biggest changes in each batted ball-type, using a similar process of average as before. For those players, I decided to use the trait ”Substantial Line Drive Increase”, ”Substantial Fly Ball Increase”, ”Substantial Ground Ball Increase” and ”Substantial wOBA Increase”. The same is true for decreasing values.

Then, I decided to see which players had a sustainable ”Substantial wOBA Increase”. What I wanted was to link every positive baseball process that I know that could derive into an increase of wOBA.

So, between all those links, it came the moment to answer a pretty easy question, which was: ‘‘Which players who hit the ball relatively hard on the last three seasons have made a batted ball type adjustment in order to increase their wOBA production”?

With this question, I thought I would get a narrow list of 5 – 10 players whose wOBA Increase would be backed up by this adjustment. To my surprise, I only found one name on it: Michael Conforto.

Conforto had a great season last year. He upgraded his BB%, upgraded his batting line to .279/.384/.555 and had a great .392 wOBA. Also, he became less pull happy and slightly upgraded his Hard Contact %. What most people could point out as a step back was that he lifted the ball less than last year.

While this might be true, Conforto showed an impressive upgrade on his LD%, which is a major factor behind his production upgrade. Going back to the name of this post, he was the only player who has hit the ball hard for the last three seasons (which I call ”Proven Hard Contact%), to upgrade substantially his LD% and wOBA. Another interesting aspect of this analysis is that no player who was tagged with the ”Ball Murderer” trait showed a substantial increase on his FB% and wOBA.

Conforto had a breakout season last year. He was characterized for lifting the ball in his career, and he lifted it less last year. But his great increase on LD% indicates that his results of 2017 were valid, and that he is a great and unique choice for your 2018 fantasy baseball team.

Looking Ahead to More Batted-Ball Data

Over the past two to there months, I have noticed a continuing trend in the baseball community of asking questions about batted baseball information. Coaches are seeking answers, community members are interested in finding potential diamonds in the rough for their fantasy teams, and real teams. I think with the baseball data which is currently public on the web it seems that we have now plateaued in terms of the information many smart people have gone through. Meaning that the most obvious, and illuminating answers have come from the data. Now the public is picking through the scraps.

I want to preface what I am saying by pointing out that the amount of data that we currently have is amazing and that shouldn’t be overlooked. People (myself included) are now thirsty for more information. More pieces to the puzzle. More answers to the game. More data to try to help players. As players and coaches continue to use devices like HitTrax, Rapsodo, and TrackMan to collect, organize, and judge players it is important to continue to not only educate the public but to also continue to push the envelope of information. These devices collect data and present it in a visual form to help players improve. Some of the metrics include Launch Angle, Exit Velocity, Distance. Which all are pretty common knowledge, and a general understanding among the community. One piece of information that is presented that I feel needs further research is spin rate, and spin axis of batted baseballs.

I suspect that the next big data for information that is released from MLBAM is going to be something on batted ball spin. Ideally, we could look into players data on both spin rate of batted baseballs and spin axis of those baseballs. There are pockets of the internet which now have access to a limited amount of data on this topic, and often times these people are putting out amazing work. Alan Nathan has published several studies on this topic, and a couple of articles looking at batted ball spin. As great as this data is you can get a general sense of the important from reading over the information which is now public. To GENERALLY SUMMARIZE lower Launch Angle Balls need more spin to gain distance, Higher Launch Angle balls would need an average amount of spin to go further. Baseballs that are over-spun or under-spun can lead to reduced distances in both cases.

Coaches have talked in the past about how true backspin on a batted baseball is one of the most important factors to hitting, but we don’t’ have an accurate depiction of this. How important is true backspin? Teams if they had access to Spin Axis of batted baseballs. This information can lead to a whole different conversation. One in which we can look into players who are slicing a ball or fading a ball. Think about it like a golf swing per say. We could have a more accurate look at not only which players are best at creating this backspin, but how much of an effect does this backspin have on batted balls Vs. side spin on batted baseballs?

Which devices like the ones mentioned above becoming more common among coaches and players I feel that these coaches, and players have no context for what they should be looking for. Great you created 4000 rmps of backspin on the ball? What does that mean for you? Oh you side spun it at 3500 rmps, what does that look like if you had more backspin on it? These coaches and players are thirsty for the knowledge, and they are left to make up their our thoughts on what these things mean for players. It isn’t fair to players, and even worse for coaches who are actively looking for the information to help these players. My last thought on this topic is that making the information public makes sense as more coaches are becoming familiar with this.

What happens after the batted ball data is public? (If that were to happen) It brings up an even more interesting topic in my mind. Once we have another piece to the batted ball puzzle we can now start to create mathematical formulas that look into how player are striking the baseball. People have already figured out how to get the attack angles (estimated) on batted baseballs, the spin rate would help to finalize these types of formulas.

Lastly, we would be able to hopefully calculate the Offset of the bat-ball collision. Simply put this “offset” is point that the baseball makes contact with the baseball. Hitting the baseball square, vs. miss hitting it above or below the center of the barrel. This would bring up a bunch of other questions like which player are best at successfully missing the barrel effectively enough to create the best attack angle, back spin, and offset.

More information will continue to be produced, and this data will help baseball move forward. People are asking the questions. Coaches, and Players are seeking the answers. Data holds the answers.

What’s Next for the Pirates?

It’s been about several weeks since the Pirates parted ways with both Gerrit Cole and Andrew McCutchen, the former to Houston, and the latter to San Francisco. Most fans and analysts expect Josh Harrison to be next, and by the looks of it, that’s what he’d prefer.

Some would consider the Pirates to be rebuilding, while others suggest it may be somewhat of a retooling, hoping that some names that were expected to work out, but suffered setbacks either last year or culminating throughout the last several years (like Marte and Polanco) will bounce back or reach expectations.

That, coupled with players breaking through and reaching their potential (like Bell and Taillon), along with other young players (as though the Pirates have any other type of player now) like Trevor Williams, who showed a lot of promise last year, or Steven Brault, who pitched very well at AAA Indianapolis, perhaps the Pirates can field a winning team. It doesn’t hurt that they inked one of the top relievers in the game, Felipe Rivero, who emerged with a breakout season last year, to a four-year deal.

But most Pirates fans aren’t buying it. There was even a petition started on for “MLB to force Bob Nutting to sell the Pirates”, and to this date it has reached 59,456 signatures. Of course, there is basically no chance that this petition will actually result in anything.

Before both trades, the Pirates projected win total by FanGraphs was a whopping 81. After the Cole trade, it went to- er- stayed at, 81. It did move, though, once McCutchen was dealt, dropping from 81 to 78, which would still be three wins better than last year’s club, which might cause some to say that technically the team is improving, even if it’s by the most basic metric; of course, most would say that’s nonsense.

Last year, the Pirates were plagued by a multitude of problems, from Marte’s PED suspension, a plethora of injuries to, well, everyone, and even to Taillon missing time due to testicular cancer (which he brilliantly rebounded from, appearing as a starting pitcher just five weeks after surgery). Not to mention Jung-ho Kang’s off-field issues and inability to return to the team. The Bucs suffered a six-game setback from last year’s projections where they were expected to go 81-81. They finished six games below that total, winning 75 contests.

The Pirates were basically destined to fail last year. Now many believe that the Pirates are in store for the same fate this year after departing with two of the franchise’s marquee players.

A lot of the Pirates roster will look strikingly similar to last season, except for those received in the trades, which includes: 3B Colin Moran, P Joe Musgrove, P Kyle Crick, P Michael Feliz, among several other pitchers not involved in either of those trades, Nik Turley, Jack Leathersich, and Jordan Milbrath. It is unlikely that those players will make that big of an impact.

It’s possible, without any major injuries, the contributions the Pirates expected to receive last year will be more likely to reach fruition this year. If Gregory Polanco has the kind of breakout season people felt like he might have when the Pirates first acquired him, it’s possible for him to be a 5.0 WAR player. A litany of injuries prevented him from coming anything close to that last year, registering a 0.5 WAR, but with glimpses of power in his minimal contributions.

The same is true for Starling Marte. In 135 games for the Pirates in 2013, Marte posted a 4.8 WAR and 122 wRC+. We are all aware of Marte’s 80 game suspension following him testing positive for performance-enhancing drugs prior to the 2017 season. When he returned, he failed to be the player the Pirates hoped he’d be, and of course, he’ll have a lot to prove after his suspension in his first full season, but it isn’t completely insane to think he might experience a resurgence.

Josh Bell had a breakout season for the Bucs in his rookie campaign, perhaps positioning himself to be the next face of the franchise. Bell registered a 1.4 WAR last year and 113 wRC+, a .338 wOBA, and an OPS of .800. He hit for significantly more power than was expected of him, blasting 26 cannonballs, which was 12 higher than his 2016 total in AAA Indianapolis, playing nearly every game (159) in 2017. If Bell continued to grow this offseason, it’s entirely possible he’ll repeat in some statistical categories, like home runs wOBA, and OPS, and improve in others, like BABIP (.278), making him a very legitimate threat in the middle of the order.

Joe Musgrove, whom the Pirates acquired from Houston, showed that he may have the stuff for a solid third in the rotation type pitcher. Musgrove appeared in 38 games for Houston last year, starting 10 of them, posting a FIP of 4.38, an ERA- of 113, and an xFIP of 4.03. Those numbers are about in-line for a 5 starter, most likely, but PNC is one of the most “pitcher friendly” parks in baseball. Also, I’m not one to chalk up occurrences to magic, but Ray Searage has worked some serious voodoo in the past, and that could likely be the case here, especially with Musgrove who is by no means a lost cause pitcher to begin with. Additionally, Musgrove throws pretty hard, last year registering his fastball around 93.5 mph, his cutter a tick over 90, and a slider around 92.

Colin Moran will likely see the most time at 3B this season, as David Freese’s production levels just don’t quite reach what they should to warrant starting everyday, especially with a young player like Moran waiting in the wings. Jeff Sullivan wrote an article highlighting Colin Moran’s swing change, and some of the numbers were glaring. During seasons 2013-2016, Moran sat around 50% ground balls, and with the way baseball’s evolved, that’s not really a good thing. But in 2017, that number was strikingly different. Moran hit a ground ball only 34% of the time. With his decrease in ground balls came an increase in home runs. He had a previous high of 10 in AAA with far more at bats than his 18 in 2017 during his AAA campaign.

Lastly, Michael Feliz, another piece from the Astros, comes to Pittsburgh after having posted interesting numbers in 2017. Firstly, Feliz throws hard, reaching the high 90s with his fastball, averaging nearly 97 mph in 2017. He posted a FIP of 3.78, an xFIP of 3.58, and an xFIP- of 81. Feliz will likely be a strong complement to Felipe Rivero out of the pen.

Help will have to also come from players being called up from AAA for the first time (Meadows, who suffered setbacks last year on the DL, Keller, perhaps Bryan Reynolds, among others), but if some things break the right way, the Pirates may experience more success than originally anticipated. Don’t misunderstand me, I’m not saying the Pirates will be in contention for the NL Central this year, or even a Wild Card spot. I’m saying the potential is there for them to rebound from last year and finish the season above .500 at 82-80, especially if they can capitalize on a flailing Reds team, as well as in games against the largely inept NL East.

But barring a major outbreak by a lot of guys, the Pirates will likely be an average to below average team (somewhere along the lines of 75-87 to 79-83). It wouldn’t surprise me for them to finish better than last year, if not just for the sheer manpower versus last year, and hopefully not having to deal with such setbacks.

But when is it most likely the Pirates will be able to actually contend? The front office will say 2019 at the earliest, and there’s some credence to that.

Mitch Keller is projected to make his debut sometime this season, ranked 16th overall, and is the Pirates best prospect. Austin Meadows, ranked 45th, is expected to make his debut this season, as well. The last of the Pirates top 100 prospects, Shane Baz (67th) isn’t expected to make his debut until 2021, and hopefully, the Pirates are competing before then.

From the Pirates own top 30 list, several potentially important players are expected to debut in 2018, including Nick Kingham and Bryan Reynolds (the latter of whom came over in the McCutchen trade). 2019 will see a string of more players, and if they make an impact right away could yield a winning ball club, like Ke’Bryan Hayes and Cole Tucker. If you combine their potential productivity with the progression of guys that are already there and guys that are debuting this season, the Pirates could be returning to a similar place as their winning years, 2013-2015, in as little as two seasons.

Although it should be noted, the Pirates most successful years weren’t necessarily fueled by prospects. When Gerrit Cole debuted in 2013, one of the Pirates most successful seasons, he was really the only major prospect getting to play at that time, while the majority of the roster was comprised of veteran holdovers from the season before.

What that could potentially mean is that perhaps 2019 isn’t necessarily a possibility in terms of being competitive. Perhaps a more realistic timetable is 2021. By that time, Starling Marte will be in the final year of his contract at age 32, and likely his last year as a Pirate, and assuming he’s able to rebound, will be in the latter part of his most productive years. Gregory Polanco, if he’s able to reach his potential, will be in his Age 29 season and possibly at the peak of his ability. Moreover, by 2021, most of the players we’ve discussed will have had time to fully develop, like Josh Bell and Jameson Taillon, plus any guys coming up over the next two seasons.

All of these guys won’t pan out, but there’s a pretty good chance some of them will, and that’s the best an organization and fan base can hope for (except for the Astros who have seemed to hit the jackpot in every regard). The team will also need to be supplemented by veterans, and not just the cheap ones. For the Pirates to make a run and win between 2019-2023, the front office is going to need to spend more money than they were willing with Gerrit Cole and Andrew McCutchen.

There are a lot of hypotheticals in the Pirates future, but there truly is a lot to be excited about. I know it’s a difficult thing to request of Pirates fans, but this transition will require patience. That, and the front office attempting to provide more from the outside in free agency or big trades, and probably both. There is a lot the front office has done right in the past; unfortunately, though, there is also a lot its done wrong. We’ve seen the front office make some truly good trades, having the insight to know when guys have passed their peak and flipped them at the right time (like the acquisition of Rivero). But there will have to come a time where they send prospect packages for big-time players; if not, the Pirates may not see a real contender until ownership changes.

Joe Biagini’s xwOBA and RISP Spread

Would you believe me if I told you that Joe Biagini did a better job minimizing contact quality last year than Marcus Stroman?  I didn’t believe it at first, but it turns out he had slightly better contact quality control on the whole. See the arranged summary table below: (min 500 pitches, showing data for batted balls)

player_name xwOBA
1 Danny Barnes 0.306589888
2 Aaron Loup 0.307355422
3 Roberto Osuna 0.326513158
4 Joe Biagini 0.334128342
5 Ryan Tepera 0.33518593
6 J.A. Happ 0.339169336
7 Dominic Leone 0.340634286
8 Marco Estrada 0.341392086
9 Marcus Stroman 0.355793677
10 Joe Smith 0.368621951
11 Francisco Liriano 0.371231373
12 Aaron Sanchez 0.37646281
13 Mike Bolsinger 0.378370079

xwOBA in this case is a statcast proxy for contact quality, based on launch speed and angle. I’d go on a limb to say it essentially imputes an expected number of wOBA based on the quality of how the hitter squared up the ball, irrespective of what happens after that. Last year, the average xwOBA for a Blue Jays pitcher included in this sample above was 0.344.

Interesting. Biagini’s contact quality was fourth best on the team, but his ERA was third highest among the group. The two higher ERAs were Liariano and Bolsinger (also 11th and 13th highest expected wOBA). This is an example of how situational pitching can ruin you if you let it. Let’s factor in runners in scoring position and compare the same analysis. Below is the same table, except now showing one for runner in scoring position (RISP), 0 for not:

player_name RISP xwOBA
1 Joe Biagini 0 0.299694737
2 Aaron Loup 0 0.302101695
3 Roberto Osuna 0 0.309736842
4 Ryan Tepera 0 0.325156463
5 Danny Barnes 0 0.339150794
6 J.A. Happ 0 0.350223214
7 Marco Estrada 0 0.350911628
8 Marcus Stroman 0 0.352419087
9 Francisco Liriano 0 0.363611702
10 Dominic Leone 0 0.364512605
11 Aaron Sanchez 0 0.369082353
12 Mike Bolsinger 0 0.372388235
13 Joe Smith 0 0.395754386
14 Danny Barnes 1 0.227692308
15 Dominic Leone 1 0.289892857
16 J.A. Happ 1 0.30239604
17 Joe Smith 1 0.30676
18 Marco Estrada 1 0.308904762
19 Aaron Loup 1 0.320270833
20 Ryan Tepera 1 0.363538462
21 Marcus Stroman 1 0.369462185
22 Roberto Osuna 1 0.376842105
23 Mike Bolsinger 1 0.39047619
24 Francisco Liriano 1 0.39261194
25 Aaron Sanchez 1 0.393888889
26 Joe Biagini 1 0.444393258

Look at the two Biaginis! At the very top and very bottom. Without runners in scoring position, Biagini was the best pitcher on the roster in terms of limiting contact quality. Put a guy in scoring position, and he starts getting lit up. Here’s that same table, but sorted by the differences.

player_name RISP.x xwOBA.x RISP.y xwOBA.y diff
1 Danny Barnes 0 0.339150794 1 0.227692308 -0.111458486
2 Joe Smith 0 0.395754386 1 0.30676 -0.088994386
3 Dominic Leone 0 0.364512605 1 0.289892857 -0.074619748
4 J.A. Happ 0 0.350223214 1 0.30239604 -0.047827175
5 Marco Estrada 0 0.350911628 1 0.308904762 -0.042006866
6 Marcus Stroman 0 0.352419087 1 0.369462185 0.017043098
7 Mike Bolsinger 0 0.372388235 1 0.39047619 0.018087955
8 Aaron Loup 0 0.302101695 1 0.320270833 0.018169138
9 Aaron Sanchez 0 0.369082353 1 0.393888889 0.024806536
10 Francisco Liriano 0 0.363611702 1 0.39261194 0.029000238
11 Ryan Tepera 0 0.325156463 1 0.363538462 0.038381999
12 Roberto Osuna 0 0.309736842 1 0.376842105 0.067105263
13 Joe Biagini 0 0.299694737 1 0.444393258 0.144698522

Biagini was not the same person on the mound when threatened with a runner past first. To offer some perspective, that very difference is larger than that between Mike Trout (1st @ 0.437) and Kevin Pillar (130th @ 0.302). You must wonder what some possible explanations of this could be .Sign stealing? The yips? Pitch selection? Let’s look at the 2016 differences table and see if this affected him at all. (min 500 pitches)

player_name RISP.x xwOBA.x RISP.y xwOBA.y diff
1 Jason Grilli 0 0.429316 1 0.229538 -0.19978
2 Drew Storen 0 0.475368 1 0.320645 -0.15472
3 Joe Biagini 0 0.345207 1 0.270319 -0.07489
4 R.A. Dickey 0 0.39697 1 0.337583 -0.05939
5 Aaron Sanchez 0 0.366077 1 0.328248 -0.03783
6 Roberto Osuna 0 0.386113 1 0.350638 -0.03547
7 Brett Cecil 0 0.411931 1 0.379067 -0.03286
8 Marcus Stroman 0 0.362472 1 0.334 -0.02847
9 Francisco Liriano 0 0.351901 1 0.369333 0.017432
10 J.A. Happ 0 0.363058 1 0.386705 0.023646
11 Marco Estrada 0 0.335267 1 0.359102 0.023835
12 Jesse Chavez 0 0.340885 1 0.48161 0.140725

Runners on second and or third in 2016, Biagini pitched to better contact quality.  He was coming out of the bullpen, but it still leaves our question of consistency from last year unresolved. It wasn’t Biagini’s pitch selection either. Based on the table below, his distribution of pitches with and without RISP last year was more or less the same. It’s not as though he wasn’t throwing the breaking ball with RISP.

pitch_type RISP n Frequency
1 CH 0 207 0.146393
2 CU 0 278 0.196605
3 FC 0 145 0.102546
4 FF 0 784 0.554455
5 CH 1 80 0.154739
6 CU 1 138 0.266925
7 FC 1 39 0.075435
8 FF 1 260 0.502901

I don’t know what the real explanation for this is. It likely could just be chance, but I’d like to think there’s a more probable explanation for it. I say the yips! Pitchers aren’t robots, some pitchers must get phased more than others by the pressure of potential runs scoring. But last year on the whole Toronto pitching allowed very similar contact quality regardless of having runners in scoring position.

1 0 0.343178
2 1 0.345718

P.S. first time posting! let me know what you think. had a lot of fun doing this.