Hardball Retrospective – What Might Have Been – The “Original” 1908 Cardinals

In “Hardball Retrospective: Evaluating Scouting and Development Outcomes for the Modern-Era Franchises”, I placed every ballplayer in the modern era (from 1901-present) on their original team. I calculated revised standings for every season based entirely on the performance of each team’s “original” players. I discuss every team’s “original” players and seasons at length along with organizational performance with respect to the Amateur Draft (or First-Year Player Draft), amateur free agent signings and other methods of player acquisition.  Season standings, WAR and Win Shares totals for the “original” teams are compared against the “actual” team results to assess each franchise’s scouting, development and general management skills.

Expanding on my research for the book, the following series of articles will reveal the teams with the biggest single-season difference in the WAR and Win Shares for the “Original” vs. “Actual” rosters for every Major League organization. “Hardball Retrospective” is available in digital format on Amazon, Barnes and Noble, GooglePlay, iTunes and KoboBooks. The paperback edition is available on Amazon, Barnes and Noble and CreateSpace. Supplemental Statistics, Charts and Graphs along with a discussion forum are offered at TuataraSoftware.com.

Don Daglow (Intellivision World Series Major League Baseball, Earl Weaver Baseball, Tony LaRussa Baseball) contributed the foreword for Hardball Retrospective. The foreword and preview of my book are accessible here.


OWAR – Wins Above Replacement for players on “original” teams

OWS – Win Shares for players on “original” teams

OPW% – Pythagorean Won-Loss record for the “original” teams

AWAR – Wins Above Replacement for players on “actual” teams

AWS – Win Shares for players on “actual” teams

APW% – Pythagorean Won-Loss record for the “actual” teams


The 1908 St. Louis Cardinals 

OWAR: 29.2     OWS: 247     OPW%: .375     (58-96)

AWAR: 13.5       AWS: 146     APW%: .318   (49-105)

WARdiff: 15.7                        WSdiff: 101  

Despite a dismal effort and last-place finish, the “Original” 1908 Cardinals bested the “Actual” Redbirds by a 9-game margin and a confounding Win Shares differential of 109. “Turkey” Mike Donlin (.334/6/106) tallied 198 base knocks, pilfered 30 bags and recorded a career-high in ribbies. Fellow outfielder Charlie “Eagle Eye” Hemphill swiped 42 bases and batted .297 for the “Original” Cardinals. Red Murray supplied a .282 BA with 48 stolen bases for both the “Original” and “Actual” Redbirds.

Mordecai Brown ranks twentieth among pitchers according to Bill James in “The New Bill James Historical Baseball Abstract.” “Original” Cardinals teammates listed in the “NBJHBA” top 100 rankings include Ed Konetchy (48th-1B) and Mike Donlin (52nd-CF).

  Original 1908 Cardinals                             Actual 1908 Cardinals

Charlie Hemphill LF/CF 3.11 25.83 Joe Delahanty LF -0.89 13.78
Red Murray CF 2.92 25.78 Red Murray CF 2.92 25.78
Mike Donlin RF 5.8 31.2 Al Shaw RF/CF -0.3 10.83
Ed Konetchy 1B 1.65 16.9 Ed Konetchy 1B 1.65 16.9
Chappy Charles 2B -2.75 2.31 Billy Gilbert 2B -1.13 3.61
Freddy Parent SS 1.89 11.89 Patsy O’Rourke SS -1.02 0.64
Bobby Byrne 3B -1.61 3.31 Bobby Byrne 3B -1.61 3.31
Art Hoelskoetter C -0.24 2.21 Art Hoelskoetter C -0.24 2.21
Joe Delahanty LF -0.89 13.78 Shad Barry RF -0.53 4.25
Al Shaw CF -0.3 10.83 Chappy Charles 2B -2.75 2.31
Al Burch LF 0.18 10.72 Jack Bliss C 0.12 2.18
Spike Shannon LF -0.85 7.58 Bill Ludwig C -0.02 1.4
Jack Bliss C 0.12 2.18 Wilbur Murdoch LF -0.21 1.3
Bill Ludwig C -0.02 1.4 Champ Osteen SS -0.81 0.41
Wilbur Murdoch LF -0.21 1.3 Charlie Moran C -0.44 0.28
Patsy O’Rourke SS -1.02 0.64 Walter Morris SS -0.65 0.25
Art Weaver C -0.1 0.33 Doc Marshall C -0.07 0.18
Charlie Moran C -0.44 0.28 Tom Reilly SS -0.58 0.13
Walter Morris SS -0.65 0.25 Ralph McLaurin LF -0.14 0.09
Tom Reilly SS -0.58 0.13
Ralph McLaurin LF -0.14 0.09
Simmy Murch 1B -0.06 0.06

Mordecai “Three-Finger” Brown, in the midst of six straight seasons with 20+ victories, furnished a 29-9 record with a 1.47 ERA and a career-best WHIP of 0.842. He completed 27 of 31 starts and saved 5 contests in 13 relief appearances for the “Original” Cardinals. Billy Campbell contributed 12 wins with a 2.60 ERA and a 1.116 WHIP in 221.1 innings. “Actuals” ace Bugs Raymond suffered through a 15-25 campaign despite a 2.03 ERA and 1.021 WHIP. Johnny Lush (11-18, 2.12) endured similar results as the Redbirds rotation was unable to overcome a lackluster offense.

  Original 1908 Cardinals                            Actual 1908 Cardinals

Mordecai Brown SP 6.62 31.34 Bugs Raymond SP 1.97 21.04
Billy Campbell SP -0.96 10.38 Johnny Lush SP 0.26 14.3
Art Fromme SP -1.45 3.61 Fred Beebe SP -2.13 5.63
Slim Sallee SP -1.61 3.19 Ed Karger SP -1.87 3.69
Jake Thielman RP -0.34 3.78 Art Fromme SP -1.45 3.61
Irv Higginbotham SP -0.9 3.1 Slim Sallee SP -1.61 3.19
Charlie Rhodes RP -0.05 1.67 Irv Higginbotham SP -0.9 3.1
Stoney McGlynn SP -1.16 1.23 Charlie Rhodes SP 0 1.4
O.F. Baldwin SP -0.46 0 Stoney McGlynn SP -1.16 1.23
Buster Brown RP -0.39 0 O.F. Baldwin SP -0.46 0
Fred Gaiser RP -0.13 0 Fred Gaiser RP -0.13 0

Notable Transactions

Mordecai Brown

December 12, 1903: Traded by the St. Louis Cardinals with Jack O’Neill to the Chicago Cubs for Larry McLean and Jack Taylor.

Mike Donlin

Before 1901 Season: Jumped from the St. Louis Cardinals to the Baltimore Orioles.

Before 1902 Season: Released by the Baltimore Orioles.

August, 1902: Signed as a Free Agent with the Cincinnati Reds.

August 7, 1904: Traded as part of a 3-team trade by the Cincinnati Reds to the New York Giants. The New York Giants sent Moose McCormick to the Pittsburgh Pirates. The Pittsburgh Pirates sent Jimmy Sebring to the Cincinnati Reds.

Charlie Hemphill

March 2, 1901: Jumped from the St. Louis Cardinals to the Boston Americans.

Before 1902 Season: Signed as a Free Agent with the Cleveland Bronchos.

June, 1902: Released by the Cleveland Bronchos. (Date given is approximate. Exact date is uncertain.)

June 4, 1902: Signed as a Free Agent with the St. Louis Browns.

August 23, 1905: Purchased by the St. Louis Browns from St Paul (American Association). (Date given is approximate. Exact date is uncertain.)

November 5, 1907: Traded by the St. Louis Browns with Fred Glade and Harry Niles to the New York Highlanders for Hobe Ferris, Danny Hoffman and Jimmy Williams.

Honorable Mention

The 1983 St. Louis Cardinals 

OWAR: 54.8     OWS: 310     OPW%: .517     (84-78)

AWAR: 36.1     AWS: 237     APW%: .488   (79-83)

WARdiff: 18.7                        WSdiff: 73 

The “Original” 1983 Cardinals seized the National League Eastern Division flag by a single game over the Expos. The flock featured left fielder Jose Cruz (.318/14/92), the NL leader with 189 base hits. “Cheo” reached the 30-steal mark for the fifth time in his career. Terry Kennedy (.284/17/98) registered a personal-best in RBI. Keith Hernandez earned the sixth of eleven consecutive Gold Glove Awards. John Denny (19-6, 2.37) merited the NL Cy Young Award. Larry Herndon notched personal-highs in batting average (.302), hits (182), doubles (28) and RBI (92). Ted “Simba” Simmons delivered a .308 BA with 39 two-baggers and 108 ribbies. Steve “Lefty” Carlton whiffed 275 batsmen and fashioned a 3.11 ERA. George Hendrick (.318/18/97) received his fourth All-Star invitation and posted a career-high in batting average for the “Actual” Redbirds.

On Deck

What Might Have Been – The “Original” 1975 Astros

References and Resources

Baseball America – Executive Database


James, Bill. The New Bill James Historical Baseball Abstract. New York, NY.: The Free Press, 2001. Print.

James, Bill, with Jim Henzler. Win Shares. Morton Grove, Ill.: STATS, 2002. Print.

Retrosheet – Transactions Database

The information used here was obtained free of charge from and is copyrighted by Retrosheet. Interested parties may contact Retrosheet at “www.retrosheet.org”.

Seamheads – Baseball Gauge

Sean Lahman Baseball Archive

Where Art Thou, Yan?

It seems that more and more often, we as baseball fans are constantly trying to “diagnose” the cause of a specific player’s struggles, and give our two cents on if everyone should — in the words of Aaron Rodgers — relax, or be concerned about the player’s deficiencies. I am not sure what it is; maybe it’s because talking about other people’s problems makes us forget about our own. Maybe it’s because we as humans simply like to tell other people how to do their jobs, because it makes us feel important. No one will truly ever know the exact answer to that question. With that being said, however, I am going to do exactly what I just talked about the previous four sentences; I am going to try to explain what is going on with Yan Gomes. In his first two seasons with the Tribe (223 games total), he accumulated 7.8 WAR, won a Silver Slugger award in 2014, and drew positive reviews for his framing abilities according to Baseball Prospectus (ranked 17th out of 417 catchers in 2013 and 32nd out of 382 in 2014 in the Framing Runs statistic). Framing runs essentially shows how many runs a catcher saves throughout a given season based on how many extra strikes they are able to get their pitchers from their framing abilities. The Indians, seeing a young and talented player still required to go through the arbitration process for several more years, locked Gomes up to a six-year, 23-million-dollar contract before the 2014 season. Taking a look at this chart, the Indians’ felt they were in for a huge bargain.

Year Age Salary (in millions) WAR est. $/WAR Value (in millions)
2014 27 0.6 3.5 7.6 26.6
2015 28 1 3.15 8.2 25.8
2016 29 2.5 2.84 8.8 24.9
2017 30 4.5 2.55 9.4 24.0
2018 31 6 2.17 10.0 21.7
2019 32 7 1.84 10.6 19.5
Total 23 (includes 0.5 million signing bonus) 142.6
Surplus Value 119.6 M


To briefly explain my methodology, I used the estimates for dollars per WAR (which adjusts for inflation) from an article by Matt Swartz from Hardball Times, and adjusted Gomes’ overall WAR per year by the generally accepted decline rates laid out by Dave Cameron of FanGraphs a few years back. Players on average perform at 90% of their previous year’s WAR output through age 30, 85% from 31-35, and 80% from 36 and up. When the Indians signed Gomes, he was coming off a 3.3 WAR season. Considering he was going into his age-27 season, he was probably nearing his peak year in terms of WAR. Therefore, right or wrong, I believe his true-talent level (and what the Tribe were expecting from him) in 2014 was right around 3.5 WAR. I adjusted his yearly totals accordingly until his contract expired — I did not incorporate team options for 2020 and 2021 into this. The Indians receive roughly 120 million dollars in surplus value for the length of Gomes contract, which would be an incredible deal for a small-market team.

Obviously, Gomes went out in 2014 and produced a 4.5 WAR season, even further increasing the bargain for the Tribe in the early goings of the deal. Since 2014, however, Gomes hasn’t been the same player at the dish. His defense still grades out favorably according to many defensive metrics, but his bat appears to have taken a big step back. It isn’t fair to judge him on 2015, considering he was injured early on in the season and never fully recovered. This year, there isn’t an injury excuse — that we know of anyways. Gomes is slashing a dismal .167/.204/.353 at the plate, and has been worth just 46 wRC+, meaning his hitting has been 54% worse than league average. Few things of merit before jumping into a more detailed analysis: he is running a .174 BABIP, which is tremendously lower than his career average of .302 and upon regression will raise his average. His walk rate is about the same, and he is only striking out 3% more than his “peak” season of 2014. While a 3% rise in strikeout percentage isn’t minuscule, Gomes has always been known as a free swinger (over the last four years, he is in the 75th percentile in swinging strikes and 83rd percentile in swing percentage).

So, the big question here is, what specifically is causing Gomes’ struggles? I am going to try to be as systematic as possible here, so that everything kind of builds upon itself. To quickly summarize his plate discipline statistics — because I don’t think there are really any surprises here — his out of zone, zone, and overall swing percentages in comparison to his career have increased, and his out of zone and overall contact percentages have decreased. I am not sure why his Z-Contact% has increased, but I don’t think that is of much consequence. It is clear that Gomes is swinging more, and making contact less.

Turning to his batted-ball statistics, there are several important changes that start to paint a better picture of why Gomes is struggling. For ease of communication, I have split the information into two tables below.

Season Team GB/FB LD% GB% FB% IFFB%
2012 Blue Jays 1.28 14.9% 47.8% 37.3% 8.0%
2013 Indians 1.12 17.8% 43.5% 38.7% 11.2%
2014 Indians 0.93 24.0% 36.7% 39.4% 9.6%
2015 Indians 0.84 26.4% 33.6% 40.0% 11.3%
2016 Indians 0.76 18.9% 35.1% 45.9% 14.7%

Notice how in all of Gomes’ professional seasons, his groundball-to-fly-ball ratio has gone down. This could be considered a good thing, since he does possess a ton of raw power, and everyone knows you can’t hit home runs on the ground — okay, technically you can, but Gomes doesn’t have Dee Gordon speed. The next thing that jumps out is his 14.7% pop-up rate, which is good for 25th highest out of 192 qualified hitters. His increased fly-ball rate, coupled with his bloated IFFB%, could explain why his BABIP is so low — balls in the air are caught more often than balls on the ground. More importantly, though, it seems that there could be a pitch-recognition problem, considering his isn’t quite squaring up balls as consistently as he has in the past. To go into this concept further, let’s take a look at the next chart.

Season Team Pull% Cent% Oppo% Soft% Med% Hard%
2012 Blue Jays 52.9% 31.4% 15.7% 7.1% 62.9% 30.0%
2013 Indians 42.2% 31.7% 26.1% 14.3% 53.5% 32.2%
2014 Indians 42.6% 30.2% 27.2% 16.4% 52.6% 31.0%
2015 Indians 37.4% 37.0% 25.7% 16.6% 55.5% 27.9%
2016 Indians 44.6% 40.5% 14.9% 20.3% 54.1% 25.7%

Gomes is pulling the ball more than he ever has in his entire career — excluding the cup of tea he had in the bigs in 2012. Not to mention, he has basically abandoned taking the ball the other way. Looking at his quality of contact stats, he is hitting the ball “hard” less often than he typically has throughout his career, too.

Sure enough, Gomes has been below the league average in exit velocity for the majority of the season. So, to recap what I have already found, Gomes is hitting a ton of fly balls and pop-ups, is pulling the ball more and taking it the other way less, and is hitting the ball softer than usual. What does this all mean? I think it illustrates that Gomes is struggling with breaking balls.

Looking at Gomes’ spray angles against hard, breaking, and offspeed pitches, it appears that he is not recognizing breaking balls well this season.

For those that aren’t familiar with Brooks Baseball’s spray angle data, it essentially shows the average direction which balls are hit on the field. So, a positive spray angle (as depicted on the graph) means that the hitter tends to pull that pitch, and a negative spray angle means they tend to take it the other way. A recent FanGraphs Community Blog post by an author named Brad McKay explained the significance of spray angle well, in my opinion. He surmised that similar spray angles for different pitch types suggests that a player “was able to recognize and wait back equally well for both pitch types,” something that I happen to agree with. Looking at Gomes’ Silver Slugger Award winning year, it appears that Gomes tracked and hit fastballs and breaking balls at a similar spray angle, while also hitting offspeed pitches almost identical as well. This shows that Gomes was picking up the ball well in 2014. Fast-forward to 2016, and you can see that those angles have changed, and Gomes is now pulling breaking balls more than he does against fastballs. This suggests that something isn’t right with Gomes’ pitch recognition. He has almost reverted back to more of what he was in 2013. Interestingly enough, Gomes hit really well that season in 88 games played. The difference from then to now, however, is the pitch sequencing.

The approach against him has done a complete 180. The lefties — who used to pound him with fastballs when ahead in the count — now go to their breaking balls, while the righties — who used to pound him with breaking balls when ahead in the count — now attack him with fastballs. Essentially, the way pitchers (both lefties and righties) attacked Gomes in 2013 is consistent with how one would traditionally pitch to an aggressive, right-handed power bat. Here’s what I think has happened now. Pitcher’s have realized that Gomes is not picking up breaking balls the way he was in the past, causing him to have to sit breaking ball on the majority of pitches. He does this with the hopes of picking up the breaking ball early enough to decide whether to swing or not swing. With this in mind, right-handed pitchers know that because Gomes is sitting breaking ball, he will have a harder time catching up to the fastball many times. Simultaneously, left-handers know that they can attack him earlier with their fastballs (which are generally a pitch righties see well from lefties) to get ahead in the count, and then try to put him away with the breaking ball. In a sense, Gomes is completely and utterly discombobulated at the plate. Here are his heat maps vs. righties, broken down into “hard stuff” and “breaking balls.”As expected, the “hard stuff” is up, while the breaking balls are started over the middle of the plate and break down and away. Next, the lefties.

Lefties have attacked him with fastballs low, and inside, and use this to set up the breaking ball on Gomes’ back foot, which is incredibly difficult to hit (especially for someone not picking up those types of pitches well). Gomes is hitting .177 against the 55 sliders he’s seen this year, and is hitting .000 against the 35 curveballs he’s seen. His averages against harder pitchers are not much better.

Now that we have identified the problem, is there a way to fix it? I don’t know what Gomes is doing behind the scenes, but in my opinion there are three different ways to go about this. For one, I think Gomes should study the way pitchers are attacking him (which I would assume he is already doing). Using this knowledge, I think Gomes could benefit from being a little more patient at the plate. Instead of swinging out of his shoes all the time, he might be better suited remembering how pitchers are attacking him, and waiting on a pitch he not only can drive, but knows is most likely coming (helping to eliminate the guessing game he is playing right now). Lastly, I think he could simply practice recognizing pitches on the pitching machines teams have in the clubhouse. Gomes could spend time every day tracking a set amount of pitches, working to improve his ability to discern spin on the baseball upon its release. Then, he could put that pitch recognition to the test by actually attempting to hit the pitches when they are thrown. These are pretty simplistic solutions, and I am sure Gomes is working tirelessly trying to break out of his slump already. These are just my best guesses on how to improve this deficiency in Gomes’ game going forward in 2016.

I still believe in Yan Gomes, and so should you. He has proven he can be a successful big-leaguer, and one of the top catchers in the league. Catchers are judged more on their defense than on their bat, and catchers who can do both are considered a premium. In other words, Gomes could still be considered a solid MLB catcher, even if he doesn’t ever regain his old form at the plate. It is my opinion, however, that we should not sell him short at the plate. The ability is there, it just needs a little refining right now. For the sake of Indians’ fans everywhere, let’s hope Gomes can unleash his inner “Yanimal” sooner rather than later; the fate of the Indians season depends on it.









The Future of Analytics In Baseball: How Will Small-Market Teams Fare?

This post originally appeared on the Pittsburgh Pirates blog Bucco’s Cove.

A recent episode of the Baseball Prospectus podcast Effectively Wild (and if you don’t listen to it, this is one of the best baseball podcasts out there) had two analysts from the LA Dodgers’ front office as guests. During the episode, one of them said, “Even though we have grown substantially in the last year…” and went on to talk about the size of their analytics department and how they work together. This is a scary prospect for small-market teams like the Pirates; embracing analytics before such things were en vogue allowed teams like the Moneyball A’s, the Royals, the Pirates, and many others to gain a competitive advantage over their comparatively retrograde competition still throwing money at their problems every offseason.

The window of opportunity for small-market teams to use advanced analytics to their advantage may be closing faster than we think. Most (and possibly all, I don’t have access to every team’s front office payroll) teams have some sort of analytics department (or “Baseball Operations Department,” as they’re often dubbed). According to this ESPN article from about 14 months ago, only two woeful teams are listed as “nonbelievers,” the Marlins and the Phillies, and the Phillies have since seen some significant shuffling in their front offices. Larger teams are beginning to emulate their smaller counterparts to varying extents, with results that will bear fruit over the coming seasons. As a fan of a small-market team, this is concerning; the limited dividends paid from the analytics advantage may mean a return to the old power structure in baseball in which larger-market teams with more money have the ability to acquire players at will. The difference, however, will be that stats will have informed the signings, so if two teams are targeting the same player for “sabermetric reasons,” the team with more money will obviously still have the upper hand.

Scarier still for fans of small-market teams is that the greater financial capital available to geographically-favored franchises is that these financial resources can not only be employed to sign the best players, but also the most talented analysts and more of them. The premise that teams all have access to effectively the same data and analysis is rendered moot if larger franchises can secure a stronger analytics department, both in terms of the number of analysts and the talent of the analysts (money could even be used to lure talented analysts to the richer franchises in the same way that players are). For example, the Cubs thus far this season seem to be a perfect confluence of young talent, effective free-agent signings based on a strong analytics department, and a hell of a lot of money, which is exactly where you want to be if you’re trying to create a dynasty and win multiple Commissioner’s Trophies.

Parity in the league is still greater than that of the NFL, but we could be witnessing the last generation of such parity. How is such a situation solved? The one obvious choice is a salary cap; the player’s association would be loath to support such an idea, although it’s perhaps beginning to be in their interest. As the league’s revenue increases, players haven’t been getting the same share of that revenue, according to Nathaniel Grow on FanGraphs. A quote from that article:

“The biggest difference between the NBA and MLB, then, isn’t the fact that the former has a salary cap while the latter does not. Instead, the primary difference between the two leagues’ economic models is that by agreeing to a “salary cap,” NBA players in turn receive a guaranteed percentage of the league’s revenues, while MLB players do not.”

According to the same article, the players’ share of revenue has fallen about 13% to 16% since 2002 or 2003. While this argument is unlikely to induce the MLBPA to support a salary cap, a downturn in league parity could force their hand at some point in the future. This would be a long-term effect, however; many years of a “lack of parity,” coupled with a downturn in the popularity of the sport as a whole, would be required to even have the MLBPA thinking about acquiescing to a salary cap.

Coming back to the proliferation of analytics departments among MLB teams and their effect on important advantages held by those willing to embrace statistics: I don’t know what’s going to happen. There are many facets to analytics, more than just comparing players based on the BABIP or K% or arm slot or determining what players to acquire and how much they’re worth. For example, one of the Effectively Wild guests from the episode I cited earlier was a biomedical engineering major during her undergraduate studies, implying that the front office is becoming interested in the medical side of analytics: preventing injuries, improving player health, and looking at the biomechanical aspect of baseball, which takes a significant toll on players’ bodies. This is not too dissimilar from what the Pirates have done in recent years and is just one of the many components to assembling and maintaining a competitive squad.

This line of thinking admittedly removes the human component from the equation, which is still incredibly important to this entire process. There will always be GMs who are more willing to try new strategies to win and those who are unwilling to change (*cough* Ruben Amaro, Jr.). Coaching and player development, especially in the minor leagues, will continue to be extremely important for MLB franchises and is largely outside the purview of the type of statistical analysis that is widely considered in evaluating players. Rather, this part of baseball can be thought of, to a certain extent, as producing the statistics that analysts ultimately study. As a result, there will always be opportunities for smaller-market teams to hire talented personnel, including trainers, coaches, scouts, and other employees outside the scope of the Major League analytics departments that will influence franchises’ success and failure.

However, analytics at the MLB level may start to be influenced by money. Ultimately, stories like the Pirates’ repeated acquisitions of undervalued Yankee catchers who are stellar pitch framers, the Royals’ World Series win relying on great defense and a crazy strong bullpen, and the general parity of the league beyond the traditionally great franchises may be fewer and further between. Those franchises with more money may regain the competitive advantage that the sabermetric revolution has wrested away from them for the past decade, and smaller-market teams will have to find yet another way to adapt to the ever-changing baseball landscape.

Got Projections?

Back in college, I remember being fascinated by a concept I learned in one of the first chemistry classes I took: the atomic orbitals. Contrary to what I thought at the time, electrons don’t orbit around the atom’s nucleus in a defined path, the way the planets orbit around the sun. Instead, they move randomly in the vicinity of the nucleus, making it really hard to pinpoint their location. In order to describe the electrons’ whereabouts within the atom, scientists came up with the concept of orbitals, which, simply put, are areas where there’s a high probability of finding an electron. That’s pretty much how I see baseball projections.

A term that is very often used by the sabermetric community is “true talent level,” and just like an electron’s position, is a very hard thing to pinpoint. Projections, however, do a very good job of defining the equivalent of an atomic orbital, sort of like a range of values where there’s a high probability of finding a certain stat. I know what you’re thinking; projections are not a range of values. But you can always convert them very quickly just by adding a ±20% error (or any other percentage you consider fitting). So, for example, if a certain player is projected to hit 20 home runs, you can reasonably expect to see him slug 16 to 24 homers.

As a 12-year veteran fantasy baseball manager (and not a very good one at that), I’ve never used projected stats as a player-evaluating tool when I’ve gone into a draft. For some reason (probably laziness), I’ve mainly focused on “last year’s” stats, and felt that players repeating their last season’s numbers was as good a bet as any. This year, after taking a lot of heat for picking Francisco Lindor and Joe Panik much higher than what my buddies thought they should’ve been taken, I started wondering how much of a disadvantage was using a simple prior-year data instead of a more elaborate method.

To satisfy my curiosity, I decided to evaluate how good a prediction are “last year” numbers, and compare them to other options such as using the last two or three years, and using some projections publicly available. In this particular piece, I’ll limit the study to offensive stats, but I’ll probably tackle pitching stats in a second article.

The first step for this little research was to establish the criteria with which to compare the different projections. A simple way to evaluate projection performance is using the sum of the squared errors; the greater the sum, the worse the projection (in case you’re wondering, squared errors are used in order to make negative errors positive so they can be added, it also penalizes bigger errors more than smaller errors). In this particular case however, I wanted to evaluate projections for a number of different stats, so a simple sum of squared errors would have an obvious caveat in that stats with bigger values have bigger errors. For example, an error of 10 at-bats is a very small one, given that most players log 450+ of them per season. On the other hand, an error of 10 HR is huge. Additionally, not every stat has the same variation among players. Home runs, for example, have a standard deviation of around 70% of the mean, while batting average’s standard deviation is only about 11% of the mean. So, you could say that it’s harder to predict HR than it is to predict AVG.

Long story short, I divided each squared error by the squared standard deviation, and calculated the average of all those values for each stat. Finally, I converted those averages to a 0 to 1 scale, with 1 being a perfect prediction (in reality, these values could be less than zero when errors are greater than 1.5 standard deviations, but I scaled it so that none of the averages came out negative).

For this study, only players with at least 250 AB on the season were considered. Also, players that were predicted to have less than 100 AB were not considered, even if they did amass more than 250 AB on the season. The analysis was done on five different sets of predicting data:

  1. Last season stats.
  1. A weighted average of the two preceding seasons, with a weight of 67% for year n-1, and 33% for year n-2.
  1. A weighted average of the last three seasons, with 57.5% for year n-1, 28.5% for year n-2, and 14% for year n-3.
  1. ZiPS projections (Created by Dan Szymborski, available at FanGraphs)
  1. Steamer projections (Created by Jared Cross, Dash Davidson, and Peter Rosenbloom. Also available at FanGraphs)

The following graph shows the average score of each of the 5 projections for each individual stat considered in this study. The graph also shows the overall score for each stat, in order to have an idea of the “predictability” of each one of them. Remember, higher scores indicate better performance, with 1 being a perfect prediction.


Other than hinting that it is in fact a very poor decision to use only last year’s data, this graph doesn’t tell us much about which predicting data has a better overall performance. It does provide, however, a very good idea of the comparative reliability of each stat within the projections.

Aside from stolen bases (which honestly surprised me as being the most predictable stat of the bunch), the three most reliable stats are the ones you would’ve expected: HR, BB, and K. They’re called “true outcomes” for a reason, they depend a great deal on true talent level, and involve very few external factors such as luck or opponent’s defensive ability.

On the other end of the spectrum, it’s really no surprise to find three-baggers as the least reliable stat. This may seem counterintuitive at first, given that players that lead the league in triples have a distinctive characteristic in being usually speedy guys. Nonetheless, 3B almost always involve an outfielder misplaying a ball and/or a weird feature of the park such as the Green Monster in Fenway or Tal’s Hill in Minute Maid’s center field, making triples unusual and random events. Playing time (represented in this case by at-bats) has also an understandably low overall score. Most injuries, which are a major modifier of playing time, are random and hard to predict. Also, managerial or front-office decisions can affect a player’s playing time. It does surprise me, however, to see doubles so far down in this graph, and I really can’t find a logical explanation for it.

Let’s move on now to the real reason why we started doing all this in the first place. Here’s a graph that shows the average score for each predicting data, for years 2013, 2014, and 2015. It also shows the three-year average score.



The one fact that clearly stands out in this graph is that last-year numbers are a very poor predicting tool. Its performance is consistently and considerably worse than any other set of data used. So my initial question is answered in a pretty definite way: it is a huge mistake to rely on just last season’s number when trying to predict future performance.

Turning our attention to the other four projections, it becomes a bit harder to separate them from each other, especially using only three years’ worth of data. The average performance of the three-year period gives us a general idea of the accuracy of each option, but looking at the year-by-year numbers, it’s not really clear which one is better. Steamer seems to be the winner here, since it had the better score on all three years. ZiPS, on the other hand, despite having a better overall score than the three-year weighted average, has a worse score in two of the three years. They were really close in 2014 and 2015, but ZiPS was considerably better in 2013, which interestingly, was a less predictable year than the other two.

The biggest point in favor of ZiPS when comparing against the three-year weighted average is that ZiPS doesn’t actually need players to have three years’ worth of MLB data in order to predict future performance, and that makes a huge difference. Another major point in favor of ZiPS is that it’s doing all the work for you! Believe me, you do not want to be matching data from three different years every time drafting season comes around (I just did it for this piece and it’s really dull work).

After all is said and done, projection systems such as Steamer or ZiPS do a fine job of giving us a good indication of what to expect from players. We’re much better off using them as guidelines when constructing our fantasy teams than any home-made projection we could manufacture (unless you’re John Nash or Bill freaking James). I know next March I’ll be taking advantage of these tools, hoping they translate into my very elusive first fantasy league title.

Tyler Wilson and His Five Plus Pitches

Let me preface this article by saying that I watch A LOT of baseball.  I also have an extensive analytical background and am always analyzing baseball stats looking for value in players.  Last week, I was watching an Orioles game and the starting pitcher was a player I have never heard of.  His name is Tyler Wilson.  While watching the game, I was very impressed with his overall make-up and the confidence he displayed in each one of his pitches.  Many times what separates a pitcher from being able to start at the big-league level versus being destined for the bullpen is the ability to throw multiple pitches.  The ability to throw each of those pitches effectively, however, can be what separates a good starting pitcher from a great starting pitcher.  The more I watched of Wilson, the more intrigued I became about his future outlook, and the more motivated I became to write this article.  (I went back and watched all of Wilson’s starts this year before writing this article.)

To give you a little background, Tyler Wilson has never been an elite prospect.  He attended college at the University of Virginia, where he was overlooked by fellow staff-mate, and future 1st round pick, Danny Hultzen.  Wilson was drafted by the Orioles in the 10th round of the 2011 MLB Draft.  Ever since being drafted, he has quietly excelled at every level.  He doesn’t have the dominant strikeout numbers that you look for in pitching prospects, which is a big reason he has gone overlooked for much of his career.

After climbing his way through the organizational ladder, Wilson made his major league debut with the Orioles last year and eventually made the team this year out of spring training.  Although he made the team in a bullpen role, early season injuries to the Orioles pitching staff opened up an opportunity and Wilson has really taken advantage of it.  Enough of the background though.  Let’s move on to what I saw while actually watching him pitch.

Tyler Wilson features a cutter and a two-seam fastball.  Each of these pitches sit in the 89-91 mph range and both show a great amount of movement.  The cutter is most effective against right-handed batters when thrown on the outside portion of the plate.  Check out the video below to watch him fool Kansas City Royals outfielder Lorenzo Cain with three straight cutters:

He essentially gave Cain, a very good hitter, three of the exact same pitches in a row…and Cain couldn’t touch them.  In every start this year, Wilson has pounded the outside corner with this cutter and has had fantastic results.  Don’t think by any means though that he is a one trick pony.  As soon as you start to expect that cutter on the outside corner, Wilson will come right back in on you with a two-seam fastball:

Look at the horizontal movement on that pitch!  Absolutely filthy!  Wilson has showed a ton of confidence in both of those pitches so far this season as he uses them to pound both sides of the strike zone and his command of them has been exceptional.  He is not afraid to throw them in any count and they are equally effective vs both left-handed and right-handed batters.

While his fastballs both seemed to be plus pitches upon first glance, I started to have thoughts that this guy might be for real as soon as he started throwing his curveball.  Wilson’s breaking ball sits in the 77-79 mph range.  I was astonished by how well he was able to locate his curve and the amount of movement on each and every one he threw.  Watch him send White Sox slugger Jose Abreu down swinging in the video below:

Abreu had no chance.  In his most recent start against the Twins, Wilson’s curve looked even better.  Check out the one he threw to Byung-Ho Park:

Both of those pitches came in a 2-2 count.  Many pitchers are scared to throw a breaking ball in a 2-2 count, especially to players with plus power such as Abreu and Park.  If you miss your target, two things can happen.  One — you leave the ball up in the zone and it gets hit out of the stadium.  Two — you throw it in the dirt; the hitter lays off; and now you have to pitch to this slugger with a full count.  Wilson isn’t scared to throw his curveball in any count and that is what makes him so dangerous.  You never know when to expect it, but at the same time you have to expect that he can throw it at any moment.

The last pitch in Wilson’s arsenal is his changeup.  This pitch has a ton of downward movement and produces a lot of groundballs.  While there were many better examples that I could have shown you of his change-up in action, I wanted to show one of his bad ones.  Even when he missed his target, the batter was still fooled by the amount of movement on this pitch.  Check out the following pitch to Royals SS Alcides Escobar:

The catcher set up down in the zone and Wilson clearly misses his target.  Luckily it didn’t seem to matter as the pitch had an insane amount of horizontal movement, running in on Escobar and jamming him.

Take a look at the chart below, showing the vertical and horizontal movement on each of Wilson’s pitches:

Tyler Wilson Movement

The middle portion of this chart is empty.  All five of his pitches have a tremendous amount of movement, and none of them move in the same direction.  The fact that he is able to command each of these pitches so well and keep hitters guessing with which one will come next is the reason why he has had so much success.  A big reason why hitters are having trouble guessing his pitches is because of how well Wilson is able to repeat his delivery.  The chart below shows Wilson’s release point for each type of pitch:

Tyler Wilson Release Point
As you can see, his release point is almost identical with all five of his pitches.  At this point, I have watched all of his starts from this season and was very impressed.   I then decided to do some research and was immediately impressed with stats such as his career BB rate and low WHIP, but wanted to dig further.  I began to look through the PITCHf/x data because I was curious to see how effective each of his pitches actually were.  Based on the PITCHf/x value metric, all of his pitches so far this year have graded as above average.  If you are not familiar with the PITCHf/x value scale, someone who has a fastball ranking of zero means that he possesses an average fastball.  Any value above zero means that pitch is above average.  Obviously the higher the number, the better the pitch.  The same goes for negative numbers and pitches being below average.  See the table below for the breakdown of Wilson’s arsenal:

Screen Shot 2016-05-15 at 1.19.17 AM

Based on the above values, the change-up has been Wilson’s most valuable pitch this season with his curveball close behind.  Obviously it is very early in the season and we are working with a small sample size…but that doesn’t mean we can’t have fun!  While doing this research, I set out the goal to find every starting pitcher who throws five or more above-average pitches.  Below is the list of players who fit that description:

Screen Shot 2016-05-15 at 1.41.09 AM
IP = Innings Pitched
FA = Fastball
FT = Two-Seam Fastball
FC = Cut Fastball
SI = Sinker
SL = Slider
CU = Curveball
CH = Change-up
KC = Knuckle Curveball
EP = Eephus

There are only five pitchers who have thrown five or more pitches above average so far this season!  Wilson is in great company, as the other four pitchers are all All-Star-caliber players and borderline household names.  Being that this is such a small sample size, I decided to look back at last year’s stats to see how many players fit this description over a full season.  Using the same parameters and setting the minimum IP to 100, the following table was produced:

Screen Shot 2016-05-15 at 2.05.17 AM

Once again, the names on this list are some of the top pitchers in baseball.  A few of these pitchers have a pitch that graded out as below average, but since they had five or more different pitches all individually grade as above average, they made the final cut.

As you can see, it is very rare to have a pitcher who has five legitimate plus pitches.  I am very interested to see if Tyler Wilson can maintain these results over the course of a full season, and I really hope he is given the opportunity to do so.  If he continues to pitch the way he has been, the Orioles will have no choice but to leave him in the rotation.  Although he has had limited success, Wilson has struggled in each of his starts when facing the lineup the third time around.  This could be due to the fact that he is still in the process of being stretched out from his bullpen role.  When in the bullpen, you don’t have to prepare to face the same hitter three times.  I am hopeful that once he is fully stretched out and back into his starter mentality, he will be able to make the necessary adjustments and continue to throw all of his pitches with confidence.  If he can continue to make quality pitches as he faces the lineup for a third time, I believe Tyler Wilson has the chance to become a very special pitcher.

Memorable quotes I heard during the TV broadcasts:

“Everyone thinks that I pitch with a chip on my shoulder but I really don’t.  I just go out and compete.  I don’t think of it that way.” – Tyler Wilson

“I think he understands himself.  He can maintain his game-plan throughout the game.  He’s going to keep us in the game and give us a chance to win.  What more can you ask for?” – Pitching Coach Dave Wallace

“I love that he can make the ball run in and then cut away.  He pitches to both sides of the plate.  Not a lot of young pitchers can do that.” – Manager Buck Showalter

…no Buck, not a lot of young pitchers can do that.

Twitter – @mtamburri922

Drew Pomeranz Is Here to Stay

After shutting out the Chicago Cubs offense over six innings of 10-strikeout ball, Drew Pomeranz lowered his season ERA to 1.80 and FIP to 2.61. He currently ranks 3rd among qualified starters in K% and is tied for 11th in WAR. Furthermore, Pomeranz has faced four of the top five offenses in the National League, as well as having had a season opener at Coors Field, hence we cannot claim stat padding against mediocre competition. While a .250 BABIP and 82.1 LOB% may not exhibit the greatest signs of stability, Pomeranz is finally reaching the potential that garnered him a top-30 prospect ranking from Baseball America. So what has Pomeranz done to unlock this potential?

Pomeranz has discovered his newfound success by neutralizing right-handed bats. Earlier in his career, Pomeranz’ relative struggles against righties led many to wonder whether his ultimate fate rested in the bullpen. In fact, heading into 2016 many doubted whether he could even earn a spot in the Padres rotation; he couldn’t even earn a mention in Jeff Sullivan’s positional preview post. This sentiment was understandable given his career .340 wOBA against and 7.1 K-BB% when facing right-handed hitters up to this point. In 2016, however, he has lowered the wOBA against to a measly .240 while striking out 34% of righties. By dropping 100 points of wOBA, he’s essentially transformed his average opposite-handed plate appearance from Kyle Seager to Omar Infante. As with any dramatic improvement in performance, a confluence of factors has led to Pomeranz’ success.

Since debuting in 2011, Pomeranz has gradually raised his vertical release point up nearly half a foot. This more over-the-top delivery has undoubtedly provided him greater deception against righties. More noticeably, however, Pomeranz has brought his changeup back from the dead. Early in his career, Pomeranz threw his change roughly 9% of the time to righties. From 2013-2015, when 72% of his appearance came out of the bullpen, Pomeranz lowered that rate to 3%. This season, however, Pomeranz is utilizing his change-piece over 15% of the time against right-handers. Throwing it around 87 mph, Pomeranz’s change nearly perfectly mimics his sinker in both velocity and movement, but to differing results. Pomeranz generates an above-average 44% fly balls on balls in play with his change, while the sinker gets 67% groundballs. This deception, combined with Pomeranz’s pitcher-friendly home park, have led to a dearth of quality contact on the changeup, as illustrated by the .111 ISO against on the pitch.

Despite the resurgence of Pomeranz’s changeup, his improved curveball has been the true game-changer.  He trails only the enigmatic Rich Hill in percentage of pitches that are curveballs; likewise, he employs it over 43% of the time against righties, up from 23% over his career before joining San Diego. His 4.6 curveball pitch value trails only the Phillies duo of Aaron Nola and Jerad Eickhoff, and their club’s experimental pitching philosophy, so far in 2016. After leaving the breaking-ball-murdering confines of Coors Field in 2014, Pomeranz witnessed a significant increase in both vertical movement and velocity. This, however, does not explain his recently-discovered success. Similarly, he has kept his Zone% on the curve right around his career average of 43%. The key lies in where out of the zone he locates the ball. This season, Pomeranz is hitting low-and-gloveside off the plate with almost 30% of his curves to both righties and lefties alike. Prior to this campaign, Pomeranz only hit that spot about 10% of the time, as he more evenly distributed his curveballs across the zone horizontally. Whether a change in approach or simply improved mechanics and command, Pomeranz is finding tremendous success with his hook. Using the curve against righties, Pomeranz has raised his Whiff% to a career-high 16.4% in addition to generating a career-high 39.6 Swing %. Furthermore, nearly three-quarters of his balls in play off the curve are grounders and he has yet to permit a single fly ball on the pitch vs. right-handed hitters.

As Eno Sarris noted in his discussion with him last December, Pomeranz’s success hinges on three things: “his health, his changeup, and his curveball.” Seven starts into the season, Pomeranz’s progress on these three fronts has led him to success against righties and helped him unlock his prior potential. He’s gone from a guy the Athletics traded for spare parts to a solidly above-average starter for the Padres. Perhaps the most encouraging aspect of this emergence: Pomeranz is still only 27 years old. With almost three more years of service time left, and an inevitable sell-off of Tyson Ross, Andrew Cashner, and James Shields on the horizon, Pomeranz could potentially parlay his improvement into an ace role on the Padres staff. Of course, Pomeranz could find himself on the market in the near future, and he would certainly fetch more than Yonder Alonso and Mark Rzepczynski this time around.

xHR%: Questing for a Formula (Part 5)

This is the long-delayed fifth part in the xHR series. If you really want to read the first four parts, they can be located here, here, here, and here.

More than a month late, the highly anticipated follow-up to the first iteration of xHR has arrived. Once more, that increasingly trivial metric will grace the page of FanGraphs, wallowing in the mostly prestigious Community Research section (on the other hand, this section is most definitely the best section on the World Wide Web for experimental metrics and amateur analyses).

Unless the reader has an impeccable memory for breezily scanned, frivolous articles, he or she likely needs a reminder as to what xHR% is and aims to be. xHR% is a metric that describes at what rate a player should have hit runs over a given season. From this, expected home runs, a more understandable counting statistic, can be found by multiplying plate appearances by xHR%. It cannot be emphasized enough that the metric is not predictive; it only aims to describe. Without further ado, the formula is here:

I know that’s a lot to look at, and it isn’t exactly self-evident what all of the variables mean. As such, an explication of each part is necessary and provided below. (For logical rather than chronological purposes, the Kn variable will be analyzed last.)

AeHRD – One of the biggest differences between this formula and the last one is that this one does not use home run distance. This iteration uses expected distance, rendering it a combination of simple math, sabermetric theory, and physics. As such, expected home run distance strips out one of the biggest factors in luck — the weather.

Expected home run distance is found by utilizing a method taken from Newtonian Mechanics to calculate how far objects go. By using ESPN’s HitTracker website, I was able to obtain launch angles and velocities for nearly every home run hit in 2015. From this, I was able to resolve velocity into its respective parts, velocity in the x-direction (Vx) and velocity in the y-direction (Vy). After that, I calculated the amount of time the ball would be in the air with the formula vf=vi+gt, where vf is final velocity (0 m/s), vi is initial velocity (Vy), and g is simply the gravitational acceleration constant. Finally, I multiplied Vx by time in order to get the total expected distance.

I repeated that process for every home run hit by a given player in order to find his average expected home run distance. By doing this, I was able to strip out all weather-related components.

AeHRDH – Utilizing the same process as above, I found the average expected home run distance for every stadium. This is the player’s home stadium’s average home run distance, regardless of team.

AeHRDL – The same as above, but done for every home run hit in the majors last season.

When put together in the numerator and the denominator, the above variables serve as a “distance constant” of sorts that will at most adjust the resulting expected home runs by plus or minus two. Occasionally, the impact is negligible because the average expected distance is very close to that of the player’s home stadium and the league. Averaging the mean expected home run distance of the league and of the home stadium allows the metric to paint a more accurate picture of where the player hit his home runs and whether or not they should have left the park. Nevertheless, it’s important to note that this formula still fails to account for fly balls that fell just short of the wall due to the wind and other factors, meaning that there are still expected home runs unaccounted for.

FB% – If you remember correctly, or took the time to briefly review the previous posts, then you will recall that in the prior iteration of the formula there was a section very similar to this one. The only differences are that the weights on each year of data have changed (those are still somewhat arbitrary, however, but I am working on getting them to more precisely reflect holdover talent from past years) and the primary statistic used.

Previously, HR/PA was used, but it had to be abandoned because the results were too closely correlated with reality. This time, I looked at how similarly descriptive formulas were quantified. Oftentimes, those metrics did not use the target expected metric in their formulas. Rather, they utilized other metrics that correlated moderately well or strongly with their expected metric. In this case, I decided to use FB% because it’s a relatively stable metric (especially in comparison with HR/FB), and it has a strong correlation with HR% (about .6).

As a clarification, the subscript Y3, Y2, and Y1 indicate the years away from the season being examined, where Y1 is really Y0 because it’s zero years away. So just to be clear, Y1 is the in-season data from the year being examined. In the data to be examined, for example, Y1 is 2015, Y2 is 2014, and Y3 is 2013.

Kn – As you can well imagine, FB% numbers are always far greater than HR% numbers*, resulting in some truly ridiculous results if a constant isn’t applied that relates HR% to FB%. For instance, without a constant to modify the results, Jose Bautista would have been expected to hit 304 home runs last season. That’s a lot of home runs. Just two and a half seasons of playing at that level and he’d have the home run record in the bag. Luckily, I’m not stupid enough to think that that’s actually possible, and so I initially related FB% and xHR% with a constant, called KCon.

Unfortunately, KCon didn’t work as well as I’d hoped because it skewed expected home run results way up for terrible home run hitters and way down for the best home run hitters. By skewed, I mean bad by more than six home runs. And so I, in my infinite (and infantile) amateur mathematical wisdom, made it into a seven part piecewise** function. By this, I mean that there’s a different constant for each piece of the formula, defined by HR% at somewhat arbitrary, though round points. For clarity, here they are:

K1 = HR%<1

K2 = 1≤HR%<2

K3 = 2≤HR%<3

K4 = 3≤HR%<4

K5 = 4≤HR%<5

K6 = 5≤HR%<6

K7 = 6<HR%

It works quite well. I am very excited about the current iteration of xHR%, its implications, and all it has to offer. Of course, it is not finished, but I think I’m getting closer. Please comment if you have any questions, an error to point out, or anything of that nature. There will be a results piece published soon on the 2015 season, so keep an eye out.

*It wouldn’t be surprising if Ben Revere became the first player to have a HR% equal to FB% (both at 0%, naturally).

**It is neither continuous nor differentiable.

The Cubs, the Astros, and Tank Warfare Revisited

Last year the once lowly Cubs won 97 games, and the also once lowly Astros won 86. Because both clubs had been as bad as Trump’s rug for years, many attributed these successes to the practice of tanking — intentionally losing games to acquire high draft picks with which to rebuild. This year, the Astros have gone a bit backward in the early going, thanks mainly to an incendiary pitching staff (if you had this guy second among Houston pitchers in WAR by mid-May, stop reading this right now and go fix world hunger). The Cubs have continued to roll, and as you know are currently on a pace to win 3.4 billion games this year. Those tanks seem unstoppable.

The interwebs were aflame with tanking debates during the offseason, with some saying it’s destroying Our Way of Life, and others saying well, no, it isn’t. This seems like a question susceptible to analysis using a new statistic with a vaguely humorous name. But before we get to that, we need to define the “tank” — I consider it to be the bottom six teams in the majors in any year. I arrived at six by rigorously counting the number of divisions in major-league baseball, and assuming that in most years the bottom six teams will be in their respective divisional cellars. This won’t always be true, but it will seldom be egregiously false.

So a team in the tank gets one of the top six draft picks in the following June draft. The new statistic, TankWAR, is simply the WAR attributable to each player the team drafted with a top-six pick, or to players obtained by trading one of those top-six players.

The Cubs and Astros each had four tank picks in the last ten drafts, twice the random expectation. The italicized players have reached the majors.

Cubs Tank Picks 2006-2015

Albert Almora (6) 2012

Kris Bryant (2) 2013

Warbird (4) 2014

Astros Tank Picks 2006-2015

Carlos Correa (1) 2012

Mark Appel (1) 2013

Brady Aiken (1) 2014

Alex Bregman (2) 2015

Last year the Cubs accumulated 50.2 WAR. Bryant contributed 6.5 of that, while Kyle Schwarber added another 1.9. So the Cubs’ TankWAR last year was 8.4, or 16.7% of the team total. On the one hand, the Cubs probably would have come close to 90 wins without these guys. On the other hand, wins 90-97 are among the most valuable in baseball. On the third hand, last year it wouldn’t have made a difference. At 89 wins or 97 the Cubs were the second wild card. On the fourth hand, that’s probably pretty rare.

Also note that of the Cubs’ starting 13 (eight position players plus five starting pitchers) only Bryant and Schwarber were Cubs draftees. The team acquired the other 11 through trades and free-agent (including international) signings. To put it another way, 42 of the Cubs 50 WAR came from players that every other GM had access to regardless of the previous year’s record.

This year, the Cubs’ TankWAR is just 1.4 (with Bryant contributing 1.5 and Schwarber subtracting 0.1 before suffering his season ending injury). That’s just under 10% of the Cubs’ total WAR of 15.6. So however important tanking was to the Cubs last year, this year it’s mattered less thus far.

For the Astros, Carlos Correa put up a 3.3 TankWAR in 2015, just over 7% of the Astros total of 44.6. Those three wins put the Astros in the playoffs — without them, The Fightin’ (and I do mean fightin’) Scioscias would have been in. To no one’s great surprise, in the current season Correa has just about doubled his contribution to the team — his 0.8 TankWAR is 14% of the team’s 5.6 total. (In theory, Ken Giles‘ -0.3 WAR could also be considered TankWAR since Mark Appel was one of the Ryder-load of prospects Houston traded for him, but Appel seemed to be an afterthought in that deal.)

The Astros were a more draft-dependent team than the Cubs in 2015, with six of their 14 regulars (including the DH) being Houston draftees. George Springer was by far the highest pick of the lot, costing Houston the 11th overall pick, thanks to the Astros bad-but-not-especially-tankly 76-86 finish in 2010 (good for fourth of six in the then-bloated NL Central). Most of the Houston draftees were guys that the other 29 GMs had passed over, and over, and sometimes even over again.

Both teams still have solid farm systems, if somewhat less spectacular than in recent years thanks to graduations and in the Astros’ case, that ill-advised Giles trade. The tank picks currently in their respective systems could help their teams relatively soon. But these teams are already very good. The remaining tank draftees won’t be turning their teams around so much as extending their respective windows of success, either by joining the big club or anchoring key trades.

So the evidence that tanking works is mixed. Both teams have benefited from their tank picks, but it is a significant exaggeration to say the Cubs’ and Astros’ recent successes are solely or even primarily because of tanking. However, Bryant and Correa in particular are players that can move their teams from good to great. These are the kinds of players that will typically be available only to the very worst teams under the current draft system. Thus, the worrywarts aren’t entirely … wartless — there will always be some incentive under some circumstances to get one of those top picks.

That said, the case for making major rules changes in response to tanking remains thin. While it’s clear that in recent years the Cubs and Astros lacked quality major-league talent, it isn’t at all clear that they were deliberately trying to sabotage their rosters (the case of Kris Bryant’s AAA hostage drama is a different problem). And, as noted above, most of the Cubs’ and Astros’ WAR during their recent resurgence has come from players who they could have obtained whether they had tanked or not. Indeed, one of the most tank-dependent teams of all time, your 2008 World Series Rays, obtained less than a quarter of its WAR from tank picks.

Another thing to bear in mind is that every team is different. For some teams, attendance is highly correlated with winning percentage, and for others, not so much. Tanking will probably cost the highly correlated teams more revenue, making it harder for those teams to finance the other rebuilding components. The low correlation teams have more patient fans and thus may have the room to explore more radical roster revision approaches.

Thus, a patient fan base is an asset. Changing the rules to prevent death-and-resurrection rebuilds isn’t a neutral solution — it would directly favor the teams whose fans desert them in the lean years (these are discussed in detail in the preceding link), and disfavor the teams with patient fans (like the Cubs and the Astros). The case hasn’t been made that the patient fan problem is so egregious that it needs to be legislated out of existence; indeed, it isn’t clear there’s a problem here at all. Each franchise (well, maybe except this one) tries to win by maximizing the advantages it has over its competitors while minimizing the impact of its relative weaknesses.

That doesn’t sound very nefarious. In fact, it sounds a lot like baseball.

Don’t Worry About Brett Cecil (Too Much)

My friend posted something interesting on Facebook. It said:

“Dear Jays bandwagoners, stop booing Brett Cecil. Form is temporary, class is permanent.
2014 April: 5.14 ERA, May-Sept: 2.09 ERA
2015 April: 5.23 ERA, May-Oct: 2.09 ERA
2016 April: 5.79 ERA”

Maybe he is a slow starter and he should be able to go back to his second-half form as the season goes on. What I am slightly concerned about is that his April 2016 season ERA is worse than Aprils from the two previous seasons.

Let’s examine his pitches. He struggled big time in June 2015 when he posted an abysmal 9.00 ERA, but he did not allow a single run after June 30th that season. He has a 5.59 ERA as of May 11th. I went to brooksbaseball.net and researched his four-seam fastball, curve, and sinker between these three periods.


Usage: 31%(June 2015) -> 21%(After June 30th of 2015 season) -> 13% (This season, as of May 4th)

Velocity: 93.9 mph -> 93.0 mph -> 92.8 mph

Horizontal movement: 3.6 inches -> 4.4 inches -> 5.1 inches

Whiff/Swing rate: 8% whiff/swing -> 17% whiff/swing -> 8% whiff/swing

GB/BIP: 13% -> 39% -> 11%

LD/BIP: 38% -> 30% -> 33%

FB/BIP: 38% -> 26% -> 56%

Horizontal release point: 0.83ft (June 2015) -> 0.89 (July 2015) -> 0.55 (August 2015) -> 0.61 (Sep 2015) -> 0.64 (This season)

Vertical release point: 6.57ft (June 2015) -> 6.49ft (July 2015) -> 6.58ft (August 2015) -> 6.51ft (Sep 2015) -> 6.54ft (This season)

Brett is relying less on his four-seam fastball as time goes. He is trying to adapt to the ‘sinker-ball’ trend. While his four-seamers have some movement, he may have felt the need to opt for a new pitch with more movement. His fastball velocity is in the low 90s and he can reach for 94 on occasion. That’s not ideal for a relief pitcher. His four-seamer is gaining more horizontal movement as time goes. He, in this season, has 1.5 more inches of horizontal movement than last season. He had big success with his four-seamer after June 2015 — it induced a 17% whiff rate, which is 9% higher than June 2015.

He also recorded a 39% GB/BIP using his four-seamer in his last three months of 2015 season, which is 27% higher than June 2015 (39% GB/BIP means that he induced 39 ground balls in every 100 balls in play off his four-seam fastball). His LD/BIP and FB/BIP also had substantial decreases in the last three months of the 2015 season, which helped him record a 0.00 ERA in that span. One of my theories of his successful 2015 season is that he changed his horizontal release point throughout the 2015 season. You can see the changes above. You can also observe the changes in the graph that I created using R:

z0 vs x0Blue plots indicate his release points from April to June 2015 when he struggled to get batters out. Red plots indicate his release points from July to October 2015. You can definitely see that red plots clustered away from the blue plots. He made this adjustment and his command significantly improved, as well as other metrics.

April-June 2015: 25IP 11BB 5.40 ERA
July-Oct 2015: 29.1IP 2BB 0.00 ERA

Batters have adapted to him this season. His release points of this season are consistent with his 2015 second half, but he is struggling this season. His four-seam fastball is being hit hard again this season. His whiff/swing rate in the second half of 2015 was 17% and his 2016 season whiff/swing rate is 8%. If you refer to the ball-in-play stats above, his 2016 season ground ball/BIP, line drive/BIP, and fly ball/BIP rates are also worse than in the second half last season. But I don’t see velocity drop and change in release points for his four-seamer. Movement of his four-seamer is actually better. I can’t seem to diagnose what is wrong with his four-seam fastball this year and it leads to me to assume that his lackluster breaking balls are hindering the effects of his fastball as well. Now I am going to continue on researching with his other pitches and examine some specific situations.

Cecil is throwing significantly less four-seam fastballs for the first pitch of at-bats. He seems to be afraid of throwing it for the first pitch. Maybe he thinks that batters are waiting for it. Or maybe he wants to try to induce groundballs more and decided to throw a sinker more. You can see that he throws more sinkers for a first pitch instead of four-seamers.


His sinkerball approach for the first pitch seems to be a good one because most of the sinkers he throws for the first pitch are strikes. Last year, he threw 64% of his first-pitch sinkers for a strike. 19% of sinkers he’s thrown this year in his first pitch have been balls. Refer to pitch outcomes below:

However, he should avoid throwing a curveball for the first pitch, if he doesn’t want to get behind. Out of 12 curveballs that he’s thrown for the first pitch this year, nine of them were called a ball. If you look at the tables above, he did much better last year with his curveball for the first pitch.

He should not throw a curveball if he wants to get further ahead either. Look at the table below for pitch outcomes in 0-1 counts. You will notice that batters are not chasing it, and they don’t whiff on it when they swing after it. Although Cecil’s 2016 season 0-1 curveball sample is limited with only nine, you can see the pattern. 12% more balls taken by batters against Cecil in 0-1 counts this year compared to the  second half of 2015. 36% less swings have been taken this year against Cecil’s curve. No batters have whiffed against Cecil’s 0-1 curveball this year. His 0-1 curveball in the second half of 2015 served him so well, inducing whiffs in 26% of occasions. Now that he can’t do that, he is failing to get ahead 0-2 as often as last year, which gives him more trouble getting outs.

Screen Shot 2016-05-11 at 6.00.56 PMScreen Shot 2016-05-11 at 6.01.02 PM

And when he does get to an 0-2 count somehow, he is struggling to get guys out with curveball. You can see here:

Screen Shot 2016-05-11 at 6.12.26 PMScreen Shot 2016-05-11 at 6.12.31 PM

Half the curveballs he’s thrown in 2016 in 0-2 counts were called a ball. Worse rate than last year. Batters swung at it 61% of time in the second half last season, while they now swing at it only 39% of the time. Batters are also making more contact with 0-2 curveballs this year than last year. It’s the same story when considering when he is ahead. (In other words, all counts when he is ahead)

His refined curveball in the second half of the 2015 season was the reason why he was doing so well. According to FanGraphs, his wCu/C in the 2014 and 2015 seasons were 2.5 and 2.8, respectively. This year, it is an awful -5.2. His curveball must be refined because batters figured it out.

Let’s figure out what could be wrong with his curveball then.H-mov cv

His curveball’s horizontal movement deviates from last year’s second half. His curveball was great in the first half of last season as well. Last season, the horizontal movement of the curveball ranged between 0 to 1 inch. This means that his curveball’s horizontal last year moved 0 to 1 inch away from the catcher’s glove side. This season, it is moving toward the glove side of the catcher. I don’t know whether that has a negative impact. It’s inconclusive.

Screen Shot 2016-05-11 at 6.33.57 PMScreen Shot 2016-05-11 at 6.34.04 PM

h-rel cv

Brett’s horizontal release points of 2016 curveballs are up to par with the second half of 2015. So I don’t think horizontal release point has had any impact on his curveball this year.

v-mov cv

He has more vertical depth on his curveball this season than the last. More vertical depth on his curve is a good thing. But I don’t think improving vertical depth will fix anything, given that his curveball got its job done last year with less vertical depth.

v-rel cv

Vertical release point of his curveball this season is within the range of second half of 2015. I don’t think vertical release point of his curveball is a problem either.

velocity cv

His curveball velocity is down this year. This is likely the biggest problem with Cecil. This implies that batters have some more fractions of a second to judge whether the curveball is a ball or strike. This gives batters some more time to decide whether to swing or not. I am convinced that a velocity increase will help him. Fortunately, he experienced a velocity increase throughout each of his last four seasons (2012 to 2015), as you can see in this graph:


It does seem to explain his improved ERA throughout each of the last two seasons. We should monitor his velocity this May to see if there is any sign of velocity improvement. In the meantime, it’d be best to let him pitch in low-to-medium leverage situations until he is warmed up for home stretch. He looks to me like he will be okay. He is only 29 this year and I don’t think we need to worry that his velocity drop is a permanent thing yet. Message to Brett: “Just relax and stop thinking about your disappointing start to this season. It’s likely nothing and time will only solve it. Congratulations on the birth of your daughter.”

David Price Should Be Okay

(Written before Price dominated on Thursday)

Obviously there is some concern about David Price.  So I went and dove into his numbers to see what I could figure out. (All data below was obtained through FanGraphs, who coincidentally also wrote an article about Price, with similar methodology and results.)

So let’s start at the top and look at his ERA.

Career | 3.19
3 Year Average | 3.01
2016 | 6.75

Yikes!  His ERA this season is more than twice what we’ve ever seen out of Price.  This is no surprise to anyone. But we all know that historical ERA isn’t really a good predictor of future ERA (it includes too much “noise” from things that the pitcher can’t control).  So let’s look at some metrics that are better indicators of the way he’s pitching.

Career | 3.36 | 3.34
3 Year Average | 3.09 | 3.07
2016 | 2.99 | 2.94

Okay, so according to both xFIP and SIERA, Price is actually pitching as well as he’s ever pitched.  Nothing to be concerned with here, and in fact we should be really happy with how he’s pitching.

In most cases, when a pitcher’s ERA is significantly higher or lower than their xFIP and SIERA, it can usually be chalked up to variance and you should expect things to settle back to their historical numbers.

Over his career Price’s ERA has actually outperformed his xFIP by almost half a run per 200 innings pitched.  Which makes it even more peculiar why this season his ERA would be *lagging* his xFIP by such a significant margin.

So let’s go a little deeper and try to figure out *why* his ERA is so much higher than his xFIP.

Well, the obvious first things that jump off the page are his BABIP and Left on Base % (LOB%)

Career | .286 | 75%
3 Year Average | .298 | 74%
2016 | .373 | 54%

His BABIP is 75 points higher than his three-year average and he’s stranding 20% fewer runners.  It’s easy to look at these numbers and say he’s just getting unlucky on balls in play and getting unlucky on batter sequencing.

The LOB% I can buy being just bad luck, but the BABIP I want to check on.  Let’s look at his batted ball profile and see how unlucky he’s been on balls in play:

| LD% | GB% | FB% | Soft % | Med % | Hard %
Career | 20% | 44% | 36% | 18% | 56% | 27%
3 Year Average | 22% | 42% | 36% | 17% | 55% | 28%
2016 | 29% | 40% | 31% | 17% | 42% | 41%

Uh-oh.  His soft-hit and ground-ball ratios are constant, but in 2016 he’s giving up more line drives and harder contact by a significant margin.  Giving up more line drives and harder hit balls helps explain his elevated BABIP… It’s not just bad luck.  By my calculation his xBABIP based on this batted ball profile is .361.  That’s slightly lower than his actual BABIP (.373), but still way higher than his career average.

This is definitely a bit concerning, but let’s see if we can figure out why he’s giving up such hard contact.  First place I like to look is his command and velocity numbers.

| Fastball Velocity | Fastball %
Career | 94.6 | 35%
3 Year Average | 93.6 | 23%
2016 | 91.8 | 12%

Another red flag.  His fastball velocity is down almost 2mph from his three-year average.  I did check, and his velocity went up about 1.5mph between April and August last year so we should see his velocity pick up as the year goes on, but this isn’t something you want to see out of a guy you just spent $217M on.  To go along with the reduced velocity, you are seeing Price rely way less on his four-seamer.  He’s basically replaced it with two-seam fastballs and cutters, hoping the movement he gets out of them makes up for the reduced velocity.

But how is he doing with his slightly altered pitch selection?

| K% | BB% | Zone % | Contact % | SwStr%
Career | 23% | 6% | 47% | 80% | 9%
3 Year Average | 25% | 4% | 48% | 79% | 10%
2016 | 29% | 7% | 48% | 71% | 14%

First takeaway is that his strikeouts are actually up!  Despite the reduced velocity, he’s striking out more batters and inducing more swing and misses.  These are good signs that his “stuff” is still there.

Not shown above, but he’s not getting guys to chase pitches like he used to (3% drop in swing rate on balls out of the zone compared to his three-year average), but on pitches in the zone he’s getting way *more* swing and misses (12% improvement on batter contact rate on pitches in the zone).

**So what does this all mean?**

As far as I can tell, Price will be fine.  He’s lost some velocity, so you are seeing him switch from a four-seam fastball to a two-seam fastball.  Because of the movement on these pitches, he’s getting more swing and misses when he throws strikes.  But with the drop in velocity, when guys do put the bat on the ball, they are doing so with more authority. What this means for Price is he will need to get his offspeed pitches working to keep batters off balance and induce more swings on pitches out of the zone.  Namely his changeup which has seen a big drop in value so far this year.

His LOB% should stabilize and if he can start commanding his changeup better, his BABIP should drop as well, which will make his ERA start to resemble that of the Price the Red Sox paid for this offseason.

The best news of all? It’s only May, so we have a lot of baseball left.  No need to panic yet, as far as I can tell.