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:
- Final rotisserie points by year for each team
- 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)
The strength of this formula is that empty cells are excluded from the average calculation. So the one year this league had only 11 teams does not distort the calculation.
The example spreadsheet has eight years of overall standings data and averages out to the following:
I’ve seen arguments before that you only need to beat the second place team to win. If second place averages a total of 88.56 points, this argument suggests that it’s going to take 89 points to win the league. A quick look at the historical data shows that, 89 points would have won the league only five out of the eight years (89 would have beaten the second place team five times). The first place average of 93.875 would have beaten the second place team seven out of eight times. So for purposes of this exercise, I’m going to assume my target for rotisserie points is 93.875 (shoot for the stars, right?).
If my goal is 93.875 and the league has 10 categories (5×5 league), I need to average 9.4 points per category (93.875/10). Said another way, I need to finish third in about half of the categories (in a 12-team league, a first place finish earns 12 points, second place 11, and third place 10) and fourth in about half of the categories (fourth place earns 9).
That’s it! You only need to finish in third or fourth to win the league. That’s where the historical statistic averages come in.
2. FINAL STATISTICS BY TEAM FOR EACH LEAGUE ROTO CATEGORY
Knowing that I need to finish in third or fourth for a particular category, let’s look at the league’s home run averages for the last eight years:
To earn third place, a team needs to hit 272 home runs (going back to the earlier argument about if you need to actually get to 272.25 or if you just need to beat 258.375, I’m going “shoot for the stars” again).
Using the other statistical averages from the example spreadsheet, I’ll go aggressive and set my goal to come in third place in every category. Here’s what it’ll take:
What If I Don’t have a Recurring League or a Lengthy History of Standings Data?
If your league has an unusual scoring system, you may not be able to develop an accurate estimate. But if you play in a typical rotisserie scoring system, Razzball.com has some data that you can use as a guide.
Please note that the actual data for the average Yahoo! or ESPN league isn’t publicly available. I have seen it mentioned in specific articles on those sites in the past (sometimes Andy Behrens or Matthew Berry will refer to them), but I can’t even locate those references now. Razzball uses surveys of its users and a data model to estimate these figures (someone asked them, you can read the exact description here). It’s not perfect. But neither is my example. And it’s at least a very strong starting point.
Reverse Engineering a Team
Now that you have the ending statistics for your team, use projections and available draft/auction averages to construct a team that can reach those stat levels. Participate in mock drafts or mock auctions, then run the projected stats from your team to see how things shake out. You don’t need to shoot for an all around team that finishes in third place for each category. For every category that you win, you can finish in fifth in another.
This is can be a powerful strategic exercise. Let me know if you have any critiques or similar practices that you’ve found to be successful. Please let me know if I’m making any logical flaws in my analysis.
Thanks for reading! Make smart choices.