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 Bryce Harper scored 150 points over the past 10 games and his expectation was 120 points, he would have a +/- of 30 total points, or +3.0 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 Harper costs $5,000, 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 batter costs the same on DraftKings as he does on FanDuel, for example, he will have an extremely high Bargain Rating for DraftKings and a very low Bargain Rating for FanDuel since the latter site has a smaller salary cap and thus prices their batters 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
Upside figures show the percentage of games in which a player has scored at least one-half standard deviation above his point expectation based on salary
To identify high-upside players for tournaments
Our Dud stat calculates the percentage of games in which a player has fallen below his point expectation based on salary by at least one-half standard deviation.
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, especially for pitchers. Most of the best pitcher models heavily weigh Pro Trends.
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.
In baseball, 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
wOBA—Weighted On-Base Average—is a catch-all advanced MLB stat that does a nice job of capturing a hitter’s overall value to his team. Our wOBA numbers are always broken down into splits to reflect the handedness of pitcher a batter is facing in a given day.
To identify pure value for cash games
Our “Differentials” show the difference between a player’s wOBA split for the day and his wOBA split against the opposite handedness of pitcher. Josh Donaldson crushes left-handers, for example, and has a very high positive wOBA Diff when facing southpaws. Against righties, his wOBA Diff is negative because he struggles against that handedness compared to lefties.
To identify underpriced batters in cash games
ISO—Isolated Power—is a very simple advanced stat that measures a batter’s raw power by quantifying how often he hits for extra bases. Like wOBA, all of our ISO numbers are shown as splits versus the handedness of pitcher a batter is facing in a given day.
To identify pure power for tournaments
ISO Diff uses the same methodology as wOBA Diff: “In” ISO split minus “out” ISO split. A positive number means a batter is better against the current handedness of pitcher he’s facing for that day.
To identify underpriced upside for tournaments
We have umpire data that helps quantify the effect of particular umpires when they are behind home plate. WithinPlayer Models, we show an umpire’s Plus/Minus: the points above or below expectation for hitters/pitchers in games in which he’s the home plate umpire. If an umpire has a Plus/Minus of 1.0 for DraftKings batters, for example, it means hitters have scored one additional point above what you’d expect based on their salaries in games with that umpire behind the plate.
Good for all league types to gain an edge few realize
Runs and Opponent Runs are pretty self-explanatory, but it is worth noting how we calculate these numbers. Instead of using a complicated formula to determine projected runs, we just look at historical data; we use the over/under and moneyline for a particular game—look back at very similar games in the past—and examine how many runs have actually been scored in those games. This is the most accurate way to project runs because it can accounts for any potential biases in Vegas, as well as non-linear scoring distributions.
Vegas data can and should be used as a major component of all your MLB research. You can use projected runs to identify high-upside offenses to stack, to select the safest pitchers in a given night, and a whole lot more. Most successful player models weigh Vegas to a strong degree.
Run Change (Delta)
This is the change in a team’s projected run total from the opening line to the current line. If the Blue Jays opened at 4.5 runs and are currently projected at 4.0 runs, their Run Change would be -0.5.
There’s a correlation between run line movement and fantasy scoring. When a team’s run projection moves up even 0.3 runs, for example, batters in those games have a big jump in their Plus/Minus.
Our Weather Rating displays the hitter-friendliness for a particular game. It is a proprietary Fantasy Labs model and shown on a scale of 0 to 100. The Weather Rating does not account for potential rainouts; rather, it simply examines all relevant atmospheric conditions (temperature, altitude, wind speed, humidity, and so on) to determine how many home runs are likely to be hit in a given game. Not even considering offensive strength or ballparks, games with a Weather Rating of 100 have produced nearly 2.5 times as many home runs as those with a 0 rating.
Relevant for all league types, but particularly for choosing high-upside stacks