Fantasy Labs’ mission is pretty simple: deliver the best data and provide the tools necessary to transform that data into winning daily fantasy sports lineups.
And we offer a lot of data. Some of the stats are pretty self-explanatory, but others either aren’t widely used or were created by us, meaning you might have no idea what the hell they mean.
We have tool tips across the site that you can hover over to learn more about specific stats, but I wanted to create a guide to all of the numbers we offer and just fill you in a bit more about the philosophy behind each stat, why we use the numbers we do, and how to get the most out of the data.
About +/- (Plus/Minus)
A lot of our DFS-focused stats—the stuff that’s sport-agnostic—are centered around a concept we call “Plus/Minus” (+/-). Simply put, a player’s plus/minus is his actual points minus his expected points. So if Andrew Luck scored 300 points over the past 10 games and his expectation was 250 points, he would have a Plus/Minus of 50 total points, or +5.0 points per game.
Cool. So how do we know what to “expect” from each player? We know based on our database of historic salaries and fantasy performances on each daily fantasy site. Instead of using a fragile $/point system (or, even worse, sorting players into completely arbitrary tiers), we use historic performance data to help calculate exactly what to expect out of a player based on his cost. So if Luck costs $10,000, we know he should produce X points, on average.
Using Plus/Minus, we can calculate all kinds of really cool stats and identify league-winning trends. Our Consistency stat, for example, shows how often a player has exceeded his expected points. Instead of using a “well-how-often-does-he-reach-4x-his-salary?” system that naturally overvalues cheap players, we put every player on a level playing field since all of our stats are adjusted for cost.
Our Bargain Rating is a historical percentile rank representing how much of a bargain a player is on one daily fantasy site versus the other. We look at the typical difference in site salaries at a position and then rank a player based on how much of a bargain he is in a particular game relative to the historical data.
If a quarterback costs the same on DraftKings as he does on FanDuel, for example, he might have a high Bargain Rating for DraftKings and a low Bargain Rating for FanDuel since the latter site has a smaller salary cap and thus tends to price certain players much lower, on average.
The Bargain Rating stat is extremely powerful and useful in a number of ways. First, there’s a strong link between Bargain Rating and player value (Plus/Minus). That shouldn’t be surprising since Plus/Minus is determined based on price; the cheaper a player, the more potential value he can offer (assuming the same skill level). It is smart to use Bargain Rating in your player models, especially in a sport like basketball in which it pays to be price-sensitive.
Second, Bargain Rating is an excellent way to determine where to get exposure to certain players. If you play daily fantasy sports on both DraftKings and FanDuel, you should get exposure to the players you like where they’re the cheapest. A big part of finding value is leaving yourself a cushion to soften the negative impact of assessment errors, and Bargain Rating does that better than any other stat.
The percentage of games in which a player has produced within a standard deviation of his expected points based off of historical scoring and pricing
To identify high-floor players for cash games
A standard deviation based value metric. Matches players who have most frequently posted the highest Plus/Minus scores.
To identify high-upside players for tournaments
Our Dud stat calculates the percentage of games in which a player has scored fewer than half his salary-based expectation.
To identify low-floor players to avoid in cash games
Our Trends product lets you leverage our massive database of historical salaries and fantasy performances to determine in which situations players traditionally offer value. You can create your own trends or utilize our DFS-pro-created ‘Pro Trends,’ which already show up in models and player cards.
Our Pro Trends are very strongly linked to value, and they allow you to truly customize your models based on angles you find.
Moneyline percentage, or the percentage of bets coming in on each team in a game. We aggregate all our Vegas data from seven different sportsbooks.
There’s a positive correlation between public betting trends and player value. You can also use public betting trends to help predict player/team ownership in tournaments.
A player’s change in salary over a given period of time
To help identify players whose price might be artificially inflated/deflated due to variance
Ceiling: Projected point total a player is expected to surpass with 15% probability; we use a PECOTA-style sim score model to analyze how the 30 most comparable players have performed in the past as the basis for our ceiling projection.
Floor: Projected point total a player is expected to fail to reach with 15% probability; we use a PECOTA-style sim score model to analyze how the 30 most comparable players have performed in the past as the basis for our floor projection.
To find value in either GPPs (ceiling) or cash games (floor); it’s also useful for visualizing each player’s range of outcomes so you can get exposure to certain players who have access to the type of range you need.
Simply put, this is median projection minus salary-based expectation. We just compare how we think a player will perform most of the time versus what he should do (historically) based on his cost.
A great way to identify pure value for all leagues
This is the typical “points-per-dollar” you see across the DFS industry. It has its uses and can be valuable for cash games, although we think it should be just one component of your model. It is simply projected points for every $1000 of salary.
A way to identify pure value for all leagues
Opponent Plus/Minus Allowed
This is also known as “Defensive Unit Rating” in our NFL sliders within Player Models. It is a highly predictive stat that shows the points above or below expectation a defense has allowed to a particular position.
It is so powerful because it naturally adjusts for opponent strength. Remember, our Plus/Minus stat is a function of salary. If a defense gets torched by four Pro Bowl quarterbacks to start the season, their Opponent Plus/Minus allowed won’t be as poor as if they had gotten beat by crappy quarterbacks. That’s because the latter group would cost less on the DFS sites and thus be expected to score fewer points.
An extremely predictive way to judge matchups for all leagues
Simply the points we expect a player to score (historically) based on his cost.
More as a way to visualize expectations