DFS Strategy: Following Trends & Analyzing Statistics

Following trends and analyzing statistics are important
pieces in anyone’s daily fantasy sports strategy. Luckily, no
one who hates statistics participates in this hobby. After all,
statistics are what daily fantasy sports are all about.

On this page, we offer a broad overview of how to follow
trends and analyze statistics that can affect winning
percentages and profitability.

Tracking Your Own Statistics

Most people reading this page are probably looking for advice
about how to analyze trends and statistics for various sports.
We’ll get to that, but tracking your own statistics is the place
to start. If you’re not keeping up with your own numbers, you’re
doomed to fail.

We once read a great book about poker. We came away from the
book with only one solid piece of advice, but it changed our
thinking forever.

This was a light bulb moment. You can go from being an
amateur fish to a serious player by making a single change.

Making that change and tracking results will improve anyone’s
game dramatically.

The same holds true for fantasy sports. If you want to be a
serious player, track your results. Here’s why.

The key to profiting consistently is making predictions based
on your theories. When those predictions pan out, players can
gain confidence that their theories are accurate. More positive
results add to a player’s level of certainty.

It’s easy at most sites to collect data. Major daily fantasy
sports sites usually offer the option of downloading your entire
history. The spreadsheet they provide usually includes the
following data.

• Which contests you entered.
• How well you placed.
• How much was scored.
• How much was won.
• How much the entry fee was.

We like to use these statistics in multiple ways.

Average Scores

Tracking our average scores is what enables to see if we are
making a gradual improvement in our scores over time. Eventually
this improvement levels off, but everyone should always be
trying to improve.

Keep in mind, though, that rule changes can affect scores.
Sites change their scoring systems and their salary caps. When
they do, the statistics change, too.

Win Rates

A more important statistic to track is win rate. We like to
play a lot of head-to-head and 50/50 contests. In fact, we’ll
even play in a large number of low buy-in contests just to see
how strong our lineups are. Anything can happen in a single
contest, but when you’re looking at the results of 100 or 200
contests a day (or per week, in football), you’re starting to
get some statistical accuracy.

If your scores are consistently in the bottom half of the
field, you’re doing something wrong. Figure it out. Make changes
to your drafting strategy until you start winning a larger
percentage of contests. In daily fantasy sports, the winning
percentage for break-even players is 55.56%. Set that as your
first target.

Return on Investment

A common misconception is that someone has to be winning in
order to track their return on investment. That is not correct.
Even losing players have a return on investment—a negative
return.

The math isn’t complicated, as it’s just one simple
calculation.

The result then needs converting into a percentage.

Here’s an example.

• Someone plays 72 contests with a $1 buy-in.
• Their total amount invested is $72 (72 x $1)
• They win 37 contests, winning $1.80 each time.
• Their total return is $66.60 (37 x $1.80)
• Their net loss is $5.40 ($66.60 – $72)
• $5.40/$72 = 7.5%

This player is a net loser. So his return on investment of
-7.5% is a negative number.

If that overall number changes this week to -5%, then he’s on
the right track. On the other hand, if it goes up to 8% or 9%,
he’s doing something wrong. Or maybe he’s just gotten lucky or
unlucky.

That’s something else everyone should keep in mind when
looking at statistics. In the short term, anything can happen.
72 contests is a small sample size. You’re not looking at any
kind of statistical accuracy until you start looking at results
of 1,000+ games.

The Value of Overlays

An overlay is when there’s more money in the prize pool than
that generated by the players. Here’s how that works.

Daily fantasy sports tournaments with guaranteed prize pools
don’t always “fill up”. Sites count on a certain number of
players entering, but when too few players enter, the site
covers the difference. Here’s an example.

• FanDuel has an upcoming contest called the $50k Sun NFL
  Bomb.
• It’s $25 to enter, and the total prize pool is $50,000.
• The top 472 players get prize money in this one.
• The site expects 2298 entrants.
• This will cover the prize pool and the site’s profits.
• If only 1500 people sign up for this tournament, the
  site only collects $37,500.
• The other $12,500 is covered by the site.
• That extra $12,500 is what poker players call “dead
  money”.

It’s easy to calculate what an overlay is actually worth to
you. Take the amount of the overlay and divide it by the actual
number of entrants. That’s the dollar amount of equity that each
person has in the contest on top of their entry fee. In
this example, the overlay is worth $8.33 per person.

When a player consistently puts himself into overlay
situations, he increases his upside. In the example above, if
the contest had filled, his chances of winning—assuming everyone
were exactly the same skill level—are 472/2298, or about 20.5%.
But in this overlay situation, his chances of winning increase
to 472/1500, or about 31.5%.

That’s a huge difference.

Projecting Players’ Fantasy Points

Most readers are probably more interested in knowing how to
use statistics and trends to project players’ points. In fact,
that’s a critical aspect of strategy, too.

Take multiple statistics into account when making a
projection. For one thing, you need to have an idea of how many
points a player scores on average, regardless of competition.
You can calculate that as a long-term average, but it’s as much
an art as it is a science—especially for new players who don’t
have much historical data.

Also, look at who the player’s opponents are. If a player is
facing a strong opponent, he’s liable to score fewer points than
average. On the other hand, if his opponent is weak, he’ll
probably score more points than you might expect.

Those two factors determine your projections, but the actual
score for that player is affected by a third factor, variance.

In any given game, a player might perform better than or
worse than expected. That’s the random nature of the game. The
good news is that this variance is only a short term factor. It
matters in tomorrow’s game, but over a large number of games,
assuming your estimates are correct, your projections will start
to near the averages.

Where do you get these statistics?

One place to look is historical data. In some sports, like
baseball, you can look at an average player’s performance over
the last 50 games or so to get an idea of what you can expect.
In other sports, like football, you have to take into account
the competition in those historical games—that’s because there
are so many fewer football games each season.

Another place to get data is from the Vegas sportsbooks. If
you know the point spread for a game and who the favorite is,
you can project each team’s total performance, point-wise.
Combine that with the playing tendencies for that team, and you
can get an idea of how the strength of their opponents might
affect an individual’s performance.

Dozens of sites on the web estimate how each player will
perform in any given game. You can use those sites as a starting
point, but make informed decisions based on your own educated
opinion. Otherwise, you’ll never be able to get an edge over
other players who are using those same sites for their research.

Here’s an example.

• You can
  visit ESPN to see the batting averages for all the
  hitters this season.
• You’re trying to decide between drafting Miguel Cabrera
  or Bryce Harper.
• Cabrera has a slightly better batting average: .361
  versus .331.
• Cabrera’s salary is $4800. He’s playing against
  Cleveland.
• Bryce Harper’s salary is $4600. He’s playing against
  Atlanta.
• Harper is playing against Julio Teheran, though. He’s a
  strong pitcher.
• Cabrera, on the other hand, is playing against Kyle
  Lobstein.
• He’s not a bad pitcher, but he’s not nearly as strong as
  Teheran.

In this case, Cabrera and Harper are more or less equal,
statistically. Cabrera’s a little better, but he also costs a
little bit more, so it’s hard to make a decision based purely on
value.

But since Cabrera is playing against the weaker pitcher, he’s
the obvious choice.

Players Don’t Become Due

Something else to keep in mind is that players don’t become
due for a good game just because they’ve had poor performance in
the previous game or games. By the same token, just because a
player is on a losing streak doesn’t imply that he’s a bad
choice.

Don’t count on variance to help you make your predictions.
Variance is just the level of randomness in the game. Over time,
players are going to perform in a statistically predictable
manner. But narrowing that down into how a player is going to do
in a specific game is folly.

On the other hand, situations can change what you expect.

Here’s an example.

An NFL team suffers a humiliating loss on the road. This
week, they’re playing at home versus a reasonably weak opponent.
They have something to prove. Their home game advantage takes
some of this into consideration, but not the “something to
prove” part.

One might use this data to inform his decision on whether or
not to take that team’s RB1 versus another team’s.

These are the kinds of situations which take into account
player psychology. They’re not the same thing as assuming that a
team is due for a win because they lost last week.