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add EWM method for 3PAT averaging #1

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add EWM method for 3PAT averaging #1

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7 changes: 6 additions & 1 deletion main.py
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Original file line number Diff line number Diff line change
Expand Up @@ -8,9 +8,13 @@
parser = argparse.ArgumentParser(description='Run the model.')
parser.add_argument('-b', '--boostrap_samples', default=100_000, help='Set # of boostrap samples')
parser.add_argument('-n', '--n_simulated_games', default=10_000, help='Set # of simulated games')
parser.add_argument('-m', '--fga_method', default='simple', help='Set mean method to mwa')
#parser.add_argument("-f", "--fff", help="a dummy argument to fool ipython", default="1")

args = parser.parse_args()
bootstrap_samples = int(args.boostrap_samples)
n_simulated_games = int(args.n_simulated_games)
fga_method = str(args.fga_method)

#logging settings
root = logging.getLogger()
Expand All @@ -27,5 +31,6 @@

model.run_model(
bootstrap_samples=bootstrap_samples,
n_simulated_games=n_simulated_games
n_simulated_games=n_simulated_games,
fga_method=fga_method
)
5 changes: 4 additions & 1 deletion model/__init__.py
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Original file line number Diff line number Diff line change
Expand Up @@ -52,7 +52,9 @@ def compress_lines(self):
def run_model(
self,
bootstrap_samples:int=100_000,
n_simulated_games:int=200_000
n_simulated_games:int=200_000,
fga_method:str="simple",

):

self.load_data()
Expand Down Expand Up @@ -81,6 +83,7 @@ def run_model(
defensive_matchup,
bootstrap_samples=bootstrap_samples,
n_simulated_games=n_simulated_games,
fga_method="simple",
plot=False
)
except Exception as e:
Expand Down
24 changes: 19 additions & 5 deletions model/models/threes.py
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Original file line number Diff line number Diff line change
Expand Up @@ -25,6 +25,7 @@ def run_model(
n_components:int=5,
bootstrap_samples:int=100_000,
n_simulated_games:int=200_000,
fga_method:str="simple",
min_samples:int=25,
plot:bool=False,
plot_args:dict={
Expand All @@ -41,9 +42,15 @@ def run_model(
:param n_components: The number of clusters to use for the GMM
:param bootstrap_samples: The number of bootstrap samples to use (suggested 100,000 - 500,000)
:param n_simulated_games: The number of simulated games to run (suggested 10,000 - 200,000)
:param fga_method: The method to simulate field goals attempted ('simple' moving average or 'ewm')
:param plot: Whether to plot the results
:return: numpy array of simulated shot results (1 = made, 0 = missed), length = n_simulated_games
"""

fga_attempt_types = ['simple', 'ewm']
if fga_method not in fga_attempt_types:
raise ValueError("Invalid fga_attempt_type. Expected one of: %s" % fga_attempt_types)

try:
player_df = get_player_shot_loc_data(player_name, context_measure_simple='FG3A')
except Exception:
Expand All @@ -60,7 +67,9 @@ def run_model(
threes['SHOT_MADE_FLAG'] = threes['SHOT_MADE_FLAG'].astype(np.int64)

threes_train_xy = threes[['LOC_X', 'LOC_Y']].values.reshape(-1, 2)
fga_per_game_data = threes.groupby('GAME_ID')['SHOT_ATTEMPTED_FLAG'].count().values
# fga_per_game_data = threes.groupby('GAME_ID')['SHOT_ATTEMPTED_FLAG'].count().values
fga_per_game_data = threes.groupby('GAME_ID')['SHOT_ATTEMPTED_FLAG'].count().reset_index().sort_values('GAME_ID')


#get league shot data

Expand Down Expand Up @@ -114,16 +123,21 @@ def run_model(
def_adjustment = opponent_fg_percent_by_cluster / league_fg_percent_by_cluster

#bootstrap resample from FGA data to find normal distribution fo estimated mean FGA per game
fga_per_game_est = [np.random.choice(fga_per_game_data, size=len(fga_per_game_data), replace=True).mean() for _ in range(bootstrap_samples)]
fga_per_game_est_mean = np.mean(fga_per_game_est)
fga_per_game_est_std = np.std(fga_per_game_est)
if fga_method == 'simple':
fga_per_game_array = fga_per_game_data['SHOT_ATTEMPTED_FLAG'].values
fga_per_game_est = [np.random.choice(fga_per_game_array, size=len(fga_per_game_array), replace=True).mean() for _ in range(bootstrap_samples)]
fga_per_game_est_mean = np.mean(fga_per_game_est)
fga_per_game_est_std = np.std(fga_per_game_est)
elif fga_method == 'ewm':
fga_per_game_est_mean = fga_per_game_data['SHOT_ATTEMPTED_FLAG'].ewm(span = len(fga_per_game_data)).mean().values[-1]
fga_per_game_est_std = fga_per_game_data['SHOT_ATTEMPTED_FLAG'].ewm(span = len(fga_per_game_data)).std().values[-1]
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@bendominguez0111 bendominguez0111 Feb 28, 2023 •
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No bootstrapping is getting done here, won't be able to compress into a list comprehension but I think we can write a for loop to do it:
eg.

bootstrapped_emas = []
for _ in range(bootstrap_samples):
    fga_boot = np.random.choice(fga_per_game_array, size=len(fga_per_game_array), replace=True)
    fga_boot_ema = pd.ewma(fga_boot, span=len(fga_boot)).mean()[-1]
    bootstrapped_emas.append(fga_boot_ema)
fga_per_game_est_mean = np.mean(bootstrapped_means)
fga_per_game_est_std = np.std(bootstrapped_means)

I've never actually done this before and I'd like to actually examine the distribution here in a notebook or something. Since EMA is dependent on the ordering I'm not sure this is valid, but I think it does work (mentioned this point earlier)

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Actually having messed around with this in a notebook i dont think it does work. ordering does matter


fg3m_s = []
#simulate n_simulations games
for _ in trange(n_simulated_games, desc=f'Simulating 3PM outcomes for {player_name} vs {opponent}...'):

#simulate FGA
fga_i = np.random.poisson(np.random.normal(fga_per_game_est_mean, fga_per_game_est_std))
fga_i = np.random.poisson(max(np.random.normal(fga_per_game_est_mean, fga_per_game_est_std),0))
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@bendominguez0111 bendominguez0111 Feb 28, 2023 •
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So above you're not actually bootstrapping (for ema) so I don't think it's fair to draw from a normal distribution to input into the poisson. In fact I think this may be skewing the results for simulations quite a bit since the rolling std should be much larger than std of the bootstrapped emas. I could be wrong on that second part though.


if fga_i == 0:
fg3m_s.append(0)
Expand Down