Welcome to Smart Fantasy Baseball

Welcome to Smart Fantasy Baseball

Welcome to Smart Fantasy Baseball.  The goal of this site is simple – to make you a smarter, better, more knowledgeable fantasy baseball player.  

If thinking about strategy, doing your own player analysis, creating your own projections, developing your own rankings, and diving into spreadsheets of baseball data are your thing, then I think you’ll enjoy the site.  Take a few minutes to look around.  If you like what you see and want more, register for the Smart Fantasy Baseball Newsletter and you’ll get instant access to two free e-books.

Listenings & Readings – Week Ending February 10th, 2013

I started keeping better track of what I’m reading…  A lot more content this week.


  • Baseball Prospectus “Towers of Power Fantasy Hour” with Jason Collette and Paul Sporer had their first base preview episode.  One main point they reiterated several times is that staying healthy and being in the lineup every day is a skill.  Keep this in mind when you’re looking over projections.  Players like Prince Fielder and Billy Butler who play 155+ games year-in and year-out are locked in production.


  • CBSSports.com released a 2013 fantasy outlook for each MLB team.  Each outlook includes a summary of offseason transactions, the projected starting lineup/batting order, projected rotation, a player to beware of, a breakout candidate, and some prospects to be aware of.  There is A LOT to read here.
  • Keith Law released an updated Top 100 prospects list (requires ESPN Insider).  For reference, Mike Trout, Bryce Harper, Matt Moore, and Manny Machado were the top four last year.  Those guys helped some fantasy teams last year.  You need to be familiar with the top names on this year’s list.

Baseball, But Not Fantasy Baseball Related

Geeky and Fantasy Baseball Related

  • I’m overwhelmed by but very interested in this series “Saberizing a Mac“, which takes you from ground zero to creating a database, obtaining data, and running baseball database queries from your mac.  

Understanding DIPS and FIP

Defense Independent Pitching Statistics (DIPS)

“There is little if any difference among major-league pitchers in their ability to prevent hits on balls hit in the field of play.” – Voros McCracken, Pitching and Defense

McCracken’s article mentioned above was extremely influential in pioneering a new wave of baseball statistics.  McCracken began the process of separating pitching statistics from the defensive players behind the pitcher.  The question being, “Can we measure the effectiveness of a pitcher by using statistics that only a pitcher can control?”.

In attempting to answer this question, McCracken created Defense Independent Pitching Statistics, or “DIPS”.  A key finding in McCracken’s work is that a pitcher’s walk rate, strikeout rate, and home run rates were somewhat consistent from year-to-year, while BABIP was not.

If a player can consistently maintain walk rates, strikeout rates, and home run rates, any fluctuation in statistics like ERA or BABIP must be influenced by defense and luck, which are factors outside a pitcher’s control.

With this in mind, let’s examine the five possible outcomes for a given pitcher vs. batter plate appearance:

  1. Ball hit into play for a hit
  2. Ball hit into play for an out
  3. Home run
  4. Strike out
  5. Walk (or HBP)

Of these categories, items one and two are clearly dependent upon defensive players and luck (is the frozen rope hit directly at the third basemen or six inches out of his reach?).  Items three, four and five are completely independent of defensive players.  And while some luck is involved in home run rate, the pitcher’s skill is a factor as well (some pitchers give up a lot of home runs, some can prevent them).

That’s where FIP comes in.  No not that FIP.  This one.

Fielding Independent Pitching (FIP)

FIP, developed by Tom Tango, attempts to evaluate pitchers only on factors under their control.  Or independent of fielding.  Tango’s calculation uses the measures that are significantly within a pitcher’s control (HR, BB, K) to approximate what the pitcher’s ERA “should” be.  FIP is an easy stat to use and calculate because it has a simple calculation:

FIP = (13 * HR + 3 * BB – 2 * K) / IP + 3.20

The addition of 3.20 is to more closely align FIP with ERA.  Otherwise you end up with numbers like 0.50 or 0.77.

FIP turns out to be an incredible predictor of ERA (check out this analysis of the top 10 ERA and FIP leaders since 1962 by Tom Tango).

Is FIP Always an Accurate Measure of ERA?

No.  In an individual season, ERA and FIP can differ significantly (up to 1.00).  Further, some pitchers display a perpetual difference between ERA and FIP.  For example, Zack Greinke has a career ERA of 3.77 and a career FIP of 3.45 (his actual results are worse than expected).  While Mark Buehrle has a career ERA of 3.82 and a career FIP of 4.14 (better than expected).

A significant difference between ERA and FIP over the course of a lengthy career suggests other factors at play that FIP does not account for.  Perhaps there is some intangible quality that Grienke does not possess that leads him to have an ERA greater than his FIP.  Maybe Mark Buehrle has this quality and it allows him to regularly outperform his FIP projections.

How Do I Apply FIP to Fantasy Baseball?

Granted, this Harball Times article is from 2005.  But the results are impressive.  Of the 22 pitchers whose ERA exceeded their FIP the most, 18 saw their ERA decline the next year (and two didn’t even play!).  Of the 30 whose ERA was lower than FIP, 23 saw their actual ERA increase.  Applying this, we can look for pitchers whose FIP varied greatly from actual ERA to identify candidates likely to improve upon last year’s ERA or to identify those likely due for an increase in ERA.

What Do You Think?

Please leave your comments below.  Have you added FIP to your repertoire yet?

Thanks for reading.


Tom Tango, the creator of FIP, is also well known for The Book: Playing the Percentages in Baseball. This is recommended reading if you’re looking to understand optimal baseball strategy.


Listenings & Readings – Week Ending February 3rd, 2013


I listened to more Baseball Prospectus “Towers of Power Fantasy Hour” with Jason Collette and Paul Sporer. I didn’t pay very close attention to baseball during the “Hot Stove” season, so listening to the archives of this podcast have brought me back up to speed.

This week I listened to the following episodes:

I also gave the Fantasy Pros 911 podcast at try, but could not get into it. I just didn’t find the experts as insightful or entertaining as Sporer and Collette. It seemed like a lot of “I think” or “I feel” analysis; whereas Sporer and Collette seem to lead with facts and statistics. Facts + Statistics = Smart.


I can’t remember how I stumbled across these, but I ended up reading some old articles that sounded interesting to me:

Get your thinking cap on for this one.  An article by Mike Podhorzer of fangraphs.com to predict HR/FB rate.  There are a few layers to this article I’m going to have to peel into someday.  One interesting layer is a tool here to see the average distance a player’s batted balls travel.  Or this tool to see the average flyball distance leaders for a given year.  Both of these are from baseballheatmaps.com.  There’s not an official conclusion, but the article has some data to suggest that Chase Headley’s 2012 season may not be a fluke.

What did you listen to or read this week?

Let us all know in the comments below.

Listenings & Readings – Week Ending January 27th, 2013


I’ve done more listening than reading lately, and have recently discovered the Baseball Prospectus “Towers of Power Fantasy Hour” with Jason Collette and Paul Sporer.  While the name sounds a bit goofy, it really is an all-business podcast.  I have long been listening to ESPN’s Fantasy Focus Baseball podcast, and while I find Matthew Berry and Nate Ravitz entertaining, I find Collette and Sporer more insightful.  The weekly episodes are lengthy (average about two hours), but are packed with “smart” content.  Another bonus is that they have been releasing new podcasts all off-season.  So there is a lot of good listening available.

Collette writes for Baseball Prospectus and Rotowire.  You may be familiar with Sporer’s annual  “Starting Pitching Guide“.

I really liked episode 29(iTunes, other).  The guys were joined by Todd Zola of Mastersball.com and talked about draft strategy, player valuation, and developing your own dollar values.


I’m dabbling with the idea of creating my own projections, and would at least like to improve my understanding of how others develop their projections.  Mastersball gives an overview of their process here.

I’m also perusing the Sporting News Fantasy Source Baseball 2013 magazine. Yes, I know. Using a static magazine that was written in December is not necessarily “smart”. But the values in the magazine are used as the player salaries in my favorite league.

What did you listen to or read this week?  Do tell.

Do You Know What It Takes To Win Your League?

Even a blind squirrel occasionally finds a nut.  Do you really know what it takes to win your league?  Or are you just blindly drafting your team?

This may seem a little basic, but if I had to guess, I would say this is a simple exercise most fantasy owners don’t do.  Before the draft, do you know (or at least have an estimate of) what it’s going to take to win the league?  And I’m not talking generically.  I mean, do you know how many rotisserie points you’ll need to accumulate to win it all?  Do you have an idea of the total HRs or Ks you’ll need?

How Can Anyone Know That?

Maybe one can’t “know”.  Each season is unique.  But you can do some research to develop a strong estimate.  And once you have the end goal in mind, you can then use projections and average draft positions (ADPs)/auction dollar values to reverse engineer a winning team.

Instead of blindly drafting a team, you can strategically build a team to win.

It helps if you have a recurring league with some established history and preferably not a lot of manager turnover.  If you play in public leagues on large websites (Yahoo!, ESPN, etc.), you may be able to find estimates as well.

OK.  I Have a Recurring League.  HOW DO I START?

Access you historic league standings and start a spreadsheet to track what it has historically taken to win the league.  You can use my example here.

I track these two things:

  1. Final rotisserie points by year for each team
  2. Final statistics by team for each league roto category (e.g. final BA, HR, W, K)

The following example is for a 12-team league with 23 man rosters, using standard 5×5 rotisserie scoring and players from the AL and NL.

 1.  Final Rotisserie Points by Year for Each Team

This is the first 15 rows on the example spreadsheet.

This historical data is then used to create an average.  This calculation will yield the estimated total points needed to come in 1st place.  There are several ways you can calculate an average in Excel, but I use the following formula:

=AVERAGE(B4, F4, J4, N4, R4, V4, Z4, AD4)


xBABIP? Let’s Start Putting Random Letters in Front of Statistics!

In our discussion of BABIP, we mentioned that it’s frequently misused or misinterpreted.  An example of its misuse might be, “Miguel Cabrera’s BABIP was .331 in 2012.  He’s due for a drop in production as we expect his BABIP comes back down to the league average of about .300″.  That’s where the “x” comes into xBABIP, or Expected Batting Average on Balls in Play.

What Do We Use xbabip for?

xBABIP is a projection of what a given player’s BABIP will/should be.  It’s not a measure of past performance like BABIP.  So we can use it to project a player’s statistics for the year.

Should we expect all hitters to have a BABIP of around .300?

No.  As you know, there are many different types of hitters.  Even without statistics to support the argument, you would probably expect a hitter with power to have a different BABIP than a hitter with little power.  You’d expect a hitter that tends to hit more ground balls and line drives to have a greater BABIP than a fly ball hitter (a fly ball that stays in play has a low chance of being a hit).

How Is xbabip calculated?

This is a difficult question to answer.  As far as I can tell, because this is a projection, there is no definitive calculation.  This article at the Hardball Times may be the first to reference the phrase “xBABIP”.

The harder I look for an agreed upon calculation, the more variations I find.  In my not-so-expert opinion, I see two ways to calculate without needing a degree in statistics:

  1. Use a player’s historical BABIP to project future BABIP
  2. Break down batted ball data into categories of ground balls, line drives, and fly balls (some will then further break fly balls into infield fly balls and outfield fly balls). (more…)
Using the Site

Using the Site

One of the main goals of this site is to provide you with access to usable statistics and other data.  In order to do that, many posts will include embedded Microsoft Excel files.  These files are hosted in the cloud using Microsoft’s SkyDrive service.

You’re welcome to view any of the spreadsheets (if you can make sense of them outside the context of a post) using this link.

Alternatively, you will have several options to view, download, or interact with the spreadsheets within a given post.

Each embedded Excel file will have the Excel Web App footer shown below.  Clicking on the Excel icon will allow you to download the file to your computer for you to manipulate (you’ll likely need Microsoft Excel 2007 or greater installed for the file to open).  Clicking the rightmost icon will open the Excel file in the Excel Web App.  You don’t need Excel installed to use this, but please note that many features of Excel are not available in the Web App.


Additionally, if you see downward pointing triangles in a spreadsheet, you can sort that data by any column or apply a filter if you’d like to search for specific data (like a certain player or players with statistics over a certain criteria).


It’s my hope that having access to the data and being able to interact with it will help make you a smarter fantasy baseball player.  Leave a comment below or use the contact page if you have any ideas on how to make data more accessible or how to allow more interaction with statistics on the site.

Thanks for reading, and make smart choices.

What the flip is BABIP?

Batting Average on Balls in Play, or BABIP, is a measure of a hitter’s batting average on batted balls that can be fielded (thus are “in play”).  It would include all ground balls, line drives, fly balls (including sacrifice flies), and fielded foul outs.  It does not include at bats where the batter strikes out or hits a home run (the ball is not put “in play” during these at bats).

For example, assume a player has 10 at bats.  Within those ten at bats the player strikes out three times and hits one home run.  That leaves six balls that were batted in play (10 at bats – 3 Ks – 1 HR = 6 balls in play).  Of those six balls in play, two were for hits and four were various outs (ground outs, fly outs, etc.).  In this example, the player’s BABIP would be .333 (2 hits / 6 balls in play).

The official formula for BABIP is:

BABIP = (H – HR) / (AB – K – HR + SF)


On a very simplistic level, BABIP is a measure of a batter’s luck.  The theory is that a player’s skill contributes significantly to their contact rate (avoiding strike outs) and hitting for power (home runs), but there are other factors (like luck, which is beyond the hitter’s skill or control) that come into play when a ball is batted into play.

When a player hits a hard line drive they may be unlucky and have it hit directly at an opposing fielder.  Or a player may be lucky and hit a soft blooper over the infield.

Figure 1 below shows the BABIP for all hitters with at least 250 ABs for the last five years.  You can see that BABIP hovers consistently around the .300 – .305 mark.

Figure 1 – BABIP for All Hitters with >250 AB


I think the main weakness is simply (more…)