Is pitch velocity an indicator of a good pitcher?

Over this past summer, the Twins struck a deal with the Boston Red Sox to send specialist Fernando Abad to Boston for prospect Pat Light. Light, 25, first pitched in the majors in 2016, where in two innings with the Red Sox, he had allowed 8 runs (7 earned). After the deal, he has spent the rest of the season with the Twinkies. His numbers do not look much better, with an ERA of 10.22 in 12.1 innings pitched. Over his minor-league career, he has posted a 4.35 ERA in five seasons. Why did the Twins want this guy? He was 25, fully established as a reliever, and has only dominated the minors in 2016.

One of my theories is that the Twins saw that Light is a flame-thrower. Recently, he hit 101 miles per hour on a pitch. Are the Twins fixated on his high velocity? Looking at the Twins’ bullpen, another below-average pitcher, Ryan Pressly, is also touted for his high velocity.

I am not saying definitively that the Twins are focusing on pitchers’ velocities to value prospects and players; previously I wrote about how teams have focused on batters’ exit velocities, so perhaps the Twins have tried to apply this mentality toward pitchers.

Either way, I decided to delve into this topic, seeing if a pitcher’s velocity indicates a lower ERA, FIP, and BABIP, or a higher strikeout rate and walk rate. Using MLB’s Statcast, I was able to parse their data to record a pitcher’s average velocity. Using these data, I tried to establish the skill set of a flame-thrower.

To do this, I performed linear regressions between these different factors, seeing if any of these values are highly related to or influenced by faster pitching.

First, I looked at FIP and velocity. Below are the results:

Not a strong relationship, yielding an R-squared of 0.09. This relationship does show that as velocity increases, FIP tends to decrease, but again, not a very convincing relationship.

Next, I looked at ERA and velocity:

It yielded a similar result, a weak negative relationship, if any.

While the results for ERA and FIP were disappointing, I figured BABIP might look better. If a pitcher can throw faster, it would make sense that the batter would have a tougher time making contact, leading to weaker contact and a lower BABIP. Did the results agree? Have a look:

Disappointing. No relationship at all.

On to strikeout rate and walk rate.

I immediately thought of Aroldis Chapman. He has the fastest heater in the league, and his strikeout rate is above 40%, nearing the top of the league. I was much more optimistic for these metrics.

Here is velocity to strikeout rate:

Not a great relationship, yielding an r-squared of .13. It is a little stronger than anything else we have seen, but that is not saying much at all.

Finally, here is velocity and walk rate:

Not much going on here as well.

What does this all mean? Well, for starters, it shows that there are other factors that determine how effective a pitcher is. These data show that these metrics are not the end-all-be-all of a pitcher’s skill. Velocity is not a key indicator of an effective pitcher. Sure, the fastball probably needs to be upward of 85 miles an hour, but speed is not the most important factor. Rather, other skills, such as control, deception, and quality of breaking pitches might be just as important, if not more important, than velocity.

I don’t know if the Twins specifically targeted Light because of his velocity, but in his stint with the Twins, he’s averaged 10.9 walks per 9 innings. What good does his speedy fastball do if he cannot get it over the plate?

After my analysis, I’ll admit I’m a little surprised. I would think a higher velocity would mean a higher strikeout rate. But I am wrong. I guess for every flame-throwing Aroldis Chapman, there is an equally effective Andrew Miller, who does not posses the 105 mile-an-hour heater, but has a higher strikeout rate.

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 1992 San Diego Padres 

OWAR: 52.6     OWS: 324     OPW%: .595     (96-66)

AWAR: 37.3      AWS: 246     APW%: .506     (82-80)

WARdiff: 15.3                        WSdiff: 78  

The ’92 Friars fiercely engaged the Braves but when the dust settled, the San Diego crew emerged two games behind Atlanta. The Padres led National League in OWAR and OWS. Roberto Alomar (.310/8/76) nabbed 49 bags in 58 attempts and registered 105 tallies. Carlos Baerga (.312/20/105) collected 205 base knocks, rapped 32 doubles and merited his first All-Star selection. Shane Mack supplied a .315 BA and scored 101 runs. Dave Winfield drilled 33 two-baggers, walloped 26 big-flies and plated 108 baserunners. Dave “Head” Hollins manned the hot corner and responded to full-time status with personal-bests in home runs (27), RBI (93) and runs scored (104). John Kruk laced 30 two-base hits and posted a .323 BA. In the final season of a 13-year consecutive Gold Glove Award streak, Ozzie Smith aka “The Wizard of Oz” delivered a .295 BA and succeeded on 43 of 52 stolen base tries. “Mr. Padre” Tony Gwynn contributed a .317 BA with 27 doubles.

Gary Sheffield (.330/33/100) and Fred “Crime Dog” McGriff secured their first invitations to the Mid-Summer Classic and accounted for a substantial chunk of the “Actuals” offensive production. “Sheff” claimed the batting title and placed third in the 1992 NL MVP balloting. McGriff topped the Senior Circuit with 35 bombs while driving in 104 runs.

Tony Gwynn rated sixth among right fielders in the “The New Bill James Historical Baseball Abstract” top 100 player rankings. “Original” San Diego teammates enumerated in the “NBJHBA” top 100 lists include Ozzie Smith (7^th-SS), Roberto Alomar (10^th-2B), Dave Winfield (13^th-RF), Kevin McReynolds (45^th-LF), John Kruk (72^nd-1B), Ozzie Guillen (74^th-SS) and Carlos Baerga (93^rd-2B). Fred McGriff (21^st-1B), Tony Fernandez (24^th-SS) and Gary Sheffield (54^th-RF) attained top-100 status among those who played exclusively for the “Actual” 1992 Padres.

  Original 1992 Padres                               Actual 1992 Padres

Andy Benes fortified the “Original” and “Actual” Padres rotations with 13 victories and a 3.35 ERA. Rich Rodriguez and Mike Maddux enhanced the “Actuals” bullpen with identical 2.37 ERA’s while southpaw Bruce Hurst contributed to the starting rotation with a 14-9 record. Omar Olivares registered 9 wins with a 3.84 ERA and Bob Patterson posted a career-best 2.92 ERA for the “Originals”.

  Original 1992 Padres                                Actual 1992 Padres

Notable Transactions

Roberto Alomar 

December 5, 1990: Traded by the San Diego Padres with Joe Carter to the Toronto Blue Jays for Tony Fernandez and Fred McGriff. 

Carlos Baerga 

December 6, 1989: Traded by the San Diego Padres with Sandy Alomar and Chris James to the Cleveland Indians for Joe Carter. 

Shane Mack 

December 4, 1989: Drafted by the Minnesota Twins from the San Diego Padres in the 1989 rule 5 draft. 

Dave Winfield

October 22, 1980: Granted Free Agency.

December 15, 1980: Signed as a Free Agent with the New York Yankees.

May 11, 1990: Traded by the New York Yankees to the California Angels for Mike Witt.

October 30, 1991: Granted Free Agency.

December 19, 1991: Signed as a Free Agent with the Toronto Blue Jays. 

Dave Hollins

December 4, 1989: Drafted by the Philadelphia Phillies from the San Diego Padres in the 1989 rule 5 draft.

Ozzie Smith

Traded by the San Diego Padres with a player to be named later and Steve Mura to the St. Louis Cardinals for a player to be named later, Sixto Lezcano and Garry Templeton. The San Diego Padres sent Al Olmsted (February 19, 1982) to the St. Louis Cardinals to complete the trade. The St. Louis Cardinals sent Luis DeLeon (February 19, 1982) to the San Diego Padres to complete the trade.

Honorable Mention

The 1986 San Diego Padres 

OWAR: 47.6     OWS: 298     OPW%: .518     (84-78)

AWAR: 29.2       AWS: 222      APW%: .457    (74-88)

WARdiff: 18.4                        WSdiff: 76

The ’86 Padres ended the season in a virtual tie with the Dodgers. Tony Gwynn (.329/14/51) paced the Senior Circuit with 211 base hits and 107 runs scored. He swiped 37 bases in 46 attempts and collected his first Gold Glove Award. Kevin McReynolds (.288/26/96) began a streak of five successive seasons with at least 20 round-trippers. Ozzie Smith succeeded on 31 of 38 stolen base attempts. Dave Winfield crushed 24 moon-shots and plated 104 baserunners. Johnny Grubb contributed a .333 BA with 13 jacks in a part-time role and John Kruk delivered a .309 BA in his inaugural campaign. Eric Show fashioned a 2.97 ERA and tallied 9 victories for the San Diego starting staff.

On Deck

What Might Have Been – The “Original” 2002 Blue Jays

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

Todd Frazier has 38 home runs this year. That’s probably a strange way to start off a post about Juan Uribe but hang with me.

Todd Frazier has 38 home runs this year. Todd Frazier also has a wRC+ of 100 this year. That is a pretty remarkable combination. According to wRC+ Frazier has been exactly an average hitter this year despite the fact that he is currently 8th in all of baseball in home runs. This interesting and seemingly unlikely union piqued my curiosity and sent me down a statistical rabbit hole in search of home runs and terrible wRC+’s. At the bottom of that rabbit hole is where I ran into Juan Uribe.

Juan Uribe has not played an MLB game since July 30th. In that game he went 0-for-3 and he was released by Cleveland a few days later on August 6th. This probably wasn’t a surprise to most people as A) Most people probably would be more surprised to learn he was still in the league to begin with, and B) he was running a 54 wRC+ over 259 PA with Cleveland this year.

But I’m not here to argue that someone should give Uribe a job because his current talent level deserves one (although you probably could; he was nearly a 2-WAR player as recently as last year). I’m here to argue for someone to give him a job because Juan Uribe is on the cusp of history. Juan Uribe has 199 career home runs.

You might think that 200 career home runs isn’t that much of a milestone and it’s only because humans love round numbers that we even recognize it as a milestone. And you would be absolutely correct in saying that. But much like Todd Frazier’s 38 home runs this year, Juan Uribe’s 200 career home runs would be fairly unique. In fact they would be entirely unlike anyone before him because Juan Uribe would be the worst hitter to ever hit 200 home runs.

That is the board of directors of the Terrible 200 Club (patent pending) and as you can see Juan Uribe is poised to unseat Tony “why the hell am I standing sideways at the plate” Batista as CEO with one more measly home run, and by a pretty decent margin. Obviously though his bid is now under threat because he is 37 years old, has been without a team for over a month now and was absolutely awful when he did have a team. It is entirely possible, maybe even likely, that he never hits another MLB home run. And it’s not like there is another current player who is a slam dunk to make a run at Batista if Uribe never steps into the batter’s box again:

–Brandon Phillips will get to 200 but he is sneaky old. He turned 35 in June, so while he is nowhere near what he was earlier in his career it seems unlikely that he plays long enough to see his career wRC+ fall below 90.

–AJ Pierzynksi is all but done at this point. At 39 years old and nearly a win below replacement level this year it’s probably more likely that the ghost of Clete Boyer gets signed and hits 38 home runs to get to 200 as it is Pierzynski hits 12 more in his career.

-Which bring us to James Jerry Hardy. Hardy seemed to be doing his best to crater his wRC+, posting a dreadful 50 last year, but he has rebounded (relatively speaking) to post a 93 so far this year. One has to wonder if he can even get to 200 home runs (he still needs 16 more to get there and he has hit only 26 over his past 1404 PAs), and secondly, if he does, will he post a wRC+ low enough to “best” Batista? You could probably argue that any version of Hardy that is good enough to get to 200 homers is probably also good enough to not decimate his career wRC+.

The easiest solution is for some intrepid and/or awful team to just give Uribe a spot so that he can chase history with each swing. Atlanta, Arizona, Minnesota, what have you guys got to lose? Would a Kickstarter or GoFundMe to pay some of his salary help? It would just be such a shame for the baseball public to be denied a potentially marvelous thing when it’s so close to realization. Like teasing a dog by pretending to throw a ball or every season after the first one of Homeland.

Somewhere Tony Batista is sitting in a recliner, probably in some crazy way that no one else sits in recliners because he is Tony Batista, just waiting for the news that Uribe has been picked up by someone so he can hand the crown to the new king of the Terrible 200 (patent pending). He just needs a little help. Let’s make this happen, MLB.

Bases Produced is the keystone stat in a paradigm for baseball statistics that I have been developing, off and on, for the past 18 years.* Bases Produced measures a player’s overall offensive productivity by counting, quite simply, the number of times that player enables either himself or a teammate to advance to the next base. Each time this happens, a player is considered to have “produced a base.” Counting these events is important because producing bases is quite literally the only way that a baseball player can contribute to the scoring of runs by his team. When a player scores a run, after all, he has done nothing more than advance to all four bases in succession.

The Bases Produced system assigns credit for the production of these bases in a way that is based on traditional baseball statistics, but is also an expansion thereof. This expansion enables most traditional numbers to be tied together into a unified whole, evaluated in terms of Bases Produced, rather than remaining the haphazard collection of unrelated counts that they have always seemed to be.

How does it work? To calculate Bases Produced (BP), I first unify all of a player’s productive batting stats into one sub-total called “Batting Bases Produced” (BBP). This counts each base the player reaches on his own base hits, walks, or times hit by pitch:

BBP = 1 * 1B + 2 * 2B + 3 * 3B + 4 * HR + BB + HBP

A player’s success at producing BBP may be contextualized by dividing his BBP by his total number of “Batting Base Production Chances” (BBPC). This total includes all of a player’s plate appearances (PA), except for those times when a player has attempted to lay down a sacrifice bunt (SHA) — where his primary goal is ostensibly to produce bases for his teammates, rather than himself — and also his catcher’s interferences (CI), where the defense literally takes away his ability to put the ball in play.

BBPC = PA – SHA – CI

The ratio of BBP to BBPC then becomes a player’s “Batting Base Production Average” (BBPAVG):

BBPAVG = BBP / BBPC

Secondly, a player may produce bases for himself as a runner, by either stealing bases (SB), advancing on fielder’s indifference (FI), or “gaining” bases (BG). “Gaining Bases” is the term I use for a player who advances a base when the defense attempts to make a play on a runner somewhere else on the basepaths. For example, if a runner tries to score from second on a single, the batter may advance to second when the defense tries to throw out the runner at the plate. In this case, the batter/runner “gains” second base.

Taken altogether, the bases a player produces for himself as a runner are then called “Running Bases Produced” (RBP):

RBP = SB + FI + BG

Lastly, an offensive player can produce bases for teammates who are already on base by either drawing walks, getting hit by a pitch, or by putting the ball in play. Collectively, these bases are known as “Team Bases Produced” (TBP). The number of times a batter enables a teammate to reach home (TBP4) can be intuitively understood as the number of RBIs he has produced for his teammates, without including any that he has produced for himself. Overall, Team Bases Produced expands this concept by including the number of times a player enables his teammates to advance to second (TBP2) or third (TBP3), as well:

TBP = TBP2 + TBP3 + TBP4

While of course the batter depends on the presence — and subsequent baserunning actions — of a teammate on base to produce these bases, I assign the credit for producing them solely to the batter, without whose actions the runner(s) would not be able to advance on the play. The presence of the runners on base, however, is important to recognize when trying to evaluate how successful a batter is at producing team bases; each runner on base therefore counts as one “Team Base Production Chance” (TBPC) for a batter. (Note: When a batter draws an intentional walk, I do not count TBPC for runners whom the batter cannot force ahead to the next base.)

A batter’s Team Base Production Average (TBPAVG) then becomes, generally (and simply):

TBPAVG = TBP/TBPC

Overall, a player’s total Bases Produced (BP) is simply the sum of his Batting Bases Produced, Running Bases Produced and Team Bases Produced:

BP = BBP + RBP + TBP

This number may also be evaluated in terms of the player’s total number of chances to produce bases (BPC), including his Plate Appearances, Team Base Production Chances, and the number of times he enters the game as a pinch runner (PRS):

BPC = PA + PRS + TBPC

Rounding out this approach, I calculate a general measure of “Base Production Average” as the ratio of Bases Produced to Base Production Chances:

BPAVG = BP / BPC

On my website, www.basesproduced.com, I fill in the blanks of this general paradigm with similar breakdowns for “Outs Produced” and “Bases Run” (= bases a player reaches, but does not necessarily produce); interested readers may follow the link to learn all of the gruesome details for themselves. On the same website, I also calculate and update the BP stats for the current MLB season on a daily basis. You are welcome to check it out to follow along and see how they play out in real life.

While the Bases Produced paradigm may not enjoy all of the mathematical sophistication that goes into many modern sabermetric measures of offensive performance, it does have the advantage of reflecting straightforward facts and events that take place in every baseball game that any fan can quickly recognize and easily count for themselves (with or without a smartphone!). A grand slam home run, for instance, counts as 10 BP: 4 for the batter, 3 for the runner at first, 2 for the runner at second, and 1 for the runner at third. 10 Bases Produced is also a pretty good standard for an excellent game of baseball: I’ll mention in passing that there were just 7 performances of 10 BP or greater in last night’s (9/16) slate of 15 MLB games, with 14 BP topping the list (by three different players).

On basesproduced.com, I have also tabulated the same stats, using data from retrosheet.org, going back to the 1922 season. For those who are curious, the highest single-season BP total in history is 1005, by Lou Gehrig in 1927, while the highest BPAVG of all time is Barry Bonds’ .885, in 2004. There are still many bases produced statistics left to be calculated from the very olden days of baseball, however, before any of these numbers might be considered “records.”

Although Bases Produced is not, strictly speaking, a system that was designed to determine who ought to be the “Most Valuable Player” in any given season (whatever you might interpret that to mean), it is fun to use as another data point in the never-ending discussions about who most deserves the MVP award each year. So let’s consider what the system can show us about the best players in the American and National Leagues in 2016.

The AL MVP race has generally been described this season as a five-man horse race between David Ortiz, Mike Trout, Jose Altuve, Josh Donaldson and Mookie Betts. The Base Production Average numbers back that perception up, as all five of those players sit on top of the current AL BPAVG leaderboard, as of September 16th:

Player                             BPAVG      BBPAVG     TBPAVG

1. David Ortiz               .709            .673              .760

2. Mike Trout               .649            .628              .613

3. Jose Altuve              .645             .590             .652

4. Josh Donaldson      .644             .630             .651

5. Mookie Betts            .605             .564             .607

Although these numbers should ideally be normalized to account for the influence of hitter-friendly venues like Fenway Park, Ortiz is still enjoying his best season there ever (his previous season high BPAVG was .697, in 2007), and he’s well ahead of his career BPAVG of .620, too. As far as base-production statistics are concerned, David Ortiz is unambiguously the 2016 AL MVP.

Over in the National League, I have heard many people talk about the great year that Kris Bryant is having, but his performance fails to even register in the NL’s top five base producers, by average:

Player                             BPAVG      BBPAVG     TBPAVG

1. Daniel Murphy         .665            .619              .718

2. Anthony Rizzo         .634            .607              .659

3. Joey Votto                .619             .602             .617

4. Nolan Arenado        .617             .607             .624

5. Freddie Freeman    .612             .612              .597

(9. Kris Bryant             .601             .618             .541)

Daniel Murphy of the Nationals has clearly had the standout year, instead. And it is worth noting that Bryant’s teammate, Anthony Rizzo, is actually doing considerably better than Bryant in overall BPAVG. The big difference amongst these three players can largely be attributed to Bryant’s mediocre TBPAVG, which is near the National League median of .529 (Aledmys Diaz). That difference can, in turn, be attributed to a combination of Bryant’s high strikeout percentage (.219) and very low ground-out percentage (.113). The one outcome of a plate appearance that never produces bases for teammates is a strikeout, and ground outs tend to be about three times as team-productive as fly outs, in those situations where a batter hasn’t succeeded in producing a base for himself. Bryant’s current numbers place him squarely on the wrong side of both of these team-base-production tendencies.

While Kris Bryant has had a great baserunning season this year…these numbers give reason to question any suggestion that he might have been the best player in the league this season — or even, for that matter, the best player on his own team. But at least it is manifestly clear that Joe Maddon has Bryant and Rizzo in the correct order in the Cubs’ lineup. 🙂

*While I am not as up on the current literature in baseball statistical analysis as I should be, I do know that others have developed similar statistical measures independently of me, including at least Gary Hardegree, Alfredo Nasiff Fors, and someone named EvanJ on this forum. If there are other similar thinkers out there, then I apologize for my ignorance of their work.

“The Coors Effect.”

These three words evoke a strong reaction from most people and are impossible to ignore when discussing the offensive production of a Rockies player. Ask anyone who was around for the Rockies of the ‘90s and they will tell horror stories of games with final scores of 16-14. Ask anyone at FanGraphs and they will laugh and point at the Rockies’ 2015 Park Factor of 118. Heck, ask Dan Haren and see what he has to say:

I would count out the days about a month in advance to see if I was gonna pitch in Coors field.

— dan haren (@ithrow88) January 4, 2016

Suffice it to say that Coors is a hitter’s park. Nobody will argue that. But there have been murmurs recently about another effect of playing 81 games at altitude, an effect that actually decreases offensive production. These murmurs have evolved into a full-blown theory, which has been labeled the “Coors Hangover.”

This theory supposes that a hitter gets used to seeing pitches move (or, more accurately, not move) a certain way while in Denver. When they go on the road, the pitches suddenly have drastically different movement, making it difficult to adjust and find success at lower elevations. In other words, Coors not only boosts offensive numbers at home, it actively suppresses offensive numbers on the road, which can take relatively large home/road splits for Rockies players and make them absolutely obscene.

The concept seems believable, but thus far we have no conclusive evidence of its merit. FanGraphs’ Jeff Sullivan recently tested this theory, as did Matt Gross from Purple Row. Although neither article revealed anything promising, Jeff is still a believer, as he recently shared his personal opinion that the Coors Hangover might simply last longer than any 10-day road trip. With this is mind, I decided to approach the problem by examining the park factors themselves.

If you haven’t read the article about how FanGraphs calculates its park factors, I highly recommend you do so before continuing. The basic approach detailed in that article is the same approach that I use here. As a quick example, the park factor for the Rockies is calculated by taking the number of runs scored in Rockies games at Coors (both by the Rockies and the opposing team) and comparing that to the number of runs scored in Rockies games away from Coors. Add in some regression and a few other tricks, and we have our final park factors.

This method makes a number of assumptions, most of which are perfectly reasonable, but I was interested in taking a closer look at one critical assumption. By combining the runs scored by the Rockies with the runs scored by their opponents, we are assuming that any park effect is having an equal (or at least, an indistinguishable) impact on both teams. This seems like an obvious assumption, but it becomes invalid when the Rockies play on the road. According to the Coors Hangover, Rockies hitters experience a lingering negative park effect after leaving Coors which the opposing team is not experiencing.

In other words, we expect a gap to exist between a hitter’s performance at Coors and his performance at an average park. If the Coors Hangover is true, this gap would be larger for Rockies hitters than anyone else.

Let’s start by taking a look at the park factors we have now. The following tables only contain data from NL teams for simplicity sake.

No surprises. Teams score a ton of runs at Coors and hardly ever score at AT&T Park in San Francisco. Now let’s split up those middle columns to get a closer look at who is scoring these runs.

These are the two pieces of run differential — runs scored and runs allowed — and we generally see agreement between the home and away stats. If a team out-scores their opponents at home, they can be expected to do the same on the road. Good teams are better than bad teams, regardless of where they play. Although, if you subtract a team’s run differential on the road from their run differential at home, the difference will actually be around 100 runs due to home-field advantage. Doing this for all 30 teams yields a mean difference of 83 runs with a standard deviation of 122.

Where do the Rockies fall in this data set? Not only have they scored over 400 more runs at home than the next-best NL team — they have also scored almost 200 runs less on the road than the next-worst NL team. Comparing their home and road run differentials, we see a difference of 483 runs (+44 at home, -439 on the road), or 3.3 standard deviations above the mean. To put it plainly: that’s massive. This is a discrepancy in run differentials that cannot be explained by simple home-field advantage.

Furthermore, I followed the same process of calculating park factors for each team explained above, but I split up the data to calculate a park factor once using the runs scored by each team (tPF), and again using the runs scored by each team’s opponents (oPF). Generally, these new park factors are closely aligned with the park factors from before…except for, of course, the Rockies.

On average, a team’s tPF is about two points higher than its oPF — again, this can be attributed to home-field advantage. The Rockies, however, are in an entirely different zip code with a discrepancy of 17 points. We aren’t talking about home-field advantage anymore. We are talking about something deeper, something that should make us stop and think before averaging the two values to get a park factor that we apply to the most important offensive statistics.

We have no reason to believe that any team should have a 17-point difference between their tPF and oPF; the fact that the Rockies are in this situation either means that they are enjoying hidden advantages at home, or they are suffering hidden disadvantages on the road. To date, we don’t have a theory supporting the former, but we do have one supporting the latter. This is the Coors Hangover.

Does this mean that the Rockies’ Park Factor should actually be their oPF of 110? Should it be some weighted average of different values? I don’t know. But I do know these numbers can’t be ignored. Something is going on here, and we need to talk about it.

I’m working under the hypothesis that you can use launch angle on balls hit to the outfield to determine an outfielder’s relative strength.

The more I look at the data, the more convinced I’m becoming.

So I downloaded the 2015 and 2016 KC Royals Statcast data to see if I could compare their major outfielders’ performance year to year and see a couple things. What I’ve done is bucket hits to the OF by launch angle (in two-degree increments) and calculate a percentage of that contact resulting in a HIT or an OUT. Simple as that. So what I’m comparing between years is:

1) Are the hit likelihood percentages for each angle by OF reasonably projectable year to year
2) Does improvement in my angle metric result in improvement in other defense metrics

First let’s look at Jarrod Dyson. He’s one of the best outfielders in MLB. He recorded, per FanGraphs, 11 DRS in 2015 and to date has 18 DRS in 2016. His 2015 UZR/150 was 18.4 and in 2016 to date it’s 28.7. So both of the “new-traditional” type defense stats are saying, he’s not only good but he’s getting better in 2016 versus 2015. What does my angular stat suggest?

The red points are for ’16 Dyson while the blue is ’15. The left linear regression equation (with the .837 R2) is 2015 while the right (R2 .7796) is 2016. This shows Dyson as a similar player year to year, but likely a bit better. On the higher-angle fly balls, it does appear that Dyson has done a better job this year tracking them down; however, it also appears that in 2015 he did a bit better catching some of the lower-angled fly balls. So it’s not entirely clear, from this graph, why Dyson is per DRS and UZR having such a better defensive year. To have something like this happen, it could indicate that maybe Dyson is starting to play deeper than before. This would limit the likelihood of him catching the low-angled line drives to the OF, but help track down more true fly balls. I’d certainly be interested to see if Dyson is actually doing that very thing this year.

When it comes to projecting year to year, the R2 for Dyson’s ’15 to ’16 hit likelihood % was: 0.532. In real life this is a pretty strong correlation, so I’d say it’s a reasonable estimator.

How about we look at KC OF defensive darling Alex Gordon:

Again the red points are for ’16 Gordon while the blue is ’15. The left linear regression equation (with the .939R2) is 2015 while the right (R2 .8424) is 2016. It jumps right out to you how much smoother Gordon’s regressions are than Dyson’s. Maybe experience leads to that, who knows. So the 2016 regression line (the dashed one) shows that contact to him in the OF is a bit more likely to land for a hit now in 2016 than it was in 2015. This would suggest that Alex Gordon is having a worse year defensively in ’16 than ’15.

How do DRS and UZR/150 compare? Well, Alex has a DRS of 3 in 2016 and had a DRS of 7 in 2015. So he does seem to be trending a bit lower, though not too much. And he has a UZR/150 in 2016 of 9.9 whereas that was 10.5 in 2015. So in this case it all sort of agrees. Gordon seems to be a step or two slower (age and injuries easily could account for that) and as a result his defense has stepped backward a bit. Interestingly he’s still doing about the same job on balls that are high-likelihood hits — the more difficult plays. It’s really at the end of the spectrum where the balls are unlikely to be hits anyway that Alex seems to be struggling. So maybe the “skills” are still there, but the athleticism has just faded a bit and he can’t run down those long fly balls anymore. This is sort of the opposite of Dyson. Maybe Gordon is in fact playing too shallow, cheating to ensure his reputation for robbing sure hits stays intact while losing a bit of overall range, creating a situation where some balls land that probably should have been outs.

When it comes to projecting year to year, the R2 for Gordon’15 to ’16 hit likelihood % was: 0.778. This is excellent and I think it is clearly visible from the chart just how projectable year to year this would be.

What about All-Star and defensive stalwart Lorenzo Cain?

Again the red points are for ’16 Cain while the blue is ’15. The left linear regression equation (with the .8876 R2) is 2015 while the right (R2 .9073) is 2016. Well this is interesting — it’s just as though you shifted the line up ever so slightly. A 2016 higher trendline would indicate that contact to the outfield around Lorenzo would be more likely than last year to result in a base hit. This would indicate he too has backslid some from his 2015 self. So what do UZR and DRS say? DRS in 2016 is 11 whereas it was 18 in 2015. But UZR/150 is currently 15.4 in 2016 and it was only 14.1 in 2015. So there is a bit of confusion as to Cain’s 2016 performance, relative to ’15. Clearly he is still an excellent outfielder by all measures, but I would lean toward him trending in the negative direction in ’16 and moving forward.

Given the two linear regressions and data sets, you’d have to believe you could use this data to project very accurately the future year. And you’d be right. Cain’s year-to-year R2 checks in at 0.955.

Well what about newcomer Paulo Orlando? he already seems to be living up to the newfound tradition of excellent KC outfield defense:

Paulo Orlando is sort of the exact reverse of Cain. His trend has basically just taken an entire step down. This means balls are less likely to be hits now than before. So do UZR and DRS agree with Orlando taking what appears to be a reasonable step forward? Surprisingly no. DRS from ’15 to ’16 has jumped from 8 to 12, but Orlando has played a lot more innings which more or less would explain that growth. And his UZR/150 went from 14.0 in 2015 to 8.7 now in 2016. So these metrics both seem to think Orlando is the same if not a little worse than in ’15.

Projecting using Orlando’s earlier year is, like with Cain, excellent. There is an R2 of .90 between the two data sets.

So for my questions:

1) Are the hit-likelihood percentages projectable year to year? This seems to be a resounding yes, at least in the case of KC Royals. The R2 was always greater than 0.5 with two instances of the four being over 0.9! I’m starting to believe this really could mean something in regards to defense evaluation.
2) How does my angle measure compare to UZR/DRS? There do seem to be some differences; however, this is basically the norm in the “new” defense evaluations. No universal system has been developed and there are plenty of cases where UZR and DRS themselves have disagreements.

I do think in the end this has some merit and I will be looking further into it. I also think similar work can be done with regards to hit speed, as I already alluded to in my earlier article:

http://www.fangraphs.com/community/using-statcast-to-substitute-the-kc-outfield-for-detroits/

I think it’s important to view both the angle and hit speed as two pieces and going forward that’s something I’m hoping to include for these players.

Baseball’s home-run surge this season is already well-documented, and analysts have turned over several theories for why this could be happening. Are steroids back? Has MLB juiced balls to give them more carry? Is this increase a result of an intentional shift toward power by baseball’s young sluggers? No matter what is happening, home runs are flying out of the park at record pace. At 4,459 home runs through 3,834 games, the 2016 HR/G rate is 1.16 – just barely trailing the all-time record of 1.17 HR/G set in steroid-heavy 2000.

Lately, baseball fans have been treated to a rookie performance for the ages, as New York Yankees catching phenom Gary Sanchez has hit .403/.459/.883 with 10 HR through only 20 games. Sanchez is only the third player in MLB history to swat 10 HRs through his first 20 games, joining George Scott of the 1966 Boston Red Sox and Trevor Story of the 2016 Colorado Rockies.

Sanchez has been a highly-regarded prospect for several years after signing with the Yankees as an international free agent in 2009, but he has never slugged at a rate like this before. Last season Sanchez swatted 18 HR in 365 minor league at-bats, and in 2014, he hit 13 HR in 429 minor league at-bats. In fact, Trevor Story is somewhat similar – he hit 14 HR in 396 minor-league at-bats in 2014 and 20 HR in 512 minor-league at-bats in 2015. Now in the big leagues, Story’s smashed 27 HR in 372 at-bats. Story’s home runs cannot all be credited to the homer-happy Coors Field which he calls home. Story has hit 11 HR in just 196 road at-bats, far outpacing his 2015 home-run rate.

While the success of Sanchez and Story can somewhat be credited to their power-friendly home-run parks and the natural tendency of talented ballplayers to grow into their power – they’re both only 23 years old – there may be more to this story than meets the eye. Below I compiled a list of all 2016 MLB rookies with more than 200 at-bats and compared their 2016 MLB home-run rates to their 2015 minor-league home-run rates. I had to exclude rookies who did not play in the U.S. minor-league system last year – Hyun Soo Kim, Dae-ho Lee, and Byung Ho Park. While this is far from a perfect science, the 200 at-bats should give us an interesting-enough sample size to examine.

Of the 16 rookies who qualify, 13 of them saw their AB/HR rate drop significantly, a counter-intuitive result as MLB pitching is far superior to that of Double-A or Triple-A pitching. Two of the three remaining rookies saw their AB/HR rates remain basically unchanged (Cheslor Cuthbert and Tyler White). And finally, Ramon Flores was the sole rookie who saw his AB/HR rate rise notably, though we could possibly point to the severe ankle injury he suffered at the end of last season as a partial culprit for his slip in play. Flores has seen dips in his batting average, on-base percentage, and other offensive rates as well this year.

The rookie home-run bounce is almost universal and includes: Jefry Marte (23.8 AB/HR in AAA in 2015 to 19.6 AB/HR in MLB in 2016), Alex Dickerson (21.7 AB/HR in AAA this season to 19.9 AB/HR after getting called up), and of course Sanchez (20.3 AB/HR in AAA in 2015 to 7.7 AB/HR in 2016).

Just two years ago, analysts were arguing that the jump from AAA to MLB may be getting harder for young players, but now we’re seeing exactly the opposite, at least for position players.

Let’s see how minor-league players transitioned to the major leagues in the past. With the HR spike occurring late in 2015, we’ll use data from 2014 rookies and their 2013 minor-league seasons. I compiled a list of 18 MLB rookies with at least 300 at-bats in 2014. I excluded Jose Abreu who did not have 2013 minor-league numbers.

This looks much more natural. The majority of rookies (11) saw their AB/HR rates rise, often dramatically, while others saw their AB/HR rates basically stay the same and a few others saw an decrease. Again, this aligns with the common knowledge that MLB pitching is tougher than minor league pitching.

So why are the 2014 and 2016 rookie tables so different? The data would indicate that something happened between these years to make graduating to the MLB so much easier for rookie position players.

Finally, we can look at rookie pitchers and compare their home runs allowed per 9 innings pitched from last year in the minor leagues to this year in the majors. I’ve compiled a list of the 13 MLB rookies to cross the 75-innings-pitched plateau this year. I had to exclude rookies Tyler Anderson (didn’t pitch in 2015) and Kenta Maeda (didn’t pitch in the U.S. in 2015).

Of the 13 rookies, nine saw their HR/9 rates rise notably, two saw their rates basically stay the same, and two saw their rates lower and improve in the majors. (It should be noted that Archie Bradley Jr. threw only 29.3 IP in 2015, and Devenski has shifted from a starter to primarily a reliever this year. This may have skewed their numbers.)

This chart should not come as a surprise, as rookie pitchers have historically allowed more hits, walks, and home runs to superior competition, at least in their first few months of big league time.

Yet the near-universal increase of home runs, whether hit or allowed, by players making a transition from the minor leagues to the major leagues indicates that something is happening at the major-league level specifically. We can likely dismiss sudden steroid use, as the majority of users historically have come from the minor leagues. (Unless major-league players have sole access to a super-drug that goes undetected in urine tests, but now we’re wading into something else completely.) We may also be able to disregard theories such as “young players are altering their swings to hit for more power and strikeouts,” because wouldn’t these “altered swings” result in more home runs in the minor leagues against inferior pitching? Once again an altered or juiced baseball at the major-league level appears to be the most obvious culprit, although no hard evidence has been discovered.

An increase in power transitioning from the minor leagues to the major leagues is counter-intuitive to everything we know about the game’s structure.

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 1978 Pittsburgh Pirates 

OWAR: 49.0     OWS: 345     OPW%: .559     (91-71)

AWAR: 40.0      AWS: 263     APW%: .547     (88-73)

WARdiff: 9.0                        WSdiff: 82  

Pittsburgh emerged victorious from a three-team battle with Montreal and Philadelphia for the National League Eastern Division crown. The “Original” Pirates paced the Senior Circuit in OWS and accrued an 82-point Win Shares differential compared to the “Actual” Bucs.

Dave Parker (.334/30/117) collected his second straight batting crown and earned NL MVP and Gold Glove honors. “Cobra” scored 102 runs and topped the League with 340 total bases and a .585 SLG. Willie Randolph recorded 36 steals in 43 attempts and coaxed 82 bases on balls. Willie “Pops” Stargell (.295/28/97) achieved All-Star status for the seventh time. Al “Scoop” Oliver drilled 35 two-base knocks and posted a .324 BA. Mitchell Page (.285/17/70) supplied a solid sophomore season after placing runner-up in the Rookie of the Year balloting in the previous campaign. Don Money batted .293 with 30 doubles to secure his fourth All-Star invitation. Omar Moreno and Frank Taveras ran wild on the base paths, swiping 71 and 46 bases, respectively.

Willie Stargell rated ninth among left fielders in the “The New Bill James Historical Baseball Abstract” top 100 player rankings. “Original” Pirates teammates registered in the “NBJHBA” top 100 rankings include Dave Parker (14th-RF), Willie Randolph (17th-2B), Al Oliver (31st-CF), Manny Sanguillen (42nd-C), Dave Cash (50th-2B), Don Money (55th-3B), Richie Hebner (56th-3B), Richie Zisk (69th-RF), Freddie Patek (73rd-SS), Bob Bailey (79th-3B), Tony Armas (89th-RF) and Rennie Stennett (90th-2B). Jim Fregosi (15^th-SS), Bert Blyleven (39^th-P) and Phil Garner (41^st-2B) achieved top-100 status among the individuals who played solely for the “Actual” 1978 Pirates.

  Original 1978 Pirates                                Actual 1978 Pirates

Don “Caveman” Robinson (14-6, 3.47) produced a WHIP of 1.139 and placed third in the NL Rookie of the Year balloting. “The Candy Man” John Candelaria contributed 12 victories and a 3.24 ERA following a 20-win effort in the previous campaign. The bullpen trifecta consisted of Doug Bair (1.97, 28 SV), Gene Garber (2.15, 25 SV) and Kent Tekulve (2.33, 31 SV). Bert Blyleven tallied 14 victories for the “Actuals” while posting a 3.03 ERA.

  Original 1978 Pirates                               Actual 1978 Pirates

Notable Transactions

Willie Randolph 

December 11, 1975: Traded by the Pittsburgh Pirates with Ken Brett and Dock Ellis to the New York Yankees for Doc Medich. 

Al Oliver 

December 8, 1977: Traded as part of a 4-team trade by the Pittsburgh Pirates with Nelson Norman to the Texas Rangers. The Atlanta Braves sent Willie Montanez to the New York Mets. The Texas Rangers sent Tommy Boggs, Adrian Devine and Eddie Miller to the Atlanta Braves. The Texas Rangers sent a player to be named later and Tom Grieve to the New York Mets. The Texas Rangers sent Bert Blyleven to the Pittsburgh Pirates. The New York Mets sent Jon Matlack to the Texas Rangers. The New York Mets sent John Milner to the Pittsburgh Pirates. The Texas Rangers sent Ken Henderson (March 15, 1978) to the New York Mets to complete the trade. 

Mitchell Page 

March 15, 1977: Traded by the Pittsburgh Pirates with Tony Armas, Doug Bair, Dave Giusti, Rick Langford and Doc Medich to the Oakland Athletics for Chris Batton, Phil Garner and Tommy Helms. 

Gene Garber

October 25, 1972: Traded by the Pittsburgh Pirates to the Kansas City Royals for Jim Rooker.

July 12, 1974: Purchased by the Philadelphia Phillies from the Kansas City Royals. 

Don Money

December 15, 1967: Traded by the Pittsburgh Pirates with Harold Clem (minors), Woodie Fryman and Bill Laxton to the Philadelphia Phillies for Jim Bunning.

October 31, 1972: Traded by the Philadelphia Phillies with Bill Champion and John Vukovich to the Milwaukee Brewers for Ken Brett, Jim Lonborg, Ken Sanders and Earl Stephenson.

Craig Reynolds

December 7, 1976: Traded by the Pittsburgh Pirates with Jimmy Sexton to the Seattle Mariners for Grant Jackson.

Honorable Mention

The 2012 Pittsburgh Pirates 

OWAR: 46.1     OWS: 303     OPW%: .597     (97-65)

AWAR: 24.2       AWS: 236      APW%: .488    (79-83)

WARdiff: 21.9                        WSdiff: 67

The “Original” 2012 Bucs bested the Brew Crew by four games and trounced the “Actuals” by an 18-game margin. Andrew McCutchen (.327/31/96) established personal bests in batting average, home runs, RBI, runs (107), hits (194) and SLG (.553). He placed third in the NL MVP race and earned his first Gold Glove Award. Aramis Ramirez (.300/27/105) topped the circuit with 50 two-base hits. Pedro “El Toro” Alvarez dialed long distance 30 times and knocked in 85 baserunners. Jose A. Bautista bashed 27 long balls despite missing nearly half the season due to injury. Jeff Keppinger boasted a .325 BA in a platoon role.

On Deck

What Might Have Been – The “Original” 1992 Padres

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

Last season, MLB released Statcast, an innovative tool used to evaluate player movements and athletic skill. Defensively, it can track how efficiently a player’s line to the ball was, how much ground he covered, arm strength, top speed, and many other factors. It also can track baserunning metrics, such as lead distance, grabbing an extra base, max speed, and home-run trot, among other things. Statcast also tracks pitching and hitting metrics. MLB teams can now use iPads in the dugout, meaning they have an endless supply of information at the touch of a finger.

Recently, Albert Chen of Sports Illustrated wrote a piece on various teams’ use of Statcast. The article notes how Pirates hitters would review a pitcher’s spin rate before an at-bat. If the spin rate was high, they would expect something lower in the zone. Even Kris Bryant credits Statcast, saying he improved his launch angle, aiding in his breakout, possibly MVP season. All teams have been using the data, says Chen, and teams have used the data in different ways. Daren Willman, who heads BaseballSavant, describes the use of Statcast as an “arms race,” as teams now have this bank of information at their disposal. Willman analyzes this Statcast data himself, looking at player comparisons and evaluations. The tricky thing, according to Willman, is knowing what information to look at. He claims “It’s so massive, it’s just about asking the right questions . . . the answers are all there.”

The Tampa Bay Rays, a forward-thinking club, tell their players on the first day of spring training that the Rays value their batted-ball velocity, rather than batting average. Similarly, the New York Mets decided to take Lucas Duda over Ike Davis to be their 1^st baseman of the future. Duda soon started to mash the ball, before struggling with injuries. Davis, on the other hand, is still looking for major-league employment.

Some of the highest exit velocities belong to sluggers like David Ortiz, Josh Donaldson, Miguel Cabrera, and Giancarlo Stanton. Perhaps this is not surprising. There are, however, some players who are not in the upper echelon of MLB, such as Chris Carter or Khris Davis. Both of these sluggers have low batting averages, but high exit velocities. At the same time, both of these players have solid slugging percentages, both fluttering around .500. What can this data tell us? Is exit velocity related to batting average? Slugging percentage? wOBA?

My initial thoughts pointed me towards BABIP (batting average on balls in play). My thinking was that if these players hit the ball harder, on average, then their contact will more likely than not will find its into being a hit. If the ball is hit harder, the defense has less time to react and make a play. I was looking at BABIP instead of just batting average, since BABIP will overlook a player’s tendency to strike out. A lot of the guys with high velocities are big swingers, so it would make sense if they tend to swing and miss. So I set out to test these hypotheses, and the results may surprise you.

At first, I looked at the relationship between BABIP and exit velocity by performing a linear regression between the two. Here is the result:

No relationship, at all. R-squared of 0.03. Looks like I’m 0 for 1 so far. My theory that harder-hit balls would result in more hits, on average, looks to be proved incorrect, as there is no relationship between the two in the data. Perhaps this aligns with the idea that a pitcher really has no control of a ball once it is put in play (unless it is a HR), as unless the batter hits a HR, he still has little or zero control over the result (as a reminder, HR is not included in BABIP since the ball is not in play).

So, I will continue to my next ideas. If these players are big swingers, they probably strike out more, right? Well, sort of; a weak correlation exists, if any at all. I’ll take the loss on this one — 0/2. With a correlation of 0.11, it is hard to say a relationship exists. Here is the graph:

I then looked at other hitting metrics to see if a relationship exists. Specifically, I looked at the stats generally associated with exit velocity: Home runs, slugging percentage, and isolated power.

First, I’ll show the relationship between the two. A relationship definitely exists here. It may not be a direct relationship, but players with high exit velocities had more home runs. Now, some of this is tied to other factors, such as how often they could make contact with a pitch, what their fly-ball and ground-ball rates are, and how often they strike out. These various factors will also play a role in the amount of home runs hit, as will exit velocity. Nonetheless, as one might expect, a relationship exists. The R-squared on the regression is 0.37. Here is the graph:

Next, I looked at slugging percentages as well as isolated power. The difference between these two metrics is that isolated power equals batting average subtracted from slugging percentage. It tracks how often a player hits for extra bases, since singles are subtracted out of the equation. Nonetheless, both of these metrics track total bases and include more information about the hitter’s power.

After running my regression between slugging percentage and exit velocity, the graph shows another relationship. Again, it is a weaker relationship, but a relationship exists. The R-squared on the regression again was 0.37, so about the same value as home runs and exit velocities. So again, players with higher exit velocities are more likely to have a higher slugging percentage. Here is the graph:

Isolated power again shows a similar relationship, as the R-squared on the regression was 0.39. Other factors explain isolated power, just as they do with slugging percentage and home runs, which goes to show that other factors are important as well, such as strikeout rate. Nonetheless, isolated power is related to exit velocity in a positive notion.

For those wondering, I left out metrics such as OBP and wOBA because they incorporate how often a player walks, which has nothing to do with how hard a player hits the ball. I did run the regressions, and the R-squared values were around 0.30 for both metrics.

So what does this all mean? Should teams focus on exit velocity? What about launch angle?

For the record, launch angle did seem to have a weak relationship with HR, with an R-squared value of 0.25, so another relationship seems to exist.

Wrapping it all up, it seems that exit velocity is a good way to determine the power of a player. Yes, there are other things, such as launch angle, strikeout rate, fly-ball and ground-ball rate, and other factors. Is it the end-all, be-all of a player? No, of course not, but it may be better able to tell a player’s true power than a recent stretch of hot play. Also, players must also learn to work the count and draw walks, which is separate from exit velocity.

Nonetheless, it is smart to look at exit velocities. There are other important factors, and teams should not neglect these factors, but focusing on exit velocities is a good way to determine the raw power of a player. Also, it can show the potential in an undervalued player, who may have a low batting average, but has an ability to hit for power that is hiding beneath a cold stretch.

Anyways, it looks like major-league baseball teams do know more than me. Oh well, I’m working on it.

In 2015, the Boston Red Sox had one of the worst bullpens in Major League Baseball. Red Sox relievers were worth -1.3 WAR with  a FIP of 4.64, finishing 30^th in the league in both measures. They allowed opposing hitters to hit .261 with a BABIP of .300. Unsurprisingly last offseason, newly-installed president of baseball operations Dave Dombrowski set out to remake Boston’s bullpen. Throughout his long and storied career as a general manager, Dombrowski has consistently turned lagging franchises into contenders. His one weakness, as Dave Cameron pointed out last year, has been constructing bullpens. After examining Dombrowski’s tenure with the Detroit Tigers, Cameron wrote, “There was not a single aspect to pitching that the Tigers bullpen excelled at during Dombrowski’s tenure.” In the 2015 offseason, Dombrowski made two significant trades to bolster the back end of the Red Sox pitching staff. He shipped four prospects to the San Diego Padres for closer Craig Kimbrel and sent left-handed starter Wade Miley to the Seattle Mariners in exchange for reliever Carson Smith. Both of these moves reveal that despite his years of experience, Dombrowski still has difficulty properly valuing relievers.

THE KIMBREL TRADE

From 2011-2015, Craig Kimbrel led all relievers with 12.6 WAR. He struck out 40.9% of opposing hitters, allowing a .159 batting average with a 1.73 FIP. Only Aroldis Chapman struck out more hitters over the same time period. Kimbrel’s league-leading 224 saves were 58 more than the closest reliever, Huston Street. The difference between Kimbrel and Street is roughly equivalent to the difference between Street and Addison Reed, who had the 15^th-most saves from 2011-2015.

A closer examination of Kimbrel’s peripheral stats, however, reveals that he’s been slipping from his career peak in 2011 and 2012. In 2015, Kimbrel’s FIP rose to 2.68. Opposing hitters hit more home runs against him and their batting average against his four-seam fastball rose from .180 from 2011-2014 to .212 in 2015. In 2016, this decline has continued. Kimbrel’s walk rate has ballooned to 12.2%. His ground-ball and fly-ball rates have reversed themselves and he’s allowing much more hard contact. Just take a look at the chart below.

Opposing hitters are now hitting more of Kimbrel’s pitches as fly balls, they’re grounding out less often, and they’re making more hard and less soft contact than ever before. These factors have turned Kimbrel from an otherworldly reliever to merely an effective one. Looking at his yearly WAR figures, we can see that this transformation has been underway for a while now.

In 2015, Kimbrel ranked 19^th in reliever WAR, right between Justin Wilson of the Yankees and Keone Kela of the Rangers. That’s hardly inspiring, especially since Kimbrel earned $9 million in 2015 while Wilson and Kela made the league minimum.

Considering the price in prospects the Red Sox paid to acquire Kimbrel, they need him to perform at an elite level. In November 2015, Boston sent 3B Carlos Asauje, SS Javier Guerra, OF Manuel Margot, and LHP Logan Allen to the Padres for Kimbrel. Asuaje profiles as a utility infielder. According to Ben Badler of Baseball America, Logan Allen, whom the Red Sox drafted in the 8^th round, had the talent of a 2^nd or 3^rd round pick. Margot and Guerra were both among the top 100 or even top 50 prospects in the minors depending on which prospect list you prefer. Using the prospect valuation system developed by Kevin Creagh and Steve DiMiceli (you can read about their methodology here), I’ve estimated the cost to the Red Sox in terms of the surplus value of Margot and Guerra. Due to the varying nature of prospect valuations I’ve included the players’ rankings in Keith Law’s Top 100 prospects and Baseball America’s Top 100 as of February 2016.

Even if Kimbrel were the pitcher of 2011-2012 that would still be an astronomically high price to pay for a reliever who throws 60-70 innings per year. Now that Kimbrel is a 2-WAR reliever, it’s even worse.

THE SMITH TRADE

After acquiring Kimbrel, Dombrowski wasn’t finished remaking the Red Sox bullpen. On December 7, 2015 he traded left-handed starter Wade Miley and right-handed reliever Jonathan Aro to the Seattle Mariners for right-handed reliever Carson Smith and left-handed pitcher Roenis Elias. Aro is currently pitching at Triple-A Tacoma and Elias has a grand total of three appearances for the Red Sox this season, so the crux of the trade is Smith for Miley.

Based on their salaries and performances in 2015, Smith and Miley were both valuable pitchers and trade assets. Relying heavily on his slider, Smith held opposing hitters to a .194/.278/.262 batting line. He struck out 32.4% of opposing hitters with a 2.12 FIP and finished fifth among relievers with a 2.1 WAR. Additionally, Smith comes with five more years of team control. He isn’t arbitration-eligible until 2018 and won’t become a free agent until 2021. In 2015, Miley was a 2.6-WAR pitcher, best among any qualified starter on the Red Sox. From 2012-2015, Miley threw an average of 198 innings per season. Prior to the 2015 season, he signed a team-friendly three-year, $19.5-million contract from 2015-2017 with a $12-million club option in 2018.

After signing David Price to a seven-year contract in December 2015, the Red Sox believed they had an excess of starting pitching. With Price, Rick Porcello, Miley, Clay Buchholz, Joe Kelly, and Eduardo Rodriguez, they had six starters for five rotation spots. Additionally they had prospects Henry Owens, Brian Johnson, and knuckleballer Steven Wright waiting in the wings. In order to bolster the bullpen, Dombrowski decided to trade Miley, recognizing that he was the most valuable trade chip among the remaining starters. Porcello had just underperformed in 2015 and was entering the first year of a four-year, $82.5-million extension. Joe Kelly, while having an electrifying arm, had not really shown himself to be an effective starter. While Buchholz had pitched well in 2015, he managed only 18 starts. And Eduardo Rodriguez, the 23-year-old left-hander and potential top-of-the-rotation starter, was untouchable. This left Miley as the most logical trade chip.

By trading Miley, a serviceable innings eater, the Red Sox left themselves open to injuries and ineffectiveness. While Steven Wright effectively stepped into the rotation after Rodriguez dislocated his kneecap in spring training, Buchholz and Kelly were disasters. In 22.1 innings as a starter, Kelly allowed opposing hitters to hit .316/.437/.564 for a wOBA of .419 or the equivalent of Mike Trout this season. He sported a walk rate of 16% and a 5.88 FIP. In his 88 IP as a starter, Buchholz allowed opposing hitters to hit .268/.347/.470, good for a .349 wOBA and a 5.68 FIP. Since 2010, Buchholz has never been healthy and effective at the same time. For all of the talk about Kelly improving last season, a look at his peripheral numbers revealed a pitcher that was merely getting lucky with stranding runners as opposed to improving his underlying performance. By trading away Miley, the Red Sox cost themselves a cushion for the failures of Buchholz and Kelly. In order to fill the rotation void, Dombrowski traded highly-regarded pitching prospect Anderson Espinoza (the 19^th-best prospect in baseball according to Baseball America) to San Diego for Drew Pomeranz. Carson Smith, meanwhile, underwent Tommy John surgery in May after straining a flexor muscle in spring training.

In trading for Craig Kimbrel and Carson Smith, Dave Dombrowski has revealed that his biggest weakness remains properly valuing bullpen talent. For a baseball executive with a generally sterling record, this may seem like a minor flaw, but it’s one that caused him to overpay for a declining closer, to trade Miley while relying on a pair of risky starters, and then to swap a prospect who garners comparisons to Pedro Martinez to fill the resulting void in the rotation. With Smith’s injury and the failings of Buchholz and Kelly, Dombrowski has little to show for all his bullpen efforts other than generously restocking the Padres’ farm system.