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MLB Daily Games Strategy: Adding ISO To Your Arsenal

Renee Miller

Renee Miller

Neuroscientist at the University of Rochester and author of Cognitive Bias in Fantasy Sports: Is your brain sabotaging your team?. I cover daily fantasy basketball for RotoWire and write for RotoViz about fantasy football.


Adding ISO to your DFS arsenal at CI

I could start every single strategy article I write about MLB DFS the exact same way...frustration with the day to day variance inherent in the game. From the first week of this season, when I committed to playing daily MLB, every day, I've worked hard on refining my personal strategy for building a successful lineup. I've added in various tools, dropped others that were too time consuming and yielded too little reward. I've written about all of this before. The underlying theme is that baseball is just a lot harder to predict than basketball or even football.

Actually, it's not harder to predict. Baseball has the best data available of any of the major sports, and there are several algorithms that argue that they weight all the variables optimally to spit out the best plays of the day, some even digging down to batter and pitcher success against/using specific pitches. That's incredible. So, with tools like those, it's not hard to predict baseball. It's hard to be right.

How often does your most logical, most researched, most justifiable play go 0/4? Half the time? Seems about right to me. And that's crazy. It doesn't matter how much salary cap you expended on said player, how good the circumstances are, he might not score you a single fantasy point. Now that is nearly impossible in NBA, and pretty darn rare in NFL. MLB is the only sport where money doesn't buy you any security, especially for your hitters.

I've tried to let a lot of the frustration go, take it in stride, know that everyone else is in the same boat, etc. That hasn't stopped me from trying to figure out the best way to predict DFS success for hitters. Today I want to focus on the corner infield spots. You typically are hoping for power in the form of homeruns and extra base hits from these two positions. I usually pay up here while saving in middle infield and outfield. So how to make that investment a little more sound?

I took a look at the top 15 DFS 1B and 3B according to fantasy points per game (FPT/G):

POS PLAYER TEAM DD Salary FPT/G
1B Jose Abreu White Sox 12800 18.3
1B Edwin Encarnacion Blue Jays 11650 18.3
1B Paul Goldschmidt Diamondbacks 12500 18.2
1B Miguel Cabrera Tigers 12000 16.5
1B Anthony Rizzo Cubs 11300 16.4
1B Brandon Moss Athletics 10400 15.5
1B Albert Pujols Angels 10900 15.2
1B Freddie Freeman Braves 10200 15.2
1B David Ortiz Red Sox 10100 14.7
1B Steve Pearce Orioles 8450 14.1
1B Michael Cuddyer Rockies 9650 14.1
1B Adam LaRoche Nationals 8500 14.1
1B Mike Napoli Red Sox 9350 13.9
1B Mark Teixeira Yankees 9350 13.6
1B Joey Votto Reds 9400 13.2


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POS PLAYER TEAM DD Salary FPT/G
3B Josh Donaldson Athletics 10100 15.6
3B Anthony Rendon Nationals 10350 15.6
3B Adrian Beltre Rangers 10200 15.4
3B Todd Frazier Reds 10250 15.3
3B Kyle Seager Mariners 9800 14.2
3B Ryan Zimmerman Nationals 10450 13.6
3B Brock Holt Red Sox 9350 13.5
3B Neil Walker Pirates 9550 13.3
3B Lonnie Chisenhall Indians 7600 13.1
3B Conor Gillaspie White Sox 9050 12.8
3B Trevor Plouffe Twins 8250 12.7
3B Aramis Ramirez Brewers 8550 12.6
3B Carlos Santana Indians 9000 12.4
3B Matt Carpenter Cardinals 8650 12.4
3B Casey McGehee Marlins 7350 12.3


Notables who don't make the cut include Adrian Gonzalez, Adam Dunn, Justin Morneau, and Chris Davis at first, David Wright and Evan Longoria at third. As you can see, salary follows DD pts closely.

Next, I went to FanGraphs to get the 2014 season data for these positions (no splits, just full season data for this year) to see what statistics most reliably sorted players into the lists shown above. In other words, I want the player who is going to succeed best in my DFS scoring system, so I want to know what actual baseball statistic of his predicts that. So I sorted first and third basemen on things like HR, SB, RBI, R, wOBA, OPS, and ISO.

Some of my instincts going in:


  • Most DFS sites weight HR extremely heavy in their scoring.
  • Sometimes it's SB that push otherwise similar players apart.
  • A lot of experts and professional DFS players rely on wOBA.


None of these produce a ranked list of corner infielders that approximates the goal, however. I found that the best metric to rank these players on to match the DFS FPT/G ranks was ISO, or isolated power. Here's a link to the FanGraphs excellent description.

First Base:

Name Team HR R RBI SB ISO wOBA
Jose Abreu White Sox 29 51 74 1 0.327 0.405
Edwin Encarnacion Blue Jays 26 57 70 2 0.314 0.411
Brandon Moss Athletics 22 48 67 1 0.261 0.377
Paul Goldschmidt Diamondbacks 18 71 65 8 0.253 0.414
Anthony Rizzo Cubs 23 65 53 2 0.242 0.392
Miguel Cabrera Tigers 14 58 76 1 0.22 0.383
Lucas Duda Mets 14 38 49 2 0.217 0.361
Michael Morse Giants 14 36 47 0 0.213 0.358
Mark Teixeira Yankees 17 39 48 1 0.212 0.339
Adam Dunn White Sox 14 34 39 1 0.209 0.351
Albert Pujols Angels 20 59 65 4 0.205 0.344
Matt Adams Cardinals 12 32 43 3 0.204 0.371
Chris Davis Orioles 16 39 50 2 0.197 0.31
Freddie Freeman Braves 13 65 53 0 0.191 0.373
Justin Morneau Rockies 13 38 60 0 0.189 0.366


Third Base:

Name Team HR R RBI SB ISO wOBA
Kyle Seager Mariners 16 40 64 4 0.216 0.366
Josh Donaldson Athletics 21 65 70 3 0.215 0.338
Todd Frazier Reds 20 58 54 15 0.211 0.371
Adrian Beltre Rangers 14 52 52 1 0.199 0.391
Anthony Rendon Nationals 13 69 53 8 0.196 0.352
Mark Reynolds Brewers 14 34 33 5 0.179 0.303
Lonnie Chisenhall Indians 9 41 41 2 0.179 0.395
Carlos Santana Indians 14 40 40 2 0.176 0.332
Trevor Plouffe Twins 7 43 44 0 0.172 0.323
Pedro Alvarez Pirates 15 41 47 6 0.169 0.322
Luis Valbuena Cubs 5 34 25 0 0.162 0.327
Pablo Sandoval Giants 12 44 43 0 0.161 0.33
Nick Castellanos Tigers 6 30 35 2 0.137 0.314
Matt Dominguez Astros 12 37 43 0 0.136 0.291
Evan Longoria Rays 11 52 48 4 0.132 0.318



As you can see, the top 6 1B are identical when sorted by ISO. For 3B, we get the top 5 correct. While it's certainly not perfect at predicting fantasy points, ISO is a metric I'll be looking at some more. As FanGraphs notes, ISO is not considered predictable for small samples, yet it performed better in this setting than HR, wOBA, or anything else I checked. I also haven't applied any rigorous test of which stat is a better predictor, I'm simply matching lists by hand. Remember that DFS is not the typical context in which sabermetric stats are used. Most people are using these numbers to predict seasonal fantasy output or actual baseball career output.

One way I will try to take advantage of this ISO correlation is to look for players with great pitching matchups, that perhaps have really high (favorable) ISO splits. For example, tonight Jose Abreu vs LHP Chen (ISO vLHP .310) and Adam Jones vs LHP Santiago (ISO vLHP .340) look like strong plays on this statistic alone. Facing RHP tonight, Trout vs Gonzalez (ISO .289), Seager vs deGrom (ISO .272), Smith vs Hendricks (ISO .239), and Duda vs Ramirez (ISO .249) look like good plays. I happen to believe in deGrom, so I will be avoiding Smith and all the Padres as usual.

Predicting one game is always going to be a nightmare, but by adhering to some logical process (see many of my previous MLB DFS Strategy articles), refined over time with new information, we can rest a little bit easier. ISO is one of the new factors I'll be considering when deciding between similar players. Particularly when you're going to be spending top dollar, having one more piece of data to justify a selection is nice. Good luck this week! As always, feel free to comment or question below!