lspinheiro

def calc_glicko2_rank(df: pd.DataFrame, tau:float, rd_0:float, sigma_0:float = 0.06, date_freq:str = '180D') -> pd.DataFrame:
    glicko_scorer = Glicko2(tau=tau)
    fighter_set = set(df['fighter_slog'].unique().tolist() + df['opponent_slog'].unique().tolist())
    for fighter in fighter_set:
        glicko_scorer.add_player(fighter, rd=rd_0, sigma=sigma_0)
    
    for group_idx, (date_idx, date_group) in tqdm(enumerate(df.groupby([pd.Grouper(key='date', freq=date_freq)]))):

        # pre-rating map
        rating_map = {fname: glicko_scorer[fname].rating for fname in fighter_set}

lspinheiro

import math
import numpy as np

cdef class GlickoRating:
    cdef public double mu, phi, sigma
    cdef readonly double elo_scale
    cdef readonly double glicko_scale
    cdef public list fights
    cdef public list scores
    

lspinheiro

def calc_elo_rank(df: pd.DataFrame) -> pd.DataFrame:
    elo_scorer = Elo(k = 20)
    fighter_set = set(df['winner_slog'].unique().tolist() + df['loser_slog'].unique().tolist())
    for fighter in fighter_set:
        elo_scorer.add_player(fighter)

    elo_f1 = np.zeros((len(df,)))
    elo_f2 = np.zeros((len(df,)))

    for rowidx, fs_row in tqdm(enumerate(df.itertuples())):