Supports and junglers are the players tasked with contributing most to the vision game. In the first 15 minutes of games, some players at the 2016 Mid-Season Invitational have taken up that role with more gusto than others.
The tables below count the average number of wards placed by the supports and junglers at MSI in the first 15 minutes of group stage games.
Royal Never Give Up’s star, Mata, stands head and shoulders above the crowd, placing the most pre-15-minute wards of any player.
Surprisingly, a jungler took second spot: SK Telecom T1’s Blank has earned quick access to his Tracker’s Knife, it seems, lighting up the map with an average of 6.6 green wards in the first 15 minutes and buying plenty of pink wards to go with them.
Counter Logic Gaming’s Aphromoo and the Flash Wolves’ Karsa have both fallen off the pace for their positions. Aphromoo, on average, has placed only 73% as many wards as Mata in the first 15 minutes. Karsa’s early-game ward output has been just 67% of Blank’s.
For more player vision stats, check out the MSI 2016 Player Stats page, where you can see average wards per minute (WPM) and average wards cleared per minute (WCPM) for all players.
I’m experimenting with a new feature for the site by presenting the first Oracle’s Elixir calculator page, where you can estimate a team’s probability of winning a game as of the 15-minute mark.
This calculator uses the formula and coefficients that generate my early-game ratings and mid/late ratings.
Take the calculator for a spin, and let me know if you have questions or find any issues with the tool. I’m still exploring the options and limitations of the plugin I’m using. For example, it doesn’t seem to like it if you type in a negative number, which is why the calculator asks you to enter everything from the perspective of the team with the gold lead.
The LCK Spring split regular season has wrapped up, and the gauntlet-style playoffs will be kicking off soon. Did the “right” five teams qualify? Here are a couple of alternative ways to rank the teams based on their game results.
Pure win percentage leaves almost all of the teams in the same order, because it’s used as the first tiebreaker in standings. CJ Entus did benefit one spot in the official standings by winning seven of their ten Game 3s, which made them look slightly better than they may deserve.
By Gold Spent Percentage Difference, the standings are shaken up pretty significantly. Suddenly KT Rolster and the Jin Air Green Wings don’t look so good, while the Afreeca Freecs seem like legitimate contenders and Longzhu Gaming are bumped all the way up into a playoff spot.
GSPD rewards teams who win decisively and lose by narrow margins, and that definitely fits the profile of the Freecs, who are one the LCK’s better early-game teams but sometimes struggle to close things out in the mid/late-game. In Longzhu’s case, their GSPD is higher than their place in the standings in part because of their struggles in Game 3s, where they had just a 2-5 record.
The table below has the actual numbers for each of these metrics.
Based on these numbers, it could be argued that Longzhu deserved a playoff spot over the Jin Air Green Wings, but in the end the wins are what matter.
For complete LCK team statistics from the Spring regular season, hit the Team Stats page.
The systemic Match History issues have returned. Riot is working on it, but until resolved, I won’t be able to update stats from the NA LCS and EU LCS playoffs, or from the LCK week 12, day 4.
Stay tuned for updates, or follow me on Twitter for live notifications when stats are uploaded.
Update April 6: All issues have been resolved.
Way back in January, I put out a pair of articles previewing the NA LCS Spring split. I ranked all 10 teams from weakest to strongest, based on what I thought of their offseason moves and the potential strength of their new rosters.
After a two-and-a-half month regular season, including an array of roster changes and substitutions, it’s time to look back at those preseason rankings as a way to remind ourselves of the expectations we held and to dissect some of the unexpected outcomes. We also get to see how accurate I was with my predictions, so that’s fun!
Let’s kick things off with the team I, and the rest of the world, predicted for a 10th-place finish.
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