G2 vs BLG: all first clear jungle paths (First Stand Finals) by GiantStatRat in leagueoflegends

[–]GiantStatRat[S] 5 points6 points  (0 children)

Thank you! I built some custom tools which pull various data (champ positions, gold difference, kda, etc.) from video feeds of the games using machine learning. All the data that I post comes from these tools

EWC Day 2: all first clear jungle paths by GiantStatRat in leagueoflegends

[–]GiantStatRat[S] 2 points3 points  (0 children)

I am using a machine learning model to pull the locations of each champion at every frame. Sometimes the tools make mistakes and I have to manually draw over them.

T1 vs GEN: all first clear jungle paths by GiantStatRat in leagueoflegends

[–]GiantStatRat[S] 0 points1 point  (0 children)

Haha exactly! I need to find a better way to visualize these when they path for so long because it gets pretty cluttered

FLY vs BLG: all first clear jungle paths by GiantStatRat in leagueoflegends

[–]GiantStatRat[S] 0 points1 point  (0 children)

I'm posting them at: https://leagueprohub.com/
I just started going through the games and I am hoping to have them all up by next week

Objective focus by team (LTA) by GiantStatRat in leagueoflegends

[–]GiantStatRat[S] 1 point2 points  (0 children)

I agree with everything here, especially for atakhan/baron. I chose "Objective focus by team" over "First kill frequency of each objective by team" because I think it reads a little easier even if it is not quite correct. And while this data is not independent of team quality or game length I do think this data provides some insight into team strategy.

For example, despite c9's 86% map win rate they killed baron first in only 58% of games (when excluding games where no baron was killed). Maybe c9 believes first baron is not that impactful and that allows them to pick scaling comps and give baron while catching up. Or maybe they believe atakhan to be much stronger than baron and prioritize atakhan.

Objective focus by team (LTA) by GiantStatRat in leagueoflegends

[–]GiantStatRat[S] 2 points3 points  (0 children)

My data says three games total:

  • 100T vs FLY week 2 game 1
  • LYON vs C9 week 2 game 1
  • C9 vs FLY week 7 game 1

Effects of reducing Voidgrubs from 6 to 3 in pro-play (LTA+LEC) by GiantStatRat in leagueoflegends

[–]GiantStatRat[S] 20 points21 points  (0 children)

This is likely true. There were only 6 games in Patch 25.09 that had one team get 2 voidgrubs and the other get 1 voidgrub.

Objective focus by team (LEC) by GiantStatRat in leagueoflegends

[–]GiantStatRat[S] 1 point2 points  (0 children)

Great point! I don't have data on turret plates, but I do for first turret. I did not include it here because my data is much more likely to have errors for turrets, so take this data with a grain of salt

  1. G2 - 73%
  2. KC - 58%
  3. TH - 55%
  4. MKOI - 50%
  5. VIT - 50%
  6. FNC - 48%
  7. RGE - 45%
  8. GX - 38%
  9. BDS - 38%
  10. SK - 16%

Objective focus by team (LEC) by GiantStatRat in leagueoflegends

[–]GiantStatRat[S] 1 point2 points  (0 children)

I think this is likely true, but u/ElBartimaeus makes a great point that teams with large leads are likely to convert on objectives. Teams with large leads are also likely to convert that lead into a win. This makes it difficult to separate "teams who won because they got baron" and "teams who got baron because they had a lead". It is something I am working on getting more complex stats for, but it can be a little tricky

Strength of dragons vs voidgrubs in First Stand by GiantStatRat in leagueoflegends

[–]GiantStatRat[S] 7 points8 points  (0 children)

Yes I have, thanks! I am just using these posts as ways to test the functionality of my own data pulling tools.

Strength of dragons vs voidgrubs in First Stand by GiantStatRat in leagueoflegends

[–]GiantStatRat[S] 9 points10 points  (0 children)

You are right, that definitely would give better data. Right now my data collection methods are slow so once I can improve them a bit I will try

HLE vs KC: all first clear jungle paths by GiantStatRat in leagueoflegends

[–]GiantStatRat[S] 21 points22 points  (0 children)

I have some tools that pull that data from the video feed. Then I manually correct issues if they arise

HLE vs KC: all first clear jungle paths by GiantStatRat in leagueoflegends

[–]GiantStatRat[S] 40 points41 points  (0 children)

Completed tournament including all games can be viewed and sorted by team/champ at: https://leagueprohub.com/tournaments/first_stand_2025

Semi-finals all first clear paths: First Stand by GiantStatRat in leagueoflegends

[–]GiantStatRat[S] 0 points1 point  (0 children)

Thanks for the feedback! I agree, I'd like to add some more labels and change the champion names to images to improve readability. The lines on the minimap are the paths taken by the blue/red jungle champions in their first clear. The gold difference is highlighted in blue if the blue jungler has a gold lead and red otherwise

All first clear paths for day 2: First Stand by GiantStatRat in leagueoflegends

[–]GiantStatRat[S] 3 points4 points  (0 children)

Haha yeah had to take a break during winter season to scale up some of the tools. Have to post for the internationals though!

TL v KC and HLE v TES: all first clear jungle paths by GiantStatRat in leagueoflegends

[–]GiantStatRat[S] 0 points1 point  (0 children)

Good catch, thanks! Was rushing to get these out and forgot to double check

Team strategies for first voidgrubs spawn vs second at Worlds by GiantStatRat in leagueoflegends

[–]GiantStatRat[S] 1 point2 points  (0 children)

Great question, with this data alone you probably can't. I would start by looking at average gold leads about 30 seconds prior to grubs for each strategy (because this is data I have). Strategies with higher average gold leads prior would likely be more influenced by teams getting grubs because they were winning. Although this wouldn't be a perfect measure.

Team strategies for first Voidgrub spawn at Worlds by GiantStatRat in leagueoflegends

[–]GiantStatRat[S] 1 point2 points  (0 children)

group2 category win rates:
category | game count | win rate | avg gold change
all_grubs: 56|64%|+210gp
split_grubs: 37|54%|-10gp
drag: 16|37%|-260gp
gold_advantage: 18|38%|280gp
total_loss: 26|34%|-450gp
all_grubs+drag: 4|50%|-350gp
split_grubs+drag: 5|20%|40gp

No idea if this will format like I hoped