Never Start the Wrong Guy Again

Fantasy Math
SIMULATES YOUR MATCHUP
thousands of times, taking into account factors regular rankings can't, like:
whether anyone you're considering is CORRELATED (playing in the same NFL game) as anyone else in your matchup
each players
variance
(boom or bust-ness)
whether you're favored and by how much — interacts with the above
Fantasy Math let's you take it all into account.

Here's how it works

1. Enter your matchup.

Including scoring systems, any midweek points (so you can run it after the Thursday night games, etc).
Once you enter your team, FM will remember them so it's easy to enter them again.
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2. Behind the scenes, Fantasy Math models the distribution of each player's outcome

The computer simulates your matchup thousands of times, sampling from each player's
probability distribution
.
These distributions are fit using the expert fantasy consensus.
The more the industry disagrees about a player, the wider his range of possible outcomes.
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3. Fantasy Math samples these distributions to simulate YOUR matchup

The computer simulates your matchup thousands of times, sampling from each player's
probability distribution
.
Right: 15 random sims from a few of these players (in reality the model does 1-15k samples for each player on both teams, as well as all the guys you're contemplating starting).
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4. These samples are correlated

These simulations not only fit well (gray lines above) and make intuitive sense:
Team 1 Sum Stats
They're also
correlated
. For example, our opponent here had Watson + David Johnson, who were playing the Ravens and Mark Andrews. Here's the
correlation matrix
:
HOU-BAL
You can see Watson and DJ's simulations are positively correlated at 0.13, which is historically the correlation for pts between a starting QB and RB. Watson is also positively correlated with Andrews and Lamar Jackson, and (strongly) negatively correlated with the BAL defense.
These are just a few players. The Fantasy Math Model factors in ALL of the following correlations:
Correlations By Position

5. You get back who to start to maximize your chances

Once you click submit, and FM has simulated every scenerio (only takes a few seconds), you get back who you should start.
Along with your probability of winning with each player, FM also returns how often you can expect the advice to be WRONG (a backup outscores the reccommended starter)
It also tells you how often this decision will cost you your matchup (LOSE).
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Finally, you also get a closer look at each individual player, as well as a look at your team (with the recommended starter) vs your opponent.
scoring
scoring

6. Also get in depth looks at the players on your team.

Toggle individual players distributions by position to see where you stack up vs your opponent and where you might improve.
It's also useful for sending people trade offers, showing them how a proposed trade helps their win probability this week.
Sound good?
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Live for 2020!

completely rebuilt models
more
simulations
— simulate your matchup up to 10k times to detect the smallest edge
more
correlations
— takes into account 1600+ pairs of correlations every week
always current
— data automatically updates every few hours (more frequently before games)
"...an
outstanding
way to visualize the choices we make every week and why there are not necessarily one size fits all "right" answers,
different curves make more sense for different lineups/matchups
."
Sigmund Bloom
Co-Owner of footballguys.com
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Rebuilt Models for 2020

Fantasy Math has always projected performance as distributions, but up until this year it was a single, closed form distribution.
In the two pictures below — note:
read this if you aren't sure how to interpret these
— the blue line is how the top 5 ranked RBs performed from 2017-2019, standard scoring.
The old model is the single gray curve on the left. It fits performance pretty well, but there's only so much you can do when you're working with a single curve.
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OLD model
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NEW model
The NEW model uses probabilistic, Bayesian and MCMC sampling methods to fit fit thousands of distributions and better represent the curve. You can see how the model (all the gray curves) captures actual performance (blue) a lot better.
The FM model calculates thousands of these distributions for each player, every week.
Top 5 QBs, 2017-2019
Above, how the model does on QBs. Sorry the blue line is kind of hard to see... since it's
right in the middle of the model!!

What People Are Saying

"
I freaking love this
. It's a beautiful idea and so far looks like a great execution of it."
Dylan Lerch, aka u/quickonthedrawl
"I think
@nathanbraun
is one of the sharpest dudes around."
Adam Harstad
"This is an
outstanding way to visualize the choices we make every week
and why there are not necessarily one size fits all "right" answers,
different curves make more sense for different lineups/matchups
."
Sigmund Bloom, Co-owner of footballguys.com
"
No other tool I've ever come across has done this
... I've considered [incorporating correlations] as a competitive advantage of mine for a number of years, but wasn't able to properly and consistently quantify those values in a truly objective measure..."
Seth K
"It’s an
incredible site
. I have no idea how you worked the math into this."
Ben R
"...helped me to
two championships
last season!"
Paul H
"...this is the first site I have seen that looks like it might
match the standards I'd have for a subscription service.
"
David S

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