Oh my, it’s prediction time. Can you feel it? Another season is upon us. Another chance for us to look like huge idiots. Like last season, when none of us pegged Kansas City to make the playoffs, and just five of us pegged the Mets as a playoff team. Being wrong on the internet can be an annual thing, but rarely do we crystallize just how wrong we are in one single location. Yes, it’s good to be back.

This year, there are a couple of no-doubt teams, according to everyone who voted. Chicago and Houston enter the season as our two locks to make the postseason. Considering that, as recently as 2013, these teams won 66 and 51 games, respectively, this seems pretty remarkable. We can save the tanking discussion for another day, but no matter your name for it, these franchises have really built themselves up into something. Houston has had an especially sharp transformation. Last season, they got zero votes for the postseason, and this year they got every single vote.

The Astros and Cubs aren’t the only teams in which we’re big believers, of course, but let’s get right to looking at the actual predictions.

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Spring Training stats don’t matter. Right? Right. Well, mostly. It’s true — you shouldn’t be paying attention to things like batting average, ERA, or even home-run figures in the spring. There’s just too little time, too much volatility in the statistics, and too much uncertainty surrounding the quality of opposition for those numbers to have, well, any meaning. But last year, Dan Rosenheck’s excellent work in The Economist, later nominated for a SABR Analytics Conference Research Award, revealed that certain peripheral Spring Training statistics actually can have some predictive value for the regular season.

It needs to be stressed: even then, the effects are small. Nothing that happens in Spring Training should drastically alter your perception of a player. And for most guys, nothing should change. But, for the few players at the very end of each spectrum in these particular statistics, it’s okay to move your expectations up or down a tick or two.

You should read Rosenheck’s article and also view the slides he used to present his research at the MIT Sloan Sports Analytics Conference, but I’ll briefly summarize his findings here anyway:

• For batters, the three most predictive statistics that stabilize most quickly are: strikeout rate, walk rate, and isolated power on contact.
• For pitchers, the three most predictive statistics that stabilize most quickly are: strikeout rate, walk rate, and ground-ball rate.
• Each player’s Spring Training figures should be compared against his own projections to find the largest outliers.
• We can learn the most in the spring about younger players, who have less major-league playing time, therefore less significant data to fuel the projections, therefore more uncertainty within those projections

Using those four basic principles from Rosenheck’s work, we can fairly easily use the Spring Training leaderboards from MLB.com and our depth chart projections here on the site (a mix of ZiPS and Steamer projections with author-updated playing-time estimates) to find the players who changed their outlook the most this spring (though still not that much!).

The Hitting Winners

Jake Lamb

• 2016 projections: 25.6 K%, 8.4 BB%, .200 isolated power on contact
• 2016 spring stats: 24.2 K%, 19.7 BB%, .405 isolated power on contact

By this measure, Arizona’s third baseman Jake Lamb has had the single most encouraging spring of any batter in baseball. He’s still striking out more than the average batter, but he also has the highest walk rate of any qualified batter in the spring — more than double his projected rate — and he’s doubled his power output, perhaps thanks to a change in his swing path, inspired by his teammate, A.J. Pollock.
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Previous editions: Arizona / Baltimore / Boston / Chicago AL / Chicago NL / Cincinnati  / Cleveland / Colorado / Detroit / Houston / Kansas City / Los Angeles (AL) / Los Angeles (NL) / Miami / Milwaukee / Minnesota / New York (AL) / New York (NL)  / Oakland / Philadelphia / Pittsburgh / San Diego / San Francisco / Seattle / St. Louis.

Back in November, lead prospect analyst Dan Farnsworth published his excellently in-depth prospect list for the Atlanta Braves. In this companion piece, I finally get around to looking at that same Atlanta farm system through the lens of my recently refined KATOH projection system. The Braves have the 13th-best farm system in baseball according to KATOH. Read the rest of this entry »

The other day, in a spring-training game against the Braves, Miguel Gonzalez was basically pitching for his rotation slot. That might’ve been surprising enough, given how much Gonzalez has meant to the Orioles in the past, but he did have a rough 2015 and a rougher month of March. Anyhow, Gonzalez went five innings, allowing a run with no walks and four strikeouts. The results were solid, even if they could’ve been worse. Yet Gonzalez still got released. Something just wasn’t good enough, and the Orioles decided to go with other options.

Maybe “stunning” would be too strong, but the reality of Gonzalez getting released is unexpected. The Orioles are anything but deep in the rotation, and apparently Gonzalez’s former teammates are less than ecstatic. It’s a hard thing to wrap your head around, given Gonzalez’s presence, but it seems to me there are three ways to think about this. One of them, I prefer over the others.

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Ben and Sam preview the Dodgers’ season with Vice Sports editor Eric Nusbaum, and Jeff talks to Los Angeles Times Dodgers beat writer Andy McCullough (at 21:22).

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Perhaps you already thought it inevitable, but now it’s official: Out of the gate, Travis Shaw will be starting for the Red Sox at third base, over Pablo Sandoval. Through the end of his contract, Sandoval is owed more than seventy-five million dollars. Shaw, meanwhile, is owed an amount of money you could actually imagine in your own bank account. This is surprising, because of the commitment the Red Sox made to Sandoval the previous offseason. But this is not surprising, because Sandoval was a disaster. Hanley Ramirez might’ve been a more conspicuous disaster, but Sandoval managed to beat him, ever so slightly, in negative WAR.

There are just a few things that have to be said in response to the news. The first, which is critical, is this is non-binding. I mean, Shaw will start on opening day, but beyond that, no one’s really said anything. It stands to reason Sandoval is going to play; he’s not going to be a full-year pinch-hitter. It is legitimately unusual for a team to rule against its own financial commitments, at least this soon. And then — well, this decision was probably easy. This is the right time to give Shaw his chance. He might just be a better baseball player than Sandoval is, and after back-to-back seasons of misery, the Red Sox are in the business of maximizing wins.

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I got my first look at Blake Rutherford (Chaminade College Prep, Calif.) at USA Baseball’s Tournament of Stars showcase last summer. The 18-year-old outfielder, whom evaluators considered a top 10-draft prospect entering the spring, reinforced that status at last weekend’s National High School Invitational at the USA Baseball complex in Cary, NC, perhaps elevating himself given the underwhelming performances of some of his similarly talented peers.

The video below merges Rutherford’s batting practice from Tournament of Stars and his four at-bats from the Chaminade Prep vs. Walton HS (Ga.) contest at NHSI.

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Teams are setting their Opening Day rosters, another page is about to flipped on the wall calendar, heralding the dawn of the regular season. Never mind those pesky Opening Day temperature forecasts of sub-40 degrees in my neck of the woods. Today, we’ll open the second half of our ball-in-play-based analysis of 2015 starting pitcher performance. Most recently, we examined the NL West. We begin our look at the junior circuit with the AL East.

First, some ground rules. To come up with an overall player population roughly equal to one starting rotation per team, the minimum number of batted balls allowed with Statcast readings was set at 243. Pitchers are listed with their 2015 division mates; those who were traded during the season will appear in the division 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:

Starting Pitcher BIP Profiles – AL East

	AVG MPH	FB/LD MPH	GB MPH	POP %	FLY %	LD %	GB %	CON	K %	BB %	ERA –	FIP –	TRU –
M.Estrada	88.57	91.95	84.82	6.1%	46.2%	15.5%	32.2%	74	18.1%	7.6%	78	110	75
Pineda	88.14	92.07	86.03	2.7%	27.2%	21.9%	48.2%	103	23.4%	3.1%	109	83	79
Archer	90.10	92.00	89.29	2.6%	31.3%	20.0%	46.1%	112	29.0%	7.6%	81	72	81
Warren	88.06	90.97	85.42	3.2%	28.8%	22.8%	45.2%	87	19.5%	7.3%	82	90	82
Gausman	87.43	91.26	85.45	4.5%	33.6%	17.1%	44.7%	97	21.9%	6.2%	106	102	83
E.Ramirez	89.30	92.38	87.25	4.1%	27.7%	20.4%	47.8%	94	18.9%	6.0%	94	94	87
Tanaka	89.86	93.21	87.21	3.2%	30.6%	19.2%	47.0%	111	22.8%	4.4%	88	99	89
W-Y.Chen	87.61	91.74	86.37	5.5%	33.9%	20.1%	40.5%	100	19.3%	5.2%	83	104	89
Karns	89.20	92.03	87.87	3.4%	33.1%	21.6%	41.9%	104	23.4%	9.0%	92	102	92
Odorizzi	88.82	92.35	85.90	4.3%	36.4%	22.0%	37.3%	106	21.4%	6.6%	84	90	92
Sabathia	87.86	90.45	86.08	3.1%	29.3%	21.7%	45.9%	99	18.9%	6.9%	118	117	93
E.Rodriguez	87.58	91.39	85.74	4.8%	28.6%	23.5%	43.0%	98	18.8%	7.1%	96	98	93
Miley	87.52	92.13	84.23	2.1%	28.3%	20.8%	48.8%	96	17.7%	7.7%	111	95	95
U.Jimenez	88.07	91.63	86.54	3.5%	25.3%	22.1%	49.1%	104	21.2%	8.6%	102	100	95
Dickey	88.08	91.72	85.02	5.1%	32.2%	20.8%	41.9%	93	14.3%	6.9%	98	112	97
Porcello	88.99	92.36	86.53	1.7%	30.8%	21.8%	45.7%	118	20.2%	5.2%	123	103	101
Buehrle	87.23	92.50	83.94	2.8%	29.9%	21.4%	45.9%	97	11.0%	4.0%	95	106	102
Eovaldi	88.28	90.86	86.79	1.8%	24.2%	21.8%	52.2%	107	18.0%	7.3%	105	85	103
J.Kelly	90.01	91.89	89.68	1.7%	27.7%	25.1%	45.6%	108	18.8%	8.4%	120	104	103
M.Gonzalez	89.56	92.76	86.50	3.4%	32.4%	23.9%	40.3%	107	17.5%	8.2%	122	125	106
Tillman	90.33	91.92	90.16	4.8%	30.5%	21.2%	43.5%	102	16.2%	8.6%	124	111	106
Hutchison	88.39	92.36	85.36	4.4%	32.0%	24.0%	39.6%	123	19.4%	6.6%	139	110	111
AVERAGE	88.59	91.91	86.46	3.6%	30.9%	21.3%	44.2%	102	19.5%	6.8%	102	101	93

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 pitchers’ 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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Previous editions: Arizona / Baltimore / Boston / Chicago AL / Chicago NL / Cincinnati  / Cleveland / Colorado / Detroit / Houston / Kansas City / Los Angeles (AL) / Los Angeles (NL) / Miami / Milwaukee / Minnesota / New York (AL) / New York (NL) / Philadelphia / Pittsburgh / San Diego / San Francisco / Seattle.

Earlier today, lead prospect analyst Dan Farnsworth published his excellently in-depth prospect list for the St. Louis Cardinals. In this companion piece, I look at that same St. Louis farm system through the lens of my recently refined KATOH projection system. The Cardinals have the 15th-best farm system in baseball according to KATOH, and rank second best in terms of pitching.

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