Hopefully More Than Just Another Draft Recap

The Great Fantasy Baseball Invitational
Click here to see a list of participants The Great Fantasy Baseball Invitational. I’m in League 13.

I recently participated in my The Great Fantasy Baseball Invitational (TGFBI) draft, which, if you’re a Twitter user and follow anyone in the fantasy baseball landscape, you could not have avoided. I do want to share with you some observations I had during the draft, but similar to my other writings, the goal here is to give some actionable advice (even if you’re reading this in the future) and not get too hung up on my team and specific players.

The Context

The invitational is made of 13 separate 15-team leagues. Each of these leagues will compete like any traditional rotisserie league and crown a champion within that league. The twist is that there is also an overall competition across the 13 leagues, whereby all 195 teams are competing in one massive rotisserie competition to crown an overall champion (similar to how the NFBC works). The one person that emerges atop 194 other experts can surely claim to be one of the best fantasy baseball players around.

This is the inaugural year of the competition, but it’s such an innovative idea that there’s no shortage of well-known folks competing. You can see the full list of participants here.

I’m participating in League #13. I happen to be the last name on the roster of the last league! What does that tell you, ha! You can see the draft results here. I was picking from the fifth spot.

My Feelings Going Into the Draft

While I’m obsessed with fantasy baseball, I really don’t view myself as anything special in this arena. Sure, I’ve MacGyver’ed up some neat spreadsheet tools over the years. But I don’t view my preparation process as anything special. I DON’T DO ANYTHING YOU’VE NEVER HEARD OF BEFORE. I’m not holding back any secret tricks of the trade.

And because I don’t do anything special, I was nervous as hell heading into this draft. I joked a few paragraphs back about being the last name on the last league. I don’t really know if that’s indicative of anything, but even if it is, I get it! I don’t even think I’ve written five legitimate articles in the past two years. It wouldn’t surprise me if that’s the lowest output of any participant involved. Meanwhile, many of the others are busting their backs to write articles and create podcasts on an aggressive and regular schedule.

These guys and gals are painstakingly combing over StatCast data, spin rates, hard hit rates, launch angles, swinging strike rates, and more… Meanwhile, I pretty much just let them do the work, read their articles, listen to their podcasts, plop some projections into a spreadsheet, make some manual adjustments, and I’m ready to rock with a comprehensive list of players and expected earnings dollar values.

Imagine my feelings when I’m now competing against some of the folks I admire the most in this business… Mike Gianella, Mike Podhorzer, Jeff Zimmerman, Rudy Gamble, and Rob Silver, to name a few.

Alright. Enough about me. Let’s try to make this useful. I apologize if some of what follows comes across as inflammatory or soap-boxy. Not everything can be sugar-coated. Here are my top lessons learned and observations after participating in this draft.

#1 – Exploit the League Rules

I really, really, really didn’t want to start with this one. It’s what EVERY SINGLE introduction to fantasy sports article ever written in the history of the world has started with.

So you would have expected that every one of the 195 participants would have done this, right?

But guess what??? I’m speculating, but I’d bet less than half of the TGFBI participants gave the rules a worthwhile look (I do realize saying “the TGFBI” is probably redundant, but it looks too weird not to do it). They probably assumed we were playing by prototypical standard rules and just checked to determine if we were using batting average or on-base percentage. But there are two rules we are playing by that are not exactly “standard” and each was something that I think needed to be known going into the draft. These two rules should have affected your behavior in the draft, and possibly in a significant way. Those two rules are:

  • Starting rosters include only one catcher but two utility spots
  • Rosters allow for five reserves and up to five DL spots for injured players
Pretty simple stuff, but small differences in rules, like starting one catcher instead of two, can have a significant effect on player valuation.
Pretty simple stuff, but small differences in rules, like starting one catcher instead of two, can have a significant effect on player valuation.

Why does this matter? In a 15-team league, I show the effect of going from two starting catchers to one as having around a $10 swing in value! That is an ENORMOUS detail (Note: the values in the image reflect the change from 2 C & 1 UTIL to 1 C & 2 UTIL, not just the move to 1 C).

The fact that this change is so significant surprises some people. But these are the same objective calculations that tell me Mike Trout, Trea Turner, Jose Altuve, Chris Sale, Clayton Kershaw, and Max Scherzer should be the highest valued players for the 2018 season. This isn’t speculation or “feel” about how to adjust for position scarcity. If you want to read more about the reason for this, here’s an illustrated example I put together from a few years ago.

As some of the faster drafting leagues started to get into the second and third rounds, we saw the big name catchers start to go. Then word quickly spread over Twitter, “This is a one-catcher league.” The effect quickly kicked in and the catchers starting plummeting. I’m a little disappointed this had to spread like a juicy rumor. I’d have expected everyone to know this going in.

I also suspect that many folks were worried the rules allowed for five reserve spots and no recourse for injured players. I believe this is how NFBC leagues and Fantrax leagues that allow for transactions operate (e.g. NFBC Main Event). I don’t have hard evidence to support this claim, but it just seemed like injured players like Michael Brantley, Michael Conforto, Jimmy Nelson, and Alex Reyes were going later than they should have been. My guess is they’d have been pushed up draft boards aggressively had everyone known this.

Small tip here. I don’t mind pushing up these injured players when you have a realistic way of replacing them that won’t burden you (force you to keep dead weight on your roster). Not only do you secure a talented player at a discount, you get the added benefit of being able to take chances on the waiver wire early in the season, when the odds are higher that you’ll be able to find a hidden gem.

My takeaway here is to not take anything in the rules for granted. Comb over them. Think about what the wrinkles in the rules might allow or incentivize you to do. Tailor your rankings and calculate your dollar values with these rules in mind. And don’t assume your enemies are doing the same. This can be an edge.

#2 – Use Dollar Values Tailored to Those League Rules to Make Decisions

My stance on this is simple and straightforward. If you’re not drafting with a set of projection-based dollar values in mind, you can do better.

I don’t care if you calculate them yourself, if you use the Fangraphs auction value calculator or the Rotowire custom dollar values, or if you buy a piece of software that does it for you… You’re not optimizing your chances of winning if you’re not drafting from values. You need a framework for comparing two hitters to each other, for comparing a hitter to a pitcher, and for making educated decisions. This is what dollar values do! Without dollar values, you’re being subjective. You’re letting biases creep into your decision making.

Do I have evidence that this approach works? The folks at Friends With Fantasy Benefits (specifically @smada_bb) undertook the incredible task of tracking and logging all 13 drafts into a Google Sheet with projected standings and it seems to support what I’m saying.

I don’t know exactly how every analyst drafts, but based on following folks on Twitter, reading certain sites, and discussions I’ve had with people, I’m pretty certain these folks all do value-based drafting:

Continue reading “Hopefully More Than Just Another Draft Recap”

Tools for the 2018 Season – Now Available!

Here are the Excel tools and books I have available for the 2018 season. You won’t find draft lists or player profiles here. But if you’re looking to build skills and to develop your own methods for ranking and valuing players, these are for you! All of the spreadsheet tools listed below have been updated for the 2018 season.

Title Description
AGGREGATOR Projection Aggregator
***UPDATED for 2018***

An easy-to-use Excel spreadsheet that can combine (or average) up to three different projection sets. The aggregator can use just about any well-known projection set you can find on the web (if you find one that doesn’t work, let me know!). Simply download your favorite projection sets, fill out some settings, and you’re done. No complicated formulas or VLOOKUPS for you to add.

Using Standings Gain Points to Rank and Value Fantasy Baseball Players

Ever wanted to create your own rotisserie rankings? This is my instructional guide written specifically to show you how to create customized rotisserie player rankings, dollar values, and inflation dollar values, in Microsoft Excel, tailored to your own league. No more downloading rankings from the web, hoping they apply to your unique league. 10, 12, or 15-team league? $260 or $300 budget? AL-only or mixed league? 10 hitters or 14? It doesn’t matter. This book will guide you through the process of developing rankings for just about any kind of rotisserie league.

How to Rank and Value Fantasy Baseball Players for Points Leagues

My step-by-step guide to building custom rankings, dollar values, and inflation dollar values, in Microsoft Excel, for your points league. This book will guide you through the process of developing rankings for just about any point-based scoring format.

Start Preparing for the 2017 Season!

It’s time! Are you getting the itch to start thinking about fantasy baseball again? Are ready to take on a new challenge this year and calculate your own rankings or create your own projections? All spreadsheet templates have been updated for the upcoming 2017 season. Take a look at the available books and tools below.

Title Description
Bundle Image The Projecting X 2.0 Bundle
***UPDATED for 2017***

The Projecting X 2.0 Bundle comes with Mike Podhorzer’s instructional guide to creating your own baseball projections, as well as an accompanying Excel template to help save you hours and hours of time as you work through the projection process.

The Projecting X 2.0 Excel Template Only
***UPDATED for 2017***

(NOTE: the Excel template requires you to enter certain formulas from the book, Projecting X 2.0. If you purchased the bundle prior to the 2016 season, this is being offered to save you the time of having to manually update the player names, teams, and positions in the spreadsheet in order to start projecting the 2017 season.)

AGGREGATOR Projection Aggregator
***UPDATED for 2017***

An easy-to-use Excel spreadsheet that can combine (or average) up to three different projection sets. The aggregator can use just about any well known projection set you can find on the web (if you find one that doesn’t work, let me know!). Simply download your favorite projection sets, fill out some settings, and you’re done. No complicated formulas or VLOOKUPS for you to add.

Using Standings Gain Points to Rank and Value Fantasy Baseball Players

Ever wanted to create your own rotisserie rankings? This is my instructional guide written specifically to show you how to create customized rotisserie player rankings, dollar values, and inflation dollar values, in Microsoft Excel, tailored to your own league. No more downloading rankings from the web, hoping they apply to your unique league. 10, 12, or 15-team league? $260 or $300 budget? AL-only or mixed league? 10 hitters or 14? It doesn’t matter. This book will guide you through the process of developing rankings for just about any kind of rotisserie league.

How to Rank and Value Fantasy Baseball Players for Points Leagues

My step-by-step guide to building custom rankings, dollar values, and inflation dollar values, in Microsoft Excel, for your points league. This book will guide you through the process of developing rankings for just about any point-based scoring format.

How to Project Plate Appearances

Projecting X Mike Podhorzer
Click here to create your own player projections.
Going through the process of projecting individual players is one of my favorite parts of the year. I started creating my own projections two seasons ago, using Mike Podhorzer’s book Projecting X.

There are parts of the projection process I feel very comfortable with. I can look at a player’s recent plate discipline, batted ball mix, and power ratios to arrive at an accurate projection for most of that player’s stat line…

But when it comes to projecting playing time, I feel like I’m throwing darts with a blindfold on. How can I realistically make a determination between 675 PAs and 690 PAs?

Until now, I’ve really just relied upon a player’s recent seasons and used qualitative information about injuries, role on the team, and playing time competitions to come up with an estimate for total plate appearances.

Thankfully, a reader of the site recently commented on a post I wrote about the effect of batting order on runs and RBI, and his question helped me arrive at the much more sound approach for projecting playing time I’m about to share with you. Here’s his question:

Interesting stuff. In your research, I am wondering if you happened to look at Team Runs/Plate Appearances on a per game basis?

That is, if a team scores Y runs in a game, what would you predict their Team PAs to be. Something like Y = Ax + B.

~DMM

That question got the wheels turning in my rapidly deteriorating middle-aged brain… There have to be better ways to think about playing time. And I certainly need to take the team’s overall run scoring into account.

Team Plate Appearances vs. Team Runs

To answer the question, I downloaded the last ten years of MLB team offensive stats from Baseball-Reference.com (click here to see the data).

Then I created a scatter plot in Excel by graphing team runs against team plate appearances.

TEAM_RUNS_VS_PLATE_APPEARANCES

I’ve mentioned it many times on the site already. I’m no statistician. I don’t play one on TV. And I’m not pretending to be one on the internet. I am squarely in the area of having enough knowledge about statistics to offer no help but to only be dangerous. With that amazing qualifier I’ll try to explain what you see in that chart above.

Each of the blue dots represents one team’s season in the last 10 years (2006-2015). For example, the dot in the top right corner is the 2007 Yankees, who scored 968 runs (holy crap, A-ROD!).

The dotted red line represents a trend line or line of best fit. It’s the best estimate of the relationship between team runs scored and team plate appearances. The equation on the graph is the formula used to chart out the red line and is the exact answer to reader DMM’s question (where x is team runs scored and y is team plate appearances).

y=1.141x+5375.6

I suppose that could be helpful at the daily game level too. That equation would become y=0.007x+33.18 if you were trying to project a team’s plate appearances in an individual game (where x is runs per game, not season-long runs).

Projecting Individual Plate Appearances

That answers the original question. But I still wasn’t quite satisfied with stopping there.

Sure, it’s helpful to know that if I think Angels will score 700 runs that I should project that whole team for about 6,175 plate appearances (5,375.6 + 1.141 * 700 = 6,174.3). But what does that mean to Mike Trout if I think he will bat second in the lineup? And what if I think he’ll bat third?

Is there a way to add a third variable to the chart above? So we can see how leadoff hitters on teams scoring 700 runs have fared? Or how cleanup hitters on teams scoring 800 runs have performed?

The Data

Baseball-Reference has a really interesting split table that shows the hitting stats each team had from each spot in the lineup (click here to see Kansas City’s 2015 team split).

Kansas City Royals 2015 team batting splits

I downloaded that split table for all 30 teams for each of the last 10 seasons (300 CSV files!). You can see all the raw data here. Again, thanks to Baseball-Reference for making this data available.

Then I grouped the data by team runs scored, putting teams into categories of 500-549, 550-599, 600-649, 650-699, 700-749, 750-799, 800-849, 850-899, 900-949, and 950-999 runs. Here’s a table showing the number of teams in each of these categories for the AL and NL:

Runs Scored AL Teams NL Teams Total
500-549 1 2 3
550-599 2 7 9
600-649 19 34 53
650-699 23 33 56
700-749 33 43 76
750-799 30 25 55
800-849 19 9 28
850-899 12 4 16
900-949 3 0 3
950-999 1 0 1

Continue reading “How to Project Plate Appearances”

How To Calculate Custom Rankings for a Points League: Part 1 – Download Projection Data and Player ID Map

Welcome to the first part of a series in which we’ll go step-by-step through the process of using Microsoft Excel to calculate your own rankings for a fantasy baseball points league (as opposed to rotisserie or head-to-head rotisserie).

Whether you’re in a standard points league at a major site like ESPN or a more advanced Ottoneu league at Fangraphs, this process will help you develop customized rankings for your league.  These instructions can be used for a season-long points league or a weekly head-to-head points league.

If you’re looking for info on how to rank players for a roto league, look here.

In this first part of the series we’ll start a new Excel file and download projection information.

Why I Created This Series

Points leagues seem to be like fingerprints and snow flakes. Each one is a little different than the others. I’m a big believer that in order to be a strong fantasy player, you need to create your own rankings and dollar value calculations that are tailored specifically to the league(s) you play in.

I’ve also felt that nobody takes the time to explain exactly how to create your own rankings. If you look hard enough you might find an article that gives a quick bullet point list.  Maybe something like:

  • Download projections
  • Multiply projections by your point system
  • Adjust for replacement level
  • You’re done!

I’m going to be a little more thorough than that.

In Part 1

In this first part of the series we’ll download hitter and pitcher projections, take a look at and download player ID information, and bring all of this information into one Excel file.

ABOUT THESE INSTRUCTIONS

  • The projections used in this series are the Steamer 2015 preseason projections from Fangraphs.  If you see projections that you disagree with or that appear unusual, it’s likely because I began writing this series in December 2014, still early in the off-season.
  • For optimal results, you will want to be on Excel 2007 or higher.  Some of the features used were not in existence in older versions.
  • I use Excel 2013 for the screenshots included in the instructions.  There may be some subtle differences between Excel 2007, 2010, and 2013.
  • I can’t guarantee that all of formulas used in this series will work in Excel for Mac computers.  I apologize for this.  I don’t understand why Excel operates differently and has different features on different platforms.

Where To Get Projections

There are many solid projection systems available for download.  Some very fine projections are available at membership sites like Baseball Prospectus or Baseball HQ.

But if you’re like me, when I started out on the adventure of calculating my own rankings, I wasn’t looking to pay for something I wasn’t sure I’d be able to translate into fantasy success.  It’s great to have accurate projections, but how do you use them if you can’t take the next step to rank and value those projections?

For that reason, I’m partial to the Steamer projections.

They’re available in easy-to-use Excel downloads (specifically CSV) at Fangraphs. There are also daily rest-of-season updates, meaning on any day you can see the projections for the remainder of the season for any player.  Steamer does a good job of projecting playing time (if a player gets hurt, they try to estimate the effect on playing time).

And they’re free.

Use Whatever Projection System You Prefer

I’ll be using Steamer in this example.  But you can use the system of your choice.  Just try to pick a system that uses some form of player ID system.

What is a Player ID?

Just like you and I are identified by Social Security Numbers or employee IDs (at work), most of your major fantasy or MLB websites use some form of an ID number that is unique to each player.

Using an ID is a more reliable way of identifying a player than a simple name.  Two players can have the exact same name (think Chris Young and Alex Gonzalez) which could cause big problems when ranking players.

And players can go by different name variations (Mike and Michael, Jon and Jonathan, JP and J.P., AJ and A.J.) or even change their name (Mike to Giancarlo Stanton, Fausto Carmona to Roberto Hernandez).

Here’s a quick look at some player IDs for various systems:

Player ID Source Mike Trout Clayton Kershaw Giancarlo Stanton
Baseball Reference troutmi01 kershcl01 stantmi03
Fangraphs 10155 2036 4949
MLB 545361 477132 519317
CBS 1739608 1221725 1630093
ESPN 30836 28963 30583

If you’re wondering how to determine someone’s player ID, visit their player page on a particular website.  You can usually find the player ID in that web page’s URL.  For example, here’s Mike Trout’s MLB.com player page:

MIKE_TROUT_PLAYERID

In looking at that table above, you can see here that there is not one universal numbering system.

To alleviate this problem, I maintain a “Player ID Map” (click here to download in Excel).  The Player ID Map lists out all “fantasy-relevant” players and their ID for each of the major systems (Fangraphs, Baseball Reference, Baseball Prospectus, Yahoo, ESPN, etc.).

PlayerIDImage

I stumbled across the concept of the player map from Tim Blaker at Crunchtimebaseball.com and tailored it to meet my needs.  This provided me with an excellent starting point.  Tim maintains his own version and updates his more frequently than I do.  I only maintain my own because I’ve wanted to add some new columns.

On this site I will typically work with the Baseball Reference ID.  I like those IDs more than the others because I can look at an ID and usually determine who the player is (troutmi01 is Mike Trout).  Most other sites use a straight ID number that has no inherent meaning (10155 or 545361).

If you’re familiar with Excel and using VLOOKUPs, Player IDs are the item we’ll be matching upon to start pulling information around our Excel rankings file.  If you have no idea what I just said, don’t worry.  We’ll get there soon.

STEP-BY-STEP INSTRUCTIONS

Continue reading “How To Calculate Custom Rankings for a Points League: Part 1 – Download Projection Data and Player ID Map”