Archive for SB

Yesterday, I wrote about how sprint speed relates to wOBA minus expected wOBA (wOBA–xwOBA). Today, I summarize my investigation into what factors most readily affect a player’s stolen base success rate (SB%).

This invitation from BatFlip Crazy, embedded in this lengthy Twitter exchange, served as the catalyst for the research. In hindsight, I’m not sure I totally answered the question. Manipulating data from multiple different sources (in this case, Baseball Reference and Baseball Savant) can be exhausting.

I used my final Frankenstein data set, which contained statistics for all players from 2016-18 with at least 100 stolen base opportunities (SBOs) in a given season, to investigate relationships among the following various stolen base metrics:

Read the rest of this entry »

I think there are not one, but many, questions because there are not one, but many, ways Adalberto Mondesi and Byron Buxton are similar.

Here’s one answer to one possible question:

who has the best fantasy season in 2019?

— Alex "Oxlade" Chamberlain (@DolphHauldhagen) August 29, 2018

I can’t say I’m surprised, but I’m kind of surprised. I asked this question very deliberately, its design not remotely accidental, the response options dripping with subtext. Mondesi, with his elite speed, decent power for a speedster, and very questionable contact skills, in 2018 is almost a dead ringer for Buxton in 2017. Mondesi doesn’t quite have Buxton’s baggage — he doesn’t carry the weight of expectations of a No. 1 prospect — but he has his own, continuing a familial legacy. But they do have a lot in common, as aforementioned, which can be summarily boiled down to this great quip from our Eric Longenhagen: “wholly untamed physical abilities.”

Read the rest of this entry »

Edit (3/29/17, 7:55 pm EDT): Brent Hershey of BaseballHQ and Ron Shandler’s Baseball Forecaster (very politely) brought to my attention that this has been done before! By Bill Macey back in 2012. Formerly behind a paywall, it has now been made public for your reading pleasure. I didn’t even know this research existed (so I’m really glad Murphy brought it to my attention); I am always reluctant to ever claim to break ground in this field that progresses so quickly but also has such a rich history of research. Please consider the following research a companion to and external validation of Macey’s work.

* * *

I welcome all constructive criticism. This research is not especially rigorous, but given the nature of the claim — a legitimately significant spring training statistic! — it merits the disclaimer.

I found a statistically significant spring training statistic.

I’d rather not rehash the history of research and speculation regarding The Spring Training Stat(s) That Matter. Just know that, outside the modest results from this Dan Rosenheck piece in The Economist, it’s generally accepted that Spring Training statistics mean virtually nothing, and you’ll read all manners of baseball writers bashing this notion.

The big caveat is most of this research concerns individual players. Mine: team-level statistics. Alas, it’s an inherently different beast with which I’m dealing. Despite small within-year populations (30 teams rather than hundreds of players), the observation-level sample sizes are much larger (hundreds of plate appearances rather than dozens), making the odds of finding meaningful correlations much better despite fewer data points.

Per usual, I buried the lede: a team’s rate of stolen base attempts (calculated from stolen bases [SB] plus caught stealing [CS]) during spring training is actually meaningful. I’ll get to the implications of this later because there are many. First, let’s dig into the guts of the research. I gathered team-level spring training statistics from 2006 through 2016 and paired it with regular season statistics from the same span plus 2005.

Read the rest of this entry »

Playing time can make or break a baseball player’s fantasy value. An elite player may not finish above replacement level if he suffers an injury and plays only half the season, and a lackluster player could finish above replacement level simply by playing every single day. This is all intuitive, and the fantasy community generally approaches these kinds of things rationally. In other words, most players are appropriately valued, outside of the market inefficiencies that inevitably warp player values.

One-dimensional speedsters — dudes who steal a bunch of bases and do little else — are much harder to peg. Their value is tied up primarily in one category, as stolen bases (SBs) do not directly correlate with other categories the way home runs would with runs and RBI, for example. The issue becomes all the more confounding when one considers the contemporaneous scarcity of SBs relative to home runs. There’s more to value than just SBs and plate appearances (PAs), but the fact of the matter is the two statistics by themselves correlate very strongly with a player’s end-of-season (EOS) value (which, here, are informed by Razzball’s Player Rater).

In the last five years, baseball has seen 75 player-seasons of 30-plus SBs — 15 steals a year on average, a trend that didn’t fundamentally change in 2016 (although that doesn’t mean SBs aren’t scarce). A simple linear regression of SBs and PAs, the latter of which serves as a proxy for other counting stats such as runs and RBI, against EOS value produces a remarkable 0.71 adjusted R²:

Read the rest of this entry »

A couple weeks back I provided some (hopefully) useful tables demonstrating how R and RBI are affected by the team’s overall run scoring projection and where the hitter is positioned in the batting order.

At the time I assumed that a similar analysis for stolen bases would be unnecessary. I knew that runs and RBI are largely affected by team context and batting order, but had a feeling that stolen bases were simply a function of player skill. Maybe with a small hint of lineup effect.

Thankfully, our very own Birchwood Brothers questioned that assumption and asked if I had come across any similar research for stolen bases. So here I am to present those findings. Does a player’s position in the batting order affect stolen base frequency? Read the rest of this entry »

Let’s cut straight to the chase. Take a look at the statistical snapshots below:

Obviously, Player 1 is benefiting from a higher batting average on balls in play while Player 2 is getting burned a bit by his. Still, take away their triple-slash lines (but leave the isolated power) and you have two players with almost identical numbers, down to the six steals on eight attempts and the meager isolated powers (ISOs). Where they differ a bit is in plate discipline: Player 1 has a much healthier walk rate than Player 2 and a couple fewer strikeouts. So while Player 1 is benefiting from the a higher BABIP, he can also reasonably be expected to post a marginally higher batting average and noticeably higher on-base percentage. Most importantly, the two hitters are eligible at the same position and are, thus, substitutable.

Read the rest of this entry »

Last week, I identified potential stolen base streamers for daily fantasy leagues and weekly leagues with daily transactions and lineup changes. I used a pitcher’s career caught-stealing and pick-off rates as criteria to determine if a particular matchup was primed for streaming a speedster with the hope of him stealing a base (or two or four).

I like how it turned out, but it felt hastily constructed. A pitcher’s career rate seemed too broad a scope, especially considering the possibility that a pitcher can get better (or, perhaps, worse) at limiting steals and picking off runners over time.

With a little more time and care, I fleshed out everything a bit more and added an additional criterion: catcher effectiveness, which can be most obviously measured by caught-stealing rate. But I think there also is merit to calculating the frequency at which runners attempt to steal on a catcher. In a sense, it measure runners’ perception of a catcher’s skill, especially for those at the tails of the distribution.

Read the rest of this entry »

I’m really not privy to the whole daily fantasy baseball thing, as proctored by FanDuel or DraftKings. It’s probably good that I’m not because I’m 98 percent certain I would immediately fall in love with it.

Still, I’m intrigued, mostly because it takes streaming to the extreme. And I love streaming. It’s a tedious, somewhat painstaking process, what with combing through splits, looking for the juiciest matchups that are also cost-effective.
Read the rest of this entry »