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Extreme Makeover: B.J. Upton Edition

Back in 2007, B.J. Upton was thought to be a future megastar, a young tools-filled player, whose future seemed to almost certainly include MVP consideration and numerous other awards. As a 24-year-old he put up back-to-back 4+ WAR seasons. For the past two years however, he has posted a total -0.2 WAR. What has happened to B.J. Upton? He is only 30 years old, which for an athlete of his caliber, is still potentially only the tail end of his prime.

Defensively speaking, he has had ups and downs, but the swings in performance were never too drastic, posting a -7 DRS and -1.9 UZR/150 in 2014 compared to -3 and 8.4 back in 2008. His Defense rating has dropped from 9.8 to 0.1 from 2008 to 2014. Again, obviously not good, but not enough to account for such a big drop in WAR. So his troubles must mostly be tied to his offensive production.

I first tried to identify the problem with his hitting by dividing our options into two groups. The biomechanics processing results versus possible telling statistics on his approach. Let us look at the latter to start. A couple things I want to focus on would be his O-Swing% and how he performs in hitter-friendly counts. From 2008 to 2014, his O-Swing% has jumped up 11% from 16.8% to 27.8%, which is not a great indication that he has a plan when stepping into the box. Hitter’s counts are all about the approach, not only working yourself into this count, but being ready for your pitch because you can be selective at the plate.

Avg. / wRC+ Through 2-0 Through 3-1 Through 3-0
2008 .265 / 176 .262 / 212 .353 / 280
2014 .091 / 134 .097 / 150 .100 / 204

Now obviously the wRC+ numbers will be inflated due to the higher walk rates when in a hitter’s count, so I am focusing more on average. To help put this into perspective, in 2014, through 0-2 counts, Upton hit .085. That is scarily similar to his performance in hitter’s counts. Clearly something is off. When in a hitter’s count, the batter typically sits on a fastball, so naturally my next focus was to look at his wFB. His wFB in 2008 was 1.1 and in 2014 was -11.1. So while he used to be above average, he has now become much worse at handling fastballs, which would correlate to his lack of success hitting in hitter’s counts. In order to survive in this league as a hitter, you must be successful hitting against the fastball. His pitches seen rate has remained relatively consistent except for a slight increase in sliders seen. His lack of production in these areas really makes me question whether he is ready to hit when stepping into the box.

If you have ever seen a B.J. Upton swing you know there are a lot of moving parts. This in and of itself is part of the problem. Double loads, bat wraps, too much rotational and not enough linear movement, dipping, and changing eye levels are all apparent. But first let’s start with the numbers. A couple of things jump out when looking at the numbers. To begin with, his GB/FB rate in 2008 was 1.65 in comparison to a rate of 1.11 in 2014 while his 2014 HR/FB rate is lower than his career rate, it is higher than his 2008 All Star rate at 9% compared to 7.4%. His line drive rate is consistent throughout.

However Upton’s BABIP tells you most of the story. Upon entering the league full time in 2007, B.J.’s BABIP was .393 then .344 in 2007-08 compared to .286 in 2014. This is ridiculous and impossible to sustain! The question is, does this then make his two best years a fluke? Back in 2010 and 2011 he had near-league average BABIP years and posted close to a 4 WAR in both. So as ridiculous as those 2007-08 numbers were, I can’t blame his entire decline on not being as lucky.

Another one of the most worrisome numbers is to see his Z-Contact% drop 10%, almost as much as his overall Contact% which dropped 12%. This to me screams mechanics. So let’s take a look (and I apologize in advance for the youtube link, but it serves our purposes decently enough — I still haven’t figured out the .Gifs). Ironically enough, he hits a home run in this video. It just goes to show all the holes in his best of swings.

The first thing you see is at the peak of his negative move his hips slant upwards (2 second mark). The reason this happens, is because his leg kick doesn’t gain any ground. It immediately makes him more likely to have a bit more uppercut in his swing and it also changes his eye level due to the flexion in his knees changing while the distance between his feet do not change. Both factors make it more difficult to produce solid contact. Once at toe-touch, he then loads again and inverts his front leg, making it a double load (5-6 second mark). This leads to the potential to over-rotate (think Newton’s 3rd law — for every action there is an equal and opposite reaction). By inverting and coiling his body, he will uncoil, or over-rotate off the pitch, causing his shoulders and hips to pull off the pitch and not stay square.

Once he finishes inverting his front side he commits to swinging. His hands/upper half look okay up to this point (7 second mark), and they’re very active. However his upper half and lower half are completely out of sync. Once he initiates his swing, his bat immediately wraps because he still hasn’t come set with his barrel, the bat has been moving the entire time. At this point there is nothing going forward at all, no backside drive. It is all rotational, making it harder to stay on the ball if his timing isn’t near perfect. In other words, due to having a more rotational swing versus a linear swing, his margin of error with timing is much narrower.

Once at the contact position (11 second mark) he looks okay. He hits the ball off his front foot, his elbows are at slightly obtuse angles, and his front side is stiff. During his bat path his hands dip a bit, giving him a high finish, most likely due to the pitch being low.

B.J. Upton’s biggest problems in his swing come before his contact position. This is a very good explanation for why he struggles against fastballs and in hitter’s counts. Simply put, he isn’t ready to hit. He has way too much going on, the main problems being his double load and lack of linear movement. In an age of power bullpens and power fastballs it is no wonder that he is struggling as badly as he is. B.J. Upton needs to simplify and settle everything down in the box. His swing is fixable, but these issues need to be addressed and changes have to be made if he is ever to be successful again.

Billy Butler In: The Good, The Slightly Above Average, And The Ugly

For the past two years or so, Kansas City has been torn about breakfast… Billy “Big Country Breakfast” Butler that is. During this past offseason there were many rumors that the Royals were going to trade him and it seemed inevitable upon entering talks with then free agent Carlos Beltran. Billy Butler is part of the home-grown youth movement in Kansas City with Alex Gordon, and later followed by Salvy Perez, Mike Moustakas, Eric Hosmer, and company. From 2009 through 2013, Billy Butler has offensively been above average, and even great! However, after failing to meet expectations last year, and in some opinion already being in decline at the age of 28, Billy came out and struggled mightily to start the 2014 season.

But he has turned it around somewhat, and with the Royals making headlines this August, Big Country played a big part. So I wanted to look at what he did differently comparing his April dud, to his career average, and to his being a stud again in August. We will measure his overall offensive prowess with WRC+, which in this study would be 50 in March/April, 118 for his career average, and 126 in August. So let’s look at the more telling processing stats.

April 8.3% 18.3% 0.45 0.275 2.82 18.8% 60.0% 21.3% 0.0%
Career Average 8.9% 14.4% 0.62 0.325 1.51 19.9% 48.3% 31.9% 11.1%
August 5.8% 13.2% 0.44 0.308 1.35 23.2% 44.2% 32.6% 12.9%


One of the first things to pop out at you is the BB/K ratio. While under his career margin (and by a decent margin too), his BB/K rate is nearly the exact same in April and August. A lot of times credit for a hitter’s success is given to an increase in the BB% and decrease in the K%, but here Butler cuts down on both, therefore increasing the amount of balls he puts into play bringing us to BABIP. Both his April and August are way below his career norms. Perhaps dealing with a little unluckiness? Or just weak contact? Fact is even with his BABIP down and his home run rate relatively consistent he can still create above average production.

Now comes the most telling rate, which is the type of balls that he hits. As someone who is an AL DH, Billy Butler is not only expected to hit, but to slug. That big goose egg for HR’s in April is just an absolute killer, and the culprit is the GB%. It is no wonder why a big, SLOW (we all know about his base running and uncanny attraction to double plays), gap to gap power hitter has one of the worst months of his career considering his GB% is up almost 12% and his FB% is down nearly 10%. Billy Butler will never be Aoki. He has to get the ball in the air. He lives on hitting doubles into the deep gaps at Kauffman Stadium and with ratios such as those it is no surprise he puts up a WRC+ of 50.

When your BB/K ratio is so nearly identical but yet you put up such drastically different numbers, not to mention the fluctuations in his BABIP, it has to come back to his swing mechanics and getting to a consistently good contact position where he can drive the ball.


Split O-Swing% Z-Swing% Swing% O-Contact% Z-Contact% Contact% Zone% F-Strike % SwStr%
April 30.0% 58.6% 43.7% 77.4% 92.9% 87.4% 48.0% 57.8% 5.5%
Career Average 28.0% 63.0% 44.3% 69.4% 90.0% 83.1% 46.7% 56.0% 7.2%
August 37.8% 62.1% 49.5% 70.1% 91.5% 83.1% 48.2% 71.9% 8.5%

Billy’s discipline at the plate has been waning. But the month he really lacked discipline is the same month he did so well in: August. In April he was within his career norms for all of his discipline stats except O-Contact%. Overall he was swinging less and missing less. And that is where the problem may lie! It is not so much that he was struggling with pitch selection, because clearly he was even worse with discipline in August, but the fact that he didn’t miss when he swung.

In a sense Butler was too good at making contact! With his swinging percentage up along with increasingly bad pitch selection, the higher his swinging strike percentage, the better! And perhaps with his swing percentage, his first pitch strike percentage, and his O-Swing percentage all up, he has changed to a more aggressive approach? Again all of this can lead back to the assumption of Butler making poor contact in April. Which leads to the question of what has he done differently, if anything, with his swing?

Split Fastball % Slider % Cutter % Curveball % Changeup % Splitfinger %
April 52.5% 19.5% 8.5% 10.3% 8.8% 0.5%
Career Average 56.3% 18.1% 5.6% 8.6% 9.9% 1.0%
August 50.4% 22.9% 8.3% 9.5% 8.5% 0.7%



Split Fastball % wFB/c wSL/c wCT/c wCB/c wCH/c wSF/c
April 52.5% -2.45 -0.92 0.56 1.96 0.86 -11.47
Career Average 56.3% 1.09 -0.81 0.16 0.29 0.16 -1.45
August 50.4% 2 1.89 -1.74 -5.1 -2.11 25.04


Now the main reason I bring these stats up is that I am a huge believer in fastball hunting. These charts may not be the most reliable in telling of pitch selection, but they do tell you if he has been seeing certain pitches better and the rates at which he has been seeing pitches.  So I wanted to look closely at his fastball rate in particular just to see if there was anything funky going on. And what was so funky is that in August he was crushing it! The more fastballs you see the better chance you have to hit well. While I am not sure of the exact quantity of fastballs he faced, for the most part he has been seeing the same consistent rate of different pitches he always has and he definitely has done one of his better jobs of taking advantage of the fastballs he has seen. Can a correlation be made between his April failures and August success against fastballs to a possible new approach and/or adjustment in his swing mechanics? Or just unlucky, bad contact?

After searching through the KC Star (hometown newspaper) as well as other media report outlets, I have not been able to find much of anything indicating adjustments being made. There was some talk of just his timing being off, but other than that there are not many clues. I wish I knew how to make video clips of swings and find a couple angles of Billy Butler’s swing in April compared to his swing in August and dissect them both. I would like to see what, if anything, is different. If we could see his timing and especially his bat path, I believe we can tell a lot about what he is doing wrong or right. If anyone can provide those, or teach how to make them, please do and send to me!

However, going off of what I have seen here, everything to me points back to weak contact consistently being made. Whether due to timing or mechanics, I am not sure. Normally I would say this is due to poor pitch selection, but as I showed above, he had even worst discipline and pitch selection in August than April and still put up very stellar numbers. To be clear hard contact is not good enough for a player of Billy Butler’s style. He NEEDS to get air under his pitch. Now they say that this is a game of adjustments. I would love to know what, if any, adjustments Billy “Big Country Breakfast” Butler has made. After all, could it really have just been a string of bad luck?

Biogenesis Players: Then vs. Now

After watching Nelson Cruz this year and all the noise he has been making, on top of a recent report by Buster Olney stating, “The average distance of the fly balls pulled by Ryan Braun this season is down 42 feet, from 302 to 260…”, it inspired me to look up the numbers for players suspended in the Biogenesis case. The big four suspended were Alex Rodriguez, Ryan Braun, Nelson Cruz, and Jhonny Peralta. Other position players involved and suspended were Everth Cabrera, Jesus Montero, Francisco Cervelli, and Jordany Valdespin.

This article will focus on the big four with the exception of A-Rod because he has been suspended all season. Obviously enough this is a small sample size so take heed. I will be making a couple of assumptions, the main one being that these players had been using steroids for at least 3 years (2010-2012) prior to their being caught and suspended. The other assumption being that enough time has passed for the effects of the steroids to have worn off and that their bodies/abilities are back to their more natural state.

Ryan Braun 2010 2011 2012 2014 2014 (ZiPSU)
HR/FB 14.00% 18.80% 22.80% 15.10%
Slug% 0.501 0.597 0.595 0.496 0.505
ISO 0.197 0.265 0.276 0.211 0.231
WRC+ 134 171 160 129 133
OFF 32.5 58.8 52 12.5 21
True Distance (ft) 408.2 406.7 406.9 387.9
Average Speed Off Bat (mph) 105.1 104.7 104.2 102.1


Nelson Cruz 2010 2011 2012 2014 2014 (ZiPSU)
HR/FB 15.20% 18.70% 13.10% 20.00%
Slug% 576 509 460 513 505
ISO 258 246 200 253 246
WRC+ 147 116 105 130 127
OFF 26.6 7.7 0.8 14.9 18.5
True Distance (ft) 405.2 411.6 418.6 398.9
Average Speed Off Bat (mph) 105.2 106.4 106.8 104.2


Jhonny Peralta 2010 2011 2012 2014 2014 (ZiPSU)
HR/FB 7.50% 10.80% 8.30% 12.50%
Slug% 392 478 384 447 441
ISO 143 179 145 187 180
WRC+ 91 122 85 122 120
OFF -12.7 11.2 -13.8 8.4 10.3
True Distance (ft) 392.5 388.4 391.9 397
Average Speed Off Bat 101.2 102.3 101.7 102.8


The main thing that jumps out at you is that Cruz and Peralta are statistically putting up some of the best numbers of their careers (without a doubt, top 3)! Braun, however, is having his worst season of the 4 above, while Peralta and Cruz both are having their most powerful seasons yet. Their HR/FB rates are each at their highest as well as their ISO numbers, while again Braun’s are at his worst of the 4 seasons. Looking at WRC+ and OFF, Peralta is having his 4th best season ever, Cruz is having his 2nd best ever, and Braun is having the worst season of his career to date (with the possible exception of 2008).

Using ESPN’s I looked up each player’s True Distance on home runs this year as well as the average exit speed velocity of their home runs. Ryan Braun has lost 3 mph which has correlated to a shortage of almost 20 feet on his balls. Nelson Cruz has lost about 2 mph and 20 feet off his home run balls from his peak of the four years. Jhonny Peralta, on the other hand, is showing his best numbers this year.

So what does all this mean? In summary, I believe the main thing we can take away from this is that each player who used steroids should be assessed on a case by case basis. Every player is affected differently. We cannot group all steroid users together. Using the above statistics as proof, after being charged in the Biogenesis case, 2 players are having among the best seasons of their careers while another is having his worst. In addition the best all-around athlete and youngest of the 3 (so therefore closest to his prime) is the one who is struggling most, Ryan Braun! Whether it is the HOF vote, or evaluating future value of perceived steroid users, we can’t lump them all into the same group and assume that they will automatically decline. Yes, using steroids is absolutely cheating, however it doesn’t necessarily mean that those players wouldn’t have been just as productive had they chosen legal supplements or nothing at all.

Possible Side Impacts of Base Stealers

Having grown up playing catcher from Little League through college, I always recognized the temptation and situational changes that occurred in terms of strategy and pitch selection with runners on, particularly base stealers, versus with no runners on base.  As a catcher, my thought process with a base stealer on, is always to try and have my pitcher get the ball to me as quickly as possible.  An earlier study I read dealt with the correlation between pitchers’ times to home, and that being a much stronger factor in throwing out a base-stealer than catcher pop times.  Logically, in thinking of pitch selection as a way of controlling the run game, the quickest way to get the catcher the ball is with one’s fastest pitch.

To evaluate the impact of base-stealers I defined a base stealer as a player who swiped 20 plus bags in 2013.  Using Baseball Reference, I slotted 6 pairs of base stealers and their following hitters.  The criteria for those hitters being 400 plus plate appearances in the same slot in the batting order.  Nick Swisher however is an exception because he had 250 plus appearances behind both Michael Bourn and Jason Kipnis, but I decided to include him.  I should also note that all the statistics in this study are from 2013.  Using Baseball Savant’s Pitch f/x database I defined a fastball as a 4 seam, 2 seam, sinker, splitfinger, and cutter and every other pitch as a breaking ball.  I then compared the fastball and breaking ball rates with each hitter with a runner on 1st or nobody on.

It is taken from granted that for a hitter the best pitch to hit is a fastball.  While there are many different approaches, one of the most common is “fastball adjust,” meaning the hitter always looks, or anticipates, a fastball as you get in the box.  However, if you recognize something different out of the pitcher’s hand, you should have more time to adjust.  Hitters are always fastball hunters first, that’s why we call 2-0, 3-1 counts “hitter’s counts” because they will most likely get a fastball and at the same time are sitting fastball.  As proof we used the probability of scoring a run per 100 pitches of a certain pitch above the prototypical average players.  The league average probability of scoring runs against what I defined as a fastball type pitch for every 100 pitches in 2013 was 0.0167 and for every 100 off speed pitches was -0.07.  That is over an 8/100ths difference in the likelihood of scoring a run above average, which added up over the thousands of pitches a player can see a year can make an impact.  Below are the 6 hitters I used for this study and their run probability rates against different pitches:


Name Team wFB/C wSL/C wCT/C wCB/C wCH/C wSF/C wKN/C
David Wright Mets 1.74 -0.13 2.75 1.95 2.01 -4.82
Shane Victorino Red Sox 1.53 1.29 -1.28 -0.52 -0.33 1.16 0.11
Dustin Pedroia Red Sox 0.11 -0.72 3.87 1.86 1.47 9.6 -2.77
Nick Swisher Indians 1.02 0.23 0.97 0.37 -0.55 -0.77 -4.47
Jean Segura Brewers 0.19 0.45 0.82 -0.18 2.7 -5.61
Manny Machado Orioles 0.17 0.23 1.15 -1.73 1.2 2.31 -1.34


As the data above supports, the best pitch to hit, the pitch a hitter is most likely to score more runs from, is a fastball.

So that being said, if a reputed, or habitual, base stealer is on base, then will the hitter at bat see an unusually high rate of fastball-like pitches?  With a higher rate of fastballs the hitter should therefore have a greater chance of success.  The theory being that an offense built more on speed and base stealing should see a higher rate of fastballs which then gives that team a greater probability of scoring more runs.

Now the total overall fastball rate for the league as a whole for the 2013 season was 57.8%.  The total fastball rates I arrived at were derived from simply taking the situational fastball rate and dividing it by the total pitch percentage or fastball percentage plus breaking ball percentage: fastball% / (fastball% + breaking ball%).


Base Stealer: Following Hitter: Runners on Fastball%: Runners on Breaking Ball%: Nobody on Fastball%: Nobody on Breaking Ball%: Total Fastball% with runner on: Total Fastball% with Nobody on:
Norichika Aoki Jean Segura 20.3001% 9.5322% 37.5552% 20.4325% 68.05% 64.76%
Jacoby Ellsbury Shane Victorino 16.8302% 9.5191% 38.2237% 22.8165% 63.87% 62.62%
Daniel Murphy David Wright 21.0498% 9.534% 33.5833% 18.3717% 68.83% 64.64%
Nate McLouth Manny Machado 18.1782% 11.9856% 36.5961% 21.8138% 60.26% 62.65%
Shane Victorino Dustin Pedroia 22.1729% 11.0694% 34.1647% 17.2532% 66.70% 66.45%
Michael Bourn/Jason Kipnis Nick Swisher 19.8731% 12.0587% 31.4954% 21.4597% 62.24% 59.48%


Looking at the results, in particular the totals, there is no significant difference in percentages of fastballs vs off speed seen with a runner on first or not.  The biggest difference is a 4.46% difference with David Wright.  And David Wright scores 21.1 runs above average against fastball type pitches (wFB).  While maybe an extra 4.46% increase does not make a world of difference it still contributes to overall run production and as we know in baseball 1 run can decide a game and 1 game can decide a season.  However, it appears that my hypothesis is false and there is no significant difference in situational pitch selection with a base stealer on 1st.

Now I will be the first to admit that there are definitely ways to improve upon the accuracy of my theory.  The biggest problem being that I could not find a database on the internet that allowed me the option of isolating at bats with only specific runners on, so the next best thing was Baseball Savant’s option of isolating at bats with the option of runners on certain bases or a combination thereof.  So all these plate appearances measured are just with a generalized runner on 1st who could be anybody or nobody on at all.  This study is assuming that the runner on 1st, for a majority of the time, is the base stealer who hits 1 spot in front of the selected hitter.  BIG assumptions I realize.  Also this is only covering 6 hitters in their 2013 season, which is a small sample size considering.  Unfortunately I did not have all the resources necessary for the most accurate representation for this study as a whole and on that note I hope many of you who perhaps have more available to you, can dig deeper and build on my theory.

This is my first time posting something like this so if you have any helpful questions/comments/criticism/advice please feel free to comment.  And if you have a way to more thoroughly complete this study please do so!  Thanks and I hope you enjoyed.