Last week, I began an examination of stolen base rates. The process is messy with too many variables and nuances to consider. I’m examining the information through several different lenses and seeing what applies. Today, I’m going to look at how success rate plays a role.

Team Level Analysis

As sabermetric principles are being utilized more and more by front offices, they quickly came around to the idea that for stolen bases to be helpful, the success rate needs to be high. In 2000, the success rate was 69% for the entire league and it has increased to 73% last season.

Knowing that each team is made of different players and their individual success rate are a factor, here are the three-year success rate along with total stolen base attempt percentage ((CS+SB)/(1B+HBP+BB)).

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In case you missed the announcement in Paul Sporer’s latest post:

FanGraphs now has NFBC ADP data!

NFBC ADP data used to be hosted at Stats, Inc. Prior to last week, 2018 data had only been available to NFBC contestants.

Anticipated FAQs:

Where can I find the data?

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We added the average draft positions for National Fantasy Baseball Championship leagues to our projections pages! Right now only Steamer is up and so you can click here and find the ADP in the last column on the right. Once I was told they were live I thought let’s take this info and use it with the Steamer 600 projection (their normal projection normalized to 600 PA for everyone) to find some potential gems. Essentially, it’s a playing time sleeper list. If these guys were to find 600 PA, Steamer is suggesting they’ve got the skills to shine. I’m looking at players currently being drafted outside the top 200 in NFBC leagues.

Here are 12 names that stood out to me:

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Several years ago Alan Nathan, Jeff Kensrud, Lloyd Smith, and Eric Lang brought an air cannon and a few boxes of brand new baseballs to Minute Maid Park. If you’re anything like me, you like where this is going. They set up their cannon to fire balls roughly 96mph on a 28° angle and used Trackman to measure their distance and spin rate. They tested four groups of balls, two groups composed of MLB balls, one MiLB, and one NCAA. One group of MLB balls, group A, were tested using reasonably low spin rates, about 1800. The other, group B, had variable spin rates, ranging from 2100 to 3300. The results of their study were published in an article titled  How Far Did That Fly Ball Travel (Redux)? on Baseball Prospectus, although it can also be found here. I encourage you to read the piece, but today I want to focus on the MLB-A and MiLB groups.

The major league ball traveled 28 feet further than the minor league ball. Albeit with a higher spin rate. Presumably, the higher spin rate should translate to increased distance, but it is difficult to imagine that a difference of 200 rpm could bridge a gap of 28 feet. More on this in a moment. Read the rest of this entry »

Earlier this month, I asked our readers for any aspects of the fantasy game which are missing. Okra stepped up and said:

“I feel like we still do a poor job of predicting stole bases. I think we could better utilize the new Sprint Speed data and speed scores to predict SBs. Taking it one step further would be to try and quantify each managers propensity for SB attempts.”

This statement is 100% true. We really don’t know which measurable factors fantasy owners should focus on when looking for stolen base breakouts. I’ve gone ahead and dived into the topic of just the manager influence with positive results.

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As somewhat of an ode to the will-be-missed Mr. Swydan, I want take a page out of Paul’s book to highlight a number of players I’m excited to watch in 2018 (this is part one).

Freddie Freeman

Having fun with some random comparisons, Justin Smoak finished a terrific 2017 with a career-high .371 wOBA (.270/.355/.529).  Freddie Freeman also finished the summer with a .371 wOBA (.291/.378/.513)…after spending six weeks on the DL with a broken wrist.  Before that DL stint, Freeman was arguably the best hitter in baseball, slashing .341/.461/.748 (.485 wOBA) over the first six weeks of the season.  He was essentially unstoppable during that stretch, and at the age of 28, armed with one of the most consistent batted ball profiles in the game and a full season of health (plus 3B eligibility in some leagues), Freeman has all the ingredients for a truly special season in 2018.

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I think at the crux of it, the question is asking whether people [call them “the market”] are adjusting middle infielders’ values upwards artificially as compared to other positions.  But another way to ask this (from a fantasy baseball drafting perspective), is perhaps:

“Due to the market’s perceived value of the scarcity in the Middle Infield (MI) position, are other positions better valued at the draft table?”

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“… pros were more likely to ride a wave of irrational exuberance than to fight it. One reason is that it is risky to be a contrarian. ‘Worldly wisdom teaches that it is better for reputation to fail conventionally than to succeed unconventionally’” – Richard Thaler in Misbehaving

At the root level, fantasy baseball is about acquiring more undervalued assists than your opponents. Everyone wants a first-round talent for a last round price (e.g. Aaron Judge). With teams clamoring to acquire every advantage, they are insistent on wasting away an early draft advantage. In early 2018 drafts I’ve participated in, an early emphasis on middle infielders is inflating their value way beyond their projected production. Is the observation wrong? If so why? If not, how can an owner take advantage of this mispricing?

Note: For this article, I will lump second basemen and shortstops together into one middle infield position. Neither position has more talent than the other and the bottom players will be used to fill a middle infield position.

For those who have recently created mixed-league valuations, positional scarcity doesn’t exist besides with catcher. I use the method outlined in Larry Schechter’s book, Winning Fantasy Baseball to determine my values. I’m not going to go into the process’s exact details but it’s the standard procedure used by fantasy experts to prep for auctions. Even a couple years ago a small amount of positional scarcity existed but a huge influx of good middle infielders has raised the group’s overall talent level up to the other positions.

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Jonathan Villar burst onto the scene in 2016 with 19 HR and 62 steals. He was going 19th overall in 2017 NFBC drafts and was a huge disappointment when he hit posted a .293 OBP, 11 HR, and 23 SB. With 2018 drafts starting, I’ve seen his ADP (200) way below where he should go using his Steamer projection (~118). The disconnect is understandable but not to the current level. Opportunity exists for huge upside.

Just for reference, here are Villar’s basic stats from the past four seasons and his 2018 Steamer projection.

Of all the values which changed his value from 2016 to 2017 was the 76-percentage point drop in OBP. Less times on base meant fewer steals and runs scored. An OBP under .300 is kill for any hitter.

Besides the scoreboard stats, here are his 2nd order stats over the same time frame.

His plate discipline is the biggest discrepancy over the past two season seasons with his K%-BB% jumping from 14% to 23%. Even though he maintained similar power and groundball rates, the overall decline in contact rate while swinging more did him in.
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In leagues with Runs and RBIs as categories, predicting how a player’s mix will change with a new team can be guessing. Some clown at Fantrax yesterday wrote the following:

“As for Headley, his value drops by going to a worse offensive team in a pitcher-friendly park. Part of the decline could be offset by a move up in the lineup since he mainly batted seventh for the Yankees last season.”

It could go up, it could go down, who really knows? While writing the statement, I needed a better answer so I created a couple quick and simple tool. If an owner can estimate a few stats, they can predict changes in plate appearances, Runs, RBI when a hitter moves from one team to another.

The key was to be simple and quick. For simplicity, only the following stats are needed.

• New likely lineup location
• Estimate of projected home runs
• Estimated games played such 150 out of 162 games as a percentage.
• Estimated Runs scored by a team. Used over on-base percentage because team level runs scored is easier to find and remember.

The estimated runs scored is the toughest value to come up with. I’d just go to FanGraphs team projection page to get a decent idea. Just take that year’s RS/G and multiply it by 162. Another method is to take the previous season value and plug it into the following regression equation:

`RS in Y2` = .575 * `RS in Y1` + 311

The goal is just to get a basic idea of possible changes.

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