Archive for July, 2014

In Which the Value of Kevin Kiermaier is Probably Grossly Overstated

Jose Abreu has seemed a virtual lock for the AL Rookie of the Year Award ever since Masahiro Tanaka went down with what could be a season-ending elbow injury. Abreu has done nothing but hit the hell out of the ball since storming into the Majors after fine-tuning his craft in Cuba. Rookies like Brock Holt and George Springer have had nice seasons of their own, to be sure, but they’re certainly no challengers to the might of Abreu.

Then, of course, there’s the best rookie that mostly nobody’s heard of. Let’s say hello to Mr. Kevin Kiermaier. A 31st round pick in 2010, Kiermaier fought his way through the minors and finally, after a single game appearance last year, was called up for good on May 17th. He’s since appeared in every game since May 31st.  In 57 games (198 plate appearances), he’s hit .311/.362/.544. More importantly, he’s produced a .391 wOBA and 157 wRC+. Those are certainly pretty numbers. But can he keep it up? Kiermaier hit well at almost every level in the minors, but was always known for his superb glove work. Let’s look at his and Abreu’s numbers together, shall we? All stats herein are as they were prior to the inception of action on Monday the 29th.

Player Games PA wOBA wRC+ HR K% UZR DRS fWAR
Abreu 91 393 .406 159 30 23.9% 1.1 -5 3.4
Keirmaier 57 198 .391 157 8 18.7% 10.5 8 3.1

Abreu is numerically the greater offensive producer, if only by a slim margin. He’s only out-produced Kiermaier relative to the league (wRC+) by two points, but his wOBA is indicative of the fact that he’s driving the bar a lot further (.619 slugging percentage versus Kiermaier’s .544). Yet while chicks most certainly dig the long ball, it isn’t everything.

Kiermaier’s .362 OBP is a fantastic mark. It’s about twenty points higher than Abreu’s, and it’s incredibly beneficial for his spot in the lineup. For all his famous lineup tinkering, Joe Maddon has been primarily using Kiermaier out of the 9 spot of late. He even went as far as batting the pitcher (Alex Cobb) eighth on July 23rd in an inter-league game against the Cardinals so that Kiermaier could bat ninth. Putting him there allows him to serve as what basically amounts to a second leadoff hitter when the lineup turns over, and there’s one more runner on base for the big boppers in the heart of the order.

Kiermaier also is much more of a two-way player. UZR and DRS are both very pleased with his defensive skill set. That’s not a surprise, as many scouting reports on Kiermaier always gave glowing reviews of his instincts and range in the field. It’s that defense that has allowed him to be right on Abreu’s tail in total value, in far fewer games. While UZR says that Abreu isn’t a total loss at first base, DRS says he leaves much to be desired. Naturally, first basemen traditionally aren’t employed for their gloves, but nobody complains when someone like Carlos Santana comes along and dazzles on both sides of the ball.

Now, the hard part. We want to extrapolate Kiermaier’s value over the same time span that Abreu’s accrued his, but how? It would be easy to simple do it over the same number of games played. However, “games played” doesn’t account for late-inning substitutions, or early exits. A better (but still not perfect) way of looking at it would be to extrapolate it over innings played. This is still not perfect, as a batter can hit in the top of an inning and then be replaced in the field in the bottom, and vice versa. However, I’m not a good enough statistician to develop my own metric for this (check back with me in a few months) and I can’t find anything out there to show just how much time a player has seen.

I used Baseball-Reference’s game logs for this, as they have a count of how many innings the player saw. Here’s how these numbers work out.

Player Innings Played PA fWAR fWAR/I
Abreu 812 393 3.4 ~ .00418
Kiermaier 464 198 3.1 ~ .00668

This is far from an exact science. Abreu and Kiermaier didn’t produce exactly that many wins every inning they plated, but it’s how the numbers work out. Now, if Kiermaier’s fWAR/I (fWAR per innings played) is multiplied by Abreu’s total innings played, the result is an fWAR total of 5.425. So far this season, Mike Trout leads all of baseball with 5.7 fWAR. Troy Tulowitzki is in second place with 5.1. That’s assuming, of course, that Kiermaier maintains his current level of play. And as I said before, “But can he keep it up?” The answer to that is “I haven’t a clue.” Here’s why.

.353 50.7% .336 .225

There are two glaring realities. One is that Kiermaier hits a lot of ground balls. His BABIP would seem to indicate that’s he’s getting lucky, and his grounders are finding holes in the infield. For what it’s worth, here’s the league leaders in ground ball rate out of everyone who’s qualified for the batting title. It’s a mixed bag for sure. There are some good hitters around Kiermaier’s range, like Yasiel Puig, Alexei Ramirez and Melky Cabrera. Derek Jeter’s made a heck of a career out of a high ground ball rate and good BABIP. So while that isn’t an immediate cause for concern, it’s something worth watching to be sure. For reference, here’s his Brooks Baseball spray chart on the year.

The other is that southpaws have completely owned him. While right-handers are more common (and Kiermaier certainly has no problem with them), in the age of specialized bullpens it’s one awful quality to have. Managers (and the think-tanks in front offices) are surely catching on to this. Eventually, the scouting report will read to get a lefty reliever to face him in high-leverage situations, if they can. This also, of course, could be a result of small samples sizes. Kiermaier only has 43 plate appearances against lefties, and 155 against right-handers. Another thing to keep an eye on going forward.

However, what Kiermaier is doing is not wholly unsustainable. Players have made livings on putting ground balls in the right spots, and good players at that. However, the projections for the rest of the season aren’t too pretty. ZiPS predicts that he’ll produce a .259/.312/.385 line for the rest of the season. Steamer has him at .257/.311/.382. The projection systems are generally not too far off from reality, but it’s fun to think about what could be. Will Kiermaier end up being a 5-win player? The odds aren’t good. It’s certainly possible, though, and if it does happen prepare yourselves for a wonderful offseason debate on whom the rightful winner of the Rookie of the Year Award really was. In the meantime, let’s marvel at how darn good this former 31st round pick has been.

Nicolas Stellini is a student, college baseball announcer, and amateur baseball writer. Check him out over at @StelliniTweets. 

Royals are Foolishly Thinking about Lackey and Miller

Social media is fun a tool to use. It makes following the news a lot easier. You can find hundreds of stories within a matter of minutes on Twitter. I was scrolling through my twitter feed when I found this cute little rumor:

This is just rumor, and it doesn’t necessarily mean that the Royals are in  hot pursuit for those two arms, but  it is a rumor worth exploring.

Dayton Moore wants his team to win this year, and that’s not completely unreasonable. After all, Moore gets paid to build competitive teams, and if he consistently put lousy products on the field he wouldn’t hold onto his job very long. However, the Royals situation isn’t as competitive as Moore would like to think.

Tigers 57 45 .559 0.0 91.2 70.8 .570 89.1 % 4.9 % 94.0 % 91.4 % 53.3 % 27.4 % 15.7 %
Royals 53 51 .510 5.0 81.8 80.2 .496 4.7 % 10.0 % 14.7 % 8.9 % 3.8 % 1.5 % 0.7 %

The problem appears to be that the Tigers have the AL Central locked up with nearly a 90% chance of winning the division. That would leave the Royals in the midst of a wild-card race. A wild-card berth is not as valuable as it used to be because it results in a one-game toss up. This year, that one-game toss-up would be a difficult one for a team like the Royals to win because they’re likely to face one of the two of the best teams in baseball: the Angels and the Athletics.

In a hypothetical situation where the Royals somehow secure a wild card, they get to face either the Angels’ dynamic offense — including Mike Trout — as well as a fortified bullpen, or they could face the all-around, well-built Oakland A’s. While it’s extremely hard to predict the result of one game, and I don’t possess a magical crystal ball, the odds that the Royals advance to the ALDS don’t look very promising. Given the likelihood that the Royals a) don’t make the playoffs and b) if they do it would most likely be through a wild card matchup that they’re unlikely to win, they should probably not be looking to acquire the services of John Lackey and Andrew Miller.

Based on the work that Jeff Sullivan did a couple of weeks ago, we know that a team that acquires an ace like David Price would roughly increase their playoff odds by about 10%. This is a rough estimate, but that’s the upside of adding someone like Price. I doubt that Lackey would increase playoff odds by 10%, but ZiPs/Steamer projects Lackey to have between 0.9 and 1.1 WAR for the rest of the season. For context, Shields is projected have a WAR of 1.3 for the rest of the season, and Price is projected to have a WAR of 1.5 for the rest of the season.

The difference between Price and Shields is marginal for the rest of the season. However, the difference between Lackey and Price is somewhat significant, so just to be a little more accurate, we have to scale back those increased playoff odds — for Lackey — from 10% to 8%.  I have done no calculations; we’re just simply guesstimating here.

Say that the Royals decide to add a guy like Lackey, and they get the upside of a 8% increased chance of making the playoffs. That still only puts them at roughly a 22.7% chance of making the playoffs. That’s assuming that Lackey performs the way the projections expect him to perform. There’s still a chance that they make a run for a wild-card spot, but the other thing to take into consideration is the price of Lackey and Miller.

Relievers are considered extremely valuable assets if you’re going to the playoffs — so the price on Miller is going to be high — especially if you plan on being in wild-card games, as the strategy in those one-game matchups is to empty the bullpen.

Boston controls a large part of the market, because as the Rays have started winning they’re probably having second thoughts on moving Price. Boston has many arms, and as we’ve seen this week with the Peavy trade, they’ve committed to being sellers at the deadline. They have two of the best available pitchers on their roster: Lester and Lackey.

Therefore, since they have two of the better pitchers on the market the asking price is probably going to be high even on Lackey, given that the next best pitchers available are Bartolo Colon and A.J. Burnett. Rumors have been circling that the Red Sox want at least an average major-league starter in return for a package of Lackey and Miller. There would probably have to be some sort of prospect thrown in, as well. Selling the farm to increase your odds of making the playoffs that aren’t that high to begin with isn’t the best use of the Royals’ resources.

You could make the argument that trading for Lackey is justifiable because he has 2015 club options for a team-friendly $0.5 million, and if the Royals are building for 2015, then they would be bolstering their rotation. The problem is that Lackey’s contract is done after one year — at which point he would be 36 years old — and his skills would be declining. Trading a young player that you could control for multiple years for an aging veteran that you control for one year doesn’t sound like a very good deal.

The Royals simply are not in the position to upgrade with the present in mind. They should probably think about selling rather than buying, but they still want to be relevant in 2014. If the Royals are set on hanging around for a wild card, they should follow the model that the Yankees have set of acquiring low-cost/high reward upgrades; a guy that you hope to squeeze two months of good baseball out of. Otherwise, paying a premium for a pitcher when your team is unlikely to make a run at the playoffs is not the best move that the Royals could be making right now.


Streaks in sports are looked at a lot, just Google hot hand baseball, basketball, etc.  There is a lot out there on whether or not players can actually get into a groove or if it is completely luck-based.  I want to look at team streaks though, not that this hasn’t been done before, and see which teams are the streakiest so far of 2014 to see which teams might have a run in them as they are chasing the playoffs.

To measure this I wanted to treat all games as part of a streak, so each game was given a value.  A loss is defined as -1 and a second loss in a row would then become -2 and so on until the team won which would then be given a value of 1 with additional wins adding on top of that until a loss occurred.  If you then just look at the standard deviations of each team by this measure it should be easy to see who has been the most streaky.  One of the expectations of this measure would be that this would lead toward higher values for teams farther away from .500 as you have to have to string together wins (losses) to diverge significantly above (below) that mark, but those teams also don’t tend to have long losing (winning streaks) so their one-directional streakiness keeps them from being at the top of the list.

Streakiest (St.Dev.)                                              Least Streaky (St.Dev.)

Tampa Bay (3.14)                                                  LA Dodgers (1.62)

Boston (2.89)                                                          Baltimore (1.89)

Kansas City (2.76)                                                 St. Louis (1.89)

Detroit (2.75)                                                         Pittsburgh (1.91)

Atlanta (2.70)                                                          Arizona (1.97)

*data through games on Sunday, July 27th

Streakiness, or lack thereof, does not make you a good or bad team.  Detroit and Atlanta are streaky and good, Boston is streaky and bad, and Tampa and KC are streaky and near .500 on the season.  On the not streaky side Arizona and the Dodgers are on extreme opposites of the spectrum.  Just to make sure the measure didn’t bias a lot as you moved away from .500 in either direction I modified it by taking the standard deviation as a percent of the greater of wins or losses.  The top 5 still included Tampa Bay, Boston, KC, and Detroit in a slightly different order with Atlanta falling to 6th and being replaced by Miami.  The low end behaved similarly, so I will stick with the first measure as it looks like there is no bias toward good or bad teams.

One of the other things I wondered was whether or not streaky teams had high volatility in their runs scored or given up.  Looking at both standard deviation of runs scored and allowed, and then those as a percentage of average runs scored/allowed it does not look like this is the case.  The correlations for volatility in runs scored or allowed with streakiness are low, so I took it a step farther and looked only at teams that have high relative volatility in both runs scored and runs allowed.  This group has an average streakiness rank of 11.3 versus and expectation of 15.5, so maybe there is something there, but it is not even close to convincing.  I am going to need a lot more than one partial season of data to see what makes a team streaky.

As we head into pennant chase season this idea of streakiness may make things more interesting.  For instance, Kansas City and Detroit are atop the AL Central and streaky, which could make that race a lot more fun to watch as the standings are likely to vacillate more than most, especially since Cleveland has been relatively streaky as well.  On the other hand, the Dodgers might be harder to make up ground on as they consistently avoid long streaks.  Tampa Bay and Baltimore are on opposite ends of the spectrum with Baltimore hoping the Rays will fall back into the negative streaks after gaining a lot of ground recently.  They of course have an average streakiness Yankee team and a little bit streaky Blue Jays team to worry about as well.

Gregory Polanco’s Power Struggle

Baseball has spoiled us. For the past two years baseball teams, spectators, and analysts have been in awe of the young talent in the game. Last year,  the likes of Yasiel Puig and Jose Fernandez came out of nowhere and dominated the league. The year before, Mike Trout and Bryce Harper came up  and were beyond exceptional baseball players.

This year has been a little bit dry in terms of top prospects coming up and turning into the best players in the league. George Springer has been an exciting player to watch but not dominant like Trout, Puig or Fernandez. There’s a case to be made for Jose Abreu, but he was an international signing. Abreu wasn’t developed in the White Sox’ farm system for multiple years. Instead, Abreu was major league ready upon signing his contract.

The other top prospect whose performance hasn’t been captivating — at least during his first month in the big leagues — is Gregory Polanco.

Polanco is considered an extremely toolsy young player. Most scouting reports agree that Polanco has a pretty good glove — profiling as a corner outfielder — and he has a good arm.  Polanco’s bat has been more of a projection than a reality. In the minors it was easy to see that he had some talent. In his minor league career he put up an .842 OPS. There were still some rough edges around Polanco’s game as his ability to hit for power fluctuated during his time in the minors.

Season Team PA ISO SLG
2010 Pirates (R) 200 0.085 0.287
2011 Pirates (R) 203 0.124 0.361
2011 Pirates (A-) 10 0.000 0.100
2012 Pirates (A) 485 0.197 0.522
2013 Pirate (A+) 241 0.161 0.472
2013 Pirates (AA) 286 0.144 0.407
2013 Pirates (AAA) 9 0.000 0.222
2014 Pirate (AAA) 274 0.194 0.540

There were times in Polanco’s minor-league career in which he hit for decent power, but he definitely bounced back and forth between having above-average power and power outages. In his 2014 Pittsburgh Pirates Top Prospects post, Marc Hulet described some of Polanco’s difficulties:

“At the plate, he flashes the ability to hit for both average and power but he’s still learning to identify and handle breaking balls.”

Polanco has been in the big leagues for about 40 games. In his first couple of plate appearance he looked like a pretty dynamic toolsy outfielder. However, as he has accrued more plate appearances, it has become apparent that he is still an unfinished product. Polanco has hit .247/.324/.352. which has been good for a WAR of 0.2. This is a pretty small sample size of only 183 PA’s, however Polanco has been having difficulty hitting for power. Right now Polanco is getting on base a decent amount, but his lack of power offsets the value of his ability to get on base.

Fourseam 230 46 6 8 0 11 2  0  0 .283 .326 .044 .325
Sinker 207 37 6 8 0 6  0  0 0 .162 .162 .000 .194
Change 57 13 3 0 0 6  0  0  0 .462 .462 .000 .600
Slider 111 32 9 2 0 5  0 0 3 .250 .531 .281 .250
Curve 56 12 5 1 0 1 0  0 1 .167 .417 .250 .167
Cutter 66 14 5 1  0 3  0 0 1 .286 .500 .214 .375
Split 26 9 2 0  0 1  0 0 0 .111 .111 .000 .143
Slow Curve 1 0 0 0 0  0  0 0 0 .000 .000 .000 .000

Pitchers have mostly fed Polanco fastballs. In his first 40 games, Polanco has seen a fastball around 60% of the time, and that’s the pitch against which he seems to be able to have the most success. Polanco also doesn’t hit for a lot of power when he does make contact with a fastball (.044 ISO against fastballs). Despite that, Polanco was known for not being able to handle breaking balls — in the minors — so far he has actually been able to take advantage of some curveballs, cutters and sliders. All five of the home runs he has hit have come off of either a slider, a curveball or a cutter.  The story seems to be that he can catch up to the hard stuff, but doing something meaningful with it is a different story.

We’re dealing with a small sample size,  so we don’t know whether Polanco has a propensity for hitting sliders, cutters, and curveballs a long ways. Other off-speed/breaking pitches — such as sinkers and splitters — have been difficult for Polanco to hit.

2014 O-Swing% Z-Swing% Swing% O-Contact% Z-Contact% Contact% Zone% F-Strike% SwStr%
League 30.6% 65.7% 46.4% 66.0% 87.4% 79.6% 45.1% 60.1% 9.2%
Polanco 28.3% 59.3% 41.4% 53.3% 88.3% 74.4% 42.1% 54.2% 10.0%

Polanco is still showing good knowledge of the strike zone as he’s drawing a lot of walks this season. Despite his proclivity for drawing walks, there are some issues with his plate discipline stats. These mainly have to do with his ability to make contact. While Polanco does swing at lower number of pitches outside of the zone  — with an O-Swing%  of 28% — he only makes contact with those pitches 53% of the time.

The only good part of Polanco’s offensive performance has been his ability to draw walks. However, if you’re not making enough contact and not hitting for power, that doesn’t make you a productive major-league hitter.

Polanco has not necessarily been a great hitter in his first couple of months in the majors. I’m sure some people expected Polanco to step in and be one of the best hitters in the game. The truth is that this game is incredibly hard, and there are certainly growing pains when it comes to young players. Polanco has to figure out how to hit for more power to be a useful major leaguer. He hasn’t done that.

That doesn’t mean that Polanco won’t do it; we are dealing with a small sample size of just 183 PA’s, and it’s completely plausible that he makes the adjustment necessary to fix his problems. Polanco showed that he could adjust quickly.  That’s why he rose so quickly through the minors; he was able to adjust to the new challenges each level presented him. Demoting Polanco is completely pointless as he hasn’t been bad, and he has nothing left to prove in the minors.

Polanco now needs to prove himself in the majors, and that might just take some time.

Collins Working the Lineup

Over the course of 162 games, there’s only so much influence a manager of any baseball team could have over their outcome. After 105 games the Mets actual record is 3 wins shy of their projected record of 53-52, making this a .500 team. Several factors contribute to this discrepancy like losing your ace pitcher to injury, scrambling for a closer to begin the season, developing a major league catcher, adapting to a new hitting coaches philosophy, and setting the most productive lineup possible just to name a few. What Terry Collins has done with this team to this point can only be admired, but help has arrived and changes must be made to maximize team production.

The move of Curtis Granderson from the cleanup to leadoff role proved to be successful as the team surged from June’s end through July. Daniel Murphy and Curtis Granderson’s slash line numbers are almost identical, batting average is the only big difference which Daniel Murphy leads Granderson by about.060 AVG points and make him a more ideal leadoff hitter. Curtis Granderson hit 6 home runs from the leadoff spot which minimized his RBI potential which essentially is the reason Sandy Alderson signed him. In moving Daniel Murphy into the leadoff spot, the Mets actually increase their leadoff OBP while putting Curtis Granderson into a role where his RBI opportunities increase dramatically.

Daniel Murphy’s SLG% is nearly that of Curtis Granderson with half as many HRs, meaning that Daniel Murphy is doing a better job of getting into scoring position than our current leadoff hitter. The only 2 reasons the Mets have kept Murphy out of the leadoff spot in the past were lack of speed on the basepaths and low OBP. Now Daniel leads our starting players in SB showing he has some speed and base running ability and his OBP is amongst the team leaders. David Wright being the best hitter on the team (despite struggles in 2014) deserves the 2nd spot in the order. His power has declined this season, however his OBP is still respectable and he should remain in a table-setting role followed by Granderson. Lucas Duda has earned his cleanup role as he’s hit over .280 in the past couple of months with at least 5 HRs per month. He is driving the ball to all fields and should be a key contributor to driving in runs once our table-setters do their jobs.

The top 4 lineup spots should be configured as follows:

1  2B Daniel Murphy        (.293/.340/.412) 28 2B, 7HR, 11SB

2  3B David Wright           (.278/.339/.401) 24 2B, 8HR, 5SB

3  RF Curtis Granderson  (.232/.339/.415) 18 2B, 15HR, 8SB

4  1B Lucas Duda               (.259/.356/.500) 22 2B, 18HR, 3SB

For the next spot in the lineup, this player has had a tale of 2 seasons. Travis d’Arnaud has adjusted quickly since his demotion to AAA on June 6th. Since being recalled on June 24th, d’Arnaud has a slash line of (.302/.337/.646). He has lengthened our lineup and has earned the spot of the 5 hitter.

5  C Travis d’Arnaud

Before June 6th demotion    (.180/.271/.320) 3 2B, 3HR

Since June 24th Promotion (.302/.337/.646) 7 2B, 4HR

Season Stats                            (.232/.298/.379) 10 2B, 7HR

Right after Travis d’Arnaud in the Mets order is when they begin to look thin offensively. Having early success in the season but struggling as of recent is Juan Lagares, the defensive wizard and minor league doubles machine. This kid showed an advanced approach to lead off the year and is capable of making the bottom of our order a productive one. He isn’t seeing the ball well like he was in the first half, but we need to remember he is in his first full season in the bigs and known primarily for his route to catch baseballs and cannon for an arm, any offense is a plus.

6  CF Juan Lagares (.271/.306/.375) 16 2B, 2HR, 2SB

7  RF Chris Young/Eric Young/Kirk Nieuwenhuis/Bobby Abreu/denDekker

Our right field position is a question mark. I’m not saying the Mets haven’t produced anything from the position, but they don’t have an everyday right fielder which is a need to be addressed in the off-season or via trade before Thursday’s deadline. Though not one player has stepped up and taken over this position, I still believe they have produced more than my “ideal” 8 hitter, Ruben Tejada. In every championship team there is that one scrappy player that is on the squad solely for defensive prowess. Through the course of the season I have seen many different Ruben Tejadas. I’ve seen the defensive shortstop, the slap hitter, the kid in way over his head, and the wanna-be slugger with warning track power. This player is undoubtedly our 8 hitter and those who look too dependently on his OBP must take into consideration how many times he has walked for the sole reason that the worst hitting pitching staff is just 4 pitches away.

Ruben has been intentionally walked 10 times, twice as much as any player on the Mets. Ruben Tejada hasn’t defended the way he has in the past which quieted his lack of offense. In a New York setting, he shouldn’t start and the Mets executives know that. Ruben is a bridge to the future, an inexpensive filler until we land in a position of contention where an offensive producer is necessary at the position. Until then we have a shortstop with a strong arm and instincts but lacks the speed to get too many balls up the middle or steal a base when we need him to. He has no power and is offensively irrelevant as his slash line below shows. A shortstop with any tools is an upgrade here.

8  SS Ruben Tejada (.226/.351/.281) 9 2B, 2HR, 1SB

xHitting (Part 4): 2014 Fantasy Edition!

Welcome to the fourth installment of xHitting!  As always, reader comments and feedback are super encouraged and appreciated.  (Links to parts one, two, and three)

Briefly recapping the method, the gist is to estimate the expected rate of each individual hit type based on a player’s underlying peripherals, and in turn recover all the needed components to compute expected versions of wOBA, OPS, etc.  The only real change to the model since last time is that I now utilize a “hybrid” predicted home run rate, that averages between actual and (raw) predicted home run rate, with the weight given to actual HR rate increasing in the number of plate appearances.  (This is explained in part three, for those curious.)

Perhaps the more exciting change, though, is that this time I actually have results for an ongoing season, which potentially can help for fantasy purposes.  (Not that most readers need my help necessarily.)  Related to fantasy usage, there were a few requests to see a full spreadsheet of past results (2010-2013 seasons), which I have posted here.  Again feel free to take it or leave it at your leisure.

Note: I collected most of these data at the All-Star Break, so numbers may be a few weeks behind, but they’re still mostly true.  Also, for time considerations I only fetched 2014 stats for qualified leaders.  This even leaves out a few big names, but I couldn’t justify time to fetch every player.

So far, I’ve typically posted the biggest “over-” and “under”-achievers for a given season.  And I suppose I’ll continue that tradition today.  But while these lists are useful for highlighting which players seem most likely to regress, it overlooks another main use of the model, which is to assess the realness of a player’s apparent “breakout” or “decline;” at least in-sample.  (In some cases, the model may think that a player’s breakout is entirely justified, given peripherals, while others it may view more skeptically.)  Thus, today I’ll also post a second list, of players who seem to have taken a pronounced step forward/step back this season, and what the model thinks of their season-to-date performance.

Okay, time for results!  I’ll start with the list of “over-” and “underachievers.”

2014 Underachievers (1st half) 2014 Overachievers (1st half)
Name wOBA xWOBA Diff Name wOBA xWOBA Diff
Jean Segura 0.256 0.305 -0.049 Casey McGehee 0.345 0.277 0.068
Chris Davis 0.306 0.353 -0.047 Yasiel Puig 0.398 0.340 0.058
Mark Teixeira 0.352 0.397 -0.045 Matt Adams 0.376 0.324 0.052
Gerardo Parra 0.289 0.327 -0.038 Mike Trout 0.428 0.381 0.047
Brian McCann 0.298 0.330 -0.032 Marcell Ozuna 0.343 0.300 0.043
Torii Hunter 0.323 0.355 -0.032 Lonnie Chisenhall 0.396 0.359 0.037
Joe Mauer 0.308 0.340 -0.032 Scooter Gennett 0.355 0.320 0.035
Jimmy Rollins 0.320 0.352 -0.032 Marlon Byrd 0.344 0.309 0.035
Brian Roberts 0.304 0.334 -0.030 Giancarlo Stanton 0.397 0.363 0.034
Buster Posey 0.326 0.352 -0.026 Hunter Pence 0.359 0.325 0.034

A general pattern I notice is that, having worked with this model for a while now, there do seem to be players that give the model some trouble and have a disproportionate tendency to appear on this list from year to year.  A few of these players appear on this list… more on that later.

Partly for that reason, I wouldn’t necessarily say to “buy low” the guys on the left, nor “sell high” the guys on the right; although you can if you want.  I won’t address every player, but I have some scattered comments:

  • For readers who prefer OPS, .020 wOBA translates to about .050 OPS, on the margin.
  • .397 predicted for Teixeira?  Not sure where that came from…
  • Poor Segura.  All things considered, I think nobody deserves a big second half more than he does.
  • Whatever happened to Casey McGehee’s power?  The guy once hit 23 home runs in a season, but now has ISO of .073, with surprisingly low fly ball distance.
  • Although Chisenhall’s breakout is not as impressive if you take out what the model thinks is luck, it’s still a pretty impressive improvement.
  • Chris Davis is sort of the reverse of Chisenhall.  Adding back in what the model thinks has been bad luck, he’s still way down from what he did last year, but not nearly as disappointing as he probably has been to many owners thus far.

As mentioned, certain players do seem to be able to over/underperform the model somewhat consistently; the same way we think some pitchers are usually better or worse than their FIP.  With now 4.5 years of data to work with, however, I think I can make educated guesses about which players systematically deviate from the model predictions.  I’ll term this deviation the “player fixed effect.”

(Requiring at least 1000 PA from 2010 through 2014 first half)

Model loves too much Model loves too little
Name Player FE
estimate (wOBA)
Name Player FE
estimate (wOBA)
Brian Roberts -0.033 Wilson Betemit 0.032
Todd Helton -0.026 Brandon Moss 0.032
Jean Segura -0.026 Ryan Sweeney 0.028
Jose Lopez -0.025 Mike Trout 0.027
Mark Teixeira -0.025 Peter Bourjos 0.026
Russell Martin -0.024 Matt Carpenter 0.025
Darwin Barney -0.023 Brandon Belt 0.025
Chris Getz -0.023 Melky Cabrera 0.025
Jimmy Rollins -0.021 Carlos Ruiz 0.024
Jason Bay -0.020 Chris Johnson 0.024


  • Again, .020 wOBA is equivalent to about .050 OPS, on the margin.
  • Taking out their apparent fixed effect, Teixeira is only underperforming his xWOBA by about .020, and Brian Roberts is actually doing about par.
  • On the reverse side, Mike Trout’s “adjusted” xWOBA jumps up to .408, where really it probably doesn’t surprise us that he’s outperforming even that, since he’s Mike Trout.  And although Giancarlo Stanton misses the Top 10 cutoff above, his apparent fixed effect of +.022 would be 11th; so his “adjusted” xWOBA is more like .385.
  • Yasiel Puig (.058) would also be on the list of “positive fixed effects” if we relaxed the PA requirement (he has 826 during this time).  And Matt Adams (~.040) might also be well on his way to that list; although he has fewer plate appearances still than Puig.
  • I don’t really have good explanations/know any common themes for players with negative fixed effects.  Maybe readers can help?
  • For Trout, home runs are pretty clearly the area where the model underestimates him.  In any given season (2010-2014), he hits about twice as many HR as the model thinks he should in the “raw” prediction.
  • And Trout’s not the only “HR rate defier,” either; just the most salient.  In general, the model has never done as well with home runs as it does with singles, doubles, and triples.  It seems there are other important determinants of home run hitting that really should be in the model, but currently are not.  Intuitively, I sort of would like velocity and angle of the ball off the bat, but so far have not found a good data source to actually include these.  (Maybe that will change in the coming years as MLBAM releases “Hit F/X” style data?)  Until then, reader suggestions are also super welcome here.

And now, finally, for the other usage: here’s a partial list of players who have taken either a pronounced step forward or back this season, relative to established norms.

2014 “Decliners” 2014 “Improvers”
Name Career wOBA 2014 wOBA 2014 xWOBA Name Career wOBA 2014 wOBA 2014 xWOBA
Nick Swisher 0.352 0.285 0.305 Michael Brantley 0.324 0.394 0.404
Joe Mauer 0.373 0.308 0.340 Lonnie Chisenhall 0.328 0.396 0.359
Allen Craig 0.350 0.289 0.309 Seth Smith* 0.334 0.389 0.356
Billy Butler 0.352 0.300 0.309 Victor Martinez 0.362 0.416 0.422
Evan Longoria 0.365 0.315 0.323 Jonathan Lucroy 0.342 0.383 0.354
Domonic Brown 0.315 0.267 0.267 Anthony Rizzo 0.342 0.382 0.382
Chris Davis 0.351 0.306 0.353 Nelson Cruz 0.356 0.393 0.380
Matt Holliday* 0.385 0.342 0.318 Jose Altuve 0.319 0.356 0.325
Jean Segura 0.299 0.256 0.305 Brian Dozier 0.311 0.344 0.362
David Wright 0.377 0.335 0.305 Kyle Seager 0.334 0.367 0.344
Buster Posey 0.366 0.326 0.352 Dee Gordon 0.297 0.329 0.318
Shin-Soo Choo 0.369 0.333 0.346 Alcides Escobar 0.284 0.312 0.300
Dustin Pedroia 0.356 0.325 0.337 Casey McGehee 0.321 0.345 0.277
Jed Lowrie 0.327 0.297 0.305
Jay Bruce 0.343 0.315 0.326

* – To avoid inflation from Coors Field, for these players I’ve taken the total from 2011-13 seasons only


  • At least in-sample, Brantley’s breakout seems to be pretty much entirely justified.  Of course this doesn’t mean that he won’t regress somewhat, but if I were to guess, I’m a little more optimistic than ZiPS and Steamer (which currently project .341 and .333 RoS, respectively).  Similar deal for some others.
  • “Yikes” for Billy Butler and Domonic Brown, whose declines this season seem (at least in-sample) to be entirely justified.
  • I’m not sure why the model dislikes Casey McGehee so much.  Obviously his fly ball distance (mentioned earlier) isn’t doing him any favors, and his .369 first-half BABIP is probably unsustainable.  Still, .277 xWOBA?  Seems harsh.

As with any fantasy advice, don’t take any of this too literally…  Take it or leave it as you see fit.

Lastly, although I hyped this piece from a fantasy perspective, the overall goal remains that I would love to see more work done to de-luck hitter stats, the way people do so often for pitchers.  (FIP for pitchers, and xWOBA or xWRC+ for hitters! Is the dream.)

Reader thoughts on how to improve the model, or requests for players not already mentioned?

Projecting the Mariners

At the time of writing, the Seattle Mariners are 54-50 and 0.5 games back in the race for the second wild card spot. With rumors flying as to the upcoming trade deadline, the benefit of selling prospects to improve their odds varies heavily based on how view their current roster. Dave Cameron rang the warning bell for all the of the second wild card contenders, pointing out that these teams are vying for a one game playoff in Anaheim. Not such a great prize.

But. The Mariners haven’t made the playoffs in 13 years and the prospect of a wild card spot has made this most exciting campaign in a decade. So how good are the Mariners? At 54-50, the Mariners have won at a clip of .519, but FanGraphs’ projection models paint a very different picture of the team. FanGraphs’ playoff odds page believes the Mariners are a .503 team going forward. Not so hot.

But. The Mariners’ season-to-date-stats projection from FanGraphs is a lot more favorable, suggesting the true talent level of M’s, based on CoolStandings’s version of Pythagenpat adjusted for remaining schedule, is .557. To put that in context, it’s the 4th best in baseball, behind the third-place Angels (.572) and 5th place Dodgers (.549). The Mariners, by this measure, are the both the 3rd best team in the AL West and the American League.

The CoolStandings model, like that of Baseball Prospectus, relies on base runs and a modified Bill James Pythagorean W-L. As best I understand it, this means that the Mariners, by base runs, have been ‘unlucky’ to the tune of the gap between their projected rest of season W% and their current W%, for a loss of 0.038 W%. 3.8% of the M’s 104 games already played comes out to 4 wins, so the Mariners’ ‘true talent level’ record would be 58-46. That’s still a good margin worse than the Angels, but it’s also well above all the other wild card contenders. By this model, the M’s would be expected to win 86.1 games this year, beating out the next-best Blue Jays by almost 2 wins.

Conversely, by the FanGraphs Zips-Steamer projection system, the M’s should win 83 games and come a game or so short of the Blue Jays. By these numbers, the M’s still finish the as the best team not to make the playoffs.

There is a huge gap between these projections, coming out to about 8 wins over a full season and 5 over the roughly two-thirds of the season already played. The rest of this article is an amateur attempt to account for that gap, and to assess how good the Mariners might actually be.

The CoolStandings model relies on past performance and therefore absorbs some mathematical ‘luck.’ For example, the Mariners’ runs allowed this season has to have been impacted by a league high strand-rate. For that reason, the figures are probably overly optimistic of the M’s chances.

But that isn’t to say the M’s look like a .503 ballclub either. To account for the difference between the M’s .519 W% and their .503 Zips-Steamer talent level, I looked at the ZiPS projections for the rest of the season, but in ignoring Steamer, this is only a rough guess at how the projection could be too pessimistic .016% of 104 games is about 2 wins, so that’s the extent of the disagreement between reality and luck against the projections. For the remainder of the season, that .016% is worth about one win.

For example, Michael Saunders has been worth 1.7 fWAR this year in 219 PAs, a pace of approximately 4.2 fWAR over 550 PAs, but Zips says he’ll be worth 0.7 fWAR over 161 PAs the rest of this year, a clip of 2.39 fWAR/550. If we say the M’s have played two-thirds of their season already, that says that Saunders should’ve been worth 1.6 fWAR this year, and has overperformed to the tune of .1 of the M’s wins.

Looking a little deeper, we can see that ZiPS doesn’t punish Saunders playing time projection very much despite his injury trouble, in that it sees him basically playing full time the rest of the way. If we take the ZiPS projection and put it over his 219 PAs of 2014 service time, the picture is a little clearer: ZiPS says Saunders should’ve been worth 0.95 fWAR this year. So Saunders has already given the M’s almost a full win more than he should’ve, says ZiPS.

Other contributors to the M’s supposed over-performance include Felix and Ackley.

Ackley’s defense has been fine so far, per UZR, but ZiPS says it should be bad. So despite underperforming at the plate by 9% per wRC+, ZiPS says Ackley’s glove has already given the M’s a full half-win more than can be expected.

A topic of some discussion, Ackley’s fielding is hard to assess. To the naked eye he’s looked ok, not terrible, and UZR seems to agree. There was his spectacular catch the other day, and there’s also his mediocre arm, which possibly has been taxed at a below-average rate, but this is all speculation. But if we trust Ackley’s to date figures and performance, we can give the Mariners a third of a win back over their remaining schedule.

And then there’s Felix, who has already contributed a remarkable 5.5 fWAR. ZiPS penalizes Felix quite a bit, expecting him to regress quite a bit. If Felix continues pitching at his current level, the M’s again would be expected to win another half game more than ZiPS suggests. Felix both has reached new highs with his changeup and has benefited quite a bit from Zunino’s pitch framing, as have the M’s, and Zunino’s framing is neither accounted for by the projection systems nor his season-to-date fWAR.

Then there’s Chris Young. Young has been worth 0.6 fWAR and 2.7 rWAR. Young alone can account for the gap between FanGraphs’ and ZiPS’ perception of the Mariners and their current performance. A lot has been written about Young’s season, and going back to 2009, the last season ZiPS looks at for their projections, Chris Young’s 2014 is the single greatest overperformance of ERA against FIP. Young probably hasn’t been as good as his ERA nor nearly as bad as FIP, but I can’t speculate at his true talent.

Between Young, Zunino’s framing, Ackley’s defense, Saunders’ somewhat expected improvement, and Felix’s dominance, the Mariners seem like an especially tough team to project. For a counter example, the Blue Jays are expected to win 84.3 games by ZiPS-Steamer and 84.9 by Cool Standings, versus the Mariners 3 game swing between the projections. Clearly ZiPS-Steamer is the more reliable model and clearly it’s missing a significant piece of the picture. But were I a betting man, I’d certainly bet the M’s finish better than 83 wins.

What these numbers suggest is that by both the models the Mariners are close enough to be competitive for the wild card, and that acquiring marginal talents like Marlon Byrd or a DHing Matt Kemp (not going to happen) could have a real impact on the team’s chances. By both models, going all in for Ben Zobrist at SS and a right-handed OF might not be be such a bad idea, nor would be so ludicrous to pursue David Price. That said, a bad trade is a bad trade no matter the context of standings, and the M’s suffer from an overpay in any event.

What the holes in the ZiPS projections say, however, is that maybe the M’s recent slide isn’t especially important, and that while this probably isn’t the 4th best squad in baseball, it’s still probably a good team, and a team to be excited about. Because even if the M’s fail to make another roster move, they should be a competitor.

Looking at Attendance after Aces are Dealt

As baseball season and the summer months heat up, so too do the trade rumors. Almost every year, baseball media and fans postulate and prognosticate who might be traded before the annual trading deadline.

This year, the big fish on the market is Rays left-hander David Price. With only one year left on his contract, it is unlikely the Rays can afford to keep the former Cy Young Award Winner. But with the team winning eight in a row and 19 of their last 24, trading their ace doesn’t seem like a sure deal anymore. Most recent reports say the Rays management will wait until the absolute last minute to make a decision on if, where, and for whom the popular lefty will be traded.

With the Rays’ status with regards to popularity and market, some of the talk in regards to trading David Price has wound into the realm of attendance. The Rays are currently last in the Major Leagues in attendance, and some are concerned attendance could drop even lower if they traded their best pitcher. There are those who think Rays fans would consider the trade a message from ownership to wait until next year. And if that’s the message, why not wait until next year to buy a ticket?

To estimate how Rays attendance might react to a possible trade of David Price, I looked at 12 prior trades of ace pitchers over the last 37 years. Via, I looked at attendance before and after each trade. I also looked at winning percentage before and after.

My goal is to see if two maxims hold true:

  1. Attendance goes up when teams win and goes down when teams lose.
  2. A team that trades its best pitcher will have a worse record after the trade.

Hence, if attendance is attached to winning and ace pitchers are attached to winning, attendance should drop after ace pitchers are traded.

Is this really the case? Or is attendance in some cities more sensitive to major trades than others?

Let’s begin by looking at the granddaddy of superstar pitcher trades: the Tom Seaver trade. On June 15, 1977, after a slight tiff with ownership, the Mets shipped the franchise’s first ace to the Reds for Steve Henderson, Pete Flynn, Pat Zachary, and Dan Norman. The Mets were bad before but worse after and attendance followed suit.

Tom Seaver trade attendance

Twelve years later, in 1989, two aces were traded during the season. On May 25th, the Mariners moved ace Mark Langston to the Expos for a bevy of prospects headlined by future ace Randy Johnson. Mariners fans reduced their attendance by nearly the same amount Mets fans did in 1977. Although playing .500 baseball prior to the trade, the Mariners winning percentage dropped significantly after the trade.

Langston trade

Two months after the Langston trade, the Minnesota Twins traded 1988 Cy Young Award winner Frank Viola to the Mets for Rick Aguilera, Kevin Tapani, and three other pitchers. The Twins were two games under .500 at the time of the trade, and then played .500 after the trade. Despite their slight improvement, attendance dropped 12.95% after the Viola trade.

Viola trade

We fast-forward to 1998 and another Mariners trade. During the 1998 season, the Mariners dealt the aforementioned Johnson to the Astros for Freddy Garcia, Carlos Guillen, and John Halama. While Johnson immediately did well in Houston, the Mariners played better after his departure, going 28-25 after the trade. Like the 1988 Twins, however, the positive play did not lead to an increase in attendance, as the average per game attendance went down after the trade.

Johnson trade

Our next trade is the Bartolo Colon trade in 2002. On June 27, 2002, the Indians shipped Colon and Tim Drew to the Expos for Cliff Lee, Grady Sizemore, Brandon Phillips, and Lee Stevens. The Indians played .467 baseball before the trade and a lesser .447 clip following the deal. Attendance, however, jumped after the trade, up 10.04% over the team’s final 45 games.

colon trade

We look at Cleveland again in 2008, when the Indians moved CC Sabathia to the Milwaukee Brewers for Michael Brantley, Matt LaPorta, and three other players. After trading Sabathia, the Brewers vastly improved their record, finishing the season 44-30. Attendance also went up after the Sabathia trade, from 25,964 to 27,766 per game, an increase of 6.94%.

Sabathia trade

The 2009 season saw the trade of three high profile pitchers. Two were legitimate aces, and the other a former ace that might give us insight to a Rays attendance prediction.

The first major pitcher trade in 2009 again involved the Indians. On July 29th, the Tribe shipped Cliff Lee and Ben Francisco to Philadelphia for Jason Knapp, Carlos Carrasco, Jason Donald and Lou Marson. Unlike the Colon or Sabathia trades, following the Lee trade, the Indians winning percentage and attendance per game both decreased.

Lee trade

Two days after the Indians traded Lee, the San Diego Padres moved right-hander Jake Peavy to the Chicago White Sox for Clayton Richard and three other players. Like the Twins in 1989 and the Mariners in 1998, the Padres played better after moving their ace, finishing the remaining 59 games with a 34-25 record. Unfortunately, also like the ’89 Twins and ’98 Mariners, less fans came out to see their now-winning team.

Peavy trade

Our final pitcher trade of 2009 occurred on August 29th, when the Rays moved former ace Scott Kazmir to the Angels for Sean Rodriguez, Alex Torres, and Matthew Sweeney. Kazmir was no longer the Rays ace in 2009, handling over the title to James Shields and the up-and-coming David Price. But Kazmir still had name value in the Tampa Bay area, despite his decreased effectiveness.

After trading Kazmir, the Rays stumbled to a 15-20 finish. They went from being 4.5 games out of the wildcard to finishing 11 games out of the playoffs. Per game attendance following the Kazmir trade also dropped considerably, from 24,169 per game to 19,574 per game. This attendance decrease of 19.01% is the biggest drop of any of our surveyed trades.

Kazmir trade

The next year, two of our most frequent subjects collided when the Mariners traded Cliff Lee. After signing with Seattle in the offseason, Lee was sent to the Rangers for the stretch run. After the trade, the Mariners, who had played .400 baseball prior to trading Lee, finished the season with a .350 winning percentage and saw attendance drop 4.99% over the last 39 home games.

Lee trade 2010

In 2012, the Brewers were on the dealing side when they sent Zack Grienke to the Angels for Jean Segura and two other players. While the Brewers were 10 games under .500 before the trade, they reversed fortune after the deal, going 39-25, a .609 clip. Attendance also increased after moving Grienke, albeit by 124 fans per game, or only 0.3%.

Grienke trade

In our final trade, we look at the Chicago Cubs. Prior to trading Matt Garza on July 22, 2013, the Cubs were 10 games under .500 and averaging exactly 33,000 fans per game. After trading Garza, the Cubs dropped to 30 games under .500 and lost 919 fans per game in the seats, a 2.78% decrease.

There are many other trades and fanbases I could have looked at (the Ubaldo Jimmenez trade in 2011 comes to mind), but this small sample set gives a wide spectrum of possible outcomes resulting from trading an ace pitcher. From what we looked at, we found:

  • 50% of the data set decreased in both record and attendance
  • 25% increased in record and decreased in attendance
  • 16% increased in both record and attendance after trading their ace
  • 8% decreased in record but increased in attendance

attendance graph

The Indians are particularly interesting, seeing a different outcomes each time they traded an ace. The Mariners saw an attendance drop after both the Langston and Johnson trades but played better after trading Johnson and worse after moving Langston. Perhaps Langston had a bigger effect on the team in 1989 than Johnson did in 1998.

So what would happen if the Rays traded David Price? Given their current winning streak and the attendance sensitivity seen after the Kazmir trade, my initial estimate would have them in the same category as the 1989 Twins, 2009 Padres, and 1998 Mariners – an improved winning percentages but lower attendance. An better record post-trade might not be difficult considering the beginning of the Rays season was a disaster marred by injured players who are slowly returning (Alex Cobb, Jeremy Hellickson, David DeJesus, and possibly Wil Myers).

But with the Rays struggling to fill seats, moving fan favorite David Price might be a bad public relations move. From the studies I have done, games David Price has pitched in have drawn 6% more than average. That could be because Joe Maddon sometimes aligns the rotation so Price faces prime opponents such as the Yankees and Red Sox, teams that traditionally draw well at Tropicana Field. But some of Price’s “bump” could be the allure of seeing one of the best pitchers in the American League.

My estimate is the Rays would suffer an initial attendance drop if they traded David Price. Games against the Red Sox and Yankees (especially Jeter’s last series in Tampa Bay) will continue to do well. Bobbleheads and other promotions will also do well (expect a good turnout for the Don Zimmer sno-globe). And if the team plays well enough to contend, attendance may recover, but even then, the Rays won’t average over 20,000 per game.

Then again, doubtful they would draw 20K on average even with David Price in the rotation.

Using Triple-A Stats to Predict Future Performance

Over the last couple of weeks, I’ve been looking into how a players’ stats, age, and prospect status can be used to predict whether he’ll ever play in the majors. I used a methodology that I named KATOH (after Yankees prospect Gosuke Katoh), which consists of running a probit regression analysis. In a nutshell, a probit regression tells us how a variety of inputs can predict the probability of an event that has two possible outcomes — such as whether or not a player will make it to the majors. While KATOH technically predicts the likelihood that a player will reach the majors, I’d argue it can also serve as a decent proxy for major league success. If something makes a player more likely to make the majors, there’s a good chance it also makes him more likely to succeed there. This hypothesis may be less true for players at the Triple-A level since such a high proportion of these players make it to the majors, but I still think it provides some insight. To address this issue, In the future, I plan to engineer an alternative methodology that takes into account how a player performs in the majors, rather than his just getting there.

For hitters in Low-A and High-A, age, strikeout rate, ISO, BABIP, and whether or not he was deemed a top 100 prospect by Baseball America all played a role in forecasting future success. And walk rate, while not predictive for players in A-ball, added a little bit to the model for Double-A hitters. Today, I’ll look into what KATOH has to say about players in Triple-A leagues. Due to varying offensive environments in different years and leagues, all players’ stats were adjusted to reflect his league’s average for that year. I also only considered what happened during or after the sample season. So if a former big leaguer spends the full season in Triple-A, he’s only considered to have “made it to the majors” if he resurfaces again. For those interested, here’s the R output based on all players with at least 400 plate appearances in a season in Triple-A from 1995-2011.

AAA Output

This output looks pretty similar to what we saw for Double-A hitters, including the “I(Age^2)” coefficient, which adds a bit of nuance into how a players’ age can predict his future success. But in this version, there’s also an interaction between ISO and age. Basically, this says that the ability to hit for power is much more important for older players than younger players at the Triple-A league level.


By clicking here, you can see what KATOH spits out for all players who logged at least 250 PA’s in Triple-A as of July 7th. . I also included a few interesting players who missed the 250 PA cut off, including Mookie Betts, Rob Refsnyder, Ramon Flores, and Kris Bryant. Here’s an excerpt of the top players from Triple-A this year. Joc Pederson tops the charts with an impressive 99.91% probability. Many of these players have already played in the majors, so these values can be interpreted as the odds that said player will play in the majors in the future.

Player Organization Age MLB Probability
Joc Pederson LAD 22 100%
Gregory Polanco PIT 22 100%
Kris Bryant CHC 22 100%
Mookie Betts BOS 21 100%
Arismendy Alcantara CHC 22 100%
Oscar Taveras STL 22 99%
Stephen Piscotty STL 23 98%
Steven Souza WSN 25 98%
Javier Baez CHC 21 98%
Maikel Franco PHI 21 97%
Taylor Lindsey LAA 22 97%
Domingo Santana HOU 21 97%
Enrique Hernandez HOU 22 96%
Chris Taylor SEA 23 95%
Jake Marisnick MIA 23 95%
Mikie Mahtook TBR 24 94%
Rob Refsnyder NYY 23 94%
Alfredo Marte ARI 25 93%
Carlos Sanchez CHW 22 93%
Nick Franklin SEA 23 93%
Ramon Flores NYY 22 92%
Ronald Torreyes HOU 21 92%
Joe Panik SFG 23 91%
Tyler Saladino CHW 24 91%
Giovanny Urshela CLE 22 90%

Now that I’ve gone through all levels of full-season ball, I’ll start at the bottom and cycle through the short-season leagues. These samples will be pretty small, but perhaps not completely useless now that those players have a few weeks’ worth of games under their belts. At the very least, it will be interesting to see what KATOH’s able to tell us about batters so far away from the big leagues, even if it’s a little premature to ask KATOH about 2014’s players.

World’s Best Pitcher Faces World’s Best Hitter

If you’re an East Coaster  — like myself — and you stayed up late enough to watch the Angels-Mariners game on Saturday night, you were in for treat. The game, based on the pitching matchup alone, would have been an exciting one to watch: Garrett Richards versus Felix Hernandez. There’s a fair argument that Hernandez is the best pitcher in baseball right now. The same could be said about Clayton Kershaw, but if you’re talking about pitchers who have been healthy for the whole season, then King Felix is your guy.

Richards has been no slouch either, turning himself into the bona-fide ace in Anaheim, pitching to a ERA/FIP/xFIP line of 2.55/2.69/3.21, which is good for a WAR of 3.2. This game previewed as more of pitcher’s duel than a slugger’s fest.

However, this game didn’t just feature the game’s best pitcher.  It also featured arguably the game’s best hitter: Mike Trout. So, how does the world’s best pitcher pitch to the world’s best hitter?

First Pitch

trout pitch 1

This is in the fourth inning, and it’s Trout’s second time facing Hernandez.  Hernandez starts out with a fastball. Sucre set up for a pitch low and away, and Hernandez misses with a 92 MPH up and in. A slight mistake, but definitely not a costly one. The count is 1-0.

Second Pitch

Like any great pitcher, Hernandez fixes the mistake he made on his first pitch. Sucre sets up for the same location, and Hernandez throws a 92 MPH fastball low and away. Hernandez nails it right on the money. The pitch was well executed, and Trout even thought about going around on it. Trout doesn’t go around, but that doesn’t matter because the pitch is a called strike. Hernandez evens up the count at 1-1.

Third Pitch


Clearly,  the Mariners had a plan: pitch Trout low and away. This is the third time that Sucre set up in that location, however this time Hernandez decided to flash his signature changeup. This hard changeup  probably would have bounced in the dirt had Trout not fouled it off. It seems as though Hernandez plans on going after Trout by  first establishing the fastball in a particular location, and then attack with the off-speed stuff. The count is in Hernandez’s favor at 1-2.

Fourth Pitch

Hernandez comes after Trout with another changeup, and Sucre sets up in the same location as the last pitch. Hernandez — like any pitcher — is clearly going for the punch out. Hernandez is trying to execute the same pitch, while hoping for better results. Unfortunately he misses inside for a ball. Trout has worked the count to 2-2.

Fifth Pitch

When you throw a pitch two times in a row, you run the risk of becoming predictable. Hernandez decides to break his streak of throwing changeups, and goes for the hard stuff. Sucre sets up in the same location that he always does, however Hernandez floats 93 MPH fastball up and away for a ball. The count is full. The best thing about a full count is that you know that one of three things will happen: strikeout, walk or the batter makes contact.

Sixth Pitch

Well, something did happen. Trout made contact, but it was nothing  meaningful,  as he fouled off a 94 MPH fastball from Hernandez. Nothing is really new from the Hernandez-Sucre side. Sucre sets up in the same location he has been setting up for the entire at bat, and Hernandez probably would have hit his spot had Trout not fouled it off. The count is still 3-2.

Seventh Pitch


Hernandez comes back with a 94 MPH fastball, hoping to hit the low and away location that Sucre sets up. However, Hernandez leaves the pitch up a little bit just enough to slightly miss the location that Sucre had set up. Trout is one of the best hitters in the game, and the best hitters in the game can take advantage of a pitcher’s slight mistake. Trout almost takes this pitch yard , as it bounces off the wall for a double. This double ended up being nothing significant, as Hernandez managed to work his way out of the inning without Trout scoring.

Crisis averted for Hernandez.

Trout happened to come out successful in that at-bat as he got himself a pretty big hit against Hernandez, however later on in the game Hernandez managed to strike out Trout.

Hernandez finally gets Trout on his signature nasty changeup that breaks on the inside part of the plate. If you look at Mike Trout’s heatmap,  you know that going inside on him is risky business. Dave Cameron even wrote a whole article about it. Luckily, Hernandez’s changeup is good enough that he can get away with pitching Trout inside.

One of the best hitters in the game faced off against one of the game’s best pitchers. You could see why these two are the best at what they do. They recognize mistakes, capitalize on those mistakes, and correct their own mistakes. There are a lot of at-bats each year in baseball, and to someone who knows nothing about baseball, this might look ordinary.  However,  if you look closely enough, you can see the intricacies of a particular at-bat. That’s when you start to realize how complex baseball is.