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Pitcher Contact-Management Update: New Qualifiers

A few weeks back, we took a look at the 2016 contact-management performance of qualifying pitchers in both leagues. Since then, a number of new qualifiers have emerged. Today, we’ll utilize tools such as plate-appearance-outcome frequencies, exit-speed and launch-angle allowed to see how these hurlers have performed in this vital area.

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Hitter Contact-Quality Report: New Qualifiers

Over the last few weeks in this space, we’ve been evaluating regular position players’ contact quality utilizing granular data such as plate appearance outcome frequencies, exit speed and launch angle. (Catchers represented the last installment in that series.) Over that time, players not included in our original analysis have overtaken previous incumbents in terms of total plate appearances. Today, we’ll add players who did so as of July 4 to the mix. Next time, we’ll look at newly qualified pitchers.

The data examined today runs through July 4. Players are separated by league, and are listed in Adjusted Production order. Adjusted Production expresses, on a scale where 100 equals average, what a hitter “should have” produced based on the exit speed/launch angle of each ball put in play. Each player’s Adjusted Contact Score, which weeds out the strikeouts and walks and states what each player should have produced on BIP alone, is also listed. Here goes:

AL Adds’ BIP Profiles
Name Avg MPH FLY MPH LD MPH GB MPH POP% FLY% LD% GB% ADJ C K% BB% wRC+ ADJ PR Pull%
Grossman 87.4 88.0 87.3 86.6 0.0% 39.0% 23.0% 38.0% 113 22.2% 18.1% 142 124 39.2%
Forsythe 91.9 92.3 95.0 89.2 1.9% 29.8% 25.5% 42.9% 126 21.6% 7.4% 129 114 34.8%
Hardy 93.1 91.1 98.3 94.6 4.9% 32.5% 19.5% 43.1% 86 13.3% 4.7% 67 93 47.2%
Merrifield 89.2 92.5 90.2 87.0 0.0% 24.2% 28.8% 47.0% 102 21.3% 2.8% 95 86 34.1%
Barney 87.0 89.8 88.8 84.4 3.5% 25.9% 23.1% 47.6% 75 14.6% 7.0% 96 84 38.2%
Gattis 89.2 88.7 92.6 89.6 5.1% 33.8% 15.9% 45.2% 91 23.7% 8.5% 82 84 48.4%
Buxton 89.8 86.5 93.3 90.7 7.4% 35.8% 23.5% 33.3% 81 39.4% 3.9% 48 43 43.7%

Most of the column headers are self-explanatory, including average BIP speed (overall and by BIP type), BIP type frequency, K and BB rates, wRC+ and Adjusted Production, which incorporates the exit speed/angle data. Each hitter’s Adjusted Contact Score (ADJ C) is also listed. Adjusted Contact Score applies league-average production to each hitter’s individual actual BIP type and velocity mix, and compares it to league average of 100.

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Hitter Contact-Quality Report: Catcher

The All-Star Game is behind us, the unofficial second half of the season is set to kick off and, today, we present the last installment in our position-by-position look at hitter contact quality. Last time, it was right fielders; this time, catchers. Granular ball-in-play data such as BIP frequencies, exit speed and launch angle are the key inputs in this analysis.

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Hitter Contact-Quality Report: Right Field

The All-Star break is beckoning as we come down the homestretch of our position-by-position look at hitter contact quality. We will again use granular ball-in-play data such as BIP frequencies, exit speed and launch angle to perform the analysis. Two positions to go. Last time, it was center fielders; today, it’s the right fielders’ turn.

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Hitter Contact-Quality Report: Center Field

With the extended holiday weekend behind us, we get back to the business at hand: our position-by-position look at hitter contact quality. Only three positions to go. Last time, it was left fielders. This time: a fun-filled group of center fielders. As we have in the previous installments, we’ll use granular ball-in-play data, such as BIP type frequencies, exit speed and launch angle to perform this analysis.

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Hitter Contact-Quality Report: Left Field

Our position-by-position review of contact quality grinds on. In the last installment, we examined third basemen. Today, we move into the outfield. It’s two starkly different stories with regard to left-field production, as National League regulars have dramatically out-produced their junior circuit counterparts. As we have in the previous installments, we’ll use granular ball-in-play data, such as BIP type frequencies, exit speed and launch angle to perform this analysis.

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Hitter Contact-Quality Report: Third Base

Our position-by-position tour of hitter contact quality reaches its midway point today. Last time, we looked at shortstops. Today, hot-corner regulars. As we have in the previous installments, we’ll use granular ball-in-play data, such as BIP type frequencies, exit speed and launch angle to perform this analysis.

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Hitter Contact-Quality Report: Shortstop

The winter sports have crowned their champions, Cleveland has its first title in eons thanks to their prodigal son LeBron, and baseball now owns a greater part of the sporting stage for the rest of the summer. In that spirit, we continue to take a position-by-position look at hitter contact quality, utilizing granular ball-in-play data, such as BIP type frequencies, exit speed and launch angle. Last time, it was second basemen. Today, the shortstops are at bat. Read the rest of this entry »


Hitter Contact-Quality Report: Second Base

Earlier this week, we began a position-by-position look at hitter contact quality with a review of the first-base and DH population. Today, we continue to use granular ball-in-play data, such as BIP type frequencies, exit speed and launch angle, to review second basemen.

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Hitter Contact-Quality Report: First Base and DH

Over the last couple of weeks, we’ve taken a look at the 2016 contact management ability of ERA-qualifying starting pitchers in both leagues, utilizing granular batted-ball data. Now it’s the hitters’ turn. Over the next few weeks, we’ll take a position-by-position look at hitters’ contact quality, using exit speed, launch angle, and BIP type frequencies as our tools. Today, let’s look at each team’s primary first basemen and designated hitters.

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American League Contact-Management Update

Starting pitchers get the job done in various ways; some excel at bat-missing and/or command. Others are more adept at managing contact on balls in play. The very best are able to clear the bar in all three areas. Sample sizes for the 2016 season have increased in size to the point that we actually should begin paying attention. Last week, we checked in with NL ERA qualifiers regarding their early-season contact-management performance; this week, it’s the AL’s turn.

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National League Contact-Management Update

Another page has been ripped off of the calendar, and sample sizes are finally getting to a point where they actually matter. This, then, represents a good occasion to take a first look at starting-pitcher contact-management trends. Today, it’s the National League.

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The 2016 Single-Game Pitching Belt: Kershaw vs. Velasquez

Earlier this week, we again utilized granular batted-ball data to determine whether Vince Velasquez could hold onto the championship belt for the best single-game pitching performance of the season. He did so, beating out Max Scherzer‘s 20-strikeout performance. To this point, we’ve also matched the Phils’ righthander against Jaime Garcia‘s one-hitter and Jake Arrieta‘s no-hitter.

When one is discussing pitching excellence, it’s only a matter of time before Clayton Kershaw enters the discussion. Today, let’s match up Velasquez’16 K, 0 BB vanquishing of the Padres on April 14 to, well, Kershaw’s entire body of 2016 work.

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The 2016 Single-Game Pitching Belt: Scherzer vs. Velasquez

A few weeks back, we matched up three of the most dominant pitching performances from April, utilizing granular ball-in-play data, to determine which of Vince Velasquez, Jaime Garcia or Jake Arrieta had the best day. Velasquez won that time around, and with Max Scherzer recently authoring a 20-strikeout, no-walk complete game shutout over the Tigers, we have a worthy contender for the single-game pitching championship belt.

There’s one rule for entry into this competition: you had to finish what you started. Only complete games apply. Then we simply look at every batted ball allowed, and first calculate each pitcher’s single-game Adjusted Contact Score based on exit speed and angle data. Then, we add back the Ks and BBs, and calculate each pitcher’s single-game “tru” ERA-. With these two performances, we don’t need to worry about adding back any BBs.

Velasquez vs. Scherzer – Exit Speed/Angle Data
AVG ALL AVG FLY AVG LD AVG GB AVG VERT
Velasquez vs. SD 14-Apr 88.1 89.1 87.2 87.4 20.8
Scherzer vs. DET 11-May 86.6 93.1 93.5 56.8 19.1
MLB Avg. Thru 18-May 89.4 90.0 93.5 87.4 11.0

Both of these pitchers followed similar paths in their dominant outings. Besides striking out 36 and walking none between them, both pitchers allowed very high average exit angles, and very few grounders. Only extreme fly-ball/pop-up pitchers sustain average exit angles near 20 over a full season, the Chris Youngs and Jered Weavers of this world.

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Fun With Early-Season Park Factors

The introduction of granular ball-in-play data has changed baseball analysis in numerous ways. While traditional methods of evaluation remain invaluable, they can now be supplemented by hard data that can explain what our eyes are telling us, just as our eyes can at times help explain the numbers.

Park factors have been a part of baseball analysis for at least a generation now. Some versions are calculated very simply, others are much more complex. Most would agree that a single year is way too little data upon which to generate meaningful park factors; rolling three- or four-year metrics are often utilized.

Well, I would submit that there is a lot we can learn from park factors generated over very short periods of time, provided that granular exit speed and angle data is integrated. Today, let’s look at some fairly crude context-adjusted park factors based on data from opening day through May 11 of this season.

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Is It Time to Worry About David Price?

The Red Sox were a fairly popular pick to win the AL East entering this season. The continued maturation of their young position players combined with an improved starting rotation — fronted by big-ticket free-agent acquisition David Price — was the recipe for success.

Here we are, over a month into the campaign, and the Sox are battling the Orioles for the top spot in the division. The offense has been even more potent than expected, with David Ortiz fighting off father time and Xander Bogaerts taking the next step toward stardom. The pitching staff, however — with the exception of knuckleballing savior Steven Wright — haven’t gotten the memo. Price, in particular.

Price enters his start this evening with an AL-worst 6.75 ERA. It’s not like his stuff has evaporated: he still possesses a strong 53/12 strikeout-to-walk ratio, and his swinging-strike rate stands at a career best 14.1%. Today, let’s dig into some granular ball-in-play (BIP) data and draw some conclusions as to whether it’s OK to start worrying about Price.

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Chris Sale: New and Improved?

The historic start of the club residing on Chicago’s north side has obscured some pretty amazing things going on at US Cellular Field, as the White Sox have raced out to the best record in the American League. Hopes weren’t all that high entering the season, with the club’s only spring-training noise emanating from the aftershocks of Drake LaRoche-Gate.

A month-plus in, however, the poor-fielding and weak-hitting Chisox of 2015 are a distant memory. A fine starting staff, led by perennial Cy Young candidate Chris Sale and his wingmen Jose Quintana, Carlos Rodon and Mat Latos, are thrilled to find that most of the batted balls they allow are finding leather this time around.

About those batted balls: much is being made of the fact that Chris Sale is posting the best, small-sample traditional numbers of his career while pitching to much more contact than in the recent past. Today, let’s dig inside the numbers a little bit to see whether Sale is, in fact, new and improved.

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Kenta Maeda: One Month In

Major League Baseball has taken steps toward becoming a truly global game in recent years. Cuban players have joined their Venezuelan, Dominican and other Latin American counterparts in making a significant impact on today’s game, and talent from the Far East, particularly from the Japanese and Korean Leagues, has made its presence felt as well.

This year’s most heralded Japanese rookie is Kenta Maeda, who signed a long-term deal with the Dodgers this past offseason. After concerns were raised following a medical examination, he signed a deal that was heavily discounted from the originally negotiated terms, paying him $25 million over an eight-year period. This put the Dodgers in a fantastic position: a low-risk, potentially high-reward scenario. One month in, the Dodgers simply have to be thrilled as Maeda’s posted a 3-1, 1.41 mark with a 28/6 strikeout-to-walk ratio in 32 innings.

Sure, the season remains young, and the sample sizes are small, but it’s not too early to form some early hypotheses regarding whether Maeda is for real. Today, let’s use granular batted-ball data, examining his plate-appearance frequency and production by BIP type data, to see how Maeda is getting it done, and whether we can expect his success to continue moving forward. Read the rest of this entry »


Ranking April’s Most Dominant Pitching Performances to Date

It’s almost time to rip the first page from the regular-season calendar, and many players and moments have already left indelible marks that will live on in our memories. From Trevor Story to Kenta Maeda, from the Cubs and Nationals on the good end to the Twins and Astros on the bad, it’s been an exciting ride thus far.

There are a number of dominant pitching performances already in the books, with Jake Arrieta‘s second no-hitter in as many years an obvious highlight. Just a week before his vanquishing of the Reds, the Phils’ Vincent Velasquez and the Cards’ Jaime Garcia unfurled identical game scores of 97 in complete game victories over the Padres and Brewers, respectively. Since it’s still early in the season, and sample sizes remain quite small, let’s use batted-ball data in a more laid-back, fun manner, and attempt to split some hairs among these three gems, and crown one as April’s most impressive pitching performance.

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2015 Relief Pitcher Ball-in-Play Retrospective – NL

Over the last few weeks in this space, we’ve conducted ball-in-play based analyses of position players’ and starting and relief pitchers’ 2015 performance. Last time, we considered AL relievers. Today we’ll present the last installment of this series, focusing on NL relief pitchers. It’s admittedly a little dicey to evaluate relief pitchers in this manner. The sample sizes are much smaller, and filled with more noise. Still, it’s a worthwhile exercise that can show us the different ways in which closers, set-up men, et al, get it done.

First, some background on the process. I identified the 214 relief pitchers from both leagues who yielded the most batted balls in 2015, making sure that all team save leaders were included in the sample. From that group, I selected 28 pitchers from each league for further scrutiny. Pitchers are listed with their 2015 league mates; those who were traded during the season will appear in the league in which they compiled the most innings. Pitchers are listed in “tru” ERA order. For those who have not read my previous articles on the topic, “tru” ERA is the ERA pitchers “should” have compiled based on the actual BIP frequency and authority they allowed relative to the league. Here we go:

Relief Pitcher BIP Profiles
AVG MPH FB/LD MPH GB MPH POP% FLY % LD% GB% ADJ C K% BB% ERA- FIP- TRU-
Jansen 88.07 91.64 86.56 7.4% 46.3% 11.1% 35.2% 85 40.0% 4.0% 64 56 43
Kimbrel 89.42 92.05 86.82 3.9% 30.5% 19.5% 46.1% 62 36.4% 9.2% 69 70 45
A.Chapman 83.53 86.39 79.65 8.1% 33.0% 21.8% 37.1% 83 41.7% 11.9% 41 49 51
Storen 87.16 90.47 84.11 5.1% 32.6% 23.9% 38.4% 75 29.4% 7.0% 87 73 58
AJ.Ramos 86.80 90.72 82.82 4.4% 35.9% 16.4% 43.4% 76 31.4% 9.4% 59 80 60
W.Smith 88.72 91.15 88.36 1.4% 37.5% 15.3% 45.8% 91 34.5% 9.1% 67 61 63
Romo 84.14 90.03 80.39 4.1% 27.6% 23.4% 44.8% 95 30.9% 4.4% 83 53 64
Strop 89.71 90.49 89.35 4.6% 24.3% 19.7% 51.3% 74 30.0% 10.7% 74 81 64
Kelley 88.13 92.83 85.75 4.9% 33.0% 19.4% 42.7% 88 30.7% 7.3% 66 67 66
Melancon 87.99 92.05 85.13 3.3% 19.3% 19.8% 57.5% 73 21.2% 4.8% 59 75 66
Benoit 83.22 90.00 77.46 4.3% 32.1% 17.3% 46.3% 69 24.8% 9.1% 63 98 66
Dyson 88.41 91.73 87.26 1.9% 12.5% 16.8% 68.8% 73 23.0% 6.8% 66 76 67
Familia 86.23 90.18 85.06 2.5% 19.1% 20.1% 58.3% 90 27.9% 6.2% 50 71 69
R.Delgado 84.95 89.42 82.25 5.7% 35.0% 18.0% 41.2% 67 23.7% 10.7% 80 97 69
H.Rondon 87.60 89.05 85.96 1.6% 25.6% 20.4% 52.4% 84 24.6% 5.3% 43 69 70
Maurer 84.09 88.76 78.90 4.7% 25.5% 22.1% 47.7% 68 18.9% 7.3% 81 86 70
Fr.Rodriguez 85.64 89.08 82.26 2.1% 27.9% 23.6% 46.4% 97 28.7% 5.1% 55 72 71
Grilli 87.98 91.56 82.00 5.9% 41.2% 25.9% 27.1% 104 32.1% 7.1% 76 57 73
Rosenthal 87.76 91.57 87.65 4.5% 30.5% 19.2% 45.8% 93 28.9% 8.7% 55 63 74
Ziegler 88.89 89.20 88.51 0.5% 13.1% 13.6% 72.8% 65 13.7% 6.5% 45 89 75
Papelbon 88.57 90.98 89.24 2.8% 32.2% 15.3% 49.7% 95 21.5% 4.6% 54 95 81
Giles 88.55 90.67 87.58 2.2% 31.1% 21.9% 44.8% 107 29.2% 8.4% 46 54 82
Casilla 86.68 92.23 81.26 2.6% 27.1% 23.9% 46.5% 102 25.4% 9.4% 77 100 89
Nicasio 85.93 89.70 82.34 2.5% 29.3% 24.8% 43.3% 94 25.0% 12.3% 103 74 90
Jeffress 86.72 89.96 84.98 0.0% 18.0% 23.8% 58.2% 109 23.5% 7.7% 65 80 95
Cishek 86.47 90.89 83.03 0.6% 31.5% 21.8% 46.1% 98 19.8% 11.1% 92 102 103
Axford 91.65 93.10 91.09 1.3% 25.8% 16.8% 56.1% 120 24.8% 12.8% 92 85 112

First, a little background. The larger group of 214 relievers had a cumulative strikeout rate of 22.2% and walk rate of 8.2%. Both rates are higher than the comparable marks for starters (19.8% and 7.0%, respectively). The larger group of relievers also conceded less authoritative contact than starters, allowing lesser overall (88.02 mph for relievers, 88.46 mph for starters), FLY/LD (91.24 vs. 91.78) and grounder (85.76 vs. 86.30) authority. With regard to BIP frequency, relievers outpaced starters in the key grounder-rate category by 45.6% to 45.2%, and matched them in pop-up rate (3.2%).

The subset of relievers listed above generally represents the cream of the relief crop. Most of the column headers are self-explanatory, including average BIP speed (overall and by BIP type), BIP type frequency, K and BB rates, and traditional ERA-, FIP-, and “tru” ERA-. Each pitcher’s Adjusted Contact Score (ADJ C) is also listed. Again, for those of you who have not read my articles on the topic, Unadjusted Contact Score is derived by removing Ks and BBs from opposing hitters’ batting lines, assigning run values to all other events, and comparing them to a league average of 100. Adjusted Contact Score applies league-average production to each pitchers’ individual actual BIP type and velocity mix, and compares it to league average of 100.

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