Quote:
Originally Posted by Chris is me
In games where the scoring actions of different teammates are more separable, like in 2010 or 2013, OPR is more accurate. In games where scoring actions are less separable, like 2014, OPR is much less accurate.
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In terms of the sample sizes we're given, I absolutely agree.
However, over an infinitely large sample I'd love to see what OPR could do in a game like 2014. Jared made reference to "secondary effects" in his post, referring to freeing up resources on your alliance that could be spent elsewhere. That's the type of thing, in theory, OPR could be better at tracking than manual data entry. It's easy to manually track how teams complete objectives and directly impact the scoresheet, it's much tougher to determine how they impact the match in less obvious ways. The most obvious example is defense, which is very hard to quantify accurately (and "DPR" has rarely done a good job at it). In a game like 2014, where so much of the match is spent playing "away from the ball" (playing defense, positioning for the next cycle, blocking for teammates, etc), it can be really hard to determine how effective some teams are at impacting the score sheet. This is even true in professional sports, where broadcasters and analysts frequently talk about "intangibles" and how players impact the game in ways other than scoring (think good defensemen in hockey or offensive linemen in football, for instance). Sports have also turned to more advanced metrics to try and solve this, ranging from the sabremetrics movements in baseball and hockey to the motion tracking in basketball and soccer. That's the type of area where OPR/DPR/CCWM could potentially have significant value. However, a 12 match sample size (with random alliance partners/opponents) is nowhere near enough data to iron out the noise.