match2kd

match2kd aims at overcoming "Callof" API rate limits by estimating a match difficulty ("lobby k/d") from its features (players' metrics in that match).
Instead of getting every player matches history (hundreds of calls) to calculate the score, a model predicts the difficulty with a single prior API call (a given match metrics).

The model is one of the 3 component of a more global personal side project centered about COD API / metrics intelligence:

• wzkd : a Streamlit-based dashboard that collect, aggr. and visualize player's stats from Call of Duty Warzone (1), where we also deploy the model.
  
• wzlight (also on pypi) : a light, asynchronous python wrapper for 'Callof' API that was (also) used to build our dataset / power the dashboard.
  Edit - June 2023. While our approach is still valid, Activision discontinuated its API with the launch of Warzone 2 and apparently has no plan to bring it back.

Under-the-hood pain points one tries to resolve

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• Calculation of an accurate "lobby kd" (players' average kills/deaths ratio), the main metric used by players to estimate a match difficulty, isn't possible without some form of special access / partnership with Activision like cod tracker / wzranked stats tracker websites and/or a continuous matches / players profile data retrieval, caching & storage.
• One of the rate limit of the COD API (not documented) is said to be 200 calls / 30 mn or so.
• Our target, "Lobby KD" or more accurately "avg players' kills/deaths ratio" is calculated as :
  Mean (n players' kills / deaths ratio, their x last matches)
• This metric is not directly provided by the API and is only retrievable by querying and aggregating n (player) times x (player recent matches) the API.
• E.g. for a single match of 40 players with, let's say an history of 30 matches per player, we would need a minima 40 (players) * 30 (matches) = 1200 calls to calculate that metric.
• As a player we can often "feel" what is the difficulty of a match and the API provides post-match metrics (one call with a given match ID), with players performance metrics this match, that could be used to model game difficulty without querying all players profiles and performance in their last recent matches.

Workflow - things that you might re-use in this repo

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1. Data collection tools

• Two modules were used to retrieve data:
  • wzranked.py : custom Cls / wrapper to crawl/parse a --quite accurate, "lobby kd" from wzranked.com (unofficial, graphQL-based) API.
  • collect_matches_details.py : an async wrapper to collect detailed match stats, based on my side project wzlight
    

2. Dataset

Final custom dataset (match2kd/dataset_warzone_kd_bigger.parquet.gzip) with 55k rows (1170 unique matches with +- 40 players), 152 features (kills, deaths, pct time moving...) and the associated target (game difficulty / "lobby kd"). The data was collected around Sept 2022, from random COD Warzone players/matches (Rebirth mode only, solos to quads)

2. Model

• Final XGB model available (match2kd/xgb_model_lobby_kd_2.json), currently deployed in Streamlit app
• Notebook with the workflow to train our model (match2kd/model_v3.ipynb)
• Include my findings to improve model accuracy : features selection & engineering, as well as final aggregations made to retain enough information.

3. Best features to improve model accuracy

• See notebook for more details
• Features selection : 20+ retained out of 150
• Features creation :
  At dataset level (all rows)
• binning on match time : morning (1), noon (2), afternoon (3), evening (4), late evening (5)
• normalization by time played (x/time) of players' kills, deaths, damage done, damage taken
• normalization by kills (x/kills) : players' damage and headshot by kills
  At match level, aggregation of players' stats per match
• mean, std, median for a fixed set of features
• pct_players with 0,5, 10 kills
• pct_players with kills streak, double kills, with headshots

Challenges

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• Custom dataset : the usual tedious work (scrapper, API wrapper) to gather our precious data.
• Skewed continuous target + 4 "types" of matches (team of 1,2,3 or 4 players) even if XGB is allegedly ok with that.
• multi-level data : we want to predict "lobby kd" for a match. Picture every match as a table of +- 40 rows (players) with 150+ features (players metrics) and a single unique target (lobby kd). To train our model we will need to compress our data (one row with 150+ features per match -> our target). Will the model retain enough information ?
• Ressources and examples quite scarce when dealing with multi-level data. Also specific models exist but won't extend on the matter :-p.

Model Performance

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• Our model has a mean rmse of +- 0.1 ; FYI "lobby k/d" usually navigates between 0.6 (rare) and 1.5 (rare).
• From post production personal tests (far from exhaustive), captures quite well lobby kd variations, just using compressed players-in-a-match features.
• Overall feeling (would need further proofing) is that lowest and highest lobby kds could be more accurate. E.g True [.7 - .9] k/d or > 1.1 k/d matches seem less finely predicted that those revolving around 1.
• We would have wanted our RMSE to be below the 0.1 mark more often as a lot of lobby kd revolve around the "1" threshold and we can "feel" the difference between a "0.9 kd" and a 1 or 1.1 kd match.

Further leads, ideas

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• A lot of work was puth in back-and-forth tweaking/testing on features; some missing features (not provided/updated by COD api) would have been useful... or more engineering... ;)
• Playing around "ranking" models or the "rank" features (players/team placement in a match).
• Augment and/or add more balance our dataset due to the the inherent skewed nature of Warzone matchmaking systel (a lot of matches revolve around the 1 kd mark).
• Cf. new techniques around synthetic data generation with DL for tabular data.
• Benchmark vs. other models. As of nov 2022 and after some searches, didn't bother to test DL methods (tabnet & Co) for our tabular exercise despite and interesting (small) revival in the domain.
• Prediction power gain is difficult due to the nature of the available data / degree of randomness of players' performances in a battle royale match ?
• 4, 8 or 2 models (solos, duos, trios, quads Resurgence) instead of 1 ? Though, squad size and map type (Fortune Keep vs Rebirth Island) did not seem that important in our model though.