Q: How are team games rated
QLStats doesn't care about teams. This may sound wrong, and to a certain degree it is. But there are reasons for this:
In general, public Quake Live servers do not enforce balancing. Making someone lose points for playing in a bad team is unfair.
The social aspect of using team win/loss is that nobody wants to join a team mid-game when it is likely to lose.
The mathematical aspect is that win/loss of unbalanced teams adds volatility to the rating data and makes it less accurate for predicting results and matchmaking.
Aside from that, Glicko (and Elo) can only deal with 1-vs-1 outcomes, so splitting up the bounty between winners is not handled by the system. (There are approaches like an imaginary aggregated/averaged opponent, but that also has its flaws).

Therefore, qlstats rates players individually, regardless of teams.

Q: How are players rated after a match
First step is to filter out matches and players which don't meet certain requirements (round limit, match participation time, ...).
Then a score is calculated for each player's performance in the match. The exact formula depends on game type.
The score is adjusted for the player's match participation, either based on time or rounds where applicable. This is what you see in the "Perf" column.
Then the player list is sorted descending by score. Each player loses to all player above him and wins against all players below him - with a small margin to allow draws, which depends on game type.

Then for each player pair their ratings and the outcome ("win/loss/draw" according to scoreboard position) is passed to the Glicko system.
Glicko does not care about the calculated scores, only win/loss/draw matters. By 1 point or 1000 is irrelevant. Your rating is adjusted by winning multiple times and not by winning 1 time with a big margin. On Toxicity 20:0 wins are common, on Lost World not so much. The score is only used to put you in order with other players in the same match.

Each multiplayer match is treated like n*(n-1)/2 duels. All the gains/losses are accumulated and then the ratings are updated.

Q: What does the rating value mean
Glicko ratings consist of 2 values, the estimated rating "r" and a rating deviation "RD" which expresses confidence/uncertainty in that estimate.
In general RD gets lower with every match you play (except for very unlikely outcomes) and increases over time for inactivity.
So Glicko gives you a rating as r±RD as an interval for a player's skill rather than a single number. It's like an estimate and a standard deviation.
To keep things simple, qlstast.net shows r-RD as as the rating value, which puts the player somewhere at the lower side of the confidence interval.

The Glicko rating system distributes players and their ratings in a way that forms a statistical standard distribution (Gaussian bell curve) in the range 0-3000. That makes it possible to say something like "If player A is rated 200 point higher than player B, he has a 68% chance of winning".
(The numbers here are examples, the exact values would have to be derived from the data first, but that's past my math skills).

Q: My team won 10:3 but I lost points. This system is broken!
Not necessarily. If teams were unbalanced to begin with, winning is easy. If you were overrated or other players underrated, it is a normal that your rating will go down in a single match. Statistics works on averages and large data sets, not single data points.

Q: Why do I only gain 3 points but lose 50
Same as above. Statistics work over averages and large data sets, not single events (match results).
If you are higher rated and expected to win with 80%, it means you will often gain a small set of points and lose a big amout a few times so on average you will stay on the skill the system estimated you.
Normally ratings should not be applied after a single match, but instead over a rating period. The you would not observe these jumps and just stay on your estimated skill. But the jumps are the price to pay when you want to see immediate changes after each game instead of averaging over a week.

Q: Why Glicko and not Elo, Glicko2 or TrueSkill?
TrueSkill is considered "the best/only rating system for multiplayer games", but it is patentend and none of several Javascript implementations I tested produced the correct results for even a single match. Only a Python implementation worked, but I don't speak Python.

All other rating systems were designed for 1-on-1 matches (chess) and require some workarounds to deal with multi-player games, which have an impact on data quality.
Elo is mathematically simple, uses a single rating value per player and the total amount of points lost/gained in a match is always 0.
If an unrated Pro beats you in his first match, you'll lose a big share of points that you might need quite some time to recover from.

Glicko improves Elo by adding the RD value for uncertainty. If there is low uncertainty in your rating but high uncertainty in your opponent's rating, your rating will change by a smaller amount than his.

Glicko-2 adds a measure for volatility on top of that and more magic parameters, and when I put to the test with QL match data, the results showed some anomalies.

So Glicko-1 was the only viable solution for me with a working implementation and plausible results.

Q: Why are there no global rankings
If you let 1000 bad players play only on one server and a 1000 top players play on another server and they never mix, then both groups will create a standard distribution in the range 0-3000 independantly. So you have a 2200 rated Noob on one server and a 1300 rated Pro on the other.

For matchmaking purposes the numbers make perfekt sense. They are good for the players on the Noob server and also for the players on the Pro server.
But you can't compare them. Only when they all play against eachother and ratings adjust to eachother, you can compare them.

For the same reason you can't compare AU, EU, US ratings or ratings from a European Vampiric PQL 8v8 CA server with a closed community with the rest of the European CA players.

Q: Which matches are rated, how is the score calculated, ...
Match filters: https://github.com/PredatH0r/XonStat/blob/master/feeder/modules/gamerating.js#L108
Match/player filters: https://github.com/PredatH0r/XonStat/blob/master/feeder/modules/gamerating.js#L374
Score formulas: https://github.com/PredatH0r/XonStat/blob/master/feeder/modules/gamerating.js#L503