Glicko 2 Benchmarking
Glicko2 is a ranking system for duel games. I use it to rank Quake Live duels. The idea is to find a value, the strength of a player, called R in the system. The higher the R value, the better the player.
How does Glicko2 estimate R? It looks at historical data and uses bayesian methods to estimate the current strength of a player. Like all other systems, there are parameters we can tune for a set of players and a set of duels:

What initial R value should we pick for new players? We could pick 1500 as the base, but it may be that there are better initial values than this.

What initial RD value should we pick for new players? The RD value measures a belief in a given Rvalue. A large RD means we have relatively little information and hence can’t trust the R value as much for rathing purposes.

What about the initial Sigmavalue? The Sigmavalue is used to adjust ratings for players which are consistently fooling the rating system. It accounts for the situation where a player has been ``locked'' to a given rating and then suddenly improves. In other rating systems, it would take a long time to move that players R value to the correct position. But Glicko2 will detect this and adjust the Sigma for the player, allowing for greater Rstrides.

What about the value of Tau? Tau is a global value which controls how much power Sigma has. Games with high random variability needs a low Tau around 0.2 to account for randomness in games. Games with lower variability can run with higher Tauvalues. Perhaps as high as 1.2 at maximum. In game types with high Tauscores, there is very little random behaviour. Hence a win or a loss is more decisive and Sigma should adjust with a greater factor.
The 4 different values spans a 4dimensional space. We can try to tune the values for the game type we are looking at. By doing so, we can hope to find good values which optimize the ranking system to the game type.
NelderMead numerical optimization
There are many optimization algorithms. The original algorithm when I operated in pure Erlang was quite computationally intensive. I used the concept of Simulated Annealing as an optimization heuristic. This algorithm works well, but requires many computations in order to find good values.
I had experiments of Glicko2 computations in Erlang, Go & OCaml. Given that the fastest Erlang code runs a round in 8us, OCaml in 2us and Go in 1us, I decided to focus on Go for the next round of work.
I want a more intelligent search for an optimum. One algorithm is NelderMead from 1965. This algorithm has some trouble in certain situations. And it may converge to a wrong point. But it often works well in practice. So I set out to implement the algorithm for Go. The efforts are here, including tests and benchmarks: http://github.com/jlouis/nmoptim
NM is nice for our ranking work since it does not need numerical derivatives of the function being computed. Rather, you start with a simplex which you then iteratively move around. You can imagine a fishing net spanning the whole sea. NM proceeds by moving the fishing net according to some rules in order to find the fish you are searching for. In this case the minimum of the function at hand.
NelderMead by itself is not easy to make parallel. But the ranking function is trivially parallelizable. So I opted to do operations in parallel and then speed up the ranking code. By using sync.WaitGroup in Go, I immediately got to around 500% CPU usage on an 8 core machine. So now I am blocked on speedup. Getting 5/8 of the cores to do meaningful work and a factor of around 5 in speedup is nice. My ranking runs are about 5 times faster in wallclock as well. And that is disregarding other possible optimizations.
Furthermore, we find a good value in 22 iterations of NelderMead with 45 function evaluations. I guess it was not that important to speed up the computations after all since Erlang would have been able to run this, albeit in hours rather than minutes. Go completes the run in in about 3 minutes on my current Ivy Bridge Corei7 laptop, which is a fine speed for 2.5 million quake matches. It also ranks in around 925ns per match which is around the numbers I got in my Glicko2 benchmarks for a single test match.
The next steps
I can now optimize in Go, but there are still more work to be done before it is on par with the Erlang code:

We need to be able to rank pairs of \{Player, Map}. This is rather easy.

The prediction code only runs on the last round out of 99. It should preferably run prediction on the last 45 rounds instead so the predictions even out over a larger area. This will account for a single round of matches becoming too crazy.

Ranking expected scores need some clamping which I am not doing currently, but that should be easy to add as well.