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.
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 »
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.
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.
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.
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.
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.
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.
| 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.
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.
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.