exploring stat comparision

sandbox/data-exploration/src/api/__init__.py

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+from .core import *

sandbox/data-exploration/src/api/core.py

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+import requests
+
+class Leetify:
+    api_base_url = "https://api.leetify.com/api"
+    profile_base_url = f"{api_base_url}/profile"
+    match_base_url = f"{api_base_url}/games"
+
+    def __get_page(self, url: str) -> dict:
+        resp = requests.get(url)
+        return resp.json()
+
+    def get_profile(self, id: str) -> dict:
+        url = f"{self.profile_base_url}/{id}"
+        return self.__get_page(url)
+
+    def get_match(self, id: str) -> dict:
+        url = f"{self.match_base_url}/{id}"
+        return self.__get_page(url)
+
+

sandbox/data-exploration/src/tools/recent_player_comp.py

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+import click
+import pandas as pd
+from api import Leetify
+from tools.utils import playerStats_df, get_n_matches
+
+
+@click.command()
+@click.option("--user", prompt="Your user id")
+@click.option("-n", prompt="number of games", type=int)
+@click.option(
+    "--include-self",
+    help="Includes self in overal stat averages",
+    default=False,
+    type=bool,
+)
+def comp_users(user, n, include_self):
+    api = Leetify()
+    cols_of_interest = [
+        "preaim",
+        "reactionTime",
+        "accuracy",
+        "accuracyEnemySpotted",
+        "accuracyHead",
+        "sprayAccuracy",
+        "counterStrafingShotsGoodRatio",
+        "hsp",
+        "mvps",
+        "totalKills",
+        "kdRatio",
+    ]
+
+    profile = api.get_profile(user)
+
+    meta = profile.get("meta")
+    if not meta:
+        print("Missing user meta data(could be malformed response from api)")
+        return
+
+    name = meta.get("name")
+    print(f"{name}")
+
+    matches = get_n_matches(profile, n)
+
+    m_ids = [m.get("gameId") for m in matches]
+    dfs = [playerStats_df(id) for id in m_ids]
+
+    df = pd.concat(dfs)
+    players_of_interest_df = (
+        df if include_self else df[df["leetifyUserId"] != meta["leetifyUserId"]]
+    )
+    stats_of_interest_df = players_of_interest_df[cols_of_interest]
+    total_means = stats_of_interest_df.mean()
+    my_means = df[df["leetifyUserId"] == meta["leetifyUserId"]][
+        cols_of_interest
+    ].mean()
+    print(my_means - total_means)
+
+
+if __name__ == "__main__":
+    comp_users()

sandbox/data-exploration/src/tools/utils.py

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+from api import Leetify
+import pandas as pd
+
+def get_n_matches(profile, n):
+    key_f = lambda match: match.get("gameFinishedAt")
+    games = sorted(profile.get("games"), key=key_f, reverse=True)
+    return games[:n]
+
+def playerStats_df(id, api = Leetify()):
+    m_details = api.get_match(id)
+    pStats = m_details.get('playerStats')
+    return pd.DataFrame(pStats)