Hardball Retrospective – The “Original” 1992 San Diego Padres

In “Hardball Retrospective: Evaluating Scouting and Development Outcomes for the Modern-Era Franchises”, I placed every ballplayer in the modern era (from 1901-present) on their original team. Therefore, Bobby Grich is listed on the Browns / Orioles roster for the duration of his career while the Phillies declare Dick Allen and the Pirates claim Jose A. Bautista. I calculated revised standings for every season based entirely on the performance of each team’s “original” players. I discuss every team’s “original” players and seasons at length along with organizational performance with respect to the Amateur Draft (or First-Year Player Draft), amateur free agent signings and other methods of player acquisition.  Season standings, WAR and Win Shares totals for the “original” teams are compared against the “actual” team results to assess each franchise’s scouting, development and general management skills.

Expanding on my research for the book, the following series of articles will reveal the finest single-season rosters for every Major League organization based on overall rankings in OWAR and OWS along with the general managers and scouting directors that constructed the teams. “Hardball Retrospective” is available in digital format on Amazon, Barnes and Noble, GooglePlay, iTunes and KoboBooks. The paperback edition is available on Amazon, Barnes and Noble and CreateSpace. Supplemental Statistics, Charts and Graphs along with a discussion forum are offered at TuataraSoftware.com.

Don Daglow (Intellivision World Series Major League Baseball, Earl Weaver Baseball, Tony LaRussa Baseball) contributed the foreword for Hardball Retrospective. The foreword and preview of my book are accessible here.

Terminology

OWAR – Wins Above Replacement for players on “original” teams

OWS – Win Shares for players on “original” teams

OPW% – Pythagorean Won-Loss record for the “original” teams

Assessment

The 1992 San Diego Padres          OWAR: 52.6     OWS: 324     OPW%: .595

GM Jack McKeon acquired 84.2% (32/38) of the ballplayers on the 1992 Padres roster. Based on the revised standings the “Original” 1992 Padres won 96 contests but came up two games short of the Atlanta Braves for the division title. San Diego led the National League in OWAR and OWS.

The Padres’ offense featured seven players that registered at least 20 Win Shares. Roberto Alomar (.295/8/76) scored 105 runs, stole 49 bases and topped the Friars with 31 Win Shares. Carlos Baerga (.312/20/105) accrued 205 safeties and earned his first All-Star appearance. Shane Mack posted a .315 BA with 101 tallies and 26 steals. Dave Winfield crushed 33 two-baggers and 26 big-flies while plating 108 baserunners. The corner infield was anchored by John Kruk (.323/10/70) and Dave “Head” Hollins (.270/27/93). Ozzie “The Wizard” Smith batted .295 and continued his dazzling defensive displays to earn his 13th consecutive Gold Glove Award. Tony Gwynn aka “Mr. Padre” batted .317 in the midst of an 19-year streak in which he hit .300 or better.

Gwynn ranked sixth among right fielders according to Bill James in “The New Bill James Historical Baseball Abstract.” Eight ballplayers from the 1992 Padres roster placed in the “NBJHBA” top 100 rankings including Ozzie Smith (7th-SS), Roberto Alomar (10th-2B), Dave Winfield (13th-RF), Kevin McReynolds (45th-LF), John Kruk (72nd-1B), Ozzie Guillen (74th-SS) and Carlos Baerga (93rd-2B).

LINEUP POS WAR WS
Ozzie Smith SS 3.24 22.13
Tony Gwynn RF 1.69 17.86
Roberto Alomar 2B 5.37 31.53
Shane Mack CF/LF 6.17 27.47
John Kruk 1B 4.35 25.38
Dave Hollins 3B 3.61 25.6
Kevin McReynolds LF 1.27 12.89
Benito Santiago C 0.81 8.17
BENCH POS WAR WS
Carlos Baerga 2B 4.83 28.54
Dave Winfield DH 3.53 25.75
Joey Cora 2B 0.66 3.98
Mark Parent C 0.25 1.42
Warren Newson RF 0.25 4.04
Paul Faries 2B 0.19 0.82
Ron Tingley C 0.13 3.36
Sandy Alomar C 0.09 8.2
Rodney McCray RF 0.09 0.45
Gary Green SS 0.08 0.46
Guillermo Velasquez 1B 0.08 0.7
Thomas Howard LF 0.05 6.44
Ozzie Guillen SS -0.01 0.41
Jose Valentin 2B -0.03 0
Luis Quinones DH -0.04 0.02
Jim Tatum 3B -0.1 0.08
Mike Humphreys LF -0.15 0.12
Jerald Clark LF -0.67 9.94

Andy Benes furnished a 3.35 ERA and notched 13 wins for the ’92 squad. Omar Olivares crafted an ERA of 3.84 and managed 9 victories in 30 starts. Bob Patterson saved 9 contests while Jim Austin fashioned a 1.85 ERA in 47 relief appearances.

ROTATION POS WAR WS
Andy Benes SP 4.22 15.68
Omar Olivares SP 1.89 8.33
Jimmy Jones SP 0.41 4.89
Greg W. Harris SP 0.4 3.81
Ricky Bones SP -0.35 4.22
BULLPEN POS WAR WS
Jim Austin RP 1.21 6.79
Bob Patterson RP 0.95 7.52
Mark Williamson RP 0.4 2.48
Matt Maysey RP -0.01 0.08
Steve Fireovid RP -0.18 0.3
Mitch Williams RP -0.27 4.99
Doug Brocail SP -0.23 0

 

The “Original” 1992 San Diego Padres roster

NAME POS WAR WS General Manager Scouting Director
Shane Mack LF 6.17 27.47 Jack McKeon Sandy Johnson
Roberto Alomar 2B 5.37 31.53 Jack McKeon
Carlos Baerga 2B 4.83 28.54 Jack McKeon
John Kruk 1B 4.35 25.38 Jack McKeon Bob Fontaine Sr.
Andy Benes SP 4.22 15.68 Jack McKeon
Dave Hollins 3B 3.61 25.6 Jack McKeon
Dave Winfield DH 3.53 25.75 Peter Bavasi Bob Fontaine Sr.
Ozzie Smith SS 3.24 22.13 Bob Fontaine Sr.
Omar Olivares SP 1.89 8.33 Jack McKeon
Tony Gwynn RF 1.69 17.86 Jack McKeon Bob Fontaine Sr.
Kevin McReynolds LF 1.27 12.89 Jack McKeon Bob Fontaine Sr.
Jim Austin RP 1.21 6.79 Jack McKeon
Bob Patterson RP 0.95 7.52 Jack McKeon Sandy Johnson
Benito Santiago C 0.81 8.17 Jack McKeon Sandy Johnson
Joey Cora 2B 0.66 3.98 Jack McKeon
Jimmy Jones SP 0.41 4.89 Jack McKeon Sandy Johnson
Mark Williamson RP 0.4 2.48 Jack McKeon Sandy Johnson
Greg Harris SP 0.4 3.81 Jack McKeon
Mark Parent C 0.25 1.42 Bob Fontaine Sr.
Warren Newson RF 0.25 4.04 Jack McKeon
Paul Faries 2B 0.19 0.82 Jack McKeon
Ron Tingley C 0.13 3.36 Bob Fontaine Sr.
Sandy Alomar C 0.09 8.2 Jack McKeon Sandy Johnson
Rodney McCray RF 0.09 0.45 Jack McKeon Sandy Johnson
Gary Green SS 0.08 0.46 Jack McKeon Sandy Johnson
Guillermo Velasquez 1B 0.08 0.7 Jack McKeon
Thomas Howard LF 0.05 6.44 Jack McKeon
Ozzie Guillen SS -0.01 0.41 Jack McKeon
Matt Maysey RP -0.01 0.08 Jack McKeon
Jose Valentin 2B -0.03 0 Jack McKeon
Luis Quinones DH -0.04 0.02 Bob Fontaine Sr.
Jim Tatum 3B -0.1 0.08 Jack McKeon
Mike Humphreys LF -0.15 0.12 Jack McKeon
Steve Fireovid RP -0.18 0.3 Bob Fontaine Sr.
Doug Brocail SP -0.23 0 Jack McKeon
Mitch Williams RP -0.27 4.99 Jack McKeon Sandy Johnson
Ricky Bones SP -0.35 4.22 Jack McKeon
Jerald Clark LF -0.67 9.94 Jack McKeon

 

Honorable Mention

The “Original” 1989 Padres    OWAR: 46.4     OWS: 303     OPW%: .552

Tony Gwynn collected his fourth batting crown with a .336 BA and topped the circuit with 203 base knocks. Roberto Alomar batted .295 and pilfered 42 bases during his sophomore season. Ozzie Smith contributed 30 doubles and nabbed 29 bags while Kevin McReynolds jacked 22 long balls and knocked in 85 baserunners. Greg W. Harris accrued 8 wins and 6 saves to complement an ERA of 2.60, pitching primarily out of the bullpen. The Friars tied the Giants for second place in the National League West, two games behind the division-leading Reds.

On Deck

The “Original” 1986 Mets

References and Resources

Baseball America – Executive Database

Baseball-Reference

James, Bill. The New Bill James Historical Baseball Abstract. New York, NY.: The Free Press, 2001. Print.

James, Bill, with Jim Henzler. Win Shares. Morton Grove, Ill.: STATS, 2002. Print.

Retrosheet – Transactions Database

Seamheads – Baseball Gauge

Sean Lahman Baseball Archive


Ian Desmond’s Second Half Resurgence

It’s been just over a month since Ian Desmond’s mid-season outlook. Things were not going well for Ian Desmond, playing in his contract year in 2015 he was hoping to set himself up for a massive pay day. After turning down a reported $107 million dollar extension, Desmond was hoping for a productive 2015 season. Things could not have gone much worse in the first half of the season.

Desmond’s monthly splits reveal a roller coaster season for the soon-to-be free agent. March and April started out slowly, his play picked up in May, and then June came. The month of June was simply abysmal, so of course let’s take a more in-depth look at his numbers that month. His performance that month compared to his career averages were all much worse. He walked only 3% of the time while striking out 33.3% of the time (just over 10% higher than his career average). Any time you combine a low walk rate and a high strikeout rate you can expect a really poor OBP. In the month of June his OBP (note: NOT HIS BATTING AVERAGE!) was below the Mendoza line and his wRC+ was 22. That means in the month of June Ian Desmond created 78% less runs than league average. For a player in his walk year and especially someone who turned down over $100 million, it should be concerning to say the least.

Monthly BB% K% OBP SLG ISO BABIP wRC+ wOBA
Mar/Apr 6.90% 22.80% 0.287 0.326 0.109 0.279 70 0.274
May 4.30% 28.70% 0.310 0.444 0.167 0.375 106 0.326
Jun 3.00% 33.30% 0.194 0.269 0.108 0.207 22 0.204
Jul 8.00% 33.00% 0.253 0.392 0.203 0.234 73 0.278
Aug 8.20% 24.70% 0.353 0.500 0.205 0.358 135 0.369
1st Half 4.90% 28.40% 0.255 0.334 0.124 0.279 60 0.259
2nd Half 8.60% 28.60% 0.338 0.512 0.236 0.342 133 0.366
Career 5.90% 23.10% 0.312 0.425 0.161 0.321 101 0.321

Then something strange happened: Ian Desmond started turning his season around after the All-Star break. His stats in the second half have been a complete turnaround. He’s walking more, striking out less but still more than his career average, and generally just performing better. His August BABIP is well above his career average suggesting that we can expect some regression at some point.

While only 35 games into the second half, his performance compared to the first half is night and day. He has already hit more home runs and stolen more bases in less than half the games, and his RBI total is inching closer to his first half mark. Most importantly, in the second half of the season he has been worth 1.1 WAR (Bryce Harper for comparison has been worth 1.5 WAR in the second half). Not only is this good news for Desmond’s free-agent stock, but the Nationals will need all the help they can get while they try to chase down the teams in front of them for a playoff berth. As of right now, the Nationals are 5.5 games back of the Mets for the division lead and 10.0 games back of the Cubs for that second wildcard.

Monthly G PA HR R RBI SB WAR
First Half 84 348 7 36 24 5 -0.6
Second Half 35 140 8 21 22 6 1.1

As an added bonus, I thought it might be useful here to show a plot of Ian Desmond’s career trajectory as predicted by his seasonal OPS. This model was created using the methods presented in the book “Analyzing Baseball Data with R” by Max Marchi and Jim Albert, and I’ve excluded Desmond’s age-23 season where he only played 21 games.

Based on the age trajectory graph it looks like Desmond may have already peaked in his career. What this means for his potential earnings this upcoming offseason remains to be seen. Any GM looking to add a top-tier hitting shortstop for the next few seasons will inevitable come calling his agent, but the data tells us that his best days may be behind him.


Final Month Fantasy Fun With Excel

The Major League Baseball season is just past the three-quarter mark, which means just under one-fourth of the season is left to be played. If you play fantasy baseball, you should know by now whether you have a chance to win this year. If you’re still in contention, now is the time to really take a good look at the important categories for your team. If you’re not in contention, don’t be a chump and just give up. At the very least, play an active lineup each day as a courtesy to the other owners in your league.

By this point, trades may no longer be an option. Most leagues have trade deadlines set before late August, so you are more likely looking at waiver-wire additions and setting your lineup in a way to optimize the points you can gain and minimize the points you can lose.

The vast majority of fantasy baseball leagues have both counting stat categories (runs, home runs, RBI, stolen bases, wins) and rate-stat categories (batting average, ERA, WHIP). In general, it’s easier to see how many points you can gain or lose in the counting categories. With so much of the season done, some of the counting-stat categories have taken on greater importance. Perhaps steals is a very tight category in which you have room to move up or down and could gain or lose a few points. It’s clear that you have to make add/drop moves and set your lineup to address steals, while also keeping an eye on any other hitting categories that would suffer with the addition of a low-power basestealer.

With rate-state categories, it’s a bit trickier than just looking at the standings and making an estimate of how much you can move up or down. I’ll use pitching as an example. In my standard 12-team Yahoo league, there is an innings limit of 1250 innings. In this league, the top team in innings pitched has used up 1037 innings (83% of the limit), while the bottom team has just 932 innings (75% of the limit). Moving forward, this will make a difference in the counting-stat categories of wins and strikeouts. It will also make a difference in ERA and WHIP.

I like to have an idea of how much my team can move in ERA, WHIP, and Strikeouts, so I created a spreadsheet to track this. Even though this leagues uses raw strikeouts, I want to figure out my K/9 so I can more easily compare my strikeouts to teams with different innings pitched totals (you could also use K/IP).

Below is my spreadsheet. In this spreadsheet, ER stands for “Earned Runs,” BR stands for “Base Runners,” and K stands for “Strikeouts.” I plug in my current innings total (955), with my current team ERA, WHIP and Strikeouts, then calculate ER [(ERA x IP)/9], BR [WHIP x IP], and K/9 [(K/IP)*9].

In the row labeled “Remaining IP,” I use the same formulas as above for ER and BR, then use this formula in the K column: ((K/9)*IP)/9.

For the “Projected Stats” row, I add up the INN, ER, BR, and K columns, then use formulas to figure projected ERA, WHIP, and K/9 (the yellow squares).

This gives you the framework of the spreadsheet. Now it’s time to get an expectation of how your team’s pitching numbers will play out.

In the grayed-out cells, I put in various projected ERA, WHIP, and K/9 numbers. I start with an optimistic view of my team’s future pitching abilities and work down to a pessimistic view. My team currently has a 3.44 ERA, 1.18 WHIP, and 8.94 K/9. In the top of the chart, I put in 3.00, 1.00, and 9.20 in the grayed out cells for ERA, WHIP, and K/9. This tells me that if my team puts up a 3.00 ERA, a 1.00 WHIP, and a 9.2 K/9 from this point forward, my final ERA will drop to 3.34, my final WHIP will drop to 1.14, and my final K/9 will rise to 9.0. This could be considered a best-case scenario.

On the other hand, if my pitchers post a 4.00 ERA, a 1.30 WHIP, and an 8.6 K/9 from this point forward, my final ERA will be 3.57, my final WHIP will be 1.21, and my final K/9 will be 8.86.

Here is the spreadsheet with various levels of projected performance:

The main idea is to get an estimate of how much your ERA, WHIP, and K/9 can change over the final five weeks of the season. If I use the numbers from this example, I can expect my final ERA to be between 3.34 and 3.57, while realizing a more realistic estimate would be between 3.40 and 3.50 unless I’ve made some big changes to my pitching staff. It’s a similar story for WHIP, with a likely estimate being a final WHIP of 1.16 to 1.20. The range for K/9 would be from 9.0 to 8.85. As you can see, there isn’t much movement available in these pitching categories. The particulars of your league’s standings will tell you how many points you can gain or lose based on rest-of-season expectations.

Once you’ve created the spreadsheet, you can take a closer look at ERA, WHIP, and K/9 and make the moves that will help you the most.


The Leadoff Hitter: Is Speed the Answer?

Classical baseball line-up construction involves putting your fastest player in the lead-off spot. This is due to the belief that speed generates runs (a la Rickey Henderson). In order to test this theory I went back to 1998 (since the last expansion) and looked at how may runs were scored in each season and then looked at 3 indicators, OBP, wOBA and stolen bases to test which indicator would be most useful in predicting runs. Although OBP and wOBA are very similar stats I decided to include both of them in the analysis because of differences in calculation. To put simply OBP gives a home run the same weight as a single and considers them equal (which they are not) while wOBA gives different types of hits more weight (see the OBP and wOBA pages for more information). I’ll admit that I am a huge fan of stolen bases, there is nothing like watching a player steal second or third to try and get a rally started. But the question is, can you expect to score more runs by being fast or by getting on base?

To get started I only looked at data from 2015 and pulled out the top 25 players from each stat category in order to define the “fast” players and the players who get on base the most. I also standardized runs scored to runs per game (RPG) to account for rest days and injuries which may have kept players out of the lineup for short periods of time. In the plot below it appears that the leaders in stolen bases have been scoring fewer runs per game than players who get on base more often. Based on the 95% confidence intervals of the top 25 players the difference was not significant, but the results are interesting nonetheless.

Now let’s look at some long-term data with how many runs were scored each year since 1998. In the plot below we can see that there was a large spike in runs scored in 1999 and 2000 before scoring evened out. The trend seemed to remain relatively stable from 2001 up until around 2006 or 2007 and then we see a dramatic decrease in runs scored up until last year. MLB started testing for steroids in 2003 and perhaps this is why we’ve begun to see that decrease in runs scored, but that is outside the scope of this article so let’s just focus on runs.

Runs are the most important aspect in baseball, whether that means scoring runs or preventing them. In the end, if your team can’t score any runs then you can’t win any games and unless a team have a titan of an offense you need to prevent runs as well. Here we are going to focus on run generation so we can forget about run prevention from here on out. Let’s look at the seasonal stats for our indicators and see how they look over time. I’m going to note here that OBP and wOBA shown in the plots are the league average, while the stolen bases are the league total for each season. A quick look tells us that OBP and wOBA are very closely related to the trend we saw in the second figure while stolen bases have a lot of variability over time. This seems to give a lot of evidence to getting on base, but let’s go one step further and see if we can develop a linear model to predict how each predictor affects the expected runs scored in a season.

In the final plot below I’ve put runs per game on the y axis and each stat on the x axis. In order to test how changes in league performance affects run scored I predicted the number of runs scored based on the 10%, 50% and 90% quantiles to see how many runs a player would generate over a 162-game season.

I’ve created a summary table for easy comparison of each stat and the thing that really jump out is that stolen bases doesn’t have any effect on runs scored. Based on the model, in a season where players steal almost 700 more bases collectively they generate less than 1 extra run.

OBP Expected Runs (Per Season)
0.319 56.51
0.333 60.93
0.340 63.15
wOBA Expected Runs (Per Season)
0.315 56.64
0.328 60.77
0.336 63.31
Stolen Bases (Season) Expected Runs (Per Season)
2583 59.74
2918 60.21
3281 60.72

In the end, getting on base is the most important (Thanks Moneyball!). For many the results should be unexpected, players who get on base more give their teams more opportunities to score runs. There doesn’t seem to be a significant advantage to using OBP or wOBA to predict runs, but based on advanced analytics people should probably consider wOBA more useful since singles, doubles, triple and home runs are all treated differently in the calculation.


Quantifying Outlier Seasons

I’ve always been fascinated by the outlier season where a guy puts up numbers well above or below his career pattern (Mark Reynolds’ 2009 steals total is one of my favorite examples). I wanted to take a look at the biggest outlier seasons in baseball history. To do this, I ran the data on every player-season since 1950 and calculated a z-score for each season based on the player’s career mean and standard deviation for that stat (only including qualified seasons). While the results were interesting, in my first pass through I did not control for age and the results were largely what you would expect – lots of guys at the beginning or ends of their careers.

On my second pass, I rather arbitrarily restricted the age to 25-32 to attempt to get guys in the middles of their careers. I think these results ended up being pretty interesting. The full list is here, but I’ll highlight a few below:

null

I had never heard of Bert Campaneris, but it turns out he was a pretty good player who put up 45 career WAR, mostly as a speedy, light-hitting, great-fielding shortstop. But in 1970, he briefly turned into a power hitter. He hit 22 home runs, his only season in double digits. He hit two in 1969 and five in 1971, playing full seasons both years. So this wasn’t even a mini-plateau. This was a ridiculous peak that he would never come close to again. We don’t have the batted ball data to dig further, but I would love to know just what was going on that year.

Dawson, on the other hand, was a pretty good home run hitter who usually hit 20-30 a season, except in 1987 when he blasted 49. Usually guys hitting crazy amounts of home runs in the late 80s through the 90s wouldn’t be that interesting, but these guys played for a long time after, never coming close to their 1987 totals again.

The guys on the downside are all fantastic home run hitters. With guys playing a full season and falling this short of their numbers, it’s always a possibility that they were playing hurt. Schmidt did indeed play hurt in ’78, but a quick Google for Thomas and Carter brought up nothing, making it all the more inexplicable.

null

As I mentioned above, in 2009 Mark Reynolds went 44 HR/24 steals. That was Reynolds’ only season stealing more than 11, but it “only” registered a z-score of 2.0. The three guys listed here blow that out of the water. Zeile had his season early in his career so it could have been a case of a guy losing speed or getting caught too many times and then being told to stay put. But Palmeiro and Yaz did it right in the middle of their careers. Palmeiro’s stolen base record consists of usually stealing 3-7, and getting caught 3-5 times. But in 1993, he decided to steal 22 while only getting caught 3 times. The next year he was back to his plodding ways.

On the negative side, Crawford’s struggles have been well documented. Driven by a .289 OBP and possibly declining health, Crawford’s 18 steals in his dismal 2011 season were the lowest amount of his career in a qualified season by far. We knew it was a shocking performance at the time, but I didn’t fully grasp its historical significance.

null

The last things I will look at are plate discipline numbers. They differ from home runs and steals because they represent hundreds of interactions, thousands if you consider individual pitches, rather than the dozens that the former two represent.

Mantle’s 1957 season deserves some attention (although he put up 11.4 WAR so it probably gets plenty of attention). That year, he put up the second best walk rate and the best strikeout rate of his career, at age 25. After that he went right back to being the great player he was before, albeit with slightly worse plate discipline stats.

Except for Money who was a guy early in his career working his way into better walk rates, this is something I don’t have a great explanation for so I’d love to hear theories. Why did Ripken in 1988, right in the middle of his career, take a bunch of walks and then never do it again to that degree? Likewise, how was Brett Butler able to cut his strikeout rate from 8.7% to 6.3% in 1985 then jump back up to 8-10% for the rest of his career?

Before I corrected for age, I got a bunch of results of guys at the tail end of their careers doing what you would expect. I do want to highlight one of them, however. In 1971 at age 40, Willie Mays had a 3.7z walk rate and a 3.1z strikeout rate. He walked a ton, but also struck out a ton. Added with his 18 home runs, that season he had a robust 47% three true outcome percentage. As the z scores show, it was a radical shift from anything he had done in his career and impressively, he used this new approach to put up a 157 wRC+ and 5.9 WAR. Apparently that guy was pretty good.

This piece identifies the biggest outlier seasons in history, but is crucially missing the why. And unfortunately, for most of these that’s not something I have a great answer for. If you have enough player-seasons, you’re going to expect some 3z outcomes. But historical oddities are one of the joys of baseball and each of the 3z outcomes is the product of a radical departure in underlying performance. I think it would be fascinating to talk to some of these guys and see what they have to say about why things went so differently for one season.


Introducing Two New Pitching Metrics: exOUT% and exRP27

exOUT%

In the early 21st century, Oakland Athletics’ General Manager Billy Beane revolutionized baseball forever. He was the first general manager in baseball to heavily utilize sabermetrics in his baseball operations. This isn’t a history lesson though, I bring him up because of his idea that outs are precious, and as a hitter your goal is to not make out, thus him prioritizing OBP so heavily. In the following years, baseball statistics have seen phenomenal progress on both offense and for pitchers. While I believe FIP and xFIP are both very useful statistics in really measuring a pitcher’s skill, my problem is that they essentially ignore all the batted ball data that we have (GB%, FB%, LD%). SIERA and tERA have solved some of these problems, but are far from perfect, and I believe the more statistics we have, the better.

As I mentioned with Beane, while we largely focus on a hitter’s ability to not make out, we still don’t have a catch-all statistic to realize how effective pitchers are at getting batters out, because if the batter’s goal is to not make out, the pitcher’s goal is to get the batter out. So I present to you expected out percentage, or exOUT% (the name is certainly a work in progress). exOUT% sets out to answer a simple question: For any plate appearance, what is the likelihood that the pitcher will get the batter out? This can easily be found by just looking at a pitcher’s opponent OBP, but that is rather primitive, and we can get a better estimate by focusing more on pitchers’ skills to strike people out, not walk batters, and the type of contact they are giving up, and also trying to negate the effect of the defense by him, by just using league averages. So to calculate a pitcher’s exOUT%, I used K%, BB%, GB%, LD%, FB%, lFFB%, and 2014 league averages on ground balls, line drives, and fly outs. (HBPs are essentially ignored but can certainly be incorporated in a future version, this is pretty much exOUT% v1.0)

I want to give full disclosure, I am not a statistician or close to it. Math and statistics are an area of interest and I am currently pursuing a degree in math-economics, but I am far from a professional, so I recognize there are going to be errors in my data. This is an extremely rough version; there’s even a combination of data from this year and last year so there will be inconsistencies, as I don’t have the resources to gather all the data I need. If after reading this, you are interested in this and would like to take this further, please feel free to contact me if you have the skills necessary to advance this further (or even if you don’t).

I will first post a simple step-by-step breakdown of how to calculate exOUT%, and then get into more detail and take you through it with Clayton Kershaw, because well, he is awesome.

1- Add K% and BB%, subtract this percentage from 100%, this leaves you with a balls in play%, let’s just say BIP%

2- Multiply the pitcher’s GB% (make the percentage a number less than 1, for example 40% is .4) and BIP% (leave it between 1 and 100, ex 40%), this gives you a GB% for all PAs, not just balls in play, we’ll call this overall GB%, or oGB%… now multiply this percentage (in between 1 and 100) times the league average percentage of ground balls that don’t go for hits (league average is .239 on ground balls in 2014, so out percentage on ground balls is 76.1%, but make it .761…. this will give you a percentage you can leave between 1 and 100, if the number is 20%, that means that there’s a 20% chance that pitcher will induce a ground ball out that PA, assuming league average defense, we can assume this because we’re using the league average for batting average on groundballs… we’ll call this exgbOUT%

3- Now follow the same steps but with LD%, exldOUT%, the percentage chance for any given PA that the pitcher will produce a line drive out. (The league average on line drives last season was .685 (!) so that means there is a 31.5% chance a line drive will result in an out)

4- Same thing with FB%, sort of, because we also want to incorporate IFFB%. So multiply a pitcher’s FB% by their IFFB%, this gives you the percentage of balls in play that the pitcher produces an infield fly ball (bipIFFB%). Multiply this percentage by their BIP% to get his overall percentage of PAs that result in an infield fly, and this will also be their exiffbOUT%, because any infield fly ball should be converted to an out, and if not, it’s to no fault of the pitcher, so we won’t punish him. Next subtract a pitcher’s IFFB% from 1 or 100, whatever, and this is their balls in play percentage of fly balls that are normal fly balls, to the outfield. Multiply this number by their BIP%, this gives you the overall normal FB% for a pitcher, not just balls in play. Multiply this number by .793 (the league average on fly balls in is .207, so there’s a 79.3% that a fly ball will result in an out). This number is the percentage chance that for any given PA, the pitcher will produce a fly ball out to the outfield. Add this exnfbOUT% (n for normal) and his exiffbOUT% and you have his exfbOUT%, the percentage that for any given PA, the pitcher will produce a flyball out, to the infield or outfield.

5- Add K% + exgbOUT + exldOUT + exfbOUT

6- You have your exOUT%

 

The terms are not that technical or scientific so I don’t confuse anyone — I tried to simplify a very complicated procedure as much as possible. To clarify and give you an example, let’s go through Clayton Kershaw.

Kershaw profiles like this (I compiled this data on 8/21): 32.3 K%, 4.9 BB%, 52.8 GB%, 26 FB%, 11.8 IFFB%, 21.2 LD%.

So let’s look at the balls that don’t go in play, strikeouts and walks. Add the two and balls not in play percentage is 37.2, 4.9% are walks and thus won’t be an out, and 32.3% are strikeouts so will be an out. Thus far, Kershaw’s exOUT% is 32.3 (of a possible 37.2 so far)

Now let’s look at the balls in play. People will usually say that a pitcher can’t control what happens when a ball is in play, but I vehemently disagree, the type of contact the pitcher gives up can’t be ignored and largely effects what will happen to the ball in play. I will quote a FanGraphs article here to explain it, “Generally speaking, line drives go for hits most often, ground balls go for hits more often than fly balls, and fly balls are more productive than ground balls when they do go for hits (i.e. extra base hits). Additionally, infield fly balls are essentially strikeouts and almost never result in hits or runner advancement.” And FanGraphs also gives us this data from 2014.

GB: AVG- .239, ISO- .020, wOBA- .220

LD: AVG- .685, ISO- 190, wOBA- .684

FB: AVG- .207, ISO- .378, wOBA- .335

 

So this means that fly ball pitchers are most likely to get outs, although they may be less effective because when they don’t get outs, it’s more trouble than for ground ball pitchers. But remember, this statistic is just finding the chance that the pitcher will get a hitter out.

 

All right, so, let’s calculate Kershaw’s exgbOUT%, exldOUT%, and exfbOUT%; you can follow the numbers along with the steps I listed above.

 

GB%- 52.8

62.8 x .528 = 33.1584

(33.1584 x .761)=  25.23354424 exgbOUT

 

LD%- 21.2

62.8 x .212 = 13.3136

(13.3136 x .315) = 4.193784 exldOUT

 

FB%- 26

26 x .118= 3.068 bipIFFB%

26 x .882= 22.932 (bipFB%)

62.8 x .22932= 14.401296 (onFB%)

14.401296 x .791= 11.3914251 exnfbOUT%

62.8 x .03068= 1.926704 oIFFB% and exiffbOUT%

exnfbOUT% + exiffbOUT% = 13.3469317 exfbOUT%, if you followed my math exactly a decimal may be off, like 13.31 something, but this is the number the excel doc chugged out, so I’m trusting that, my iPhone calculator can’t carry all the decimals sometimes.

Now add them all up

32.3 + 25.23354424 + 4.193784 + 1.926704  + 11.3914251 = 75.07%

K% + exgbOUT% +  exldOUT% + exiffbOUT% + exnfbOUT% = exOUT%

The league average exOUT%, using league average statistics from 2014 for the ones involved, is 69.8%. Scherzer leads the majors (well the 89 pitchers I was able to export data from FanGraphs) with a 76.43 exOUT%. If you want to look at it as a more concise and better version of opponent OBP, his is .236, so, you know, good. Here is a picture of the data for the top 37 — the J column is what you are looking at. Betances is in their because I wanted to calculate one reliever. 

View post on imgur.com

All right, I’ve explained it a bit in the prologue, but now that you’ve seen it, let me explain more why I like this stat. Well first, I created it and calculated, so, well, yeah… but I also like this stat because it answers a very simple question “How good is a pitcher at getting people out?” Pitching in its simplest form, is exactly that, getting people out. The stat recognizes that there’s basically only these outcomes for an at bat: strikeout, walk, ground ball, line drive, and fly out, and looks at the pitcher’s stats in these categories to determine how many people he should be getting out. The stat is more predictive than evaluative in nature, because you can calculate a pitcher’s actual out percentage, but that doesn’t nearly tell the whole story, because a lot of luck is involved with balls in play, and other fluky outcomes.

This operates under the basis that a ground ball will perform the way the average ground ball does, a line drive performs the way an average line drive does, and a fly ball behaves the way a typical fly ball does. There could be guys getting very fortunate with ground balls: having a great infield behind them, balls not squeaking through the holes; with line drives: being hit right at people; and fly balls: staying in the park, having outfielders who cover a lot of ground. And there could be guys who are getting unlucky: the ground balls are getting through the holes, the infielders don’t have range; line drives seem like they are always going for hits, and fly balls are falling in. This says that a pitcher can’t control that, but they can control how much they strike out people, how much they walk people, and how often they give up ground balls, line drives, and fly balls, and if these balls in play behaved the way they should, the pitcher should be getting this percentage of people out.

I will address the flaws I have found with it. As much as getting people out is important, sometimes what happens in the plate appearances that don’t end in outs are almost as important. This only deals in batting average regarding balls in play, but wOBA is very important too. Fly balls are more likely to be outs than ground balls, but the wOBA on fly balls is over 100 points higher. Additionally, I’d prefer instead of ground balls, line drives, fly balls, to use soft contact, medium contact, hard contact, because that is a truer test of pitcher skill, however, I did not have this data at my disposal as far as league averages on what the batting average is for soft contact, medium contact, hard contact (if someone does, please contact me like I said). So what I have for now will do and this batted ball data is still a good measure. I set out to calculate what percentage of batters a pitcher should be getting out, and that is exactly what I found out. So while it’s not perfect, it has its use, and it’s something to build on.

 

exRP27

And build on I did. While the out percentage is nice, it doesn’t give us a measure like ERA or FIP or xFIP, that tells us how many runs a pitcher should be giving up. So using the data I used to calculate exOUT%, I present to you exRP27 (expected runs per 27 outs, a stupid name for a hopefully not stupid stat).

The basis for this stat is this data from FanGraphs, “Line drives are death to pitchers, while ground balls are the best for a pitcher. In numerical terms, line drives produce 1.26 runs/out, fly balls produce 0.13 R/O, and ground balls produce only 0.05 R/O.” (I don’t know how this was calculated, or when it is accurate for, but this is what I got). We don’t know this for soft contact, medium contact, hard contact, so again I’m sticking with ground balls, line drives, and fly balls. 

All right, so what I am going to do using this stat and the pitcher’s K%, BB%, GB%, LD%, and FB% is see how many runs the pitcher should be allowing over 27 outs, and then adjust it to get it on a scale similar to ERA, FIP, and xFIP.

Keeping Clayton Kershaw as our example, let’s take a look.

Kershaw’s K% is 32.3 — we’re multiplying this by 27 (for outs in a game), and we get 8.721 K’s, so 0 runs so far because a K will never produce a run

Now GB%. His exgbOUT% is 25.23354424, multiply this by 27 and we get 6.8 (ish, final number will be exact via the Excel doc). Multiply this by .05 (the runs per GB out he gets) and we get .34 runs.

LD%- his exldOUT% is 4.193784, multiply by 27 and get 1.13232168, and multiply this by 1.26 for LD runs/out and we get 1.43 runs

His exfbOUT% is 13.3181291, now multiply by 27 get 3.6 and then that by .13 and you get .47 runs

Add up all these exRUNS and Kershaw’s total is 2.24. However, we can’t stop here because the number of outs he’s recorded is only 20.3 (8.7+6.8+1.1+3.6) approximately. 20.3 is the rounded up total. So get this 20.3 (or whatever the pitcher’s exOUTS is) up to 27  by multiplying by whatever it takes, and then multiply his exRUNS by this same number. For Kershaw you end up with 2.97 exRP27. The league average would be 3.78. Last year’s average ERA/FIP/xFIP was 3.74, but when I adjust everything to that, everyone’s exRP27 just goes down slightly (Kershaw’s from 2.97 to 2.94), but I want it to be on a more realistic scale where everyone’s totals are lower and a really good exRP27 is comparable to a really good FIP, like in the low 2s. 

So I don’t know what the statistic’s correct way is, but here is what I did to make it work. I calculated what his “ERA” would be using by multiplying his exRUNS by 9 and then dividing that by his exOUTS. His was .99, the league average was 1.26. I then did .99/1.26 to get .78 or so, I then multiplied that by his exRP27 and got 2.34. I felt like this was more realistic and in line with his ERA/FIP/xFIP. Obviously, can’t be the same because they measure different things, but just got in in the area. And the same is done for all pitchers. Obviously, not everyone gets multiplied by .78 of course. The league average remains 3.78, between last season and this season’s average for ERA/FIP/xFIP.

Here is the leaderboard for that (S column):

View post on imgur.com

 I really like this stat a lot, and feel like it does what I wanted to accomplish: figure out how many runs a pitcher should allow per 27 outs given his K%, BB%, GB%, LD%, FB%, and the notion that balls in play will behave the way they normally do, as anything else is likely luck and not indicative of the pitcher’s performance.

I look at Sonny Gray as someone this stat is perfect for. His ERA is outstanding at 2.04, but his FIP is 3.00, his xFIP is 3.47 and his SIERA is 3.50. The problem is, at least with FIP and xFIP for sure, is that they ignore what happens when the ball is in play. He doesn’t strike out too many people, he has a good BB% but not spectacular, and he’s given up 10 home runs, a fair amount, so this hurts his FIP and whatnot. However, instead of saying “well he will regress, look at his FIP/xFIP/SIERA” this looks at why he’s having this success, and it has to do with the balls in play, which is getting ignored. Gray’s LD% is just 14.6! That is really good! Second best of the 90 pitchers I did this for. And his GB% is 54%, 9th best, also really good. The pitcher does have control over the type of contact he allows, and the fact that Gray is producing a ton of ground balls, and very few line drives, is why he’s been so successful. His 2.34 exRP27 suggests that he has not been as good as his 2.04 ERA suggests, but he’s not as far off as the other stats suggest. 

Obviously exRP27 is far from perfect, and is in no way supposed to replace FIP/xFIP/SIERA, but it is something to look at with them. I am a big believes in aggregation, so I think that averaging some combination of these 4 stats together or them all, is an even better way to evaluate a pitcher. We’ve got more data than ever, so it makes sense to use it, exRP27 and exOUT% are just more examples of utilizing this data to help better evaluate pitchers.  

I hope you guys enjoyed. Any feedback please comment or contact me. Next I will be looking at exWOBA against for pitchers using similar data, and exWOBA for batters using the data but for hitters.


The Improvement of the Indians Starting Rotation

Remember at the end of last season and before this season when we all foresaw an Indians rotation that could possibly feature somewhere between 2 and 5 really good, and possibly great, starting pitchers?  Don’t get bogged down on the slight exaggeration of that 1st sentence – To recap what we were looking at coming into this season for the Indians’ rotation:  Corey Kluber won the 2014 AL Cy-Young; Carlos Carrasco had a string of starts to end 2014 in which he seemingly (finally) figured out how to harness all of his powers in a bid to ascend his name to an echelon where only Clayton Kershaw’s name resides; Danny Salazar has always had elite swing and miss stuff and was also excellent in the second half of 2014;  Trevor Bauer and his Costco-sized arsenal of pitches have made some of us incredulously, if not warily optimistic since he was taken 3rd overall in 2011; and even T.J. House made us pause and take notice with his strong second half of 2014.

Then, like hype men with a special blend of Cleveland Kool-Aid being intravenously administered, Eno Sarris and Daniel Schwartz posted one of my favorite FanGraphs articles ever, Pitch Arsenal Score Part Deux, and the anticipation over the Indians’ rotation pulsated like a vein in the neck of John Rambo in the midst of fleeing from man-hunters.

The supporting cast, the lineup, looked poised to support the staff with plenty of runs.  Returning would be: break out star Michael Brantley; bounce-back candidate Jason Kipnis; now-full-time-first-basemen, Carlos Santana; a supposedly healthy Michael Bourn; an offense-first but totally-respectable-defensively, Yan Gomes; and an actually-not-that-horrible-in-2014, Lonnie Chisenhall.  Slugger Brandon Moss, and contact-happy-supposedly-glove-first Jose Ramirez had secured full-time spots as well in RF and SS respectively.  So even though it wasn’t without flaws, it seemed like they would allow the pitchers to rack up plenty of fantasy-relevant wins.

Note: This post isn’t about the disappointment of the Indians, though they have been disappointing; it’s more about what factors beyond luck have contributed to the numbers of the Indians’ starting rotation at various points throughout the year, and the disparity (big or small) between the pitchers’ rates and predictors at those points.

The Indians’ starting pitchers, or at least the top 4 (Kluber, Carrasco, Salazar, and Bauer) have, for the most part, been putting up good, albeit, inconsistent numbers all year despite posting some elite peripheral rates and ERA indicators.  A number of reasons have caused these numbers to grow apart (bad), come together, and then grow apart again (good).  Luck can work like a bit of a pendulum, swinging from one extreme, through the middle, and to the other extreme before evening out and that is at the core of what the Indians’ starting pitchers have experienced this year — although they have yet to experience the final stabilization phase.

We will examine plenty of numbers (Beginning of season to August 18th) based on this time frame: (Spoiler alert – this article is long and dense, and this timeline serves as a sort of cliff notes as to how the staff’s numbers have improved throughout the year – so if you’re the type of person who feels like looking at a bunch of data is superfluous when the bullet points are in front of your eyes, just read the timeline and be done with it.)

timeline

April 6th – May 23rd/May 24th – June 15th

One week into the season, before it was evident that the team’s defense was very sub-par, Yan Gomes hurt his knee and hit the disabled list for over a month.  Roberto Perez filled in quite nicely, and looking at just a couple numbers, could be considered the more valuable catcher (1.4 WAR compared to 0.5 WAR for Gomes).  Brett Hayes (0.0 WAR) was called up and was the secondary catcher during this period.  Behold, a table from StatCorner:

statcorner

 

 

 

 

 

 

Perez has had the least amount of pitches in the zone called balls and the most amounts of pitches out of the zone called strikes.  Overall, despite receiving fewer pitches than Gomes, he has saved more runs (4 DRS to Gomes’ 1) and their caught stealing rates are basically identical with a slight edge going to Perez – 38% to Gomes’ 35%.  Gomes was much better in terms of framing in 2014, and it’s possible the knee injury has limited his skills all around this season.  Anyways, from April 6th – May 23rd, the combined stats of Kluber, Salazar, Carrasco, and Bauer look like this:

ERA FIP xFIP SIERA K-BB% GB%
Kluber 3.49 2.16 2.46 2.51 25.3 48.6
Salazar 3.50 3.27 2.46 2.30 28.7 43.8
Carrasco 4.74 2.60 2.67 2.82 22.3 48.9
Bauer 3.13 3.23 4.09 3.94 14.2 35.7
3.75 22.7 44.7

Gomes returned as the primary catcher on 05/24, and from that point through June 15th, the cumulative numbers aren’t too different, although there is a dip in both K-BB% and GB% that we’ll have to look into.

ERA FIP xFIP SIERA K-BB% GB%
Kluber 3.67 3.26 3.20 3.19 19.8 43.8
Salazar 3.60 3.72 3.36 3.43 17.3 47.7
Carrasco 3.65 2.83 3.29 3.17 20.2 44.1
Bauer 3.96 4.72 4.47 4.30 11.5 36.8
3.74 17.2 43.1

So despite lower K-BB and ground ball percentages (leading to higher ERA predictors), the group’s ERA in the segment of the season when Gomes was reinstated is essentially exactly the same as from the first block of time with Perez.  Now, I am not a big believer in CERA because there is a high level of variation and too many unknown variables pertaining to how much of the responsibility/credit goes to the catcher, the coaching staff, or the pitcher; but I do think that it’s possible Gomes’ extra service time has enabled him to be more in tune with his staff as well as understand hitter tendencies better than Perez and Hayes.  I realize we’re getting into a gray area of intangibles, so I’ll reel it in with some results based on pitch usage%.

% Difference in Pitch Usage with Yan Gomes compared to Roberto Perez

Pitcher FB% CT% SL% CB% CH% SF%
Corey Kluber -9.0 8.8 -17.3 5.0
Danny Salazar 9.8 -12.6 -4.4 17.1
Carlos Carrasco -6.5 9.4 49.2 13.3
Trevor Bauer -2.9 -15.0 -8.9 78.5 25.8

Using BrooksBaseball Pitch f/x data, let’s painstakingly find out how different each pitcher’s pitch usage was in regards to different counts, or better known as Pitch Sequencing.  We’ll look at first pitches, batter ahead counts, even counts, pitcher ahead counts, and 2 strike count situations.  As good as pitch f/x is, the data still isn’t perfect.  There may be discrepancies if you look at usage at Brooks compared to the usage at FanGraphs, so for each pitcher we’ll split the pitches up into three categories: Fastballs (four-seam, sinkers, cutters), Breaking Balls (sliders, curve balls), and Change Ups (straight change/split finger) – I’m aware that splitters are “split fingered fastballs”, but I liken them to change ups more because of the decreased spin rate and generally lower velocity.

*Having a table for each pitcher in regards to pitch sequencing made this article quite messy, so I’ve included a downloadable Excel file, and briefly touched on each pitcher below.

Pitch Sequencing Excel Doc.

Corey Kluber

Looking at the data, Gomes stays hard with Kluber more than Perez until they get ahead in the count.  Perez swaps some early count fastballs for curve balls, but they both see his curve ball as a put-away pitch.  Gomes tends to trust Kluber’s change-up more than Perez later in counts and Perez likes it more earlier in counts.

Danny Salazar

Much like with KIuber, when Gomes catches Salazar, they have a tendency to stay hard early.  Gomes pulls out Salazar’s wipe out change up after they’re ahead whereas Perez will utilize it in hitter’s counts as well.

Carlos Carrasco

Carrasco has 5 good pitches and he’s pretty adept at throwing them for strikes in various counts which is why there is some pretty even usage across the board, at least in comparison to Kluber and Salazar.  There is quite a bit more usage of Carrasco’s secondary pitches in all counts and there are pretty similar patterns when Gomes and Perez are behind the plate.  With Hayes, it doesn’t look like there is much that changes in sequencing until there are two strikes on a hitter.

Trevor Bauer

Bauer is probably a difficult pitcher to catch because of the number of pitches he has and the constant tinkering in his game.  Side note: Gomes is the only catcher to have caught a game in which Bauer threw cutters, and in their last game together, Bauer threw absolutely no change-ups or splits.  Bauer’s highest level of success has come with Hayes behind the plate and perhaps that’s from their willingness to expand his repertoire in more counts than Gomes and Perez do, but there is no way I can be certain of that.

Pitch sequencing can effect the perceived quality of each pitch and therefore, can produce more favorable counts as well as induce higher O-Swing and SwStrk percentages (or less favorable and lower).  So despite the framing metrics favoring Perez, the group throws more strikes with Gomes and also induces more swings at pitches outside the zone – although, as previously noted, there is some regression with Gomes behind the dish in terms of SwStrk% and K-BB%.

swing tendencies

 

 

 

 

 

 

 

 

 

aaa0ide

 

 

 

 

 

 

 

 

**These graphs represent numbers through the entire season to garner a bigger sample size.

With lower line drive rates and more medium + soft contact, and (in the case of the Indian’s defense), more fly balls, a conclusion could be jumped to that the staff’s BABIP has trended downward since Gomes regained his role.  A look at BABIP throughout the course of the season:

babip

 

 

 

 

 

 

 

 

 

Woah!  It was well above league average in April and then plateaued at just above league average through mid June, but has been plummeting ever since.  Obviously a catcher is not responsible for this dramatic of a swing in BABIP, so the Indians’ defense must have improved.

June 16th – August 18th

The rotations’ traditional stats look even better if you use June 16th as the starting point:

Pitcher IP H K BB W ERA WHIP
Corey Kluber 84 61 82 16 5 3.11 0.92
Danny Salazar 71 46 69 23 5 2.79 0.97
Carlos Carrasco 77.1 56 77 13 3 2.91 0.89
Trevor Bauer 68.1 69 63 24 4 5.80 1.37
300.2 232 291 76 17 3.59 1.03

 

So let’s take a look at the Indians’ defensive alignment by month (Player listed is the player who received the most innings played at the position).

 

POS April May June 1 – 8 June 9 – 15 June 16 – 30 July August
C Perez Perez Gomes Gomes Gomes Gomes Gomes
1B Santana Santana Santana Santana Santana Santana Santana
2B Kipnis Kipnis Kipnis Kipnis Kipnis Kipnis Ramirez
3B Chisenhall Chisenhall Chisenhall Urshela Urshela Urshela Urshela
SS Ramirez Ramirez Aviles Aviles Lindor Lindor Lindor
LF Brantley Brantley Brantley Brantley Brantley Brantley Brantley
CF Bourn Bourn Bourn Bourn Bourn Bourn Almonte
RF Moss Moss Moss Moss Moss Moss Chisenhall

If you’ve paid attention to the Indians at all, you know they’ve made some trades and called up a couple prospects.  But just how different is the new defense?  Well, we only have a small sample with the current configuration, but it appears to be A LOT better. If BABIP wasn’t enough of an indicator, and it’s not, because there has to be some regression to the mean – it can’t stay that low – here are some numbers from the players who were playing the most in May compared to the players who are playing the most in August (again, numbers represent full-season stats):

 

MAY PLAYER FLD% rSB CS% DRS RngR Arm UZR UZR/150
C Perez .994 2.0 38.5 4
1B Santana .997 -6 0.0 0.7 1.2
2B Kipnis .988 4 4.5 3.6 7.0
3B Chisenhall .963 7 3.1 3.3 10.5
SS Ramirez .948 -2 -2.4 -5.2 -21.9
LF Brantley .992 1 0.3 -2.1 -1.4 -3.3
CF Bourn 1.000 4 -7.2 1.1 -5.8 -11.4
RF Moss .975 -4 1.7 -2.5 -1.1 -1.8
AUG PLAYER FLD% rSB CS% DRS RngR Arm UZR UZR/150
C Gomes .996 0.0 35.0 1
1B Santana .997 -6 0.0 0.7 1.2
2B Ramirez 1.000 1 1.1 2.8 23.2
3B Ursehla .973 2 4.5 6.0 15.7
SS Lindor .967 6 6.0 4.9 14.9
LF Brantley .992 1 0.3 -2.1 -1.4 -3.3
CF Almonte 1.000 2 0.4 -0.2 0.9 10.0
RF Chisenhall 1.000 4 1.6 0.5 2.3 27.3

What’s interesting is that the biggest difference in the infield is Francisco Lindor (Giovanny Urshela has been very solid, but Chisenhall was pretty similar this season at 3B).  I’m sure someone at FanGraphs could churn out a really cool article (if someone hasn’t already) that shows us a quantifiable difference an above average to well above average shortstop makes for a team even if you just keep the rest of the infield the same, as the control.  The 2015 Tigers come to mind – a healthy Jose Iglesias has made a difference for a team that still features Nick Castellanos at 3B and Miguel Cabrera at 1B.  Teams are willing to sacrifice offensive contributions if a SS has elite defensive skills (Pete Kozma, Andrelton Simmons, Zack Cozart to name a couple off the top of my head).  Lindor, to this point, has been an above average offensive player, too, so this could be special.

At this point the Indians are in last place and are out of contention.  Abraham Almonte is their starting center fielder and with Kipnis back from the DL, Jose Ramirez is not playing 2B, but is instead getting reps in left field while Michael Brantley DHs due to his ailing shoulder.  Perhaps all this means is that they don’t have better replacements; OR PERHAPS they’re planning to establish a more defense-oriented squad next year…

Now there’s no doubt that this research has led to some frustrating conclusions.  With Gomes behind the plate, the K rate and GB rate of the staff has trended in the wrong direction in regards to ERA indicators; so is the difference in the batted ball profile plus an improved defense enough to make up for these facts?  This small sample size thinks so, but it could 100% just be noise.  However, there are clubs that are succeeding by using similar tactics right now:

Team ERA FIP ERA-FIP GB% (rank) SOFT% (rank) OSWING% K-BB% (rank)
Royals 3.57 3.93 -0.36 42.1 (29th) 18.1 (16th) 30.9 (19th) 10.5 (26th)
Rays 3.63 3.79 -0.16 42.4 (28th) 18.7 (13th) 31.2 (17th) 14.8 (7th)
Indians (as a reference) 3.85 3.65 0.20 44.7 (17th) 18.2 (15th) 33.3 (2nd) 16.9 (1st)

Granted, the Royals and Rays have the 1st and 2nd best defenses in baseball, and their home parks play differently than the Indians, but they also don’t boast the arms the Indians do.

The Indians have their noses deep in advanced metrics and having rid themselves of Swisher, Bourn, and Moss during 2015’s trading period has allowed them to deploy a better defensive unit which has amplified their biggest strength – their starting pitching.  Furthermore, their unwillingness to move any of their top 4 starting pitchers also leads me to believe they see next year as a time for them to compete.  I’m not going to speculate what moves the Indians will make in the offseason, but I hope they stick with this defense-oriented situation they have gone with recently because it’s been working (and because I own a lot of shares of Kluber, Carrasco, and Salazar in fantasy).


Exploring Three True Outcome Quality

INTRODUCTION & EXPLORING THE QUESTION

So there’s been a lot of attention paid to Three True Outcome guys recently. The subject was touched upon in a recent article by Craig Edwards, as well as in this community blog by Brian Reiff. These articles brought attention to guys who are notable for putting 7 of the 9 defensive players to sleep. However, what caught my attention the most was a comment on Craig’s piece by “steex” who proposed a hypothesis about these sluggers:

I think this makes selecting TTO players strictly by the numbers difficult. For me, the spirit of TTO is a player that does enough good (HR+BB) to balance out for a lot of bad (K). Harper and Votto don’t really fit that definition in the intended way, but rather show up on the list because they do SO much good (HR+BB) that their total HR+BB+K makes the cut despite having not as much of the bad (K).

I wonder if a better list of players comparable to one another would be obtained by first sorting by TTO%, then subdividing that by the percentage that Ks represent from the TTO events (i.e., K/[HR+BB+K]). That provides a lot of separation between guys like Harper, Votto, and Goldschmidt who have strikeouts represent less than 50% of their TTO events and guys like Carter, LaRoche, and Belt who have strikeouts as more than 65% of their TTO events.

This was also supported by follow up comments speaking about how they differentiate the players into two groups, those who strike out at a higher clip and those who have BB% and HR% compensate for a reduced K%. My goal was to figure out whether the quality aspect of the Three True Outcomes was different between these high-K% players and the low-K% players, beyond the walks and strikeouts.

 

THE PROCESS

First, let me define how I picked out my sample, and how I classified the players into two groups, and then I’ll begin to discuss the details of the study. I pulled all the data from 2010-2014 for player seasons who qualified for the batting title (minimum 502 Plate Appearances). This gave me a sample of 723 player-seasons (where a single player may be listed as a qualifier separately for up to five seasons). Of these 723 player-seasons, I set the Three True Outcome bar at 40%. Why 40%? Well the simple average (weighted to PA) was 29% Three True Outcome (I’ll abbreviate to TTO from now on), with a standard deviation of approximately 8%. So that would make 40% TTO somewhere around 1.5 standard deviations above the mean, which seemed like a reasonable line to draw in the sand.

There is now a sample of 52 player-seasons (7.2% of the qualifiers). From here, I had to draw a new line, and I wanted to go by “steex”’s suggestion of using the proportion of strikeouts to TTO% as the barrier. The key was getting a decent number of player-seasons on either side. I started off with 50% (using the formula K/[HR+BB+K]), but that would have left me with only two player-seasons (2011 Bautista, 2013 Votto, for those who are curious). I bumped it up continually until I reached a 60% ratio, which seemed to be reasonable. That placed 11 player-seasons in the low-K TTO group (which will be referred to as TTO-L) and 41 player-seasons in the high-K TTO group (which will be shown as TTO-H).

The whole TTO population is now divided into two groups, TTO-L (with 11 player-seasons) and TTO-H (with 41 player-seasons). Now what? I was truly curious about how these two groups differed in their hitting abilities. It seems fairly obvious that those who have lower K% and higher BB% will have higher (better) wOBAs, wRC+s, and the like (just due to trading strikeouts for walks). As Craig showed in his article, the average TTO player is an above average hitter due to a typically lumbering stature and a penchant for not being great at defense. Those who aren’t above average hitters and are bad at defense usually find themselves riding minor league buses around the country. But I’m not trying to compare TTO hitters to non-TTO hitters, rather comparing the two halves based on TTO quality.

 

BATTED BALL DISTRIBUTIONS

I decided to compare them using  statistics that might glean differences between good and bad hitters. I looked at batted ball distributions to start. I compiled the GB%, LD%, FB% and IFFB%, as well as the PULL%, CENTER% and OPPO% from the leaderboards (plus HR/FB for good measure), and computed the mean, standard deviation, and p-value based on a two-tailed T-Test. The results are in TABLE ONE:

 

(legend)Statistical Significance
p < 0.1
p < 0.05
p < 0.01

 

TABLE ONE: Batted Ball Distributions

TTO-H TTO-L t-test
Measure     mean-H     StDev-H     mean-L     StDev-L     p-val  
COUNT 41 plyr-sea 11 plyr-sea
GB% 38.1% 5.5% 38.9% 3.9% 0.654
LD% 19.6% 3.0% 19.0% 3.5% 0.572
FB% 42.3% 5.3% 42.2% 5.5% 0.956
IFFB% 8.3% 4.1% 9.8% 5.2% 0.314
Pull% 43.9% 5.1% 45.8% 7.7% 0.332
Cent% 33.6% 3.7% 31.4% 2.5% 0.070
Oppo% 22.6% 3.7% 22.9% 6.9% 0.846
HR/FB 19.4% 4.9% 19.4% 4.0% 1.000

 

Interestingly enough, the batted ball distributions are very similar between the two groups. The groups are pretty much interchangeable, with the only thing close to being statistically significant is the percentage of balls hit to center field. However, when looking at that in the bigger picture of pull/center/opposite, the numbers are nearly identical. So far, the two groups are relatively indistinguishable from one another.

 

BATTED BALL AUTHORITY

At this point, my mind went in another direction: do TTO-L player strike the ball better than their TTO-H counterparts? If you’ve got a good eye and can take a walk more easily, then you’re probably able to see the ball better, and therefore are able to drive the ball harder. So, even though it may not have manifested itself in the GB/LD/FB numbers, perhaps these “elite” players in the low-K group have better pop. To evaluate this, I pulled the HARD%, MED%, and SOFT% of balls by each group, along with BABIP for good measure, summarized in TABLE TWO:

 

TABLE TWO: Batted Ball Authority

TTO-H TTO-L t-test
  Measure     mean-H     StDev-H     mean-L     StDev-L     p-val  
COUNT 41 plyr-sea 11 plyr-sea
Soft% 15.5% 3.5% 16.0% 3.4% 0.925
Med% 48.6% 4.2% 46.6% 4.9% 0.182
Hard% 35.9% 4.0% 37.5% 3.4% 0.231
BABIP 0.297 0.034 0.307 0.043 0.417

 

Again, a little surprising to me. There’s no statistically significant difference between these low-K guys and high-K guys in terms of batted ball authority. Each group hits roughly the same, with the low-K guys trading a few medium hit balls for some hard hit ones (albeit not enough to differentiate the groups). BABIP would manifest itself in these guys striking the ball harder, and it comes out roughly even. One note that BABIP would control itself here more than in most hitter studies because the subset of TTO players typically have similar builds and are not artificially increasing BABIP by beating out infield hits (neither group would have a distinct advantage).

 

BATTING SELECTIVITY & CONTACT RATES

So where do these two groups separate? Something has to cause the disparity between the groups and show a differential in ability. And that something is at the plate in their selectivity – which only makes sense. Players who draw walks are those who lay off bad pitches out of the zone, and those who strike out typically struggle to identify strikes from balls, or lack the ability to contact balls when they swing (usually not both, or else they wouldn’t be in the majors). The data is summarized below in TABLE THREE:

 

TABLE THREE: Batting Selectivity & Contact

TTO-H TTO-L t-test
Measure   mean-H     StDev-H     mean-L     StDev-L     p-val  
COUNT 41 plyr-sea 11 plyr-sea
Z-Swing% 68.0% 5.0% 65.9% 4.2% 0.208
O-Swing% 29.5% 5.2% 25.5% 3.3% 0.020
Swing% 46.1% 4.1% 42.5% 2.1% 0.007
O-Contact% 53.9% 5.3% 57.5% 6.7% 0.065
Z-Contact% 79.4% 3.6% 83.3% 3.3% 0.002
Contact% 70.1% 3.6% 74.3% 4.6% 0.002
SwStr% 13.6% 2.4% 10.7% 2.3% 0.001

 

 

Here’s all that red you’ve been waiting for. Starting with the first three rows, there’s a statistically significant difference (p < 0.05) between the two groups in swinging at balls (O-Swing%), which goes to show the selectivity of the TTO-L group is better than the TTO-H group. In rows four to six, we see that for swings on pitches both in and out of the zone, the TTO-L group makes contact more often, with in-the-zone contact being statistically significant at the p < 0.01 level. To summarize this table, the TTO-L hitters don’t swing as often, but when they do they are better at making contact with the pitch as compared to the TTO-H batters.

 

GROUP SUMMARY

The final table, TABLE FOUR, summarizes the groups for anybody who was curious. 

TABLE FOUR: Group Summary

TTO-H TTO-L t-test
  Measure     mean-H     StDev-H     mean-L     StDev-L     p-val  
COUNT 41 plyr-sea 11 plyr-sea
HR% 4.8% 1.3% 4.9% 1.2% 0.819
K% 29.2% 2.8% 24.0% 3.5% 0.000
BB% 11.0% 2.0% 15.2% 2.5% 0.000
wOBA 0.341 0.031 0.379 0.034 0.001
TTO% 45.0% 4.3% 44.0% 2.8% 0.470

 

Obviously above you see that the K-rates and BB-rates are statistically significant, which only makes sense because that’s how we divided the groups, so that was artificially implanted. And, of course, you’ll always have a better wOBA if you walk more and strike out less, because walks count for approximately 0.7 runs based on linear weights each.

 

SUMMARIZING THE FINDINGS

Of the 723 player seasons between 2010 and 2014, inclusive, 52 were deemed to be Three True Outcome seasons (with 40% of the plate appearances ending in BB, K or HR). From there, the group was subdivided into two by the relative amount of K’s compared to total TTO% (with [K%/TTO%]>60% as TTO-H, and [K%/TTO%]<=60% as TTO-L.

The groups were compared against one another on Batted Ball Distributions, Batted Ball Authority, and Batting Selectivity & Contact. The vast majority of the statistically significant differences between the groups appeared in the third table, with the TTO-L group displaying a better eye for strikes, while also contacting the ball better when they decided to swing. Perhaps the most interesting finding of the study was that this increased contact did not manage to create better authority when hitting the ball, nor did it change the batted ball distribution significantly. Just because the TTO-L group made contact more often on their swings did not mean they were able to drive the ball better than the TTO-H players.

Just a quick thank you to end this, to the FG community comments that inspire people to write things like this and make my last college summer a little more (less?) exciting.


Three Undervalued Hitters to Help Down the Stretch

We’re officially in the dog days of summer, which means a few things of note: NFL is almost upon us; the fantasy baseball playoffs have begun for many; and finally, whether you’re in a roto league without playoffs or otherwise, you’re still looking to find value on your waiver wire.

I define value as something like: Players who produce counting stats (and/or average), who, for whatever reason, have low ownership rates and thus can be found on waivers for free, or in my case, for a few FAAB dollars (of which, I have zero remaining). The players I’m referring to are generally valuable in deeper mixed leagues or NL- or AL-only formats, but some, like Dexter Fowler, whom I’ve written about in the past, can offer solid numbers for leagues of any size/format.

I’ve recently written about guys like David Peralta, Fowler, and Jung-Ho Kang, and my advice on these players remains the same as it’s always been: pick them up ASAP. Their low ownership rates on ESPN continue to leave me flummoxed; E.g., David Peralta and his .294 average, 48 R, 13 HR, 66 RBI, and 5 SB is owned in just 70% of ESPN leagues. Go figure. Better yet: Go pick him up.

Here are a few more hitters I like who can help you down the stretch:

Yangervis Solarte: Solarte hit his tenth home run on August 21 and third in as many games. A switch-hitter, Solarte has multi-position eligibility (1B; 2B; 3B) and is owned in just 34% of ESPN leagues. With a triple-slash line of .269/.325/.425, Solarte has 47 R, 10 HR, and 49 RBI. Those stats play in most leagues, and while he is a bit streaky and on a power surge in August, his ambidexterity keeps him in the Friars’ lineup on a near-daily basis. Solarte has solid on-base skills (29:46 BB/K), hits for decent power, above league-average batting average, and the vast majority of his AB’s come in the leadoff or 2-holes in the lineup (110 and 142 AB, respectively).

That said, hitting in front of a hot Matt Kemp and a hopefully-getting-hot Justin Upton should help keep his run totals healthy, and he’s showing some nice HR power in August. His .283 BABIP is in line with career norms, so I don’t expect much regression in terms of batting average; if anything, that number seems somewhat low for a player who runs well, but ZiPS projects a BABIP of .280 the rest of the way. At any rate, you could certainly do a lot worse than Solarte, a player who might be finding his stride in the second half.

Colby Rasmus: In short, Rasmus is who he is: He hits for power and not much else. His power, particularly against righties, is the real deal: Rasmus owns a .451 slugging percentage and a solid .222 ISO in 2015 (with a career-norm .297 BABIP); his 17 HR and .750 OPS suggest he can help in AL-only or deeper mixed-leagues.

Owned in just 6.5% of ESPN leagues, Rasmus has 44 R, 17 HR, 44 RBI, and 2 SB to his credit (along with an unsightly .228 batting average), with the two most recent of his 17 Colby Jacks courtesy of Detroit lefty Matt Boyd. While he does sit against most LHP, Rasmus’ OPS against lefties in 2015 is a respectable .815 across 80 AB’s (compared to a .726 OPS vs. RHP over 244 AB). That said, you will see him in the lineup against a few soft-throwing lefties, but that will likely stop when Springer returns.

For perspective, consider Brandon Moss relative to Rasmus:

Moss is batting .211 with 38 R, 15 HR, and 51 RBI. He was recently ranked OF number 52 and 49 by two CBS analysts, whereas Rasmus is ranked 63 and 88. Although Rasmus’ power is less proven than that of Moss, Moss has been miserable since June and Rasmus has been steady, if unspectacular, effectively all season. But despite hitting more HR—and being projected to hit just 3 fewer HR than Moss (8 HR projected for Moss ROS seems totally absurd, incidentally)—Moss is owned in roughly 8 times more leagues than is Rasmus. In short: Colby is either massively under-owned, or Moss is hugely overvalued; or, I guess, both.

ZiPS has another 5 HR and 13 RBI projected for Rasmus rest of season, but those number seem a bit soft in the absence of Springer for a player hitting at Minute Maid Park. Rasmus won’t win a batting title anytime soon, but his solid OPS vs. lefties this year (an outlier, to be sure) and strong defense at all three OF positions keeps him in the lineup on a near-daily basis, especially given the recent, albeit short-term, demotion of Preston Tucker. Colby is a funk since his 2-HR game on 8/16, but like most power hitters, Rasmus is prone to streaks; my advice to you is exactly the same advice I took myself: pick him up and enjoy the HR power, but don’t expect him to suddenly become Bryce Harper.

Asdrubal Cabrera: Arguably the hottest hitter in baseball since he returned from the DL on July 28, Cabrera is hitting .404 with an OPS of 1.078 since the All-Star break. Those are not typos, though his numbers are propped up by a massively inflated BABIP. Also since the break, Cabby has 20 runs, 4 HR, 13 RBI, and 2 SB across 89 AB’s. He’s on fire, no two-ways about it.

What we’re seeing here, I think, are two things: 1) a player out-of-his-mind hot and 2) a veteran with proven, decent power and a solid hitter regressing to the mean. Currently batting .264 with 49 R, 9 HR, 35 RBI, and 5 SB (.730 OPS), Cabrera has hit at least 14 home runs every season since 2011 (career high of 25), and he’s on pace for roughly 12 this year. A career .267 hitter, Cabrera was miserable in April, May, and some of June, and while he’s hitting an unsustainable BABIP of .320, he was certainly due for a few bloopers to drop.

With dual 2B/SS eligibility, his ownership rate on ESPN has spiked from sub-20% in mid-August to 39% at the time of this writing. If you’re looking for help at a very weak SS position, or a possible Howie Kendrick replacement, Cabrera can certainly help you out; and as a switch-hitter, you’ll find him in the 5- or 6-hole in the Ray’s lineup on a daily basis.


The Evan Gattis Triples Game

There are 13 qualified hitters in baseball with at least six triples.  12 of the 13 players have at least five SB and the average among those 12 players is 18 steals.  Among the league leading ranks in triples stands one man who defies the common narrative that triples hitters are speedy.  He’s known as ‘El Oso Blanco’, which translates to “The White Bear” for non Spanish-speaking readers, and listed at a whopping 6’4”, 260 lbs, it’s easy to see why they call him that.  His story is one of modern day folklore, and it’s fitting that his wandering days eventually would lead him to an Astros squad that have taken the American League West by surprise.  Evan Gattis, has as many stolen bases as he has batting gloves, or as many as he appears to have, which is zero, because if you’ve witnessed him hit at all, one of the first things you notice about him is that he does not wear batting gloves.  Yet there his name is, one triple ahead of the likes of Adam Eaton and David Peralta; Evan Gattis, with nine triples, the man in sole position of second place for the most triples in major league baseball.

Consider this: he had 1 triple in his first 783 PA (or even 1 in his first 928, if we want to include all of his career PA up to May 28th, 2015 – the date of his first triple this year), and that one triple was hit into Triples Alley at AT&T Park in San Francisco on May 13th, 2014 (No, this was not a Friday the 13th).  Triples Alley is aptly named for the high volume of balls that are hit there that result in triples (relatively speaking).  So that was Gattis’ one and only, and yet he’s hit 9 in his following 446 plate appearances (or even scarier, 9 in 301 PA).  Before delving too much into this, I thought, “Conditions for an Evan Gattis triple would have to be perfect.  I bet at least 6 of these triples are due to Tal’s Hill“, which is the 90 foot wide, 30 degree incline, that extends the area of balls in play about 34 feet beyond where the fence would normally end at Minute Maid Park.  It is a whopping 436 feet to the wall at the top of Tall’s Hill.  However, a quick peek at Gattis’ home/away splits would reveal that he has just 5 triples at home and 4 on the road.

Well then he must have hit his triples in “triple-friendly” parks; below is a table showing where he has hit his 9 triples this year:

STADIUM 3B FACTOR
AT & T Park 1.211
Minute Maid Park (5) 1.549
Kauffman Stadium 1.240
Comerica Park (2) 1.465

Okay, that was predictable and makes a lot of sense to me.  Now here is a spray chart that shows his hit types (if you don’t read keys, the red dots are the triples):

chart (3)

*There is a sneaky red dot signifying a triple hiding behind a home run dot in left center just to the right of the most far left red dot*

Looking at the plotting of the red dots and considering what stadiums he hit his triples at is where I got the idea for this article – and I will now switch to writing in present tense to portray the feeling of spontaneity I felt when I first started this writing. Considering the factors, I get the feeling that I can guess which stadium each of his triples have been hit at – an exhibition of frivolity to be sure, but this is just the kind of thing that we’re looking for while we’re at work, trying to look busy, isn’t it?  If you wanna play, keep reading and guess along.  I am going to take a liberty and use the pronoun “we” instead of “I” so this feels more like a group effort.  And I also have a disclaimer: If you continue reading, you are assuming the risk that this could be a jarringly disjointed, moderately sarcastic, and gif cluttered article – it is.

The Evan Gattis Triples Game

Let’s consider my first hypothesis – that Tal’s Hill is responsible for a majority of these triples.  Looking at the red dots it looks like 3 of them may have very well landed there.  In order to kind of stick with my original idea, we’ll take the five most centrally located red dots and say that those are the triples he hit at home.

chart home

For reference into this reasoning, here’s the stadium layout of Minute Maid Park (all ballpark layouts are courtesy of Clem’s Baseball).  Note the massive depth of center field.

MinuteMaidPark

Using FanGraphs’ Game Logs I’ll pinpoint the dates of his 5 home triples and then plug those dates into Gattis’ spray chart over at BrooksBaseball.

1st Triple at home; 3rd Triple of Season: 06/28 vs NYY

triple1

That ball is not hit to Tal’s Hill, but it is one of his 5 most centrally hit triples of 2015, so that’s 1/1 if you’re scoring at home.

Now here’s the GIF – and here’s where I have to pause and give credit to another article.  When I started to write this post I hadn’t planned on including so much media, but as the post evolved it really did call for GIFs of these triples.  When I searched ‘Evan Gattis triples’ on google, the first link that popped up is this SB Nation post by Murphy Powell, and it’s the source for 6 of the 8 GIFs here and is, by all accounts, VERY similar and a much better article than mine, so check it out.  Any other GIFs were created using Baseball Savant media and makeagif.com.

gattis_3.0

“ARGH!”  That’s the sound of Michael Pineda groaning as he grimaces and falls on to bended-knee while telepathically willing the ball to stay in the park, which it does, barely.  Pineda is groaning because that was not a quality slider.  This information could probably be an entirely new post altogether, but I did warn you about this post being disjointed, so let’s to a quick detour.

This triple took place at the end of June – a table tracking velo and movement of Michael Pineda’s sliders shows that Pineda was throwing sliders of a lesser quality during this period.

Date(s) Velo x-movement v-movement BAA
Pitch to Gattis (06/28) 87.9 2.15 1.25 1.000 (obviously)
April 2015 84.08 4.54 -0.30 .208
May 2015 85.76 4.00 -0.41 .191
June 2015 87.12 2.47 0.02 .250
July 2015 87.10 1.34 0.46 .231

Whether it has been a conscious decision to throw his slider harder or it is a product of his ailing elbow, the results have not been so good.

Anyways, at this point, three triples into the season – and 3 in his last 36 games – Gattis’ reputation as a triples machine is really starting to build momentum (I warned you about the sarcasm, too) and as soon as the ball bounces away from Brett Gardner and is left to be retrieved by a scurrying Garrett Jones, Gattis is off to the races.

2nd Triple at home; 4th Triple of the season: 06/30 vs KCR

triple2

Bingo! This is a Tal’s Hill special and would be a home run at 29 other ball parks.

gattis_4.0

Lorenzo Cain, who has to at least be in the conversation for the smoothest looking active baseball player, is rendered looking like a reckless drunkard, smashing head-first into the wall and then toppling over on to his side after heaving the ball in towards a cut-off man from his knee.  Nonetheless, Gattis has his 4th triple of the year and we are 2 for 2.

3rd Triple at Home; 5th Triple of the season: 07/17 vs TEX

triple3

That one is not quite as impressive as the last one in terms of distance, but he laid into this one pretty good, too.

gattis_real_5.0

This hit scoots up on to Tal’s Hill after it nicks off Leonys Martin’s glove and then bounces off the wall – are you already missing the antics that Tal’s Hill won’t be causing in 2016?  The main thing here is that we are now 3 for 3 in this game.  I knew this would be easy.

4th Triple at Home; 7th Triple of the year: 07-28 vs LAA

triple4

So we’re wrong on this one and that brings our tally to 3 for 4 – and I’ll take most of the responsibility for the ones we get wrong – my bad.  “My bad” suffices when a player makes an errant pass out of bounds in a professional basketball game, so it should be enough here, too.

gattis_5.0 (1)

This one hit just under the yellow line against the Papa John’s sign, and it had to careen off the wall in such a way that it caused the ball to bounce into another empty center field where Shane Victorino finally picks it up and hurls it in just in time for Gattis to pull in to third base with a stand up triple.

5th Triple at Home; 7th Triple of the year: 08-14 vs DET

plot_hc_spray

This is technically another one of the 5 most centrally located triples so we are 4 out of 5.

Gattis Triple 5 Gif

 

 

 

The ball comes off the bat hard enough (99.3 mph) and then takes a generously frictional hop and loses speed as it trickles up against the wall in the deepest part of right center field at Minute Maid.  I don’t care if even the great Roberto Clemente was in right field, that is a long relay throw and there is plenty of time for Evan Gattis to lock down his 9th triple of the season.  Gattis is immediately pulled from the game as he is probably completely out of juice at this point in the season, but fans rejoice over his exploits and even Evan Gattis can’t believe his recent output of triples:

7fx3An

 

 

 

So we are hitting .800 after the home stand, but now let’s take on the triples hit away from home.  Here are the triples that we have left to identify:

chart (3)

The media, for whatever reason, has started to get smaller, so I will point out the locations of the triples: there is one to deep, left center; one to deep center, one to right-center, and one down the right field line.

For reference, here are the stadium layouts for Comerica (where he’s hit 2 triples), AT&T Park, and Kauffman Stadium.

Comerica

ballpark

AT&T

triple7

Kauffman Stadium: has the largest outfield in major league baseball as measured by total square feet.

KauffmanStadium

Let’s start with the one triple hit to deep center that did not take place at Minute Maid and say that one took place at Comerica Park, since, like Minute Maid, Comerica has a cavernous center field.

1st Triple of the Year: 05/21 vs DET @ Comerica

triple6

Huzzah! That was kind of obvious and maybe shouldn’t have elicited a Tobias Funke jubilation, but the fact that we’re five for six does.

gattis_half_1.0

Let’s jump ahead to what should be considered the other obvious pick, his triple hit at AT&T park.  There’s a triple that was hit to right center and we’ll say this triple it was a throwback piece; inspired by his first triple in the bigs, in that it was hit to Triples Alley.

8th Triple of the Year: 08/11 vs SFG @ AT&T Park

triple8

This one is wrong and that stings because I felt like this one would’ve been obvious.

qzp-eX

I’m not sure how much of the ball Gregor Blanco gets when he leaps – he may have ultimately sandwiched the ball between his back and the wall – but it looks like he prevented an Evan Gattis HR; but still can’t prevent yet another Evan Gattis Triple.  We’re 5 out of 7.

So of the two triples left, there is one that goes to deep right-center, and one that scurries down a right-field line.  The ballparks left are Kauffman and Comerica.

We’ll play the odds and guess that the one down the right-field line is hit at Kauffman Stadium because it would make sense for the one to right-center to have ended up in that little enclave at Comerica.

6th Triple of the Season: 07/26 vs KCR @ Kauffman Stadium

Oddly enough there is no data for this on Brooks Baseball and there is also no GIF for this triple; Who’s padding the stats?? At least that builds some suspense…

2nd Triple of the Season: 05/24 vs DET @ Comerica

triple9

Wrong – which also makes us wrong on the triple hit at Kauffman so we miss the final 2 – “my bad”.

gattis_2.0

It looks like Rajai Davis was positioned towards the gap and therefore had to hunt this ball down while El Oso Blanco set the base paths aflame.

So our (my) final score is 5/9, which is good but not great considering my 100% accuracy prediction.  While I’m completely aware of the vast, expansive magnitude of my ignorance, I really did believe I could pick out where each of these 9 triples happened…it’s probably this same hubris that causes me to lose $3 daily over at Draft Kings.

Trying to elicit some meaning out of this article would be contrived, so I’ll just say (tongue-in-cheek-ly), Gattis is likely to experience some regression to the mean (whatever that mean is in regards to triples).  I can’t imagine a reality where Evan Gattis highlights aren’t home runs and continue to be centered around him tearing around the basepaths – his massive, rippling thighs simultaneously inspiring awe, terror, and a few chuckles among his teammates – but what do I know?  The last time I tried to predict something about Evan Gattis, I was only 55.6% right.