Every baseball stadium is different.  This is an obvious fact, but its obviousness can obscure its importance.  Every baseball stadium is different, so baseball is different in every stadium.  Some of these differences are easy to discern such as HRs in Denver and Cincinnati.  Others though are more easily masked — did you know that the White Sox’ U.S. Cellular Field raises walks by 7%?  Each game is a combination of outcomes affected by each team’s talent and, to a lesser extent, these park factors.  FanGraphs is nice enough to publish its park factors here.

With the league-wide increase in exit velocity and home runs, I was interested to know if any park factors may be changing as well.  With roughly half of the 2016 season in the books, I thought now was as good a time as any to take a look.  Rather than go through the laborious calculations necessary to find park factors like those at FanGraphs, I came up with a quick and not at all exact way to look at just this season.  Essentially, I found each team’s home and away rates of 1B, 2B, 3B, HR, SO and BB per plate appearance.  I then compared each to league average on the same scale as wRC+ (100 is average).  I then calculated a quick park factor on the same scale for each of the above stats as follows (1B factor shown below):

((Team Home 1B Rate – (Team Away 1B Rate – 100)) + 100) / 2 = 1B Park Factor

For example, the Marlins have hit 4% more singles than average at home (104 1B+), and 27% more singles than average on the road (127 1B+), so the Marlins Park 1B park factor would be 88 (depresses singles by 12%).

I am fully aware of the many problems with the methodology (ignores half of the data, small sample, not enough regression included, team road schedules aren’t guaranteed to have average park factors, etc.), but like I said, I wanted something quick, and I am only focused on the extremes anyway.  This should at least show us which parks to consider monitoring or examining further.

I know that is a lot to digest, and I apologize it is not sortable due to my lack of coding skill — but there are some interesting differences buried in that table.

1B Park Factor

Two parks stick out at the extreme ends for singles.  The aforementioned Marlins Park went from slightly single-friendly to the worst park for singles.  I don’t have a good explanation for this, though the fences were moved in prior to this season which we would expect to set off a ripple affect with the park factors.  The Blue Jays’ Rogers Centre went the opposite direction of the Marlins, showing a move from slightly below-average for singles to the second-best park for singles.  The Jays did change to a dirt infield from turf for 2016, but I would expect that to decrease 1Bs rather than increase them.  Maybe dirt slows infielders down giving them less range?  The Jays have recorded more infield and bunt hits at home than on the road as well, which would increase singles.

2B Park Factor

Coors Field has seen a marked increase in doubles (and triples) in 2016 with a small decrease in HRs, which is very interesting considering they raised several areas of the outfield walls.  The Cubs, Braves, Nationals, Phillies and Pirates have all seen at least a 10-point decrease in 2Bs.  Of that group, the Braves, Phillies and Pirates seem to have traded those doubles for triples which I wouldn’t necessarily expect to hold up as a change in the park factor given the limited samples.  The Phillies also made a change to a longer-cut grass, so a decrease in 1Bs and 2Bs makes some sense.  I am not sure what is going on in Chicago (wind patterns?) and Washington as the decrease in doubles does not seem to be offset by an increase in other similar batted balls.

3B Park Factor

As expected with the extremely limited number of triples, there is a ton of variation across the half-season sample.  The two most likely to represent a true change to the park factors in my mind are the decrease in triples in Marlins Park (moved fences in) and the increase in triples at Coors Field (raised fences), though both likely won’t hold up to this magnitude.

HR Park Factor

There have been large and unexpected decreases in home runs in Toronto and Texas, while the Marlins and Dodgers have seen upticks in homers at home.  Probably nothing but small-sample noise here.  It will be worth checking more rigorously to see if these hold up, particularly at Marlins Park given the change to the fences.

Strikeout and Walk Park Factors

Given the way I have calculated each component park factor, I expected all of them to need an adjustment for home-field advantage.  Interestingly, that was not the case for 1Bs, 2Bs and HRs as the average observed park factor for each was 100 across the league.  I wrote off the 108 average observed 3B factor as small-sample noise, but I believe I picked up some measure of home-field advantage in strikeouts and walks.  On average across the league, home parks decreased strikeouts by 3% and increased walks by 5%.  These have been regressed and the samples for each are among the largest of the component park factors (more PAs end in a K than any specific batted-ball outcome, and there are more BBs than anything except 1Bs), so it feels like this reflects something.

The extreme parks for changes in strikeouts are the Twins’ Target Field and Diamondbacks’ Chase Field.  Adjusting for the home-field difference (the unadjusted numbers are shown in the table above), the Twins’ park seems to be decreasing strikeouts by about 8% more than usual, while the Diamondbacks’ stadium is increasing Ks by 8% more than FG expects.  The Twins did make a change to their CF seating that could be affecting the hitters’ ability to pick up pitches (and thus strike out less), but if that is the case an increase in walks would also be expected — and that is not the case, as the Twins have actually walked less than expected when including the home-field adjustment.

For changes in BBs (after adjusting for home field), the parks in Oakland and Cleveland stick out.  The Coliseum has allowed 12% less walks than expected, while the Indians’ Progressive Field has inflated walks by 16%.  These may be worth exploring as both parks have also affected strikeouts, with the A’s park increasing strikeouts and the Indians’ park decreasing Ks.  It is possible hitters are not picking up the ball in Oakland while they are seeing it well in Cleveland.

***

So there you have it.  Noisy, likely inaccurate 2016 park factors.  It will be very interesting to see if any of the observed changes detailed above turn out to reflect a true change in the park factors.  My best guess is Colorado, Miami and Toronto will need some type of adjustment from the 2015 park factors given the fairly significant changes to each park debuting in 2016.  It would be fascinating to hear thoughts from the players on the extreme differences found above as well.  The fact that each park is so different is part of baseball’s appeal to me.  Every game really is totally unique, all the way down to the field itself.

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On June 25, this was David Price’s tweet to family, friends and fans.  It was a clear signal that he knew the patience of the Boston fans and media was wearing thin.

Fast forward to the All-Star break and his “Made for TV” stats (those that casual fans know best) are underwhelming: a 9-6 record with a 4.34 ERA, which is worse than the MLB average of 4.23.  It’s not so much his ERA that’s the problem to fans, but more his inability to be consistent from start to start.  Price has three starts of six-plus innings allowing two or fewer runs, but also has four starts of allowing six or more runs.  With the rest of the rotation producing an atrocious 4.86 ERA, the Sox desperately needed Price to be the one to stop the bleeding, something he hasn’t been able to do.  But that doesn’t mean his underlying skills have deteriorated and all of a sudden he’s become a league-average pitcher.  In fact, the advanced metrics say he’s been extremely unlucky and that he’s due for a big second half. 

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* Rank is solely being used to establish a baseline for Price as a top 10 pitcher.

In 2014 and 2015 combined, Price was ranked in the top 10 of all pitchers in four of the skill-based statistics: K%, BB%, xFIP and SIERA (the latter two being ERA estimators with a weighting towards more pitcher-controlled outcomes).  Through the 2016 All-Star break, Price has maintained or improved his top-10 rank in K%, xFIP and SIERA but dropped a few spots in walk rate.  Despite the move from 9th to 10th in K% rank, his K rate is actually up from 26.2% to 27.1%.  The reason for the drop in rank is that 2016 newcomers to the list Jose Fernandez, Noah Syndergaard and Drew Pomeranz did not meet the minimum innings qualifier for the 2014/2015 combined list.  On the flip side, Price’s xFIP and SIERA are higher than they were the past two years, but he has improved his ranking versus his peers.  This is because xFIPs and SIERAs are both up 10% league-wide versus last year (due to all the home runs being hit) while Price’s increases are smaller.

So what is happening?  If his base skills are fine, why is his ERA so high and his performance so inconsistent?

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So everyone is familiar with ERA and can easily infer that 4.34 is no bueno for a $217-million pitcher.  But there is a reason these stats are labeled “Non Skill-Based” — that’s because these stats are influenced by factors outside of the pitcher’s direct control (defense, luck, sequencing, variance, etc…) and therefore have wide variability over small samples.  Three of these stats (HR/FB%, BABIP and LOB%) explain why David Price is a great rebound candidate for the second half.

HR/FB%

Price’s current HR/FB (home runs per fly ball) rate is 15.2% — which is good for being ranked 76th out of 97 qualified starting pitchers.  The past two years combined he ranked 19th.  To put this in context, Price’s career average is 9.4% while the 2016 league average is 12.9%.  Price has never recorded a full season (>150 IP) HR/FB rate higher than 10.5%.  Also, on balls hit into play against Price this year, 31.3% of them are fly balls, the second-lowest rate of his career.  The only season in which he allowed a lower fly ball rate was in 2012 when he won the AL Cy Young award.  Price is giving up fewer fly balls this year, but of the fly balls he is allowing, they are going over the fence at the highest rate of his career.  Those that remember Price giving up a HR in 10 consecutive starts this year are nodding violently right now.  His HR/FB% will regress towards his career norm (9.4%) and this should be the main reason for a big second half.

BABIP

Price is also suffering from an unsustainable BABIP (batting average on balls in play).  His current mark of .321 is well above his career rate (.289) and even above his highest full-season rate (.306).  Once a ball is put into play it is out of the pitcher’s control what happens from there.  This is why defense and luck influence this stat more than skill.  And with that said, statistical outliers here tend to regress towards career norms.  Even though Price is allowing ground balls at a higher rate than the past two years, his 2016 GB% is still lower than his career average.  BABIP can be influenced by the number of ground balls a pitcher allows, but he’s not allowing vastly more than his career average.  His BABIP should have some positive regression in it, which is another predictor of improved second-half performance.

LOB%

Price’s Left-On-Base% (percentage of runners a pitcher strands over the course of a season) is currently 70.9%, which is also below his career rate (74.7%) and would be his second worst full-season rate (70.0%) if the season ended today.  Similar to HR/FB%, he is ranked 73rd out of 97 qualified starting pitchers.  The past two years he ranked 22nd.  A pitcher with a higher than average strikeout rate should be able to sustain a slightly higher than average LOB%, but it’s playing out the exact opposite way for Price.  This is partly due to his inflated BABIP and HR/FB%; as these statistics continue to regress towards his career norms, the LOB% will creep up to expected levels.

Much has been made of Price’s velocity being down this year compared to any point in his career.  At the start of the season, his velocity was over 2.0 MPH lower than his career average (94.1).  He has since closed this gap almost entirely.  Here is his average fastball velocity by month (with number of starts):

April: 92.0 (5)

May: 92.5 (6)

June: 92.9 (6)

July: 94.0 (2)

If this upward trend in velocity stabilizes somewhere at or above 93.5, then nearly all the performance metrics within his control — velocity, K%, BB%, xFIP and SIERA — will be at or near his career norms.

Let’s dive a little deeper into that early-season velocity issue.  Below are two charts.  The first shows combined performance of 2014 and 2015 for ERA-qualifying starters while the second chart is the same data for the 2016 season through the All-Star break.  The orange circle is David Price.  The red circle (if shown) represents Price’s career average.  The blue circles are a hand selected peer group of the top 10 pitchers in the game (Kershaw, Sale, Arrieta, Scherzer, Bumgarner, Greinke, Strasburg, Syndergaard, Salazar and Fernandez).  Remember those rankings where Price was right around the top 10 — these are the guys usually outperforming him.  The gray circles represent everyone else.  Note: For these first two charts the top-right quadrant is Good, and the bottom-left quadrant is Bad (unless you’re a knuckleballer).

2014-2015 K/9 vs FBv

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2016 K/9 vs FBv

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The first graph shows David Price clustered where you would expect him — right at the middle-to-bottom of his top-10 peer group, with a healthy average fastball velocity and K/9.  The second graph (2016) shows Price in a similar relationship to his peers, but with slightly lower velocity and a higher K/9.  Note the gap between the orange (Price’s 2016) and red (Price’s career average) dots depicting his improved strikeout numbers this year despite the slightly lower velocity.  This graph also shows what freaks Noah Syndergaard, Jose Fernandez and (to a lesser degree) Jered Weaver are.

The final two graphs show the relationship between ERA and xFIP where xFIP is the more predictive estimator of a pitcher’s skill.  The bottom-left quadrant is Good (think Kershaw) and the upper-right quadrant is Bad (think Buchholz).  Anyone in the upper-left quadrant (Price in 2016) is a candidate for positive regression.

2014-2015 ERA vs xFIP

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2016 ERA vs xFIP

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The first graph again shows Price in his usual place — at the tail end of the top 10.  In 2014 and 2015 combined he had a very similar ERA (2.88) and xFIP (2.98).  The second graph (2016) shows the disparity between his ERA (4.34) and xFIP (3.16).  Pitchers with this large of a gap between ERA and xFIP are great candidates for regression.  The important takeaway is that his xFIP, relative to his peers, has stayed in that top-10 range.  This supports the point that some bad luck is the main element depressing his ERA.

David Price can easily be the best pitcher in the American League over the next two and a half months.  He already owns the lowest xFIP in the AL at 3.16 — the next-closest is Corey Kluber, at 3.34.  The skills above show he can sustain the xFIP level, but with some change in luck and maintaining his improved velocity, he doesn’t need to “pitch better”; he just needs to keep pitching — and the results will follow.

This article was originally published on Check Down Sports. 

Predictions are hard. Getting them right is harder. But everyone loves them, so I’m going to attempt to predict which starting pitchers will improve in the second half of the season, and which are poised to put up worse numbers. This information may be especially helpful for a GM thinking about acquiring a pitcher before the trade deadline, or, maybe more applicably, a fantasy owner trying to surge his team into playoff position.

How do you exactly predict starting-pitcher performance in MLB? Well, it’s pretty commonly known among baseball-thinkers that FIP is more accurate at predicting a subsequent year’s ERA than ERA itself. FIP is a statistic on an ERA-scale that only accounts for what the pitcher can control (strikeouts, walks, and home runs). There’s been a lot of research that looks at differences between ERA and FIP, but to my knowledge, there’s nothing out there to see if it can predict second-half performance. So that’s what I’m going to do here.

I compiled all the starting pitchers who were qualified in both the first and second halves of 2015 (57 total), and ran a basic scatter plot of their first-half ERA, FIP, and xFIP against second-half ERA, to see which of the former was best at predicting the latter.

First-Half ERA and Second-Half ERA

First up is first-half ERA and second-half ERA. A fairly weak correlation — 7% of a pitcher’s second-half ERA is explained by his first-half ERA — albeit significant (p-value < 0.10).

First-Half FIP and Second-Half ERA

Next is first-half FIP and second-half ERA. It’s hard to tell but the dots are, on average, a bit closer to the fit line — 11% of second-half ERA is explained by first-half FIP (p-value < 0.05).

First-Half xFIP and Second-Half ERA

Lastly, we have first-half xFIP and second-half ERA. While FIP uses a pitcher’s actual home-run totals, xFIP uses league-average totals because home run rates fluctuate year-to-year. You can clearly see the dots are much closer to the fit line than in the previous two graphs — 15% of second-half ERA is predicted by first-half xFIP (p-value < 0.01).

Is 15% good? Using the same method as above, I looked at the correlation between 2014 xFIP and 2015 ERA — and found an r² of 27%. So while half-season predictions don’t seem to be as accurate as season-to-season predictions, if MLB teams are making real moves based on a 27% correlation, I’m going to take a leap and say my fantasy team can makes moves based on a 15% correlation.

Now the part you (and I) have been waiting for: Here are the top 10 pitchers poised for second-half improvement followed by the top 10 pitchers who may get worse (sorted by the difference between ERA and xFIP, as of 7/9).

Some interesting things to note on the first list:

• Smyly is owned in 48% of Yahoo Fantasy leagues, Nola in 47%, Ray in 11%, and Bettis in 4%. Pick them up.
• The rest could be solid buy-low trade options (minus Eovaldi, unless your league values middle relievers).
• A common theme among the members are high BABIPs and home-run rates (>.300, >15%) — which suggests they have been victims of bad luck.

And the second list, where the opposites are mostly true:

• While Teheran’s name has come up in trade talks, his numbers suggest he may regress in the second half.
• Sell-high trade options in fantasy leagues.
• Low BABIPs and home-run rates (<.275, <10%).

Do you ever look at a daily lineup and find yourself disappointed with one of the names in it? Do you ever ask why the manager continues to bat a clearly inferior player when there are clearly better options on the bench or in the minors or in your softball league? Do you ever celebrate when a player gets designated for assignment and you never have to see them bat second in front of like six clearly better hitters? Well then I am very sorry, but it’s time to relive some bad memories, team by team, from the past ten years.

Yes, it’s time to talk about the black holes of the recent past.

Now what makes a player a black hole?

Read the rest of this entry »

In late May, I posted a version of expected ISO (xISO), inspired by Alex Chamberlain’s work, which incorporated the publicly available Statcast data, easily accessible from the Baseball Savant leaderboard. I’ve been tinkering with it since, and figured I would post an updated version, as well as some second-half predictions based on the current “leaders and laggards”.

MODEL UPDATE

The original version of xISO was a simple linear regression model using GB% and average LD/FV exit velocity (LDFBEV). The only feature of any real note was the inclusion of the square of LDFBEV as an additional term. I knew then that I could get better correlation to data if I used LD% and FB% and removed GB% from the model, but I thought the simpler model would be better. I also thought it would be weird to have LD% and FB% as separate terms, and then one combined term for average exit velocity. I guess I just changed my mind. Whatever, it’s all empirical, and the only rule is it has to…predict better. Let’s examine the model, again trained on 2015 qualified hitters, and using LD% and FB% instead of GB%.

As you can see, the coefficient of determination went up a little bit from the previous version. It’s not a big deal, but it’s basically free, so we’ll take it. The updated model equation is as follows:

Now, we also have a fair bit of data for this year. I don’t yet want to update the model parameters using 2015 and 2016 data to train, but I will at least check how the model correlates to this year’s outcomes so far. I arbitrarily selected a minimum of 175 batted ball events (BBE), which limits the pool to 141 players, as of July 8th.

Look at that! Not too bad overall. Armed with some confidence in the method, let’s now take a look at some of the hitters who most over- and under-performed xISO in the first half (numbers current as of July 9). I will also attempt to avoid talking about any of the players I mentioned previously, or that Alex mentioned in his June xISO report.

OVERPERFORMERS

Jay Bruce: ISO = .274,  xISO = .187

Bruce is actually hitting his line drives and fly balls with less authority than last year (92.8 mph down from 93.2). His overall batted-ball profile looks similar as well. After a couple down years, it’s nice to see Bruce succeeding, but I’m not betting on it to continue.

Anthony Rizzo: ISO = .282,  xISO = .201

At the risk of enraging my pal, league-mate, and curator of Harper Wallbanger, we might need to calm down a little bit on Rizzo. Don’t get me wrong, I think he’s a very good player, but odds are he won’t continue to hit for quite this much power.

Jake Lamb: ISO = .330,  xISO = .256

Right now, Jake Lamb is second in the majors in ISO behind David Ortiz. He does hit the ball hard (97.9 mph LDFBEV), but he hits 46% of his balls on the ground. Even a .256 ISO would be quite good, given his decent walk rate. This will likely go down as a true breakout season for Lamb.

Wil Myers: ISO = .242,  xISO = .188

While some of the guys on this list play in hitters’ parks, Myers is an example of a first half overperformer in a pitcher’s park. Between expected power regression and his spotty injury history, I’m nervous about the second half.

UNDERPERFORMERS

Andrew McCutchen: ISO = .165,  xISO = .233

Now, ‘Cutch is hitting more popups this year than last year, which could be fooling xISO a bit. Still, I like his ISO to get back to around .200. Of more concern might be his spike in strikeouts.

Ryan Zimmerman: ISO = .181,  xISO = .236

Zimmerman’s exit velocity is up from last year (96.8 mph from 95.0). He probably won’t hit for average, but if he continue making hard contact, he should accumulate plenty of RBIs in the second half.

Yasiel Puig: ISO = .133,  xISO = .188

xISO basically expects Puig to get back to his career average of .183. My main worry with the burly Cuban is his struggle to maintain a healthy pair of hamstrings.

Colby Rasmus: ISO = .157,  xISO = .211

At this point, we basically know who Rasmus is. He is a player who consistently sports an ISO over .200. After a bump in fly balls last year, he’s sitting below his career average this season. That’s not ideal for power output, but he’s also hitting the ball a bit harder. I’ll still bet on him doubling his homer total over the remainder of the season, and surpassing 20 for the second season in Houston.

That’s it! Please feel free to to leave comments, questions, or suggestions for improvement. I’m working on a public document with the xISO calculation available for every player, updated daily-ish. Feel free to follow me on Twitter for updates, or badger me in the comments.

The Yankees have been a beacon of mediocrity this year, currently sitting under .500 and in fourth place in the resurgent AL East. Pitching is often a soft spot for the Yankees, as Yankee Stadium routinely ranks in the top half of the league in terms of worst parks for pitchers, according to park factors on FanGraphs. The short porch in right field is especially troublesome for hurlers. For this reason, park factors on FanGraphs have listed Yankee Stadium (tied with others) as the easiest park for a lefty to hit a homer from 2013-2015. Thus, there are bound to be some interesting trends among their starters. One such starter is the always entertaining Masahiro Tanaka. Let’s explore!

Tanaka struggled mightily with the longball during his first two seasons with the Yankees, posting a 1.24 HR/9 ratio from 2014-2015, which would have been good (or bad) enough for the eighth-highest among starters, had he qualified. That includes a ridiculous 1.46 HR/9 from last year, which would have ranked sixth (a few spots behind teammate CC Sabathia, who ranked third with 1.51 HR/9). At the same time, that high HR/9 may have been because Tanaka was victimized by a 15.7% HR/FB ratio during those two seasons. Since Tanaka is a righty (opposing teams are more likely to play lefties against him) who pitches half of his games at Yankee Stadium (lefty heaven), his HR/FB ratio is bound to be high, but not that high. Thus, it’s understandable that his HR/FB ratio decreased to a more normal 9.5% this year. It will probably be higher than that in the future, but this is just the ebb and flow of things: sometimes stats are higher than they should be, and sometimes they are lower.

Tanaka actually allowed a palatable 0.99 HR/9 in his first season with the Yanks, despite a 14.0% HR/FB ratio. His groundball rate remained virtually the same from that first season to the second, but one thing that made his home-run total lower in his first season as opposed to the second (other than a slight uptick in HR/FB) was a 9.3 K/9. When you allow fewer balls in play, you will allow fewer homers. However, in Tanaka’s second season, that number sunk to 8.1 K/9, a substantial drop. It has dropped even further this season, considerably below league average now at 7.1 K/9. What has brought about this dip in Ks? Changes in velocity often coincide with changes in K-rate:

Interesting. His velocity has remained quite stable throughout his time with the Yankees (if you look year to year). Let’s look elsewhere. Another determining factor for K-rate is movement. Here is Tanaka’s horizontal movement over the years.

His slider, cutter, and curve have lost a bit of that typical movement away from righties, but at the same time, the four-seam and split have improved their arm-side run, inside to righties. The sinker has pretty much held steady. How about vertical movement?

All of his pitches are dropping less/rising more, which is good for the four-seamer and cutter, but not so good for everything else. There are some good and some bad movement trends, but nothing that I would think would completely evaporate Tanaka’s strikeouts. There has to be something else. Has Tanaka’s whiff rate changed at all?

Indeed it has. The splitter has seen a sharp drop in swinging strike rate. Why is this happening? Did Tanaka change something? Let’s see if his pitch mix has any answers.

Woah! Tanaka has completely scrapped his four-seamer in favor of his sinker. Everything else has remained relatively stable. Here’s where we come full circle — back to the homers. I have a feeling that his reasoning for this change was that he wanted to improve his groundball rate in order to allow fewer home runs. Sure, the groundball rate has improved a little (up 3.4% in since last year), but this pitch swap seems to be the biggest reason I can think of for the sharp decrease in swinging-strike rate for Tanaka’s splitter. My theory is that his four-seamer, a relatively straight, hard pitch with a bit of rise, can help set up the splitter better than the sinker. The sinker and splitter are almost the same pitch; the split has a bit more drop, a bit more run, and is a bit slower. The velocity difference and movement difference between the splitter and four-seamer is much more drastic than those differences between the sinker and the split.

So, Tanaka seems to have traded some strikeouts for a few more groundballs/fewer homers. I’m not sure I like this change; his HR/FB rate was bound to normalize anyway. However, one thing I certainly like from Tanaka this year is that he has stayed off the DL. An asterisk has been over his name on draft day since the UCL injury a couple of summers ago. Despite staying off of the DL this year, people have been pointing to his better record on extra rest as a sign of fatigue. In 10 starts on extra rest this year, Tanaka has a 1.72 ERA, versus a 5.28 ERA in seven starts on regular rest. However, this has never been a noticeable split before this year, and it’s also worth noting that seven of those 10 starts have come away from Yankee Stadium, which I have deemed a bad park for Tanaka to pitch in.

Graphs and tables are from Brooks Baseball.

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

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

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

Terminology

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

OWS – Win Shares for players on “original” teams

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

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

AWS – Win Shares for players on “actual” teams

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

Assessment

The 2004 Kansas City Royals 

OWAR: 40.4     OWS: 264     OPW%: .483     (78-84)

AWAR: 16.8      AWS: 173     APW%: .358     (58-104)

WARdiff: 23.6                        WSdiff: 91  

The “Original” 2004 Royals placed third in the American League Central division, 12 games behind the Indians. The “Actual” 2004 Royals lost 104 contests. Carlos Beltran (.267/38/104) enjoyed a monster campaign as he narrowly missed the 40/40 club. The Royals center fielder compiled 121 tallies and swiped 42 bags in 45 attempts. However he only earned 11.4 Win Shares for the “Actual” Royals (vs. 29 WS for the “Originals) due to a mid-season trade to the Houston Astros. Fellow outfielder Jeff Conine contributed 35 doubles while first-sacker Mike Sweeney went yard on 22 occasions.

Juan Gonzalez of the “Actuals” placed 52^nd in the “The New Bill James Historical Baseball Abstract” top 100 player rankings. 

  Original 2004 Royals                                    Actual 2004 Royals

Jon Lieber recorded 14 victories and yielded only 18 bases on balls in 27 starts. Glendon Rusch fashioned a 3.47 ERA as he split time between starting and relief roles. Zack Greinke delivered 8 victories and a 3.97 ERA in his inaugural season. Tom “Flash” Gordon (9-4, 2.21) whiffed 96 batsmen in 89.2 innings and achieved All-Star status.

  Original 2004 Royals                                  Actual 2004 Royals

Notable Transactions

Carlos Beltran

June 24, 2004: Traded as part of a 3-team trade by the Kansas City Royals to the Houston Astros. The Oakland Athletics sent Mark Teahen and Mike Wood to the Kansas City Royals. The Houston Astros sent Octavio Dotel to the Oakland Athletics. The Houston Astros sent John Buck and cash to the Kansas City Royals.

Johnny Damon

January 8, 2001: Traded as part of a 3-team trade by the Kansas City Royals with Mark Ellis to the Oakland Athletics. The Oakland Athletics sent Ben Grieve to the Tampa Bay Devil Rays. The Oakland Athletics sent Angel Berroa and A.J. Hinch to the Kansas City Royals. The Tampa Bay Devil Rays sent Cory Lidle to the Oakland Athletics. The Tampa Bay Devil Rays sent Roberto Hernandez to the Kansas City Royals.

November 5, 2001: Granted Free Agency.

December 21, 2001: Signed as a Free Agent with the Boston Red Sox.

Tom Gordon

October 30, 1995: Granted Free Agency.

December 21, 1995: Signed as a Free Agent with the Boston Red Sox.

November 1, 2000: Granted Free Agency.

December 14, 2000: Signed as a Free Agent with the Chicago Cubs.

August 22, 2002: Traded by the Chicago Cubs to the Houston Astros for players to be named later and Russ Rohlicek (minors). The Houston Astros sent Travis Anderson (minors) (September 11, 2002) and Mike Nannini (minors) (September 11, 2002) to the Chicago Cubs to complete the trade.

October 29, 2002: Granted Free Agency.

January 23, 2003: Signed as a Free Agent with the Chicago White Sox.

October 27, 2003: Granted Free Agency.

December 16, 2003: Signed as a Free Agent with the New York Yankees.

Honorable Mention

The 2009 Kansas City Royals 

OWAR: 45.7     OWS: 268     OPW%: .544     (88-74)

AWAR: 25.3       AWS: 194      APW%: .401    (65-97)

WARdiff: 20.4                        WSdiff: 74

Kansas City clinched the American League Central division title by a lone game over Minnesota. Zack Greinke (16-8, 2.16) merited the 2009 AL Cy Young Award as he paced the Junior Circuit in ERA and WHIP (1.073) while posting career-highs in strikeouts (242) and innings pitched (229.1). Johnny Damon (.282/24/82) tied his personal-best in home runs, slashed 36 two-base hits and registered 107 tallies. Billy Butler aka “Country Breakfast” drilled 51 doubles and swatted 21 big-flies. David DeJesus contributed 13 jacks and knocked in 71 runs. Carlos Beltran supplied a .325 BA but missed more than two months of the season due to injury. J.P. Howell saved 17 contests and collected 7 victories as the Royals’ relief ace.

On Deck

What Might Have Been – The “Original” 1969 Reds

References and Resources

Baseball America – Executive Database

Baseball-Reference

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

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

Retrosheet – Transactions Database

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

Seamheads – Baseball Gauge

Sean Lahman Baseball Archive

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

“Speed kills.” –Al Davis

Starling Marte is having a hell of a season stealing bases and this is one of the things that should have propelled him into the All Star Game without needing the stupid final vote. He is the top baserunner in the NL this year, he’s second in the league with 25 stolen bases, and he has a better percentage than the leader Jonathan Villar.

A recent game against the Cardinals gave a strong piece of evidence of Marte’s preternatural baserunning ability when he stole second base with Carlos Martinez pitching in the sixth inning:

The Pirates’ announcers commented on the fact that people very rarely steal off of Martinez. I went to look at the numbers: in 399 2/3 innings since entering MLB, Martinez has yielded only 10 stolen bases in 19 attempts. I think the 19 attempts is really indicative of his abilities to control the running game; people just don’t even try to run on Carlos Martinez. Martinez ranks 36th among all pitchers in SB/IP who have thrown at least 200 innings since 2013, which is decent. However, if we limit ourselves to looking at only righty starters over this time period (since it is much harder to steal against lefties and game circumstances are a bit different between starters and relievers), Martinez ranks 13th among pitchers with the same innings restrictions. (I’m not including tables for all of these stats, but if you want to see for yourself, mosey on over to the Baseball Reference Play Index, where all of this data comes from.)

Martinez has really shut down the running game since becoming a full-time starter in 2015, however. Over that time period, he ranks fourth among all pitchers (lefty, righty, starter, and reliever alike) having at least 250 innings in SB/IP, yielding only four SB in nine attempts over 282 innings.

(Note: If you bump this down to 150 innings to get more relievers on the list, Martinez is still in the top 10 for SB/IP.)

Of the four pitchers ahead of him in the table, two are lefties. (Sidebar: How the hell is R.A. Dickey on this list given the fact that he throws a knuckleball? It seems like it should be really easy to steal on him given that.) Martinez is similarly ranked (fifth) if you look at SB/Total Baserunners over the same period; in short, Martinez is really, really good at controlling the running game.

Furthermore, reigning eight-time Gold Glove winner Yadier Molina was behind the dish attempting to throw Marte out. Molina ranks first among all catchers since 2002 in his ability to control the running game by the defensive metric rSB. Obviously Martinez’s ability to prevent the stolen base is helped by having Molina behind the dish, but the combination of these two has been deadly over the past season and a half, making Marte’s accomplishment all the more impressive.

The Martinez/Molina duo (and Martinez in general) has only allowed one other stolen base this season so far. Who was it? None other than Bartolo Colon! Actually, I’m kidding, it was Starling Marte, which is almost as crazy! He has the only two stolen bases this season against a guy who only gave up two all of last season. These are also the only two attempts against Martinez all season. On May 6, after a bunch of false starts, pickoff moves, foul balls, and laughs between Molina and Marte, he finally got his stolen base:

The thing I find most entertaining is how loose everyone seems, goofing off and laughing about the play that just happened, which seems relatively rare in this day of boring interviews and generic soundbites. Molina had a good laugh about that one, but he was pretty upset about the more recent stolen base.

There’s nothing to be mad about, though; Martinez and Molina simply got burned by the best baserunner in the game right now.

Ever since he was taken with the second pick in the 2013 draft, the spotlight has continually followed Kris Bryant. After mashing his way through the minors in less than two years, Bryant had a spectacular rookie season with a slash line of .275/.369/.488 with 26 homers and a 136 wRC+. Deservedly, he was rewarded with the NL Rookie of the Year award. Although he did strike out over 30 percent of the time, he showed great plate discipline along with immense power. His 6.5 WAR ranked 10th among major league hitters. However, this year he has taken his production a step further.

With his league-leading 25 home runs to go along with his slash line of .278/.370/.578, one major change sticks out. Although his average and on-base percentage remain around the same as his 2015 totals, his slugging percentage has taken a huge jump. Halfway through the season, he is one home run shy of last year’s total and around half of his hits have gone for extra bases (44 out of 87). He’s also cut down on his strikeouts while making even more hard contact than he did last year — shown by his 42% hard-hit rate which will allow him to continue to tap into his power.

One noticeable change sticks out in his batted-ball profile. Although his ground-ball, line-drive, and fly-ball rates remain relatively constant, Bryant has pulled the ball more in his second big-league season. This has caused more of his fly balls to leave the park. Taking a look at his 2016 home-run spray chart, you can see that all of his home runs have been pulled.

Source: FanGraphs
Next take a look at his 2015 home-run spray chart.

Source: FanGraphs
Most of his home runs have been in the same general area with the exception of a few opposite-field home runs to right. Since he has been pulling the ball with more authority in 2016, the balls that he pulls in this sweet spot to left will allow him to continue to leave the yard at a ridiculous rate. With his picturesque swing to go along with his strong hands and 6-foot-5, 230-pound frame, swings like this

…will continue to be common for Cubs fans to see from Kris Bryant.

However, it would be foolish to simply call Bryant a home-run hitter when in fact what makes him so special is his all-around hitting ability to go with this insane power. He walks, hits the ball hard, and his only flaw is his propensity to strike out — and even that he has improved upon this year. A two-time All Star already with 4.3 WAR this season, he ranks fourth among major-league hitters and first among NL hitters in WAR while also possessing a 149 wRC+. At this point, if the NL MVP is going to go to a player not named Clayton Kershaw, Kris Bryant deserves to be the one holding up the trophy. But the trophy most important to him is the one won at the end of October. With the Cubs holding a comfortable lead atop the NL Central, Bryant looks destined to lead them on a deep playoff run with the hope of finally shattering their 108-year-old curse.

On June 30, Mets manager Terry Collins sent left-hander Steven Matz to the mound to face the Chicago Cubs. Matz turned in a modest performance, striking out six in 5 1/3 innings while surrendering two homers. Not a terrible outing, but a club as offensively challenged as the Mets can only afford so many starts like this. What made the outing of more than the usual interest was that this was Matz’ first appearance after the world learned he had a bone spur in his left elbow.

Bone spurs are not generally in and of themselves debilitating, but they can inflict significant pain. And since pain is your body’s way of saying “don’t do that again, you stupid git,” the pain a bone spur causes may in turn cause other changes to the pitcher’s usage patterns and delivery. Those changes might end well, or they might not. A cascade of other injuries and mechanical problems can follow.

So a bone spur presents player and team with a choice: the player can pitch through the injury, at reduced and perhaps increasingly decaying effectiveness, or opt for surgery, which resolves the problem but sidelines the player for several months. In Matz’ case, surgery could doom his season.

If Matz were an entirely independent actor, surgery would seem the rational choice. Like all players, Matz’ overriding goal is to get The Contract: the multiyear 7-8 figure deal that will provide financial independence for Matz and his family for as long they subsist on this benighted orb. (Matz will tell you his overriding goal is to win a World Series, but that’s probably number two on his list.) By skipping the rest of this season and coming back healthy next year, Matz probably boosts the odds of making it to The Contract before critical elements of his body begin to rebel.

But Matz is not truly independent: the Mets organization, his teammates, and Baseball Tradition all exert substantial influence. Peer pressure may play a significant role here. Even John Smoltz, one of the most intelligent minds in baseball broadcasting today, discussed Matz’ bone spur (and Noah Syndergaard’s apparently smaller one) in a recent Fox broadcast with the quit-whining-and-rub-some-dirt-on-it machismo that would hardly have been out of place a century ago.

Smoltz said the pitchers can deal with bone spurs by changing their pitch selection, and there is some evidence Matz is doing just that. He used his slider at a 15% clip in April and May; in June he abandoned it. His velocity, however, is essentially unchanged, and he’s using his other pitches more or less as he always has.

So maybe Matz is reacting to the pain, maybe not. But he would certainly pay a price if he seemed to be reacting in a highly visible way. For all the analytical advancements of the past quarter-century, players are still expected to suffer in silence. Those who don’t may “lose the manager’s trust,” and have fewer opportunities to establish that they merit The Contract. I’m no fan of conformity, but it is sometimes the economically rational decision.

Mets’ GM Sandy Alderson views the Matz dilemma through a substantially different risk-assessment prism. As long as the Mets have a good shot at the playoffs, Alderson has little incentive to see Matz hit The List for any significant length of time, at least unless and until his performance seriously deteriorates. The supposedly pitching-rich Mets have nothing behind their current top five starters. No, not even Rafael Montero, who is putting up a 6+ ERA in Las Vegas this year. What stinks in Vegas stays in Vegas.

Further concentrating Alderson’s mind are the Mets’ playoff odds. This is a borderline playoff team; FanGraphs says the Mets have a 56% chance of making the playoffs, but most of that 56% just puts the Mets in the wild card. Still, that’s a five-point jump from last Wednesday, before the Mets ripped off a four-game sweep of the 1927 Yan– er — I mean, the Cubs. But sadly for the Mets, the division-leading Nationals have also been swatting aside opponents with cavalier disregard — most of the Mets’ playoff gain came at the expense of the Fighting Lorians.

Like Matz, Alderson faces a tough decision: how to balance the future against the now. The Mets are neither clearly bad enough to play for next year, nor clearly good enough to play for this one. Their roster is largely set for the near future; of their significant contributors only Neil Walker will walk at the end of the season, likely to be replaced by Dilson Herrera. The Mets are ninth in attendance, 14th in local television revenue, and 16th in payroll. They are also first in BMI (Bernie Madoff Influenza). This isn’t a team that can likely add a lot of payroll, particularly if they intend eventually to fork over some major bitcoin for at least some of the current starting rotation.

The Mets have three prospects in MLB’s top 100, but just one (Dominic Smith) in the top 50, and Smith barely clears that hurdle at #45. Although showing some increased power this year, Smith threatens to develop into the next James Loney, a threat so grave that Alderson fended it off (momentarily at least) by bringing in the current edition to fill in for the wounded Lucas Duda at first. Young shortstop Gavin Cecchini is raking at AAA to the tune of an .871 OPS, but he only recently found the rake in the back of his garage behind the broken foosball table; his career minor-league OPS is a pedestrian .745. Alderson is showing his faith in Cecchini by filling the Mets’ yawning chasm at third with an incipient public relations disaster.

Alderson has a little time. Playoff chances can swing wildly during the season, as he discovered last year. In three weeks he’ll have a better idea of where the Mets stand, and can then make a decision regarding Matz. If the Mets collapse, then the incentives for both team and pitcher come into alignment, and Matz will likely have surgery. If the Mets surge (they did, after all, finally find Nimmo), then he’ll ask Matz to shut the hell up and rub some dirt on it. Unlike some of his co-rotationists, Matz isn’t a superhero. He’ll do what he’s told, while watching with trepidation as The Contract recedes into the future.