Куда сохраняются лучшие моменты матча overwatch: Где сохраняются лучшие моменты Overwatch — Overwatch Center

смотрите на свои матчи под другим углом — Overwatch — Новости Blizzard

Функция повторов, которая сейчас доступна на PTR, позволит посмотреть на свои игры под совершенно новым углом. Повторы позволяют посмотреть на ваши прошлые матчи с любой точки. Хотите вид от первого лица, от третьего, с высоты птичьего полета? Не вопрос.

Оттачивайте свои навыки, замедлив или ускорив игру и посмотрев, что именно произошло в ключевой момент, или получите общее понимание ситуации, посмотрев на поле боя сверху (также можно убрать интерфейс). Функция повторов сейчас доступна только на PTR для PC, но скоро появится и на консолях.

Доступ к повторам

Имеющиеся повторы можно просмотреть в своем профиле на вкладке «Повторы». Там вы сможете загрузить 10 последних матчей во всех режимах игры (кроме «Обучения» и «Учебного полигона»). Повторы будут доступны до выхода очередного обновления игры — так же, как лучшие моменты — после чего список будет очищен.

Управление

Вот полезные сочетания клавиш, которые помогут понять, как работает новая функция.

• N: показать/спрятать элементы управления
• W, A, S, D: движение камеры в плоскости
• Q и E: движение камеры вверх и вниз
• Колесико мыши вверх:увеличение скорости просмотра
• Колесико мыши вниз: уменьшение скорости просмотра
• (Ctrl+P): воспроизведение/пауза
• Пробел: переключение между видами от первого и третьего лица
• Левая/правая кнопка мыши: переключение с вида от первого или третьего лица на режим свободной камеры
• F1–F12: наблюдать за игроком (клавиши соответствуют списку игроков в настройках медиа)
• 0: наблюдать за объектом
• (Ctrl+L): показать карту
• (Ctrl+1) – (Ctrl+9): сохранить положение камеры
• 1–9: восстановить положение камеры

Полный список сочетаний клавиш можно найти, нажав Esc и выбрав Настройки > Управление > Наблюдение. Сочетания клавиш полностью настраиваются.

Наблюдайте как профессионал

Вот несколько советов, как вывести просмотр повторов на новый уровень.

• Если хотите понять, как конкретный игрок играет за любимого героя, — наблюдайте за ним, но не забывайте следить и за командой соперника, чтобы почувствовать отличие в их стилях игры.
• Использование карты (Ctrl+I) при просмотре игры поможет вам внимательнее следить за тимфайтами. Значки героев отображают их состояние — это позволяет понять, что происходит с игроком.
• Для лучшего обзора с использованием карты разместите камеру в небе и направьте ее точно вниз. Так вы сможете параллельно видеть карту и происходящее на поле боя.

• Сохранив положение камеры в определенных местах вы сможете наблюдать за боями с разных ракурсов и быть в курсе смены позиций команд. Не забывайте каждый матч сохранять положение камеры.
• Готовя лучшие моменты для своих роликов, не злоупотребляйте функцией замедления. Для наилучшего эффекта используйте ее только для подчеркивания ключевых моментов схватки.

90000 Introducing Overwatch Replays: See Your Past Games From New Perspectives — News 90001 Introducing Overwatch Replays: See Your Past Games From New Perspectives — News — Overwatch Hex Mask UI Icons Arrow Down Arrow Left Arrow Right Arrow Up Brackets Calendar Checkmark Chevron Down Chevron Left Chevron Right Chevron Up Clock Close Connect Copy crown Ellipsis External URL Favorite (off) Favorite (on) Help Home Info Live Mobile Overflow Paginate Dot (filled) Paginate Dot (empty) Pin Placeholder Icon Play Video Circle Play Video Plus Search Team No-Team-Logo_Fallback_003 Ticket Trophy User Input Video Video Play Icon World X Game Mode Icons Hero Icons Map Icons .90000 The Math Behind Your Competitive Overwatch Match | by Lance McDiffett 90001 90002 Very broadly, this is how Overwatch’s Competitive game mode functions: 90003 90004 90005 Players play a round of placement matches after which they are assigned a numerical rating from 0 to 5000 — which is the player’s 90006 SR 90007, or Skill Rating. A player with a higher 90006 SR 90007 is expected to win when matched up against a player with a lower 90006 SR. 90007 90012 90005 A ranked match begins after two suitable teams have been formed.The matchmaking system assembles teams of six similarly ranked players and determines which teams should be paired up against each other based on the average 90006 SR 90007 of the players on each team. 90006 90017 Presumably, the system attempts to make the matches as even as possible for the two teams based on the resulting Expected Score calculation. 90018 90007 90012 90005 After the game is finished, the system adjusts the 90006 SR 90007 of players up or down depending on if they won or lost, respectively, with no change in the event of a draw.90012 90025 90002 So things look pretty similar to the Elo system we discussed above. 90003 90028 Digging into the Game Data 90029 90002 In order to determine if Overwatch’s Competitive mode is using the standard Elo formula for Expected Score or a modified version, I will calculate the experimental Expected Score for varying magnitudes of rating differences. That is the same as asking the question — 90017 «how often did a team win when they had a 50 point rating advantage? What about a 25 point rating advantage? » 90018, and so on.I will then fit those points to the equation below to determine the value of 90006 k ‘90007 (which is 400 in the standard Elo formulation). 90003 90002 After realizing that it would take far too long to collect a sufficient amount of game data myself (typically matches last anywhere from 10-30 minutes), I came across this incredible data set by Myles O’Niell with data on over 3,000 Competitive Overwatch games. 90003 When you find that perfect data set … 90002 Keeping in mind the Elo formula, the columns of interest from the data set are: 90003 90004 90005 90006 my_team_sr: 90007 the average rating of the team 90012 90005 90006 enemy_team_sr: 90007 the average rating of the enemy team 90012 90005 90006 team_sr_delta: 90007 the difference between the two teams ‘ratings [enemy_team_sr — my_team_sr] 90012 90005 90006 result: 90007 the outcome of the match 90012 90025 90028 90006 Some Quick Data Exploration 90007 90029 90002 Looking at the distribution of the average team ratings (below), we see that the median, IQR, and shape of distribution are almost identical for both 90006 my_team_sr 90007 and 90006 enemy_team_sr 90007.Additionally, they range from about 1900 to 3100, which gives us a good representation of the typical Competitive Overwatch player. 90017 Note that if there is a different behavior in the extreme lower or higher rankings, we will not be able to see that. Thus, we must remember the group of players to which our findings will apply. 90018 90003 This violin chart shows almost identical distributions for both teams across the recorded games (the width of the blob corresponds to the density of data points at that value).The median (dot) and IQR (dotted line) are also nearly identical. 90002 Below, we see that the differences between the teams ‘ratings for each match appears to be normally distributed. We also see that the vast majority of games played occur between teams that have average ratings that are within 100 SR of each other. 90003 Distribution of rating difference between teams. Looks pretty normal.Summary statistics for team_sr_delta 90072 90002 The distribution is very slightly negatively biased, as evidenced by negative median and mean.90003 90075 Breakdown of match results 90072 90002 Additionally, it does not appear that the results of the games are particularly biased towards wins or losses. 90003 90075 90028 90006 Determining Expected Score at Varying SR Deltas 90007 90029 90002 The next step is to get an understanding of how the Expected Score changes as the rating between the two teams changes. Recall that the formula for Expected Score is: 90003 90002 90006 90017 Expected Score 90018 90007 90017 = Probability of winning + 1/2 (Probability of Drawing) 90018 90003 90002 So we need to know how likely teams are to win and how likely they are to draw as their opponent’s rating increases or decreases (relative to theirs).I chose to do this via a simulation that randomly chooses games from the data set and calculated the probabilities based on the number of those games played, won, and drawn. 90003 90028 90006 The Simulation 90007 90029 90002 Since the distribution of team_sr_delta is normal, that means we will have a very large group of games where the ratings of the two teams are very close together, and far fewer games where the ratings are further apart. So I can be pretty confident when finding the win rate of games played with SR deltas between say — 0 and 25.However, if I’m looking at games with SR deltas between 75 and 100, that win percentage is more susceptible to erroneous findings due to smaller sample size and noise in the data. 90003 90002 To overcome this, I binned each game based on the SR delta and created a simulation that would do 90017 very simple 90018 oversampling and undersampling for games in each bin. The simulation selects 100 games at random from each bin — with replacement since some of the bins are quite small. It then counts the number of games that resulted in wins and draws and calculates the Expected Score.It does this 1,000 times. You can find the R code for the simulation loop below (if you’re into that kinda stuff). 90003 90106 simOutput <- data.frame (matrix (ncol = 5, nrow = 0)) 90002 colnames (simOutput) <- c ( "Bucket", "GamesWon", 90108 "GamesDraw", "GamesPlayed", "SimRun") 90003 90002 90017 # because I'm too lazy to do the math required for output rows 90018 90108 row <- 1 90003 90002 90017 # loop through iterations of simulation 90018 90108 for (i in 1: 1000) 90108 {90003 90002 90017 # loop through each bucket for this iteration 90018 90108 for (j in 1: 8) 90108 {90108 90017 # sample the games with replacement for bucket j 90018 90108 simData <- data%>% filter (sr_delta_bucket == j)%>% sample_n ( 100, replace = T) 90003 90002 90017 # determine how many games were won, drawn and played for that bucket 90018 90108 simOutput [row, «Bucket»] <- j 90108 simOutput [row, "GamesWon"] <- sum (simData $ win_count) 90108 simOutput [row, "GamesDraw"] <- sum (simData $ draw_count) 90108 simOutput [row, "GamesPlayed"] <- nrow (simData) 90108 simOutput [row, "SimRun"] <-i 90003 90002 row <- row + 1 90108} 90003 90002 90017 # for my own sanity 90018 90108 if (i% in% seq (0,1000,100)) {print (i)} 90108} 90003 90002 90017 # Numerator for expected score 90018 90108 simOutput $ WonHalfDraw <- simOutput $ GamesWon + (simOutput $ GamesDraw / 2) 90003 90002 90017 # Calculate expected score 90018 90108 simOutput $ ELOScore <- round (simOutput $ WonHalfDraw / simOutput $ GamesPlayed, 4) 90003 90159 90002 After running the simulation, I aggregated by bin so I can get a good idea of ​​the distribution of Expected Score by bin.90003 90106 90017 # now determine the probabilities of winning at each bucket 90018 90108 simResults <- 90108 simOutput%>% 90108 group_by (Bucket)%>% 90108 summarise (90108 TotalGamesWon = sum (GamesWon), 90108 AvgGamesWon = round (mean (GamesWon ), 2), 90108 SDGamesWon = round (sd (GamesWon), 2), 90108 TotalGamesDraw = sum (GamesDraw), 90108 AvgGamesDraw = round (mean (GamesDraw), 2), 90108 SDGamesDraw = round (sd (GamesDraw), 2 ), 90108 TotalGamesPlayed = sum (GamesPlayed), 90108 WinProb = round ((TotalGamesWon / TotalGamesPlayed) * 100, 2), 90108 DrawProb = round ((TotalGamesDraw / TotalGamesPlayed) * 100, 2), 90108 TotalELOScore = WinProb + (DrawProb / 2), 90108 ELOScoreSD = round (sd (ELOScore) * 100, 2) 90108) 90159 90002 Now that they are binned by the SR delta between teams, we can look at the distribution of Expected Scores from the simulations based on the bin ( shown below).We can see that at the top left where the enemy team has a rating disadvantage of between -100 and -75, «my team» has a higher expected score (probability of winning) than when the enemy team has a rating advantage of between 75 to 100 (bottom right). I’ve included the dashed line at 0.5 to make comparisons between bins easier. 90003 Distribution of Expected Score by Bin from Super Magical Overwatch Simulator 1000 90002 We also see that distribution in each of the bins is approximately normal, which means that we can do things like regression and confidence intervals without too much concern of violating underlying assumptions.90006 We are now ready to compare the simulation data to the Elo Expected Score equation! 90007 90003.90000 May 2020 Overwatch Tier List: Competitive Season 22 90001 90002 90003 [The June Tier List is Now Out] 90004 90005 90002 Competitive Season 21 has come to a close. And with it, the majority of the Overwatch playerbase waves goodbye to Hero Pools. From now on, the pools will only affect matches in Masters and above (3500 SR). Additionally, map pools no longer exist in Competitive play. Horizon Lunar Colony and Paris have been removed from rotation for retuning or reworks, but the rest of the maps are available for play.90005 90002 Since the last tier list, the biggest patch was the sweeping crowd control changes. Mei, McCree, Reinhardt, Ana, Sigma and Roadhog all saw changes to their stuns and sleep dart. These changes were geared towards toning down the times where a player loses control of their character. None were overly reduced, but the nerfs to Earthshatter, Sleep Dart and Mei’s freeze are definitely noticeable. 90005 90002 Echo has been out for a month now, seeing frequent use in the Overwatch League.She has a high skill ceiling and is incredibly powerful in capable hands. But at the same time there is a lot of room for error. If the wrong target is duplicated, her ultimate can get little value and be wasted. Time has shown she may be overtuned but is not nearly as broken as initially anticipated. 90005 90002 The following tier list exists precisely to help guide players through this more dynamic meta, reflecting how individually well-kitted Overwatch’s 32 heroes are and how well they synergize with other powerful heroes.With each month, there will be a new tier list released to update changes in each hero’s performance from month-to-month (90003 April 2020 90004) so ​​be sure to check back next month to see what changes lie ahead. 90005 90016 90003 Tier 1: Optimal heroes with high win rates and synergy 90004 90019 90020 90021 Reinhardt 90022 90021 Echo 90022 90021 McCree 90022 90021 Mei 90022 90021 Ana 90022 90021 Lucio 90022 90033 90002 Reinhardt still remains in a strong position despite the Earthshatter nerf.The ultimate does not feel as powerful anymore, requiring more team coordination to capitalize on it. Likewise, Ana is still strong despite the nerf to sleep dart. Her Biotic Grenade can still win fights on its own, yet she might fall out of Tier 1 soon as they are experimenting with nerfing her healing output. 90005 90002 The new hero, Echo, rewards high skill. Thus she is has been a staple in the Overwatch League since release. But she is quite vulnerable to hitscan damage, plus if played poorly her ultimate ability can get little value.Mei still remains strong despite more nerfs to her freeze abilities. Smart usage of her Ice Wall can isolate targets and makes for easy cleanup. McCree’s flashbang was also nerfed, but he still remains one of the strongest DPS heroes in the game. 90005 90002 All in all, if these heroes are not banned, they’re clear favorites. 90005 90002 90003 90042 [Related: 90043 90004 90003 90042 When does Competitive Season 23 Begin? 90043 90004 90003 90042] 90043 90004 90005 Image Courtesy of Blizzard Entertainment 90016 90003 Tier 2: Good alternatives to Tier 1 and still viable overall 90004 90019 90020 90021 Ashe 90022 90021 Torbjorn 90022 90021 D.Va 90022 90021 Tracer 90022 90021 Sigma 90022 90021 Brigitte 90022 90021 Baptiste 90022 90021 Moira 90022 90021 Winston 90022 90033 90002 Several of these heroes are either good substitutions to those in Tier 1 or can provide good counters to those heroes. Ashe has seen a handful of improvements and quality of life changes in the past months, leaving her in a solid spot. Against coordinated teams, her ultimate can struggle to get value, which keeps her out of the top tier. Dynamite and her Coach Gun provide nice utility to keep away from enemies and rain down damage on them.90005 90002 Sigma has risen through the ranks as he can provide a lot of extra protection alongside Reinhardt. He and D.Va are equally viable, with D.Va gaining an edge on maps with high ground and Sigma preferred on those without. D.Va has also seen a return to Dive compositions against Echo. She is not as much of an obvious choice for off-tank anymore, so she has fallen to Tier 2 just as Sigma has risen from Tier 3. 90005 90002 Overall, look for these heroes to maintain the same style of play as the ones in Tier 1, just with slightly less oppressive kits.90005 90016 90003 Tier 3: Good situational picks but less strong overall 90004 90019 90020 90021 Pharah 90022 90021 Soldier: 76 90022 90021 Doomfist 90022 90021 Hanzo 90022 90021 Mercy 90022 90021 Reaper 90022 90021 Symmetra 90022 90021 Orisa 90022 90021 Wrecking Ball 90022 90021 Widowmaker 90022 90033 90002 These heroes can all be perfectly good in niche situations or when being played by players who are specialists in that hero. Some, like Symmetra, can be good on certain sections of certain maps.However, when it comes to rating them against the rest of the Overwatch roster, they are currently more easily exploitable or have better options. Look for these when certain heroes are banned to come up with mini-metas comprised of less popular picks. 90005 90002 90003 90042 [Related: 90043 90004 90003 90042 When is the Next Overwatch Event? 90043 90004 90003 90042] 90043 90004 90005 Image Courtesy of Blizzard Entertainment 90016 90003 Tier 4: Difficult to have success in the current meta 90004 90019 90002 90003 90004 90005 90020 90021 Sombra 90022 90021 Roadhog 90022 90021 Zenyatta 90022 90021 Zarya 90022 90021 Junkrat 90022 90021 Genji 90022 90021 Bastion 90022 90033 90002 Overall, these are slightly less powerful overall.than other heroes in the game. Some, like Sombra and Genji, are too reliant on their ultimate abilities. This means the team has to play slower to accommodate that, and the current meta does not favor that passive style. There are moments where they can fit into niche compositions, however, there are typically more viable options that are available. Look elsewhere, if possible. 90005 90002 90003 90042 As a reminder, this tier list is currently operating off of the latest live patch, 90043 90004 90003 90042 1.47.1.0. 90043 90004 90003 90042 This list will be updated as patches continue to come out throughout the month of May and a new list will be published in June. 90043 90004 90005 90002 90005 90168 90003 Stay Connected 90004 90171 90002 90042 Featured image courtesy of Blizzard Entertainment. 90043 90005 90002 90042 You can 90043 90042 like 90043 90042 The Game Haus on Facebook and 90043 90042 follow 90043 90042 us on Twitter for more sports and esports articles from other TGH writers along with Matthew.90043 90005 90002 90042 You can also follow Matthew 90043 90042 @setanta_ow 90043 90042 or message him on Discord (setanta # 2155). 90043 90005.