👾Proof-of-Performance Protocol
Let's dive further into the protocol!
Athlete Work to Power Conversion
The way to conceptualize this concept is to think of the work that an athlete produces in the form of energy. Every game, athletes perform work and the output of this works is captured in primary, secondary and tertiary statistics. For example, the graphic below demonstrates how this works for basketball.
Through the use of real-time sports data, we can build a model to predict the number of calories (cal) that are consumed and convert it into power (watt). In the case of the NBA, a player burns on average 650-750 cal / hour or 12 cal / min. We can apply this across a multitude of sports such as Basketball, Football, Hockey, MMA, Golf and Baseball (although this would be the most difficult given the various roles on the field).
For example, the energy generated by an NBA player who is playing a 48 min game can be calculated as:
0.2 x 4.18 = 0.826 watt / second 0.826 watt / second x 60 second / min x 48 min = 2407.68 watt (or 3.22 HP)
Reward Distribution Algorithm
The $PROS supply and distribution is provided in the Tokenomics.
In the case of ProspectPicks, users predict a combination over/under players stats and stake a predetermined number of $PROS (similar to a parlay). If they get all of their picks correct, they receive a multiple (up to 25X) on their staked $PROS. The distribution of $PROS is a closed loop where winnings and incentives are distributed from the Prospect Treasury and losses are returned.
In the case of ProspectArena, users challenge other users in specific categories (energy, points, goals, rebounds, etc.). Users select how many $PROS they would like to stake, then depending on the outcomes in real-life, the $PROS are redistributed accordingly. Users receive a percentage of $PROS they staked based on their results (200%, 70%, 50%, etc). Users can earn bonus $PROS for things such as a bad beat, big upset, winning streaks or breaking a losing streak.
Statistical Performance Benchmarking
Most fantasy sports models take a linear approach to measuring performance, for example 1 TD = 6 pts, or 10 yards = 1 pt. Prospect uses statistical methods such as Performance Benchmarking to incorporate both the gross production and the relative performance of an athlete. This enables the PoP protocol to look across time and sports.
For example, after an NBA game, if there are 5 players that Athlete NFTs staked. Prospect will calculate the median for each in-game statistical category based on these 5 players. Median [points] Median [rebounds] Median [assists] Median [steals] Median [blocks] Median [3-pts] Median [turnovers] Median [field goals missed] Median [free throws missed] Once the Median for the day is calculated, the algorithm will provide a Median Deviation Percentage [MD%] for every player relative to the Median.
Example: if the median for points is 15, and Lebron scored 25, then than median deviation % is (25 - 15)/15 = 66.67%
The second piece is Performance Efficiency, which dictates which players will earn more $PROS vs. other players. The concept here is pretty straightforward. It's important to capture how well an athlete performs relative to peers and how efficiently. Those athletes that perform inefficiently despite putting up statistics would earn a lower reward while those who have efficient performances would earn a larger reward.
Last updated
