Daily Fantasy Strategy — June 29 — For Draftstreet

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June 29, 2014

There are a lot of different ways to evaluate bad offenses, which we’ve done a handful of times in this space of later. Simply grabbing a team’s wOBA against a certain handedness of pitcher is a quick and easy way. We’ve also looked at the average fantasy score of an opposing pitcher against that offense, as well as the distribution of those scores to get a better idea of the boom-or-bust nature of an offense.

Today, we’ll look at one other way to skin that cat, one that focuses entirely on the ultimate upside of a match-up.

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If we’re talking about the upside of streaming against a team, looking at the top pitching performances can be instructive. So I took the top 600 performances of the season (by game score) so far, and looked at which opponents produced the most high-end outings. The average fantasy total for these 600 games was 13.08 points and the average game score was 67.22, with a .725 R-squared between the two.

Here are the opponents who come up most often:

TEAM #
SDP 31
CHW 29
ATL 28
MIA 28
PHI 27
HOU 26
CIN 24
MIN 24
WSN 23
BOS 23
ARI 22
SEA 22
SFG 21
CHC 20
NYM 20
PIT 20
BAL 19
TEX 19
COL 18
MIL 18
DET 17
STL 17
TOR 16
NYY 15
LAA 14
LAD 14
CLE 14
KCR 11
OAK 10
TBR 9

So once again, pick on the Padres, and don’t expect to make hay against the A’s.

The Daily Five
Mike Bolsinger – $11,071
Look, I’m not a big Bolsinger fan, but Steamer likes him for a 19.3 percent strikeout rate the rest of the season, he does a terrific job keeping the ball on the ground and he’s got the Padres, who are basically playing The Show with the difficulty cranked all the way up and the controller out of batteries.

Jose Quintana – $11,410
The White Sox were lucky to trot out three straight lefties against the Jays in this series, and over the last two games the blue birds have actually surprised with a total of seven runs. Why is that a surprise? Well, on Friday their four-through-nine was Dioner Navarro, Brad Glenn, Steve Tolleson, Munenori Kawasaki, Anthony Gose and Josh Thole, and on Saturday it was Navarro, Glenn, Tolleson, Colby Rasmus, Darin Mastroianni and Kawasaki. This is a team that ranks ninth in wOBA against lefties overall thanks to the long ball but without Jose Bautista has just two above-average bats against lefties. Quinatana is a lefty.

Rays stack – The Rays offense hasn’t been great, ranking just 18th in wOBA on the year, 17th over the past 30 days and 20th against righties. It’s been enough to depress prices, even against a homer-prone pitcher like Miguel Gonzalez. The Rays are slight favorites at Camden, a favorable park, in a game with an 8.5 over-under.
Matt Joyce – $4,841
Ryan Hanigan – $5,768
James Loney – $6,578
Evan Longoria – $7,047

This post, covering one of the leading sites for daily fantasy, is sponsored and made possible by the generous support of Draftstreet. FanGraphs maintains complete editorial control of the postings, and brings you these posts in a continued desire to provide the best analytical information on the latest in baseball.





 

Blake Murphy is a freelance sportswriter based out of Toronto. Formerly of the Score, he's the managing editor at Raptors Republic and frequently pops up at Sportsnet, Vice, and around here. Follow him on Twitter @BlakeMurphyODC.

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napster
9 years ago

I am a statistics teacher. I hope you realize than if you select only the top 600, and remove from the population a sample that is only from the ranked top (removing the lower end) you are creating bias.

— because people are selecting from the entire population — not just the top 600 outcomes !!!!

You cannot eliminate elements of the sample space and have reliable inferences about the population, when the sample IS NOT RANDOM, because if you take only the top 600 OUTCOMES, not every pitcher is equally likely to be selected from the population of pitchers before the games actually got pitched.

No one could not predict the outcomes before the selection, so removing them after the fact is biasing the data based upon the outcomes.

Also, the R squared that you obtain is based upon outcomes, not selection. You aren’t comparing the expected (pre-game) versus the observed (outcome, after-game) data at all. Measuring the Game score versus the Fantasy points are still measuring only the OUTCOMES, and those two groups are not orthogonal data sets ANYWAY (ie., not independent) because they are really just two different ways of measuring the same stat outcomes.

This is like measuring all of the A and B students in an AP Calculus class, and excluding the rest. Except that pitchers are more variable than students, so that a lot more of the “pre-game” pitchers would have been expected to get A’s and B’s (or D’s and F’s) that with students taking a class.

This is parlour statistics, mate. But jolly good show. Hat tip for the effort though.

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Blake Murphymember
9 years ago
Reply to  napster

I think you misunderstood the point.

“ee’ll look at one other way to skin that cat, one that focuses entirely on the ultimate upside of a match-up.”

It was entirely just to see which teams are the victims of the highest-output games. It was not a statistical study. It was counting.

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Blake Murphymember
9 years ago
Reply to  napster

I get I could have used just correlation instead of R2, but I was very clear that this was nothing but seeing which teams are victim to the highest-output games. Quite an overreaction to counting.

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Kris Gardham
9 years ago
Reply to  Blake Murphy

To the second point, regarding your r-squared comment, I think has to do more with your sentence structure than your math. It’s not exactly clear what you’re measuring. An r2 on gamescore/fpts, as the commenter points out, really doesn’t tell us anything. An r2 of 0.725 of opposition vs. fpts is kind of a big deal, a big deal that should be an entire article. I remember reading something similar to this (maybe even on fangraphs!) but I’d read a fantasy perspective on it.

If that’s the case, it’d be interesting to see what starter vs. fpts would account for. In which case, we’re back to the first point: We need all the starts.

So anyways, you two should play nice with each other and build a streaming model. Once completed, deconstruct the model and write a pretty little article.

I wouldn’t mind, personally, if you ran the model with both the full pool and spit-balled it with *streaming options* or under arbitrary percent owned options.

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