NHL Team Clustering

Idea is to use data from 2023-2024 seasons statistics and group teams into clustering based on their similarities. After clustering we can find the teams you might enjoy the most. For example if you like to see goals then teams for you would be from the cluster which has highest average goals per game.

Data

Data is from NHL offical website. Data has statistics on the games played and goals for and against.

Data

	Team	Season	GP	W	L	T	OT	P	P%	RW	ROW	S/O Win	GF	GA	GF/GP	GA/GP	PP%	PK%	Net PP%	Net PK%	Shots/GP	SA/GP	FOW%
0	Vancouver Canucks	20232024	35	23	9	--	3	49	0.7	22	23	0	135	88	3.86	2.51	24.2	77.8	24.2	81.5	28	30	50.7
1	New York Rangers	20232024	32	23	8	--	1	47	0.734	18	22	1	107	88	3.34	2.75	31.1	85.7	27.2	87.8	30.9	29.7	54.8
2	Vegas Golden Knights	20232024	35	21	9	--	5	47	0.671	15	17	4	118	95	3.37	2.71	22.5	81	21.7	85.7	32.3	30.1	49.6
3	Colorado Avalanche	20232024	34	21	11	--	2	44	0.647	20	20	1	124	103	3.65	3.03	23.6	83.3	19.7	87.5	32.2	29.4	49.2
4	Dallas Stars	20232024	32	20	8	--	4	44	0.688	14	18	2	112	99	3.5	3.09	22.1	86.4	19	93.2	29.8	30.7	53.5
5	Boston Bruins	20232024	32	19	7	--	6	44	0.688	15	17	2	98	85	3.06	2.66	24.5	85.8	22.5	85.8	31	32.3	50.3
6	Winnipeg Jets	20232024	32	20	9	--	3	43	0.672	18	20	0	109	81	3.41	2.53	18.2	75	16.2	78.1	31.1	28.8	47.3
7	Los Angeles Kings	20232024	30	19	7	--	4	42	0.7	17	18	1	106	71	3.53	2.37	20	86.6	16.2	92.8	33.9	26.6	50.8
8	New York Islanders	20232024	33	16	8	--	9	41	0.621	12	15	1	103	105	3.12	3.18	24.7	71	23.7	79	30.1	35.7	50.7
9	Florida Panthers	20232024	33	19	12	--	2	40	0.606	17	19	0	96	86	2.91	2.61	18.9	82.9	14.2	83.8	34.3	27.1	50.3
10	Philadelphia Flyers	20232024	33	18	11	--	4	40	0.606	12	15	3	98	91	2.97	2.76	10.6	85.7	8.7	92.4	32.9	28.6	46.9
11	Toronto Maple Leafs	20232024	31	17	8	--	6	40	0.645	10	13	4	111	104	3.58	3.35	26.4	79.2	22	83.3	32.6	32.5	53.2
12	Tampa Bay Lightning	20232024	35	17	13	--	5	39	0.557	14	16	1	117	120	3.34	3.43	30.4	79.6	28.6	79.6	30.4	31.1	52
13	Washington Capitals	20232024	31	17	9	--	5	39	0.629	11	14	3	74	83	2.39	2.68	12.4	82.7	11.2	84.7	28.1	30.7	47.3
14	Nashville Predators	20232024	34	19	15	--	0	38	0.559	14	18	1	106	104	3.12	3.06	20.5	77.3	18	80.9	29.8	31	49.2
15	Carolina Hurricanes	20232024	34	17	13	--	4	38	0.559	13	16	1	110	108	3.24	3.18	24.4	82.7	18.3	88.2	33.9	25.6	51.2
16	New Jersey Devils	20232024	32	17	13	--	2	36	0.563	14	17	0	109	114	3.41	3.56	30	77.7	27	79.6	31.7	29.3	53.4
17	Arizona Coyotes	20232024	33	17	14	--	2	36	0.545	14	15	2	101	95	3.06	2.88	23.2	80.2	18.8	82.2	27.1	31.3	47.5
18	Detroit Red Wings	20232024	34	16	14	--	4	36	0.529	13	15	1	120	113	3.53	3.32	21.6	79.5	18	82.8	30.1	32.2	48.8
19	St. Louis Blues	20232024	33	17	15	--	1	35	0.53	14	16	1	99	110	3	3.33	11.6	78.9	7.4	87.8	30.7	32	49.5
20	Montréal Canadiens	20232024	33	15	13	--	5	35	0.53	7	12	3	92	109	2.79	3.3	17.7	73	12.6	75.7	29.3	33.6	53.9
21	Pittsburgh Penguins	20232024	32	15	13	--	4	34	0.531	12	13	2	91	89	2.84	2.78	13.7	82.7	9.8	84.6	32.9	30.9	54.7
22	Minnesota Wild	20232024	32	15	13	--	4	34	0.531	10	12	3	97	101	3.03	3.16	18.2	72.2	13.6	73.9	30.2	30.3	45.3
23	Calgary Flames	20232024	34	14	15	--	5	33	0.485	11	14	0	102	111	3	3.26	11.8	83.3	8.2	91.7	31.6	29.8	50.4
24	Seattle Kraken	20232024	35	12	14	--	9	33	0.471	9	11	1	94	108	2.69	3.09	20.8	79.2	17	81.2	29.9	29.2	48.9
25	Buffalo Sabres	20232024	35	14	17	--	4	32	0.457	13	14	0	106	120	3.03	3.43	14.1	80.2	8.1	82.9	30.2	29.8	45.9
26	Edmonton Oilers	20232024	31	15	15	--	1	31	0.5	13	14	1	107	106	3.45	3.42	26.2	78	22.3	81.7	34.2	28.4	51.8
27	Columbus Blue Jackets	20232024	35	11	18	--	6	28	0.4	9	11	0	108	127	3.09	3.63	15.3	81.4	12.2	81.4	29.8	34.2	47.6
28	Ottawa Senators	20232024	29	12	17	--	0	24	0.414	9	11	1	99	103	3.41	3.55	17.7	71.3	14.3	72.3	32.5	30.7	50.6
29	Anaheim Ducks	20232024	33	12	21	--	0	24	0.364	8	11	1	85	111	2.58	3.36	21.7	79.9	20.8	82.6	29.3	31.8	48.5
30	Chicago Blackhawks	20232024	33	10	22	--	1	21	0.318	8	10	0	80	122	2.42	3.7	12.5	74.3	7.7	76.2	26.7	32.7	45.7
31	San Jose Sharks	20232024	34	9	22	--	3	21	0.309	7	9	0	73	137	2.15	4.03	20.2	72.9	14.6	74.6	25.7	35.6	50.7

Preprocessing

Preprocessing for the data was to remove unnecessary variables and set Team as index. These were

1. Season
2. GP
3. W
4. L
5. T
6. OT
7. P
8. RW
9. ROW Since they carry no information on how the team plays. For example GP (Games played) only tells about the schedule of that team.

Data after variable elimination

Team	P%	GF/GP	GA/GP	PP%	PK%	Net PP%	Net PK%	Shots/GP	SA/GP	FOW%
Vancouver Canucks	0.7	3.86	2.51	24.2	77.8	24.2	81.5	28	30	50.7
New York Rangers	0.734	3.34	2.75	31.1	85.7	27.2	87.8	30.9	29.7	54.8
Vegas Golden Knights	0.671	3.37	2.71	22.5	81	21.7	85.7	32.3	30.1	49.6
Colorado Avalanche	0.647	3.65	3.03	23.6	83.3	19.7	87.5	32.2	29.4	49.2
Dallas Stars	0.688	3.5	3.09	22.1	86.4	19	93.2	29.8	30.7	53.5
Boston Bruins	0.688	3.06	2.66	24.5	85.8	22.5	85.8	31	32.3	50.3
Winnipeg Jets	0.672	3.41	2.53	18.2	75	16.2	78.1	31.1	28.8	47.3
Los Angeles Kings	0.7	3.53	2.37	20	86.6	16.2	92.8	33.9	26.6	50.8
New York Islanders	0.621	3.12	3.18	24.7	71	23.7	79	30.1	35.7	50.7
Florida Panthers	0.606	2.91	2.61	18.9	82.9	14.2	83.8	34.3	27.1	50.3
Philadelphia Flyers	0.606	2.97	2.76	10.6	85.7	8.7	92.4	32.9	28.6	46.9
Toronto Maple Leafs	0.645	3.58	3.35	26.4	79.2	22	83.3	32.6	32.5	53.2
Tampa Bay Lightning	0.557	3.34	3.43	30.4	79.6	28.6	79.6	30.4	31.1	52
Washington Capitals	0.629	2.39	2.68	12.4	82.7	11.2	84.7	28.1	30.7	47.3
Nashville Predators	0.559	3.12	3.06	20.5	77.3	18	80.9	29.8	31	49.2
Carolina Hurricanes	0.559	3.24	3.18	24.4	82.7	18.3	88.2	33.9	25.6	51.2
New Jersey Devils	0.563	3.41	3.56	30	77.7	27	79.6	31.7	29.3	53.4
Arizona Coyotes	0.545	3.06	2.88	23.2	80.2	18.8	82.2	27.1	31.3	47.5
Detroit Red Wings	0.529	3.53	3.32	21.6	79.5	18	82.8	30.1	32.2	48.8
St. Louis Blues	0.53	3	3.33	11.6	78.9	7.4	87.8	30.7	32	49.5
Montréal Canadiens	0.53	2.79	3.3	17.7	73	12.6	75.7	29.3	33.6	53.9
Pittsburgh Penguins	0.531	2.84	2.78	13.7	82.7	9.8	84.6	32.9	30.9	54.7
Minnesota Wild	0.531	3.03	3.16	18.2	72.2	13.6	73.9	30.2	30.3	45.3
Calgary Flames	0.485	3	3.26	11.8	83.3	8.2	91.7	31.6	29.8	50.4
Seattle Kraken	0.471	2.69	3.09	20.8	79.2	17	81.2	29.9	29.2	48.9
Buffalo Sabres	0.457	3.03	3.43	14.1	80.2	8.1	82.9	30.2	29.8	45.9
Edmonton Oilers	0.5	3.45	3.42	26.2	78	22.3	81.7	34.2	28.4	51.8
Columbus Blue Jackets	0.4	3.09	3.63	15.3	81.4	12.2	81.4	29.8	34.2	47.6
Ottawa Senators	0.414	3.41	3.55	17.7	71.3	14.3	72.3	32.5	30.7	50.6
Anaheim Ducks	0.364	2.58	3.36	21.7	79.9	20.8	82.6	29.3	31.8	48.5
Chicago Blackhawks	0.318	2.42	3.7	12.5	74.3	7.7	76.2	26.7	32.7	45.7
San Jose Sharks	0.309	2.15	4.03	20.2	72.9	14.6	74.6	25.7	35.6	50.7

Clustering

Clustering method was chosen to be K-means since it's a highly flexible and fast clustering method.

Selection of K

Selection of K was done by plotting inertia for different k-values and finding an "elbow" which means that higher values of k do not bring significantly more information.

Inertia to value of K

From the plot value of k was chosen to be 4. K=4

K-means with K=4

K-means clustering was done using sklearn library with k of 4 and initial value of 10. Results for the cluster were added to the dataframe

Data with clusters\

Team	Cluster	P%	GF/GP	GA/GP	PP%	PK%	Net PP%	Net PK%	Shots/GP	SA/GP	FOW%
Vancouver Canucks	1	0.7	3.86	2.51	24.2	77.8	24.2	81.5	28	30	50.7
New York Rangers	1	0.734	3.34	2.75	31.1	85.7	27.2	87.8	30.9	29.7	54.8
Vegas Golden Knights	0	0.671	3.37	2.71	22.5	81	21.7	85.7	32.3	30.1	49.6
Colorado Avalanche	0	0.647	3.65	3.03	23.6	83.3	19.7	87.5	32.2	29.4	49.2
Dallas Stars	0	0.688	3.5	3.09	22.1	86.4	19	93.2	29.8	30.7	53.5
Boston Bruins	0	0.688	3.06	2.66	24.5	85.8	22.5	85.8	31	32.3	50.3
Winnipeg Jets	2	0.672	3.41	2.53	18.2	75	16.2	78.1	31.1	28.8	47.3
Los Angeles Kings	0	0.7	3.53	2.37	20	86.6	16.2	92.8	33.9	26.6	50.8
New York Islanders	1	0.621	3.12	3.18	24.7	71	23.7	79	30.1	35.7	50.7
Florida Panthers	0	0.606	2.91	2.61	18.9	82.9	14.2	83.8	34.3	27.1	50.3
Philadelphia Flyers	3	0.606	2.97	2.76	10.6	85.7	8.7	92.4	32.9	28.6	46.9
Toronto Maple Leafs	1	0.645	3.58	3.35	26.4	79.2	22	83.3	32.6	32.5	53.2
Tampa Bay Lightning	1	0.557	3.34	3.43	30.4	79.6	28.6	79.6	30.4	31.1	52
Washington Capitals	3	0.629	2.39	2.68	12.4	82.7	11.2	84.7	28.1	30.7	47.3
Nashville Predators	0	0.559	3.12	3.06	20.5	77.3	18	80.9	29.8	31	49.2
Carolina Hurricanes	0	0.559	3.24	3.18	24.4	82.7	18.3	88.2	33.9	25.6	51.2
New Jersey Devils	1	0.563	3.41	3.56	30	77.7	27	79.6	31.7	29.3	53.4
Arizona Coyotes	0	0.545	3.06	2.88	23.2	80.2	18.8	82.2	27.1	31.3	47.5
Detroit Red Wings	0	0.529	3.53	3.32	21.6	79.5	18	82.8	30.1	32.2	48.8
St. Louis Blues	3	0.53	3	3.33	11.6	78.9	7.4	87.8	30.7	32	49.5
Montréal Canadiens	2	0.53	2.79	3.3	17.7	73	12.6	75.7	29.3	33.6	53.9
Pittsburgh Penguins	3	0.531	2.84	2.78	13.7	82.7	9.8	84.6	32.9	30.9	54.7
Minnesota Wild	2	0.531	3.03	3.16	18.2	72.2	13.6	73.9	30.2	30.3	45.3
Calgary Flames	3	0.485	3	3.26	11.8	83.3	8.2	91.7	31.6	29.8	50.4
Seattle Kraken	0	0.471	2.69	3.09	20.8	79.2	17	81.2	29.9	29.2	48.9
Buffalo Sabres	3	0.457	3.03	3.43	14.1	80.2	8.1	82.9	30.2	29.8	45.9
Edmonton Oilers	1	0.5	3.45	3.42	26.2	78	22.3	81.7	34.2	28.4	51.8
Columbus Blue Jackets	3	0.4	3.09	3.63	15.3	81.4	12.2	81.4	29.8	34.2	47.6
Ottawa Senators	2	0.414	3.41	3.55	17.7	71.3	14.3	72.3	32.5	30.7	50.6
Anaheim Ducks	0	0.364	2.58	3.36	21.7	79.9	20.8	82.6	29.3	31.8	48.5
Chicago Blackhawks	2	0.318	2.42	3.7	12.5	74.3	7.7	76.2	26.7	32.7	45.7
San Jose Sharks	2	0.309	2.15	4.03	20.2	72.9	14.6	74.6	25.7	35.6	50.7

To make understanding of the clusters they were plotted to teams rank in the standings.

Cluster on the team rank

From the plot above it can be seen that in cluster 0 and 3 teams are from very similar rankings. Where as clusters 1 and 2 are more evenly spread around the rankings.

Finding the most similar team using NearestNeighbor search

Idea was to find for each team a team that is most similar to them. This could be helpful if a customer likes to watch Minnesota Wild but they are not playing today so the most similar team could be suggested instead.

NearestNeigbor was found by using sklearns NearestNeigbors functions using number of neighbors of 2 to find itself and the closest one. Algorithm for the search was set to "auto" and distance used was eucledian distance.

These closest teams and the distance to them were added to the data.

Data with closest team and distance\

Team	Cluster	Closest team	Closest Distance	P%	GF/GP	GA/GP	PP%	PK%	Net PP%	Net PK%	Shots/GP	SA/GP	FOW%
Vancouver Canucks	1	Anaheim Ducks	6.05589	0.7	3.86	2.51	24.2	77.8	24.2	81.5	28	30	50.7
New York Rangers	1	Boston Bruins	9.8878	0.734	3.34	2.75	31.1	85.7	27.2	87.8	30.9	29.7	54.8
Vegas Golden Knights	0	Colorado Avalanche	3.81856	0.671	3.37	2.71	22.5	81	21.7	85.7	32.3	30.1	49.6
Colorado Avalanche	0	Vegas Golden Knights	3.81856	0.647	3.65	3.03	23.6	83.3	19.7	87.5	32.2	29.4	49.2
Dallas Stars	0	Los Angeles Kings	7.34026	0.688	3.5	3.09	22.1	86.4	19	93.2	29.8	30.7	53.5
Boston Bruins	0	Colorado Avalanche	5.41633	0.688	3.06	2.66	24.5	85.8	22.5	85.8	31	32.3	50.3
Winnipeg Jets	2	Nashville Predators	6.0148	0.672	3.41	2.53	18.2	75	16.2	78.1	31.1	28.8	47.3
Los Angeles Kings	0	Dallas Stars	7.34026	0.7	3.53	2.37	20	86.6	16.2	92.8	33.9	26.6	50.8
New York Islanders	1	Vancouver Canucks	9.53377	0.621	3.12	3.18	24.7	71	23.7	79	30.1	35.7	50.7
Florida Panthers	0	Seattle Kraken	7.61098	0.606	2.91	2.61	18.9	82.9	14.2	83.8	34.3	27.1	50.3
Philadelphia Flyers	3	Calgary Flames	4.85649	0.606	2.97	2.76	10.6	85.7	8.7	92.4	32.9	28.6	46.9
Toronto Maple Leafs	1	Edmonton Oilers	5.05003	0.645	3.58	3.35	26.4	79.2	22	83.3	32.6	32.5	53.2
Tampa Bay Lightning	1	New Jersey Devils	3.63893	0.557	3.34	3.43	30.4	79.6	28.6	79.6	30.4	31.1	52
Washington Capitals	3	Buffalo Sabres	5.49288	0.629	2.39	2.68	12.4	82.7	11.2	84.7	28.1	30.7	47.3
Nashville Predators	0	Seattle Kraken	2.88506	0.559	3.12	3.06	20.5	77.3	18	80.9	29.8	31	49.2
Carolina Hurricanes	0	Colorado Avalanche	4.99783	0.559	3.24	3.18	24.4	82.7	18.3	88.2	33.9	25.6	51.2
New Jersey Devils	1	Tampa Bay Lightning	3.63893	0.563	3.41	3.56	30	77.7	27	79.6	31.7	29.3	53.4
Arizona Coyotes	0	Anaheim Ducks	3.61712	0.545	3.06	2.88	23.2	80.2	18.8	82.2	27.1	31.3	47.5
Detroit Red Wings	0	Anaheim Ducks	3.14187	0.529	3.53	3.32	21.6	79.5	18	82.8	30.1	32.2	48.8
St. Louis Blues	3	Calgary Flames	6.45886	0.53	3	3.33	11.6	78.9	7.4	87.8	30.7	32	49.5
Montréal Canadiens	2	San Jose Sharks	6.29773	0.53	2.79	3.3	17.7	73	12.6	75.7	29.3	33.6	53.9
Pittsburgh Penguins	3	St. Louis Blues	8.26245	0.531	2.84	2.78	13.7	82.7	9.8	84.6	32.9	30.9	54.7
Minnesota Wild	2	Ottawa Senators	6.16118	0.531	3.03	3.16	18.2	72.2	13.6	73.9	30.2	30.3	45.3
Calgary Flames	3	Philadelphia Flyers	4.85649	0.485	3	3.26	11.8	83.3	8.2	91.7	31.6	29.8	50.4
Seattle Kraken	0	Nashville Predators	2.88506	0.471	2.69	3.09	20.8	79.2	17	81.2	29.9	29.2	48.9
Buffalo Sabres	3	Washington Capitals	5.49288	0.457	3.03	3.43	14.1	80.2	8.1	82.9	30.2	29.8	45.9
Edmonton Oilers	1	Toronto Maple Leafs	5.05003	0.5	3.45	3.42	26.2	78	22.3	81.7	34.2	28.4	51.8
Columbus Blue Jackets	3	Washington Capitals	6.21811	0.4	3.09	3.63	15.3	81.4	12.2	81.4	29.8	34.2	47.6
Ottawa Senators	2	Minnesota Wild	6.16118	0.414	3.41	3.55	17.7	71.3	14.3	72.3	32.5	30.7	50.6
Anaheim Ducks	0	Detroit Red Wings	3.14187	0.364	2.58	3.36	21.7	79.9	20.8	82.6	29.3	31.8	48.5
Chicago Blackhawks	2	Minnesota Wild	9.79179	0.318	2.42	3.7	12.5	74.3	7.7	76.2	26.7	32.7	45.7
San Jose Sharks	2	Montréal Canadiens	6.29773	0.309	2.15	4.03	20.2	72.9	14.6	74.6	25.7	35.6	50.7

Finding teams with the highest average goals for and against.

Finding the teams was done by selecting the cluster with the highest total goals (sum of goals for and against). Average goals was calculated using groupBy on cluster and then taking the mean for goals.

Average goals per game

From the plot it is clear that cluster 1 has highest average total goals per game. Teams in that cluster can be seen below.

Team	Cluster	Closest team	Closest Distance	P%	GF/GP	GA/GP	PP%	PK%	Net PP%	Net PK%	Shots/GP	SA/GP	FOW%
Vancouver Canucks	1	Anaheim Ducks	6.05589	0.7	3.86	2.51	24.2	77.8	24.2	81.5	28	30	50.7
New York Rangers	1	Boston Bruins	9.8878	0.734	3.34	2.75	31.1	85.7	27.2	87.8	30.9	29.7	54.8
New York Islanders	1	Vancouver Canucks	9.53377	0.621	3.12	3.18	24.7	71	23.7	79	30.1	35.7	50.7
Toronto Maple Leafs	1	Edmonton Oilers	5.05003	0.645	3.58	3.35	26.4	79.2	22	83.3	32.6	32.5	53.2
Tampa Bay Lightning	1	New Jersey Devils	3.63893	0.557	3.34	3.43	30.4	79.6	28.6	79.6	30.4	31.1	52
New Jersey Devils	1	Tampa Bay Lightning	3.63893	0.563	3.41	3.56	30	77.7	27	79.6	31.7	29.3	53.4
Edmonton Oilers	1	Toronto Maple Leafs	5.05003	0.5	3.45	3.42	26.2	78	22.3	81.7	34.2	28.4	51.8

Conclusion

Clustering and search methods and algorithms can be used on various tasks. On this project they were only used for what they are designed to do but during research I found multiple other use cases. For example nearestNeighbor can also be used for classification or to find outliers in data. But overall an positive learning experience from whichs results I will benefit from next time my favourite NHL team doesn't play.