## Relationship Between OBP and Runs Scored in College Baseball

There is a segment of the population of the United States which meets the following criteria:  between the age of 18-21, devout FanGraphs reader, and was mesmerized by the movie “Moneyball.”  I have read the book and watched the movie a number of times, as well as dedicating time to understanding the guiding principles in the book and how they relate to professional baseball.  The relationship between on-base percentage and scoring runs in Major League Baseball is well established, but has anyone ever taken the time to examine the relationship at the collegiate level?

Collegiate baseball is volatile — roster makeups change dramatically each year, no player is around more than five years, not to mention there are hundreds of teams competing against one another. In terms of groundbreaking sabermetric principles, this study is not intended to turn over any new stones, but rather present information which may have been overlooked up to this point, which is the relationship between on-base percentage and runs scored in collegiate baseball.

To conduct this study, I compiled a list of Southeastern Conference team statistics from the 2014-2017 seasons (Runs Scored, On Base Percentage, Runs Against, and Opponents’ On Base Percentage).  I then performed linear regression on the distribution by implementing a line of best fit.  Some teams’ seasons were excluded due to inability to access that season’s data, and I felt like removing the 2014 Auburn season on the grounds that it was an outlier affecting the output (235 runs, 0.360 OBP).  Below is the resulting math:  the R2, and the resulting predictive equation:

Runs Scored = ( 3,537. x OBP ) – 933.6791

R² = 0.722849

I am by no means a seasoned statistician, but in my interpretation of the R2 value, the relationship between Runs Scored and OBP in this is moderately strong, with a team’s OBP accounting for roughly 72.3% of the variation in Runs Scored in a season.  Simply, OBP is statistically significant in determining the offensive potency of a team.

At the professional level, the R2 is found to be around 0.90.  The competitive edge the Oakland A’s used in “Moneyball” was using this correlation to purchase the services of “undervalued” players.  But what about in college?  Colleges certainly cannot purchase their players, but the above information can be useful to college programs.

For example, the average Runs Scored per season of the sample I used was roughly 347.8.  If an SEC team wanted to set the goal of being “above average” offensively, they would be able to determine, roughly, what their target OBP should be by using the resulting predictive equation from the Linear Fit:

Does this mean if an SEC program produces an OBP of .362 they would score 348 runs precisely? Obviously not. Could they end up scoring exactly 348 runs? Yes, but variation exists, and statistics is the study of variation.  Here are a few seasons in which teams posted an OBP at or around 0.362, and the resulting run totals:

The average of those six seasons’ run totals was 347.5, which is pretty darn close to 348, and even closer to the average of 347.8 runs derived from the sample.

Another use for this information is lineup construction and tactical strategy in-game.  The people in charge of baseball programs do not need instruction on how to construct their roster and manage their team, but who would disagree with a strategy of maximizing your team’s ability to get on base?

The purpose of this study was to examine the relationship between On Base Percentage and Runs Scored in college baseball, and how the relationship compares to its professional counterpart.  To conclude, the relationship between OBP and runs exists at the collegiate level, and carries considerable weight and value if teams are willing to get creative in utilizing its ability.

Disclaimer: I am a beginner-level statistician, and if you have any suggestions or critiques of this article, please feel free to share them with me.

Theodore Hooper is a Student Assistant, Player Video/Scouting, for the University of Tennessee baseball program.  He can be reached at thooper3@vols.utk.edu or on LinkedIn at https://www.linkedin.com/in/theodore-hooper/

## Statistical Analysis of a Few College Hitters

As the 2017 MLB Draft quickly approaches, I thought it may be fun to analyze some of the best college hitters available.  On May 23, Eric Longenhagen released the 2017 Sortable Draft Board on FanGraphs.  This article looks at the statistics of each college hitter on the list.  In this article, I tried to not lean on literature and scouting reports of the players.  Rather, I decided to calculate some statistics to use as guides in building an outsider’s perspective of their offensive profiles.  This body of work does not include much information about attributes or skills not published on a school’s statistics page on their website.

Nobody real cares about the counting statistics of college players.  So, for my table of numbers to fit on a page, I left them out.  The statistics I focused on are a hitter’s slash line (AVG, OBP, and SLG), OPS, BABIP, ISO, RC, K% and BB%.  These are relatively easy to calculate and provide some sort of worth when evaluating prospects.  AVG, OBP, and SLG are simple and widely understood.  OPS provides a good gauge of a hitter’s overall offensive ability.  BABIP is an important indicator of a hitter’s talent at the plate, but can be inflated or deflated depending on the talent level of the different defenses faced by the hitter.  ISO is a good indicator of how well each hitter demonstrated their power and XBH ability.  Runs Created (RC) is a crude but effective measurement of total, individual offensive output.  K% and BB% give us some idea of how well the batter demonstrated their understanding of the strike zone and discipline at the plate.  For more information on each statistic, as well as how to apply it, I suggest checking out the Glossary tab.

Below is the table of numbers I made.  Even further below is where you will find a quick summation of each hitter discussed.

 Name AVG OBP SLG OPS BABIP ISO RC K% BB% Jeren Kendall .306 .379 .570 .949 .333 .264 50.31 18.9% 20.8% Adam Haseley .400 .498 .688 1.186 .393 .288 70.21 7.7% 38.8% Keston Hiura .419 .556 .672 1.228 .486 .253 67.80 14.5% 40.3% Pavin Smith .348 .433 .581 1.013 .311 .233 53.78 3.2% 42.7% Logan Warmoth .336 .410 .562 .972 .374 .226 53.10 15.3% 20.8% Jake Burger .343 .459 .686 1.145 .319 .343 63.50 12.0% 35.1% Evan White .380 .454 .654 1.108 .414 .274 51.08 13.5% 17.3% Brian Miller .336 .412 .504 .917 .365 .168 49.78 11.7% 28.1%

Jeren Kendall (#9 on FanGraphs Sortable Draft Board)

Vanderbilt                   OF                   (B- L/ T- R)

Jeren Kendall is considered by many to be the best college hitter, outside of Louisville two-way player Brendan McKay.  Kendall showed some impressive pop out of center field this past year, knocking 15 balls over the fence in 235 at bats.  However, he also managed to record 50 strikeouts.  Kendall did manage to produce an excellent walk rate and ISO, but his total output was “middle of the pack” as far as the guys on this list go.  He should go off the board within the first 20 picks this upcoming draft.

Adam Haseley (#15 on FanGraphs Sortable Draft Board)

Virginia                       OF                   (L/L)

Hitting from the left side of the plate, Virginia outfielder Adam Haseley managed to put up the best statistical profile of any hitter on this list.  He comes into June’s draft with an impressive OPS (1.186) and an even more entertaining strikeout rate — a board-best 7.7% (only 19 punch outs in 205 ABs).  While Haseley’s power numbers may not translate at the next level, his affinity for driving the ball into deeper parts of the ballpark should make for a high doubles count at the next level.

Keston Hiura (#17 on FanGraphs Sortable Draft Board)

UC Irvine                     2B                    (R/R)

While Keston Hiura’s .486 BABIP may be a good indicator as to why his batting average is north of .400, it is also a good indicator of just how good he is with a bat in his hand.  He did not just hit singles — his 21 doubles come in second on the list.  He displayed an excellent walk rate, which contributed to the highest on base percentage on the shortlist.  While some teams may elect to take a prep shortstop over a college second baseman, Hiura still plays a premium position with solid presence at the plate and would fit in nicely in any class as a second to third-round pick.

Pavin Smith (#18 on FanGraphs Sortable Draft Board)

Virginia                       1B                    (L/L)

The second UVA Cavalier on our list slashed an impressive .348/.433/.581 this past season, and posted an impressive 3.2% strikeout rate.  While his numbers do not match those of his teammate Adam Haseley, Pavin Smith could very well be the first college first baseman off the board, assuming you do not count Brendan McKay as a first baseman.  His demonstrated knowledge of the strike zone, coupled with a list-best walk rate, are both very good indicators of a first baseman with a high ceiling.

Logan Warmoth (#20 on FanGraphs Sortable Draft Board)

North Carolina            SS                    (R/R)

Tar Heel shortstop Logan Warmoth, when compared to the rest of this list, does not really stand out.  However, he should be taken early, as he still has the best odds of being the first college shortstop off the board.  He hit well in the ACC this past season, compiling 18 doubles, 4 triples, and 9 home runs.  Though his demonstrated power will likely not follow him up the minors, any team would love to have a strong bat such as his at the most premium of all premium positions.

Jake Burger (#22 on FanGraphs Sortable Draft Board)

Missouri State            3B                    (R/R)

Our only hot corner prospect on the list is a power threat through and through, according to his numbers.  While his average will continually drop as he climbs the minors, Burger’s 20 homers showcased his raw power.  Although there may be some questions about his tendency to punch out, plus power paired with an excellent walk rate at a corner position are a recipe for success.  Everybody loves a little yak sauce on their Burger every now and then.

Evan White (#29 on FanGraphs Sortable Draft Board)

Kentucky                      1B                    (R/L)

A first baseman who hits from the right side is very common.  A First Baseman who hits from the right side but throws left is very uncommon.  A first baseman who hits from the right side but throws left with plus speed is downright unique.   Evan White legged out a list leading 23 doubles this past year, and posted all-around great offensive numbers.  He will be a very interesting draft choice, and his excellent statistics project a demonstrate a solid offensive background.

Brian Miller (#49 on FanGraphs Sortable Draft Board)

North Carolina            OF                   (L/R)

Rounding out our list is North Carolina outfielder Brian Miller.  Miller slashed a very impressive .336/.412/.504 line this past year, and should be a good mid-grade prospect in the upcoming draft.  His statistics do not lean to one type of offensive profile over another, but his high BABIP and excellent walk rate generate some reasons to believe his bat will continue to develop at the next level.

Again, this article is meant to simply provide a statistical overview of a few college prospects in the upcoming draft.  It should be looked at as a tool for anybody who cares enough to concern themselves with college statistics.

Theodore Hooper is an undergraduate student at the University of Tennessee in Knoxville.  He can be found on LinkedIn at https://www.linkedin.com/in/theodore-hooper/ or on Twitter at @_superhooper_

## Measuring Offensive Efficiency

Runs Created was one the first sabermetric statistics I took it upon myself to learn about.  After all, it was one of the first statistics developed by Bill James himself.  I am also pretty sure RC is the formula written on a whiteboard in Moneyball (the most influential Brad Pitt movie I have ever seen).  Anyways, Runs Created is not discussed much because there are other, more sophisticated alternatives – wRC, wRC+, etc.  I still appreciate RC because of its simplicity, and it is can still be used as an effective tool for measuring the efficiency of offensive production.

That is precisely what I set out to do.  The question I sought to answer with this study is, “which teams were the most efficient in scoring runs?”  A pretty basic question — which I decided to complicate.  Using team statistics from last year, I calculated the Runs Created for each team’s offense.  The largest separation between Runs Created and actual runs scored came from the San Diego Padres, who scored 686 times, despite “creating” only 621.38 runs.

While ranking in 19th in total runs, the Padres were actually incredibly efficient. I discovered this after trying to develop a way to measure offensive efficiency.  To do so, I created the Runs Conversion Rate (RCR).  While relatively rudimentary, this ratio between runs scored and Runs Created provides, in my mind, a good measurement for the efficiency of offenses.

Run Conversion Rate = Runs Scored / Runs Created

The purpose of this, again, is to gauge the overall efficiency of offenses.  All I really did was give a fancy name to the margin of error of Runs Created.  However, what I sought to do was use this statistic in a different way — to examine which teams made the most of what they produced (efficiency), and which did not.  Think of this article as a new way of looking at an old statistic, not me trying “discover” a new stat.  Below is a table, sorted by runs scored (i.e. from most productive offenses to least productive).  Green values represent teams in the top 10 of a category, and red the bottom 10.

 2016 Run Conversion Rates TEAM Runs Created Runs Scored Run Conversion Rate Red Sox 905.26 878 0.970 Rockies 856.84 845 0.986 Cubs 790.93 808 1.022 Cardinals 784.92 779 0.992 Indians 770.06 777 1.009 Mariners 769.39 768 0.998 Rangers 755.83 765 1.012 Nationals 752.18 763 1.014 Blue Jays 759.72 759 0.999 D-Backs 775.15 752 0.970 Tigers 791.98 750 0.947 Orioles 768.79 744 0.968 Pirates 724.74 729 1.006 Dodgers 709.32 725 1.022 Astros 727.58 724 0.995 Angels 700.20 717 1.024 Giants 725.10 715 0.986 Twins 742.03 690 0.930 Padres 621.38 686 1.104 White Sox 713.38 686 0.962 Reds 699.02 678 0.970 Royals 685.69 675 0.984 Rays 701.08 672 0.959 Brewers 694.02 671 0.967 Mets 707.39 671 0.949 Marlins 695.80 655 0.941 Athletics 655.47 653 0.996 Braves 671.35 649 0.967 Yankees 690.17 647 0.937 Phillies 617.22 610 0.988

After looking at the table, I noted a few observations to be made: teams ranked top 10 in scoring and top 10 RCR last year were, for the most part, the best teams in the league, the two highest-scoring teams did not score as many runs as they could have, and some teams capped out their production, albeit not a high level of scoring.

First, let’s look at the teams who ranked top 10 in scoring and top 10 in RCR in 2016: the World Champion Chicago Cubs, the American League Champion Cleveland Indians, the Seattle Mariners (second in AL West), the Texas Rangers (AL West Champs), the Washington Nationals (NL East Champs), and the Toronto Blue Jays (AL Wild Card).  All these teams were both productive and efficient.  Both are key indicators of good ball clubs.  They created an equal balance of the two, and, outside of the Mariners, played postseason baseball.

While the last paragraph was basically a no-brainer, this is where the study got interesting.  The Boston Red Sox scored 878 runs last year — short of their roughly 905 “created” runs.  According to their RCR, they were only 97% efficient.  So, what does this mean? The Red Sox, while more productive than anyone else, did not hit their ceiling.  They came close (RCR of 0.970), but still only ranked in the middle third of offensive efficiency.  What if the post-Ortiz Red Sox put up around the same numbers they did last year, but became more efficient in doing so?  In my opinion, the AL East should be scared.  Other teams falling into the top 10 scoring, middle 10 RCR category are the Colorado Rockies, St. Louis Cardinals, and Arizona Diamondbacks.  The Rockies certainly receive a boost in production because they played 81 games in Coors Field.  The Cardinals and Diamondbacks, like the Red Sox, scored often, but not as often as they could have.  So maybe their problem is not a low ceiling, but rather getting away from their floor troubles them.

Our third group of relatively important teams in this study are those who ranked in the middle 10 in scoring and top 10 in RCR: the Pittsburgh Pirates, Los Angeles Dodgers, the Los Angeles Angels, and the San Diego Padres.  Essentially, these offenses were middle of the road in terms of productivity, but scored as many runs as possible given their level of production.  The Angels, ranked in the bottom 10 in Runs Created by their offense in 2016, but were second in RCR, scoring 2.4% more runs than they “created.”  The only team ahead them were the lowly San Diego Padres, who turned in 10.4% more runs.  The Dodgers, who won 91 games in a comparatively weak NL West division, were middle-of-the-road in terms of offensive production, and came in third in terms of RCR.  These teams were ruthlessly efficient, milking the most out of what their offense provided.

I do not know what qualities are common in high-RCR teams.  Maybe a high average with runners in position, a low number of runners left on base, or maybe just plain luck.  That could be the topic of an entirely different study, perhaps.

To sum things up, a high RCR was a common denominator in the teams who saw great success in 2016, and I would like to think it is useful in measuring the efficiency of teams’ offenses.  It will be exciting to see who will rise in 2017 as the most potent offense.  For me, it will be just as exciting to see who is the most efficient.

FanGraphs and Baseball-Reference.com were instrumental in the production of this article.  Theodore Hooper is an undergraduate student at the University of Tennessee in Knoxville.  He can be found on LinkedIn at https://www.linkedin.com/in/theodore-hooper/ or on Twitter at @_superhooper_

## The 2017 Atlanta Braves: A .500 Team?

The 2016 Atlanta Braves were built to suck.  After all, starting a season 0-9 basically kills any hope left in the fan base, and gets them prepared for the contagious losing.  For the few fans who paid to go see their beloved Braves play in the now retired Turner Field, losing 93 games is heartbreaking.  A large volume of articles exists detailing the extent at which the Atlanta Braves, under both John Hart and John Coppolella, are remodeling their organization.  This article serves the purpose of examining one thing:

 2016 Atlanta Braves Record Runs Scored Runs Against Run Differential First Half 31-58 307 414 -107 Second Half 37-35 342 265 -20

That’s right! The 2016 second-half Atlanta Braves won more games than they lost!  If you did not already know this you either (a) are not a Braves fan, or (b) could not manage to care less.  However, this could have some real value behind it.  While the Braves managed to be outscored by 20 runs in the second half, they still managed to win two more games than they lost.  They scored 35 more runs in 17 fewer games.  Their runs/game increased 3.45 to 4.75, which would have placed them in between the Mariners and Cardinals in that regard had it been 4.75 the entire 2016 season.  The most important takeaway is how much better the second-half Braves were at preventing runs — 149 fewer runs allowed than in the first half.  Shaving off that many runs in only 17 fewer games is huge.

But let’s not get ahead of ourselves.  A winning record is unsustainable at a deficit of 20 runs in 72 games.  But I am not asking whether the 2017 Atlanta Braves can win even 82 games.  Can they win 81?  Could the great finish down the stretch of the 2016 season carry over into 2017?  While going .500 is technically meaningless because a .500 team will not make the playoffs, not losing more than they win in the new SunTrust Park will energize the organization and the fan base, and prepare the team for future success.

When the 2016-2017 offseason kicked off, the Braves signed two popular starting pitchers, and acquired one via trade, to eat innings so their crop of young pitching could ripen on the farm.

 Braves 2017 Offseason Acquisitions (2016 Statistics) Record ERA FIP BB/9 K/9 WHIP WAR Bartolo Colon 15-8 3.43 3.99 1.50 6.01 1.21 2.9 R.A. Dickey 10-15 4.46 5.03 3.34 6.68 1.37 1.0 Jaime Garcia 10-13 4.67 4.49 2.99 7.86 1.37 1.2

Two of the three had subpar years in 2016.  The other one became an internet sensation for his antics in the batter’s box and even hit a homer against the San Diego Padres.  But let’s assess what each pitcher brings to Atlanta’s rotation.

Bartolo Colon ages like a fine wine.  His ERA was better last year than any Atlanta starter except Julio Teheran.  While pitching record is not a statistic to measure performance, it is worth noting he won more games last year than any Atlanta starter.  He was better pretty much across the board than anyone not named Julio Teheran.  But can he keep this level of production up?  I would like to think so.  His two-seam velocity has stayed relatively consistent over the past three years.  All the Braves should ask Colon to do is turn in around 20 quality starts (he turned in 19 last year).  Consistency was a hallmark of his time with the Mets, and should continue in Atlanta for at least the 2017 season.

The other old guy the Atlanta Braves picked up this offseason happens to be knuckleballer — R.A. Dickey, 2012 National League Cy Young Award winner.  While Dickey will more than likely not be in the running for any hardware as he nears his 43rd birthday, he can still meet the immediate needs of his new team.  From 2011 to 2015, Dickey’s lowest inning count was 208.2, and peaked in his legendary 2012 with 233.2 innings pitched.  This is what the Braves need.  They need Dickey to turn in a mountain of good, quality innings.  If he could get over 200 innings again, and remain viable at the big-league level, then it is mission accomplished.

The third major addition to the Atlanta rotation is southpaw pitcher Jaime Garcia.  On December 1 of last year, the St. Louis Cardinals accepted minor-league infielder Luke Dykstra, right-handed pitcher John Gant, and righty Chris Ellis for Garcia’s services.  First, let’s look at the positives of this — Garcia is a definite mid-rotation talent, who posted a 3.73 ERA in 31.1 IP and a 3.18 ERA in 28.1 IP in April and May of last year, respectively.  He gives Atlanta a lefty in a rotation filled with righties.  The downside?  His low ERAs early in the season turned into a 5.40 ERA in June and a 5.60 ERA in the second half of the season.  So much for success in the second half driving this article, right?  Let’s remain optimistic.  After all, that is the whole purpose of this.  Garcia’s HR/FB rate was up from 7.1% in 2015 to a ghastly 20.2% in 2016.  He got consumed by the league-wide power surge.  I do not think such a high rate is sustainable or will happen again.

Let’s make a prediction.  Bartolo Colon makes us all fall back in love with “The Great Bart-Bino” all over again and he turns in around 16-20 quality starts for the upstart Braves.  Dickey, the workhorse of the staff, follows suit and dizzies batters with his knuckler for over 200 innings.  Garcia returns to early-2016 form, and posts something in the ballpark of 1.5 WAR.  Of course, the likelihood of all three scenarios playing out is small, but what I am trying to get across is it is possible.

Now, time to switch gears. The Braves lineup has changed its look dramatically since this time last year, sticking with a solid mixture of recognizable names and some guy named Dansby Swanson.  Here is a look at their projected Opening Day lineup:

 2017 Atlanta Braves Position Name Bats 2016 WAR Projected WAR CF Ender Inciarte L 3.8 2.5 SS Dansby Swanson R 0.9 2.4 1B Freddie Freeman L 6.5 3.7 LF Matt Kemp R 0.0 0.0 RF Nick Markakis L 1.7 0.5 2B Brandon Phillips R 0.8 0.7 3B Adonis Garcia R 0.2 0.2 C Tyler Flowers R 0.3 0.7

Look at the first half of their lineup.  To me, those three guys, Inciarte, Swanson, and Freeman, look like the core of a team poised to wreak havoc on the NL East before the end of this decade.  It is hard to project exactly what we are going to get out of Dansby Swanson, but most Braves fans and analysts expect him to take reign as the face of the franchise.

Starting in the leadoff spot is Ender Inciarte, who was brought over as icing on the cake in the Shelby Miller trade that landed Swanson and pitching prospect Aaron Blair.  In his first year in Atlanta, Inciarte posted a .732 OPS and won a Gold Glove for his outstanding play in center field.  I really could not think of a better leadoff guy for the Braves.  He is signed through 2021 at a team-friendly cost of \$30.5 million, with a \$9-million team option in 2022.  In his first years in the bigs, Inciarte has played in at least 118 games, posted a WAR above 3.7 (produced a figure of 5.3 in 2015), and shows no sign of slowing down as his prime years lay ahead. What if he crosses the 3.0 WAR plateau for the fourth time in four seasons, and maybe even adds another Gold Glove?  That is all his organization needs out of him.

Inciarte is a vital part of the Braves defense, which, according to 2017 PECOTA projections, leads the NL East in Fielding Runs Above Average (they are projected to attain an average figure of 3.6, while the other four teams are either at 0.0 or negative).  BaseballProspectus.com explains FRAA as an “individual defensive metric created using play-by-play data with adjustments made based on plays made, the expected numbers of plays per position, the handedness of the batter, the park, and base-out states.”  In short, the higher the number, the better the fielder, and vice versa.  The higher the team average, the better the team is overall in the field.  In his Gold Glove campaign, Inciarte registered a FRAA of 23.0, according to BP.  The graduation of Dansby Swanson and the addition of web-gem-prone second baseman Brandon Phillips will certainly strengthen the middle cone of the field.  Just how good is this team going to be at preventing runs?  Many projection systems think they will be around the top of their division, and many fans are excited to see the double-play tandem of Swanson and Phillips at work.

Freddie Freeman is the undisputed anchor of the lineup, and has finally seen the Braves ADD instead of SUBTRACT from the lineup around him.  The addition of Matt Kemp has helped tremendously.  With a recognizable slugger swinging behind Freeman, managers and pitchers had to pitch to him in the latter months of the year. With Kemp slotted behind him, Freeman hit to the tune of a .340/.456/.665 slash with 16 home runs and 18 doubles.  Kemp also matched the theme of this article with a strong second half — hitting .280/.336/.519 with 12 bombs in 241 plate appearances as a Brave.  The duo should have Braves fans excited for a full season of similar production from Freeman if Kemp is behind him.  Kemp, on the other hand, has a lower bar to pass, and could re-tool his value as an offensive player in his first full year off the West Coast.

So why is it unreasonable for the 2017 Atlanta Braves to win 81 games?  I do not think it is that far-fetched.  This article has not mentioned their incredibly deep farm system, which includes guys such as Ozzie Albies, Sean Newcomb, and Lucas Sims, but instead focuses on the immediate roster — a roster which has the potential to do unexpected things in 2017.  The dominoes would have to fall in all the right places, but this is baseball.  Anything is possible.

Theodore Hooper’s Official 2017 Atlanta Braves Prediction: 81-81

The statistics used in this study were found on BaseballProspectus.com, Baseball-Reference.com and FanGraphs.com, and the rosters on RosterResource.com were a great help in referencing players and transactions.

## Desert Optimism

I recently had the opportunity to tour Chase Field, home of the Arizona Diamondbacks.  While there, I saw a lot of banners for Zack Greinke.  After all, he is the face of the franchise (if you’re not considering Paul Goldschmidt).  After signing a six-year/\$206.5-million contract before the 2016 season, Greinke changed the focus and the philosophy of the Diamondbacks.  Suddenly, they were contenders.

After signing Greinke, the D-Backs traded for Shelby Miller, who was coming off what many considered one of the best years in baseball.  However, his price was laughable.  It cost Arizona top prospect Dansby Swanson, who has emerged as a candidate for a franchise player in Atlanta.  They also coughed up Ender Inciarte, a very capable center fielder who posted a .732 OPS and won a Gold Glove in 2016.  But wait, there’s more! The Braves also received pitching prospect Aaron Blair.

The purpose of this study is not to criticize former General Manager Dave Stewart’s transactions.  After all, he truly believed, after signing ace Zack Greinke, the Diamondbacks were in a position to win — and rightly so.  Stewart felt, as did many people inside the Arizona organization, their core was established.  Below is their lineup in 2016, with the players being who played the most at their position:

 POSITION Name 2016 WAR Total C Welington Castillo 2.4 1B Paul Goldschmidt 4.8 2B Jean Segura 5.7 3B Jake Lamb 2.6 SS Nick Ahmed 0.2 LF Brandon Drury 0.0 CF Michael Bourn 0.3 RF Yasmany Tomas -0.4 Total 15.6

AJ Pollock, who was coming off an All-Star season in which he produced 7.4 WAR and posted an .865 OPS, played in 12 games.  Inciarte was traded to the Braves after providing 5.3 WAR playing right field in 2015.  David Peralta, who started in left field in 2015, played in 48 games last year.  Nick Ahmed also had an injury-plagued season following a strong 2015 in which he put up 2.5 WAR in his first full year in the MLB.

The injuries to Pollock, Peralta, and Ahmed were unfortunate.  The Diamondbacks got near or around replacement-level production from their positions in 2016.  In a hypothetical situation, let’s say the three guys stay healthy, and, after subtracting their counterparts’ production, up the total runs scored by the Diamondbacks from 752 to 790 runs.  After some number crunching, the Diamondbacks’ Pythagorean expectation comes out to around 71 wins.  Give or take a few, a healthy trio of Pollock, Peralta, and Ahmed would have helped Arizona’s win expectation increase by between two and five games.

But let’s be optimistic — the hypothetical healthy trio helps Arizona to an expected 74-88 record, far better than their 69-93 actual record.  That would have moved them up in the standings from fourth in the NL West to…drum roll please…fourth in the NL West.  The problem Arizona experienced in 2016 was run prevention, not run support.  As a matter of fact, total runs increased to 752 from 720 in 2015, when they went 82-80.  However, the real increase was in runs allowed — up to 890 (!!!) in 2016, as opposed to 713 in 2015.

So why does a pitching staff that added Zack Greinke, a bonafide ace and top-tier talent, and Shelby Miller, who would fit well in the center of any rotation, give up such a whopping number of runs?  Catching.  Below is a chart of how many runs these two respective pitchers had prevented or added by their respective catchers in 2015:

 Pitcher Team Catcher Framing Runs Rank Zack Greinke LAD Yasmani Grandal +23.3 1st Shelby Miller ATL AJ Pierzynski -8.7 103rd

As you can see, any pitcher would love to pitch to Yasmani Grandal.  In 2015, he ranked as the best in framing runs.  Essentially, what the statistic does is quantify the catcher’s ability to get strikes called, which is incredibly valuable to a staff.  Positive is good and negative is bad.  While there is not as direct a correlation between Shelby Miller’s success and AJ Pierzynski’s lack of pitch-framing ability, it is apparent there is a direct link between Greinke’s 2015 performance and Yasmani Grandal.

In 2016, Greinke and Miller both joined a staff caught by Welington Castillo.  The best way to describe Welington is he’s an offense-first, defense-second catcher.  The theme of this study is to advocate for the use of defense-first, offense-second catchers.  Look at this chart of past World Series champion catchers:

 Year Team Name Framing Runs Rank 2012 SFG Buster Posey +20.0 4th 2013 BOS Jarrod Saltalamacchia -4.6 93rd 2014 SFG Buster Posey +21.5 2nd 2015 KCR Salvador Perez -7.5 99th 2016 CHC Miguel Montero +14.6 4th

After looking at that chart, there are a couple of observations to make.  One, three out of the five previous World Series teams have had top-four catchers in terms of pitch framing and pitch presentation.  Second, Jarrod Saltalamacchia was replaced by AJ Pierzynski who was replaced by Blake Swihart who is now competing with Sandy Leon and Christian Vazquez, both of whom are defense-first catchers lauded for their ability to frame pitches.  Third, Salvador Perez is the heart and soul of the Kansas City Royals, and I guarantee Dayton Moore could not care less about his pitch-framing abilities.

Essentially, what you should take away from this is teams that win have skilled catchers.  Luckily for the Giants, Buster Posey can also hit the baseball.  To bring this full circle back to the Diamondbacks — Wellington Castillo is the wrong type of catcher.  He does not frame like Posey or Montero, and the bat is nothing too special.

But alas! Castillo is no longer part of the Arizona organization! This offseason, freshly-appointed general manager Mike Hazen has added four new catchers to the picture: Chris Iannetta, Jeff Mathis, Hank Conger, and Josh Thole.  Let’s look at their pitch-framing stats from last year:

 Name Team Framing Chances Framing Runs Rank Chris Iannetta SEA 5,495 -13.8 102nd Jeff Mathis MIA 2,248 +7.2 15th Hank Conger TBR 2,366 +3.6 25th Josh Thole TOR 2,410 +4.6 21st

As you can see, the Diamondbacks have added a starting catcher who is not very good at framing pitches and three back-ups who do or might fit the desirable profile of this study.  Chris Iannetta signed a \$1.5 million, one-year deal; Mathis signed a \$4 million, one-year deal; the other two are minor-league contracts. Hazen, who came over to the Diamondbacks from the Boston Red Sox (who are leaning towards more defense-first options at catcher), made some efforts to boost his catching corps’ defensive ability, but was it enough?

In a perfect world, I think a guy like Jason Castro fits the bill perfectly in Arizona.  While the financial situations in Arizona may have made the price for Castro too high, he fits the type of catcher this study calls for, and the type of catcher Zack Greinke and Shelby Miller deserve.  He tallied +16.3 framing runs in 6,623 chances in 2016, good for third in MLB behind Buster Posey and Yasmani Grandal.  He signed for \$24.5 million over three years with the Minnesota Twins, and will surely help their young staff develop.

Let’s not dwell on the hypotheticals.  The Diamondbacks have five and a half million dollars invested in two guys: Chris Iannetta and Jeff Mathis.  While Iannetta had an abysmal year in 2016 in terms of framing runs, his track record is mixed.  In 2013, for example, he recorded a framing-runs figure of -16.6, which is comparable to his 2016 number.  In 2015, however, he recorded a figure of +13.1, good for fifth in all of baseball.  What caused such a dramatic, roller-coaster shift?  I do not know — that question could be the subject of an entire different study.

Should Iannetta get most of the starts, I would say Mike Hazen would not care if he hits below the Mendoza line if his defensive statistics match his 2015 numbers.  Should he not get most of the starts at catcher, they will more than likely go to veteran backstop Jeff Mathis.  Mathis, who is lauded for his skills behind the plate, is essentially a cheap Jason Castro.  If you divide the number of framing runs Mathis achieved in 2,248 chances last year, and multiply the decimal by Castro’s number of chances, you get around a number of +21.2 framing runs.  That would have ranked him third behind Grandal and Posey.  Of course, this method is unreliable because every chance is another chance for his framing runs to drop as well as increase.  With that being said, the efficiency of Mathis behind the plate makes giving him a chance to handle the Diamondbacks’ staff worthwhile.

The addition of Taijuan Walker, who was the return on shipping Jean Segura to Seattle, is a healthy investment in the pitching staff.  With him slotting in along with Zack Greinke, Shelby Miller, Robbie Ray, and Patrick Corbin, the Diamondbacks have the makeup of a sleeper-type rotation — one that could surprise a lot of people in 2017.  If the front office has embraced the importance of defense at the catcher position like their offseason moves suggest, their staff could cut down on runs allowed dramatically, putting their lineup in position to do some damage in the NL West this year.

One team who should be noted in this study is the Houston Astros.  Whether Jeff Luhnow’s front office emphasized framing runs and having defensively-elite catchers or not, two of the catchers mentioned in this study were teammates in Houston — Jason Castro and Hank Conger.  Castro and Conger were the only two backstops on the 2015 Houston Astros, the year Dallas Keuchel won the American League Cy Young award.  This serves the purpose of further validating the benefits a defense-first catcher can have on a pitching staff.

In conclusion, baseball is trending toward sacrificing offense for defense at a premium position.  One club that can change the face of their organization by embracing the principles outlined in this study is the Arizona Diamondbacks.  While the Diamondbacks may face public scrutiny for far after Shelby Miller and Zack Greinke are gone, fans should be optimistic about 2017.  An elite defensive catcher can make a world of difference in the performance of a pitching staff.

The statistics used in this study were found on baseballprospectus.com, the historical rosters and statistics were found on baseball-reference.com and fangraphs.com, and rosterresource.com was a great help in referencing players and transactions.