Regardless of what happened in the AL Wild Card Game on Tuesday, I was going to write this article. It just so happens that a whole new level of context and subtext has developed since then. In either case, the votes are in, and Zach Britton, the guy who didn’t even get into an extra-inning win-or-go-home game, either has or hasn’t won the AL Cy Young Award.

As we did last week with the NL and Clayton Kershaw, let’s use granular batted-ball data to help decide whether an unconventional candidate is worthy of the hardware. There are four AL starting pitchers who finished in a near dead heat in WAR; I dropped one, Rick Porcello, who didn’t come close to matching the others — Corey Kluber, Chris Sale and Justin Verlander — in my first pass. We’ll evaluate those latter three against Britton.

Kluber was an unheralded draftee, originally selected by the Padres in the fourth round of the 2007 draft. Upon arrival in the big leagues, his strikeout and walk prowess carried him to success, and to a Cy Young Award in 2014, one that I would have given to Felix Hernandez. Contact management was not a strong suit of his in the early going, but as we shall see, he made solid progress in that area in 2016.

Read the rest of this entry »

The numbers are almost laughable. In a season when over 100 players have hit 20 home runs, Clayton Kershaw has put up a line for the ages. Through Tuesday: a 1.65 ERA, 1.67 FIP, a nearly unthinkable 168:10 strikeout-to-walk ratio. The only blemish is his low innings total, resulting from the back injury that cost him a substantial part of the summer. With no one seeming to run and hide with the NL Cy Young Award, it’s only natural to ask whether Kershaw might still be a worthy recipient of the hardware.

Last week, I used granular batted-ball data to measure the contact-management performance of all ERA-qualifying NL starters. This group did not include Kershaw. In that article I referred to my hypothetical Cy Young ballot, if it were limited to only qualifiers; that ballot would have been headed by two above-average contact managers with very strong K/BB profiles, Max Scherzer and Kyle Hendricks. Today, we add Kershaw to that mix, comparing him to those two pitchers, again utilizing exit-speed/launch-angle data in our analysis.

Read the rest of this entry »

The season’s final week has begun, with intriguing races for playoff spots continuing in both leagues. It’s the home stretch of award season, as well; while strikeouts and walks often get lots of attention in the Cy Young discussion, the role of contact management is often overlooked. To that end, we examined the contact-management ability of qualifying NL starters last week. This week, it’s the AL’s turn in the barrel.

Read the rest of this entry »

We’re headed down the regular-season home stretch, with nearly a full campaign’s worth of data in the books. In my next two posts, we’ll measure the contact-management performance of qualifying starting pitchers in both leagues. Today, let’s look at the National League.

Read the rest of this entry »

As the battle for a handful of playoff spots intensifies in both leagues, we today complete our late-season look at MLB park factors. Earlier this week, it was the American League; today, the senior circuit. These park factors, as explained in previous installments, are based on granular batted-ball data, such as exit speed and launch angle.

As a reminder, here’s the quick-and-dirty on the method used to calculate these park factors. Through August 21, 106,962 balls were put into play during MLB regular-season contests. They resulted in an overall batting average of .328 and slugging percentage of .537, while fly balls generated a .328 AVG and .895 SLG. Line drives generated a .661 AVG and .872 SLG, and ground balls a .237 AVG and .258 SLG. (Oh, and pop ups have generated a .018 AVG and .028 SLG.) Each BIP type was split into “buckets” separated by 5-mph increments. The top fly-ball bucket begins at 105 mph, and the top liner and grounder buckets begin at 110 mph.

For each ballpark, the actual production derived from that park’s actual BIP mix was compared to the projected production, assuming that each BIP bucket generated MLB average production for that BIP type/exit-speed combination. Convert everything to run values, and voila, park factors, both overall and by BIP type.

Read the rest of this entry »

Last week, we updated our mid-May analysis of park factors based on granular batted-ball data, showing the offense-inflating impact that a hot summer can have, especially in certain parts of the country. This week, let’s take a look at the season-to-date overall and fly-ball park factors for all 30 parks, one league at a time, focusing on some interesting park-specific information.

First, here’s the quick-and-dirty on the method used to calculate these park factors. Through August 21, 106,962 balls were put into play during MLB regular-season contests. They resulted in an overall batting average of .328 and slugging percentage of .537, while fly balls generated a .328 AVG and .895 SLG. Line drives generated a .661 AVG and .872 SLG, and ground balls a .237 AVG and .258 SLG. (Oh, and pop ups have generated a .018 AVG and .028 SLG.) Each BIP type was split into “buckets” separated by 5-mph increments. The top fly-ball bucket begins at 105 mph, and the top liner and grounder buckets begin at 110 mph.

For each ballpark, the actual production derived from that park’s actual BIP mix was compared to the projected production, assuming that each BIP bucket generated MLB average production for that BIP type/exit-speed combination. Convert everything to run values, and voila, park factors, both overall and by BIP type.

Read the rest of this entry »

Way back in May, I wrote a piece here on early-season park factors. Based on 26,650 balls-in-play struck through May 11, park factors were calculated based on granular exit speed/angle data. Yes, the sample was fairly small, but some interesting data was generated. One conclusion reached was that weather seemed to be playing a fairly significant role: the upper Midwest and Northeast corridor clubs with open-air stadiums endured cool, wet springs which had a clear run-suppressing effect.

The article wrapped up by indicating that we’d check back in a couple months to see what effect the higher temperatures of summer would have on those park effects. And here we are. This week and next, we’ll update these park factors through late August. Today, we’ll focus on fly-ball park factors, and next week we’ll take separate looks at AL and NL overall park factors.

Read the rest of this entry »

Rejuvenated by a week away from baseball altogether, I’m back with the last in a series of articles on team ball-in-play profiles. In the last installment, we examined the AL Central. We’ve saved the best — well, at least the division with the best team — for last, as we take a look at the NL Central. As we have previously, we’ll use granular data such as plate-appearance frequencies and BIP exit speed/angle as of the All-Star break to project “true-talent” club records.

Read the rest of this entry »

Our series of divisional team BIP analyses rolls on. Most recently, we examined the NL East. Today, the AL Central. We’ll use granular data such as plate-appearance frequencies and BIP exit speed/angle as of the All-Star break to project “true-talent” club records.

About 90 games’ worth of balls in play is a fairly substantial sample size, one that enables us to make fairly educated guesses about the true-talent level of each team. We’ll compare our projections to club’s actual records at the break, examining the reasons for material variation along the way. Read the rest of this entry »

We’re now more than halfway through our division-by-division look at granular team ball-in-play data, as of the All Star break. Today, we take a macro-type view of the plate-appearance frequency and BIP exit speed/angle detail for NL East clubs.

About 90 games’ worth of balls in play is a fairly substantial sample size, one that enables us to make fairly educated guesses about the true talent level of each team. Let’s use this information to project true-talent team won-lost records and compare them to their actual marks at the break, examining the reasons for material variation along the way. Read the rest of this entry »