We all know that the ELO System is a mess right now. It’s far too easy to cheat the system to get infinite “FUN” bashing lower rated players. That’s why I suggest an automated AI that performs a standard analysis on “Gamestatistics” to find a better fitting ELO and also help with SMURF detection.
Capture Age already shows the most significant statistics to tell a good player from a better player apart. K/D, ECO K/D, TC IDLE TIME, etc. Paired with the information about Uptimes, Win/Loose, Civ information’s we have anyway. We should be able to find a ELO-Bracket a Player fits into. The AI can do that by evaluating the Gaussian normal distribution for every datapoints.
Perfoming a standard analysis over these distributions and datapoints we can compare players and their ELO.
If a new player now starts to play his first 10 ranked matches, we can compare his dataset to the set of all other players.
As a benefit we can also use this system for a SMURF detection:
If a player is reported for smurfing we can compare the stats of his last game with the expected stats for his ELO-Range. If the discrepancy is too high. You can immediately use the data to reevaluate his ELO. (Maybe paring him for the next 10 Matches with enemies that are from the ELO set that corresponds to the set where he was reported for smurfing)
- What do you think about my idea?
- What are relevant datapoints?
- Eco K/D,
- TC idle time (only for the first 30 minutes or more than 100 vills)
- age up times
- deathtime/reason of the first 10 vills
- used unit stances
- win game duration
- lose game duration
How should the system react after a player was reported as a Smurf and a significant discrapency between his ELO and the Systems detected ELO is found
Are there any flaws I overlooked?
to be discussed at a later point
- Can we adapt that to change the ELO distribution between players of a Teamgame, after the Team already earned their points by comparing average TeamELO as we do it right now?