Full-season team projections cause some heated arguments. If a team finishes the year with fewer wins than expected, fans want to know why their club underperformed projections. If a team overperforms its projections, meanwhile, those same fans will insist that forecasts in subsequent years lack the ability to detect their club’s particular strengths and are thus useless.

Here at FanGraphs, we have only been doing full-season projections for a couple years, but just about every week I see a mention of the 2015 World Champion Kansas City Royals’ projected record of 79-83. If I search Google for “79-83 Royals FanGraphs,” I get over 11,000 article links. Unsurprisingly, it’s a popular topic. Rarely does a club, following a pair of World Series appearances, then proceed to fail to break even. But that’s what the numbers suggest for 2016.

While FanGraphs has produced team win projections for only a couple seasons, Replacement Level Yankee Weblog (RLYW) has been publishing win projections for years. Since 2007, to be precise. Given this larger sample, I thought that it might be worthwhile to compare the projected win values produced by RLYW to the actual final win values produced by teams. So, with the permission of RLYW editor SG, that’s what I’ve done here.

I hate to disappoint anyone, but there are actually aren’t any great findings in the plethora of graphs to follow. I did find a couple interesting artifacts of the data, but no game changers. Instead, I see the following mainly as an additional data point in many past, present, and future discussions.

To start with, here is how projected and actual values have correlated.

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Deception seems impossible to quantify. How are you going to put a number on something like the Invisiball thrown by Yusmeiro Petit? The ball is there, and then it isn’t, and then it’s back! Put that in your number machine, nerd boy.

Except, hidden in that description of the Invisiball is a possible guide. The release point. The release point is a huge moment for the hitter — it can tell him about the type of pitch and type of movement he’s about to see even before the ball is released.

Think of Brad Ziegler. You see that arm dragging along the ground, and you know the ball is going to move down and away from you. You adjust. Think of Darren O’Day. You see that arm slot, and you think the ball will drop, and when it doesn’t, you miss his Submarine Riseball. Tyler Clippard! You see that over the top and you think the ball will rise — and his fastball does, but not his changeup.

So maybe deception is deviation from the expected movement given a pitcher’s arm slot. Or at least that’s maybe one sort of deception. It probably won’t help us understand Petit’s Invisiball any better, but it may give us something with which to work on this hard-to-define aspect of pitching.

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Over the past year, we have posted 32 different job postings from 20 different Major League Baseball teams and 15 job postings from TrackMan, Baseball Information Solutions, Inside Edge, STATS Inc, TruMedia, Wasserman Media Group and the Sydney Blue Sox. At Paul Swydan’s suggestion, I created word clouds to summarize these postings. These give a quick overview of what those jobs entail and the required qualifications. For those not familiar with the research and data science side of baseball, I’ll explain a few of the software tools which are prominent in the job postings and can be found in the word cloud.

To make the word cloud, I collected all the pieces we’ve published since January 2015 that contained “Job Posting” in the title. I separated the text content of each post into two different categories: job description and qualifications. From there, I took those two documents into R and used the tm package to clean the text, removing punctuation and unnecessary words like articles and prepositions. The package also tabulated the words. Additionally, I removed some other words like baseball, experience and strong. These words occurred frequently in the posts, but they were either obvious or not helpful. Then with the processed text data, I constructed the graphic using the aptly named wordcloud package. If you are unfamiliar with word clouds, larger words indicate that the specific word was found more often in the job postings.

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Two years back, after Zach Britton emerged as an effective closer for the Orioles, he drew a lot of attention for his sinker. At the time, I used PITCHf/x information to try to find some similar sinkers. For the most part it was a forgettable post — most of these are forgettable posts — but there was one thing that stuck with me. One name I haven’t been able to wipe from my mind.

It’s even more interesting now, with Britton having graduated into the class of the elites. You might not yet recognize Britton as an elite reliever, but he for sure most recently was an elite reliever, again driven almost exclusively by his fastball, which he threw 90% of the time. Compared to the year before, Britton generated way more strikeouts. Compared to the year before, Britton trimmed his number of walks. And compared to the year before, Britton somehow increased his grounder rate, which was already absurd.

You look at what Britton did, and you see that he did it mostly with one pitch, and you realize, hey, that’s one hell of a pitch. Wouldn’t you be interested in knowing there’s someone out there who throws an almost identical pitch? It’s time to get to know Blake Treinen. Blake Treinen pitches for the Nationals, and Blake Treinen throws the Zach Britton sinker.

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The Astros vaulted themselves into the playoff hunt on the backs of their young talent and smart free agent buys. Now that many of those players will be staying with the major league club, you might expect the system to be relatively barren. In fact, however, it is littered with mid- to upper-range prospects that could help as soon as this year. Though Houston has decent quality and quantity of prospects, the most interesting thing about this system is how much turnover it has seen since last offseason. Only five of the top-15 prospects from Kiley’s list last year remain eligible for this year’s list, the rest gone via trade or promotion.

One of the joys of scouting Astros prospects is the run environments of their High-A and Triple-A affiliates. Many of the top prospects in the system – AJ Reed, Derek Fisher, JD Davis, Francis Martes, etc. – spent all or or part of their 2015 seasons in High-A Lancaster, quite possibly the most hitter-friendly park in the country. That fact makes it a fun task to figure out whose skills have improved versus whose have been artificially elevated by an extreme run environment. For many of them, evaluators almost have to ignore their High-A production and wait to see them in Double-A. I have to admit, Fisher fits into that category for me somewhat. It also makes Martes’ stat line look even more impressive.

For these rankings, there aren’t too many eye-opening choices for the upper spots on the list. I have AJ Reed in the number-one spot, believing his offensive ability to be strong enough to make him a more productive big-league player despite his defensive position. Besides Tyler White and Jon Kemmer jumping up this list for their potential at the plate, there’s Kyle Tucker and Derek Fisher sliding down just outside of the 50+ FV group. I don’t feel completely confident in my evaluation of Fisher yet, and Tucker is too young and raw for me to be comfortable ranking him ahead of the others above him.

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Episode 632
Dave Cameron is both (a) the managing editor of FanGraphs and (b) the guest on this particular edition of FanGraphs Audio, during which edition he examines the similarities and differences between home-buying and free-agent signing, discusses the Chris Davis signing in the context of the economic concept of substitutability, and also addresses Bryce Harper’s awareness of his own value on the open market.

This edition of the program is sponsored by Draft, the first truly mobile fantasy sports app. Compete directly against idiot host Carson Cistulli by clicking here.

Don’t hesitate to direct pod-related correspondence to @cistulli on Twitter.

You can subscribe to the podcast via iTunes or other feeder things.

Audio after the jump. (Approximately 45 min play time.)

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This time of year, everyone’s a contender. So everyone’s talking about the things they can do in 2016, and the A’s are no different, highlighting their improved bullpen and increased power. Just last week they picked up Khris Davis, and when I was reading about that move, team officials noted that Davis will provide critical right-handed pop, along with Danny Valencia. Just from reading that sentence, you know two things: (1) the A’s won’t be anyone’s AL West favorites, and (2) Valencia has won himself some organizational fans.

It’s not as if Valencia has been hurting for chances, as teams have long recognized his ability to punish left-handed pitchers who dare enter the strike zone. Valencia has been treated as one of those useful players good enough to have but not good enough to keep. He debuted in 2010, and Oakland is his sixth major-league team, having been claimed off waivers from the Blue Jays in August. The Blue Jays had themselves a roster crunch, and they weren’t buying the initial evidence that Valencia had made himself more whole. You can understand why Toronto wound up doing what they did, but Oakland seized the opportunity and now they seem to have themselves an asset. The A’s could afford to see how real Valencia really was. All he did was conquer his biggest problem.

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As we count down the days until spring-training games begin, let’s continue to take a position-by-position look back at the ball-in-play (BIP) profiles of 2015 semi-regulars and regulars to see if we can find any clues as to their projected performance moving forward. Last time, we reviewed first basemen and designated hitters; today, let’s take a look at second basemen.

First, some ground rules. To come up with an overall player population roughly equal to one player per team per position, the minimum number of batted balls with Statcast readings was set at 164. Players were listed at the position at which they played the most games. There is more than one player per team at some positions and less at others, like catcher and DH. Players are listed in descending OPS+ order. Without further ado, let’s kick it off with AL second sackers.

BIP Overview – AL Second Basemen

Name	Avg MPH	FB/LD MPH	GB MPH	POP%	FLY%	LD%	GB%	CON	K%	BB%	OPS+	Pull%	Cent%	Opp%
Forsythe	89.22	92.10	87.23	3.2%	37.4%	19.8%	39.6%	115	18.0%	8.9%	123	39.1%	36.3%	24.6%
Altuve	86.29	90.26	83.92	3.0%	32.2%	18.1%	46.7%	104	9.7%	4.8%	122	45.3%	35.5%	19.1%
Kipnis	89.88	92.58	87.37	1.9%	26.2%	26.8%	45.0%	119	16.7%	8.9%	121	35.3%	36.1%	28.6%
Zobrist	89.05	92.22	86.89	3.6%	28.8%	18.6%	49.0%	93	10.5%	11.6%	120	45.7%	31.7%	22.6%
Cano	90.88	94.74	88.06	1.1%	24.2%	24.2%	50.5%	110	15.9%	6.4%	118	36.3%	41.1%	22.7%
Kinsler	86.38	88.29	84.69	5.2%	35.5%	25.4%	33.9%	98	11.9%	6.4%	113	41.9%	34.1%	24.0%
Pedroia	88.47	91.77	86.27	4.5%	27.3%	17.7%	50.5%	100	12.0%	8.9%	113	40.1%	38.6%	21.3%
Schoop	90.79	94.36	88.89	5.3%	32.4%	19.3%	43.0%	142	24.6%	2.8%	110	43.2%	31.0%	25.8%
Odor	88.44	92.93	86.35	7.6%	32.1%	14.6%	45.8%	105	16.8%	4.9%	107	46.9%	31.6%	21.5%
Dozier	87.51	92.32	80.34	8.7%	35.4%	22.6%	33.3%	104	21.0%	8.7%	101	60.2%	24.2%	15.6%
Holt	86.65	88.85	85.46	1.1%	22.4%	23.8%	52.7%	100	19.1%	9.0%	96	33.4%	40.1%	26.5%
Giavotella	85.62	87.87	83.78	3.0%	27.5%	23.7%	45.8%	75	11.8%	6.4%	96	36.7%	38.6%	24.7%
Goins	87.36	89.16	86.62	3.1%	24.8%	18.0%	54.1%	81	19.4%	9.1%	86	34.1%	36.4%	29.5%
Drew	86.53	88.56	84.51	6.1%	40.5%	15.7%	37.7%	66	16.6%	8.6%	78	47.0%	33.2%	19.8%
Sanchez	86.07	89.15	84.31	2.0%	21.1%	22.8%	54.1%	67	19.3%	4.5%	66	30.8%	36.5%	32.7%
Sogard	84.52	86.71	84.17	3.1%	30.5%	22.0%	44.3%	60	12.5%	5.7%	66	35.6%	39.9%	24.5%
Infante	84.41	86.88	83.75	3.8%	33.9%	21.0%	41.2%	57	15.2%	2.0%	49	43.5%	34.5%	22.0%
AVG	87.53	90.51	85.45	3.9%	30.1%	20.8%	45.1%	94	15.9%	6.9%	99	40.9%	35.3%	23.9%

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 BIP by field sector (pull, central, opposite). Each player’s OPS and Unadjusted Contact Score (CON) is also listed. For those of you who have not read my articles on the topic, Contact Score is derived by removing Ks and BBs from hitters’ batting lines, assigning run values to all other events, and comparing them to a league average of 100.

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