Archive for Meta Analysis

Suppose you think a team is going to improve significantly from last year’s performance. You’re not sure exactly how they’re going to do it, but you’re reasonably confident that they will. If you happen to live in the enlightened state of Nevada or are acquainted with a sports accountant, you can bet the team’s over-under. Is there any way to translate that belief into the acquisition of an undervalued player on draft day?

In forecasting team outcomes, we always like to look at a team’s record in 1-run games in the preceding season. The Elias Sports Bureau long ago discovered that (to simplify but not distort their insight) teams that perform poorly in 1-run games in Season 1 and then have a good record in the spring training games of Season 2 significantly outperform almost everyone’s expectations during Season 2 itself. The teams with the worst 1-run records in the majors last season were the Astros (17-28) and the Reds (22-38). Is there any reason—right now, before any spring training games have been played-to think they’ll do better in 2015? Yes, we think. Read the rest of this entry »

The Steamer and I series continues this week as I move onto starting pitcher comparisons. Today, I check in on two pitchers of whom the Steamer projections are significantly more bearish than my Pod Projections.

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Our quest for deep sleepers continues, and as former academics now seeking the Fantasy Baseball equivalent of tenure, we know the value of using as much of other people’s research as we can get away with.

Thus, first came Robert Arthur, an inhabitant of sabermetric Valhalla by virtue of his ability to combine meticulous and microscopic analysis of baseball stats with enjoyable prose. He demonstrated—we’re dumbing this down a bit, but it’s substantially accurate—that sluggers see fewer fastballs than other hitters. He noted that “fastball frequency normally varies according to the pop of the batter, so that when it changes, it may be indicating a change in the skill level of the same batter.”

Beloved Fangraphs editor Eno Sarris then built upon Arthur’s research. He observed, as Arthur had, that “the more we know about a player’s major-league work, the less powerful a change in fastball percentage is for prediction.” In other words, if we want to use fastball frequency to find players who might start hitting home runs in unexpected clusters, look at guys who don’t have much in the way of established performance. So Eno looked for hitters who (1) had fewer than 800 career plate appearances through the end of 2014, and (2) saw fewer fastballs in the second half of the season. This produced a list of power breakout candidates for 2015: Corey Dickerson, Rougned Odor, Kolten Wong, Nick Castellanos, Travis d’Arnaud, Scooter Gennett, Marwin Gonzalez, Mike Zunnino, Yasmani Grandal, and Jon Singleton.

As Eno points out, this is a group of striplings (average age 23). The thing about young players, though, is that they may be seeing fewer fastballs because pitchers have deduced that they can’t hit anything else, or perhaps even that they can’t hit anything else and swing promiscuously at anything-elses that aren’t in the strike zone. So, we wondered, how can we identify which hitters are true candidates for a power burst? We thought of a study of our own that we introduced in our second Fangraphs post. For those of you who haven’t been taking notes: We looked for hitters who saw fewer strikes and took fewer swings at bad pitches in the second half of the 2014 season, on the theory that this betokens increased wisdom for both the hitters in question and the pitchers who face them.

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Two weeks ago, I wrote about the importance of evaluating expected strikeout rate (xK%) in the context of each pitcher’s respective histories. In other words, xK% on its own can only tell you so much about a pitcher’s chance and magnitude of regression toward the mean.

And last week, I refined the expected walk rate (xBB%) metric for pitchers by adding a proxy for pitch sequencing in the form of percentage of counts that reach 3-0 (“3-0%”). This helped better explain the model’s fit with respect to the data, as pitchers who worked into more 3-0 counts tended to walk more batters. (Who knew?)

The logical next step is to combine the two aforementioned analyses: 1) comparing xBB% to BB% 2) for each pitcher over time. I’ll reiterate a couple of key points. Calculating a pitcher’s xBB% can give us a decent idea of how lucky or unlucky he may have been during a given season. Calculating his xBB% and comparing it to his actual BB% on an annual basis can give us a better idea of truly how he typically performs against his xBB% — that is, if he consistently outperforms his xBB%, perhaps the difference between his xBB% and BB% is not a matter of luck at all but a skill or characteristic not captured by the variables specified in the xBB% equation.
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Friend Eno explored the world of punted statistics yesterday. For those of you not in the know, “punting” occurs when a fantasy owner does not attempt to score in a specific stat. It’s most commonly seen with stolen bases and saves. Eno’s findings confirm that those are the optimal categories to punt.

Eno also discusses when to punt, noting that it’s a much more viable strategy in an H2H league than Roto. He explains the details, so check it out. If you’d like to discuss why H2H and Roto leagues are treated differently, let’s chat in the comments.

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It’s a tale as old as fantasy sports: pay attention to nine of the ten categories on draft day and you can dominate those categories and win. There are some accepted tenets to this strategy, called ‘punting,’ but as it is with all these things we hold for granted, it’s useful to look at what the numbers think of our Conventional Wisdom.

For one, which stats to punt?

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Last week, I posted the first comparison in this new series discussing discrepancies between the Steamer and Pod Projections. I began by looking at Corey Dickerson and A.J. Pollock, two players I was far more optimistic about this season than Steamer is. Today, I’ll look into a pair of sluggers who Steamer likes better than I.

Steamer doesn’t like many hitters better than I do. Of the 328 hitters we both project, Steamer projected at least .001 wOBA points higher than I did for just 83 of them (25.3%). On the other hand, I am more bullish on 232 hitters (70.7%), while we project identical wOBA marks for 13 hitters (4.0%).

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Some updates

• A form has been added for people to added some times. Some players have already been submitted by some readers. Thanks to those helping.
• I have the projected strikeout and walk rates added.
• I have cleaned up the verbiage a little.
• I have 100 scale information for every hitter for each of nine batted ball categories over the past three seasons.

Note: Some people were not able to open the spreadsheet on an Apple. I tried to save the spreadsheet as a .xls, but the embed feature didn’t work. Here is a like to downloadable .xls. I am trying to figure out a solution.

Pitchers’ Approach Attacking Hitters

Robert Arthur at Baseball Prospectus has shown pitchers will change their approach depending on the hitter’s talent level. Here is a complete list of the number of fastballs (including sinkers) thrown to each hitter of the past two years divided into half seasons. Also the number of pitches in the strike by half season is included along with the fastball percentage in the strike zone. I will begin adding 2015 information as it becomes available.

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You know that I develop my own player projections, of which I have lovingly dubbed Pod Projections. With over 500 players projected, I thought it would be interesting to compare them with the Steamer forecasts. So Steamer and I is a new mini-series in which I discuss a pair (maybe more, maybe less) of players who I have projected for dramatically different results than Steamer. To do this, I simply compared my wOBA projections with Steamers’, calculated the difference and then sorted.

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Back to our NFBC slow draft: 15 teams, 50 rounds, no post-draft transactions–a league that journeys beyond the merely deep to the abyssopelagic. Join us now as we relive our plunge into the second half of the draft.

Much of what we did was so boring it defeats all efforts at amusing commentary. We picked up too many high-strikeout no-save relief pitchers (Zach Duke, Shawn Kelley, David Carpenter, Tony Sipp, Shae Simmons[!]); some fourth-outfielder types whom you don’t have to squint at too hard to see decent playing time and some upside (Dayan Viciedo, Rymer Liriano, and [except for the upside] Jordan Schaefer); starting pitchers who could be good (Jose Urena), show signs of being good (Chase Anderson), have been good (Brett Oberholtzer), or whom we don’t actually remember drafting, much less know why we did (Jeremy Guthrie).

But there are a few guys who deserve comment.

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