One of the biggest problems with professional Rocket League is the lack of a clear way to determine who the best players in the game are. For some, just looking at the scoreboards is enough to get a good idea of player performance. Casters and analysts will use GPG (Goals per game), shooting percentage, or other per-game measures. But more casual fans may not have the time to research what statistics are most important when determining player performance.

This is where PER, or Player Efficiency Rating comes in. PER was created by John Hollinger in the 1990’s as a way to directly compare players in the NBA. PER is a single number that accounts for all major statistics and gives a good understanding of a player’s performance in a season, or across a span of games. Below is the formula I have created to calculate uPER (Unadjusted PER) and PER. I will go into detail regarding my methodology for each section and finish with a short note on issues with PER.

Formula for calculating uPER

In the NBA PER calculations, uPER is multiplied by [aPER * (15/lg_aPER)] in order to get the league average in every season to 15 and this final number is the PER rating. Because aPER involves the calculation of the pace of the team in question, it is impossible to calculate for Rocket League (Pace involves possessions. Read more here). Instead, after calculating the uPER using the formula above, I multiplied by the RegionalFactor. The RegionalFactor is just a number used to get the average PER to 15.

In RLCS League Play, teams from different regions do not play each other, so I used a different RegionalFactor for each region, for each season. This same principle will be used moving forward. Also, a different RegionalFactor will have to be used to set the average PER to 15 at the World Championship in November. Eventually I plan to collect enough data about all players in all regions that I can create a substitute for RegionalFactor that can be used. But because there is a limited amount of events where international teams mix, it is hard to get a good amount of data.