SumerSports’ Player Value for college football players has two main parts: descriptive Wins Above Average (dWAA) and Predictive Player Value.
- Descriptive Wins Above Average (dWAA): An opponent-adjusted measure of player contribution based on his involvement in the different roles of the game. This measures how much a player contributes to the team’s success, considering the difficulty of the opponents. We break down a player’s performance based on his role and position to estimate his overall impact.
- Larger absolute values mean more involvement in the game.
- Positive values indicate above average contributions, while negative values indicate below average contributions.
- Example: Dillon Gabriel had a 0.41 dWAA, against South Florida in 2020, which means he added over a third of a win above average in total value to his team that day.
- Predictive Player Value (PV): We consider each player’s recruiting ranking and the historical performance at each role of similar players. For every game in a player’s career, we observe the dWAA of that player and then update his forward-looking value accordingly. This estimates a player’s future performance, considering his recruiting ranking and how similar players have performed historically and is updated after every game in a player’s career to reflect their growth or decline over time. PV is designed to not overreact to outliers and set reasonable expectations for early career players, who are not as involved, as they develop and grow into roles.
- Example: After Gabriel’s game against South Florida in 2020, we updated this expected per-game contribution from 0.13 to 0.14 wins for an average team.
What does “average” mean?
We relate performance to Wins Above Average. In college football, given the discrepancies of schedules and quality of opponent, determining “average wins” for a schedule involves calculating the probability that an average team would beat an opponent and summing across seasons. We define an average college football team as one that would win 5 out of 12 games against FBS teams (.417 win percentage), with a net power rating (offense-defense) of 0. You should not think of average players or teams as bad, for example an average team by this definition is nearly bowl eligible and an average player at their position is a contributor at the college level.
What are “good” values?
For a single game, dWAA ranges from -0.4 to +0.4 in our data set. Typically, quarterbacks produce the most extreme values since their roles impact the game significantly, but no player will sustain such extremes over the course of a 12-game season. Most players are within a single (1.0) win above or below average. This is distinctly different from the familiar baseball Wins Above Replacement (WAR), as there are fewer games and dividing credit for each play among 22 players, the individual contributions can be small.
In general terms, above 0 is “good” i.e. above average, whereas below 0 is below. In the application, we color code analytics values including Player Value and Team Value in order to provide context. The colors chosen represent the same coloring scale used by Sumer’s football scouting staff to provide the user with a consistent frame of reference throughout our football products.
The SumerSports Percentile Range Color Scale
What to do with Negative Values?
It’s important to note that Player Value is a production metric. It’s possible for a player to have a negative value and still have traits desirable to NFL or NCAA teams or outside information (injuries, scheme fit) that may be reason to believe they will be better than their production suggests.
A negative value means that on average, that player is underperforming relative to an average player at that position in the roles that player was assigned. There might be external explanations for why a player might perform above or below their production, however we have found that our Player Values are predictive of future performance.
What is Team Value?
Team Value is simply the Player Values of everyone on your team added up. This allows us to present you information about your team at the Position Group, Side of the Ball, and Team level using the same color scaling as above for easy understanding. Just as a deep blue coded player is better than 95% or more of players at that same position, a deep blue coded offense is better than 95% of other offenses within their conference.
