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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%).

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.

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

You know his story: David Price is a $217-million man with a 4.74 earned run average, and the people of Boston aren’t happy. It’s another Crawford-Sandoval-Ramirez waste of money. Things are headed downhill for the 31-year old veteran. Or are they?

First, the bad news: the 2016 version of David Price has been worse than the 2015 David Price, and way worse than the top-caliber pitcher Boston signed him to be. And the ERA shows it.

The suspect is pitch selection, and the culprit is a sinker that doesn’t sink. Price has a two-seam fastball that over his seven-year career he has thrown some 30% of the time. In his prime, it clocked in at 94-95 mph, but since then he’s dropped almost two mph.

Usually, that level of velocity leak wouldn’t be a big deal, because if there’s enough movement and deception, batters will be fooled either way. But Price’s sinker is different.

Brooks Baseball reports that “His sinker has well above-average velocity, but has little sinking action compared to a true sinker and results in more fly balls compared to other pitchers.” Uh-oh. “Little sinking action?” There needs to be at least some element of vertical movement for a sinker to be fully effective, or, in Price’s case, a little extra velocity. But now he has neither.

The results show it. Last month, he surrendered 10 home runs, more than the previous two months combined. Also in June: 31% of his pitches were sinkers, nearly 10% more than the month before. Coincidence? I think not. He’s also allowing a .241 Isolated Power on sinkers, only three points less than Mike Trout this season. And maybe the most convincing statistic: hitters are pulling the ball 10% more than they did last year, which means they are making more solid contact and not having to stay back on his fastball. Price’s pitches are slower, and it’s making a difference.

Why is he losing velocity? There’s two possibilities and they point in completely opposite directions. The first is age. Price is 31 and he’s nearing the point where most starting pitchers start to fall on the aging curve and eke velocity. If this is the case, it’s going to be a long seven years for the Red Sox. But there is another possibility. Price has played in Tampa Bay for most of his career, where the temperatures are never 40 degrees like Boston in April. It’s entirely possible that the cold ‘froze’ him up this spring and as the season continues, he’ll regain his speed. Most likely, it’s a combination of both. But either way, it’s never a good sign when pitchers slow down.

Price has always gotten away with leaving sinkers up in the zone because they showed 94-95 mph on the radar gun. But now hitters are seeing 92mph fastballs fly straight down the middle of the plate and stay there.  Why doesn’t he just put the ball on a tee? Nine out of 10 major-league hitters will knock that pitch into the stands every time. Just look at the stats: He’s surrendered just two fewer home runs than he did last season even though he’s pitched 112 fewer innings (2015: 17, 2016: 15), and he’s allowed an average of 1.25 home runs per nine innings, which is 32 percent worse than his career average (0.84). Sinkers are sending the man to his grave.

They’re also killing his ERA. 38% of his earned runs are from home runs, and if you set his home runs to eight instead of 17, his ERA would be 4.01 instead of 4.74, a 0.73 difference. (8 is the number he had allowed last year at this point in the season.) In fact, his strikeout and walk totals are even better than last season, but the home runs negate all of it.

But we can’t blame everything on the sinker, either. Price has definitely been unlucky this season. His home run to fly ball ratio is 15.5%, an unsustainable mark, his .323 BABIP .035 more than his career average, and his LOB% 10 percent less than the 2016 league average. These will balance out in time. But his sinker is the real problem.

The only way to truly limit home runs is to limit fly balls, and for Price, the only way to limit fly balls is to stop throwing sinkers that don’t sink. The solution is (1) throw harder, or (2) find another pitch to replace his sinker. Option one is still TBD. Option two could be filled with either a change or slider — two pitches that he has used to complement his fastball but never to the level that he uses his sinker. The outlook is grim either way.

Price is still a very experienced pitcher, and once his HR/FB, LOB%, and BABIP rates come down to earth, things will even out. But if he wants to be successful for the Red Sox for the entirety of his stay, there’s a longer-term issue at stake, and if his velocity continues to leak, I’m not sure what type of David Price we’ll be looking at a year from now.

I have always been fascinated by pitchers that consistently post ERAs that differ significantly from their FIPs.  As a Braves fan, this interest is particularly relevant in the valuation of ace/not ace Julio Teheran.  Unfortunately for me — but very fortunately for readers — Eno Sarris tackled the specific case of Teheran and the more general case of FIP-beaters with high pop-up rates here before I could finish this post.  Regardless, the research is done, and I believe it is still relevant.

While Eno focused on a specific subset of FIP-beaters in his discussion of Teheran, I wanted to examine pitchers with extreme ERA/FIP gaps more broadly.  I included not only pitchers who overachieved based on FIP, but also those who underachieved.  I began with a sample of all pitchers since 1960 who reached 500 IP through age 25.  I then calculated the difference between ERA- and FIP- for each pitcher (FIP overachievers would have a negative number, underachievers positive).  I selected these metrics 1) because they were readily available here at FanGraphs, and 2) because I was interested in the gap relative to league average — hopefully stripping out any differences in era (should any even exist).  

I chose this age cutoff so that I had a sample of three “in-prime” seasons afterwards (age 26-28) to compare to the initial numbers below.  After I found Z-Scores for all of the u25 pitchers, I set the threshold for over/underachiever at +/- 1 standard deviation from the mean, which turned about to be an ERA- / FIP- difference of right around eight.  It is certainly arbitrary, but I felt like this adequately separated the sample so I could examine the ends of the population.

As you can see, the spread in ERA- between over/underachievers is pretty large.  Overachievers posted ERAs 12% lower (relative to league average) than expected based on FIP, while underachievers posted ERAs over 11% higher (relative to league average) than expected based on FIP.  The group as a whole posted an ERA- nearly identical to its FIP-, which is more in line with DIPS theory expectations.

The big question remains: how “sticky” is the gap between ERA- and FIP-?  To determine this, I compared the ERA- / FIP- gap for these same samples from age 26-28.

From age 26-28, the sample as a whole posted an ERA- above its FIP-.  Even adjusting for that change, the over/underachievers both regressed heavily towards the mean, retaining 32.3% and 37.0% of their difference in ERA- and FIP- respectively.  While regression is powerful, both samples did continue to post differences in ERA- and FIP-.  The overachievers continued to post lower ERAs than FIPs, while the underachievers kept on allowing more runs than FIP suggested they deserved.  Interestingly, the percentage of the gap retained is similar for over and underachievers, though it is slightly smaller for FIP beaters.

The methodology isn’t perfect, but I found the results very compelling.  It does seem like consistently beating FIP is partially skill (which jibes with Eno’s results), and consistently allowing ERAs above FIPs is more than just bad luck.  As usual, this analysis leads to more questions than answers.  How many innings are needed before one can be considered a DIPS outlier?  Do FIP underachievers actually regress less than FIP beaters?  How does age-related decline affect the gap in ERA- and FIP-?  As the sample for a DIPS outlier grows, does he retain more of the difference going forward?  Etc.  I may try to dive into one or more of those questions later.  For now, hopefully this analysis is helpful as you consider how likely a pitcher on your team is to continue over/underperforming his FIP.

The 2016 baseball season is well underway and we’re seeing an even more drastic version of the trends that we saw last year: There are more strikeouts, more home runs, and more challenges. And, notably, there has also been a steady increase in velocity across the league, assisted by the guys I’ll be highlighting here.

A “steady increase in velocity” might not be reason to stop the presses, but just soak in this Tweet real quick:

% of total pitches 95 MPH+ in #MLB since 2008:
2015 9.14%
2014 6.87%
2013 6.36%
2012 5.59%
2011 5.18%
2010 5.45%
2009 5.44%
2008 4.82%

— Daren Willman (@darenw) December 16, 2015

We’re basically seeing twice as many pitches thrown 95+ as we were in 2008. ¡2008!

Even left-handers, typically a step behind (always a bit of a quirky species, lefties), are chucking it. Across the league, lefties are throwing the ball 95+ mph just around 7.5% of the time. That’s way more often than the stereotype of the Tom Glavine-y, soft-tossing corner-nibbler would have you believe, but it’s 2016 and elite velocity isn’t just left to the elite pitchers anymore (Chris Sale is joined in that 95+ lefty fastball club by some guy named Buddy Boshers out of the bullpen for the Twins).

So…I’m not just interested in guys that throw hard; I want guys who throw hard and make the ball move, and I want them to be left-handed. (Truth: that lefty requirement is mostly an excuse so I can hopefully talk about Danny Duffy more, James Paxton for the first time, and because I already covered the right-handed side of things with my Charlie Morton post from the start of the season (The Unbelievable Emergence of Charlie Morton), and basically because lefties are more fun.)

A common refrain among pitching coaches is that movement is just as important as velocity. Velocity can get you to the majors, but big-league hitters will turn around 95+ fast if it’s straight. But when combined with some movement (and even better, control/command) 95+ is a high value commodity.

I’m after what I want to dub the best lefty fastball. Let’s start with the simple stuff: Who out there is throwing it 95+ most frequently? Note that the percentages here are for all pitches thrown, including the off-speed stuff.

There are a number of relievers in there that I should probably get to know better. Zach Britton, Sean Doolittle, and Aroldis Chapman have all been flame-throwers for a while now; somehow their gas no longer brings the flicker to my eye that it once did. But Josh Osich and Enny Romero? Those are new guys that throw quite hard and are likely on their way to relevance.

The starters on the list are the most fun for me. James Paxton is there. Danny Duffy, too. But so are Carlos Rodon and Blake Snell. I’m not going to anoint any of these young guys just yet, but I’d venture that it’s been a long time since we’ve had four lefty starters out there throwing 95+ mph heaters at least 23% of the time. But…Carlos Rodon has a 4.16 ERA, and the other three all have fewer than 10 starts on the season. Let’s see if movement has anything to do with it.

We’re in search of the best lefty fastball and the best lefty fastball must move sideways, while also moving quickly. 10 inches of run seems like a pretty good place to set up camp.

Look at that: Mr. Rodon and his 4.16 ERA bring up the rear, while Snell and Duffy dropped right off. But man, James Paxton is still up top there just behind Jake Diekman. Diekman is a very good reliever, who seems to be realizing his potential since his trade to Texas. Basically, by exclusively pounding the zone with that hard, running fastball, he’s posted an ERA below 2.00 since getting out of Philly.

Oh! Chris Sale, how did I forget to include him in my love fest of the young lefty starters in the league? Sale has thrown 110 pitches at least 95 mph, and of those, 90 have moved at least 10 inches. That’s nuts. His stuff is incredible.

We also see Steven Matz creep in there as 6% of his pitches are these 95 mph fastballs that move an unfair amount. Matz and his 2.96 ERA definitely belong in that quartet of young insanely talented left-handed starting pitching that I talked about before. He’ll be the fifth member of that group, and we instantly have to expand our Mount Rushmore of tantalizing excellence.

This is starting to feel a bit like the NBA where so much Amazing is happening. But it’s true: there’s a lot of amazing happening across the MLB landscape right now. These lefty fastballs are but one, tiny iota of all that is going on.

Let’s refine the batch of fastballs once more to include only those that have at least 10 inches of vertical movement, too. This admittedly feels like a laughable exercise. There’s no way that pitchers are actually throwing pitches that go 95 mph, while also running and rising that much….

Oh. Damn. I see you Robbie Ray, James Paxton, and Enny Romero. I also see you Rest of League. That group included Danny Duffy, Sean Doolittle, Aroldis Chapman, Matt Moore, and Chris Sale. But really this is about those top three guys.

Ray was once a prospect known more for his feel and pitchability than a premier fastball. He’s starting for the Diamondbacks now and he’s striking out over 10 per game. His ERA sites at 4.59 and his WHIP is over 1.50, which are both significantly worse than his 2015 campaign, but still, if that pitchability from his earlier career outlook meets with his clearly impressive fastball, things could turn around quickly for the 24-year-old. I’m frankly surprised to see him here.

As for James Paxton, we know he’s throwing way harder now that he’s dropped his arm slot. I’ll save my full review of his stuff for the lengthier look that it deserves.

Then there’s Enny Romero. Romero isn’t well known in baseball circles just yet. He started a single game as a 22-year-old for the Rays back in 2013, spent 2014 throwing a 4.93 ERA in Triple-A, and hasn’t exactly torn things up in the majors since then. But he’s a young player, with a solid baseball name and a clearly electric fastball. He’s 25 and capable of figuring it out just like any other 25-year-old.

To be totally honest, I’m not entirely sure what to do with this group of pitchers. The guys atop this 95/10/10 club clearly have electric fastballs, but the electric fastball has not equated to big-league success so far. I guess that’s OK, and feeds back into the last bit of the the old pitching coach refrain: Velocity is nothing without movement…and control. But control is not sexy.

Speed is sexy, and all these guys throwing 95 are great, but Aroldis Chapman is the only one guy who’s ever thrown it 105 mph. He keeps the crown of best fastball. (All this talk of horizontal and vertical movement was really just an attempt to crown the best non-Chapman lefty fastball.)

So what is the takeaway?

This discussion mostly serves as a friendly reminder that we’re in the midst of a great revolution of left handed pitchers — all of whom make Clayton Kershaw old by comparison. These guys are throwing fastballs harder than we’ve ever seen before and there’s so many of them doing it.

—

Stat of the Day: I feel like I should also note that I unearthed an insane Andrew Miller pitch where he effectively threw a 95 mph slider on June 6th to some poor soul.

With the MLB draft just past, I thought it would be appropriate to examine one of the most controversial topics surrounding the draft: the qualifying offer. Essentially, the qualifying offer intends to reward teams — presumably the small-market, low budget ones — that lose players in free agency. This reward comes in the form of an additional first-round draft pick for every player that signs with another team.

Only it isn’t that simple. Once a player reaches the end of his contract, the team can decide whether or not to offer the player a 1-year extension known as the qualifying offer. This new contract is equal to the average of the highest 125 salaries in MLB ($15.8 million in 2016). The player then chooses to either accept the qualifying offer or decline it — and thus, enter free agency with the assumption that he can earn more than a 1-year, $15.8 million contract. Once the player signs on with another team, his former team is awarded a first-round draft pick (to go along with the one(s) they already have, assuming they do) as compensation. Additionally, the player’s new team loses their first-round pick in the draft so long as it is outside the top 10 (in which case their second-round pick would be forfeited).

So, one would assume that, more often than not, a small-market team with a low payroll would benefit from this system. A budding star player reaches the end of his contract and commands a new contract worth hundreds of millions and spread over 5+ seasons. His current team does not have the financial resources to resign him, and another big-market team does. The cash-strapped team receives an additional first-round pick as compensation, while his new team willfully forfeits its first-round pick in exchange for his services over the next half-decade. And that’s that.

Not quite. I went back over the draft order for every year since 2013 (when the qualifying offer was first introduced) and summed the number of draft picks gained and lost. Results are shown below. I sorted the teams by their average payroll over the span in descending order. As you can see, the compensation is not in line with the assumption I presented above. In any way you shape it, the high-payroll teams are the ones benefiting from the current system. The 10 highest-payroll teams have received 19 additional draft picks over the four seasons — highlighted by the Cardinals who have gained four and lost none. The 10 teams with the lowest payrolls have received eight additional picks. The high payroll teams have a net draft pick gain of four, while the low payroll teams have a net loss of two.

Now, I’m not coming up with any revolutionary solutions here — I’m not that smart and I don’t get paid enough. I am simply presenting data that supports that MLB’s current free-agent compensation system doesn’t benefit the teams that need it the most. In fact, this seems to be a story of “the rich are getting richer” — big-money teams are receiving the extra draft picks that were seemingly meant for the low-budget ones. Maybe MLB scraps the compensation system altogether, maybe they extend the time frame for when a player can accept the qualifying offer (they currently have seven days), or maybe they come up with some other solution. In any case, the current CBA ends after the 2016 season so us fans will likely know the answer before next year’s draft.

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 1984 San Francisco Giants 

OWAR: 42.9     OWS: 294     OPW%: .508     (82-80)

AWAR: 27.7      AWS: 198     APW%: .407     (66-96)

WARdiff: 15.2                        WSdiff: 96  

The “Original” 1984 Giants ended the season with a winning record but merely earned a fifth place finish, 9 games behind the Astros. Gary “Sarge” Matthews established a career-best with 101 runs scored while pacing the circuit with 103 walks and a .410 OBP. Chili Davis contributed a .315 BA and merited his first All-Star invitation. Dave “Kong” Kingman walloped 35 four-baggers and knocked in a personal-best 118 baserunners. Bob Brenly achieved his lone All-Star nod with a .291 BA, 20 dingers and 80 ribbies. Jack Clark supplied a .320 BA with 11 long balls prior to a season-ending injury in mid-June. Dan “Dazzle” Gladden ignited the offense following his recall from the minor leagues in late June, posting a .351 BA and swiping 31 bags.

Jack Clark is ranked 27^th among right fielders according to Bill James in “The New Bill James Historical Baseball Abstract.” “Original” Giants teammates listed in the “NBJHBA” top 100 rankings include George Foster (34th-LF), Gary Matthews (46th-LF), Garry Maddox (56th-CF), Chili Davis (64th-RF), Chris Speier (68th-SS) and Dave Kingman (98th-LF).  Al Oliver (31th-CF), Manny Trillo (49th-2B) and Dusty Baker (54th-LF) make the register for the “Actual” Giants. 

  Original 1984 Giants                              Actual 1984 Giants

Bob Knepper rebounded from an 11-28 mark in the previous two campaigns to achieve a 15-10 record with a 3.20 ERA and 1.190 WHIP. Gary Lavelle notched 12 saves and fashioned a 2.76 ERA as the primary closer. Frank Williams collected 9 victories in a long relief role during his rookie year.

  Original 1984 Giants                                   Actual 1984 Giants

Notable Transactions

Gary Matthews

November 17, 1976: Signed as a Free Agent with the Atlanta Braves.

March 25, 1981: Traded by the Atlanta Braves to the Philadelphia Phillies for Bob Walk.

March 26, 1984: Traded by the Philadelphia Phillies with Porfi Altamirano and Bob Dernier to the Chicago Cubs for Bill Campbell and Mike Diaz.

Dave Kingman

February 28, 1975: Purchased by the New York Mets from the San Francisco Giants for $150,000.

June 15, 1977: Traded by the New York Mets to the San Diego Padres for Paul Siebert and Bobby Valentine.

September 6, 1977: Selected off waivers by the California Angels from the San Diego Padres.

September 15, 1977: Traded by the California Angels to the New York Yankees for Randy Stein and cash.

November 2, 1977: Granted Free Agency.

November 30, 1977: Signed as a Free Agent with the Chicago Cubs.

February 28, 1981: Traded by the Chicago Cubs to the New York Mets for Steve Henderson and cash.

January 30, 1984: Released by the New York Mets.

March 29, 1984: Signed as a Free Agent with the Oakland Athletics.

George Foster

May 29, 1971: Traded by the San Francisco Giants to the Cincinnati Reds for Frank Duffy and Vern Geishert.

February 10, 1982: Traded by the Cincinnati Reds to the New York Mets for Greg Harris, Jim Kern and Alex Trevino.

Bob Knepper

December 8, 1980: Traded by the San Francisco Giants with Chris Bourjos to the Houston Astros for Enos Cabell.

Honorable Mention

The 1906 New York Giants 

OWAR: 65.9     OWS: 361     OPW%: .591     (91-63)

AWAR: 50.8       AWS: 287      APW%: .632    (96-56)

WARdiff: 15.1                        WSdiff: 74

The New York Giants secured the organization’s fourth consecutive pennant in 1906 with a record of 91-63, placing three games in front of the St. Louis Cardinals. Third-sacker Art Devlin pilfered 54 bases and delivered a .299 BA. Harry H. Davis topped the leader boards with 12 big-flies and 96 ribbies. Converted outfielder Cy Seymour nabbed 29 bags and drove in 80 baserunners while “Wee” Willie Keeler batted .304 with 23 steals. Christy Mathewson furnished 22 victories along with a 2.97 ERA. Left-hander Hooks Wiltse recorded 16 wins with an ERA of 2.27 and a WHIP of 1.143.

On Deck

What Might Have Been – The “Original” 2004 Royals

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

The MLB draft has passed but its impact will last. Some selections will go down as busts (e.g. Matt Anderson by the Tigers in 1997). Others will be real bargains such as Carlos Beltran with the 49^th pick in 1995. I decided to look at the numbers in an attempt to answer the following questions I read over the last few weeks:

1. How many Round 1 picks do end up in the big leagues? What’s the average impact of a Round 1 pick? How does that compare to Round 2? Are there differences between pitcher and batters?
2. What has been the best draft class for the 1993-2008 period? (per three first rounds)
3. What teams have done a better job?
4. What is the best round (top 10 overall picks)?

As I usually do, let’s define the data sources and assumptions. First, my data source is Baseball-Reference. There are many assumptions and disclaimers in this process, but the most important ones are:

1. I am using data from 1993 to 2008 to give ample time for players to reach MLB. As I am using career WAR, I don’t want to over-penalize players that have been selected in the recent years and therefore have not accumulated MLB service time.
2. Organizations change and so do their ways of conducting business, which evidently includes draft strategy. We are looking at teams rather than specific front offices or general managers.
3. WAR refers to Baseball-Reference WAR (i.e. bWAR).
4. Teams may have more than one pick per round due to compensation and supplemental picks.
5. This methodology does not take into account the overall quality of the draft pool i.e. total WAR per draft year is not constant.
6. All WAR is allocated to the team that drafts the player. Understandably, that is not true but let’s toy with the idea through this post.

Let’s get to it.

Question 1 – How many Round 1 picks do end up in the big league? What’s the average impact of a Round 1 compare to a Round 2 pick? Are there differences between pitcher and batters?

The table below outlines how many players have been/were called up to the majors and how many actually have had a positive career WAR i.e. over 0.0. I have also added the average career WAR per player and I have broken down the data by round and by position (pitcher and batter) to grasp the differences easily. Just take a moment with this table:

Three things come to my mind:

First, this provides some empirical validation of what we intuitively thought: First-round picks produce greater WAR values than the others. While I only have data for the first three rounds, it’s worth noting that the gap between Round 1 to Round 2 (10%) is smaller than from Round 2 to Round 3 (41%).

Second, I actually found surprising that 67% of first-rounders reached MLB at some point. That is two players out of three and it’s a testament to how important raw skills are when it comes to moving up through the minors.

Lastly, the answer to the question of whether t draft pitchers or batters looks like an easy one. Batters not only reached MLB at a higher pace but delivered better results as a group and as individuals. While these results are not statistically significant, they provide a pragmatic answer to the question and suggest a sound strategy might be to draft batters and trade for pitchers later down the road.

Question 2 – What has been the best draft class for the 1993-2008 period?

This table should provide guidance on how to answer this question but does not fully explain it. If we think of it as the number of players that got to MLB, then 2008 is the best year. That year highlights Eric Hosmer, Buster Posey, Brett Lawrie, Craig Kimbrel and Gerrit Cole as the most prominent stars, but offers a very low career total WAR as most of its players are still playing – they’re the youngest generation of my sample. In this class, 27 out of the top 30 picks have reached MLB, though a few for a very short stint e.g. Kyle Skipworth or Ethan Martin.

If we think of the highest total career WAR, then the winner is 2002. This class is led by two of the best picks on the sample (Zack Greinke and Joey Votto) but also features Prince Fielder, Jon Lester and Curtis Granderson. If we think of highest concentration of skills, then the 1995 class has to be the first one with an average of 11.8 WAR per MLB player. On the other hand, only 41 players got the MLB call, the lowest among the sample. While Carlos Beltran and Roy Halladay are the most notable names in that draft, player such as Darin Erstad, Kerry Wood, Randy Winn and Bronson Arroyo enjoyed nice peaks.

Question 3 – What teams have done a better job?

Evidently, not every team has selected in the same combination of draft slots e.g. some teams have had the opportunity to choose top picks (Rays, for example), while other have frequently picked from mid-bottom draft slots (Yankees).  It would not be fair to compare total career WAR for players the Yankees has selected against those that the Rays has because the latter had more options and access to a different pool of players than that the Yankees had. How to fix that? I am comparing what each team did on the overall pick they were slotted. If we use 2016 as an example, I would be comparing how good Philadelphia was in choosing Mickey Moniak as pick 1 against the average of all other first picks in the timeframe (1993-2008). Once I know the WAR gap between a particular team and the average WAR per pick, I need to standardize that number by the standard deviation i.e. calculating Z scores. In simple terms, this is understanding how good or bad a pick was in relation to the entire distribution of a particular draft slot. The Z-score number allows us to compare how good a 14th pick was in relation to a third pick, for example. Finally, to identify which teams have fared better, I am calculating the average of Z-scores for all picks.

Again, there are many caveats here, but this should give us a ballpark estimate on how well teams have drafted from 1993-2008. Keep in mind, this methodology does not produce a linear WAR per draft slot. That would mean, for example, that overall pick 4 will produce greater WAR than pick 5. On average, the 4^th pick has produced 6.2 WAR on average, while the 5^th one has produced 14.3. While this might be counter-intuitive (it is at least for me), the empirical evidence of this sample size shows that.

Perhaps surprisingly, the Phillies come at the top of the list. The Phillies advantage came in three picks: First, Chase Utley was drafted in 2000 with the high 15th pick and has had a great career that is up to 63.4 WAR. Second, in 1993, the Phillies chose Scott Rolen (70 career WAR) with the 46th overall pick – which seems like a bargain now. Finally, Randy Wolf in 1997 was selected in the 54^th position and went on to have a 23.1 career WAR. The Nationals have had very much success on their first few years as a franchise with both Jordan Zimmermann and Ryan Zimmerman. The sample size does not include Bryce Harper or Stephen Strasburg, which may push the Nats to the top of the list in the near future.

Astros, Expos, Yankees, Cubs and Indians are the bottom five teams. Coincidentally or not, these teams have long droughts (Yankees exempted). Interesting to see if there is a relationship between draft performance and wins but I guess that’s is another post.

We could go and dig deeper for each team into what they’ve done well and not so much but that would not make sense. Teams make mistakes and it looks like the draft selection is pretty damn hard with an extremely high WAR standard deviation (11.6 WAR through the first 30 picks).

Question 4 – What is the best round (top 10 overall picks)?

This question is about finding the best selection on each of the first 10 picks. I’ve used the Z-score which pick was really ahead of the curve.

Well, this is quite a nice group of players. A-Rod is the WAR leader of our sample. Even as a first pick, which on average has yielded the highest WAR, he manages to be three standards deviations above the mean. Five other players are active and two of them (Greinke and Kershaw) still are among the best starting pitchers in the game. They will continue to cement their position as great draft picks for the Royals and Dodgers. Interestingly enough, Barry Zito and Eric Chavez were part of the A’s Moneyball team that frequently over-performed a few years ago — a reminder of how important it is to build a strong core of players.

As a bonus question – these are the top 10 picks, according to this methodology:

As always, feel free to share your thoughts and comments in the section below or through our twitter account @imperfectgameb.

Note: This analysis is also featured in our emerging blog www.theimperfectgame.com