PGA Video Tutorials
FantasyLabs’ 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 Bradley Beal 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 Beal costs $6,000, 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.
Quick note: We provide all of the following stats in long-term, recent, and course forms. Long-term stats are collected over a running 75-month period and recent over a running six-week period. Course form shows a golfer’s course history.
Vegas Odds: Implied percent chance to win the tournament according to the golf futures market
AdjRd: Average adjusted strokes per round. Adjustments are made to account for the difficulty of the course and the strength of the field
GIR (Green in Regulation): Percentage of holes where the player reaches the green in at least two strokes less than par
DD (Driving Distance): Average distance of the player’s initial drive on par 4 and par 5 holes
DA (Driving Accuracy): Percentage of the player’s drives that land within the fairway
SC (Scrambling): Percentage of holes where the player avoids bogey or worse after their approach shot does not land on the green
PPR (Putts Per Round): Average number of putts the player has per round
Field %: Average strength of the field in the player’s tournament history, based on Sagarin ratings. A higher percentages indicate the player primarily plays in stronger fields. It is an exchange ranking system used in multiple sports, similar to Elo rankings. The lower the field strength number, the stronger the field. Current Sagarin rankings can be found here.
Eagles: Average number of eagles per tournament
Birdies: Average number of birdies per tournament
Bogeys: Average number of bogeys per tournament
Count: Total number of tournaments in the player’s sample size. Sample includes all non-PGA/WGC events as well.
MC %: Percentage of tournaments in the golfer’s sample size where they missed the cut
Par 3: Average adjusted strokes, relative to par, on par 3 holes per tournament. Lower scores indicate better performance.
Par 4: Average adjusted strokes, relative to par, on par 4 holes per tournament. Lower scores indicate better performance.
Par 5: Average adjusted strokes, relative to par, on par 5 holes per tournament. Lower scores indicate better performance.
Wind Speed: forecasted average wind speed (MPH) for each player’s Thursday/Friday tee time
Temperature: forecasted average temperature for each player’s Thursday/Friday tee time
Also known as ‘X1,’ our Consistency figures show the percentage of games in which a player has reached his salary-based expectation. Instead of assessing players in an arbitrary and artificial way, we look at how production on each site is historically been connected to pricing to determine a more natural expected point total. All players are placed on an even playing field; it is just as easy for a $5,000 player to reach X consistency as it is for a $3,000 player, for example.
To identify high-floor players for cash games
Also known as ‘X2,’ our Breakout/Upside figures show the percentage of games in which a player has finished at least one-half standard deviation above his salary-based implied total
To identify high-upside players for tournaments
Our Dud stat calculates the percentage of games in which a player finishes at least one-half a standard deviation below his salary-based implied total
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.
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