Corey Kluber deserves the AL Cy Young.  Corey Kluber will not win the AL Cy Young.  Felix Hernandez got off to a hot start, establishing himself early as the best pitcher in the AL, earning himself the starting job in the All-Star game (Kluber was not even an All-Star), and even inserting himself into the MVP discussion as late as mid-August, which will be enough to carry him to this year’s award.  The first half comparison:

Felix was the best in the AL.  Since then, Kluber has been the best in the AL:

And the season totals:

Both the Indians and Mariners were teams in the playoff hunt that ultimately fell short.  If you’re into narratives (and/or small sample sizes), here’s September, just for kicks:

Felix certainly didn’t do the Mariners any favors down the stretch; the Mariners shuffled their rotation around based on opponents and off days specifically to ensure they’d have Felix going on 9/8, 9/13, 9/18, 9/23, and 9/28 (the final game of the year) in anticipation of needing him that final game, and he did not deliver.  Felix’s ERA was held down thanks to a scoring change in the 9/23 game (based on an error he himself made, no less) that turned 4 earned runs into unearned runs – hence the far higher FIP – but overall Felix underperformed in September.

Looking at the overall body of work in 2014, we see very similar lines.  Their IP, GS, and xFIP are almost identical.  We see a slightly better FIP for Kluber, and a better ERA for Felix, primarily explainable by Safeco Field and his lower BABIP (which in turn is primarily explainable by the Mariners’ superior defense and the subpar Indians’ defense).  We see a significantly better strikeout rate for Kluber which more than makes up for his slightly higher walk rate, and a markedly higher HR rate for Felix despite playing in HR-suppressing Safeco Field.

Add it all up, and Kluber’s performance ends up markedly better than Felix’s.  Even if you don’t care about the narrative and Felix’s choking down the stretch, Kluber was the best pitcher in the AL this year.

King Felix will win the Cy Young because of his hot start, the media exposure he got throughout the season, his All-Star performance, and his ERA title (for which he should thank Safeco Field, his defense, the league scorers, and to a lesser extent his bullpen) – but he won’t deserve it.

I’m mostly an NFL writer. For years, I’ve been naming an NFL All-Loser Team at the end of each regular season. It’s an all-star team comprised exclusively of players whose teams missed the postseason. You can view it as a celebration of players who may be underrated or underappreciated because their teams aren’t very good, or you can view it as a shot at people who insist you can’t be that great if your team didn’t make the playoffs. Up to you. It’s a fun project, and it’s easy to apply to MLB as well football.

Here’s what you’re getting after the jump:

* Four teams. We’ll do an American League All-Loser Team, National League All-Loser Team, MLB All-Loser Team, and an all-star team taken exclusively from the six clubs that finished last in their divisions.

* For each list, we’ll do nine position players (the NL gets a pinch-hitter instead of a DH), and I’ll show my imaginary batting order. Each team will also feature a five-man rotation, a right-handed reliever, and a left-handed reliever. So, 16 players per team.

* I’ll offer some minimal commentary on the teams, with a paragraph or two for each team to discuss surprising selections and close calls. For the MLB team, I’ll list the top three in fWAR at each position and explain my selections. There’s nothing earth-shattering here, unless you think we can’t make a wicked lineup out of players from losing teams.
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BABIPf/x: A Predictive Pitch-based Model

Jonathan Luman, September 2014

In recent years the strongest predictors of a pitcher’s future performance have been fielding independent peripherals: homeruns, strikeouts, and walks. This has largely been because of the difficulty in predicting the rate at which balls in play (BIP) (i.e., all other plate appearance outcomes) will fall for hits (i.e., batting average on balls in play [BABIP]). A major problem with using BABIP statistics is isolating a pitcher’s “true talent” level due in large part to the relatively low rate of balls in play. A typical qualified season sees 550 or so BIP which allows about a 0.030 uncertainty[1] which is well within the pitcher-to-pitcher talent variation.

It has long been known that batted ball types fall for hits at desperate rates (ground balls being favorable to fly balls and linedrives far greater to either). Naturally, BABIP predictors have traditionally relied on this data. These data are a categorization of BIP results and, due to sample size limitations, are subject to significant year-to-year variation. This data can be innovatively applied to improve its utility (Max Weinstein recently claimed a predictive correlation of 0.37, Redefining Batted Balls to Predict BABIP, Hardball Times, Feb 2014).

An estimation of a pitcher’s BABIP can be made by categorizing pitches thrown with PITCHf/x data and comparing to league wide BABIP on similar pitches, shown conceptually in the MLB gameday screen grab in Figure 1.

Figure 1 MLB Gameday screen grab. Expected BABIP of each pitch differs based on pitch location, movement, velocity and other parameters [2].

Problem statement

Using pitcher-only data (i.e., not considering batted ball results) a model for predicted BABIP (BABIPf/x) is developed with the ability to predict a pitcher’s next season and long-term BABIPs.

Overview of Approach: BABIP thru League Averaged Pitch Categories

Conceptually, batted ball results are a function of the dynamics of contact. While there are limitless trajectories a pitch can fly toward the plate there are, practically, a finite set of “ways” a ball can be thrown: a handful of pitch classes; 12 different counts; and bins of speed, location, movement, etc. The seven million or so batted balls for which we have PITCHf/x data (2008-2013) have been binned into categories of statistically relevant size (several thousand batted balls per category, 76 categories altogether) so BABIP for a pitch category can be calculated with high precision[3]. Resolution of a pitcher’s expected BABIP can then be modeled by understanding the frequency of his pitches matching the league-wide pitch categories. Modeled BABIP then takes the form:

Where:

P%_j: Pitches categorized into major categories per PITCHf/x auto-classification, a pitcher specific parameter.

Fastballs: FA, SI, FF, FS, FT, SF (two seam, four seam, sinker, split finger, others)

Changeup: CH

Slider/Cutter: SL, FC

Curveball: CU, KC (curve and knuckle curve)

f_i: The fraction of pitches thrown by a pitcher matching a particular category, a pitcher specific parameter.

BABIP_i: Batting average on balls in play for of pitch category_i, calculated league wide.

g_i: The ball in play rate for the pitch category, calculated league wide.

C’: A correlation for the frequency a pitcher works in favorable (or unfavorable) counts.

c_m: BABIP coefficient of each pitch count, derived similarly to BABIPf/x categories. Coefficients are the result of a net-count regression to handle low sample size counts, calculated league wide

fm: same as f_i, a pitcher specific parameter.

-BABIP-: average actual BABIP, calculated league wide.

p0’: A regression on the release point similarity based on most frequently used pitches.

The abstraction of PITCHf/x auto-classification is more a convenience than requirement. Because pitches will ultimately be binned together based on zone position, movement, and similar parameters failures of PITCHf/x auto-classification are of small consequence. The auto-classification facilitated in the establishment of pitch categories with different BABIP tendencies.

Neither the C’ and p0’ corrections are fundamental to the process. Long-term BABIPf/x results are shown using the C’ correction. Next-year BABIPf/x calculations exclude this term. p0’ has been preliminarily defined but not yet implemented.

This BABIP model based on pitch category rates has several advantages. Pitch mix (and pitch category mix) stabilize quickly so the BABIPf/x predictions stabilize with small pitch sample sizes and are independent of defense and opponents. This enables BABIP predictions earlier than was previously possible. Also, PITCHf/x data are independent of batted ball results; the data of two sources could be combined for an integrated BABIP model of greater accuracy[4].

Fastball BABIPf/x Category Definition

To provide insight into the pitch category a discussion of the 30 fastballs (FA,FF,FT,SI,FS,SF)[5] categories is provided. The process to develop changeup, cutter/slider and curves BABIPf/x components was similar.

Figure 2 shows a histogram of the vertical pitch location (pz) of all fastballs put into play normalized by total number of fastballs and bin size (so that the histogram integrates to 1.0). The brown line is a normal distribution with the same mean and standard deviation as the observed pz measurements. The close match demonstrates that the vertical pitch locations is normally distributed and centered on the strike zone.

Figure 2: Vertical pitch locations of all fastballs in play 2008-2013

BABIP can be computed for several groupings of vertical pitch location based on their position in distribution, as shown conceptually in Figure 3. Pitches in the lower quarter of the distribution have a higher BABIP than do the pitches in the upper quarter[6].

Figure 3: Vertical pitch location divided into uneven tertiles

Figure 4 shows the BABIP of the uneven tertiles with error bars used to depict the 90% binomial confidence intervals. Not unexpectedly, pitches near the top of the strike zone fall for hits less frequently than pitches near the bottom of the strike zone. Recall that pitches down in the zone more frequently result in ground balls which are associated a relatively higher BABIP. The lack of overlap between the confidence intervals is a strong indication that a reliable effect being demonstrated. Care should be taken to point out that this reduced BABIP does not necessarily indicate that elevated pitches are preferable (for the pitcher) than low pitches. Elevated pitches may result in more homeruns and/or called pitches (i.e., called strikes and balls ), which are excluded from BIP sets.

Figure 4: BABIP of fastballs including 90% confidence intervals for lower, mid, and upper tertiles of vertical pitch location

It was found that fastball BABIPf/x categories can be defined on six parameters in the PITCHf/x database: pz, pfx_z, px, count, start_speed, and the relative match between pitcher and batter handedness.[7] PITCHf/x parameters were ranked based on BABIP sensitivity and probing for key bilinear sensitivities. Categories were defined when measureable differences in BABIP were identified.

The fastball pitch categories comprising BABIPf/x are shown in Table 1. For continuously variable parameters the numerical values are percentiles on a normal cumulative distribution function. For example, a pz category of 0-0.75 indicates a pitch below 2.86 ft (the red and green regions of the PDF shown in Figure 3).

Table 1: Fastball pitch categories of BABIPf/x

Improvements in model effectiveness could be achieved splitting categories with large populations further[8]. Non-elevated pitches with modest vertical break are broken down by insideness/outsideness for pitches off the plate (categories 1-4) or by pitch count for pitches over the plate (categories 5-16). The BABIP categories that include pitch count were the result of a regression accounting for relative pitcher or batter advantage (R² = 0.72), the confidence interval size is an approximation. Pitch velocity becomes a significant factor for pitches breaking down out of the strike zone (17-19, 28-30). Counterintuitively, at least to the author, is that for these pitches increased velocity is correlated with increased BABIP. Categories 20 and 22 reflect pitches at the left and right extremes of the BIP zone, there is no statistical significance to the difference in BABIP of these categories. Fastballs with the lowest BABIP tended to be elevated pitches with significant downward break (23-27). Fastballs with the highest BABIP tended to be low pitches with modest vertical break thrown in hitter friendly counts. Figure 5 shows the bins sorted on BABIP and a 90% binomial confidence interval depicted with error bars.

Figure 5: BABIP and confidence intervals of fastballs BABIPf/x categories

Figure 6 is a graphical representation of the fastball pitch categories of BABIPf/x. The vertical axis is the vertical pitch location percentiles based on fastball mean and standard deviations, the horizontal axis is vertical movement percentiles based on fastball mean and standard deviations. The “strike zone” covers most all of the vertical axis, very few balls are put into play that are not within the vertical limits of the rulebook strike zone[9]. The larger regions were split into subcategories based on the BABIP parameters with the highest sensitivity. For example, pitches high in the strike zone, but with low vertical movement, the horizontal location tends to drive BABIP (categories 20-22). However, for pitches low in the stike zone with high vertical movement, pitch velocity tends to drive BABIP (categories 17-19). As few categories were defined as possible while maintaining approximately a 0.015 variation between adjacent regions to preserve sample size and small confidence intervals.

Figure 6 Graphical depiction of fastball pitch categories of BABIPf/x

Long-Term Model Results

BABIPf/x was evaluated against the ball in play results for the 200 pitchers having thrown the most pitches in the 2008-2013 seasons. Table 2 shows these pitchers actual BABIP, BABIPf/x and statistical significance test “p‑values”, the table is sorted by most pitches thrown. No pitcher has thrown fewer than 6000 pitches. The top 20 pitchers (by number of pitches thrown) have the same average p-value as do the bottom 20 pitchers, suggesting that 6000 pitches is sufficient for model stabilization. A smaller threshold is likely demonstrable. The null hypothesis states that the BIP results differ from the modeled BABIP and cannot be rejected for low p‑values. A crude model evaluation suggests that the model is ”wrong” for p-values less than 0.05.

A more precise evaluation states that there is greater likelihood that a pitchers “true-talent BABIP” differs from the model for lower p-values. p-values computed from a league-average baseline can be compared to the BABIPf/x p-values for model evaluation. For the pitchers who differ from league average substantially, BABIPf/x results in about 2% greater accuracy, see Figure 7.

Table 2: Actual BABIP and BABIPf/x with binomial p-tests for 200 top pitchers by number of pitches thrown 2008-2013

Figure 7: p-values for BABIPf/x and BABIP_{league average}

Example: Comparison of Model to career-to-date

This model has been developed to reflect a pitcher’s “true talent” BABIP performance. “True talent” level can only be established over large BIP samples. For relatively infrequent events, like balls in play, this takes a long time, often many seasons. A pitcher throws many more pitches than balls are put into play, so a model based pitch observance ought to converge more quickly than observed BABIP[10]. We can test this hypothesis by anecdote by looking at an example pitcher[11]. It is desirable for our example pitcher to have:

• Thrown many pitches—to establish reliable “true talent” performance
• Begun his career during the PITCHf/x era—so his career-to-date performance is contained in the database.
• A modestly above or below average BABIP—so that the trivial solution (i.e., league average) can be rejected.
• Had some significant year-to-year BABIP variation—to test the predictive nature of the model.
• Had a BABIPf/x p-value between 0.2 and 0.6—that is, a fair, but not great match against “true talent” so as to not “cherry pick” favorable results.

Justin Masterson meets all these requirements, so he’ll serve as our illustrative example. Justin’s 2008-2013 career is broken down into 2-month segments, three per season. His career-to-date BABIP is the summation of all hits/balls-in-play from the beginning of 2008 until “now”, where “now” is varied parametrically. Stated another way, his 2008 career-to-date BABIP includes only his 2008 season and his 2010 career-to-date includes all balls-in-play from his 2008,2009, and 2010 seasons. Career-to-date BABIP is plotted in red in Figure 8. Justin’s 2008 BABIP was a very low 0.243, suppressed by his amazing debut months where his BABIP was a mere 0.143. Not surprisingly, his career BABIP has risen and has more-or-less stabilized at slightly higher-than-average (0.301 end of 2013). Figure 8 also contains each two-month BABIPf/x prediction for Justin in green, these are not career-to-date predictions, but each is based on only 2 months of pitching. Each prediction is a fair reflection of Justin’s long-term “true talent” level. 2014 was a “disappointing BABIP year” for Justin, 0.346 as of this writing (1 September 2014), raising his career-to-date BABIP to 0.306.

Figure 8 Justin Masterson’s Career-to-date BABIP compared with his two-month BABIPf/x predictions

This anecdote doesn’t prove much, it does suggest that the BABIPf/x model might have predictive ability to evaluate future performance. Evaluating “true talent” level from small samples is powerful in its own right, and can be inferred from the long-term modeling results. Predicting next-year’s performance is valuable for other purposes and is a natural use case.

Predictive Model Results

Predicting future performance is a challenging use for any modeling. In addition to the model error due to uncertain sources, predictive modeling is also complicated by the measurement uncertainty in the future value. This is especially true of BABIP modeling which has large variation due to year to year variation. Predictive BABIP modeling has no ability to predict changes in a pitcher approach, either intentional (e.g., pitch mix) or unintentional (e.g., injury).

Predictive modeling baseline

For the years 2008-2013, sequential 6-month BABIPs[12] have been tested for statistical significance. The sequential 6-month BABIP (year 2) is tested against the preceding 6-month BABIP (year 1) the binomial p‑values[13] for year-to-year BABIP variation are shown in Figure 9. This will serve as a baseline to compare against the BABIPf/x p-values. The predictive period is regressed toward league mean BABIP in an attempt to increase the predictive value.

Figure 9 p-values testing statistical significance

The predictive value of raw BABIP is very low, 15.9% of p-values were lower than 0.05 resulting in a strong presumption against the null hypothesis (i.e., the sequential sample was not consistent with the mean of the predictive sample) a further 8.3% of p-values were less than 0.1 resulting in a low presumption against the null hypothesis (a total of more than 24% with a presumption that the sequential BABIP is not consistent with the preceding BABIP). These samples did not increase greatly when the predictive sample was regressed to the league average (also demonstrated in Figure 10, 20% with p-values less than 0.1).   This is because the measurement uncertainty in future year BABIP is a major uncertainty contributor. To combat this, the sequential sample was also regressed to league average and improved p-values resulted[14], see Figure 10. The corollary is that league average BABIP is more predictive of future BABIP than is previous year BABIP.

Figure 10 p-values testing statistical significance of year-to-year BABIP

Predictive Modeling using BABIPf/x

p-values are recomputed comparing the sequential sample compared against the BABIPf/x prediction from the prior 6-month period both with and without a Bayesian regression of ball in play results in the predictive sample. Figure 11 shows the BABIPf/x p-value distributions overlaid on the baseline year-to-year BABIP significance distributions (of Figure 10), less than 2% of correlations having a strong presumption against the null hypothesis (and less than 4% of p-values are less than 0.1). In general, at any significance level greater than 0.1, 10% fewer pitcher seasons have a presumption against the null hypothesis. That is, the BABIPf/x values are consistently more predictive than are previous year BABIP results. This is a similar level of predictability as xBABIP (Zimmerman, 2014) or pBABIP (Weinstein, 2014).

Utilizing the actual BABIP in the predictive sample did not significantly improve the predictive capability (i.e., the Bayesian inference).   A Bayesian regression of longer period would provide greater utility, however, over long enough samples the career-to-date sample becomes the dominant term. The major drawback of career-to-date as the dominant term is the inability to identify changes in the pitchers “true talent” level. A Bayesian regression utilizing batted ball data is expected to improve results considerably as the data sources are independent.

Figure 11 BABIPf/x p-values compared to year-to-year BABIP p-values

Conclusion

BABIPf/x correlates well to long term BABIP, better than does league average results. BABIPf/x is more predictive of next year BABIP than is previous year’s BABIP. Because batted ball results (GB, LD and FB rates) are an independent data source than is PITCHf/x categories (i.e, location, movement , etc.) these data sources could be combined to form a multi-source predictive BABIP model of better quality than either source alone. Additional work could be done to improve count, release location corrections to BABIPf/x, as well as refinement to the BABIPf/x categories.

Bibliography

Weinstein, M. (2014, February 17). Redefining Batted Balls to Predict BABIP. Retrieved August 30, 2014, from The Hardball Times: http://www.hardballtimes.com/redefining-batted-balls-to-predict-babip/

Zimmerman, J. (2014, July 25). Updated xBABIP Values. Retrieved August 30, 2014, from Fangraphs: http://www.fangraphs.com/fantasy/updated-xbabip-values/

[1] 90% binomial confidence interval

[2] Expected BABIPs from Table 1. Pitch 1 and 2 match category 17. Pitch 3 matches category 26. Pitch 4 matches category 23. Pitch 5 matches category 7.

[3] Binomial uncertainty is a function only of mean and number of observations.

[4] Multiple techniques exist for this sort of integration. Two data sources can result in accuracies better than either data source separately.

[5] There are some indications that Sinkers and Splitters need to be broken out separately.

[6] The regions shown are not equally sized; the middle region contains half of the area.

[7] Derivative fields were considered, it was found that the native PITCHf/x fields were entirely suitable.

[8] One of the current shortcomings is the lack of categories with low BABIP. Splitting categories with excess sample size will provide greater diversity and dynamic range of model results.

[9] The BABIPf/x model accounts for pitchers who frequently pitch above or below the strike zone with the g_i term (the league wide rate that pitches in a category are put into play).

[10] Observed BABIP may never actually “converge”. As pitcher’s pitch selection or ability may evolve more rapidly than an adequate sample size to precisely compute his BABIP may accrue.

[11] Predictive capability will be tested more thoroughly in the next section.

[12] 3 two-months samples to get more “seasons”. For example, a “season” might be August 2009-July 2010, spanning the off-season”.

[13] To qualify for a p-test, both current and sequential 6-month periods had to have 350 balls in play, 2/3 of a qualified season.

[14] Naturally. League average successes and failures are being added to both populations.

Season awards time. Not that I am anyone important in baseball and not that my opinions matter, though there is my miniscule contribution to FanGraphs Player of the Year and the Internet Baseball Awards to think about. And so I do. My usual approach had been (1) sort various FanGraphs leaderboards. Then, (2) do this:

Yes, this is a notepad leaned clumsily against the screen to cover up the player names, teams (and win-loss record why not). Why? I have my affections and biases for certain names, so I’m bound to want to come up with reasons to rate those guys higher. I’m also bound to neglect players I don’t see very often, and trick myself into thinking their seasons weren’t as good as they look. If I can’t see the names, I can go on pure statistical evaluation and put one over on my cognitive biases.

But yeah, clumsy. So I wrote up a tool that would do the name hiding in a more graceful way. Behold! The Player Name Hider. This is a fairly simple Greasemonkey script that hides the names of the players and teams on all leaders pages. When installed, I see something more like this:

All the names and teams are hidden after page load. If I want to see one, I just click the text to reveal, as with Zack Greinke and Justin Verlander above. Also note the link tucked just above the leaderboard to reveal all data at once.

Two immediate uses spring to mind:

1. As mentioned above, it’s handy around seasonal award time. You may be surprised whose statistical profile you uncover. (Rhymes with: Shrill Shoes or Schmadison Schmumgarner.)
2. Enjoy hours of delightful life-avoiding trivia games! Sort by RBI and guess who led the league, that sort of thing.

Here’s how you get the tool:

1. Firefox users: install the Greasemonkey browser extension. Chrome users: install the Tampermonkey extension. Safari users: I understand GreaseKit has the same functionality but I have not tested this. IE users: uh, sorry.
2. The script itself is available here. There, click the “Raw” button to install and confirm the prompt.

That’s it! Please let me know if you have any comments or questions.

As we hurtle into what promises to be a dramatic postseason, let’s pause a few moments to remember the rake-steppers, face-planters, and prat-fallers who helped make others’ excellence possible. Far from being stars, these players are the space debris that clogged several MLB rosters this year. So without further ado, here is your All Kuiper Belt team for 2015 (and I don’t mean Duane Kuiper). The team features, if that’s the word, the worst qualifying hitter at each position, and the five worst qualifying starting pitchers, by fWAR.

Catcher: Jason Castro, .222/.286./.386, 1.2 WAR

By far the best player on this ignominious team, Castro is here in part because only nine catchers qualified for the batting title. FanGraphs had this to say in Castro’s pre-season player profile:

Castro turns 27 in June, and there’s not much to suggest regression in his future.

Well, not exactly. This year Castro was durable by catcher standards, but he regressed severely, and you could in fact have seen it coming. A stratospheric .351 BABIP propelled Castro’s breakout year in 2013. This season it sank to .293, not far from his carer mark of .307.  Neither as good as he was in 2013, nor as bad as he was this year, Castro should be a solid, above-average backstop, but always be wary of balls in play bearing gifts.

First Base: Ryan Howard, .222/.310/.380, -0.3 WAR

One of the best Baseball Prospectus player notes ever was for Ryan Howard this year. It consisted of just four words: “We told you so.” Howard’s career has become a coal seam fire, and he still has 2 years left on his deal before what will certainly be a $10 million buyout in 2017. Howard will be a pallbearer at the funeral for Ruben Amaro Jr.’s GM career.

Second Base: Aaron Hill, .244/.287/.367, -0.7 WAR

Hill wasn’t the worst Snake this year; that dishonor goes to Mark Trumbo, who managed to cram -1.3 WAR into just 355 plate appearances. There wasn’t a moist eye in the house when Kevin Towers lost his job, but The Gunslinger won the draw that sent Kelly Johnson to the Blue Jays for Hill in late 2011. Since then, Hill has amassed 6.6 WAR, while Johnson has only put up 1.7.

Third Base: Matt Dominguez, .215/.256/.330, -1.7 WAR

The second Astro on this list, Dominguez is here on the merits. Regressing plate discipline and an oddly consistent but abysmal BABIP have conspired to deprive Dominguez of any run production value. Known in his prospect days for his glove, Dominguez’ UZR is -8.7; only Lonnie Chisenhall had a worse rating at third. Dominguez is only 26, but then again, so was Kevin Orie in his last full major league season.

Shortstop: Derek Jeter, .256/.304/.313, -0.3 WAR

I don’t know about you. I always thought the Bob Sheppard thing was kind of creepy.

Left Field: Domonic Brown, .235./.285/.349, -1.7 WAR

One of the more fascinating what-ifs in baseball is what if Domonic Brown had come up with a different organization.  During most of his time in the Phillies organization, manager and front office were much more vocal about what he couldn’t do than what he could. Left to his own devices at last this year, Brown’s power disappeared. His HR/FB rate of 8.1% is 18th out of 19 qualifying left fielders, meaning that his power surge last year is looking more like a fluke than a step forward.

Center Field: B.J. Upton, .208/.287/.333, 0.4 WAR

This near replacement-level guy made $13.45 million this year, which is the kind of fact that gets GMs fired. He’s actually improved over last year, but every rate stat continues to be worse than his career averages. His K rate of 29.9% is the second worst of his career, and the worst of any qualifying center fielder.

Right Fielder: Jay Bruce, .217/.281/.374, -1.3 WAR

Bruce had surgery for a torn meniscus in his knee in early May and was never the same. Except that’s not true. He came back in June and raked to the tune of an .892 OPS, but then completely fell apart. I have to wonder if fatigue in the knee had something to do with it. For his career Bruce has almost exactly the same number of doubles (181) as homers (182). From July to season’s end, he hit 11 HRs, but just 4 doubles, which suggests he was having trouble getting extra bases without putting the ball in somebody’s beer. Bruce is generally a solid defender, but had a UZR of -8.4 this year, by far the worst of his career, which also suggests he was not fully mobile for much of the season. Bruce is the most likely player on this list to be an All-Star next year.

Designated Hitter: Billy Butler, .271./.323/.379, -0.3 WAR

Four of the seven Royals hitters who qualified for the batting title are home grown. Only one of them, Alex Gordon, had a positive wRC+ (indeed, Gordon is the only such qualifying hitter on the team, period). Butler, Hosmer, and Moustakas were going to be part of the the core of the next Royals playoff team. Instead, the Royals have made the playoffs this year largely despite these guys. Butler’s ISO continued to erode this year, and his walks, which spiked last year, plunged into the root cellar in 2014. These are hard times for DHs, slow and massive beasts whom evolution is passing by, and Butler’s mediocre wRC+ of 97 is just two points off the national average. But if the Royals are going to build a team to get past the Coin Flip Game, they will need to upgrade at this position. Butler is the only player on this list on a playoff team.

Pitchers (ERA/FIP, WAR):

Eric Stults, 4.30/4.63, -0.6 WAR

Sproingggggg! Regression to the mean was mean to Stults this year, as his FIP rocketed from 3.53 to 4.63, or 40 points over his career number.  The gopher ball killed him, no mean feet in cavernous Petco. The Pads won’t offer him arbitration, so he’ll look to take his Veteran Self elsewhere. He won’t be this bad again, but at 34 he may not get the chance to prove it.

Roberto Hernandez, 4.10/4.85, -0.5 WAR

Forced in 2012 to change his name by the International Fausto Carmona Association, whose members no longer wanted to be associated with him.

Chris Young, 3.65/5.02, 0.2 WAR

It’s tough to say goodbye. Young hasn’t been an effective starter since 2007, but he grimly soldiers on, desperately searching for signs of pitching life on this barren world. It’s easy to root for guys like Young, but the M’s had a pennant train to catch this year, and their decision to give Young almost 30 starts probably cost them a seat.

Shelby Miller, 3.74/4.54, 0.2 WAR

If you’ve been playing along at home, most of the guys on this list probably haven’t shocked you, but did you see Shelby Miller coming? Let’s start with that yawning chasm between his ERA and FIP. Miller’s career ERA is 60 points less than his career FIP, so the gap is only somewhat worse than that this year, but still disconcerting. His strikeouts disappeared, not because of any velocity drop, but because of the wholesale failure of his off-speed pitches. He’s still just 23 and he still throws hard. If the Cards can’t fix him, maybe they can trade him to a pitching coach who can. Paging Dr. Cooper …

Kyle Kendrick, 4.61/4.57, 0.4 WAR

The phourth Phillie on this list, Kendrick’s rather offputting FIP is actually 8 points better than his career average. A tolerable innings eating presence on a high-scoring team, Kendrick is now a liability on a team chock full of them. This was his walk year, a strange expression to use in conjunction with Kendrick, since that’s the one thing he doesn’t give up.

Twenty-one year old Brandon Finnegan put his name on the map in Tuesday night’s epic wildcard game, when he tossed scoreless 10th and 11th innings before leading off the 12th with a walk to Josh Reddick, who would eventually come around to score against Jason Frasor. Drafted by the Royals with this year’s 17th overall pick, Finnegan made quick work of the minor leagues, making his big league debut on September 6th at Yankee Stadium, just 81 days — and 27 minor league innings — removed from his last appearance with TCU in this year’s College World Series.

To find comps for Finnegan, I first looked for pitchers with a similar arsenal of pitches. Using a minimum of 1,000 pitches, I sought out left-handed pitchers who threw fastballs, sliders, and changeups — Finnegan’s three pitches — at least 90% of the time since 2008, and threw each of these pitches at least 5% of the time. From there, I turned to the PITCHf/x database to find out how often these pitchers’ pitches fell within Finnegan’s middle 50% of values for velocity, break angle, break length, and spin rate, and spin direction from his eight big-league games. These are the pitchers who threw the highest ratio of pitches comparable to what Finnegan threw. The similarity percentage was calculated by dividing each pitcher’s share of pitches meeting these criteria by the share of pitches met by Finnegan himself. The ERA’s were calculated over the last seven years: 2008-2014.

Finnegan looks to have a bright future ahead of him, as his top two comps are two of the most dominant pitchers in baseball — one a starter (Sale) and one a reliever (Watson). It remains to be seen which path the Royals will choose for their hard-throwing lefty going forward. While it’s tempting to slot him in in a relief role next season, the wiser decision might be to stretch him out as  starter, where he would be able to take full advantage of his three-pitch arsenal. But either way, until the Royals’ playoff run comes to an end, Brandon Finnegan will be allowed to air it out for just an inning or two at a time on easily the biggest stage he’s ever seen. And given his lights-out stuff, he might just end up being this year’s Francisco Rodriguez.

This article originally appeared on Pinstripe Pundits.

On September 30, Royals 2014 1^st Round Draft Pick Brandon Finnegan was brought into the AL Wild Card Game against the Oakland A’s just under 4 months after being drafted out of Texas Christian University. Manager Ned Yost had little choice but to take a leap of faith with the rookie Finnegan, having used pitchers like Kelvin Herrera, Wade Davis, and Greg Holland already. Finnegan pitched very well, allowing 2 baserunners in 2.1 innings and striking out 3 Oakland batters. He was removed with a runner on base and was charged a run when the runner scored, but otherwise had a great outing.

I found it ironic and puzzling that the only team to utilize this approach of drafting a college pitcher, rushing him up the farm system, and giving him a shot at the postseason was the team that already had the likes of Herrera, Davis and Holland. After all, it seems like every playoff team could use some help out of the bullpen. When compared to other positions, predicting a relief pitcher’s success in the big leagues really doesn’t seem too hard either.

In 2014, 12 relievers pitched more than 60 innings with an FIP under 2.50. Aside from the sinker-oriented Steve Cishek and Pat Neshek, all of them averaged at least 92.5 mph on their fastballs. Everyone except Cishek generated swinging strikes at least 11% of the time, almost 2% more than the 9.4% league average. Simply put, pitchers with high velocity are safe bets when it comes to building a bullpen.

I can understand why a team might be stingy with its first-round draft pick. The first rounder is supposed to be the future of the franchise, the one who fans envision 25 years older, making his Hall of Fame induction speech. But looking at the 93 2^nd round draft picks from 2006-2008 (an arbitrary time period which I felt gave players sufficient time to reach the big leagues), it is clear that players selected this late in the draft are no sure thing.

48 picks have yet to make their major league debut, and another 21 have career WAR’s equal to or less than 0*. There are exceptions like Giancarlo Stanton, Jordan Zimmermann and Freddie Freeman, but the data looks even worse after the 15^th pick of the second round. Of the 48 picks in the 16-32 slots, only 8 players have career WAR’s greater than 0*. 29 have yet to make their MLB debut.

Since 2011, 10 relievers have posted FIP’s under 2.50 with at least 100 innings pitched. Of those drafted in the American amateur draft, only Sean Doolittle was picked before the 3^rd round. He was drafted in the first round as a first baseman. While overpaying for an elite reliever can be appealing for teams like the Angels or Tigers, both teams in win-now mode, a possible fall back option is taking a chance on the best reliever available in the draft with the second-round pick. Chances are, that pitcher will still be on the board.

Of course, there are major-league relievers who can throw hard but still do not succeed at the big-league level. Also, stats like average fastball velocity and swinging strike rates might not be available for college players. The prior is virtually impossible without Pitch F/X. If this is the case, GMs can consider reverting to the eye test to determine how hard a pitcher throws and what his command and movement look like. Generally accepted measures of command such as K-BB% can be derived from box scores.

For traditional fans who still value the human element of baseball, there are ways to gauge an NCAA pitcher’s ability to pitch in the spotlight. Stats like opposing batting average with runners on base and inherited runners stranded can be determined by simply looking at play-by-play recaps. Both measure a pitcher’s ability to perform under pressure, even if only in a limited sample size. I do not know what kinds of information are given to baseball operations teams, but I would be surprised if a college pitcher’s WPA in high-leverage situations was available.

If I was Tigers GM Dave Dombrowski or Angels GM Jerry Dipoto circa July, I would make the trade for Joakim Soria or Huston Street without hesitation. Both teams, one could argue, were a bullpen arm away from being World Series favorites. But for teams who don’t have the resources Detroit and Los Angeles have or don’t want to give up too many prospects, the best mid season bullpen pickup might not have even thrown his first professional pitch yet.

*I had to use rWAR, not fWAR in the interest of time. Baseball Reference has the draft results with career WAR readily available. Of course, data not from FanGraphs was taken from baseball-reference.com.

The Cards and Dodgers match up in the Division Series for the first time since 2009. The Dodgers swept that series which is best remember for Matt Holliday’s dropped fly ball in Game 2. They met again in the NLCS last season and the Cards won the series 4-2 by knocking Kershaw around for seven runs in four innings. The Dodgers held a 4-3 edge in the season series by winning three of four in LA and avoiding a three game sweep by winning a July Sunday afternoon game in St. Louis. Five of the seven games were decided by two or fewer runs.

Note: I wrote this over the course of the two week period beginning 9/22 so stats were up to date when I pulled them.

Catcher: After back-to-back top five MVP seasons and his first silver slugger award, Yadi was due for some regression this year. He broke his hand sliding on July 9^th and was out until August 29^th. During the Yadi-less stretch, the Cards pursued every option, but a trade, to attempt to fill in. Tony Cruz (back-up), Audry Perez (call-up), George Kottaras (waiver claim) and AJ Pierzynski (free agent) all saw time and the team treaded water by going 21-19 without him. Any time a team loses a perennial MVP candidate, the hole will be noticeable:

Barring injury, Yadi will play every game this postseason. Hand/wrist injuries are major concerns for any hitter as they tend to sap power and Yadi is no exception. He finished the season without hitting a home run since his return from the DL and posted a weak .267/.312/.326 line compared to .287/.341/.409 before his injury. Fortunately, his defense hasn’t suffered much as he has nabbed three of eight base stealers (37.5%).Yadi’s ability to control the running game will be key against the team that led the NL in steals (Dee Gordon accounted for just under half). He’ll fail to hit .300 for the first time since 2010 and play his fewest games since 2007 but he’s still one of the best catchers in baseball for his contributions on both sides of the ball.

The Dodgers will deploy a platoon of AJ Ellis and Drew Butera. If it weren’t for the disaster of a platoon in Tampa, the Dodgers combined catcher production of .181/.282/.262 would be the worst in baseball. I expect Ellis will play almost the entire series, but it really doesn’t matter who is playing. Ellis has only thrown out 25% of base stealers so look for Wong and Bourjos to take some chances if they get on base. The only other somewhat interesting thing about Ellis is he’s managed to face Carlos Martinez ten times. He hit a home run so his .250/.400/.625 line looks menacing but he has also struck out three times in those ten plate appearances.

Edge: Cardinals

First Base: Matt ‘Big Mayo’ Adams took a step back this year. After hitting no lower than .321 in April, May, or June, he faded in the second half. Through the first two months of the season he had hit only three home runs which led many to question if he had traded power for average. He responded by clubbing 6 HRs in June but then failed to go deep from July 19^th to August 22^nd. Adams does not walk – his walk rate is among the lowest of qualified hitters, so if he’s not hitting for power or average, he’s not providing much of anything. The defensive numbers rate him as one of the best first baseman in the league. While that’s a stretch, he is limber for a big man and will surprise with his mobility and athleticism. In an ideal world Adams would be on the bench vs Kershaw and Ryu (if he’s healthy) since his platoon split is over 300 points of OPS. The Cards don’t have the luxury of having a RH 1B on the roster so he’ll be exposed.

The Dodgers counter with the league leader in RBI, Adrian Gonzalez, who posted his highest OPS+ since 2011 while playing in nearly every game. Since arriving in LA from Boston, he’s been a consistent run producer and a solid three-WAR player. Gonzalez will likely be sandwiched between Yasiel Puig and Matt Kemp in the Dodgers lineup and represents their only lefthanded threat. Look for Matheny to deploy Randy Choate and Sam Freeman against him on a regular basis. Gonzalez is 2-11 lifetime vs the two Cards lefties.

Edge: Dodgers

Second Base: Last seen being picked off to end Game Four of the World Series, Kolten Wong entered Spring Training needing to win his formerly presumed starting position from veteran Mark Ellis. Ellis ended up injuring his knee and Wong hit .375/.434/.646 in 18 Grapefruit League games to make the injury not even matter. He was the Opening Day second baseman and hit second. The honeymoon lasted 20 games and he was optioned to AAA for the next three weeks until his recall on May 16^th. He then went on a ten game hot streak and won NL Rookie of the Month honors. Following his 4/5 performance on May 28^th, he hit .089/.146/.200 over his next 15 games and went on the DL with shoulder inflammation.

Following a rehab stint in Memphis, he rejoined the team and finally received consistent playing time appearing in 63 of the team’s final 77 games. He also returned with new found power. Prior to his injury on June 20^th, he had hit only 1 HR and was slugging .304. Following the injury he has hit 11 HRs and slugged .467. He’s the Cards best basestealing threat and led the team with 20 SBs in 24 attempts.  He also is an outstanding defender. Really, the only knock on him is he doesn’t walk that much, but that’s mitigated by his contact ability. Wong is still very much a work in progress but this has been a positive year. He’s shown all five tools and if he makes progress and becomes more consistent, he’ll be an All-Star second baseman.

When the Dodgers made a splash signing Cuban defector Alex Guerrero for $28 million over four years and paid Mark Ellis a $1 million buyout rather than exercise a $5.75 million option, it appeared that Dee Gordon would be left out. Gordon certainly hadn’t done much to help his cause as he had a career .614 OPS in 181 games entering this year. Gordon broke out this year, made the All-Star team, and has been worth 3.1 WAR. He’s fallen off in the second half as his OPS has fallen from .742 to .628. In the second half he’s only walked four times – once each in August and September. He’s only stolen 21 bases compared to 43 in the first half and has been successful only 68% of the time compared to 83% in the first half. There haven’t been any reported injuries. Despite all these factors, Mattingly continues to bat him leadoff. He’s the weakest hitter in the lineup and should be hitting eighth – ninth when Greinke pitches. Hopefully we see this happen again.

Edge: Cardinals

Shortstop: Jhonny Peralta has arguably been the Cardinals most valuable position player this year and certainly worth every penny of the four year, $53 million contract he signed this offseason. He batted in every lineup position from second to seventh. He fended off a late season charge by Matt Holliday to lead the team with 21 home runs. Many felt Peralta got off to a low start as he hit only .196 in April. A .178 BABIP was to blame and Peralta regressed to a .311 BABIP, not far off his career .312 mark, for the rest of the season. His walk rate was his highest since 2007 which propelled the best BB/K ratio of his career.

Much has been written about his defense as all of the defensive statistics suggest he’s one of the best fielding SS but he doesn’t quite pass the eye test. He made 98.2% of routine plays – good for fourth among qualified shortstops but quickly fell down the Inside Edge leaderboards finishing 19^th out of 22 qualified shortstops in “even” plays (40-60% of the time, the play is made) and 16^th in “unlikely” plays (10-40%). While he’s not the defensive wizard some of the metrics suggest, he’s an extremely capable defender and extremely valuable to the Cardinals as a run-producer. As bad as the team’s offense has been, they would not be in the playoffs with Pete Kozma and his career .235/.297/.318 line.

Hanley Ramirez has lived up to his injury-prone label this year. Even though he’s only been on the DL once this year, Baseball Prospectus’s injury database lists 12 additional day-to-day injuries and he missed at least one game for six of those injures. The full list is below:

In the 2013 NLCS, Hanley was hit by a pitch in the ribs in game one and had to miss game two. Hanley was never a good defensive shortstop but the wheels really came off the train this year. Only Yunel Escobar was a worse defender by UZR. Even 40 year old Derek Jeter is better defensively than Hanley. The Dodgers recognize this and have removed him in 18 of the 19 September games he has started. The primary back up is Miguel Rojas who hits to the tune of .186/.250/.229 but has 11 defensive runs saved in under 300 innings – fourth most among SS with at least 200 innings at the position – and leads that population in RZR (revised zone rating). His defensive abilities allowed him to post 0.6 fWAR despite his batting line. Hanley is in there for his offense and he had the highest wRC+ of all qualified shortstops (due to Tulowitzki’s injury). He isn’t the MVP candidate he was in 2013, but he has rebounded from his down years to post his second highest OPS + since 2009, behind last year. He doesn’t have much of a platoon split, was consistent month to month and is still able to steal some bases. He’ll probably hit fifth for the Dodgers.

Edge: Cardinals. Peralta’s defensive ability pushes him over the top.

Third Base: Matt Carpenter exploded as a second baseman in 2013 and finished fourth in the MVP balloting. After the off-season trade of David Freese, Carpenter moved back to third base. While few expected him to repeat his 2013 season, he put up similar numbers – albeit without as much power. Last year he led the league in hits and doubles, while this year he led in walks. It was a different, but effective offensive profile. Carpenter hit leadoff in every game he started and posted at least a .362 OBP each month of the season. Carpenter is an excellent leadoff hitter – only Mike Napoli, Brett Gardner and Mike Trout saw more pitches per PA than him.

One knock on his game is his baserunning. While he set a career high with five stolen bases this year, he also advanced an extra base only 33% of the time. Only Yadi at 29% took fewer extra bases among Cards full time position players. The Cards as a team are bad baserunners and Carpenter exemplifies that. After making five errors in April while getting adjusted to 3B, he’s proven to be strong defensively. Carpenter is a very solid player and should be well worth the five year contract extension he received last spring.

Who would have thought Juan Uribe would prove to be worth his three year, $21 million contract he signed in the 2013 offseason? Uribe has now posted the two best seasons of his career by OPS+ and set new career highs for OBP in both seasons. He has the most postseason PAs of anyone on the Dodgers and has two World Series rings. He’s an asset on both sides of the ball; His OPS of .778 ranks seventh on the Dodgers but would rank second on the Cardinals. The former shortstop, who had only started at 3B in 220 of his 1,287 career games prior to 2013, trails only Manny Machado and Nolan Arenado in defensive runs saved since the start of 2013 at 3B. Somehow, someway, he’s become one of the best third baseman in baseball.

His peripherals took a step back in 2014 as his walk rate was almost halved from 7% to 3.7% and he was bolstered by a .341 BABIP. He’s hit RH slightly better than LH over the course of his career. All in all, this version of Uribe is very valuable when he’s on the field. The 35 year old made two trips to the disabled list this year with hamstring strains and his lone stolen base attempt occurred in April.

Edge: Dodgers

Left Field: Matt Holliday has been everything the Cards could have hoped for when they signed him in the 2009 offseason. By FanGraphs value, he’s been worth at least $20.4 million in each season. His .274 average, this year, is the lowest of his career but he’s been plagued by the lowest BABIP of his career, too. His .300 BABIP is .339 below his career BABIP, but some of that may be deserved as he’s hitting fewer line drives than ever. He only had six home runs at the all-star break but has been on a tear in the second half to finish at 20. He’s still somewhat of a defensive liability but the Cards really need his bat in the lineup at all times so he hasn’t been removed in close and late situations as often.

I’m assuming the Dodgers platoon Carl Crawford and Scott Van Slyke here.

Too much of Carl Crawford revolves around the awful contract Boston gave him in 2011. There’s still three years and more than $60 million remaining but he’s far from a disastrous player; hardly anyone could live up to that contract. Crawford is looking like a .285/.330/.400 hitter with some speed. It’s far from the traditional LF profile but it’s serviceable. Crawford’s career OPS is more than 100 points higher vs left handed pitching than right-handers. This year he’s got a flukey reverse platoon going on, but that’s noise. I except him to get the lion share of LF time in this series since the Cards will be throwing all RH starters. He’s certainly lost a step as a 32 year old, but he’s still able to steal bases at an 80% clip. He’s a marginal defender and doesn’t have much of an arm.

The other half of the platoon is St. Louis native Scott Van Slyke. I intentionally walked Van Slyke in the spring of 2005. He was a great hitter for our tiny high school league, but no one thought he’d ever put up the MLB numbers he has this year. He’s appeared at every OF position and first base – he was a pitcher and shortstop in high school – but he’s spent the majority of his time in LF. He absolutely demolishes left handed pitching. 13 of his 21 career HRs have come against lefties and this year he has a 1.039 OPS against them compared to a .769 OPS vs right handed pitching. Since the Cards don’t have a left handed starter, he’ll probably be deployed off the bench. He’s a good athlete and is more than capable In LF.

Edge: Cardinals

Center Field: Everyone immediately thought Peter Bourjos would break out in St. Louis following the trade last offseason. That didn’t happen. We saw that Bourjos is exactly what he had previously shown: A glove first, speedy, light hitting OFer. His biggest accomplishment in 2014 was staying healthy and he’ll play the second most games of his career. He has historically had a reverse split and that widened even further this year. I would not use him as much more than a defensive replacement in this series but I’m sure Matheny will start him for a game or two. He’s a great defender and will save a run or two this series in CF.

The better option in CF is the Federalist, Jon Jay. Much like he defeated Colby Rasmus, Jay seems to have squashed Bourjos’ hope of being an everyday CF in St. Louis. Jay isn’t flashy but he makes all the routine plays and some good plays. He isn’t well suited for the corners due to an extremely poor arm. He’s hit at least .297 over every season except in 2013 and is nearing a personal best OBP this year. He’s hit the fewest HRs of his career but that’s also because he’ll fail to top 500 PAs for the first time since 2010. I expect Jay to bat second for most of this series and play CF for 7 innings. He’s been a key component of the Cards recent playoff success and appeared in all 48 of the possible games since the start of 2011. The results haven’t been pretty as he’s hit .188/.263/.219 across 183 postseason PAs. He’ has never hit a postseason HR. Jay already played his way into a contract next year and will likely be back in St. Louis as the last remaining member of the Memphis Mafia (RIP Freese and Torty, Happy Trails Danny D).

The Dodgers have played Kemp, Ethier and Puig in CF at least thirty times each. None of them are CFs. Kemp used to be but now needs to play in a corner. Puig is best suited for RF but I expect he’ll play CF.

Last season Puig only started six of his 96 starts in CF and this season that jumped to 51 of 140. He has a very strong arm and finished second in MLB with 15 OF assists – trailing only fellow Cuban Yoenis Cespedes. He doesn’t move very well in CF but the Dodgers need him and Kemp in the lineup so they take the tradeoff. Among CFers with at least 400 innings, Puig was last in plays made outside his fielding zone. His teammate and fellow COF, Andre Ethier was just ahead of him with 18 CF plays made out of zone and no other CFer had fewer than 24. Jon Jay and Peter Bourjos, the Cardinals two primary CFers, had 57 and 69, respectively.

Where the Cardinals have the Dodgers on defense, Puig brings a well-rounded strong offensive profile to the table. He both cut his strike out rate and upped his walk rates in 2014 – the marks of a maturing hitter. One area of concern is the massive power drop in the second half of the season. After hitting 43 extra base hits in the first half, Puig only tallied 18 in the second half. The power drop is concerning, but his second half line of .278/.370/.420 was still good enough for a 130 wRC+ and significantly better than anything the Cards can expect. Puig isn’t the fastest runner and is aggressive and not a great baserunner. He was only 11/17 on steals this year and as Kemp and Gonzalez heated up in the second half, he significantly decreased his stolen base attempts from 14 to four.

Edge: Dodgers

Right Field:

Right field could be patrolled by a number of players for the Cards. Jon Jay could play when Peter Bourjos plays CF, Randall Grichuk will play vs LHP and Oscar Taveras will play the rest of the time. Grichuk has really come on since his August recall hitting .352/.364/.556 in 25 games (10 games started). His first two stints in MLB, earlier this summer, didn’t go as well as he hit .136/.191/.273. Most of this success is due to how he is being utilized: He must only bat against left-handed pitching. In AAA Memphis, he had a .974 OPS vs LHP whereas he cratered to .693 vs RHP. He doesn’t walk much so he really shouldn’t be hitting second as he has in 13 of his 19 games started. He’s a good fielder and only committed two errors all season at Memphis.

At any point in the past two years, if I was told I’d be more excited about Grichuk than Taveras going into this series, I’d be shocked. Oscar is just 22 so there’s no reason to panic but he hasn’t been able to contribute in any way this year. His defense and base running are below average and those really stick out when his prodigious bat doesn’t show either. Oscar has hit better in September but he’s only stated 11 games and has two extra base hits. He’s had some success pinch hitting but that’s mostly small sample noise than anything else. He’ll be on the roster and should play, but that’s a function of the Cards not having anyone better to take his roster spot.

Matt Kemp was left for dead earlier this season. He entered the All Star break hitting .269/.330/.430 and rumors swirled that he and the $107 million remaining over the next five years of his contract would be out of LA. The Dodgers kept him and he responded by hitting .303/.360/.589 with 16 HRs in the second half. That’s not too far off from the version of Kemp that finished second in the 2011 NL MVP balloting. The major difference between those two Kemps is the speed. In 2011 Kemp stole 40 bases. This year he has only attempted to steal 13 and he was thrown out on five of those attempts. In 2011 he advanced the extra base 62% of the time; this year he only advanced 41% of the time. The wheels are gone. As such he’s been relegated to right field. The two-time Gold Glove award winner is now a below-average RFer. His arm is still strong and he’s recorded five assists in fewer than 500 innings which would put him among the leaders at the position over a full season. He’s also managed to stay healthy this and only Adrian Gonzalez played in more games for the Dodgers than he did. All in all, he’s an asset and his power makes him a threat each time at the plate. He is a career .240/.286/.327 hitter in 42 games vs the Cards, so maybe they’ll contain him.

Edge: Dodgers

Bench: The Cardinals bench is slightly better than last year, but still remains one of the weakest in the league. The one constant since the start of 2011 has been Daniel Descalso, but he had the fewest PAs of his career since his cup of in 2010. He eclipsed 300 PA in each of the next three years but didn’t top 200 this year due to stability at 3B and SS and better options at 2B. He appeared at every infield position and is capable everywhere, but should not play SS. This is likely Descalso’s final rodeo with the Cards as he’s due a big raise in arbitration and his season numbers are in line with his careers numbers only because of a BABIP induced .324/.452/.412 line in the second half compared to .182/.234/.239 in the first half. Look for rookie Greg Garcia to fill Descalso’s role for a quarter of the cost, and better SS defense, next season. Descalso won’t be much of a factor this series.

Joining Descalso on the bench will be AJ Pierzynski, Pete Kozma, Oscar Taveras and Randal Grichuck. Pierzynski joined the team shortly after Yadi’s injury and his from the Red Sox. He had three hits in his debut with the team and even with that performance he posted worse numbers than he did in Boston. Cruz is even worse but has been with the team longer and is slightly better defensively – though as shown above they’re both bad. AJP can hit right-handers respectably enough to be a backup catcher. Cruz can’t hit anyone and offers no value.AJP better be on the team and Matheny should not be afraid to use him as a pinch hitter, even if it leaves no backup catcher on the bench.

Pete Kozma is back for another October after being DFA’d in April. The team is 9-4 when he plays and 4-1 when he starts in 2014. This is not a coincidence. Kozma is a proven winner and brings clutch hits and amazing defense. Look for him to appear at SS or 2B in close and late situations and even get a start or two against Kershaw and Ryu. The best games of Kozma’s career happen in October and expect another standout performance or two from him. Kozma is also short for Kershaw killer as the Pistol is 4-8 with 3 2Bs and a walk against him.

The Dodgers have a much better bench as I’ve already discussed whichever of Crawford and Van Slyke isn’t playing, above. Joining them will be one of the most surprising hitters in the majors this year, Justin Turner, long time righty killer, Andre Ethier, the aforementioned defensive wizard, Miguel Rojas, and a backup catcher. Turner and Ethier are the players of note here. Not many fans know Turner posted an .897 OPS in322 PA this year. In his previous 926 MLB PAs, he hit eight HRs. This year, he hit seven. He’s always been a good contact hitter and got BABIP luck of .404 to post a .340 average while appearing at every infield position. He didn’t have much of a platoon split. Look for him to get contact oriented pinch hits opportunities and potentially relieve an injured Dee Gordon or Hanley Ramirez.

Ethier will be Mattingly’s foil for the Cards RH dominant bullpen. He’s not the hitter he once was and has a disastrous contact, but he managed a .253/.325/.385 line against RH pitching this year and hit all four of his HRs against them. He saw his playing time dwindle and only started 22 games in July, August, and September after starting 19 in each of the season’s first three months. 40 of his 115 career PH appearances came this year and he hit .290 in them. He’s a terrible defender and barring injury to any of the four OFers ahead of him, won’t see the field at all this series. His best hope of consistent playing time is to DH in AL ballparks vs right handed pitching in the World Series.

Edge: Dodgers, sizably.

Starting Rotation:

Any discussion of the starting pitching in this series begins and ends with Clayton Kershaw and Adam Wainwright. Through the end of April, it sure looked like the Cy Young award was Wainwright’s to lose. He had gone 5-1 with a 1.20 ERA while Kershaw was on the DL following his season opening start in the Australian series. Kershaw would return in the beginning of May and go on to lock up the Cy Young award and, in my opinion, the MVP award too. Despite opposing hitters have their highest BABIP against him since 2010, Kershaw posted the lowest ERA, FIP and xFIP of his career. He also posted the highest strikeout rate, lowest walk rate and highest ground ball rate of his career.

He’s had an ERA in the ones in each of the past two seasons and combined to allow 87 earned runs. To put that in perspective, 21 starters allowed at least that many runs just in 2014. He’s remarkable. The Cardinals as a team have a .273/.365/.367 line against him and they were able to beat him twice in last year’s NLCS. Maybe they’ve figured something out, but I’d expect Kershaw to dominate. The Cards will need to beat him at least once to win this series in my opinion.

Wainwright will opposed Kershaw, but he’s in a completely different league. Wainwright had an incredibly successful season, allowing his fewest runs since 2008 and holding batters to their lowest average against him. His velocity is down about a full MPH from last season, but he’s an incredibly intelligent pitcher and finds ways to get batters out even when his stuff isn’t at its best. He had a rough stretch of “dead arm” in August which produced a 5.17 ERA but appeared to right the ship in September going 5-0 with a 1.38 ERA and average just a hair under eight innings per start. Wainwright does an outstanding job controlling the running game and opponents only attempted eight steals against him this season with three of the eight coming in games caught by Tony Cruz. Look for Waino to work deep into the game. Only James Shields and RA Dickey have thrown more regular season innings since the start of 2012 and in that span Wainwright has 50 innings in the playoffs on his arm.

The matchup for game two will be Zack Greinke and Lance Lynn. Coming into this season, Lynn was seen as a pitcher who benefited from good run support to post high win totals and prone to breaking down in the second half. He dispelled both of those ideas this year as shaved more than a run off his previous career low ERA, thanks in large part to a strong second half. Lynn has now made at least 29 starts in each of his three seasons as a starter and has been remarkably consistent as his FIP’s for each year has been between 3.28 and 3.49. Since the start of 2012, he’s thrown the 25^th most regular season innings and has the 13^th lowest HR/9 of all starting pitchers. The Dodgers hit Lynn hard in his start against them in LA – it was his worst start of the season – but he was much better at home. Lynn, like Wainwright, does a good job controlling the running game and opposing runners were only one for four taking bases against him. Lynn has really struggled as a starter in the postseason going 1-3 with a 4.50 ERA and never making it through the 6^th inning.

Zack Greinke has been one of the most consistent pitchers since returning to MLB in 2007. He’s only been on the DL twice for flukish reasons – he broke a rib playing basketball in the offseason then broke his collarbone in a brawl. He posted his best FIP since his 2009 AL CY Young season and set a new career high in K/BB ratio while throwing the most change-ups of his career. Greinke is a very cerebral pitcher, like Wainwright, and can get hitters out by making smart pitches even when he doesn’t have his best stuff. The Cardinals need to attack Greinke early. Batters have hit .314/.351/.450 on the first 25 pitches of his starts this offseason and after he settles in, he really clamps down. Additionally, batters have hit .355/.351/.720 on 96 first pitch PAs against him. Greinke adds some value at the plate as he had the third highest OPS of any pitcher and the lowest strikeout rate. If the 2014 Lance Lynn shows up and puts his poor playoff appearances behind him and the Cardinals attack Greinke, this is a very winnable game and they’ll be back home with a 1-1 split.

Game three will likely feature former Cardinal Dan Haren and recent Cardinals acquisition John Lackey. Lackey didn’t exactly endear himself to Cardinal Nation following the controversial trade for Allen Craig and Joe Kelly. He struggled to a 3-3 record and peripherals closer to his disastrous 2011, “chicken and beer” season in Boston than anything else. It could just be noise – Lackey allowed no more than two earned runs in eight of his ten StL starts; in the two starts he allowed six and nine runs. In those eight starts, he went at least six innings in each except for one ejection. He’s thrown 104 innings in 16 starts over seven postseason series and appeared in relief on throw days twice. That’s exactly the type of bulldog mentality the Cards hope show up this October. In his short time with the Cardinals, the defense has been uncharacteristically shaky behind him as five batters have reached on error in 60.2 innings: The team made 8% of its errors in 4% of its innings. Lackey, like the rest of the staff, pitches better at home. In only five starts, he pitched 34 innings with a 2.38 ERA and only walked five batters. Game three will be in St. Louis so hopefully Lackey maintains that performance.

Dan Haren will oppose Lackey in game three (assuming Hyun-jin Ryu isn’t able to go). He’s now entered the twilight phase of his career and is at best a fourth starter. He’s bounced from the LA Angels to Nationals to the Dodgers in the past three years, but should be back in LA next year as his $10 million option for pitching 180 innings vested in his final start of the year. Haren is very susceptible to the long ball. Over the past three years only RA Dickey has allowed more home runs than him. One point in the Cardinals favor is right handed hitters have been historically better (+0.035 OPS points) against Haren than left handed batters. In 2014 he posted his lowest K% since 2005. With the Dodgers suspect defense, the Cardinals must put the ball in play vs Haren. Greinke and Kershaw will strike hitters out, but Haren is much more hittable. The Dodgers will be hopeful to start Ryu here (or game four), but if Haren pitches in St. Louis – he was 6-7 with a 4.75 ERA on the road this year – this is a MUST win game.

If Ryu is healthy, he or Haren will pitch this game. If Haren pitches game three, this one is completely up in the air – Kershaw could start on short rest if the Cards are up 2-1. That’s unlikely seeing he’s only pitched on three days’ rest once in his career and that was to start the NLCS last year vs the Cards. It’s more likely the Dodgers would throw Roberto Hernandez. The last update I’ve seen on Ryu is that he threw a pain-free 40 pitch bullpen on September 28^th. His last outing was September 12^th and he got shelled for four runs in one inning. If he pitches, it’s unlikely he’ll be as sharp as he was before his injury. He had built on his solid rookie season and lowered his FIP to a very respectable 2.62 by increasing his strikeout rate and reducing his walk rate even though his ERA moved in the opposite direction. The Cardinals have been vulnerable to left handed pitching in the past, but rebounded this year to post a higher OPS against them than right handers current Cardinals are also hitting .294/.314/.441 against him, but only have 34 ABs.

A Roberto Hernandez sighting would be welcome for the Cards, even if Ryu isn’t sharp. Over the past three seasons only eight starters have a worse K/BB ratio than him and only John Danks and Hector Noesi have a worse FIP among qualified starters. He is not very good and was acquired by the Dodgers only because they lost healthy pitchers after the non-waiver trade deadline.

The Cards’ game four starter isn’t settled either. If Michael Wacha were healthy, he’d be a shoo-in to start game two and push everyone back, but his health makes him a candidate here. Wacha returned without a minor league rehab assignment and pitched four times after not starting since June 17^th. Shoulder injuries are very dangerous for pitchers and Wacha certainly was not himself when he returned. Opposing hitters hit .294/.355/.441 against him and he faced lifeless Reds and Diamondbacks lineups in three of his four starts. In my opinion, he should not be in the playoff rotation or even on the roster. I would trust Shelby Miller with this start. I don’t completely buy into his renaissance over his seven starts since facing a boot from the rotation. Over those seven starts, he’s benefited from a .207 BABIP to limit hitters to a .190 average. This isn’t sustainable. He did somewhat reduce his walk rate below his career rate, not his strikeout rate was also down over that stretch. I would expect five innings and at least two runs against this Dodgers lineup from Shelby. At this point he’s proven to be a durable thrower, but that’s about it. He walks a lot of batters relying wholly on his fastball (71.7% of his pitches – sixth highest since start of 2013).

Edge: Dodgers. It’s very close but Kershaw pushes them over the top and a Ryu appearance would go better than a Wacha appearance.

Bullpen: The Dodgers employ two of the seven highest paid relievers in baseball in Brian Wilson ($10 million) and Brandon League ($7 million) and their bullpen is 24^th in WAR. It’s not a particularly good unit. Kenley Jansen is the only member worth more than 0.4 WAR and two members – Wilson and Chris Perez – have thrown more than 40 innings and have been negative contributors. The good news is that Kershaw leads the league in innings per start at 7.34 so Don Mattingly doesn’t have to worry about the pen much when he starts.

Wilson is still able to get strikeouts as he’s K’d 10.4 batters per nine, but walks have been a big issue at 5.48 per nine. He’s also given up as many HRs as he did in 2010 and 2011 combined and he threw more innings in each of those seasons than he did this year. There’s really no positive to Wilson’s season – his WPA/LI is 12^th from the bottom in only 40 innings. If Mattingly insists on using him, it should be exclusively against right handed hitters as left handed hitters have a .914 OPS against him, while right handed hitters have managed a .323 OBP against him but only a .686 OPS. Unfortunately for the Dodgers he has a player option for $10 million in 2015 that he’d be a fool not to exercise.

Brandon League was better than last year, but that doesn’t say much. He joins Wade Davis and Kevlin Herrera as the only qualified relievers to not give up an HR this season. Davis and Herrera are two of the best relievers in the league. League benefited tremendously from good luck. He had the eighth lowest strikeout rate of that bunch compared to Davis and Herrera who finished 3^rd and 25^th, respectively. League “held” opposing hitters to a .358 OBP and still somehow managed a 2.57 ERA. That is astounding as that OBP would be in the top 30 of qualified hitters. He stinks and Cardinals fans should hope Mattingly goes to him with confidence.

Thirty nine year old Jamey Wright is another oft used reliever who isn’t very good (hint there’s a theme). Like League, he “limited” opponents to a .341 OBP but managed to avoid home runs to keep his ERA down.  As mentioned above, he’s 39 and this showed as he wore down this season and had a 5.74 ERA in the second half including 9.00, 5.14, and 7.88 ERAs in July, August, and September. Additionally, here are his splits by leverage index:

Synopsis: He breaks under pressure and becomes even worse than he is normally.

JP Howell is better than Wright and League. The three of them all ranked within the bottom 30 of qualified relievers in K/BB ratio, but Howell is left-handed and is able to get both hitters out. His numbers are somewhat skewed by a poor September in which he had an 11.81 ERA and if he bounces back to form (no other month above 2.35). I’d like for him to be used in high-leverage situations between the starters and Jansen. He’s been one of the Dodgers best relievers over the past two years and as long as a rough September is behind him, he’ll be fine.

Pedro Baez is a rookie of note who may be on the roster in place of overpaid, terrible right handed pitcher, Chris Young. Baez is right handed and a former position player who converted to pitching in 2013. He’s not great but he’s better than Young and possibly better than League and Wright if Mattingly gives him a chance. For a young guy, he doesn’t have great strikeout stuff, but he also does a good job of limiting walks. In a small, 24 inning sample, he was susceptible to three HRs. I wouldn’t expect him to be used in many high leverage situations but could get some work in a three run ball game.

Jansen is as lights out as they get in the ninth. Mattingly has used him once before the ninth inning this year and never when the Dodgers are down a run and all appearances when the game were tied are at home. Mattingly only deploys this weapon in save situations. He’s nasty and has struck out at least 99 batters in three consecutive seasons

The Cardinals have three lefties in Kevin Siegrist, Randy Choate, and Sam Freeman. Siegrist has been the most disappointing of the three as his ERA is 6.90. He’s allowed 23 runs after only allowing two last season and can’t even get left handed hitters out. I’d be surprised if he’s on the playoff roster. Choate has one ugly, six run outing skewing his season totals. Remove that appearance and his ERA falls from 4.63 to 3.15 and his WHIP goes to 0.96 from 1.14. He has held lefties to a .097/.212/.153 line. In essence, he’s been Randy Choate. Under no circumstances should he face a right-handed batter as they’ve clubbed him to a .385/.458/.481 clip. Additionally, Andre Ethier, Adrian Gonzalez and Carl Crawford, the three major left-handed threats in the Dodgers’ lineup have combined  to go 3-27 with three walks (all Crawford) and 6 strikeouts in their careers. Sam Freeman has a nice shiny 2.43 ERA but his FIP is more than a full run higher at 3.80 and he hasn’t been effective a strange, significant reverse platoon split in his career. Right handed hitters have only managed a .523 OPS against him while left handed hitters are knocking him around at a .749 OPS. I would not expect him on the roster.

The right side of the Cards pen is very interesting and a significant advantage in this series. The Cards will deploy Seth Maness, Carlos Martinez, Pat Neshek and Trevor Rosenthal in that order. Jason Motte, who closed out the 2011 World Series, shouldn’t be on the roster as he’s been generally ineffective due to diminished velocity in his first year back from TJS. He’ll be a free agent after this season and I’d imagine the Cards would have interest in bringing him back with an incentive laden contract since he’s one of the most well liked guy in the clubhouse and a very good member of the community. I’d be surprised if he got much more than an NRI from someone else. Justin Masterson should also be left off the roster as he was never able to solve whatever mechanical funk is hindering his ability. He hasn’t pitched since September 9^th and the trade will go down as one of Mozeliak’s worst as Masterson posted a 7.53 ERA in 28.2 IP.

We’ll start with Maness who is fourth in MLB in reliever innings. Maness now has 141.1 career innings with a 2.48 ERA, 1.15 WHIP and 3.31 FIP. He doesn’t strike many out but he hardly walks anyone. Maness and Neshek have the fourth and fifth lowest BB rates among qualified RPs. Maness succeeds by pounding the zone with his sinker (60%) and getting groundballs at a 56% clip. He’s allowed runs in four of his 39 appearances since July 1^st but in three of those four outings he’s allowed three or more runs. Look for Maness to bridge the gap between the starter and the late inning relief arms or pitch between Choate and Neshek. He’s versatile and Matheny has a lot of confidence in him. I certainly have more faith in him than I did last year when Matheny went to him in Game 5.

Pat Neshek is one of the best stories in the game. He came to Spring Training as a non-roster invite and made the All Star team in his home town. He’s also an avid baseball card and memorabilia collector and traded his jersey number to John Lackey for a Babe Ruth signed baseball. Without a doubt it’s been the best year of his career and he’ll receive a nice payday this offseason. He’s equally effective vs lefties and righties and is able to go two innings if needed. Matheny would prefer to use Neshek in the eighth inning but if Rosenthal continues to struggle, he could get some saves.

After watching Trevor Rosenthal breeze through 20.1 innings in the 2013 postseason while striking out 33, walking five, and allowing six hits, it was easy to expect him to challenge for the league lead in saves in 2014. As is often the case, the results don’t match the process as 2014 has been extremely shaky for Rosenthal. The issue has been walks. His walk rate more than doubled in 2014 as he issued 42 free passes. Everything else was about the same but the walk rate pushed his FIP more than a full run higher to 2.98. For whatever reason he’s struggled getting the first guy he faces out as those batters in his 71 games are hitting .328/.408/.443 against him. This is a new problem for him as last year he shut down the first batter, holding them to an .185/.284/.262 line. After a particularly bad August where opponents had a .754 OPS and he walked nearly as many as he struck out, he has begun to throw a breaking ball more than any other point in his career. Rosenthal is not the same weapon he was last October but he is still generally effective.

I would strongly consider putting Marco Gonzales on the post-season roster. The Cardinals obviously like his makeup as they called him up barely after a year of signing him. He has a devastating changeup and has held left-handed hitters to a .579 OPS across A+, AA, AAA and MLB this year vs a .694 for right-handed hitters. In a smaller, 34.2 IP sample at the MLB level, the split widens to .397 and .827. Two other statistics strongly point towards Gonzales’ inclusion. He has been much better as a reliever than a starter. In 25.2 IP as a starter, he walked 17 and gave up 28 hits. As a reliever, he’s walked four and given up four hits in nine innings. Additionally, he’s particularly effective against hitters the first time he faces them within a game.

Outside of a AAA game against Joc Pederson and Alex Guerrero, he’s never faced anyone on the Dodgers. While he’s never been in a postseason environment, he has experience suggesting he’ll be calm and collected. As a rising junior, Gonzales participated on the USA Collegiate National Team. There he was the MVP of the MVP of the Honkbal Week in Netherlands based on his two starts, including eight innings of one run ball against the Netherlands in the Netherlands to lead Team USA to a bronze medal. Between that start and opening the tournament for the US against Japan, he pitched 16 innings with 19 strikeouts, two walks, and only allowed two runs. This was a big performance in a big tournament.

Second, he pitched in two high leverage situations in the final series against Arizona and excelled. He entered the first game of the series to a tie game in the bottom of the ninth – the Cards needing a win to maintain a one game lead on the Pirates – and held the D-Backs scoreless and eventually got the win. He again entered on Sunday with runners on first and second and one out with a one run lead and held the D-Backs scoreless for the remainder of the game. Gonzales is a risk but I would be very upset to see Motte, Lyons, Greenwood, or even Siegrist on the roster ahead of him. He would help the Cards get out of a situation.

Edge: Cardinals, big time.

Manager: Some fine baseball minds have likened Matheny’s lineup decisions to throwing darts. I would not disagree but it has worked for whatever reason. This may have been Matheny’s best season as a manager as he had to tinker with personnel, adapt to unpopular trades, and generally deal with the media and fan’s disappointment with the team’s hitting. This is a young team and should be the same group of players for the next two-three seasons.

In his fourth season as a manager, Don Mattingly has improved the team’s win-loss record in each year of his tenure.

After sacrificing more than all but the Reds in 2013 and Brewers in 2012, Mattingly reeled it in and finished in the middle of the pack. He used slightly more lineups than the Cards this year with 124 different ones, but by the end of the season had a pretty good idea of what he wanted to do – decidedly different than Matheny.

In terms of challenges, Matheny was 13/32 (40%) compared to Mattingly’s 55% (21/38): Mattingly challenged more often and with more success. I think they’re about equal here. Mattingly prefers to sit in the shadows and let his players do the talking while Matheny has really become the face of his franchise. Both managers were fortunate enough to come into good situations and while Mattingly has the additional challenges of Puig and LA, Matheny has weathered a tough year of criticism both nationally and more importantly, locally. I’m confident neither will do too much to hurt their teams in a negative way.

Edge: Even.

Overall Thoughts: I believe the series boils down to the Cards having a much, much better bullpen and much, much better defense. The Dodgers are unquestionably better hitting team as they’ve put up a .264/.332/.404 line good for the second highest OPS in the NL against the Cards’ weak .253/.321/.371 line. While the Cards have actually allowed fewer runs than the Dodgers, the Dodgers hold the ERA edge due to 63 unearned runs compared to 39 for the Cards. Kershaw gives the Dodgers the rotation edge. The Cards are built to win close games and at least three of these potential five games should feature under 6.5 runs. Nearly half of the Cardinals games were decided by two or fewer runs and they were 46-34 in those games and the Dodgers were nearly just as good as they went 41-32 in such games.

Home field advantage is a big deal for the Cards as they play .630 baseball at home compared to .474 on the road. The numbers and Kershaw all point to the Dodgers winning this series. I made the mistake of picking them in the NLCS last year and won’t do the same this year. The pick is Cards in four. They’ll drop the first game in LA against Kershaw, but will rebound to win the next three and take the series.