AVG на FACEIT в CS:GO
Что такое Фейсит? Это отличная система, в которой игроки могут понимать и принимать свой рейтинг таким, какой он есть. Здесь можно играть против профессионалов и заниматься реальной прокачкой скилла. Причем тут AVG, кстати?
Что такое АВГ на фейсит
На FACEIT есть много параметров, одним из которых является АВГ. Это усредненный показатель игрок. То есть, если мы знаем, что у профессионала, условно 35 AVG, то у новичка не будет 35 АВГ, потому что он новичок. Это если говорить языком простых примеров.
Но АВГ на Фейсите – это всё-таки про другое. Средние показатели смотрят по количеству киллов, смертей и прочему. То есть, если есть какой-то средний показатель, то на него и смотрят. В данном случае FACEIT AVG — это среднее количество киллов в матче.
Как посмотреть AVG FACEIT
Мы можем зайти на аккаунт и посмотреть все средние показатели. Это не трудно. Нужно просто открыть профиль и статистику. Причем статистика любого профессионального игрока будет доступна в любой момент. Кроме того, вы можете посмотреть её на HLTV. Но как можно взглянуть на AVG именно по киллам? Смотрим в K/D Ratio. Тут есть и килы, и смерти.
На FACEIT Stats
Один из сервисов, который дает хорошую статистику по каждому игроку. Внутри сайта нужно просто ввести ник и сервис покажет стату фейсит конкретного игрока. В том числе его АВГ.
CSGO Faceit Stats, источник
Удобно тем, что можно посмотреть средний AVG за последние 10 матчей. Допустим, чтобы понять, стоит ли сегодня смотреть демки матчей игрока, сыгранных конкретно за этот день.
На FACEIT Tracker
Тут тоже самое, только можно посмотреть совсем средний АВГ за все время, а не только за последние 10 или 20 матчей. Удобно. Здесь же есть количество мультикиллов.
Есть и другая платформа, FACEIT Tracker, источник
Avg. Session Duration vs Avg. Time On Page – Metrics Simplified Series
Today, We’ll be looking at analytics metrics namely Avg. Session Duration and Avg. Time On Page. We’ll be answering some questions like :
- What is Avg? Session Duration?
- What is Avg. Time on Page ?
- When does the Avg. Time on Page or Avg. Session Duration equals 0?
- Why is Avg. Time on Page higher than Avg. Session Duration and vice-versa?
- Why is Avg. Time on Page different than Avg. Session Duration for one page sites?
- What is the effect of high bounce rates and high exit rates on these metrics?
In spite of these metrics are time-based, they are way more different when the calculation are concerned.
Index
What is Avg. Session Duration & Avg. Time On Page in Google Analytics?
Average session duration is: total duration of all sessions (in seconds) / number of sessions.
Individual session duration is calculated differently depending on whether there are engagement hits on the last page of a session.
As we have seen previously, only engagement hits or interaction hits are used in calculating the sessions.
Because of this, Session duration is also calculated based on these hits.
While Time on Page is simply
The average amount of time users spent viewing a specified page or screen, or set of pages or screens.
If we look at the formulas of these metrics, they look like
Avg. Session Duration = Total Session Duration / Sessions
Avg. Time On Page = Time On Page / (Pageviews – Exits)
Both metrics are totally different and should not be confused with one another. An important point I want to highlight here is,
If the ‘Sessions’ metric is used with ‘Page’ as dimension then the sessions and its related metrics are attributed to the start page of that session.
So, Avg. Session Duration of the particular page is actually the Avg. Session Duration for a session where that specified page was the start page.
Avg rate что это
#metricsql
#metricsql
Вопрос:
Как указано в заголовке, в чем разница между avg from rollup_rate() и rate() в MetricsQL?
Ответ №1:
Предположим, у нас есть временной ряд со следующими выборками по длительности d :
Тогда rate(m[d]) at tN вычисляется как (vN — v1) / (tN — t1) , в то время avg как возвращаемый from rollup_rate(m[d]) вычисляется как среднее значение для частоты выборки (v2-v1)/(t2-t1) , …, (vN — vNminus1) / (tN — tNminus1) .
What is a good "average difference" rating in the site's computer analysis?
I have been using the site’s computer analysis function to review some of my games. It has a category for «avg. diff.» I understand what this is, in that it tells you how much value you are giving away on average per move, using the value of a pawn to equal «1.» So an avg. diff. of 0.50 would mean you are basically throwing away half a pawn per move on average through less than optimal play.
What I do not understand is what is a «good» score for «avg. diff.» 0.75? 0.5? I would assume this is related somewhat to your rating, so that players rating around 1800 would tend to have an «avg. diff.» in the same range, which would be somewhat lower than the expected «avg. diff.» for players who rate around 1500.
Does anyone know if there is a generally recognized correlation between avg. diff and rating? Or what the expected/normal avg. diff. is for players at different ratings levels?
Sorry to answer your question by another question but I’m new and don’t understand this board of results of the site’s computer analysis function : what do you mean by how much value you are giving away on average per move, using the value of a pawn to equal «1.» At the end of the game how much would be a perfect game: 0? And every point is less good? 1 would be what? that per move you «losing» one pawn What means throwing away? Do less than the perfection?
Thanks by advance
If I understand your question correction, you are basically asking if 0 is perfection, or if the score can ever be negative? (In other words if you add value to a position equal to a pawn per five moves, you’d have a negative 0.20.) I do not know the answer, but my guess is that it does not go into the negative range for moves that add value. In fact, I guess since the score itself is an average, your good moves are naturally weighted against the bad so they are counted to some extent. I would guess the best you can get is zero on this. But I’m curious to know if that is right or wrong.
Maybe if you blunder a piece every 10 moves on average then the average difference would be 3/10 = 0.3. Mine was 0.17 which is equivalent to blundering a minor piece every 18 moves.
To me, an avg. dif of 0.5 per move does not necessarily mean you are giving 0.5 pieces per move, it could mean I missed taking a pawn or improving my board position. For example, in one of the games I played where it was a quite open, sophisticated, and calculation heavy board position, both me and my opponent ended up with an average difference of around 0.6. Usually I play games with around a 0.3 average difference, but I realized the reason why the average difference was so high was not because we were blundering pawns, it was because there were many times where we missed the «best move», but at a 1500-1600, especially in open positions, this can be hard to find. In closed pawn structure games, where the «best move» comes naturally since there is usually only one or two playable moves, I find that the average difference for both me and my opponent is reduced dramatically.
In conclusion, I find that average difference is relative based on the difficulty of the position, and can indicate both the loss of your own position, or missed opportunities to gain the slight upper hand, which is why this number can vary drastically from game to game.
My advice if you want to get serious about analysis — don’t use that method, use the «self analysis» method where you can step thru each move and Stockfish will give evaluations of the position, with lines. You will learn so much more this way. You will see exactly what moves were mistakes (or blunders) and where you went from winning to losing or vice-versa. Seeing the lines is also good because it lets you see why Stockfish thinks a particular move is good.
It’s the average loss from the best engine move. For example, if you score (relative difference to the best engine move) the following: 0.03, 0.00, 0.00, 0.04, 0.38, 0.05, 0.00, 0.00, 1.15, 0.14
then your avg is 0.18, if i understand correctly.
Off the top of my head, a consistently good player (1800+) can be expected to miss a chance for a pawn or two over the course of the whole game, but the average will be thrown off if he blunders repeatedly in time trouble. Ordinary not-quite-perfect play => -2 over 40 moves = -0.05 average per move. If he loses every third game badly («badly» meaning three minor-piece blunders in one game) => -11 over 40 moves = -0.28 per move for those games, => -0.13 per move over all games.
If you’re an 1100 player, then expect to make multiple serious blunders most games, say -15 over 40 moves => -0.375 per move average.
I haven’t bothered to check these numbers against anybody’s actual rating or games, because as PsYchHo_ChEsS says, this doesn’t tell you much about your play and doesn’t offer any practical path to improvement. Look at the actual lines to see what you missed.