It would appear that James Paxton is finally getting credit for the body of work he’s produced over the past two years. Jeff Sullivan wrote about this very topic last week, pointing to several metrics that support his claim that James Paxton is one of the best pitchers in baseball. I won’t rewrite his points here; instead, I will show you the many similarities between James Paxton and a relatively unknown pitcher outside Brewers nation, Jimmy Nelson. I don’t mean to imply Nelson is equally as good as Paxton, but I hope by the end of this article there will be a greater acknowledgement of what this pitcher has done this year.

Armed with a fastball that averages 96 mph, a darting 90 mph cutter, and a tight 81 mph knuckle curve, James Paxton is able to generate plenty of swings and misses and weak contact. His 28.8 K%, 7.0 BB%, 46.5 GB%, 5.6 HR/FB% in 2017 has led to a sparkling 2.70 ERA and 2.31 FIP. His 4.2 WAR would rank fifth-best in the MLB among pitchers if he had enough innings to qualify. While Jimmy Nelson doesn’t quite have the stuff of Paxton, his repertoire might sound familiar: a 94.5 mph fastball/sinker, a 89 mph cutter, and a 81 mph knuckle curve. His 27.3 K%, 5.9 BB%, 50.9 GB%, and 13.1 HR/FB% in 2017 has produced a 3.24 ERA and 3.04 FIP. His 4 WAR ranks sixth among qualifying pitchers.

The main differences in their 2017 performance in these metrics appears to be their HR/FB%. Jeff Sullivan addressed James Paxton’s ability to manage contact in his article, and there is evidence that he should be able to maintain a below-average HR/FB%. Over 399.1 innings, Paxton has a career HR/FB% of 8.1. In addition, he currently leads the majors in 2017 in xwOBA on balls in play based on launch angle and exit velocity. While it would be foolish to expect him to keep a 5.6 HR/FB%, a 8.1% might be his norm, circa Clayton Kershaw pre-2017. Despite having a well below-average xwOBA on balls in play, his career ERA is 19 points higher than his career FIP (with an even larger difference this year). I’m not sure if we can hand-wave this difference away, but we’ll accept his FIP as the more accurate measure for this analysis.

Jimmy Nelson, on the other hand, has a history of relatively loud contact. His 13.1 HR/FB% this year isn’t much different than his career 12.4%. Despite this high career rate, his xwOBA on balls in play this year is .343, good for 25th in baseball among 113 starters who have faced at least 250 batters. In addition, his average exit velocity is 85.2 mph, good for 13th-lowest among the same group. It might be that his contact management woes are coming to an end.

Perhaps 2017 will be seen as an out-of-nowhere career year for Jimmy Nelson. Perhaps 2017 will become his norm, and he’ll take his rightful place among the 10-15 best starters in baseball. Either way, he deserves more attention than he’s been given. It’d be a shame if he continues to be denied an All-Star despite producing like one (no pitcher with an equal or higher WAR missed the Midsummer Classic).

Since no posts can be complete with mere words, here’s a link to video of Jimmy Nelson’s most recent start.

There is no higher compliment that can be given to a ballplayer than to be given “The Bonds Treatment” — being intentionally walked with the bases empty, or even better, with the bases loaded. It’s called “The Bonds Treatment” because Barry Bonds recorded an astounding 41 IBBs with the bases empty, and is one of only two players to ever record a bases-loaded intentional walk. In other words, 28% of IBBs ever issued with the bases empty were given to Bonds — and 50% of IBBs with the bases loaded. Bonds was great, no denying that — but is there anyone out there today who is worthy of such treatment?

We can find out using a Run Expectancy matrix. An RE matrix is based on historical data, and it can tell you how many runs, on average, a team could expect to score in a given situation. A sample RE matrix, from Tom Tango’s site tangotiger.net, is shown below.

The chart works as follows — given a base situation (runners on the corners, bases empty, etc.) move down to the corresponding row, then move to the corresponding column and year to find out how many runs a team could expect to score from that situation. In 2015, with a runner on 3rd and 1 out, teams could expect to score .950 runs on average (or, RE is .950). If the batter at the plate struck out, the new RE would be .353.

We can take this a step further. Sean Dolinar created a fantastic tool that allows us to (roughly) examine RE in terms of a batter’s skill. Having Mike Trout at the plate vastly improves your odds of scoring more than having Alcides Escobar, and the tool takes this into account. We can use this tool to look at who deserves the Bonds treatment in 2017 (or, to see if anyone deserves the Bonds treatment): defined as being walked with the bases empty, or the bases loaded.

First, we can look at a given player and their RE scores for having the bases empty or full. In this instance, we will use Michael Conforto, who batted leadoff for the Mets against the Texas Rangers on August 9. Conforto’s wOBA entering the game was .404, and the run environment for the league is 4.65 runs per game, so Conforto’s relevant run expectancy matrix looks like this:

Batting behind him was Jose Reyes, who, entering the game, had a wOBA of .283. Let’s assume that Conforto receives the Bonds Treatment, and is IBB’d in a given PA with bases empty or loaded. What would the run expectancy look like with Reyes up? In other words, what is Reyes’ run expectancy with a runner on first, or with the bases loaded after a run has been IBB’d in?

To do this, we can look at Reyes’ RE with a runner on first and with the bases loaded. Reyes’ RE with a man at 1B is indicative of what the RE would be like if Conforto had been given an intentional free pass. For a bases-loaded walk, we look at Reyes’ RE with the bases loaded, and then add a run onto it (to account for Conforto walking in a run).

Then, we can compare the corresponding cells of the matrices to see if the Texas Rangers would benefit any from walking Conforto. If RE with Conforto up and the bases empty is higher than RE with a runner on first and Reyes up, or RE with the bases loaded and Conforto up is higher than RE with Reyes up and a run already scored, then we can conclude that it makes sense to give Conforto that free pass.

In this instance, we can see that if the Rangers were to face Conforto with the bases empty and two out, it would make more sense for them to IBB Conforto and pitch to Reyes than it would for them to pitch to Conforto, because RE with Conforto up (.172) is higher than RE with Reyes up and Conforto on (.145). As a result, Conforto is a candidate for the Bonds treatment in this lineup configuration, if the right situation arises.

Who else could be subjected to the Bonds treatment? It would take me a few months of work to run through every single individual lineup for every team to figure out who should have been pitched to and who should have gotten a free pass, so to simplify things, I looked at hitters with 400+ PA, looked at when they most frequently batted, who batted behind them most frequently, and whether or not they should have received the Bonds treatment based on who was on deck. While no lineup remains constant throughout the season, looking at these figures gave me a good idea of who regularly batted behind whom.

Three candidates emerged to be IBB’d with the bases empty every time, regardless of outs— Yasiel Puig, Jordy Mercer, and Orlando Arcia. These players usually bat in the eighth slot on NL teams, and right behind them is the pitchers’ slot — considering how historically weak pitchers are with the bat, it makes sense that RE tells us to walk them with the bases empty every single time.

The same could be said of almost anyone batting ahead of a pitcher — according to our model, given an average-hitting pitcher, any hitter with a wOBA over .243 should be IBB’d with the pitcher on deck (only one qualified hitter — Alcides Escobar — has a lower wOBA than .243). The three names above stuck out in the analysis because they were the only players with 400+ PA that had spent most of their PAs batting eighth.

So, an odd takeaway of this exercise is that in the NL, unless a pinch-hitter is looming on deck, the eighth hitter should almost always be intentionally walked with the bases empty, because it lowers the run expectancy. Weird!

The model also identified two hitters who deserved similar treatment to Michael Conforto in the above example (IBB with 2 out and no one on) — Buster Posey and Chase Headley.

Posey has batted with almost alarming regularity ahead of Brandon Crawford, who is running an abysmal .273 wOBA on the season. Headley is a little more curious — Headley is usually a weak hitter, but earlier in the season, Headley batted ahead of Austin Romine frequently, who was even worse than Crawford.

Headley technically isn’t that much of a candidate for the Bonds Treatment since Romine hasn’t batted behind him since June 30, but Crawford has backed up Posey as recently as August 3 — if he’s batted behind Posey again, the situation could very well arise where it becomes beneficial for teams to simply IBB Posey with two out and bases empty.

But ultimately, no one, aside from NL hitters in the eighth slot, emerges as a candidate to be IBB’d every time with the bases empty. And no one, regardless of the situation, deserves a bases-loaded intentional walk. Which raises the question — was it appropriate to give the man himself, Barry Bonds, the Bonds Treatment?

Bonds received an incredible 19 bases-empty IBBs in 2004 (more than doubling the record he set in 2002), so we’ll use 2004 Bonds and his .537 wOBA as the center of our analysis.

In 2004, Bonds batted almost exclusively fouth, and the two men who shared the bulk of playing time batting fifth behind him (Edgardo Alfonzo and Pedro Feliz) had almost identical wOBAs that season (.333 and .334, respectively) — so we’ll assume that the average hitter behind Bonds in 2004 posted a wOBA of .333. This yields RE matrices that look like this:

Bonds proves himself worthy not only of a bases-empty IBB with two out, but he just barely misses with a bases-loaded IBB. While no one ended up giving Bonds a bases-loaded IBB in 2004, they did give him one in 1998.

For perspective, Bonds was running a .434 wOBA in 1998, and Brent Mayne (who was on deck) was running a .324 wOBA — so this actually wasn’t a move that moved RE or win probability in the right direction.

The final spike in WPA is Bond’s IBB — it gave the Giants a better chance of winning. Ultimately, it was a bad idea that didn’t backfire in the Diamondback’s faces.

And of course, I would be remiss in not mentioning the other player to have ever received a bases-loaded IBB — Josh Hamilton.

With apologies to Hamilton, he wasn’t the right guy to get the Bonds treatment here, either — Hamilton ran a .384 wOBA in 2008, and Marlon Byrd, who was on deck, had a .369 wOBA, which means that an IBB in this instance was a really awful move. An awful move that, like Bonds’ IBB, was rewarded by Byrd striking out in the next AB.

Have there been other players deserving of bases-loaded IBBs? It’s possible, but the most likely candidates — Ted Williams and Babe Ruth — usually had good enough protection in the lineup. Of course, there are few hitters that could have protected Bonds from himself — hence why it’s almost a good idea to IBB him with the bases loaded.

While browsing Baseball Reference’s database, I encountered a strange thing. I was looking at the WAR leaders for each season. WAR stands for Wins Above Replacement, which is a statistic that measures how many wins a team gained by having a certain player instead of a replacement player, who would have a WAR of 0. The leaders were all recognizable names — MVPs, World Series Champions, Cy Young award winners, etc.

Surely the leader in WAR in 2008 finished near the top in the MVP voting. And of course he was an All-Star. And sure, maybe he even won a Silver Slugger or a Gold Glove.

Nope.

Nick Markakis led the American League in WAR with 7.4 and did not receive an MVP vote, make the All-Star team, win a Silver Slugger or take home a Gold Glove.

Markakis is now a veteran outfielder for the Atlanta Braves. But back in 2008, he was a stud for the Baltimore Orioles, and he led his team in hits, walks, batting average, and on-base percentage. Markakis had a reputable slash line of .306/.406/.491, scored 106 runs, drove in 87, clobbered 20 homers and stole 10 bases. These stats are excellent, and Markakis finished near the top of many leaderboards once the season ended.

He finished in the top ten in the AL in batting average, OPS (on-base + slugging), hits, extra-base hits, and Offensive WAR. The right fielder also closed the year in the top five in the AL in on-base percentage, runs, doubles, and walks. Markakis was also a great asset in the field, leading AL right fielders in games played and putouts while leading all AL outfielders in outfield assists.

According to Baseball Reference, there have only been 32 seasons when a player either matched or exceeded Markakis’ WAR, Offensive WAR, Defensive WAR, batting average and on-base percentage.

Markakis’ excellent stats were matched by only 32 players ever, but he was snubbed from every award.

Markakis was not selected as one of the six outfielders to compete in the All-Star Game despite his achievements.

Instead, Ichiro Suzuki, Josh Hamilton, Manny Ramirez, J.D. Drew, Carlos Quentin, and Grady Sizemore were selected. None of them recorded more than 6.0 WAR, while Drew did not even record 3.0 WAR. Markakis was thoroughly robbed of an All-Star appearance.

Looking back on it, it’s an injustice that Nick Markakis had such a standout year and did not even receive a single vote for MVP. His 7.4 WAR outnumbered every other candidate, including winner Dustin Pedroia, who recorded 6.9 WAR.

Markakis had arguably a better year than anyone in the American League in 2008, and yet was not recognized at all.

Markakis was also a standout defensively, and recorded 1.7 Defensive WAR. Yet he was not appreciated for this achievement either, as Torii Hunter (-0.1 dWAR), Ichiro Suzuki (0.8 dWAR), and Grady Sizemore (0.1 dWAR) took home the outfield Gold Gloves in 2008.

Another intriguing aspect of Markakis’ season, besides being slighted from every award, was that this explosion came out of the blue. Markakis’ 7.4 WAR in 2008 soars above his career average of 2.6 and towers above his next-best season, when he recorded 4.2 WAR in 2007.

Markakis’ defensive exploits in 2008 were also very surprising. He has never been a great defender, and he has only two years in his 12-year career in which he has a positive dWAR, 2008 (1.7) and 2016 (0.3). Markakis has a career dWAR of -6.5, which shows that his excellent 2008 season was an anomaly.

Markakis also logged a career high in Offensive WAR in 2008, as he achieved career highs in runs, doubles, walks, batting average, on-base percentage, slugging, and OPS.

I’ll never know why Markakis could not even come close to matching his exploits from 2008. Markakis was just 24 in his breakout season, but his stats decreased as he headed toward his prime. I can’t find an explanation for his decline, as injuries weren’t a factor — Markakis has played at least 155 games in every season from 2007-2016, with the lone exception of 2012. Markakis was a bit lucky in 2008, as his BABIP (batting average on balls in play) was .350, higher than his actual batting average of .306. However, his career year cannot be explained away by luck.

The lack of recognition of Markakis’ magnificent season is puzzling. He did play in one of baseball’s smallest media markets (Baltimore) for a team that stayed in the basement of the AL East, but at a certain point, efforts like his need to be noted.

Markakis never again reached the heights of his 2008 season, and I’ll always wonder two things: why he wasn’t recognized for his achievements, and why he was never able to match his production again.

Special thanks to Baseball Reference for all of these helpful statistics

In a continuation of my article from yesterday, here is part two of the MLB Dream Team. This article will showcase spots six through nine in the batting order as well as the starting pitcher.

Enjoy!

Batting sixth and playing second base…

Robinson Cano

64.4 career WAR / 50.3 7yr-peak WAR / 57.4 JAWS

Average HOF 2B:

69.4 career WAR / 44.5 7yr-peak WAR / 56.9 JAWS

9th in JAWS out of 20 Hall of Fame Second basemen

Accolades: 8x All-Star, 2x Gold Glove, 5x Silver Slugger, World Series Champion (2009)

For many years, Robinson Cano has been in the conversation as the best second baseman in baseball.

He was an integral part of the New York Yankees 2009 championship squad, and he parlayed his five All-Star appearances with the Bronx Bombers into a 10-year, $240-million contract with the Mariners in 2014. Cano hasn’t lost his luster since leaving New York for Seattle, and he has made three All-Star Games (so far) with the Mariners.

Cano had to beat out Chase Utley and Dustin Pedroia — two likely Hall of Famers in their own right — to earn his position at the keystone sack in this lineup. Ultimately, Cano received the nod because of his sustained excellence. He has played at least 156 games every year since 2007, a level of health that neither Pedroia nor Utley can match.

Cano has also redefined the second-base position because of his ability to hit for average and power. Among Hall of Fame second basemen, Cano’s average of 25 home runs per season ranks second and his 296 career home runs ranks third. In a few years Cano should hold the record for career long balls by a second baseman, and he should be known as the greatest power hitting second baseman of all time.

Batting seventh and playing center field…

Carlos Beltran

70.3 career WAR / 44.3 7yr-peak WAR / 57.3 JAWS

Average HOF CF:

71.2 career WAR / 44.6 7yr-peak WAR / 57.9 JAWS

8th in JAWS out of 19 Hall of Fame Center Fielders

Accolades: Rookie of the Year (1999), 9x All-Star, 3x Gold Glove, 2x Silver Slugger

Carlos Beltran, always a reliable asset, is now in his 20th season. Beltran has been every archetype an athlete can be: young star (Royals); decisive deadline acquisition (Astros); hero (Mets); scapegoat (Mets again); veteran contributor (Giants, Cardinals, Yankees, Rangers); and experienced old-timer (Astros again). Beltran was the fifth-youngest in the majors when he debuted in 1998, and now he’s the third-oldest player in the league.

Beltran came into the majors as a 21-year-old kid for the Kansas City Royals, and immediately showcased his skills by taking home Rookie of the Year honors.

After seven years in Kansas City, Beltran signed with the New York Mets in 2005. It was in New York that Beltran would spend the prime of his career, making five All-Star appearances and taking home three Gold Gloves. Beltran proved to be one of the best players in the Major Leagues from 2006 to 2008, crushing 37 homers, driving in 124 runs, and scoring 123 (per 162 games).

Beltran has aged well, making All-Star teams as members of the Giants, Cardinals, and Yankees since leaving the Mets in 2011. However, the 40-year-old has shown signs of decline this year, and he may decide to hang up his spikes in the near future. Beltran left a lasting impression on the game of baseball, and his 20 years of service deserve a place in Cooperstown.

Batting eighth and catching…

Joe Mauer

51.4 career WAR / 38.5 7yr-peak WAR / 45.0 JAWS

Average HOF C:

53.4 career WAR / 34.4 7yr-peak WAR / 43.9 JAWS

8th in JAWS out of 15 Hall of Fame Catchers

Accolades: MVP (2009), 6x All-Star, 3x Gold Glove, 5x Silver Slugger

Even though Joe Mauer has not caught a game since 2013, he spent 10 seasons and 920 games behind the dish.

Mauer was truly one of the finest offensive backstops ever, and in 2009 he became only the second catcher since 1980 to win MVP (Ivan Rodriguez was the first in 1999). In Mauer’s MVP season, he led the majors in batting average (.365) and on-base percentage (.444), both of which were records for catchers. He also led the AL in slugging (.587), OPS (1.031) and Offensive WAR (7.6). In addition to his MVP, Mauer was the first AL catcher to win the batting title and he holds the record for most batting titles by a catcher, with three.

As well as being one of the league’s finest hitters, Mauer was a force to be reckoned with behind the plate. His great instincts and fielding prowess earned him three straight Gold Gloves from 2008 to 2010, and his 99.51% career fielding percentage ranks seventh all-time among catchers.

Although Mauer’s body has declined over the years, he has performed well since being moved to first base in 2014, and has not made an error this year in 69 games.

Mauer will leave a legacy as one of the greatest hitting catchers ever, and he has earned his place in the Hall of Fame.

Batting ninth at shortstop…

Troy Tulowitzki

44.0 career WAR / 40.0 7yr-peak WAR / 42.0 JAWS

Average HOF SS:

66.7 career WAR / 42.8 7yr-peak WAR / 54.8 JAWS

26th in JAWS out of 21 Hall of Fame Shortstops

Accolades: 5x All-Star, 2x Gold Glove, 2x Silver Slugger

This is the hardest decision on the roster, because in my opinion there aren’t any Hall-of-Fame-worthy shortstops in the majors right now. Alex Rodriguez and Derek Jeter, the two best shortstops of this generation, have retired in the past two years.

I ended up choosing Troy Tulowitzki because he has the best chance of any shortstop in the majors to make it to the Hall.

There were a few ways I could have gone with this pick. At first I considered moving Chase Utley to short, and then I looked at the plethora of up-and-coming shortstops (Carlos Correa, Corey Seager, Francisco Lindor; to name a few).

Ultimately, I chose Tulowitzki — but this would have been a much easier decision if Tulo had stayed healthy during his career. During his prime years with the Rockies between 2007 and 2014, Tulowitzki averaged a respectable 4.8 WAR per season. However, he missed an average of 45 games per year (!) during that period. If you extrapolate his numbers to 154 games (meaning he would miss 8 games per year), he would have recorded 6.7 WAR per season, boosting his JAWS from 42.0 to 50.0.

Although Tulowitzki didn’t stay healthy most of the time, his impact while he was on the field was undoubted, and he deserves Hall of Fame consideration.

And your starting pitcher for tonight…

Clayton Kershaw

58.8 career WAR / 48.7 7yr-peak WAR / 53.8 JAWS

Average HOF P:

73.9 career WAR / 50.3 7yr-peak WAR / 62.1 JAWS

60th in JAWS out of 62 Hall of Fame Pitchers

Accolades: Pitching Triple Crown (2011), MVP (2014), 3x Cy Young (2011, 2013, 2014), 7x All-Star, 1x Gold Glove

Clayton Kershaw, in my opinion, is the best pitcher in the game right now. He has been terrorizing opposing hitters since coming up as a 20-year-old with the Dodgers in 2008.

Kershaw achieved the Pitching Triple Crown in 2011, when he led the league in ERA, wins, and strikeouts. In 2014, Kershaw joined Roger Clemens and Sandy Koufax as just the third player in baseball history to win three Cy Young awards and an MVP.

Kershaw has finished as an All-Star and a top-five Cy Young award finisher in each of the past six seasons, a nearly unparalleled run of dominance, and he has already attained a career’s worth of honors at just 29 years old.

Supposing Kershaw retires at age 37, he has eight years remaining in his career. If we extrapolate his season average of 5.7 WAR to seven more years, then his current WAR of 57.0 jumps to 102.6, which places him as the ninth best pitcher of all time, a very fair assessment.

Kershaw has the lowest career ERA of any starter since 1920* (2.35), and he deserves a plaque in Cooperstown.

*Baseball-Reference defines a starting pitcher as a player whose starts make up 60% of their appearances. Minimum of 50 Innings Pitched.

Special thanks to baseball-reference.com for all of these helpful stats. I could not have written this article without them.

Sports always allow us to ask, what if? What if a baseball lineup — complete with all nine positions and a designated hitter — was composed of all-time greats in their best seasons.

I have composed a lineup filled with the very best active players who I think will make the Hall of Fame.

These players will not be judged on their performance this year; they will be chosen based on how well they performed during their primes.

I have designated a player’s “prime” as the best seven years of their career — not necessarily consecutive — and these selections are based on the player’s likelihood to make the Hall of Fame. Some members of the team will be inducted on the first ballot, and some will take years to make it to the Hall, but ultimately I think that every player on this list has a great shot at being immortalized in Cooperstown.

This article is part one of a two-part set in which I show my Dream Team. Part two will be released tomorrow.

Metrics Explained

Wins Above Replacement, or WAR, is the most commonly used advanced metric in baseball. It is a measure of how many wins a team gained by playing a specific player instead of a replacement player, who would have a WAR of 0. If a player records 2 WAR in a season, he is considered starting material, 4 or 5 WAR is acknowledged to be All-Star value, and 8 WAR is MVP-level production.

The Jaffe WAR score system, or JAWS, is simply the average of a player’s seven-year peak WAR and career WAR. For example, if a player had 100 career WAR and 50 seven-year peak WAR, his JAWS would be 75. This metric gives us perspective on how likely it is for a player to make the Hall of Fame compared to those who played their position.

Fielding Percentage is a measure off how often a player commits an error. For example, a fielding percentage of 97% means the player committed an error on 3% of the plays he made.

Note: This list favors older players because:

• They have more career WAR
• They have more years from which to choose their seven-year peak WAR
• They are closer to entering the Hall of Fame than younger players.

Batting leadoff and playing right field…

Ichiro Suzuki

59.4 career WAR / 43.6 7yr-peak WAR / 51.5 JAWS

Average HOF RF:

73.2 career WAR / 43.0 7yr-peak WAR / 58.1 JAWS

17th in JAWS out of 24 Hall of Fame Right Fielders

Accolades: MVP (2001), Rookie of the Year (2001), 10x All-Star, 10x Gold glove, 3x Silver Slugger

Ichiro was one of the easiest selections for this Hall of Fame Dream Team. He was a trendsetter — the first Asian position player to debut in the Major Leagues.

In his rookie season, Ichiro set the baseball world ablaze, winning MVP and Rookie of the Year, and leading the league in hits, stolen bases, and batting average.

Ichiro was a revelation in the big leagues, and his game was predicated on speed not power, completely opposite to the direction baseball was trending. According to FanGraphs, Ichiro occupies the first seven spots on the list of highest single-season infield hit totals.

Ichiro was the hit king. He holds the records for most hits in a season (262) and most consecutive 200-hit seasons (10). He also tied the record for most 200-hit seasons (10), and led the league in hits seven times.

Recently, Ichiro reached the 3,000 hit plateau, and if you count his hits from his time in Japan, he broke Pete Rose’s record for most hits across all of baseball’s professional leagues.

In his prime, Ichiro was one of the best players in the world. Only Albert Pujols and Alex Rodriguez accumulated more WAR than Ichiro from 2001 to 2010. On top of being one of the greatest to ever play in the outfield, Ichiro was a cultural icon, and many of the recent advances that Asian players have made are attributable to him.

Batting second and playing left field…

Mike Trout

52.0 career WAR / 52.0 7yr-peak WAR / 52.0 JAWS

Average HOF CF (out of 19):

71.2 career WAR / 44.6 7yr-peak WAR / 57.9 JAWS

14th out of 19 Hall of Fame Center Fielders

Accolades: 2x MVP (2014, 2016), Rookie of the Year (2012), 6x All-Star, 5x Silver Slugger

Trout usually plays center field, but I had to move him over to left in order to accommodate him in the lineup.

Mike Trout is hands-down the best player in baseball right now, and is surely destined for Cooperstown.

Trout has only played five full seasons, but his numbers stack up well next to other center fielders who are enshrined in the Hall. And at just 25 years old, Trout is only entering his prime, meaning that his best years are ahead of him.

Now that’s a stunning thought.

Trout also has the sixth-best seven-year peak WAR out of the 24 center fielders in Cooperstown, in only five seasons!

Here I am talking about how Trout is a generational talent, and I haven’t even mentioned the countless honors that he has collected. Trout has made the All-Star team (for which he has won MVP twice), taken home a Silver Slugger, and been either MVP winner (twice) or runner-up (three times) in every season of his career.

That level of dominance is mind-boggling and completely unprecedented.

Batting third as the designated hitter…

Miguel Cabrera

70.0 career WAR / 44.6 7yr-peak WAR / 57.3 JAWS

Average HOF 1B:

66.4 career WAR / 42.7 7yr-peak WAR / 54.6 JAWS

10th in JAWS out of 20 Hall of Fame First Basemen

Accolades: Triple Crown (2013), 2x MVP (2012, 2013), 11x All-Star, 7x Silver Slugger, World Series Champion (2003)

Miguel Cabrera, still one of the best players in baseball, is a generational talent and already a surefire Hall of Famer. The Venezuelan has been tearing up the big leagues ever since debuting in 2003, and has brought a cheerful smile and a love of the game to wherever he plays.

In the beginning of his career, Cabrera was a young star on the Florida Marlins, one of the youngest teams in baseball. He experienced success early on when the Marlins won the World Series in his rookie year. Then, after a blockbuster trade to the Detroit Tigers in 2007, he continued to amaze in the American League.

From 2011 to 2015, Cabrera was the most feared hitter in all of baseball. During that time, he won four batting titles, took home two MVPs, and racked up five All-Star selections. In 2013, Cabrera captured the Triple Crown (leading the league in batting average, home runs, and RBIs), a feat that had not been accomplished since 1967.

Cabrera already has 2,598 hits and 458 home runs as of July 22nd, so he has a good chance to join Hank Aaron, Willie Mays, and Alex Rodriguez as the fourth member of the 3,000 hit and 600 home run club. Cabrera’s near-incomparable match of hitting for both power and average have vaulted him into the conversation as one of the best hitters of all time.

Batting cleanup and playing first base…

Albert Pujols

100.1 career WAR / 61.6 7yr-peak WAR / 80.8 JAWS

Average HOF 1B:

66.4 career WAR / 42.7 7yr-peak WAR / 54.6 JAWS

2nd in JAWS out of 20 Hall of Fame First Basemen

Accolades: 3x MVP (2005, 2008, 2009), Rookie of the Year (2001), 10x All-Star, 2x Gold Glove, 6x Silver Slugger,  World Series Champion (2006, 2011)

The easiest choice on the roster, Albert Pujols should make the Hall of Fame on the first ballot. Much like Pujols’ overflowing trophy cabinet, I don’t have room enough to praise Pujols, truly one of the greatest players ever.

Pujols has faded since he signed with the Angels on a 10-year, $240-million contract in 2012, but don’t let his struggles of late affect your judgement on his case for the Hall of Fame. He trails only Lou Gehrig in career WAR among first basemen, and is one of only 21 position players to record 100 career WAR.

Pujols’ nickname “The Machine” was an apt description of his time as a Cardinal. His 162-game average stats for his 11 years in St. Louis were: .328/.420/.617 with 127 RBIs, 123 runs, and 43 home runs. Pujols finished in the top 10 of the MVP voting all 11 years, ending up in the top five in ten seasons, and winning the award three times. But Pujols isn’t just a slugging first basemen, he is a very capable defender and has won two Gold Gloves.

Pujols became the ninth member of the 600 home run club earlier this year, and next year he should join the 3,000 hit club (as of July 22nd he has 2,908 hits). Pujols leaves a legacy as one of the best ever, and he deserves to be enshrined in Cooperstown.

Batting fifth and manning the hot corner…

Adrian Beltre

91.5 career WAR / 49.7 7yr-peak WAR / 70.6 JAWS

Average HOF 3B:

67.5 career WAR / 42.8 7yr-peak WAR / 55.2 JAWS

5th in JAWS out of 13 Hall of Fame Third Basemen

Accolades: 4x All-Star, 5x Gold Glove, 4x Silver Slugger

Adrian Beltre, still chugging along at the ripe age of 38, has graced baseball with his presence for 20 seasons. From hitting home runs off one knee, to his aversion of people touching his head, Beltre is one of the true characters of the game.

Beltre is third all-time in WAR among third basemen, trailing only Mike Schmidt and Eddie Mathews. He also figures to be the next member of the 3,000 hit club, needing only 15 more hits as of July 22nd. And if he decides to come back and play next year, he has a great chance of overtaking Brooks Robinson for most games played at third base.

Those are just some of the records that Beltre is approaching, and he does not seem to be slowing down.

There is just no debate on Beltre’s Hall of Fame candidacy. Among all third basemen, he ranks in the top five in games played, hits, doubles, home runs, RBIs, and WAR.

Beltre’s legacy will be as one of the best defensive third basemen of all time, and he trails only Brooks Robinson in Defensive WAR among players who have manned the hot corner. His highlight reel of diving stabs, barehanded picks, and throws from all the way across the diamond make him one of the best ever to play third base.

Special thanks to baseball-reference.com for all of these helpful stats. I could not have written this article without them.

Thanks for reading Part I. Part II will be released at a later date and it will include spots 6-9 in the batting order as well as the starting pitcher.

To be continued…

Christian Yelich is a very good baseball player. Since becoming a full-time major leaguer in 2014, Yelich has accumulated 13.8 Wins Above Replacement, good for 35th among qualified hitters. Yelich owns a career 120 wRC+, showing he’s a fine hitter. Yet there has always been a lingering question: Can his bat be even better?

Yelich hits the ball hard. Since 2016, only 10 players have a greater average exit velocity (minimum 2500 pitches seen). More importantly, his 94.3 MPH exit velocity off of fly balls is 25th from the same group. If we add in line drives with fly balls, Yelich’s 95.7 MPH exit velocity ranks 17th, sandwiched in between Manny Machado and Yasmany Tomas. Exit velocity is only part of the story, though. His launch angle is not ideal. Despite hitting the ball more than a mile harder than sluggers such as Bryce Harper, Michael Conforto, and Anthony Rizzo, Yelich has routinely chosen a ground-ball-based approach. Since the All-Star break, we might have gotten another indication of a possible transformation. The prospects are tantalizing. Have always been tantalizing.

Last season, Yelich saw his wRC+ rise to 130, the best of his career. This was partly related to him increasing his power level, as shown by a .185 ISO, the highest of his career. No doubt like every other batter, he was aided by a mysterious force (most likely the ball), but he also had a slight approach change. Yelich hit more fly balls, and so far in 2017, he’s expanded on that. Yelich has the 35th highest (122 players) difference between his 2016 fly-ball rate and 2017 fly-ball rate (minimum 350 plate appearances in both seasons). Slowly, Yelich might just be embracing the fly-ball revolution. This is also seen in his launch angle. In 2016, Yelich’s average launch angle was 2.5 degrees. In 2017, it’s 4.9 degrees, nearly double (more on this later).

Yelich’s 15 Game Rolling GB% and FB%

Yelich seems to have committed to some sort of approach in which fly balls are more sought after. In September of last year, Yelich carried a fly-ball rate at nearly 30%. He began April hitting fly balls at a 27.2% clip, followed by 23.6% in May, and to a low 14.1% in June. He seemed to abandon the fly-ball approach as his results weren’t up to his standards. Have you ever done something you were excited about but didn’t do well that you sort of slowly stopped? I’d imagine something like that may have happened with Yelich. During the second half so far, his fly-ball rate is 32.3%! It could very well be the result of small sample size, but it could also be a sign of Yelich looking to become a better hitter. Since the All-Star break, the Marlins outfielder’s average launch angle has been 10.4 degrees. This is what we want to see. And interestingly enough:

Yelich’s 15 Game Rolling GB% and FB%

We haven’t really seen Yelich be at this power level. He’s had spikes for sure, but nothing as high as the power streak he has shown recently. It coincides with him lifting the ball more. Since the start of the second half, Yelich has a .250 ISO. To give you an idea of the type of power output, that’s pretty much what Anthony Rizzo and Miguel Sano have this season (both at .247).

This feeds into what I mentioned above with psychological factors possibly playing a role. Yelich is seeing good results; perhaps he may experiment a little more with a greater emphasis on fly balls.

As mentioned above, Yelich hits the ball hard. But he also hits it hard to all fields. This is just another example of the kind of strength that exists within Yelich and his all-fields approach making him a tough out. Being able to hit the ball to the opposite part of the park with authority is a rare skill. It’s one of the reasons why Rafael Devers is such an exciting prospect.

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Now combine that all-field power with solid zone control and you’ve got a good hitter. Then combine someone who is has a better batted-ball mix and you might just end up with a great hitter. If Yelich shows more power, which his 6’3″, 195lb figure suggests is there, Yelich will likely be given more free passes. Basically, Yelich has the tools to be that rare hitter than can hit for average and power.

Back to the launch angle which has nearly doubled — FanGraphs Andrew Perpetua recently had an intriguing article advising caution when using Launch Angle. In the article, Andrew writes, “Launch angle is largely dependent on the particular swing and approach of a given batter. If they have an uppercut, then they will produce high launch angles with their high-velocity balls. If they swing down on the ball, then they will have lower launch angles with their high-velocity balls.” Furthermore, Andrew mentions in the comments, “I think launch angle is so intimately tied with swing mechanics that you probably shouldn’t talk about it outside the context of swing mechanics.” This does make sense. Hitters need to alter their bat path to hit the ball at specific angles. Bringing it back to Yelich, we can try to see if he has altered his mechanics. Take the following with a massive grain of salt because it’s only a couple of videos, and I’m no swing expert. From the videos I’ve seen of Yelich, he seems to have a pretty smooth swing path and uses a leg kick for additional power. Here are two of his home runs this year: the first from June 2 against the Diamondbacks and the second from July 26 against the Rangers.

I don’t see a major difference. A bit of a stronger leg kick in the homer against the D’Backs.

In both of these videos, Yelich hits an opposite-field double. Against the Braves, Yelich seems to do a double leg kick. He did this in the next game as well. It’s not something that I’ve seen stick. I’d imagine it might have been due to seeing something he may not have been expecting. Either way, it must’ve been an interesting conversation between Yelich and the hitting coach.

From the limited video evidence, I can’t decipher much. Someone more experienced might want to look into it. The numbers show Yelich very well may have altered his bat path slightly.

One of the criticisms of Yelich was his lack of damage done when pulling the ball. He’s been the fourth-best hitter when going opposite field over the past three calendar years.

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On the plus side, this is another area of improvement for Yelich.

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Christian Yelich could very well remain a ground-ball-heavy hitter and be one of the better hitters in the majors. His plate approach has been lauded for many years to go along with his full-field power. If he is part of the fly-ball revolution, Yelich very well could be one of the best hitters in the game. He’s shown signs of a different approach. With the results to back it up, the rest of the season will give us a glimpse into the hitter that Yelich both wants to be and could be.

I can’t remember if it was in a podcast or over the radio but when the trade deadline was approaching, there was talk about the effects of how a team trading away their stars would affect the playoff picture. Not in the way where a team has a hole in their rotation so they trade for a solid starter. No, this piece was talking about how trading a great player would make it easier for teams in that division to get ahead and how the newly-acquired player would make his new division harder to play in.

My first thought was there’s no way a player’s performance can impact a division so heavily, right? Baseball is a team sport and while affecting their own roster is one thing, affecting the outcome of four other teams in the process seems like a stretch. So I did a little bit of digging and here’s what I found.

For this study, I’ve included players that had a WAR of 2 or greater before being traded from 2007-2016. Additionally, I gathered data from the day they were traded of their old team’s winning percentage, new team’s winning percentage, old division’s winning percentage, and new division’s winning percentage. I also took the difference of their WAR per games played before and after the trade as a percentage.

First things first, let’s see if a great player can really impact a divisional outcome. Out of the 42 players in this study, only six (14.3%) had a positive WAR/G difference, a positive difference in winning percentage of their old division, and a negative difference in winning percentage of their new division:

Victor Martinez – 2009

Doug Fister – 2011

Hunter Pence – 2011

Mark Teixeira – 2008

Roy Oswalt – 2010

CC Sabathia – 2008

For Fister, Oswalt, and Sabathia, their new teams’ win percentage improved. For Martinez, Pence, and Teixeira, the win percentage decreased. All teams made the playoffs, however, with Fister and Oswalt making in to their respective league championship games. It’s interesting to see that the three players whose teams’ win percentage also improved are all pitchers, while the other three were all hitters.

The split between hitters and pitchers in the study was right down the middle, with 21 pitchers and 21 hitters. After their respective trades, 16 out of the 42 players had a positive WAR/G differential. Again, the results were right down the middle, with eight pitchers and eight hitters posting the positive WAR/G difference. Looking at the 26 players that had a negative WAR/G differential after the trade, you could’ve guessed it; half (13) were pitchers and the other half were hitters. I’m not 100% sure what that could mean, but I found it as a fascinating observation.

Out of the 42 teams that made trades in this study, three were under .500 when they made the trade; Reds for Scott Rolen (missed the playoffs), Red Sox for Cespedes (missed the playoffs), and Rangers for Hamels (ALDS). Let’s see how the rest of the teams that were .500 or better fared with their new trade pieces:

No Playoffs – 9 (23%)

Wild Card – 2 (5.1%)

DS – 14 (35.9%)

CS – 7 (17.9%)

WS – 5 (12.8%)

Won – 2 (5.1%)

It should be noted that the WAR/G differential doesn’t include playoff statistics. This is important to note while looking at players in this study that went to or won the World Series. For example, in 2015 the Royals acquired Johnny Cueto from the Reds. Looking at the data alone, Cueto had a -46.4% WAR/G differential and the Royals’ winning percentage dropped by 3.62% after the trade. Looks like a bad trade so far. Fast-forward to the ALCS where Cueto gives up eight earned runs in two innings against the Blue Jays. This trade looks like a disaster. Until Cueto takes the mound against the Mets in Game 2, allowing one run on two hits for the complete-game victory, edging the Royals closer to a World Series title. If given the opportunity again, do the Royals make the trade? Absolutely.

On the other side of the spectrum is Edwin Jackson, the only other player in this study to win the World Series. He as well sported a -63.5% WAR/G differential after the trade. The next question would be, would the Cardinals make the trade again? With a 5.76 ERA that postseason, my guess would be no.

The main question in this study is, “Does an impact player have so much influence in the game around them that they can shift the outcomes of a division?” The quick answer, and one that I’m sure everyone already knew, is not really. There is no correlation between the new division winning percentage change and the old division winning percentage change. A lot of the outcomes of divisional win percent changes seem to be circumstantial. Just because the new team’s division has gotten worse and the old division has gotten better doesn’t always mean that it’s the result of the player. It does seem apparent that a pitcher may have more of an influence than a hitter in these terms however (see Sabathia, Oswalt, and Fister above).

The biggest takeaway for me is that teams seem to be reluctant to overpay and make the smaller, longer-term deals as opposed to big-name rentals as seen at the deadline this year. It’s become apparent that just because you make the trade for the big-name player doesn’t guarantee a World Series victory, trip, or even a spot in the playoffs. Speaking of those big pieces, it will also be interesting to see how Quintana and Darvish affect the data after the season is over. Additionally, I would love to see the implications of a Harper or Trout trade to see if a hitter can ever truly be able to affect a divisional outcome. We can only dream.

Unlike most home-run-related articles written this year, this one has nothing to do with the recent home run surge, juiced balls, or the fly-ball revolution. Instead, this one’s about the influence of temperature on home-run rates.

Now, if you’re thinking here comes another readily disproven theory about home runs and global warming (a la Tim McCarver in 2012), don’t worry – that’s not where I’m going with this. Alan Nathan nicely settled the issue by demonstrating that temperature can’t nearly account for the large changes in home-run rates throughout MLB history in his 2012 Baseball Prospectus piece.

In this article, I want to revisit Nathan’s conclusion because it presents a potentially testable hypothesis given a large enough data set. If you haven’t read his article or thought about the relationship between temperature and home runs, it comes down to simple physics. Warmer air is less dense. The drag force on a moving baseball is proportional to air density. Therefore (all else being equal), a well-hit ball headed for the stands will experience less drag in warmer air and thus have a greater chance of clearing the fence. Nathan took HitTracker and HITf/x data for all 2009 and 2010 home runs and, using a model, estimated how far they would have gone if the air temperature were 72.7°F rather than the actual game-time temperature. From the difference between estimated 72.7°F distances and actual distances, Nathan found a linear relationship between game-time temperature and distance. (No surprise, given that there’s a linear dependence of drag on air density and a linear dependence of air density on temperature.) Based on his model, he suggests that a warming of 1°F leads to a 0.6% increase in home runs.

This should in principle be a testable hypothesis based on historical data: that the sensitivity of home runs per game to game-time temperature is roughly 0.6% per °F. The issue, of course, is that the temperature dependence of home-run rates is a tiny signal drowned out by much bigger controls on home-run production [e.g. changes in batting approach, pitching approach, PED usage, juiced balls (maybe?), field dimensions, park elevation, etc.]. To try to actually find this hypothesized temperature sensitivity we’ll need to (1) look at a massive number of realizations (i.e. we need a really long record), and (2) control for as many of these variables as possible. With that in mind, here’s the best approach I could come up with.

I used data (from Retrosheet) to find game-time temperature and home runs per game for every game played from 1952 to 2016. I excluded games for which game-time temperature was unavailable (not a big issue after 1995 but there are some big gaps before) and games played in domed stadiums where the temperature was constant (e.g. every game played at the Astrodome was listed as 72°F). I was left with 72,594 games, which I hoped was a big enough sample size. I then performed two exercises with the data, one qualitatively and one quantitatively informative. Let’s start with the qualitative one.

In this exercise, I crudely controlled for park effects by converting the whole data set from raw game-time temperatures (T) and home runs per game (HR) to what I’ll call T* and HR*, differences from the long-term median T and HR values at each ball park over the whole record. Formally, for any game, T* and HR* are defined such that T* = T – T_med,park and HR* = HR – HR_med,park, where T_med,park and HR_med,park are median temperature and HR/game, respectively, at a given ballpark over the whole data set. A positive value of HR* for a given game means that more home runs were hit than in a typical ball game at that ballpark. A positive value for T* means that it was warmer than usual for that particular game than on average at that ballpark. Next, I defined “warm” games as those for which T*>0 and “cold” games as those for which T*<0. I then generated three probability distributions of HR* for: 1) all games, 2) warm games and 3) cold games. Here’s what those look like:

The tiny shifts of the warm-game distribution toward more home runs and cold-game distribution toward fewer home runs suggests that the influence of temperature on home runs is indeed detectable. It’s encouraging, but only useful in a qualitative sense. That is, we can’t test for Nathan’s 0.6% HR increase per °F based on this exercise. So, I tried a second, more quantitative approach.

The idea behind this second exercise was to look at the sensitivity of home runs per game to game-time temperature over a single season at a single ballpark, then repeat this for every season (since 1952) at every ballpark and average all the regression coefficients (sensitivities). My thinking was that by only looking at one season at a time, significant changes in the game were unlikely to unfold (i.e. it’s possible but doubtful that there could be a sudden mid-season shift in PED usage, hitting approach, etc.) but changes in temperature would be large (from cold April night games to warm July and August matinees). In other words, this seemed like the best way to isolate the signal of interest (temperature) from all other major variables affecting home run production.

Let’s call a single season of games at a single ballpark a “ballpark-season.” I included only ballpark-seasons for which there were at least 30 games with both temperature and home run data, leading to a total of 930 ballpark-seasons. Here’s what the regression coefficients for these ballpark-seasons look like, with units of % change in HR (per game) per °F:

A few things are worth noting right away. First, there’s quite a bit of scatter, but 75.1% of these 930 values are positive, suggesting that in the vast majority of ballpark-seasons, higher home-run rates were associated with warmer game-time temperatures as expected. Second, unlike a time series of HR/game over the past 65 years, there’s no trend in these regression coefficients over time. That’s reasonably good evidence that we’ve controlled for major changes in the game at least to some extent, since the (linear) temperature dependence of home-run production should not have changed over time even though temperature itself has gradually increased (in the U.S.) by 1-2 °F since the early ‘50s. (Third, and not particularly important here, I’m not sure why so few game-time temperatures were recorded in the mid ‘80s Retrosheet data.)

Now, with these 930 realizations, we can calculate the mean sensitivity of HR/game to temperature, resulting in 0.76% per °F. [Note that the scatter is large and the distribution doesn’t look very Gaussian (see below), but more Dirac-delta like (1 std dev ~ 1.66%, but middle 33% clustered within ~0.4% of mean)].

Nonetheless, the mean value is remarkably similar to Alan Nathan’s 0.6% per °F.

Although the data are pretty noisy, the fact that the mean is consistent with Nathan’s physical model-based result is somewhat satisfying. Now, just for fun, let’s crudely estimate how much of the league-wide trend in home runs can be explained by temperature. We’ll assume that the temperature change across all MLB ballparks uniformly follows the mean U.S. temperature change from 1952-2016 using NOAA data. In the top panel below, I’ve plotted total MLB-wide home runs per complete season (30 teams, 162 games) season by upscaling totals from 154-game seasons (before 1961 in the AL, 1962 in the NL), strike-shortened seasons, and years with fewer than 30 teams accordingly. In blue is the expected MLB-wide HR total if the only influence on home runs is temperature and assuming the true sensitivity to be 0.6% per °F. No surprise, the temperature effect pales in comparison to everything else. Shown in the bottom plot is the estimated difference due to temperature alone in MLB-wide season home run totals from the 1952 value of 3,079 (again, after scaling to account for differences in number of games and teams). You can think of this plot as telling you how many of the total home runs hit in a season wouldn’t have made it over the fence if air temperatures at remained constant at 1952 levels.

While these anomalies comprise a tiny fraction of the thousands of home runs hit per year, one could make that case (with considerably uncertainty admitted) that as many as 59 of these extra temperature-driven home runs were hit in 2016 (or about two per team!).

Early on in a season, we get to talk about eye-popping numbers that players put up. We warn of sample sizes, though, and almost crave stability. We wait impatiently for the season to steady itself and almost breathe a sigh of relief when it happens — when we can start to buy into what an individual is doing.

But as the season wades on and we move toward the postseason, the biggest stories often come from singular moments. And while we can’t predict who, exactly, will define his team’s season with a single play, we might be able to take a pretty good guess.

With weighted on-base average from Statcast, we get to see just how much a player is contributing each time they step to the plate. With expected weighted on-base average, we get to see how well their results line up with their approach.

The differences in expected and actual wOBA for these players in the early going is no small thing. The 20-to-45 point gap would have put them in a completely different class of players had things gone as expected. Manny Machado figured to rank ahead of Kris Bryant; in reality, he lingered above Freddy Galvis. There’s an example like that for each of the other three, too. While the early performances of these guys might have lasted long enough to make us feel like they were a certain kind of reliable this season, their recent play highlights how fast things can change.

The rankings associated with each player give a sense of what their teams would have enjoyed had circumstances fell more in their favor. Rankings aren’t included since the start of July because the sample size may emphasize a gap that could be misleading — Kyle Seager, for instance, has the smallest difference of the four in wOBA-based production but drops 76 spots because of it.

That’s also to intentionally emphasize something else: all of these players’ teams are in the playoff hunt. Seager’s Mariners are tied for the Wild Card lead and Machado’s Orioles, despite abysmal pitching, are only 1.5 games out. Moreland’s Red Sox and Santana’s Indians each lead their division by four games. And for better or worse, their turnarounds could be playing a big role in who’s playing in October.

So consider the implications. Do the Mariners possibly lead the Wild Card at this point if Seager’s production more closely matched what was expected? Are the Orioles smashing expectations again if the same were true for Machado?

Could Santana have delivered a more comfortable divisional lead for Cleveland earlier? Is he doing that now by exceeding expectations with a white-hot bat? Moreland broke his toe in June — what impact has that had on the Red Sox building similar divisional comfort, and how big of a role could him simply being able to put pressure on his back foot play?

The answers to these questions may or may not be rhetorical, but all of these players are having a string of moments that could help define their team’s season. While we’ve longed for stable samples to dig into, their turns in production are showing us the ebb and flow of a game that remembers snapshots more than anything. As we come down to the wire, the big picture is telling us how it’s constructed of little ones.

There have been several surprising prospect performances this year, but the one that seems to top the rest has been Atlanta Braves outfielder Ronald Acuna. The 19-year-old began his season in high-A and, by my notes, was unranked in the MLB pipeline top-100 (he was 36^th on Keith Law’s list and 67^th on Baseball America).

Fast forward to now, and Acuna has been promoted twice and is a consensus top-10 prospect. In the recent prospect hot sheet chat, Baseball America’s Josh Norris speculated that Acuna was a frontrunner for minor league player of the year and next season’s #1 prospect overall.

The aspect that I could not get my head around is that Acuna is improving faster than he can be promoted. With at least 100 PA at each level, he has improved every relevant rate stat with each stop.

This led me to answer the question of whether this had ever been observed before.

I pulled every minor league player season from 2006-2017 (the farthest back FanGraphs can go). I filtered on at least 100 PA, and consolidated a list where players appeared in at least three levels. I used wRC+ as a catchall stat for offensive production and scored players that improved their output in the jump from one level to the next.

It is worth observing that this list is quite artificial — not many players appear in three different minor league teams in a single season, and only 22 have done so in the past 11 years. And the list certainly isn’t a who’s-who of top prospects, so take this analysis with a grain of salt.

Since 2006, Acuna is the only minor-league player to have posted improved wRC+ at three different stops in the same season (see chart below, labeled players with blue lines showed improvement in at least one jump). While Acuna’s high-A 135 wRC+ wasn’t setting the world on fire, posting a 183 wRC+ at his third league level in a single season is untouchable.

See graph

Owing to Acuna’s excellent hit tool, he is also the only prospect to have posted similar improvements in AVG, OBP or OPS since 2006.

On plate-discipline statistics, the only other player to show the consistent improvement in BB% is Ryan Court (2013). Two other players show a consistent decline in K% across each level (Brett Wallace and Sawyer Carroll in 2009).

For power statistics, Acuna also finds a little company. Two other players showed improvements in isolated power at each level, Tyler Pastornicky in 2015 and Rando Moreno in 2016, though their improvements went from horrific (0.038 and 0.040, respectively) to simply not good (0.111 and 0.082). Pastornicky was the only non-Acuna player to increase his SLG at each level (.314 to .394 compared to Acuna’s .478 to .576).

As was said at the outset, the players that reach 100 PA in three levels in a single season are not any sort of elite bunch. But it is telling that among them, Acuna is the only one that has shown a consistent ability to improve while facing better and older talent. I’m not holding my breath for a 200 wRC+ when Acuna makes it to The Show, but I will be watching.