# Could Median Points Per Game be Future of Fantasy Football Analysis?

An in-depth breakdown of the Median Points Per Game statistic.

The competition in fantasy football is constantly evolving, yet the industry still uses an arcane season-long grade for how players finish. You’ve heard the arguments: “So-and-so finished as the WR23 last year and I could see him finishing top-15 this year.” The baby step forward has been the push for Average Points Per Game, commonly abbreviated as PPG. Viewing a player’s “weekly” production is helpful in making weekly roster decisions for season-long leagues as well as daily fantasy games. However, there is still a better way. That way is Median Points Per Game (MPPG will work as an abbreviation). If you don’t remember your middle-school math work, don’t worry, class is about to begin!

## Why Season-long finish is not helpful

A player’s point total is impacted by far too many variables to be helpful in predicting how a player will perform in the future. A cumulative number such as 150 PPR points (the typical PPR total for the RB30) doesn’t provide any insight into how that player got to that number. There is no accounting for any variance and without games played next to the point total, there is little to create context. Take for instance Nick Chubb's 2020 season. He finished with 207.7 PPR points and was RB11. He was one spot behind Kareem Hunt who earned 218.5 PPR points. The missing piece of context is that Chubb played four fewer games than Hunt.

## Why Average Points Per Game is better, but still not good enough

Keeping our example from above, we can calculate Chubb’s and Hunt’s Average Points Per Game (APPG). Since Chubb played fewer games and had similar total points, his APPG (17.3) is significantly higher than Hunt’s 13.7. This is helpful because it provides an estimate for what can be the expected output for Chubb on a given week.

The problem is that this estimate is wildly inaccurate for fantasy football. One issue lies in the actual math used to calculate an average. APPG is simply the year-long (or another cumulative time frame) total points divided by the number of games played. So by using APPG, you have not untethered from the player’s season-long total.

The other mathematical issue is that an average assumes a normal distribution (think bell curve from middle-school science) in order to be accurate. In a normal distribution, the average and median will be the same since there is equal “weight” on both sides of the average and median. However, when the data is non-normal, or “skewed” in one direction, the average and median will have different values depending on the direction and magnitude of the skew.

To further prove the flaws of using average in vastly non-normal data sets, statisticians and middle-school math teachers tell a “joke.” A guy and four of his best friends are at the bar. They all make around the same amount in yearly income with an average of \$50,000 a year. For some unknown reason, Bill Gates, who earned over \$7 billion in 2019 walks through the door. Happy day! The guy and his friends all just became billionaires . . . on average. This is a bad joke even by dad-joke standards, but it is an attempt to explore the impact of introducing a data point that transforms a normal curve to a non-normal curve.

Scroll to Continue