- West Brom – Burnley / 160$
- Oriol Roca Batalla – Juan Manuel Cerundolo / 152$
- Genoa – Como / 197$
- Georgetown Hoyas – Lynchburg United Dragons / 200$
- Denmark – Norway / 152$
- Boyaca Chico – Atletico Bucaramanga / 220$
- Ann Li – Astra Sharma / 196$
- Marousi – Sabah Baku / 162$
- Aris – Valencia / 151$
- Shakhtar Donetsk – Young Boys / 165$
Volatility rating
In American football, a player’s volatility rate shows how consistently they do from game to game throughout the season. There are two different types of measurements that this one is not: streakiness and variance. Streakiness looks at how close or far apart results are from the mean. Instead, volatility is more about how consistent success is over different periods, from week to week, month to month, or season to season.
- Offensive Linemen: Gini indices, which are usually used to measure wealth imbalance, are used to figure out volatility for offensive linemen. In this case, it’s about how these players’ overall season success is spread out. The estimate is based on the average PFF grade and the number of successful blocks made by linemen from season to season. Offensive tackles who are very inconsistent in their speed and footwork tend to have worse games because their job is to keep defenders from pressuring or sacking the quarterback and to make the run game easier.
- Quarterbacks: The Gini coefficient can be used to measure how volatile a quarterback is by looking at how their total season EPA (Expected Points Added) is spread out game by game. This number combines rushes, passes, penalties, and games in the playoffs. “Volatility Over Expected (VOLoe)” is a way to determine how volatile a quarterback is based on their season EPA and total opportunities.
- Wide Receivers and Running Backs: The coefficient of variation can be used to look at fluctuation for wide receivers and running backs. You can find this number by dividing the standard deviation (a measure of how volatile the data is) by the set’s average. For example, a study of the top 10 running backs and wide receivers found that receivers had a higher coefficient of variation than running backs, which means their results were more volatile. The data set is less likely to be wrong when this number is near zero.
These volatility measures look at more than just average performance numbers to get a more complete picture of a player’s performance. They focus on consistency and how performance changes over time. This can be very helpful when planning to manage a team or play fantasy football.