Archive for July, 2017

The Bad Aaron Judge Comps

Aaron Judge is good.  Some might say he is great.  The front-runner for AL Rookie of the Year and MVP is the face of MLB for 2017, but the face of MLB for the future?  Unfortunately, maybe not.

It’s hard to find something negative to say about the New York Yankees right fielder, but in order to play devil’s advocate and not get our hopes up too high about Aaron Judge, just in the event that he has a down season, I was able to find some rather unflattering comps for the slugger.

First, there’s his minor-league career.  Aaron Judge was a pretty good prospect ranking first in the Yankees’ system in 2015 and 17th in baseball according to MLB Pipeline.  However, just because a prospect is ranked highly does not mean they are without flaws.  Judge would strike out in at least 21 percent of his plate appearances in all levels in the minor leagues.  This article from 2016 even identified Judge’s proficiency to strikeout:  

Judge’s Triple-A debut at the end of 2015 did not go well. He slashed .224/.308/.373, well below both his career levels and expectations. More alarming, he struck out a career high 28.5-percent of the time (74 times in 260 plate appearances). [The 2016 season] has been more of the same. His batting average is a bit deceiving sitting at .284 (heading into this weekend), considering he currently has a nice .354 BABIP compared to last seasons .289. His plate discipline is troubling.

Perhaps the lofty expectations of Judge have him pressing. You simply can’t overlook the fact that his strikeout rate is nearly identical to the small sample size of last season’s Triple-A numbers (27.2-percent). It has to be at least a slight bit worrisome that this is a trend and not a slump. His walk right is dropping daily to a new career low (6.8-percent or eight walks in 103 plate appearances).

The article seems to point to his plate discipline as his main flaw — as other evaluators have — but is overall positive with his prospect status.  But his strikeout tendency should not be overlooked.  He has failed to improve on that statistic in his short major-league career, where he has struck out in 32 percent of his plate appearances between his call-up in 2016 and now.  However, because he also takes his walks, his walk percentage is rather high, which puts him in exclusive company.

Since 2000, there have only been four players with at least 300 plate appearances who have struck out in over 29 percent of their plate appearances and walked in at least 16 percent of them: Jack Cust (2007, 2008, 2010, 2011), Ryan Howard (2007), Adam Dunn (2012), and Aaron Judge (2017).  All of these seasons resulted in wRC+ well above 100, which means that they were productive players; however, these player were known to be the embodiment of the “three-true-outcome” hitters.  Dunn had five consecutive seasons of 40 or more home runs, but also led the league in strikeouts four times; Cust led the league in walks once and strikeouts three times; and Howard led the league in home runs twice and strikeouts twice.  Admittedly, these comps are not encouraging.  Although these players were not horrible in the simplest definition, their careers were short-lived and their production sharply declined.  For Cust and Dunn, it forced an early retirement, and Howard a well-publicized and sad end to an illustrious career.

But it’s not just Aaron Judge’s strikeout and walk percentage — it’s also his raw strikeout numbers.  Judge is on pace to strike out over 200 times this season.  While it’s already been established that he is strikeout-prone, it does not serve him justice that the 200-strikeout threshold is upon him.  No player who has struck out 200 or more times in a season has had a very high average.  As the legendary Pete Rose noted, the highest single-season average for a player with 200 or more strikeouts was .262 (Chris Davis holds that honor).  The short list of 200 single-season strikeout players is a whopping five players long: Mark Reynolds, Adam Dunn, Chris Davis, Chris Carter, and Drew Stubbs.  Kris Bryant had 199 in his rookie season (he was called up late to the bigs due to service-time considerations, so it’s likely that he would have joined this club), and Ryan Howard had 199 twice and Jack Cust had 197 once.  Dunn, Howard, and Cust again…

I love Aaron Judge, and I love 500-plus foot home runs, but we also have to be realistic and rational in our love and praise for the slugger.  The worst thing that the New York sports world can do is rattle this kid if, and when, he goes from being an All-Star to the 25th man on a roster.  There is nothing I want to see more, as a Yankees fan and a baseball fan, than Judge succeed; it’s good for the sport.  But I also don’t want to get my hopes up too high, because nothing stings more than a player of his caliber going down the path of Adam Dunn, Jack Cust, or Ryan Howard.

dSCORE: Starting Pitcher Evaluations

Early this spring I did a writeup on dScore (“Dominance Score), an algorithm that aims to identify early on pitcher “true talent.” That article reviewed RP performance for 2016.

Here’s a quick review of dScore and how it works:

dScore takes each pitcher and divides them up into a bunch of stats (K-BB%, Hard/Soft%, contact metrics, swinging strikes; as well as breaking down each pitch in their arsenal by weights and movements). We then weight each metric based on indication of success–for relievers, having one or two premium pitches, missing bats, and minimizing hard contact are ideal; whereas starters tend to thrive with a better overall arsenal, minimizing contact, and minimizing baserunners. Below is a breakdown of the metrics we used in our SP evaluations:

Performance metrics: WHIP, K/BB%, Soft%, Hard%, GB%, Contact%, SwStk%, Z-Contact%, O-Contact%

Pitch metrics: wPitch, vPitch (where “Pitch”= FA, FT, CU, SL, CH)

Our current weighting for SPs is a bit more subjective and complex than our RP weighting system, but I’m looking to implement a similar weighting system to the way we weight RP metrics in this evaluation in the near future.

dScore has been around for a year or so now, and one thing I was asked when I initially posted was whether or not it has any “predictive” tendencies. The answer is a pretty clear “no”–BUT what it does do very, very well is validate performance. There’s a fine line between saying “the numbers say pitcher X’s going to stay good” and saying “pitcher X has been good, and this confirms he’s been good”. The problem with the metric is it uses per-pitch statistics, rather than Fielding-Independent metrics. What that means is at a technical level, dScore views the pitcher as directly responsible for everything that happened after a pitch is thrown. There’s been a few outside cases that I’ll get into in a later article; but generally if a pitcher’s been bad, he’s generally viewed as having been bad, or vice versa. It seems particularly bad at projecting regression from underperformance, although I haven’t been tracking pitcher movement as well as I should. I’ll look to implement some sort of evaluation by next year.


Top Performing SP by Arsenal, 2017
Rank Name Team dScore
1 Max Scherzer Nationals 55.73
2 Alex Wood Dodgers 55.54
3 Corey Kluber Indians 49.15
4 Chris Sale Red Sox 46.43
5 Clayton Kershaw Dodgers 43.53
6 Dallas Keuchel Astros 38.90
7 Noah Syndergaard Mets 33.45
8 Lance McCullers Astros 32.17
9 Randall Delgado Diamondbacks 30.50
10 Zack Godley Diamondbacks 29.69
11 Stephen Strasburg Nationals 26.92
12 Jacob deGrom Mets 25.13
13 Luis Severino Yankees 24.38
14 Luis Castillo Reds 23.65
15 Trevor Cahill Padres 23.63
16 James Paxton Mariners 21.46
17 Kenta Maeda Dodgers 20.61
18 Zack Greinke Diamondbacks 20.48
19 Nate Karns Royals 20.42
20 Carlos Carrasco Indians 19.96
21 Rich Hill Dodgers 17.86
22 Masahiro Tanaka Yankees 17.43
23 Danny Salazar Indians 17.06
24 Brad Peacock Astros 16.51
25 Marcus Stroman Blue Jays 15.48


The Studs

The top eight guys are really a who’s-who. Scherzer, Wood, Kluber, Sale, Kersh, Keuchel, Syndergaard…Only guy I’m touching on here is Thor, who’s close to begin throwing again. Lat injuries are a whole lotta “?????” for pitchers, but he’s certainly worth a buy if someone is (stupidly) wanting to sell.


The Loaded Teams

Astros – Dallas Keuchel (6), Lance McCullers (8), Brad Peacock (24) / McCullers has broken out. Consider him a stud going forward.

Diamondbacks – Randall Delgado (9), Zack Godley (10), Zack Greinke (18) / Delgado is likely more of a bullpen option at this point. Godley had an awful first outing off the break, but dScore really believes in him.

Dodgers – Alex Wood (2), Clayton Kershaw (5), Kenta Maeda (17), Rich Hill (21) / Come on, really? Give some other team a chance!


The Young Breakouts

Zack Godley (10) – I touched on him above. Although I’m pretty sure he’s due for regression, dScore continues to think he’s got premium stuff. Continue to roll with him.

Luis Castillo (14) – He’s 29 innings into his big-league career, but that’s also 29 innings vs. the Nationals (twice), Rockies (once, in Coors), and the Diamondbacks (once, in Chase). All three teams rank in the top five in the NL in runs scored. BUY. / FUN FACT: The Rockies rank third in runs scored, but are tied with the Padres for dead last in the NL in wRC+ at 81.

James Paxton (16) – He is who we thought he is.


The Still Believin’

Kenta Maeda (17)

Masahiro Tanaka (22)

Danny Salazar (23)

Tanaka’s been god-awful. dScore agrees with his 3.73 xFIP though, and says he should’ve been significantly better than he is. Salazar has somehow been worse, but once again dScore sides with his 3.57 xFIP and says BUY when he comes back from the minors, although I feel like that’s what Salazar’s always been. Every metric says he should be significantly better than he actually is. In 10 years I feel like his career is going to spawn the ultimate sabermetric “what could have been” from FanGraphs.


The Just Missed

Jacob Faria (26)

Jose Berrios (28)

Mike Clevinger (29)

Jordan Montgomery (30)

Chris Archer (31)

A whole bunch of kids and Archer, aka the pitcher we all want Danny Salazar to be.



Nathan Karns (19) – Thoracic Outlet Syndrome. Well, it was a good idea for the Royals…


Notes From Farther Down

Newly-minted Cubs ace Jose Quintana is sitting at 76th. Remember how I said this metric was bad at projecting regression from underperformance? Quintana was sitting just inside the top 100 before his last start. Even though dScore agrees he’s been bad, I’m still buying Quintana in bulk. Old Cubs ace Jon Lester is still getting love from dScore, even after his absolute meltdown vs the Pirates. He’s at 39th. Fellow lefties Sean Manaea and Eduardo Rodriguez bookend him at 38th and 40th respectively. Manaea was sitting in the high-teens for most of the season, then seemed to lose feel for his slider and effectively stopped throwing it. That really hurt his hittability and K’s. It came back around last start vs. Cleveland. I’m continuing to buy him as a #2 ROS. Boston activated Rodriguez recently. Adam Wainwright (104), Julio Teheran (108), Jake Odorizzi (123), Matt Harvey (137), Aaron Sanchez (140), Cole Hamels (143) are a whole bunch of ughhhhh. I’m out on all but Hamels, who I’d argue to hold. His strikeouts disappeared before getting shelved with an oblique strain, then got shelled in his first start back vs. Cleveland. His last three starts have been vintage, and I’m anticipating dScore to catch back up.

Who To Expect the Most Improvement From in the Second Half

Baseball is a very fickle sport; sometimes everything will be going your way, and sometimes it may be the complete opposite. There will always be guys who go through long stretches where they are seemingly doing everything right but the results just are not coming. With that being said, let’s take a look at who should improve after the All-Star break.

Miguel Cabrera

Cabrera is in the midst of one of the worst seasons of his career. His .264 average would be a career worst, the 20 home runs he’s on pace for would be the third worst. His 110 wRC+ is his worst since his rookie season.

All signs point to that coming to an end quickly, though. Cabrera’s .067 xwOBA – wOBA is the highest in the league and his BABIP has plummeted to .307. He is obviously a terrible baserunner with his age so one might expect those numbers, but the .037 xwOBA – wOBA he posted in 2015-2016 and his .346 career BABIP suggest it has been more than his age. Comerica Park is one of the more pitcher-friendly parks in the league, but still shouldn’t account for the bad luck.

Cabrera’s batted-ball profile also appears to be in great shape. He is hitting more line drives than ever before, while also utilizing all parts of the field at a career high. To go along with that, his Hard% and Soft% are career bests. His Hard% is second in the league and within 0.1% of the godly Aaron Judge. Cabrera’s contact rates are slightly down but right in line with the last couple of seasons, and his O-Swing% and Z-Swing% are also similar to his past.

The basic numbers suggest he’s having perhaps the worst season of his career, but Cabrera’s peripherals suggest one of his best. Expect bigger things from the two-time MVP in the second half.

Matt Carpenter 

Carpenter’s numbers have not disappointed quite to the extent of Cabrera’s. He is hitting only .237 and his 119 wRC+ are down, but he is also posting an absurd 17.5% BB% and just a 18.6% strikeout rate. His 14 home runs show a little bit of improved power. However, the numbers suggest he could be doing quite a bit better.

His xwOBA is .044 higher than his wOBA, which is tied for eighth in the league. Similar to Cabrera, he is not an exceptional baserunner and is not playing in a hitter’s park, but his 2015-2016 xwOBA was only .014 higher than his wOBA. He’s also experienced the same BABIP drop as Cabrera, as the .256 mark he’s running in 2017 is way off his career .322 BABIP.

Carpenter’s batted-ball profile doesn’t excite as much as Cabrera, as his line-drive rate is down and his Soft% is up. But his hard-contact rate is at a career-high 45.1%.

His season has not been a total disappointment to date, but expect it to improve in the second half.

Manny Machado 

Lastly, we have the player disappointing the most on this list. Without even looking at the numbers, Machado could easily be included on this list. Machado is still not even at the peak of his prime yet, as he turned 25 just over a week ago. The three time All-Star posted 6+ WAR in three of the last four seasons. The only other players to do the same were Mike Trout and Josh Donaldson.

So, even without digging into things, improvement in the second half is expected. Luckily, the peripherals also support an improvement from Machado. His xwOBA of .355 is far more impressive than his .319 wOBA, and Machado is actually a solid baserunner and plays in a generally neutral park at Camden Yards. The -.013 xwOBA – wOBA he had in 2015-2016 makes a lot more sense than the .036 he is running right now. The .239 BABIP in 2017, way off his .302 career mark, further suggests bad luck.

Just like Cabrera and Carpenter, Machado’s batted-ball profile is actually even a little more impressive than past seasons. His hard-hit rate of 40.2% would be a career high by a good amount and his soft-contact rate has seen a 3% decline from last year.

There may be more cause for concern with Machado than the others, though. He has basically forgotten how to hit line drives, as his LD% has cratered to 13.9% and his ground-ball rates are up. Along with that, his pull rates are creeping up. Luckily, Machado crushes his ground balls. His 89.4 average GB MPH ranks fifth in the league, which helps to offset his minuscule liner rates. But even with that, his Contact% of 76.3% would be the lowest since his rookie year, and his plate discipline is trending in the wrong direction.

It’s possible Machado is selling a bit of his contact skills for improved batted balls, but the GB/LD/FB tendencies don’t support that. Overall, considering Machado’s youth, talent, and most of the peripheral numbers, a large improvement should be expected. However, it does appear that something may be a little off with the Orioles’ franchise third baseman.

Adam Wainwright Might Have Turned a Corner

For the last year and a half, Adam Wainwright has been singing the same tune after bad starts.

“My arm feels great. My body feels great. I know what adjustments I need to make. I’ll be back.” Cardinals fans have heard those lines from Uncle Charlie since his struggles began. For all of 2016, and most of this season, the idea of Wainwright returning to pre-Achilles tear form seemed preposterous.

There have been games in which Wainwright looked like he should hang it up, like June 6 against Cincinnati (otherwise known as the Scooter Gennett game). At other times, he looked a lot like the Adam Wainwright of 2012-2014, like May 27 at Colorado. That day, he went seven shutout innings at Coors Field, and only gave up three hits.

Wainwright’s ERA is 5.20 going into Monday’s start in New York. But, if you take out the 24 runs allowed in 6 1/3 innings against Miami, Cincinnati, and Baltimore, his ERA would be 3.14. That would be top-10 in the NL, as Jose de Jesus Ortiz noted in the Post-Dispatch.

Why the wild discrepancy? I looked at each start Wainwright has made this season, and divided them into two groups: quality starts and non-quality starts.

Usage Rates

The first thing I looked at was how often he throws each pitch, broken up by quality starts and non-quality starts.

There’s not much to see here. The only significant change is that Wainwright throws more four-seam fastballs in quality starts, but that’s offset by an increase in sinkers in non-quality starts. Either way, the variance isn’t enough to account for such a massive discrepancy in outcome.


If Wainwright isn’t mixing his pitches differently, maybe he just throws them harder (or slower) on certain days. Thanks to Brooks Baseball, took the average velocity of each of his pitches in every start. Then, I calculated the quality start average velocity and the non-quality start average velocity.

Again, not what I expected. Since Wainwright is a pitcher presumably in decline due to age, I didn’t expect to see him throwing harder in his bad outings. Wainwright has only thrown his four-seamer harder in quality starts than non-quality starts, and the difference was only 0.5 miles per hour. He’s thrown every other pitch harder in non-quality starts.

At this point, after many calculations, I was beginning to get discouraged.

Changing Speeds

On Brooks Baseball, if you click on a pitchers game log, it will show usage rates, strike percentages, average velocity, and max velocity. I didn’t intend to track max velocity, but I noticed something as I went along: it seemed like the difference between Wainwright’s average velocity and max velocity was greater in quality starts.

I know that’s a lot of numbers, but bear with me. The key columns are the two right-most. In quality starts, Wainwright has more velocity variance in every pitch except the four-seamer (I excluded the change from this analysis because he doesn’t throw it often enough).

I especially want to focus on the cutter and the curve, since up to June 22 opponents were hitting .286 against the curve and slugging .512 against the cutter.

In Wainwright’s last start against the Mets, his average cutter was 82.8 miles per hour. He also ran it up to 88.5 miles per hour. On that afternoon, hitters had to deal a pitch that moves a fair amount, but could also come at them at any speed within an eight to ten mile per hour range (if the average is 82.8, there had to have been some slower than that). In that same start, he threw his curve between 71.9 miles per hour and 76.5.

Doubling Down

In his last four starts, it appears Wainwright has doubled down on changing speeds within the same pitch.

I looks like Wainwright has made an adjustment. It’s not a surprising one, as Wainwright is the type of pitcher that would alter the tempo of his delivery in order to disrupt the timing of the hitters. The league might adjust to him. However, if this is sustainable, Adam Wainwright might have found his way to continue pitching at a high level for several more years.

This article first appeared in The Redbird Daily.

Introducing XRA: The New Results-Independent Pitching Stat

There are a multitude of ways that we can judge pitchers. Most people look at earned run average to gauge whether a pitcher has been successful, while many old school announcers will still cite a pitcher’s win-loss record. ERA is a nice, easy way of looking at how a pitcher has performed at limiting runs, but it doesn’t come close to telling the whole story. In the early 2000s, Voros McCracken created the idea of Defense Independent Pitching Stats or DIPS, which credited the pitcher only with what he could actually control. Fielding Independent Pitching was born from this theory and only took into account a pitcher’s strikeouts, walks and home runs allowed. It turns out that a pitcher’s home run rate is not terribly consistent, thus xFIP was created by Dave Studeman to normalize the home run aspect of the FIP equation by using the league home run per fly ball rate and the pitcher’s fly ball rate.

In 2015, a new metric was developed by Jonathan Judge, Harry Pavlidis and Dan Turkenkopf called Deserved Run Average or DRA. This new stat attempts to take into account every aspect that the pitcher has control over and control for everything that he does not, thus crediting the pitcher only for the runs that he actually deserves. DRA, however, is still dependent on the result of each batted ball. If the batter hits a ball deep in the gap and it rolls to the wall, the pitcher is charged with a double, but if the center fielder lays out and makes a remarkable catch, the pitcher is credited with an out. When evaluating pitchers, why should it matter whether they have a Gold Glove caliber defender behind them or not? It shouldn’t, and that’s where Expected Run Average comes in.

Expected Run Average or XRA gives pitchers credit for what they actually can control. FIP attempts to do this as well but assumes that pitchers have no control over batted balls. While the pitcher does not control how the fielders interact with the live ball, he does have an impact on the type of contact that he allows. XRA is based on a modified DIPS theory that the pitcher controls three things: whether he strikes the batter out, whether he walks the batter and the exit velocity, launch angle combination off the bat. After the ball leaves the batter’s bat, the play is out of the pitcher’s hands and should no longer have any effect on his statistics. The goal is to figure out a way to measure, independently of the defense and park, how each pitcher performs on balls in play. Since 2015, StatCast has tracked the exit velocity and launch angle of every batted ball in the majors. Each batted ball has a hit probability based on the velocity off of the bat and its trajectory. The probability for extra bases can also be determined. These batted ball probabilities have been linearly weighted for each event including strikeouts and walks to give each player’s xwOBA, which can be found on Baseball Savant. This is the perfect way to look specifically at how well a pitcher has performed on a per plate appearance basis.

Once xwOBA is found, then XRA can be calculated. The first objective is to find the pitcher’s weighted runs below average. To do this, I used the weighted runs above average formula from FanGraphs except I made it negative since fewer runs are better for pitchers.

wRBA = – ((xwOBA – League wOBA) / wOBA Scale) * TBF

For example, Max Scherzer has had a .228 xwOBA so far this season and has faced 487 batters. After finding the league wOBA and wOBA scale numbers at FanGraphs I can plug these numbers into the formula.

– ((.228 – .321) / 1.185) * 487 = 38.22

Max Scherzer has been 38.22 runs better than average so far this season, but now I need to figure out what the average pitcher would do while facing the same number of batters. To find this I need the league runs per plate appearance rate and multiply that number by the number of batters that Scherzer has faced.

League R/PA * TBF = Average Pitcher Runs
.122 * 487 = 59.41

So a league average pitcher would have been expected to surrender 59.41 runs facing the number of batters that Scherzer has so far this season. Now that we know how the average pitcher should have performed we can find the expected number of runs that Scherzer should have surrendered so far this season by subtracting his wRBA of 38.22 from the average pitcher’s runs.

Average Pitcher Runs – Weighted Runs Below Average = Expected Runs
59.41 – 38.22 = 21.19

Based on Scherzer’s xwOBA, he should have only given up 21.19 to this point in the season. If this sounds incredible it’s because this is the lowest mark of any starting pitcher though the first half of the season. Finally, XRA is found by using the RA/9 formula by multiplying the expected number of runs allowed by 9 and then dividing by innings pitched.

(9 * Expected Runs) / Innings Pitched = XRA
(9 * 21.19) / 128.33 = 1.49

Max Scherzer’s XRA of 1.49 is easily the lowest of any starter through the first half. The second best starter has been Chris Sale who has a 2.15 XRA. Of course these names are not surprising as they each started the All Star Game and are both currently the front runners for their leagues’ respective cy young award.

Here is a list of the top ten qualified pitchers:

Pitcher XRA
Max Scherzer 1.49
Chris Sale 2.15
Zack Greinke 2.26
Corey Kluber 2.33
Clayton Kershaw 2.34
Dan Straily 2.87
Lance McCullers 2.89
Chase Anderson 3.11
Luis Severino 3.17
Jeff Samardzija 3.23

And the bottom ten:

Pitcher XRA
Matt Moore 6.58
Kevin Gausman 6.47
Derek Holland 6.32
Matt Cain 6.26
Ricky Nolasco 6.26
Wade Miley 6.17
Johnny Cueto 6.10
Martin Perez 5.97
Jason Hammel 5.95
Jesse Chavez 5.84

Full First Half XRA List

It is interesting to see that three members of the Giants rotation rank in the bottom seven in all of baseball. In fact, AT&T Park is such a pitcher-friendly park that once you park adjust these numbers, Moore, Cain and Cueto become the three worst pitchers in baseball. It’s not surprising then why the Giants are having such a disappointing season.

One measure of a good stat is whether or not it matches your perception. Therefore, while it is interesting to see Dan Straily as one of the best pitchers in baseball and Johnny Cueto as one of the worst, it is much more assuring to see Max Scherzer, Chris Sale and Clayton Kershaw as some of the very best in the sport. The numbers for relievers also reveal how dominant Kenley Jansen and Craig Kimbrel have been. This is all good evidence that XRA is doing what it is supposed to do, accurately displaying how good pitchers have actually been, independent of all other factors.

Another important characteristic of a good stat is how well it correlates from year to year. While ERA is the most simple and popular way to look at pitchers, it is not very consistent. XRA is much more consistent than ERA and FIP and also compares favorably with xFIP. However, it is not as consistent as DRA. DRA controls for so many aspects of the game that it should be expected to be the most consistent. However, being the most predictive or most consistent stat is not necessarily the goal of XRA. The real goal is to show how well the pitcher actually did, and XRA seems to do this remarkably. While not being as consistent as a stat like DRA, the level of consistency is extremely encouraging and puts it right in line with the other run estimators.

XRA is a stat that takes luck, defense, and ballpark dimensions out of the equation. When evaluating a pitcher, he shouldn’t be penalized for giving up a 350-foot pop fly for a home run in Cincinnati while being rewarded for that same pop fly being caught for an easy out in Miami. With XRA, no longer will people have to quibble about BABIP, since it is results-independent and removes all luck from consideration. A ground ball with eyes will now be treated the same whether it squirts through for a single or is tracked down for an out. Pitching ability will no longer need to be measured with an eye on the level of the defense. It takes a good offense, a good pitching staff and a good defense to make a great team, and with XRA we can finally separate all of these important factions.

Poll: Which Player Would You Rather Have for the Rest of the Season

I have included anonymous descriptions of three players. The descriptions include stats that were compiled by  those players a little before the All-Star break.

I have included a link to a Google Survey (at the end of this article). No information is being collected other than your responses. (The survey also includes an optional question about your personal assessment of your baseball knowledge).

The question is: Which player would you rather have for the rest of this season?

Please keep the following facts in mind when answering the question:

  1. The league average BABIP is .299.
  2.  The league average K% is 21.6%
  3. The league average BB% is 8.6%
  4. The league average HR/H is 14.5%
  5. On average in the league, 33% of the time a bat touches the ball, a hit occurs.


Player 1: “Frank”

Frank is a young hitting prospect. He has little major-league track record outside of the first half of this season, and he was considered a top prospect coming up through the minor leagues. He has been described as “freakish” in his size.

Frank strikes out nearly 30% of the time and walks nearly 17% of the time. 53% of the time his bat touches the ball a hit occurs. 30% of those hits are home runs.

Frank compiled these numbers through 81 Games and 352 PA.

Player 2: “Tom”

Tom is a young player, but he has been around long enough that he is verging on a veteran. He has been described as a “model slugger.”

Now in his eighth season, Tom has a career BABIP of .320, K% of roughly 28%, BB% of roughly 11%, and HR/H Ratio of 26%.

This season his BABIP is .299, his BB% is 10.5%, and his K% is roughly 24%. 38% of the time his bat touches the ball a hit occurs. 27% of those hits are home runs.

Tom complied these numbers through 83 Games and 352 PA.

Player 3: “Dan”

Dan is a young player, but he has a considerable track record. He has been described as one of “the most valuable properties in the game.”

Now in his sixth season, Dan has a career BABIP of .301, K% of roughly 17%, BB% of roughly 7%, and HR/H Ratio of 16%.  

This season his BABIP is .234, his BB% is 8.6%, and his K% is roughly 20%. 29% of the time his bat touches the ball a hit occurs. 24% of those hits are home runs.

Dan compiled these numbers through 82 Games and 360 PA.

Here is the survey link:

I will follow up with an article a week after this is published, showing the results, revealing who the players are, and assessing what the projections expect from those players the rest of the year.


T.J. Rivera Looks Like the Real Deal

T.J. Rivera has had a remarkably unlikely path to the majors, going from an undrafted free agent to now the Mets’ starting third baseman. He has always had his doubters, and still does, but he got to the majors by consistently putting up around a .300 average in the minors with an above-average OPS despite his lack of walks and power. In 2016, a hitter-friendly park helped him enjoy a career year in Triple-A, winning the PCL batting title with a .353 average, a .909 OPS, a 142 wRC+ and a promotion to the majors for the first time in his career at the age of 27. He continued his success into the majors, where he was a key piece in the Mets’ 2016 Wild Card run. He was able to replicate the numbers he had put up during his entire minors career, batting .333/.345/.443 with a 119 wRC+ in 113 plate appearances.

Rivera’s impressive and somewhat surprising debut stint in the majors eased some of the concerns scouts had with his game, but plenty of people still had their doubts. The expectation was that Rivera would not be able to hit for a .300+ batting average in the majors like he did in the minors due to the tougher competition and better defenses. Rivera proved them wrong by hitting .333, although he was admittedly helped out by an unsustainable but certainly not outrageous .360 BABIP. Rivera posted BABIPs comfortably over .300 in the minors, so while some regression seemed to be in store for his future, it was certainly not crazy to predict that Rivera would still be able to hold a .300 average in the majors. If he had any chance of becoming a full-time starter at the highest level, he was going to need to keep that batting average in the vicinity of .300 to make up for his lack of other skills, such as patience, power, and defensive ability.

Rivera has always been known as a line-drive hitter with an aggressive approach at the plate. He likes to swing early in counts, and as a result he doesn’t walk much, but at the same time he is a contact hitter and doesn’t let his aggressive approach negatively affect his strikeouts. He doesn’t have much natural power, so for him to be successful, he just has to continue focusing on trying to hit line drives to the gaps and swinging at the right pitches.

In his first sample of major-league pitching, he was able to hit line drives at an above-average rate of 23.9%, compared to the MLB average rate of about 21%. It’s worth mentioning that this rate was higher than his typical LD% in the minors, showing that he was actually hitting more line drives vs. major-league pitching than minor-league pitching. He hit ground balls at a rate of 42.4%, which was also lower than he generally hit in the minors, and of course, preventing the amount of ground balls you hit leads to more success at the highest level, especially when you’re hitting them to the best infielders in the world. This GB% was slightly lower than the MLB average of about 45%, showing that some work could still be done on his GB% but that it wasn’t a serious problem. He also may have been helped about by a bit of luck on some of these ground balls, as he had a .360 BABIP that was sure to regress a little. Rather than hitting ground balls, the thing he needed to work on was hitting fly balls, which he did at a slightly below-average rate of 33.7%. For someone with not a lot of raw power, hitting more fly balls would be beneficial to making the most of whatever power he did have.

Overall, Rivera’s results in the majors had been a very pleasant surprise, don’t get me wrong. The key thing he showed in his 2016 debut is that he was not over-matched by major-league pitching, continuing to do the same things that made him successful in the minors. But in 113 plate appearances, he drew a grand total of three walks, which won’t quite cut it if you want to be an everyday starter. In addition to that, he was only making hard contact (according to FanGraphs) 27.2% of the time, below the MLB average of about 31%. Being the line-drive hitter that he was, he had the ability to hit the ball harder, and the thing he needed to do was to focus on hitting more fly balls and improving his launch angle by just a tick. This doesn’t mean that he needed to become a completely different hitter, but hitting the ball a little higher in the air more rather than on the ground or in a straight line would benefit him in not only his average but his isolated power, and also help him hit for a BABIP that would be less likely to regress.

Things got off to a bit of a slow start in 2017 due to lack of playing time and a short stint in Triple-A, but as injuries have befuddled the Mets, he has received more and more playing time, and at this point has basically hit himself into a starting role at third base.

As of July 15th, Rivera has hit .304/.350/.464. A chunk of this production has come in his last 10 games, where he’s hit nearly .500 en route to a 10-game hitting streak. Still, that batting line is “classic T.J.” At first glance it might seem like a small drop-off from last year, but if you look a little deeper, Rivera has actually improved in quite a few areas compared to last year.

First off, he has slightly decreased his soft-contact rate since last year by 2% while increasing his hard-contact rate by 4.2%. Immediately this looks like a recipe for success; hitting the ball harder more often and softer less often cannot be a bad thing.

While hitting the ball harder compared to last year, he’s also hit more fly balls, improving from a slightly below-average 33.7% last year to an above-average 40.1% this year, while also decreasing his GB% by 6.9%. So he’s hitting the ball harder, he’s hitting more fly balls, and he’s hitting fewer ground balls. These were all little things that I mentioned earlier that he could tweak to become a more polished hitter, and he has improved slowly but surely in these minor aspects of his game.

But at heart, Rivera is still the same hitter, just a better version of himself. He’s still a line-drive machine, with an LD% just a tiny bit higher this year compared to last year (24.3 vs.23.9). This shows that he has improved on hitting the ball harder and in the air while still playing his usual game. And, as it should, hitting the ball harder has caused his ISO to increase from .143 to .160, meaning that he’s taking better advantage of the power he has.

While he is still aggressive and still likes to swing early in counts, he’s also improved his walk rate slightly, from a measly 2.7% to a still below-average 4.5%, as Rivera’s plate discipline has slightly improved this year. Here’s a graph of his amount of pitches swung at outside the zone (blue), inside the zone (red), and overall (yellow).


He’s become slightly more patient and selective, swinging at more pitches in the zone and fewer pitches out of the zone. The data also shows that he’s swinging at the right pitches, as here’s a graph of his contact rate outside the zone (blue), inside the zone (red), and overall (yellow).


As you can see, he’s making contact at about the same rate on pitches in the zone, while the pitches he’s going after that are outside the zone have generally been better pitches to hit, as you can see by his increased O-Contact%. Even more importantly, he’s swinging and missing less, as last year he swung and missed an above-average 12.1% of the time while this year he’s swinging and missing at a slightly below-average rate of 10.2%. Rivera will always be a contact-first type hitter, but he’s tweaked some minor flaws in his game and is actually molding into more of an all-around hitter than people may think.

So why is his batting line appear slightly worse than last year, if he’s doing so many things better? Well, it’s really only his batting average that has declined, and that’s mostly due to a BABIP .024 lower than last year. In the minors, Rivera had always been able to keep a BABIP in the mid-.300s, so with a BABIP of .336 this year and the fact that he’s hitting the ball harder and in the air, there shouldn’t be any regression this year; in fact, his batting average is more likely to go slightly up than down. He’s improved his on-base skill and power to the point where they are still below-average skills, but they are respectable enough that his excellence in hitting for average and hitting line drives outweighs them.

So T.J. Rivera really seems like a major-league starter this year, proving that his amazingly consistent minor-league numbers and impressive MLB debut were not flukes. His defense is admittedly mediocre, as he’s accumulated -2.0 defensive runs in his career according to FanGraphs. But there really is no doubt that he can hit. This guy now has a .322/.367/.439 batting line in 3,225 professional plate appearances, so I think it’s time to stop doubting what he can do and let him play every day, because with the improvements he’s made in his game and the way he’s been able to adjust to major-league pitching, he absolutely deserves it.

Aaron Judge Among 25-Year-Old Rookies

The phenomenon that was Aaron Judge’s first half was indeed a sight to behold. Thirty home runs before the All-Star break? Get in line for some hardware.

As some have noticed, as impressive as Judge’s power display has been, he is still a 25-year-old rookie. Now, it used to be that holding rookie status at 25 was perfectly fine, but Trout, Harper, Correa & Co. have jacked up rookie expectations a bit. Players as good as Judge has been at 25 are increasingly often almost this good at 22 or even younger.

But that’s not the point today.

The point today is this: Aaron Judge is on track to have an unprecedented season for a 25-year-old rookie.

Judge’s season already ranks 5th in home runs and 4th in WAR since 1901 for players with rookie status at 25 years of age. Even if he comes down to earth in the second half, he could — should — easily clear first place in both measurements. He only needs six home runs and 0.8 WAR do so. Additionally, his wRC+ has 30 points of leeway for him to maintain first place in that stat as well.

So, it will be a disappointment if Judge does not end up with the best 25-year-old rookie season ever.

Let’s not end the story there. I’d like to examine the full careers of some of those players who still, for now, rate ahead of Judge by wRC+ and home runs. We’ll keep things in the expansion era and stick to three: Tony Oliva in 1964, Mitchell Page in 1977, and Ron Kittle in 1983.

Tony Oliva, 1964 Minnesota Twins (32 HR | 148 wRC+ | 6.2 WAR)

Oliva never hit 30 home runs again and only once more exceeded a 148 wRC+, but he put together a terrific career for the Twins, with 41 WAR, 220 homers, a 129 wRC+. So he gives hope that even if Judge never hits for this much power again, he can still have a very fruitful career. This statement seems very modest now, but Page and Kittle weren’t so fortunate as Oliva.

Mitchell Page, 1977 Oakland Athletics (21 HR | 157 wRC+ | 6.2 WAR)

Page radiated brilliance as a rookie, competence as a sophomore, and was then roughly replacement level for the remainder of his career. I can’t tell you much about what happened to Page, but this newspaper article is an interesting one. Injuries and a dispute with infamous owner Charlie Finley may well both have played a role in Page’s decline.

Unlike Judge, Oliva, and Kittle, Page’s game was not reliant on home-run power. He hit 28 doubles, 8 triples, and stole 42 bases in 1977. But he probably had the best rookie season we’d seen from a 25-year-old until this year. Unfortunately, the rest of his career did not live up to that standard.

Ron Kittle, 1983 Chicago White Sox (35 HR | 118 wRC+ | 2.0 WAR)

Kittle currently holds the “record” for most home runs by a 25-year-old rookie, but he had a weaker rookie year than Oliva or Paige. He also hit 32 home runs the next year, 26 the year after that, and then 21. After that, he never hit more than 18 and could never again make 400 plate appearances in a season. Kittle was great in 1988-89 but only had a combined 450 PA those years, putting up 2.8 WAR and 29 homers.

Kittle ended up hitting a home run every 17 times he stepped to the plate in his career; he just didn’t step to the plate often enough, field well enough, or run the bases well enough to gain more than 5.2 WAR in 3013 PA.

It takes more than power to succeed at major-league baseball. Judge seems to have more than just power, with six steals and a potentially decent glove in right field. He’ll still have to maintain other skills — and stay healthy — to avoid Kittle’s fate. He is very likely to do so, but nothing should be taken for granted.


Oliva, Page, and Kittle (or Jimmie Hall, whose career ended up looking like a rich man’s Kittle or poor man’s Oliva) can’t really tell us all that much about what Aaron Judge’s future may hold.

They are only three players out of many, and we didn’t even look at 24- and 26-year-old rookies.

Kenny Lofton was also a 25-year-old rookie who performed admirably and went on to produce a fantastic career, and whose rookie year did not end up a career year. We didn’t look at Lofton because he is such a different player than Judge, but Lofton is a much better precedent for 25-year-old rookies looking to build on their success than Oliva, Page, and Kittle.

Mark Trumbo is also worth mentioning. He hit 29 home runs as a rookie and has since exceeded that mark three times, although his non-power skills have always been lacking.

This piece is getting longer than expected and it’s time to wrap up.

The careers of Oliva, Page, Kittle, and Hall do contain a couple potentially foreboding patterns. Their rookie home-run numbers remained their single-season career highs, and with the exception of Oliva in 1971 and Kittle in some parts of seasons, none of them ever improved on their wRC+ either.

Aaron Judge is his own player, and will almost certainly have a better rookie year than any of these three comparisons managed. Given that, we can also expect a better career than they managed. And, of course, it’s no shocker to suggest that a near-200 wRC+ will eventually regress.

However, perhaps it is worth wondering about the future of a 25-year-old rookie and whether to treat it the same as a, say, 22-year-old’s future.

Or perhaps it’s not. Regardless, you should ignore me anyway and enjoy Aaron Judge’s mammoth displays of power. The Yankees certainly enjoy it.

Is Kershaw Really a Postseason Choker?

Dodgers superstar ace Clayton Kershaw has already cemented himself as the greatest starting pitcher of this generation and could go down as one of the best of all time. Despite all his tremendous regular-season success, an ongoing narrative has haunted him throughout most of his career, a well-known theory that Kershaw chokes in the postseason and can’t pitch in big games.

But in reality, this actually hasn’t been the case, and the fact that so many people consider Kershaw to be a choke artist speaks more to his amazing regular-season dominance than any struggles he’s had in the playoffs. Through 282 starts in the regular season, Kershaw has an outstanding 2.35 ERA and 0.998 WHIP, so anything worse than that in the postseason is going to feel like a disappointment.

The main argument defending Kershaw’s postseason woes for awhile now has been lack of sample size. As Kershaw has reached the playoffs more and more this argument has weakened a little bit but is still relevant, as his 89 total postseason innings pitched is less than half of what Kershaw pitches in a typical regular season. It’s a large enough sample size that we can make some conclusions about how Kershaw has pitched in the playoffs, but not enough that we can judge his true-talent level. We have 1892.1 innings of regular-season data to judge his true-talent level.

Let’s start with the basic statistics. In 18 games (14 starts), Kershaw is 4-7 with a 4.55 ERA and a 1.16 WHIP. At first glance these numbers seem not horrific, but very underwhelming for what we’ve come to expect from Kershaw. This ERA is a mix of some very good starts and some not so good ones that evens out to a mediocre 4.55.

But as we start delving into the advanced statistics, Kershaw doesn’t look so bad. His FIP is a very good 3.13, with his xFIP about the same at 3.17. These stats take into account the things the pitcher can mostly control — strikeouts, walks and home runs — in an attempt to gauge a pitcher’s true-talent level in the sample size given, and are on the same scale as ERA. So in a sense, Kershaw has had some bad luck in the playoffs, and while the results still haven’t been as great as his regular-season results, he has still mostly pitched like himself.

But where does this FIP come from, and why is it so much lower than his ERA? FIP takes into account strikeouts, an area in which Kershaw has actually performed better in the postseason than in the regular season. In the regular season, he has averaged 9.88 K/9, while in the postseason, he has averaged 10.72 K/9. He has also kept his walks down in the playoffs, averaging 2.73 BB/9, which is only a little bit worse than his regular season 2.37. As a result, his 21.5 K-BB% in the postseason is nearly identical to his 21.2 regular season K-BB%. So the problems he’s had in the postseason haven’t had to do with walking too many hitters or not striking out any batters. In that regard, he’s still pitched like the Clayton Kershaw we know and love. So where have his issues come from?

The answer to that is a higher average on balls in play, a higher HR/FB%, and a bad bullpen coming in to relieve him. FIP also takes into account home runs, and he has allowed more home runs in the postseason, averaging 1.01 HR/9 (which is still good, just not Kershaw good) versus an outstanding 0.58 HR/9 in the regular season. It’s really not fair to criticize him too much for this since his postseason sample size is still less than half of a regular season. In fact, that 1.01 HR/9 is actually better than his 2017 regular season HR/9 so far, which is a very uncharacteristic 1.22 in a year where he’s been neck-and-neck with Max Scherzer for the Cy Young award. Kershaw has allowed more home runs in the postseason as a result of not only a slightly higher fly ball% but also a higher HR/FB%, 10.9 versus 7.7 in the regular season. While this doesn’t mean that he’s been unlucky, it does mean that his HR/FB% is likely to regress closer to his career norms. xFIP takes this into account and the number ends up being virtually the same as his FIP.

In addition to the extra home runs, Kershaw hasn’t been as lucky on balls in play as he has in his career. In the regular season, he’s held a .269 BABIP, which for most pitchers would be thought to be unsustainable, but Kershaw’s pitched for so long now that it’s become clear that he’s just that good. He hasn’t been quite as lucky in the postseason, where he’s allowed a .295 BABIP. And it’s not like Kershaw has allowed way more hard-hit balls in the playoffs than in the regular season, although he has allowed slightly more. He has a 20.1 line-drive rate in the playoffs, which is just slightly higher but very similar to his 19.8% in the regular season. Pitchers obviously try to prevent line drives, as they often result in hits, and Kershaw has prevented line drives from being hit about as well in the playoffs as in the regular season. So that’s not the problem.

Kershaw has allowed slightly more fly balls — 40.2 FB% versus 34.3% — and this, paired with the higher HR/FB%, makes for a bad combination and more home runs. He’s still allowed ground balls at a similar rate, only slightly less, at 39.7% versus 45.9%. So has Kershaw allowed more well-hit balls in the postseason than in the regular season? Yes, but only slightly, and not enough that he should be considered a choker. The only slight increase in line drives shouldn’t result in as big a gap in BABIP as it actually does, meaning that luck has not quite been on Kershaw’s side the way it has been in the regular season. He’s struck people out like regular-season Kershaw, he’s prevented walks like regular-season Kershaw, and he’s prevented balls from being well hit only slightly less than regular-season Kershaw. That, in addition to slightly more fly balls leaving the ballpark, has resulted in a really good pitcher that maybe is not quite as good as regular-season Kershaw, but still very good, and it certainly doesn’t warrant calling him a “choke artist.”

It can also be argued that Kershaw has been overused and over-pressured to do well. He’s been so ridiculously good in the regular season that the expectations are for him to be just as good in the playoffs and to do it practically every three or four days against the best teams in baseball. Anything less and he seem like a disappointment. People often overlook the great moments he’s had in the playoffs, like when he came out of the bullpen against the Nationals to save a tight game or when he dominated the eventual World Champion Cubs in Game 2 of the 2016 NLCS. As a result of high expectations and trust in Kershaw, he has perhaps been left in games slightly longer than he maybe should have.

An occurrence that has plagued Kershaw in the postseason a few times is going deep into games and then getting hit around before his exit from the game. He’s often left with men on base, and the relievers coming in after him haven’t exactly been kind to him, allowing nine of the 14 runners he’s left on base to score. Let’s say the bullpen comes in and dominates, stranding all 14 of those runners, and his postseason ERA drops from 4.55 all the way down to 3.64.

Also remember that in the playoffs, teams are in their full strength and effort, doing everything they possibly can to try and win. These are the best teams in baseball, the teams that had everything working well enough for 162 games to make it past all the other teams and into the playoffs. The offenses Kershaw has to face in the playoffs are going to generally be better than the average offense he might face throughout the season. It is not uncommon for great pitchers to have slightly worse results in the playoffs. Madison Bumgarner, a famous “postseason hero” for the Giants, has a postseason FIP only 0.02 better than Kershaw’s and an xFIP 0.43 worse than Kershaw’s. Luck can go in very different directions for some pitchers in small sample sizes, and this is a perfect example.

Look at Pedro Martinez. In more postseason innings pitched than Kershaw, he has a significantly worse FIP/xFIP (3.75/4.31) despite an unsustainable low BABIP of .257, lower than his regular season .279. And no one thinks of him as a postseason “choker.” Greg Maddux, another all-time great, also has a worse FIP/xFIP (3.66/4.45) than Kershaw in even more innings pitched (198). And nobody considers him a postseason choker. Roger Clemens is the same deal. 3.52 FIP, 3.91 xFIP in 199 innings pitched. These pitchers are still considered all-time greats despite having postseason numbers that are arguably worse than Kershaw’s.

This really goes to show just how good Kershaw has been in the regular season. He puts up godlike numbers and then when he puts up “only” good numbers in the playoffs, it seems like he’s bad in comparison. When you look at the aforementioned fellow all-time greats, it’s clear that Kershaw is not the first great pitcher to have a little trouble in the playoffs.

So has Kershaw been as utterly dominant in the playoffs as in the regular season? No. But has he been a choke artist who gives up eight runs every time he’s put under pressure? No, not at all. He has had some rough outings in the postseason, particularly against the Cardinals, where he hasn’t been able to dominate and take control of the game quite like normal, but he has also had plenty of good moments of great pitching and when he’s left with runners on base, his bullpen has mostly let him down. All he really needs is one great World Series run to erase this ongoing narrative once and for all. No matter what, these small hiccups in the playoffs shouldn’t diminish the legendary career that Clayton Kershaw is in the midst of.

Losing Contact: The Shift From Singles to Power Hitting

The panel on ‘The Changing State of Sabermetrics: at the 2017 SABR convention in NYC with panelists Joel Sherman, Mark DeRosa, Vince Gennaro and Mike Petriello claimed that fewer balls are going into play and singles are actually down. They posed the question, “Are singles still a thing?”

With that in mind, we aimed to verify if these claims are true and what makes people feel that players are hitting fewer singles in today’s game.

We used data that’s current as of July 2, 2017.



Below you will see two charts illustrating the number of hits, home runs and strikeouts per game.

You can conclude three things from these graphs:

  1. Over the past 10 seasons, strikeouts have been increasing dramatically — 1.94 K/Game in the AL and 1.52 per game in the NL.
  2. Over the past 3 seasons, singles per game have dipped.
  3. Over the past 3 seasons, HR per game have spiked higher than ever before.


Plot 14

To get a good picture of the change in the distribution of hits, we broke down the AL and NL in the following two graphs. From these graphs you can conclude three things.

  1. Percentage of HR are spiking higher than ever before.
    1. AL home runs are up 4.6% from 10.3% to 14.9% since 2014
    2. NL home runs are up 4.32% from 9.85% to 14.17%  since 2014
  2. Percentage of singles are lower than ever before.
    1. AL singles down 4% from 68% to 64% since 2014
    2. NL singles are down 4.85% from 68.44% to 63.59% since 2014
  3. These spikes somehow started in 2014.



Plot 20
Plot 22

With strikeouts per game over the last 20 years rising 1.752 strikeouts per game in the AL (6.456 per game to 8.210 per game) and in the NL 1.5 strikeouts per game (6.754 per game to 8.255 per game), we wanted to see how this has affected offensive performance in terms of both batting average (BA) and batting average on balls in play (BABIP). For those unfamiliar with BABIP, it measures how often non-home-run batted balls fall for hits. This metric assesses how effective a particular hitter is at putting balls in play that lead to hits. The graphs below show how BA and BABIP are correlated.

  1. In the AL batting averages have dropped .271 to .255 over the past 20 years while BABIP has remained rather steady around .299.
  2. In the NL batting averages have dropped .263 to .254 over the past 20 years while BABIP has remained rather steady around .299.


Plot 18
Plot 16


Singles are decreasing at an alarming rate, yes. However, they’re still the most prevalent type of hit in the game. This trend is supported by the panel’s feeling that the shift has led to vastly improved defense and pitchers making better use of SABR data. Conclusively tying shifts to better defense is a bit harder, however, as shift data is difficult to obtain.

Additionally, home runs and strikeouts are increasing to all-time historic highs. This confirms the general sentiment on the panel that batters are now willing to take bigger risks to go for the HR, resulting in more home runs and strikeouts.

In follow-up pieces, we are going to look into why this may be happening, and attempt to look into how this helps generate fan interest.