# 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.

### 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.

### 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).

### 4. These samples are correlated

These simulations not only fit well (gray lines above) and make intuitive
sense:

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

:

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:

### 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).

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.

### 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?

## 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

."

# 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.

OLD model

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.

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

"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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