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It’s no secret that power is up in baseball. While guys like Brian Dozier are hitting their 40th home run and Tyler Naquin are putting up Barry Bonds-type numbers against breaking and offspeed pitches, investigations into the potential of a juiced ball have persisted throughout the analytical community. We can’t say for sure why power is up, but it is. The league’s .167 isolated slugging percentage is the highest the peak of the steroid era in 2000, and the second-highest in the history of the game, and the 14-point increase in ISO from 2015-16 is the 10th-largest single-season in increase since the modern era began in 1921.

Such a sudden and drastic increase can make it difficult to contextualize player changes on the fly. When I wrote about Eric Hosmer’s season two weeks ago, this line from a comment by user “isavage” stuck with me:

It doesn’t seem like Hosmer’s performance has fluctuated too greatly, it’s more that his offense didn’t get better when the league’s offense got better.

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Devised originally in response to a challenge issued by sabermetric nobleman Rob Neyer, and expanded at the request of nobody, NERD scores represent an attempt to summarize in one number (and on a scale of 0-10) the likely aesthetic appeal or watchability, for the learned fan, of a player or team or game. Read more about the components of and formulae for NERD scores here.

Most Highly Rated Game
Baltimore at Boston | 19:10 ET
Gausman (152.0 IP, 87 xFIP-) vs. Porcello (193.2 IP, 91 xFIP-)
Were one inclined to facilitate a yelling match between a group of Boston-area residents — ideally, for the purposes of this experiment, men aged 18 to 65 — one means by which to accomplish that might be to ask them whether they regard Rick Porcello or David Price as the ace of the Red Sox. “Porcello’s got more wins,” one might declare. “But Price has better stuff,” another would almost certainly ejaculate. Is that third one, over in the corner, exhibiting signs of a heart attack? No, that’s just how people in Boston look and act. An entire people on the verge of cardiac arrest: this is an adequate characterization of Boston.

Porcello, who’s got a lower FIP-based WAR but higher RA-based WAR than Price, is scheduled to start this particular, urgent game.

Readers’ Preferred Broadcast: Baltimore Television.

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Last September, we ran an interview with Cody Allen that was titled A Cleveland Closer’s Weird Year. At the time, his BABIP was .366 (it was .342 at season’s end). Despite his balls-in-play issue, opposing batters hit just .219 against him (with a .305 SLG). He finished with 34 saves, a 2.99 ERA and a 1.82 FIP.

The 27-year-old right-hander is having another weird year. His ground-ball rate is up almost 10 points and his infield-fly rate has plummeted from 15.4% to 4.1%. Counterintuitively, his BABIP has plummeted to .248. On the season, Allen has 27 saves, a 2.70 ERA and a 3.24 FIP. Opposing batters are hitting .184 against him, with a .333 SLG and a much higher HR/FB rate (14.3% versus 3.1% a year ago).

Allen’s repertoire hasn’t changed, and his pitch ratios and velocity are essentially the same. His strikeout rate remains high. All in all, he’s the same pitcher he was a year ago, so why are some of the numbers so markedly different? Allen did his best to explain — and expounded on a variety of other pitching subjects — this past weekend.

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Allen on his numbers: “As a reliever, your numbers can change quite a bit, because your innings aren’t that high. One or two blowups can really inflate some of them. I had one against Chicago where I gave up five and got only one out. My ERA went way up in one game. As for explaining my numbers the past two years — the ones you’re asking about — I’m not sure if there are any simple answers.”

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Near the end of the game between the Blue Jays and Rays yesterday, the camera panned to center field. Evan Longoria was at the plate, and the Jays broadcast team was talking about the third baseman’s power. “He’s got some power to right field, too, now, and I think that’s why you’ll see the outfielders, especially the center fielder and right fielder playing a couple steps back,” said Dan Shulman. “Look at how deep Kevin Pillar is in center field. That’s only a couple of steps, it seems like, for Pillar, from the warning track!” he continued. “We have not seen Kevin Pillar play that deep,” concurred Buck Martinez.

It was impressive. That little dot in center is Pillar. Looks like a wallflower at a middle-school dance.

He was 361 feet from the plate at that moment. It makes sense, given Longoria’s spray chart this year. You’ll notice that Pillar is shaded a little bit to right, which is where Longoria hits many of his deep outs.

Source: FanGraphs

But the Blue Jays were pushing the envelope a bit. Call it situational defense, maybe, because Pillar was playing more than 30 feet further back than the average center fielder against Longoria this year. Given that there were two outs in the eighth inning of a tie game and Brad Miller and Nick Franklin were scheduled to hit behind Evan Longoria, there’s a certain amount of making sure to stop the big hit doesn’t sink the team. In a league where it probably pays to play deep, this was playing just a bit deeper on a guy who hits them deep.

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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.

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On Monday, the Oakland Athletics promoted a couple of hitting prospects from Triple-A: Renato Nunez and Matt Olson. Both Nunez and Olson came off the bench on Monday to make their big-league debuts. With Billy Butler out of the picture, and Danny Valencia likely soon to follow, Nunez and Olson might see a decent chunk of playing time these next two weeks.

Olson’s numbers have trended in the wrong direction since his 37-homer season in the Cal League in 2014. He slashed .249/.388/.438 in Double-A last year and only managed to hit .235/.335/.422 in the PCL this year. Throughout his minor-league career, Olson has demonstrated good power and a willingness to draw walks. He’s also a 22-year-old with a 6-foot-5 frame, which suggests he may still have some untapped upside. But his underwhelming performance, defensive limitations and 24% strikeout rate don’t bode particularly well for his future in the show.

KATOH pegs Olson for 3.7 WAR over his first six seasons by the traditional method and 2.8 WAR by KATOH+, which integrates Baseball America’s rankings. To help you visualize what his KATOH projection entails, here is a probability density function showing KATOH+’s projected distribution of outcomes for Olson’s first six seasons in the major leagues.

To put some faces to Olson’s statistical profile, let’s generate some statistical comps for the newest Brewers prospect. I calculated a weighted Mahalanobis distance between Olson’s performance this year and every Double-A season since 1991 in which a first baseman or corner outfielder recorded at least 400 plate appearances. In the table below, you’ll find the 10 most similar seasons, ranked from most to least similar. The WAR totals refer to each player’s first six seasons in the major leagues. A lower “Mah Dist” reading indicates a closer comp.

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When a below-average lineup in 2015 was followed by a quiet offseason and a Michael Brantley shoulder surgery, it was easy to make a case against Cleveland’s preseason playoff hopes that started and ended with the lineup. Yet here we are, now, in September, and Cleveland is all but a lock to win their division. They’ve gotten here due in large part to a lineup that’s exceeded expectations (third in runs scored, 10th in wRC+) and kept pace with the pitching (third in ERA-, seventh in WAR) and defense (fourth in UZR, 10th in DRS). They’ve gotten here by crushing offspeed pitches, at a near-historic rate.

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