# Chapter 3 Player Analysis

## 3.1 Dataset

The data aggregated is scraped from the OUA website. Every box score per game per season is collected and aggregated so that there are player statistics for every season from 2014-15 to 2018-19 (this is because the Player Statistics came from the OUA website which has 2014-15 data).

## 3.2 The Goal

The goal is to use player statistics to gather insights on how players contribute to the game and how to categorize players using unsupervised learning.

## 3.3 Data Preparation

The data is a subset of the player data with certain filters on the number of games played and the minutes per game. There are dataframes for every season from 2014-15 to 2018-19 with players that have played at least 15 games of at least 20 minutes per game. The games are regular season games from the U Sports division, Ontario University Athletics conference. All the variables are totals for the season except for PPG (Points per game) and MPG (Minutes per game)

## 3.4 K-Means Clustering

First the data will be normalized in order to prepare the data for k-means clustering. This is helpful because some statistics have very different ranges e.g. the number of points compared to the number of steals. Therefore the variables will be comparable.

K-Means Clustering is a popular unsupervised machine learning algorithm. The goal of K-Means is to group similar data points in a dataset of unlabeled data. It does this by dividing the data into k clusters where each observation belongs to the cluster closest to the mean (cluster centroid) by using a distance metric (most usually Euclidean distance).

Since K-Means clustering is an unsupervised algorithm, this means that the number of clusters is not known. However, there are techniques that can be used to find an optimal number of clusters such as the gap method, silhouette method, within-cluster sum of squares method, D - index, etc. Different techniques and configurations of the techniques will be used for each season’s clustering for finding the optimal number of clusters.

### 3.4.1 K-Means Results

The technique that will be used to find the optimal number of clusters for the 2014-2015 season is the D-index method (Lebart et al. 2000). The D-index is based on clustering gain on intra-cluster inertia [8]. Intra-cluster inertia can be deﬁned as:

The clustering gain should be minimized. The optimal cluster conﬁguration can be identiﬁed by the sharp knee that corresponds to a signiﬁcant decrease of the ﬁrst differences of clustering gain versus the number of clusters. This knee or great jump of gain values can be identiﬁed by a signiﬁcant peak in second differences of clustering gain.

In the plot of D-index, we seek a significant knee (the significant peak in D-index second differences plot) around 8, that corresponds to a significant increase of the value of measure. The number of clusters that the method suggests is 8 clusters.

Table 3.1: Average of each variable for each cluster for the 2014-15 season
Cluster 1 Cluster 2 Cluster 3 Cluster 4 Cluster 5 Cluster 6 Cluster 7 Cluster 8
3PointMade 31.47 40.14 12.77 19.57 2.25 17.25 23.50 19.47
3PointAttempted 81.40 112.57 37.86 64.29 8.92 46.75 74.00 56.47
Assists 38.07 56.14 22.50 47.71 22.17 36.50 59.00 35.20
Blocks 4.33 6.29 3.64 5.86 15.25 21.25 4.50 4.13
DefensiveRebounds 59.60 64.86 44.18 81.86 79.83 128.75 94.00 58.00
FieldGoalMade 84.87 118.43 42.91 87.29 78.50 132.50 149.00 50.53
FieldGoalAttempted 194.00 278.14 104.55 209.29 157.50 264.75 304.50 135.27
FreeThrowsMade 44.80 58.57 20.86 55.71 40.50 64.25 99.50 29.40
FreeThrowsAttempted 59.87 73.57 32.59 75.00 62.83 94.75 137.50 40.67
Minutes 502.93 579.86 387.45 619.14 450.75 547.25 694.00 500.93
OffensiveRebounds 25.67 16.86 19.00 25.86 44.50 52.50 26.50 16.67
PersonalFouls 39.40 35.43 39.50 50.71 46.17 33.50 44.50 44.93
Points 246.00 335.57 119.45 249.86 199.75 346.50 421.00 149.93
Rebounds 85.27 81.71 63.18 107.71 124.33 181.25 120.50 74.67
Steals 19.60 21.86 13.00 23.57 12.50 16.75 27.50 20.53
Turnovers 34.47 40.14 28.55 48.43 30.50 47.25 57.00 34.27
Home 9.53 9.00 9.27 9.14 9.50 9.50 10.50 9.47
GamesPlayed 18.73 18.29 18.00 19.00 18.92 18.75 20.00 18.93
PointsPerGame 13.15 18.39 6.64 13.19 10.59 18.47 21.05 7.95
MinutesPerGame 26.85 31.74 21.53 32.60 23.81 29.20 34.70 26.53
3P% 0.37 0.36 0.30 0.29 0.18 0.37 0.32 0.38
FG% 0.44 0.43 0.42 0.42 0.50 0.50 0.49 0.38
FT% 0.74 0.79 0.66 0.76 0.64 0.70 0.72 0.72
TrueShooting% 0.56 0.54 0.50 0.52 0.54 0.56 0.57 0.49
Table 3.2: Players from 2014-15 season with assigned clusters.
Player Team Cluster
15-Zachary Angelini Brock 1
10-Connor Wood Carleton 1
23-Philip Scrubb Carleton 1
23-Aaron Redpath McMaster 1
32-Joe Rocca McMaster 1
12-Aaron Best Ryerson 1
9-M Sahota Toronto 1
22-Anthony Spiridis Western 1
06-Mitch Farrell Windsor 1
09-Alex Campbell Windsor 1
21-Khalid Abdel-Gabar Windsor 1
8-Nathan Culbreath York 1
11-Johneil Simpson Brock 2
14-Ryan Bennett Laurentian 2
06-Will Coulthard Laurier 2
08-Johnny Berhanemeskel Ottawa 2
21-Greg Faulkner Queen’s 2
7-Jahmal Jones Ryerson 2
22-J Clarke Toronto 2
2-Jamal Mucket-Sobers Algoma 3
3-AJ Andre Barder Algoma 3
4-Thomas Chalmers Algoma 3
13-J.e Pierre-Charles Carleton 3
05-Jonathan Wallace Guelph 3
12-Michel Clark Guelph 3
09-Luke Allin Laurier 3
4-Joey Puddister Nipissing 3
7-Jerron Rhodes Nipissing 3
01-Vikas Gill Ottawa 3
05-Mehdi Tihani Ottawa 3
09-Matt Plunkett Ottawa 3
10-Cy Samuels Queen’s 3
20-Ryall Stroud Queen’s 3
8-D Ankrah Toronto 3
07-Jedson Tavernier Western 3
10-Nidun Chandrakumar York 3
4-Phillip Cunningham-Gillen York 3
5-Gene Spagnuolo York 3
33-Matt Marshall Brock 4
04-Daniel Dooley Guelph 4
15-Tychon Carter-Newman Laurentian 4
44-Sam Hirst Laurentian 4
5-Jaspreet Gill Waterloo 4
08-Quinn Henderson Western 4
31-Guillaume Boucard Carleton 5
21-Trevor Thompson Guelph 5
15-Aiddian Walters Laurier 5
20-Kyrie Coleman Laurier 5
10-Taylor Black McMaster 5
22-Rohan Boney McMaster 5
23-Marcos Clennon Nipissing 5
04-Gabriel Gonthier-Dubue Ottawa 5
07-Evan Matthews Windsor 5
11-Thomas Scrubb Carleton 6
7-D Johnson Toronto 6
12-Rotimi Osuntola Windsor 6
22-Nick Tufegdzich York 6
6-Myles Charvis Waterloo 7
12-Greg Morrow Western 7
10-Sean Clendinning Algoma 8
5-Brett Zufelt Algoma 8
03-Gavin Resch Carleton 8
11-David Aromolaran Laurentian 8
02-James Agyeman Laurier 8
03-Garrison Thomas Laurier 8
6-Jordon Campbell Nipissing 8
12-Tanner Graham Queen’s 8
5-S Usher Toronto 8
3-Jon Ravenhorst Waterloo 8
7-Ben Davis Waterloo 8
05-Tom Filgiano Western 8
10-Mike Rocca Windsor 8

Each cluster can be categorized as a type of player.

Cluster 1: Efficient Playmakers & Scorers This cluster of players have the most assists and the second most points per game. They have a big defensive impact through the number of steals they get and can control the tempo well and score.

Cluster 2: All-Around Players These players can get rebounds, pass and score well.

Cluster 3: Dominant Big Men These players are the most dominant big men in the league with the most rebounds (defensive and offensive), blocks, and points.

Cluster 4: Smart Catch & Shoot Players These players make the best decisions and turnover the ball the fewest. They do not dribble the ball much and are the most efficient shooters.

Cluster 5: Aggressive Defenders These players are aggressive and foul the most out of all the other clusters. They have a bigger impact on defense since they do not shoot well.

Cluster 6: Role Players These players contribute to many plays and work both offensively and defensively.

Cluster 7: Second Tier Playmakers These players are less dominant playmakers that can still score efficiently.

Cluster 8: Second Tier Small Players These players play small but do not shoot as efficiently as the other players or create as many plays.

### 3.4.2 2015-16 Season

For this season, the elbow method [9] will be used to find the optimal number of clusters. The elbow method looks at the percentage of variance explained as a function of the number of clusters: One should choose a number of clusters so that adding another cluster does not give much better modeling of the data. More precisely, if one plots the percentage of variance explained by the clusters against the number of clusters, the first clusters will add much information (explain a lot of variance), but at some point the marginal gain will drop, giving an angle in the graph. The number of clusters is chosen at this point, hence the “elbow criterion”. This “elbow” cannot always be unambiguously identified.

The number of clusters that will be used for this season is 6 for this season. Below are tables to show the average statistics for each cluster and also which players belong to which cluster.

Table 3.3: Average of each variable for each cluster for the 2015-16 season
Cluster 1 Cluster 2 Cluster 3 Cluster 4 Cluster 5 Cluster 6
3PointMade 14.85 22.62 18.75 13.16 31.75 44.57
3PointAttempted 48.15 67.88 55.75 41.84 86.00 133.29
Assists 39.38 57.12 43.25 24.91 40.62 52.71
Blocks 7.85 4.75 13.50 7.41 6.44 3.29
DefensiveRebounds 72.00 72.50 111.25 51.62 63.56 68.71
FieldGoalMade 101.38 56.25 150.50 48.56 85.62 111.14
FieldGoalAttempted 236.23 150.62 317.75 119.72 196.88 290.00
FreeThrowsMade 59.38 28.75 99.00 23.69 32.88 55.14
FreeThrowsAttempted 83.46 40.12 130.25 34.47 43.44 74.00
Minutes 564.00 620.00 631.50 419.31 490.81 639.14
OffensiveRebounds 30.77 25.75 44.50 21.50 18.19 14.57
PersonalFouls 44.92 48.88 47.25 39.56 38.44 35.57
Points 277.00 163.88 418.75 133.97 235.88 322.00
Rebounds 102.77 98.25 155.75 73.12 81.75 83.29
Steals 23.85 25.00 30.25 15.72 18.88 24.14
Turnovers 49.46 40.50 58.00 26.00 34.50 49.57
Home 9.69 9.62 9.75 9.31 9.25 9.71
GamesPlayed 19.08 19.38 19.00 18.38 18.25 19.43
PointsPerGame 14.58 8.47 22.02 7.34 12.99 16.58
MinutesPerGame 29.60 31.98 33.18 22.87 26.96 32.90
3P% 0.26 0.32 0.31 0.28 0.38 0.33
FG% 0.43 0.37 0.47 0.41 0.44 0.39
FT% 0.72 0.72 0.76 0.71 0.77 0.76
TrueShooting% 0.50 0.48 0.55 0.49 0.54 0.50
Table 3.4: Players from 2015-16 season with assigned clusters.
Player Team Cluster
10-Sean Clendinning Algoma 1
11-David Aromolaran Laurentian 1
44-Sam Hirst Laurentian 1
12-Matt Chesson Laurier 1
03-Leon Alexander McMaster 1
23-Aaron Redpath McMaster 1
13-Marcus Lewis Nipissing 1
07-Ben Davis Waterloo 1
10-Peter Scholtes Western 1
22-Anthony Spiridis Western 1
08-Nathan Culbreath York 1
33-Matt Marshall Brock 2
02-Simon Mikre Laurier 2
04-Joey Puddister Nipissing 2
06-Dylan Phillips Waterloo 2
05-Tom Filgiano Western 2
10-Mike Rocca Windsor 2
06-Devin Johnson Toronto 3
12-Greg Morrow Western 3
09-Alex Campbell Windsor 3
05-Brett Zufelt Algoma 4
06-Nathan Riley Algoma 4
13-Reng Gum Algoma 4
25-Tyler Brown Brock 4
15-Drew Walford Guelph 4
31-Jack Beatty Guelph 4
10-Nick Simon Laurentian 4
32-Joseph Sykes Laurentian 4
03-Garrison Thomas Laurier 4
04-Trevon McNeil McMaster 4
21-David McCulloch McMaster 4
22-Rohan Boney McMaster 4
07-Jerron Rhodes Nipissing 4
09-Kalil Langston Nipissing 4
01-Vikas Gill Ottawa 4
05-Mehdi Tihani Ottawa 4
09-Matt Plunkett Ottawa 4
13-Nathan McCarthy Ottawa 4
13-Sammy Ayisi Queen’s 4
20-Ryall Stroud Queen’s 4
06-Roshane Roberts Ryerson 4
22-Juwon Grannum Ryerson 4
05-Sage Usher Toronto 4
09-Manny Sahota Toronto 4
21-Daniel Johansson Toronto 4
06-Alex Coote Western 4
07-Jedson Tavernier Western 4
15-Micah Kirubel Windsor 4
22-Tyler Persaud Windsor 4
04-Philip Gillen York 4
05-Gene Spagnuolo York 4
03-Andre Barber Algoma 5
14-Ryan Bennett Brock 5
03-Gavin Resch Carleton 5
10-Connor Wood Carleton 5
31-Guillaume Boucard Carleton 5
41-Kaza Kajami-Keane Carleton 5
04-Daniel Dooley Guelph 5
05-Jonathan Wallace Guelph 5
11-Taylor Boers Guelph 5
05-Troy Joseph McMaster 5
12-Tanner Graham Queen’s 5
04-Ammanuel Diressa Ryerson 5
12-Aaron Best Ryerson 5
04-Devon Williams Toronto 5
11-Marko Kovac Windsor 5
11-Tommy Hobbs York 5
11-Johneil Simpson Brock 6
21-Anthony Iacoe Laurentian 6
06-Will Coulthard Laurier 6
11-Mike L’Africain Ottawa 6
03-Jon Ravenhorst Waterloo 6
13-Isiah Osborne Windsor 6

### 3.4.3 2016-17 Season

The Silhouette method suggests 2 as the optimal number of clusters for this season. This is possibly separating the players into forwards/centers and guards. Below are tables to show the average statistics for each cluster and also which players belong to which cluster.

Table 3.5: Average of each variable for each cluster for the 2016-17 season
Cluster 1 Cluster 2
3PointAttempted 51.80 73.85
Assists 30.02 42.82
Blocks 4.92 9.18
DefensiveRebounds 53.12 83.88
FieldGoalAttempted 136.37 234.88
FreeThrowsAttempted 37.14 71.27
Minutes 452.90 559.09
OffensiveRebounds 20.53 30.91
PersonalFouls 35.53 44.12
Points 153.24 279.91
Rebounds 73.65 114.79
Steals 16.88 23.67
Turnovers 28.86 47.39
Home 9.14 9.70
GamesPlayed 18.43 19.12
PointsPerGame 8.34 14.70
MinutesPerGame 24.59 29.24
3P% 0.28 0.30
FG% 0.41 0.44
FT% 0.68 0.71
TrueShooting% 0.50 0.52
Table 3.6: Players from 2016-17 season with assigned clusters.
Player Team Cluster
06-Nathan Riley Algoma 1
13-Reng Gum Algoma 1
09-Daniel Cayer Brock 1
14-Ryan Bennett Brock 1
25-Tyler Brown Brock 1
03-Marcus Anderson Carleton 1
42-Eddie Ekiyor Carleton 1
04-Daniel Dooley Guelph 1
05-Jonathan Wallace Guelph 1
11-Taylor Boers Guelph 1
15-Drew Walford Guelph 1
44-Ahmed Haroon Guelph 1
44-OJ Watson Laurentian 1
02-Matthew Minutillo Laurier 1
04-Chuder Teny Laurier 1
10-Owen Coulthard Laurier 1
12-Elliot Ormond McMaster 1
44-Lazar Kojovic McMaster 1
06-Jordon Campbell Nipissing 1
07-Jerron Rhodes Nipissing 1
10-Ismael Kaba Nipissing 1
21-Justin Shaver Nipissing 1
09-Matt Plunkett Ottawa 1
10-Brandon Robinson Ottawa 1
15-Brody Maracle Ottawa 1
05-Isse Ibrahim Queen’s 1
08-Jesse Graham Queen’s 1
13-Sammy Ayisi Queen’s 1
14-Keevon Small Ryerson 1
15-Myles Charvis Ryerson 1
22-Juwon Grannum Ryerson 1
04-Reilly Reid Toronto 1
05-Sage Usher Toronto 1
21-Daniel Johansson Toronto 1
07-Ben Davis Waterloo 1
05-Eric McDonald Western 1
07-Jedson Tavernier Western 1
11-Cam Morris Western 1
13-Ian Smart Western 1
20-Nikola Farkic Western 1
20-Lucas Orlita Windsor 1
22-Tyler Persaud Windsor 1
10-Nidun Chandrakumar York 1
10-Sean Clendinning Algoma 2
22-Jermaine Lyle Algoma 2
11-Johneil Simpson Brock 2
10-Connor Wood Carleton 2
41-Kaza Kajami-Keane Carleton 2
11-David Aromolaran Laurentian 2
23-Nelson Yengue Laurentian 2
12-Matt Chesson Laurier 2
13-Tevaun Kokko Laurier 2
11-Connor Gilmore McMaster 2
21-David McCulloch McMaster 2
22-Rohan Boney McMaster 2
13-Marcus Lewis Nipissing 2
05-Jean Emmanuel Pierre-Charles Ottawa 2
12-Tanner Graham Queen’s 2
04-Ammanuel Diressa Ryerson 2
06-Devin Johnson Toronto 2
03-Jon Ravenhorst Waterloo 2
04-Simon Petrov Waterloo 2
20-Mike Pereira Waterloo 2
23-Justin Hardy Waterloo 2
42-Nedim Hodzic Waterloo 2
08-Eriq Jenkins Western 2
12-Omar Shiddo Western 2
05-Micqueel Martin Windsor 2
10-Mike Rocca Windsor 2
11-Jayden Frederick York 2
20-Brandon Ramirez York 2

### 3.4.4 2017-18 Season

The optimal number of clusters suggested by the D-index method is 5 for this season. This is possibly separating the players into the actual posistions (Point Guard, Shooting Guard, Small Forward, Power Forward, and Center). Below are tables to show the average statistics for each cluster and also which players belong to which cluster.

Table 3.7: Average of each variable for each cluster for the 2017-18 season
Cluster 1 Cluster 2 Cluster 3 Cluster 4 Cluster 5
3PointMade 46.75 16.23 23.72 28.11 29.50
3PointAttempted 127.75 49.32 71.44 85.00 86.33
Assists 57.92 33.95 41.36 64.17 85.33
Blocks 3.83 6.68 10.04 11.06 11.33
DefensiveRebounds 70.17 60.45 74.40 93.22 137.00
FieldGoalMade 133.00 53.00 81.40 109.00 156.33
FieldGoalAttempted 309.83 129.64 189.52 255.67 340.33
FreeThrowsMade 58.42 20.86 36.56 55.17 117.17
FreeThrowsAttempted 75.50 33.68 52.52 77.28 144.00
Minutes 606.67 496.41 571.40 703.78 750.17
OffensiveRebounds 22.17 23.73 26.32 30.72 40.17
PersonalFouls 49.33 44.00 47.08 52.89 54.00
Points 371.17 143.09 223.08 301.28 459.33
Rebounds 92.33 84.18 100.72 123.94 177.17
Steals 26.50 17.73 19.88 31.11 27.67
Turnovers 48.58 31.09 34.00 49.33 65.33
Home 11.25 11.36 11.44 11.56 11.50
GamesPlayed 22.25 22.50 22.72 23.22 23.17
PointsPerGame 16.82 6.44 9.88 13.00 19.95
MinutesPerGame 27.36 22.11 25.22 30.34 32.43
3P% 0.37 0.27 0.29 0.30 0.27
FG% 0.43 0.41 0.43 0.43 0.46
FT% 0.77 0.64 0.71 0.71 0.82
TrueShooting% 0.54 0.48 0.52 0.52 0.56
Table 3.8: Players from 2017-18 season with assigned clusters.
Player Team Cluster
10-Ian Nash Algoma 1
11-Johneil Simpson Brock 1
35-Cassidy Ryan Brock 1
10-Yasiin Joseph Carleton 1
06-Ali Sow Laurier 1
11-Tevaun Kokko Laurier 1
11-Miles Seward McMaster 1
22-Jaz Bains Queen’s 1
04-Manny Diressa Ryerson 1
10-Marko Kovac Western 1
12-Omar Shiddo Western 1
07-Pedro Costa Algoma 2
09-Kascius Small-Martin Brock 2
03-Marcus Anderson Carleton 2
15-Drew Walford Guelph 2
24-Litha Ncanisa Laurentian 2
03-Ntore Habimana Laurier 2
25-Andre Toic McMaster 2
07-Jerron Rhodes Nipissing 2
10-Ismael Kaba Nipissing 2
12-Gage Sabean Ottawa 2
15-Brody Maracle Ottawa 2
04-Harry Range Queen’s 2
20-Nikola Farkic Western 2
15-Damian Persaud Windsor 2
21-Lucas Wood Windsor 2
10-Gene Spagnuolo York 2
11-Prince Kamunga York 2
15-Ricky Hudson York 2
09-Cailum White Algoma 3
13-Reng Gum Algoma 3
22-Jermaine Lyle Algoma 3
15-Daniel Cayer Brock 3
25-Tyler Brown Brock 3
13-Munis Tutu Carleton 3
42-Eddie Ekiyor Carleton 3
05-Jonathan Wallace Guelph 3
11-Taylor Boers Guelph 3
21-Anthony Iacoe Laurentian 3
02-Matt Minutillo Laurier 3
10-Matt Quiring McMaster 3
02-Sean Stoqua Ottawa 3
03-Calvin Epistola Ottawa 3
05-Jean Emmanuel Pierre-Charles Ottawa 3
06-Mike Shoveller Queen’s 3
07-Quinton Gray Queen’s 3
05-Roshane Roberts Ryerson 3
11-Christopher Barrett Toronto 3
21-Daniel Johansson Toronto 3
09-Henry Tan Western 3
05-Anthony Zrvnar Windsor 3
08-Gianmarco Luciani York 3
06-Nathan Riley Algoma 4
04-Daniel Dooley Guelph 4
11-David Aromolaran Laurentian 4
23-Nelson Yengue Laurentian 4
04-Kareem Collins McMaster 4
13-Marcus Lewis Nipissing 4
10-Brandon Robinson Ottawa 4
12-Tanner Graham Queen’s 4
07-Myles Charvis Ryerson 4
08-Jean-Victor Mukama Ryerson 4
04-Reilly Reid Toronto 4
05-Sage Usher Toronto 4
20-Justin Hardy Waterloo 4
08-Eriq Jenkins Western 4
11-Marcus Jones Windsor 4
20-Lucas Orlita Windsor 4
21-David McCulloch McMaster 5
04-Simon Petrov Waterloo 5
42-Nedim Hodzic Waterloo 5
10-Mike Rocca Windsor 5

### 3.4.5 2018-19 Season

The optimal number of clusters suggested for the 2018-19 season is 4. Below are tables to show the average statistics for each cluster and also which players belong to which cluster.

Table 3.9: Average of each variable for each cluster for the 2018-19 season
Cluster 1 Cluster 2 Cluster 3 Cluster 4
3PointAttempted 79.55 205.75 58.26 106.56
Assists 51.12 70.75 29.81 53.06
Blocks 9.03 4.25 6.85 11.12
DefensiveRebounds 83.12 96.00 60.33 114.81
FieldGoalAttempted 215.67 435.75 150.70 308.19
FreeThrowsAttempted 58.48 130.75 40.74 108.38
Minutes 613.58 775.25 478.74 708.94
OffensiveRebounds 28.67 22.00 20.56 36.44
PersonalFouls 52.00 48.50 40.37 50.38
Points 249.00 566.00 172.70 383.69
Rebounds 111.79 118.00 80.89 151.25
Steals 24.64 31.00 15.96 24.69
Turnovers 37.42 68.00 29.00 50.12
Home 11.55 11.50 10.33 11.38
GamesPlayed 22.73 23.00 20.52 22.88
PointsPerGame 11.00 24.88 8.65 16.80
MinutesPerGame 27.05 33.72 23.51 30.99
3P% 0.29 0.36 0.29 0.33
FG% 0.43 0.44 0.42 0.43
FT% 0.69 0.79 0.70 0.75
TrueShooting% 0.51 0.57 0.51 0.54
Table 3.10: Players from 2018-19 season with assigned clusters.
Player Team Cluster
03-Elijah Butler Algoma 1
08-David Bokanga Algoma 1
15-Daniel Cayer Brock 1
25-Tyler Brown Brock 1
11-Tj Lall Carleton 1
13-Munis Tutu Carleton 1
42-Eddie Ekiyor Carleton 1
22-Rasheed Weekes Guelph 1
21-Anthony Iacoe Laurentian 1
02-Matt Minutillo Laurier 1
03-Ntore Habimana Laurier 1
05-Jackson Mayers Laurier 1
11-Justin Hill Nipissing 1
03-Calvin Epistola Ottawa 1
07-Mackenzie Morrison Ottawa 1
10-Brandon Robinson Ottawa 1
07-Quinton Gray Queen’s 1
23-Jayden Frederick Ryerson 1
11-Christopher Barrett Toronto 1
21-Daniel Johansson Toronto 1
08-Eriq Jenkins Western 1
13-Julian Walker Western 1
20-Nikola Farkic Western 1
08-Chris Poloniato Windsor 1
11-Telloy Simon Windsor 1
14-Thomas Kennedy Windsor 1
20-Lucas Orlita Windsor 1
02-Chevon Brown York 1
05-DeAndrae Pierre York 1
11-Johneil Simpson Brock 2
06-Ali Sow Laurier 2
12-Omar Shiddo Western 2
10-Michael Vos Otin Brock 3
03-Marcus Anderson Carleton 3
10-Yasiin Joseph Carleton 3
05-Aaron Nugent Guelph 3
22-Josis Mikia-Thomas Laurentian 3
24-Litha Ncanisa Laurentian 3
32-Gaetan Chamand Laurentian 3
23-Sefa Otchere McMaster 3
32-Jordan Henry McMaster 3
08-Jordan Roberts Nipissing 3
12-Quintin Ashitei Nipissing 3
04-Harry Range Queen’s 3
05-Yusuf Ali Ryerson 3
14-Keevon Small Ryerson 3
04-Simon Petrov Waterloo 3
05-Colin Connors Waterloo 3
23-Justin Malnerich Waterloo 3
09-Marko Kovac Western 3
10-Anthony Zrvnar Windsor 3
04-Prince Kamunga York 3
08-Gianmarco Luciani York 3
10-Gene Spagnuolo York 3
06-Nathan Riley Algoma 4
35-Cassidy Ryan Brock 4
15-Malcolm Glanville Guelph 4
32-Tommy Yanchus Guelph 4
11-Connor Gilmore McMaster 4
21-David McCulloch McMaster 4
13-Marcus Lewis Nipissing 4
12-Gage Sabean Ottawa 4
41-Guillaume Pepin Ottawa 4
03-Jaz Bains Queen’s 4
12-Tanner Graham Queen’s 4
07-Myles Charvis Ryerson 4
08-JV Mukama Ryerson 4
42-Nedim Hodzic Waterloo 4

## 3.5 Conclusion

Unsupervised learning used on basketball data can be very helpful. It can be used to categorize players and to see what their style of play is. It can also be used for match-ups and for predicting important players. For instance, if you find that a player was in the same cluster as the catch and shoot players, a coach can assign an appropriate defender. Knowing the style of play for your opponents is very useful for defensive purposes. In my opinion the higher the number of clusters assigned, the better because it would distinguish the type of player more.