Archive for December, 2016

Edwin’s New Home

Edwin Encarnacion’s eight-year stint with the Jays is over, as he has decided to move his talents to Cleveland. This leaves a gaping hole in the Jays lineup. During his six and a half seasons in Toronto, he accumulated 239 homers and 679 RBI while hitting for an average of .268. He reinvented his career when he made the switch to first base five seasons ago, as he began to play with more confidence. He has had a higher fielding average than the rest of the league at his position since he started playing 1B in 2011 — however, his defense is nothing to write home about.

Edwin will likely be replaced with Justin Smoak and Kendrys Morales. Smoak was able to swat 14 homers with 34 RBI in just 299 at-bats, and with more plate appearances, he will be capable of cutting into the missing 42 homers and 127 RBI Edwin produced last season. Furthermore, Morales hit 30 homers with 93 RBI, which are closer to Edwin’s numbers, and with the move to a more hitter-friendly field, Morales may actually be able to replicate similar numbers. It is also important to note that right-handed hitters fare better at Rogers Centre, and Morales seems to hit the ball better from the left side, as his wRC+ is 115 hitting from the left and 109 from the right. As far as WAR goes, however, Edwin’s total through 11 seasons is 27.6, while Kendrys only has 8.4 in 10 years, and Smoak has a WAR of  0.2 through six years. In this sense, Edwin has left his old home with a gaping hole.

The Tribe, however, will be happy landing this heavy-hitting righty. Mike Napoli is yet to sign this offseason, so at this point Edwin will likely share time between first base and DH with Carlos Santana. The Indians ranked 10th in the AL with 185 homers last season, thus, Edwin’s bat will help with the lack of power in their lineup. Moreover, Encarnacion’s 3.9 WAR will make him the third-most valuable hitter (tied with Jose Ramirez) in the lineup.

A three-year contract locks Edwin in Cleveland until age 36, but the 33-year-old has shown no signs of slowing down, as his WAR has hovered around 4 since turning 29. His cumulative WAR was only 7.4 in his first six seasons, compared to a WAR of 20.2 in his past five. The three-year contract still seems most favorable to Cleveland, as, if he sees a drop in numbers next season, he will have a year to recover, and if he is unable to they can drop him in 2020. For Edwin, if the Tribe is not able to replicate the success they had last season, he is stuck watching his prime go down the drain. However, with the addition of Andrew Miller, and the experience the pitching rotation gained from their run in the postseason this past October, there is no reason the Indians should not produce the same success.

So, Edwin’s new home may be a breath of fresh air for the slugger, as his power will not be outshone by a lineup of heavy hitters. And he is still with a team that gets on base a lot, and a pitching staff that has the capability of being one of the best in the majors. Additionally, playing half of his games at Progressive Field will not hurt, as he should be able to hit a few moonshots over the shallow eight-foot wall in right field.

At any rate, Edwin’s acquisition has pushed the Indians into being arguably the projected best team for the 2017 season, and it should be a cake walk to finish first in the AL Central, especially compared to the AL East Edwin is used to. He might have found a home more suitable than the one across the border.


Imagining Shohei Otani as a True Free Agent

We all know about Shohei Otani, but in case you are the one baseball fan who doesn’t, he is possibly the best baseball player in the world.  Otani turned 22 years old in 2016.  Although he did not have enough plate appearances to qualify, if he did, Otani’s 1.004 OPS would have led the country (of Japan).  In 382 plate appearances, he posted a slash line of .322/.416/.588, in addition to hitting 22 home runs.  That sounds like a very good player who would draw serious interest from MLB teams if posted.  However, that’s not all.  Otani also posted a 1.86 ERA in 140 IP with an 11.2 K/9.  He owns the NPB record for fastest pitch, at 165 km/h (102.53 mph).  The pitching stats alone would have every team in the MLB drooling.  Combine this with his hitting, and Otani might just be the best baseball player in the world.  And the best baseball player in the world is not going to paid like his title suggests.

The problem is that Otani will not yet be 25 after next season.  The new CBA keeps all international players under 25 from being exempt of the bonus pool system.  A tweet from Jim Allen reported that Otani still wishes to be posted after the 2017 season, when he will be 23 years old.  According to an excellent Dave Cameron article also on FanGraphs, the most money Otani could receive is $9.2 million.  This figure would be equivalent in 2016 to a player worth approximately 1.15 WAR.  Otani would surely be worth more wins than 1.15.

At first I wondered if this would make Otani the most underpaid player in the MLB.  Before that question could be answered, however, I had to answer a more important one: how much would Shohei Otani be worth in wins and, by extension, in dollars?  To make this more interesting, let’s make it a one-year deal, in which Otani would be paid the 2017 projected average price of $8.4 million per win above replacement.

The NPB has no available WAR figure, and no OPS+ or ERA+ was offered either.  Unfortunately, I could not find NPB league totals, so no calculating OPS+ or ERA+ on my own, at least not accurately.  I’ll use MLB league totals to find these numbers, but it is an obvious flaw in my research.  If anyone can find NPB totals for me, post the link in the comments, and I’ll gladly redo the study with those figures.

So, using the MLB totals, here are Otani’s numbers in 2016.  OPS+ 170.  ERA+ 225.

Those numbers look really good.  If these were for an MLB player, he would be by far the best player in the league.  How good were the numbers of other Japanese players before and after they were posted though? Let’s see, using three pitchers’ ERA+ and three hitters’ OPS+.  First the pitchers, including what Otani would hypothetically produce in 2017 by what the others produced.

Masahiro Tanaka:  2013 (NPB) 305; 2014 (MLB) 138

Yu Darvish:  2011 (NPB) 274; 2012 (MLB) 112

Hisashi Iwakuma:  2011 (NPB) 163; 2012 (MLB) 121

Shohei Otani:  2016 (NPB) 225; 2017 (MLB) 113

Now for innings pitched, another component required for the crude WAR I’ll project.

Tanaka:  212.0; 136.1

Darvish:  232.0; 191.1

Iwakuma:  119.0; 125.1

Otani:  140.0; 112.2

The raw numbers of IP and ERA+ can be converted into a metric (PV) that I can change into WAR.

Tanaka:  85.227 PV; 3.3 WAR

Darvish:  103.932; 3.9

Iwakuma:  73.552; 2.0

Otani:  63.911; 1.7

Pitching, Otani would be projected for a 1.7 WAR.  That is worth $14.28 million in real value.  Now for batting, which will be OPS+.

Ichiro Suzuki:  2000 (NPB) 157; 2001 (MLB) 126

Hideki Matsui:  2002 (NPB) 205; 2003 (MLB) 109

Kosuke Fukudome: 2007 (NPB) 155; 2008 (MLB) 89

Otani:  2016 (NPB) 170; 2017 (MLB) 107

That is the quality component of WAR.  Plate appearances now for quantity.  As a side note, because I’m not factoring in defense, oWAR is going to be used instead of WAR.

Ichiro:  459; 738

Matsui:  623; 695

Fukudome:  348; 590

Otani:  382; 540

Now for my metric to convert to oWAR.  I’ll call it OV.

Ichiro:  115.305 OV; 6.1 oWAR

Matsui:  93.179; 3.1

Fukudome:  63.012; 0.6

Otani:  68.180; 0.9

On offense Otani would have a 0.9 WAR.  This translates into $7.56 million.  For a one-year deal using real value, Otani should receive $21.84 million, while producing a 2.6 WAR.  But what about a long-term deal with market value instead of real value?  Using Bill James’ stat of projected years remaining to determine the length of the deal, it would be 10 years.  The first year would not have a salary of $21.84M, but $13.72M.  This year was easy.  Now for the next nine years.  First, we’ll examine his pitching value.  I won’t bore you with all the calculations.  This article is tedious enough without it.  Just the pitching WAR for each year.

2018 2.1; 2019 2.9; 2020 3.9; 2021 4.8; 2022 5.9; 2023 5.7; 2024 3.8; 2025 2.1; 2026 0.7

Now the oWAR for each of the seasons:

2018 1.6; 2019 2.3; 2020 3.0; 2021 3.8; 2022 4.5; 2023 5.3; 2024 4.7; 2025 3.1; 2026 1.7

The total WAR for the years are as follows:

2018 3.7; 2019 5.2; 2020 6.9; 2021 8.6; 2022 10.4; 2023 11.0; 2024 8.5; 2025 5.2; 2026 2.4

Over the course of the 10-year deal, Otani would have a total WAR of 64.5.  This is not what he would likely produce.  My projections are — ahem — optimistic.  These are the numbers he could produce if played as both a pitcher and a semi-regular hitter.  Using real value and these WAR figures, Otani would have a real value of $689.14M.  You can read that number again.  I had to do a double-take.  Go ahead and do one too; it’s still $689.14M.  That is real value — however, not market value.  The market value is the much more important, and interesting, number.  What the market value turns out to be, $249.01M, is still massive, but at least the $24.901M AAV is more reasonable in the market.  In fact, this is likely what he will receive when posted, if he is eligible for this kind of deal.  It will be a shorter deal than 10 years, but the AAV should be in line with what I projected.

However, Otani is a mind-boggling player, so no contract, no matter how mind-boggling it may seem, is out of the question for him.  Even $689.14M.


xFantasy, Part II: Triple Slash Converter

Following up on the introduction of xFantasy this week, I’ve packaged the projection equations together here into a single Triple Slash Converter tool. This allows you to input a player’s PA, AVG, OBP, and SLG, and will spit out the resulting expected 5×5 stats. Check out the original post to explore the equations used in more detail.

Triple Slash Converter

A few optional things can be used to improve the projected fantasy line…

  • Batting order: Must be an integer between 1 to 9. Determines overall R/RBI/SB as well as distribution of R vs. RBI. If you aren’t sure, the sixth spot is about average production.
  • Team runs: Number of runs you expect the player’s team to score (full season). More team runs means more player R/RBI. If you aren’t sure, 720 is about average.
  • Team SB: Number of bases you expect the player’s team to steal (full season). More team SBs means more player SBs. If you aren’t sure, 85 is about average.
  • SPD: The SPD score for a player is fairly consistent year-to-year (outside of aging effects), and is useful for two reasons: 1) Faster players score more runs 2) SPD predicts SB’s well. If you aren’t sure, 3 or 4 is about average for fantasy-relevant players.

Part III, examining the predictive power of xFantasy and comparing it to projections, is still in the works, but I realized that the package of equations from Part I would be much more useful if everyone had a tool to play around with them!


Introducing xFantasy: Translating Hitters’ xStats to Fantasy

2016 has been a garbage year. At least, that’s what everyone seems to be talking about right now as the year draws to a close. But here in the baseball world, it’s been a banner year for many reasons, not the least of which is the new era of analysis that has arrived thanks to publicly available Statcast data. I, and I’m sure every other FG reader, have enjoyed following the quality Statcast analysis being developed in these electronic pages, particularly Andrew Perpetua’s “xStats”. In fact, I’m going to go ahead and stake the claim that I may have ‘coined’ (or at least influenced the creation of) the term xStats in the comments section of Andrew’s first xBABIP post. Inspired by the work of Perpetua, along with Alex Chamberlain (BIS-based xBABIP and xISO), and frequent leaguemate and Trevor-Story-lover Andrew Dominijanni (statcast xISO), I’ve decided to spend the offseason digging into xStats a bit deeper.

Perpetua has developed a great set of data using his binning strategy, most recently explained and updated this week, producing xBABIP, xBACON, and xOBA numbers based on Statcast’s exit velocity/launch angle data, along with the resulting ‘expected’ versions of the typical slash-line stats, xAVG/xOBP/xSLG. Throughout the year, I followed these stats fairly closely, often using ‘xStats’ to influence my fantasy baseball decisions. Given the opaque nature of translating a slash-line to actual fantasy stats, I generally went to the spreadsheet with the simple question “over- or under-performing?”, but that was about as far as I got. I found myself coming to probably-wrong conclusions such as “hey, maybe Sandy Leon isn’t actually that bad.” I was frustrated at my inability to turn a seemingly useful tool into actionable numbers for fantasy purposes.

This post serves as a starting point for that translation process. Way back in 2011, Jeff Zimmerman explained a basic approach for projecting R and RBI using only AVG, BB%, and HR% as inputs. I’ll similarly start here by coming up with simple models that translate rate stats (AVG, OBP, ISO) into fantasy-relevant ones, and then finally sub in the ‘x’ versions of those stats to come up with an ‘xFantasy’ line. I’ll stress that these are meant to be simple — I train the models based on all players that reached at least 300 PA in 2016, and I introduce a few team-related factors and shortcuts to improve fits, but I’m not looking to create a new Steamer or ZiPS here, just easy translations.

Home Runs

Starting with the surprisingly easy model, HR per PA is modeled well by ISO alone, with an R2 of .902 (excuse my simpleton’s application of statistics here; if you’re hoping for RMSE, p-values, etc., this will be a very disappointing post for you).

HR/PA = 0.2814*ISO – 0.01553

Runs and Runs Batted In

R and RBI per PA are interesting given their strong dependence on lineup position. To de-convolute that a bit, I’ve combined R+RBI into a single category (we can always separate them later). ‘R+RBI’ could be modeled using SLG alone, with an R2 of .758, but we can do better by separating SLG into AVG and ISO, and including terms for ‘team R+RBI total’ (player R/RBI totals are influenced by the team’s overall run production) and ‘average batting order position.’ Tanner Bell’s preseason post from this year explains and tabulates the influence of team offense and lineup position on R+RBI production. After doing some work to combine and normalize the data from Tanner’s tables, you can see the dependence of R+RBI/PA on lineup position can be roughly modeled as quadratic:

Average batting order position doesn’t appear to be easily accessible within the FanGraphs leaderboards, but thanks to the new ‘splits leaderboards’, it is possible to calculate with some elbow grease. Integrating all these factors to modify the original SLG model, R+RBI/PA is modeled by ‘SLGmod’ with an R2 of .807.

R+RBI/PA = 0.3292*SLGmod – 0.04751
SLGmod = AVG + 1.800*ISO + 2.061e-4*TeamR+RBI – 2.023e-3*ABO2 + 1.227e-2*ABO
                    TeamR+RBI = season total R+RBI for player’s team
                    ABO = average position of player in batting order

I mentioned that R+RBI could be separated later. Rather than demand the model predict the breakdown of R vs. RBI for each player, and introduce more sources of variation, I’m taking a shortcut here. The model calculates a value of x(R+RBI), and that is decomposed into R and RBI according to the actual proportion of R vs. RBI accumulated by the player in 2016. For instance, Mike Trout had 123 R and 100 RBI (223 R+RBI), and the model predicts 214.3 R+RBI, so we’ll give him (123/223)*214.3 = 118.2 R, and (100/223)*214.3 = 96.1 RBI.

Stolen Bases

SB per PA is a strange beast, a stat that’s much more dependent upon the whims and opportunities of the player and team than it is on the physical speed of the player. It can be tough to model given the large number of players that never run, or very rarely run. Much like SLG and R+RBI, I found that the SPD metric alone predicts SB/PA well, with an R2 of .662 when using a third-order polynomial fit. Is SPD cheating a bit? Maybe. For the uninitiated, it uses SB%, SB attempt frequency, triples percentage, and runs-scored percentage as inputs. You can see how SB/PA would fall directly out of that calculation, especially given the fact that teams tend to only turn runners loose on the basepaths if they are above a certain SB%. In any case, I’ll continue by modifying SPD to improve the fit, though the contribution of xStats to SB/PA will be much smaller than for the other stats.

Two rate stats serve to improve the fit, and they make intuitive sense: OBP, as players need to be on base in order to steal bases, and ISO, as players that hit for too much power tend not to spend as much time standing on first base, trying to steal second. I’ll again include a team factor, ‘team SB/PA,’ to quantify teams’ (or managers’) willingness to send runners, as well as ‘average batting order position,’ as players near the middle of the order tend not to steal as often. In this case I may have failed my initial criteria of a simple model, but it’s nevertheless a nice fit. Integrating it all into ‘SPDmod’, we can model SB/PA with an R2 of .834.

SB/PA = 0.2200*SPDmod3 – 0.3524*SPDmod2 + 0.2132*SPDmod – .04170
SPDmod = SPD/10 + 0.8206*OBP – 0.4670*ISO + 9.180*TeamSB – 9.192e-4*ABO2
                    TeamSB = average steals per plate appearance for player’s team

Average

Does batting average need its own section? I’m just going to use xAVG.

xFantasy

Now that I’ve reinvented the wheel and created a sort-of-okay way to calculate a 5×5 line based on rate stats, it’s a simple matter of substituting in the Perpetua xStats versions of AVG, OBP, and ISO to arrive at an ‘xFantasy’ line. I’ve also done a quick calculation of 2016 $ values using my normal z-score method, along with x$ values to allow easy comparison (no positional adjustments to either of them, though). The full sheet with 429 players’ 2016 xFantasy stats is found here, and I’ll include below the top-10 and bottom-10 players* whose lines improved/declined most when using xStats:

As one might hope, the top of the list is populated by several of the players that were identified as xStats’ undervalued darlings in 2016, like Mauer and Morales. In Belt, we might be seeing a place where park factors could improve xStats, though the disparity between his 17 HR and 29 xHR is still hard to ignore. Meanwhile, at the bottom of the list, it seems likely that the xSB model fails to adequately predict the SB totals for MLB’s most prolific runners, with Villar, Hamilton, and Nunez all getting hammered in the xSB category. But, it’s also possible that this is a knock-on effect from speedy players getting an unfair shake in xOBP. With Blackmon, it’s certainly possible that this is the other end of the park-factor spectrum, with his 20 xHR flagging way behind the 29 HR he put up.

Finally, one might ask how we solve the ‘Gary Sanchez problem,’ and it’d be quite useful to see what xStats project for players that only played partial seasons, to get an idea of what they ‘should’ have done over a full complement of PAs. Much like the ‘Steamer600’ projections hosted here at FanGraphs, I’ve calculated xFantasy600 values, where each player’s xFantasy line is normalized to 600 plate appearances. Or in other words, in this case, we’re evaluating players on a per-PA basis. Below, we have the top 20 players by xFantasy600 (x$600) in 2016:

Some new names rise to the top here, with Trea Turner, Gary Sanchez, and Trevor Story checking in as the third- (!!!), eighth- (!!), and 16th- (!) best players by xStats in 2016. On the one hand, they all appear to have over-performed in 2016 (check their wOBA vs. xOBA scores), but even regressing back to xStats in 2017 would comfortably land them among the best players in fantasy. The rest of this list is generally a who’s who of the best players in baseball, outside of Rickie Weeks, who was apparently highly effective as a platoon player last year. It’s fun to see that Big Papi went out on top, as the king of xFantasy. Miggy comes in at a very close No. 2, and I’ve seen him kicking around as a second-rounder on some early 2017 rankings – he might be the biggest bargain in drafts this year if that holds up. Overall, I’m very satisfied with this list’s ability to peg the best fantasy players, outside of the potential issue of underrating SBs.

Next time

The next step in this process is to evaluate xStats and xFantasy as a predictive tool. Throughout 2016, I pondered the fact that xStats might tell you more about “what happened” rather than “what will happen.” However, it’s hard to resist the allure of using them to project forward in-season, as they should stabilize faster than their standard statistical counterparts. One thing I have theorized is that xStats might be most helpful in evaluating ‘new swing’ guys, ‘new pitch’ guys, or new call-ups, as we wouldn’t expect traditional projection systems to capture these sorts of things. Craig Edwards has actually released an exceedingly timely look at “Did Exit Velocity Predict Second-Half Slumps, Rebounds?” I’ve now started work on the next chapter of the xFantasy story, comparing first-half and second-half numbers for 2015/2016 (the ‘Statcast era’) using traditional stats, xStats, and Steamer projections (h/t to Andrew Perpetua for updating his sheet to include first/second-half xStats splits).

This first look at xFantasy was a fun exploration of rudimentary projections and xStats. Hopefully others find it interesting; hit me up in the comments and let me know anything you might have noticed, or if you have any suggestions.


Exploring the Top 155 Pitchers

Happy Holidays. A new year is almost upon us. Just around the corner, pitchers and catchers will be gearing up to report. Spring-training facilities are prepping for an early start in anticipation of the World Baseball Classic, added excitement for any baseball fan ready to brush the cold off. Every new year brings change. Some more than others. This year, the new CBA was agreed upon. As the real game changes, so too does the fantasy world. Our league is entering its twelfth year, which is mind-blowing to me, considering we now represent six different states in four different time zones. Part of our longevity is attributed to adapting to the ever-changing landscape of baseball. Sabermetrics are slowing creeping into our stat categories — power is relied on less, and relievers more so. All that to say, we have changed again.

Our constant struggle has always been how to reflect the real game as best as possible without drastically changing the landscape of the league during one offseason. Recently there has been a trend toward an arms race. Pitchers were going ridiculously early in drafts and trades were featuring first- and second-round draft picks for non-keeper-eligible starting pitchers. Our solution to reduce the value of starting pitching in our league was to move from strikeouts to K/9 so as to reflect our six stat categories: Wins, K/9, ERA, WHIP, Net Saves, and Quality Starts.

Enough about our incredibly awesome keeper league. With all the talk of the winter meetings, the World Baseball Classic, and a new year, the jump on pitching is long overdue. So, the top 155 pitchers were ranked accordingly.

Method

Steamer has released their 2017 projections. These projections, of some 4000-plus pitchers, were exported to Microsoft Excel. Pitchers were then sorted by WAR: highest to lowest. The top 155 pitchers were then selected. In a 10-team standard league, no team should roster more than 15 pitchers, giving justification for cutting off the sample at 155. Five stat categories were then selected. Steamer does not project quality starts or blown saves. Therefore, to balance the importance of SP vs RP, innings pitched was selected in addition to Wins, K/9, ERA, and WHIP.

A table was then created with the stat categories on the x-axis and the pitching running down the y-axis (if you will). Each pitcher was given a positional value based on where that pitcher ranked within each stat category. For example, Max Scherzer is projected to have an outstanding 10.93 K/9 rate, which ranks sixth in the top 155. Scherzer was therefore given a value of 6 for the K/9 category. Scores were summed for each pitcher. Pitchers were than ranked by final score. Finally, a correlation using the summed scores and pitcher rank was executed to examine the relationship between stat categories and pitcher ranks.

Table 1: Example of Pitching Scores
    Wins K/9 ERA Whip IP Total
10 Rich Hill 6 10 8 12 98 134
11 Lance McCullers 3 11 18 67 31 130
12 Robbie Ray 5 12 19 39 61 136
13 Tyler Glasnow 7 13 52 130 91 293

Results

A complete list of the top 155 pitchers can be found at the end of this document. Below is a list of the top 20. Of note are Lance McCullers and Robbie Ray, who rank at 17 and 19, respectively. Not surprisingly, Clayton Kershaw is number one.

Table 2: Pitcher Rank
Rank Pitcher
1 Clayton Kershaw
2 Max Scherzer
3 Noah Syndergaard
4 Corey Kluber
5 Chris Sale
6 Madison Bumgarner
7 Jon Lester
8 Chris Archer
9 David Price
10 Stephen Strasburg
11 Carlos Carrasco
12 Yu Darvish
13 Justin Verlander
14 Jake Arrieta
15 Johnny Cueto
16 Jacob deGrom
17 Lance McCullers
18 Rich Hill
19 Robbie Ray
20 Michael Pineda

 

A correlation was then performed to explore the relationships of stat categories on pitcher total scores. Table 3 highlights K/9, ERA and WHIP as very strong correlations, with ERA being the strongest. Innings pitched had the weakest correlation.

Table 3: Correlation of Stat Categories and Total Scores
  Wins K/9 ERA WHIP IP Total
Wins 1
K/9 0.122264 1
ERA 0.333911 0.716097 1
WHIP 0.372086 0.589884 0.815055 1
IP 0.909963 -0.04049 0.138322 0.2326 1
Total 0.594921 0.752427 0.891181 0.881576 0.458243 1

 

Discussion

The goal of this exercise was to explore the impact on the changing landscape of pitching stat categories in fantasy baseball. The top 20 pitchers remain starters. However, within the top 20, one can see the impact of the change to K/9 from strikeouts. Both McCullers and Ray rank inside the top-15 projected K/9, according to Steamer. This led to the question, just how much of an impact will K/9 have on total scores?

The correlation revealed a strong relationship, but not the strongest. Therefore, the answer is, it has a strong impact, but in the end not as much as ERA and WHIP. What does strong mean? Statisticians usually agree that a correlation above .75 is considered a very strong relationship. To explore this meaning, let us take a look at an extremely early positional ranking done by ESPN.

Below, we’ll play the guessing game.

Table 4: Player Comparison
IP W K ERA WHIP
Player 1 174.1 8 218 4.90 1.47
Player 2 175.1 7 167 4.88 1.27

 

The above numbers appear somewhat similar. In a standard league, you may be inclined to lean toward Player 2. Indeed, according to ESPN, Player 2 is ranked 38th at his position and Player 1 is ranked 62nd. However, when scored using the methodology in this study, Player 2 ranks 49th while Player 1 ranks 19th. Two things when considering this. Table 4 are stats from 2016. The aforementioned rankings are based on 2017 projections. It could be that Player 1 has more room to grow. However, the change from strikeouts to K/9 is evident. Player 1 (10.11) has a much better K/9 than Player 2 (8.35). Therefore, the K/9 relationship to player ranking is correctly strong, and ranking Player 1 higher than Player 2 is logical. If you were wondering, Player 1 is Robbie Ray, and Player 2 is Drew Smyly.

Limitations

Steamer does not project quality starts or blown saves, therefore the correlation could be skewed toward starters or relievers. These results should only be taken into consideration when these five stat categories are in play. The sample size of starting pitchers is large enough, but not for relief pitchers. Only five relievers were projected in the top 155 pitchers ranked by WAR. Results of the correlation, then, could look different had more relievers been incorporated.

Future research

Future research should then include additional relievers. Expanding the pitcher rankings to the top 300 would include most relevant pitchers according to Steamer. Furthermore, additional stat categories should be explored. Would adding saves and quality starts affect the rankings? Certainly, the more variables added, the more complicated the results become. However, finding a balance between starters and relievers, reflective of the real game, should be further explored.

Conclusion

A great importance is placed on starting pitching, both in the real and fake game. However, relievers seem to have a growing importance. In 2016, three months of Chapman cost the Cubs two of the game’s best prospects, a trade usually reserved for starting pitching. How to value starting pitching compared to relief pitching is left open to interpretation, especially in the world of fantasy. A reduction on starting pitching value was in order for our league and for standard leagues. How to go about this should reflect the real game. For 10 managers, the decision was to move from strikeouts to K/9.

This initial research demonstrates that this change does not swing the pendulum too far toward relievers and away from starting pitching. A correlation demonstrates the strongest relationship to pitcher ranking is ERA. Given a head-to-head matchup, with an innings limit, having multiple starters with a good ERA will still be favorable to deploying strong relievers. The top 155 pitcher rankings further confirm this fact. Initial conclusion is that a move to K/9 is a positive switch that reflects the growing importance of a good reliever, while still favoring starting pitching.

Appendix A

Top 155 Pitchers

Name
1 Clayton Kershaw
2 Max Scherzer
3 Noah Syndergaard
4 Corey Kluber
5 Chris Sale
6 Madison Bumgarner
7 Jon Lester
8 Chris Archer
9 David Price
10 Stephen Strasburg
11 Carlos Carrasco
12 Yu Darvish
13 Justin Verlander
14 Jake Arrieta
15 Johnny Cueto
16 Jacob deGrom
17 Lance McCullers
18 Rich Hill
19 Robbie Ray
20 Michael Pineda
21 Danny Duffy
22 Steven Matz
23 James Paxton
24 Danny Salazar
25 Carlos Martinez
26 Gerrit Cole
27 Andrew Miller
28 Aroldis Chapman
29 Kenley Jansen
30 Dellin Betances
31 Zack Greinke
32 Aaron Nola
33 Jose Quintana
34 Jameson Taillon
35 Matt Shoemaker
36 Kyle Hendricks
37 Edwin Diaz
38 Dallas Keuchel
39 Cole Hamels
40 Zach Britton
41 Masahiro Tanaka
42 Kenta Maeda
43 Jeff Samardzija
44 Tyler Skaggs
45 John Lackey
46 Vince Velasquez
47 Julio Urias
48 Matt Moore
49 Drew Smyly
50 Julio Teheran
51 Jon Gray
52 Matt Harvey
53 Kevin Gausman
54 Garrett Richards
55 Rick Porcello
56 Gio Gonzalez
57 Alex Reyes
58 Alex Wood
59 Wei-Yin Chen
60 Zack Wheeler
61 Collin McHugh
62 Carlos Rodon
63 Drew Pomeranz
64 Felix Hernandez
65 Tyson Ross
66 Matt Andriese
67 Jerad Eickhoff
68 Sean Manaea
69 Anthony DeSclafani
70 Michael Fulmer
71 Marcus Stroman
72 Blake Snell
73 Taijuan Walker
74 Tyler Glasnow
75 Ian Kennedy
76 Adam Wainwright
77 Jake Odorizzi
78 Jaime Garcia
79 Yordano Ventura
80 Joe Ross
81 J.A. Happ
82 Aaron Sanchez
83 Sonny Gray
84 Jharel Cotton
85 Hisashi Iwakuma
86 Michael Wacha
87 Francisco Liriano
88 Drew Hutchison
89 Mike Foltynewicz
90 Lance Lynn
91 Ricky Nolasco
92 Jeremy Hellickson
93 Archie Bradley
94 Luis Severino
95 Nate Karns
96 Mike Leake
97 Bartolo Colon
98 Mike Montgomery
99 Tyler Anderson
100 Ervin Santana
101 Junior Guerra
102 Ivan Nova
103 Chad Green
104 Tanner Roark
105 Jason Hammel
106 Mike Fiers
107 Dan Straily
108 R.A. Dickey
109 Doug Fister
110 Marco Estrada
111 Homer Bailey
112 Jesse Chavez
113 Ty Blach
114 Jordan Zimmermann
115 Trevor Bauer
116 Brandon Finnegan
117 Edinson Volquez
118 Charlie Morton
119 Daniel Norris
120 Cesar Vargas
121 Zach Davies
122 Adam Conley
123 Eduardo Rodriguez
124 Derek Holland
125 Luis Perdomo
126 Alex Cobb
127 Jose Berrios
128 Josh Tomlin
129 Shelby Miller
130 Chad Bettis
131 Patrick Corbin
132 CC Sabathia
133 Christian Friedrich
134 Hector Santiago
135 Kendall Graveman
136 Anibal Sanchez
137 Steven Brault
138 Tyler Chatwood
139 Wade Miley
140 Chris Tillman
141 Dylan Bundy
142 Andrew Triggs
143 Jason Vargas
144 Matt Garza
145 Phil Hughes
146 Miguel Gonzalez
147 Kyle Gibson
148 Ariel Miranda
149 Tom Koehler
150 Jorge de la Rosa
151 Chase Anderson
152 Martin Perez
153 Chad Kuhl
154 Andrew Cashner
155 Wily Peralta

 


The Season’s Least Likely Non-Homer

A little while back, I took a look at what might be considered the least likely home run of the 2016 season. I ended up creating a simple model which told us that a Darwin Barney pop-up which somehow squeaked over the wall was the least likely to end up being a homer. But what about the converse? What if we looked at the ball that was most likely to be a homer, but didn’t end up being one? That sounds like fun, let’s do it. (Warning: GIF-heavy content follows.)

The easy, obvious thing to do is just take our model from last time and use it to get a probability that each non-homer “should” be a home run. So let’s be easy and obvious! But first — what do you think this will look like? Maybe it was robbed of being a home run by a spectacular play from the center fielder? Or maybe this fly ball turned into a triple in the deepest part of Minute Maid Park? Perhaps it was scalded high off the Green Monster? Uh, well, it actually looks like this.

That’s Byung-ho Park, making the first out of the second inning against Yordano Ventura on April 8. Just based off exit velocity and launch angle, it seems like a worthy candidate for the title, clocking in at an essentially ideal 110 MPH with a launch angle of 28 degrees. For reference, here’s a scatter plot of similarly-struck balls and their result (click through for an interactive version):

(That triple was, of course, a triple on Tal’s hill)

But, if you’re anything like me, you’re just a tad underwhelmed at this result. Yes, it was a very well-struck ball, but it went to the deepest part of the park. What’s more, Kauffman Stadium is a notoriously hard place to hit a home run. It really feels like our model should take into consideration both the ballpark in which the fly ball was hit, and the horizontal angle of the batted ball, no? Let’s do that and re-run the model.

One tiny problem with this plan is that Statcast doesn’t actually provide us with the horizontal angle we’re after. Thankfully Bill Petti has a workaround based on where the fielder ended up fielding the ball, which should work well enough for our purposes. Putting it all together, our code now looks like this:

# Read the data
my_csv <- 'data.csv'
data_raw <- read.csv(my_csv)
# Convert some to numeric
data_raw$hit_speed <- as.numeric(as.character(data_raw$hit_speed))
data_raw$hit_angle <- as.numeric(as.character(data_raw$hit_angle))
# Add in horizontal angle (thanks to Bill Petti)
horiz_angle <- function(df) {
angle <- with(df, round(tan((hc_x-128)/(208-hc_y))*180/pi*.75,1))
angle
}
data_raw$hor_angle <- horiz_angle(data_raw)
# Remove NULLs
data_raw <- na.omit(data_raw)
# Re-index
rownames(data_raw) <- NULL

# Make training and test sets
cols <- c(‘HR’,’hit_speed’,’hit_angle’,’hor_angle’,’home_team’)
library(caret)
inTrain <- createDataPartition(data_raw$HR,p=0.7,list=FALSE)
training <- data_raw[inTrain,cols]
testing <- data_raw[-inTrain,cols]
# gbm == boosting
method <- ‘gbm’
# train the model
ctrl <- trainControl(method = “repeatedcv”,number = 5, repeats = 5)
modelFit <- train(HR ~ ., method=method, data=training, trControl=ctrl)
# How did this work on the test set?
predicted <- predict(modelFit,newdata=testing)
# Accuracy, precision, recall, F1 score
accuracy <- sum(predicted == testing$HR)/length(predicted)
precision <- posPredValue(predicted,testing$HR)
recall <- sensitivity(predicted,testing$HR)
F1 <- (2 * precision * recall)/(precision + recall)

print(accuracy) # 0.973
print(precision) # 0.811
print(recall) # 0.726
print(F1) # 0.766

Great! Our performance on the test set is better than it was last time. With this new model, the Park fly ball “only” clocks in at a 90% chance of becoming a home run. The new leader, with a greater than 99% chance of leaving the yard with this model is ARE YOU FREAKING KIDDING ME

I bet you recognize the venue. And the away team. And the pitcher. This is, in fact, the third out of the very same inning in which Byung-ho Park made his 400-foot out. Byron Buxton put all he had into this pitch, which also had a 28-degree launch angle, and a still-impressive 105 MPH exit velocity. Despite the lower exit velocity, you can see why the model thought this might be a more likely home run than the Park fly ball — it’s only 330 feet down the left-field line, so it takes a little less for the ball to get out that way.

Finally, because I know you’re wondering, here was the second out of that inning.

This ball was also hit at a 28-degree launch angle, but at a measly 102.3 MPH, so our model gives it a pitiful 81% chance of becoming a home run. Come on, Kurt Suzuki, step up your game.


What to Make of Blake Snell’s Arsenal

I’ll give y’all a warning: This is a very random article. It’s not like Blake Snell isn’t an interesting player; he’s a young arm who is going to be a pivotal piece of the Tampa Bay Rays rotation for a while. Even though he struggles to keep the ball in the zone, he has electric stuff and does a good job of keeping the hits he gives up in the ballpark. He was a highly-touted prospect and certainly delivered on that last year, striking out 24.4% of batters while delivering a 3.39 FIP in 89 innings.

However, there were some reasons to be concerned. Snell was very mediocre, according to Baseball Prospectus’ DRA (Deserved Run Average), which is widely considered to be one of the best measures of a pitcher’s ability. In 2016, he had a DRA of 4.58 with a DRA- of 108, with 100 being considered the average performance by a pitcher. He also struggled to keep batters off base, issuing 5.2 walks per nine and sporting a 1.62 WHIP. These are some legitimate reasons for concern, but I want to try to look at the positives, and that starts by looking at the pitches he throws. The reason scouts have been optimistic about Snell this whole time is because of his stuff. He was known for having a fastball with good velocity and movement, along with a plus slider and change-up that essentially made up for his control issues.

Looking at his 2016 numbers, Snell had a pretty bad fastball, giving up 1.02 runs per 100 pitches thrown, and it got smacked around to the tune of an .893 OPS. He only threw it in the zone 51.4% of the time, and when it was thrown in the zone, it got hit over 86% of the time, which can explain the OPS. That being said, there were positives here that shouldn’t be overlooked. Snell has ridiculous vertical movement on his fastball; 10.7 inches of rise according to the Baseball Prospectus leaderboard. In fact, he ranked fourth overall in fastballs thrown with a spin rate over 2500 RPM. The higher the spin rate, the more the ball tends to “rise” in the eyes of a hitter. Overall, 32.4% of his fastballs registered over 2500 RPM, and if you watch him pitch, you can see that his fastball, when located up in the zone, has a ridiculous amount of life, and makes even the most professional hitters look silly. Also, his fastball ranked in the 70th percentile (minimum 100 fastballs thrown) for whiffs with 19.7%. Snell’s change-up was actually his best pitch in terms of runs saved, saving 2.4 runs per 100 thrown, with good arm-side fade and a 9-mph velocity gap from his fastball. Now, this is where this article takes a strange turn, and leads into why I’m writing it in the first place.

Snell’s slider had the best whiff rate in the MLB last year. Batters missed it a whopping 56.2% of the time, six points better than the NL Cy Young winner Max Scherzer‘s slider. Wow! That’s amazing! Let’s check how many runs it saved!

Well, actually, it cost Snell 2.04 runs per 100 thrown…which registered it as one of the worst sliders in baseball. That doesn’t really make a whole lot of sense. Looking deeper, I found his slider got absolutely clobbered when it got hit; it had a 100% HR/FB ratio and got smashed with an .898 OPS when batters hit it. But hitters also missed it 56% percent of the time. Yet it got hit, a lot. We could continue that back and forth forever.

Well, it turns out this isn’t the only breaking ball Snell has. He has a slow, looping curve that clocks in at the low to mid 70s with a ton of vertical drop created by 12-6 movement. He threw both his slider and curve at nearly identical rates, 12% for the slider and 12.8% for the curve. If you look at scouting reports from Baseball Prospectus and FanGraphs, you don’t see any mentions of his curve, just some blurbs about his slider and change-up being quality offspeed offerings. But, his curve was pretty damn good last year, ranking in the top five in runs saved per 100 thrown, with 2.2. It had sharp downward movement and comes out of the same arm slot as his slider, but is much slower, so it keeps batters off balance. It also held batters to a remarkable .162 OPS. It was truly one of the better curves in the game. Looking at this data, I’m left with a question: What do we make of this?

Before I attempt to answer that, I want to show a graph of Snell’s release points in 2016 — it will come up in the next paragraph.

 

 

 

 

 

 

 

 

 

Snell’s fastball has a ton of life, and is an absolutely nasty pitch when left up in the zone. If he’s throwing a “rising” fastball that comes out of the same arm slot as everything else (except the change), to me, it makes sense for him to throw his curve. His fastball becomes much harder to catch up to due to its movement if batters sit curve, and the velocity gap along with the drop he gets on his curve will get batters out if they sit fastball. The combination of the change of eye level, consistent arm slot, and the velocity difference will keep hitters off the entire game.

Not only is Snell improving both his fastball and curve this way, but he’s taking off the reliance on the slider by not having to throw a “bad pitch.” That being said, the slider still gets a ton of whiffs, but I would rather throw a pitch that batters can’t hit/do hit poorly in his curve than essentially taking a 50-50 shot of getting clobbered when throwing a slider. There’s no reason to stop throwing his change-up; it was his best pitch in 2016. It fills the velocity gap between the fastball and the curve and features movement away from righties, which is something he would otherwise lack. This brings me to my last point, and one more snippet of stats for you.

Snell’s slider vs. RHB: .650 SLG

Snell’s slider vs. LHB: .357 SLG

He threw his slider 9.7% of the time to righties. I’m not saying he should stop throwing it completely; there are obviously some redeeming qualities to it if he can get over 50% whiffs on on it. But if Snell can cut down on that slider usage and throw it more or less “exclusively” to lefties, he can eliminate the problem that he was having with it getting blasted. Since both breaking balls leave his hand at the same place, the deception will still be there, especially since batters will have to guess if it’s the harder, faster slider or the slower curve. If he can keep the walks down as well, we’re looking at a brand-new ace in the Rays rotation for 2017, assuming that throwing the better pitch can actually lead to success.


Ranking the Importance of the Five Tools

A good friend of mine with whom I argue about baseball often once posed a very interesting question to me.  He asked me, if I were to build a team completely devoid of one tool, which tool would I want to be missing?  In the ensuing argument, I was asked to rank the tools from least to most important for team success.  I put the order as arm, speed, fielding, contact, and power.  It was not until later that day that it struck me just how great a question he had asked.  Now, several months later, I will attempt to quantify the tools.

The rules for this study will be simple.  Two teams will be assembled for each of the five tools.  Each team will be considered league-average in every tool but the one for which they are being evaluated.  One of the teams for each tool will be the best possible in that one area, and the other will be the worst possible.  The runs lost from league-average by the worst possible team will be subtracted from the runs gained by the best possible teams.  The larger the difference, the more important the tool.  The teams will have one player for each position (minimum 250 PA, 450 Inn).

Note:  Pitchers are not included.  Losing arm does not mean losing value from pitchers.

Power

The players on the teams for power will be determined using isolated power.

Best Possible Team:  C) Evan Gattis (.257); 1B) Chris Carter (.277); 2B) Ryan Schimpf (.315); 3B) Nolan Arenad0 (.275); SS) Trevor Story (.296); LF) Khris Davis (.277); CF) Yoenis Cespedes (.251); RF) Mark Trumbo (.277)

This group has a combined ISO of .276, which would put their team OPS+ at about 115.4.  An average team has 6152.6 PA in a season.  Using these figures, they would score 836 runs as a team, compared to the 725 of an average team.

Worst Possible Team:  C) Francisco Cervelli (.058); 1B) Chris Johnson (.107); 2B) Jed Lowrie (.059); 3B) Yunel Escobar (.087); SS) Ketel Marte (.064); LF) Ben Revere (.083); CF) Ramon Flores (.056); RF) Flores

The combined ISO for this team was only .072, making the OPS+ about 87.8.  Runs scored for this team would then be 636.

Difference between BPT and WPT:  200 runs

Contact

The players on the teams for contact will be determined using K%.

BPT:  C) Yadier Molina (10.8); 1B) James Loney (10.1); 2B) Joe Panik (8.9); 3B) Jose Ramirez (10.0); SS) Andrelton Simmons (7.9); LF) Revere (9.1); CF) Revere; RF) Mookie Betts (11.0)

Collectively, this team would strike out in 9.7% of their plate appearances.  League average in 2016 was 21.1%, meaning the BPT is 11.4% better than league average.  The team would score 807 runs.

WPT:  C) Jarrod Saltalamacchia (35.6); 1B) Chris Davis (32.9); 2B) Schmipf (31.8); 3B) Miguel Sano (36.0); SS) Story (31.3); LF) Ryan Raburn (31.3); CF) Byron Buxton (35.6); RF) Sano

This high swing-and-miss team would strike out in 33.9% of plate appearances.  This is 12.8% higher than average.  The team would score 632 runs.

Difference between BPT and WPT:  175 runs

Fielding/Arm

As it turns out, there are really not stats for exclusively measuring a fielder’s arm.  Baseball-Reference has Arm Runs Saved, but that is not for infielders.  Additionally, the stat I originally wanted to use for Fielding, UZR/150, is not available for catchers.  To remedy both of these problems, I elected to use DRS.  DRS is available for all positions, and it takes a fielder’s arm into account.  Because I will not be taking values for fielding and arm on their own, fielding will receive about 60% of the total difference in the category.  The remaining 40% will be attributed to arm.

BPT:  C) Buster Posey (23); 1B) Anthony Rizzo (11); 2B) Ian Kinsler/Dustin Pedroia (12); 3B) Arenado (20); SS) Brandon Crawford (20); LF) Starling Marte (19); CF) Kevin Kiermaier (25); RF) Betts (32)

Kinsler and Pedroia tied for the lead at second base, so I just listed both of them.  The brilliant defensive team would be 162 runs better than the average in the field.  Of these, 97 will be attributed to fielding and 65 to arm.

WPT:  C) Nick Hundley (-16); 1B) Joey Votto (-14); 2B) Schimpf/Daniel Murphy/Rougned Odor (-9); 3B) Danny Valencia (-18); SS) Alexei Ramirez (-20); LF) Robbie Grossman (-21); CF) Andrew McCutchen (-28); RF) J.D. Martinez (-22)

The team of these players, who look like pretty good players, would have a -148 defensive value.  The value to fielding is -89 runs, and -59 for arm.

Difference between BTP and WPT (Fielding):  186 runs

Difference between BTP and WPT (Arm):  124 runs

Speed

Speed presents a problem.  It is valuable on the basepaths, obviously, but it is also valuable in the field.  More speed means more range.  Speed Score is a stat that represents the importance of both, but it does not translate well into value.  I decided to go with FanGraphs BsR, even though it does not measure speed in the field.  That value can be circumvented by routes and reactions anyway.

BPT:  C) Derek Norris (1.8); 1B) Wil Myers (7.8); 2B) Dee Gordon (6.2); 3B) Ramirez (8.8); SS) Xander Bogaerts (6.1); LF) Rajai Davis (10.0); CF) Billy Hamilton (12.8); RF) Betts (9.8)

This speed roster is a team that anyone would like to run out every day.  It is a young and athletic team.  Even so, based on speed alone, the team is just 63 runs above average.  That is the lowest value above average for any BPT.

WPT:  C) Molina (-8.7); 1B) Miguel Cabrera (-10.0); 2B) Pedroia (-4.5); 3B) Escobar (-5.6); SS) Erick Aybar (-3.9); LF) Yasmany Tomas (-5.5); CF) Jake Smolinski (-3.4); RF) Tomas

The lead-foot team is 47 runs below average.  That is the closest to average of any WPT.  Speed clearly has the least impact of the five tools.  I regret not putting it last.

Difference between BPT and WPT:  110 runs

Conclusion

I will admit that I was wrong.  Arm actually has some real value.  My excuse, I guess, is to say that it slipped my mind that arm is important for infielders as well as outfielders.  That should not have happened, and I am a little upset I made that mistake.  Fielding also beat out contact, which I did not expect.  I do not even have a defense for this one, as I do not know what I was thinking.

In all honesty, this post was written to win an argument.  However, it does have a deeper purpose.  This answers the question posed so many years ago in Moneyball.  If a general manager can afford to buy players with only one tool, which tool should it be?  This information is probably not new to any front office in baseball, but it is something to remember when considering small-market strategy.

Anyway, here is the official list of the five tools by importance, at least for 2017.

1.  Power

2.  Fielding

3.  Contact

4.  Arm

5.  Speed


We Good, Pham

Playing with the wonderful new splits leaderboard that was just rolled out on these very pages has led me down a Tommy Pham-shaped rabbit hole.

Tommy Pham has a stat line that is currently boggling my mind.

.214 ISO, 10.9% BB%, .342 BABIP, and 9 HR in 183 PA…good to excellent offensive numbers, in my opinion. Yet despite all of these good to excellent offensive numbers, he sported a major-league-high 38.8% strikeout rate (min 100 PA) that dragged his wRC+ to a barely-above-average 105. This deserves some digging into.

Looking at this 15-game rolling K%, there were times this past season that his rolling K-rate was down to 20.8% (on August 12). The AMAZING thing happens the further right you look on that graph — he begins striking out at a rate that makes Bartolo Colon look patient, hitting a high of 66.7% in the middle of September. From the beginning of the season to August 12, Pham had a wRC+ of 126 — higher than the full-season numbers of Carlos Beltran, Nolan Arenado, and Jose Bautista. After August 12, his wRC+ was 40. 40! FOUR ZERO. That’s behind nine pitchers (min. 40 PA).

AND SOMEHOW

SOMEHOW

He managed to have a higher BABIP when he was walking through life in a strikeout-induced haze. After August 12, he ran a BABIP of .417 with a K% of 59.1%, meaning he didn’t put the ball in play much, but when he did, it was finding the holes. BABIP and wRC+ have an R^2 correlation of 0.23, so you’d sort of expect them to move up and down together. However, before he started striking out like he was afraid someone was going to outlaw strikeouts so he was getting them all in while he could, his BABIP was 89 points lower — .328.

That’s not just lower. That’s much lower. That’s the difference between Dexter Fowler and Albert Pujols. And somehow an 89-point difference in BABIP resulted in an 86-point difference in wRC+ in the wrong direction.

You’d think running a much higher BABIP would be the result of hitting more line drives. After all — that is the variety of batted ball that lands for a hit more often than any type.

BUT. IT. GETS. WEIRDER. He hit line drives 28.0% of the time up to and including August 12. After August 12, he hit line drives ONLY 7.7% OF THE TIME. So with a 28.0% line drive rate, he ran a .328 BABIP, but his 7.7% line-drive rate resulted in a .417 BABIP. WHAT KIND OF MAGIC IS THIS?

Well, you know. The magic of small samples. 183 plate appearances falls nearly 70 short of being half of a qualified season’s plate appearances. Weird things are going to happen when you are looking at smaller samples. Weird things are always happening in baseball; that’s part of its charm. We just don’t always notice because over the course of a season, some weird things will balance out other weird things and we’ll forget how weird things were at some point. That’s why it’s worth it to dive into the numbers — to remind yourself that fun things are always happening in baseball. You may even find yourself surprised with how interesting you find Tommy Pham at the end of it all.


Cardinals’ Sin: Defensive Indifference

Last season the St. Louis Cardinals scored the fourth-most runs in the majors, but were a mere 13th in runs allowed. Yes, the rotation had its issues, including but not limited to Lance Lynn’s season-long absence, but the pitching staff managed to finish seventh in FIP. The large disconnect between the Cardinals’ runs allowed and FIP has the aroma of a defensive rat.

The Cards ranked 17th in the FanGraphs Def rating, and five of their top eight players by plate appearances had negative ratings. The team’s roster had a severe internal contradiction last year, putting weak defenders behind a merely average strikeout staff; the Cards were 15th in K% last year. Cardinals’ GM John Mozeliak recognizes the problem, and recently took one step to address it by signing Dexter Fowler to play center. Craig Edwards recently covered the signing in detail, calling attention to the continuing controversy regarding Fowler’s defense. The Cards will play him in center, but he might not really be a center fielder.

Randal Grichuk patrolled center last year in a manner that will make no one forget Jim Edmonds. His advanced defensive metrics, though, were not terrible; his UZR in center was a hair below average. The Fowler signing pushes Grichuk to left, but it isn’t at all clear Fowler is actually an improvement.

It is clear, however, that even Fredbird would be a defensive improvement over Matt Holliday in left. UZR liked Holliday as a defender early in his career, but hasn’t thought much of him since 2012. Holliday’s offense made up for his increasingly offensive glove, until last year. Mozeliak’s first move to right the wrongs of the Cardinals’ 2016 roster was his eminently wise decision to let Holliday walk. Fowler may or may not be better than Grichuk in center, but Grichuk will almost certainly be far better than Holliday in left. (And, heck, maybe Fowler’s defensive improvement will stick.)

This will still, in all likelihood, be a below-average defensive outfield, but 2017’s edition should be slightly more agile than the 2016 product. The good news is that St. Louis has a heavy groundball staff; they led the league in GB/FB ratio last year. The bad news is that infield defense is even worse than the outfield.

Mozeliak is moving to fix this, too. Matt Carpenter has played five different positions in his career, none especially well. Next year he will man the cold corner, his bat having developed to the point that it can carry him at that position. Giving most of the second-base starts to Kolten Wong will improve defense at the keystone. He’s not a stellar defender, but is far better than any of the other available options.

The left side of the infield, as currently constructed, will remain scary bad. Defense is the province of the young, something that Jhonny Peralta isn’t. Heading into his age-35 season, Peralta will surrender runs in quantity whether he plays short or third. The current odd man out in the infield, Jedd Gyorko, could be a solution at the hot corner. He’s not a great defender either, but he’s better than Peralta, six years younger, and probably at least equivalent offensively.

Aledmys Diaz is young, but not as young as you think, and played old at short last year, finishing 22nd out of 28 shortstops with at least 450 plate appearances in Def. It’s hard to know whether the offense he displayed last year is real; Steamer sees some regression but is still optimistic. As long he hits he’ll play, and St. Louis will have to hope the glove develops, at least a little. The farm lacks much of a shortstop crop, and the free-agent cupboard is also bare.

The pitching staff can help hide the defense’s weaknesses by striking batters out more often. The return of Lance Lynn and his career strikeout rate of 22% in April or May should help in that regard, although Tommy John survivors sometimes struggle initially upon their return. The flame-throwing Alex Reyes, with a combined career K/9 of 11.7 at all levels, could help even more if he wins a rotation spot.

But that’s a big if. Assuming Lynn and Reyes both win spots, that leaves one of last year’s starters spitting seeds in the bullpen. Lynn in effect replaces the now-departed Jaime Garcia. But who would Reyes replace? “Mike Leake” roars (or chirps) the Cardinal faithful, and on pure performance they’re not wrong. Leake projects to have the worst ERA, FIP, and K/9 of any Cardinals starter next year. He will also be entering the second year of his questionable five-year, $80-million contract, making Leake simultaneously a Cardinal and an albatross. He is a less-extreme version of Jason Heyward, a player whose contract significantly impedes benching.

Lynn may not be back on opening day, and teams frequently can avoid using a fifth starter for the first couple of weeks of the season thanks to frequent off days. It’s likely that manager Mike Matheny won’t make a decision until he has to, and he may not have to until well into May. Leake may get off to an awful start, perhaps making it easier to banish him to the pen. Michael Wacha may suffer a similar fate, or get injured again. Both the Mikes were disappointing last season, but Reyes doesn’t offer sure improvement, given his eye-watering walk rates.

So this may be a roster bug, but it’s also a feature. The Cardinals have no sure-fire No. 1-caliber starter, but they have considerable depth, including the guys mentioned above as well as Carlos Martinez, Adam Wainwright, Luke Weaver, and perhaps Trevor Rosenthal. The last two are nearly and entirely untested (respectively) in the major-league rotation, but both cook with gas and could help alleviate the team’s defensive problems if they can command their stuff.

Another way to get more Ks would be for manager Mike Matheny to get a bit more out of his bullpen at the expense of his lower-stuff starters. The Cardinals were 20th in reliever innings last year, despite having a bullpen that finished 12th in FIP and 13th in ERA — not Rivera-esque, but usable. The addition of Brett Cecil will help if he performs as his contract suggests the Cardinals are projecting. Some of the losers in the rotation sweepstakes could also be effective relievers. Rosenthal used to be one, and Reyes showed a flash of brilliance in 17 innings at season’s end last year. Few Cardinals fans will put a big stack on Matheny’s decision-making capacity, but there is at least the possibility that he might make better use of the resources at his disposal.

The Cardinals had a poorly-configured team by the end of last season, but Mozeliak is taking steps to correct it. Cardinals fans are surely hoping that whatever roster sins remain will not be mortal ones.