Introduction

After writing the previous post about clustering 24-25 Winnipeg Jets power play goals, I saw a Dallas power play goal that was similar to the largest cluster of Jets power play goals.

That made me wonder how common this kind of power play goal is, where the puck gets moved from the half-wall to the goal line and then to the slot. So I did a quick analysis to find those kinds of goals in the 24-25 season.

Results

The Winnipeg power play goals I wanted to find similar goals for are:

I was able to find 13 power play goals from the past season similar to the Winnipeg goals:

Approach

To find power play goals similar to the Winnipeg ones, I used the same hyperdimensional computing encodings of the goal tracking data used to cluster Winnipeg power play goals.
That let me sort goals by similarity to the Winnipeg goals. Then I used the NHL API to filter on just the power play goals. After that, I manually reviewed a handful goals to set a similarity threshold, and those that met that threshold were all reviewed. In all, I reviewed around 30 or so goals. You can find a reference for the NHL API here.

Example Python code:

from dataclasses import dataclass
import json
import polars as pl
import numpy as np
import requests

@dataclass
class Goal:
    game: int
    goal: int
        
@dataclass
class GoalDist:
    game_id: int
    goal_id: int
    dist: float


def dist(x: np.ndarray, y: np.ndarray) -> float:
    """
    Calculates the distance between 2 binary hypervectors
    
    PARAMETERS:
        - x (numpy array): binary hypervector of shape (d,)
        - y (numpy array): binary hypervector of shape (d,)
    
    RETURNS:
        - dist (float): Hamming distance between x and y
    """
    if x.shape != y.shape:
        raise ValueError(f'x and y have different dimensions: {x.shape} and {y.shape}')
    
    dim = x.shape[0]
    return np.count_nonzero(x!=y)/dim


# read in exported goal hypervectors that have been created by trimming
# the tracking data to the last 17 instances
df_goal_hvs_last_17 = pl.read_parquet('goal_hvs.parquet')

# sort goals by similarity to this Winnipeg one
specified_game_id = 2024020587
specified_goal_id = 1063

specified_goal_hv = df_goal_hvs_last_17.filter(
    (pl.col('game_id')==specified_game_id) &
    (pl.col('goal_id')==specified_goal_id)
).select(
    pl.col('goal_hv')
).to_series().to_list()[0]
specified_goal_hv = np.array(specified_goal_hv)

goal_dists = []

for row in df_goal_hvs_last_17.filter(
    (pl.col('game_id')!=specified_game_id) &
    (pl.col('goal_id')!=specified_goal_id)
).rows(named=True):
    other_game_id = row['game_id']
    other_goal_id = row['goal_id']
    other_goal_hv = np.array(row['goal_hv'])
    
    goal_dist = dist(specified_goal_hv, other_goal_hv)
    
    goal_dists.append(GoalDist(game_id=other_game_id, goal_id=other_goal_id, dist=goal_dist))

l_sim_goals = sorted(goal_dists, key=lambda item: item.dist)

# print out info for the most similar ones
NUM_PDS = 3

threshold = 0.141

for goal_dist in l_sim_goals:
    if (goal_dist.dist <= threshold):
        r = requests.get(f'https://api-web.nhle.com/v1/gamecenter/{goal_dist.game_id}/landing')
        landing_str = r.content.decode()
        dict_landing = json.loads(landing_str)

        for i in range(NUM_PDS): # ignoring OT pp goals   
            l_dict_goals = dict_landing['summary']['scoring'][i]['goals']

            # go through each goal in the game to find the similar one
            for dict_goal in l_dict_goals:
                event_id = dict_goal['eventId']
                strength = dict_goal['strength']
                scoring_team = dict_goal['teamAbbrev']['default']

                if (strength == 'pp') and (event_id == goal_dist.goal_id):
                    print(f'Team: {scoring_team}')                    
                    url = f'https://www.nhl.com/ppt-replay/goal/{goal_dist.game_id}/{goal_dist.goal_id}'
                    print(f'Game: {goal_dist.game_id}, goal: {goal_dist.goal_id}, dist: {goal_dist.dist}, url: {url}')

Here is a visualization of the paths the puck took in the similar goals:

Each separate line represents a different goal. The arrows show the direction the puck moves in. The large overlap between the different goals highlights the similarity between the goals.

Conclusion

When the question, “How common are power play goals like this one?” popped into my head, the goal encodings I created using the tracking data made it simple to find out. They helped narrow down the goals I had to review to just a few dozen goals.