Major thing first. Getting data on the level of game (i.e. invite vs advance etc) seems very important, but I dont know how / where to source it. If anyone knows that would be awesome!!!

If ANYONE knows how the stats healing received "real" and damage "real" are calculated, please let me know. I would think they mean non-overheal damage taken / given, but players with 10s of thousands of damage have dt_real and dmg_real stats in the hundreds or sometimes even teens. Doesnt make much sense to me.

Ive already begun implementing the role based (pocket vs roamer) distinction. However, when running into games where either the scout / soldier have a similar enough hr% i just random assign them. Not sure how else to handle that tbh.

I'm also working on map-adjustments to the model. Its more challenging than I thought, but I would love to implement something much more complicated than just letting python build and shrug my shoulders.

This approach is actually very similar to the method I am using to generate player impact! I train the model to get good at predicting whether or not a team lost based on every player's stats. Then use machine learning techniques to figure out which player's stats affected winning for that game the most. I will definitely incorporate a dpm to dtm ratio, that sounds like a good idea.

What im definitely getting is that I need some way to place more emphasis on games that are "higher level" like invite and advance. I would absolutely love to do this, but I am having a hard time figuring out a good way to get data on whether a player where a player ranked now, let alone in the past from games in 2017. If anyone has a good data source for this currently, but preferably historically as well, that would be AWESOME!

For sure on using ratios. Not a single predictor that goes into the model is in its raw form. Whether its normalized per death or per minute, having anyhting in its raw form would bias things heavily like you stated.

Volume vs raw stats was a major issue I was considering. However, I am not 100% sure how I could address accounting for game pace without heavily biasing the model to favor short game impactful stats, vs long, drawn out match stats.

I would love to include metrics about death timing and whether or not a death could actually be "good" or "bad", but the raw data from counting stats doesnt get that granular. Just like with any metric or statistic, a grain of salt needs to be taken when looking at it. Counting stats definitely cant capture everything, especially not complicated relationships like sacking. Parsing the raw demo files themselves would be super cool, but working with demo files is Very challenging. Maybe in the future I could work on stats that incorporate geospatial data on whether a kill "generated space", but I'm just one guy :(

I would say so. I want it to be a similar 1 number summary. I do not know how their stat's are calculated, but from what I've read, I would say we are trying to accomplish similar things.

Edit: After looking more into the HLTV and vlr.gg player rating, I dont think that PIM and those numbers are the same. The player rating seems to be a global rating, while PIM only calculated impact on a Per Game basis.

Making something like that for tf2 would be really interesting, but I would definitely need to figure out a way to understand what games are Invite vs pug vs all the other leagues are

1.
Roles right now are not coded explicitly, but more on that in notes reply. Although Im not sure what you mean with the firefox extension. If you mean it would be easier for the dashboard to be in an extension... Yes it would, but I don't know if i could make that happen :(
2.
I will definitely add healing% to the match overview so its easier to access
3.
Okay yeah. I will definitely be looking into model improvements to incorporate the map into the model. Its not as simple as what I am currently doing, but hearing this makes me question the output heavily.
4.
Okay good. Logs.tf outputs data in a very particular way, and if I had to refactor I think i might cry.
5.
Hm. I am aware that a lot of private servers are used for scrims, but it would be very hard to programatically figure out which of these servers are "Real" as I wanted to only focus on what would be considered "Serious" comp matches and not just random Pugs. Will def look into fireside
Notes:
Creating pocket vs roamer would be a great idea. I am currently just randomly assigning scout_1/2 solider_1/2. I was scared to assume pocket vs roamer with heal pct, as I thought that notion was largely defunct. But at least for scout now post medic heal speed buff, probably not. This would def help with debiasing the model.

I would love to include division. However, logs supplied from logs.tf do not make that data easily accessible. I could try to bind it in from other log providers, but data might get scarce

Edit:
Hm. Thats strange on the scout. I currently calculate the class role by looking at which class you played the most time on. I will definitely look into it, because on logs.tf it doesnt even show that you played scout at all....
Tysm!!! This is all amazing and is a huge help.

I’m a data scientist who just built a TF2 Competitive 6s stats dashboard using logs from logs.tf — you can check it out here: Dashboard Link.

The main feature is the Player Impact Metric (PIM), a 0–10 score made with machine learning that estimates how much a player’s performance contributed to winning, similar to advanced NBA stats. I.e. if a scout has a PIM of 6.5 it means:

“Player’s performance ranked in the top 65% of all scouts for match impact.”

I’m looking for feedback from competitive players on what’s useful, what’s missing, and what could be improved.
Questions for the community are here in this google doc: Google Doc

Check it out, offer feedback, and if you can answer any of my questions that would be amazing!