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Cal Poly Men’s Basketball: Line-up Efficiency Matthew Kiyoshi Hanamoto California Polytechnic State University San Luis Obispo San Luis Obispo, CA, USA, 93407 [email protected] 1 Introduction The game of basketball is evolving. Data analytics have emerged as a tool to help teams analyze nuances of the game that could not be previously explained. At the professional level, teams are utilizing highly sophisticated data tracking technologies to analyze players and to help executives make million dollar decisions. Unfortunately, most college level programs do not have access to the expensive tracking technologies that professional teams are using. What they do have, however, is hours of game film and a log of scores and substitutions. Any basketball program with these basic resources, can use my data tracking techniques to analyze team information and gain strategic advantages. My research shows that a basketball team’s substitution trends, strengths, and weaknesses can be understood by tracking a team’s plus/minus statistic on a possession 1 basis. Plus/minus is a very easy to understand statistic. It is the net value of points 2 that have accumulated while a player is on the floor. Plus/minus is widely used as a statistic to show influence of individual players on the court. For example, if a player has a plus/minus of +20, it is understood that a team outscored its opponent by 20 points while the certain player was on the court. The problem with using plus/minus as a measurement of individual efficiency, as detailed on ESPN’s website 3 , is that each player's rating is heavily influenced by the play of his on-court teammates.” By keeping track of plus/minus in regards to lineups, the issue of unaccounted for influence is neutralized. To put my research in context of Cal Poly’s Men’s Basketball team, below is a table that I created to help our coaching staff visualize which lineups provided the best opportunities to maximize team efficiency. 4 1 A possession is the sequence of a team attempting a shot and then defending against their opponent’s shot. 2 Positive for points scored and negative for points given up 3 http://espn.go.com/nba/story/_/id/10740818/introducing-real-plus-minus 4 Green plus/minus values show our most efficient lineups while red plus/minus values show our least efficient lineups. Rotation Usage Plus Minus CP Opp 0 1 3 4 5 10 13 14 23 25 30 34 42 PM/Poss Per 20 Efficiency compared to Highest Used 102.407 127 12 114 102 0 0 1 0 0 1 0 0 0 1 1 1 0 0.094 1.890 Highest Used 72.288 90 -26 61 87 10100 1 0 0 0 1 0 1 0 -0.289 -5.778 -406% 92.401 81 15 75 60 10100 0 0 0 0 1 1 1 0 0.185 3.704 96% 77.293 56 20 57 37 00101 1 0 0 0 1 0 1 0 0.357 7.143 278% 97.406 51 -7 45 52 00101 0 0 0 0 1 1 1 0 -0.137 -2.745 -245% 82.303 44 7 45 38 00101 1 0 0 0 0 1 1 0 0.159 3.182 68% 71.263 35 11 29 18 01001 1 0 0 0 1 1 0 0 0.314 6.286 233% 104.373 33 5 25 20 00001 1 0 0 0 1 1 1 0 0.152 3.030 60% 74.254 32 -4 21 25 10001 1 0 0 0 1 0 1 0 -0.125 -2.500 -232% 73.283 23 7 25 18 00101 1 0 0 0 1 1 0 0 0.304 6.087 222% 99.368 21 14 28 14 10000 1 0 0 0 1 1 1 0 0.667 13.333 606% 91.278 13 11 19 8 00100 1 0 0 1 1 1 0 0 0.846 16.923 796% 121.403 11 7 13 6 00001 1 0 0 0 0 1 1 1 0.636 12.727 573% Highly Used or Highly Efficient CP Rotations
Transcript
Page 1: Lineup Efficiency

Cal Poly Men’s Basketball: Line-up Efficiency

Matthew Kiyoshi Hanamoto California Polytechnic State University San Luis Obispo

San Luis Obispo, CA, USA, 93407 [email protected]

1 Introduction

The game of basketball is evolving. Data analytics have emerged as a tool to help teams analyze nuances of the game that could not be previously explained. At the professional level, teams are utilizing highly sophisticated data tracking technologies to analyze players and to help executives make million dollar decisions. Unfortunately, most college level programs do not have access to the expensive tracking technologies that professional teams are using. What they do have, however, is hours of game film and a log of scores and substitutions. Any basketball program with these basic resources, can use my data tracking techniques to analyze team information and gain strategic advantages.

My research shows that a basketball team’s substitution trends, strengths, and weaknesses can be understood by tracking a team’s plus/minus statistic on a possession1 basis. Plus/minus is a very easy to understand statistic. It is the net value of points2 that have accumulated while a player is on the floor. Plus/minus is widely used as a statistic to show influence of individual players on the court. For example, if a player has a plus/minus of +20, it is understood that a team outscored its opponent by 20 points while the certain player was on the court. The problem with using plus/minus as a measurement of individual efficiency, as detailed on ESPN’s website3, is that “each player's rating is heavily influenced by the play of his on-court teammates.” By keeping track of plus/minus in regards to lineups, the issue of unaccounted for influence is neutralized.

To put my research in context of Cal Poly’s Men’s Basketball team, below is a table that I created to help our coaching staff visualize which lineups provided the best opportunities to maximize team efficiency.

4

1 A possession is the sequence of a team attempting a shot and then defending against their opponent’s shot. 2 Positive for points scored and negative for points given up 3 http://espn.go.com/nba/story/_/id/10740818/introducing-real-plus-minus 4 Green plus/minus values show our most efficient lineups while red plus/minus values show our least efficient lineups.

Rotation Usage Plus Minus CP Opp 0 1 3 4 5 10 13 14 23 25 30 34 42 PM/Poss Per 20 Efficiency compared to Highest Used102.407 127 12 114 102 0 0 1 0 0 1 0 0 0 1 1 1 0 0.094 1.890 Highest Used72.288 90 -26 61 87 1 0 1 0 0 1 0 0 0 1 0 1 0 -0.289 -5.778 -406%92.401 81 15 75 60 1 0 1 0 0 0 0 0 0 1 1 1 0 0.185 3.704 96%77.293 56 20 57 37 0 0 1 0 1 1 0 0 0 1 0 1 0 0.357 7.143 278%97.406 51 -7 45 52 0 0 1 0 1 0 0 0 0 1 1 1 0 -0.137 -2.745 -245%82.303 44 7 45 38 0 0 1 0 1 1 0 0 0 0 1 1 0 0.159 3.182 68%71.263 35 11 29 18 0 1 0 0 1 1 0 0 0 1 1 0 0 0.314 6.286 233%

104.373 33 5 25 20 0 0 0 0 1 1 0 0 0 1 1 1 0 0.152 3.030 60%74.254 32 -4 21 25 1 0 0 0 1 1 0 0 0 1 0 1 0 -0.125 -2.500 -232%73.283 23 7 25 18 0 0 1 0 1 1 0 0 0 1 1 0 0 0.304 6.087 222%99.368 21 14 28 14 1 0 0 0 0 1 0 0 0 1 1 1 0 0.667 13.333 606%91.278 13 11 19 8 0 0 1 0 0 1 0 0 1 1 1 0 0 0.846 16.923 796%

121.403 11 7 13 6 0 0 0 0 1 1 0 0 0 0 1 1 1 0.636 12.727 573%

Highly Used or Highly Efficient CP Rotations

Page 2: Lineup Efficiency

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From that table, we can interpret which lineups were favored in games5, which lineups worked throughout the season6, and which lineups resulted in negative outcomes7.

Additionally, by keeping track of the defensive alignments of a team and of their opponent, we can understand which lineups operate most efficiently in and against certain types of defenses.

1.1 Research Question:

The focus of my study was to determine which groups of players on Cal Poly’s Men’s Basketball team performed the most efficiently together.

• Unit of efficiency: I used plus/minus per possession as my unit of efficiency. • Supplement: I also tracked and analyzed which groups of players were the most efficient

against different types of defenses.

2 Basketball Basics (Readers who are familiar with the game… skip to Section 2.4.)

If the reader is unfamiliar with the game of basketball, below are some guidelines of the game.

2.1 Objective: Score as many points as possible, while limiting your opponent to fewer points. (You accomplish this by shooting the ball through your opponent’s hoop as many times as you can.)

2.2 Rules:

• There are five players, per team, allowed on the court at one time. o The five players form what will be referenced as a “line up.”

• When your team has the ball, you collectively have 35 seconds to attempt a shot at your opponent’s hoop.

o If your team misses the shot and regains possession of the ball, your team has another 35 seconds to attempt a shot.

5 This can be determined by the usage column 6 High positive plus/minus values provide a good indication of which lineups work. One can also compare the efficiency of other highly used lineups to the starting lineup. Rotation 77.293 was a good example of a highly efficient lineup that was used sparingly and could have been utilized more often. 7 Rotation 72.288 was our least efficient lineup on the year. This was a perfect example of how the eye test can deceive you. The players who made up this lineup looked as though they would work great together, but the numbers told another story.

Rotation Usage Plus Minus CP Opp 0 1 3 4 5 10 13 14 23 25 30 34 42 PM/Poss Per 5 Efficiency compared to Highest Used92.401 17 7 17 10 1 0 1 0 0 0 0 0 0 1 1 1 0 0.412 2.059 Highest Used

102.407 13 1 15 14 0 0 1 0 0 1 0 0 0 1 1 1 0 0.077 0.385 -81%72.288 8 6 13 7 1 0 1 0 0 1 0 0 0 1 0 1 0 0.750 3.750 82%73.283 8 3 9 6 0 0 1 0 1 1 0 0 0 1 1 0 0 0.375 1.875 -9%82.303 8 1 12 11 0 0 1 0 1 1 0 0 0 0 1 1 0 0.125 0.625 -70%97.406 8 2 9 7 0 0 1 0 1 0 0 0 0 1 1 1 0 0.250 1.250 -39%77.293 6 -2 6 8 0 0 1 0 1 1 0 0 0 1 0 1 0 -0.333 -1.667 -181%72.145 5 -4 0 4 1 0 0 0 1 1 0 0 1 0 0 1 0 -0.800 -4.000 -294%

104.373 5 1 6 5 0 0 0 0 1 1 0 0 0 1 1 1 0 0.200 1.000 -51%72.297 4 4 4 0 1 0 1 0 1 0 0 0 0 0 1 1 0 1.000 5.000 143%74.254 4 -10 0 10 1 0 0 0 1 1 0 0 0 1 0 1 0 -2.500 -12.500 -707%77.298 3 5 5 0 1 0 1 0 0 1 0 0 0 0 1 1 0 1.667 8.333 305%

Effectiveness of 1-3-1 Defense

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• While your team has the ball8, you can score 1, 2, 3, or 4 points. o Point value of a shot is determined by distance from the hoop. Occasionally,

players can earn extra opportunities to score points if there is an excessive amount of contact endured while attempting a shot.

• While your opponent has the ball9, your opponent has the same scoring opportunities. • The two teams on the court play two 20 minutes halves in which they try to score against

one another as many times as possible. • At the end of the 40 minutes of playing time, whoever has accumulated the most points,

wins. o If there is a draw at the end of the 40 minutes of playing time, there are short

overtime periods in which the teams will play until there is a winner.

2.3 Types of Defenses:

1-2-2 Zone10 Man-to-Man11 1-3-1 Zone12

The purpose of positioning your players in different defensive alignments is to exploit the weaknesses of your opponent. For example, zone type defenses are employed to increase the difficulty of scoring close to the basket. Players who struggle to maintain efficient shooting percentages far from the basket typically struggle against zone defenses.

2.4 Vocabulary to know:

• Possession: the sequence of your team attempting a shot and then defending against your opponent’s shot.

• Plus/Minus: the net value of how many points your team scores while on offense and how many points your opponent scores while you are on defense.

8 This will be referred to as offense. 9 This will be referred to as defense. 10 UC Santa Barbara uses the 1-2-2 defense frequently. 11 All basketball teams will run a man to man defense. 12 Cal Poly specialized in the 1-3-1 defense.

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3 Data Collection

3.1 Note to the Data Collector

The key to an accurate analysis of one’s team is meticulous data entry and uniformly formatted data. You will need to keep your information in a common format in order for you to have interpretable information as the season progresses. It is important to note that one game of data will not tell you much about how your team will perform throughout the season. Ten games, however, should be a sizeable enough sample to make informed decisions on how different lineups are performing. I highly recommend using Microsoft Excel to organize your data and to expedite the summary statistic process. I also recommend recording each game in a separate Excel file. To make all of my recordings uniform, I created a template to use for each game. After I finished recording each game, I moved all of the data I recorded into a larger spreadsheet that served as my database.

3.2 Method

I created a database of every single possession from this past season, from scratch. For each individual possession, I included which players from our team were in the game, how many points we scored, how many points our opponent scored, which defensive alignment we were in, and which defensive alignment our opponent was in.

3.3 Player Tracking

Keeping track of who is in the game is easy. Give the player a value of one, if he or she is in the game, and a value of zero, if he or she is out of the game. You will only need to change your inputs when there is a substitution.

3.4 Point Tracking

Knowing which possession points were scored in becomes challenging when there are offensive rebounds involved. To show that a team did not score in a possession, I assign “Pts Offense” a value of zero. If there is an offensive rebound, I skip to the next line and treat the next shot as a new possession.13

13 Be careful during this part of the data entry because the way you have entered the scoring information now switches in order. If your opponent was shooting first in a possession and your team gets an offensive rebound on their shot attempt, your team’s scoring information will be now entered before your opponent’s. This seems like a minor detail now, but it can get very confusing when you are in the middle of a game and there is no stoppage in play.

0 1 3 4 5 10 13 14 23 25 30 34 421 1 1 0 0 0 0 0 0 1 1 0 0

Team Possession Half P.M Rotation Indicator CP Man CP 13 CP 23 CP 12 Opp Man Opp 23 Opp 12 Opp 13 Pts Offense Pts AgainstNevada 1 1 2 59.291 1 0 0 0 1 0 0 0 2 0

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3.5 Error Proofing

To ensure that I am only observing five players on the court at the same time, I used an if-else statement to tell me whether or not there are five players in the game.14 The line of code could be as simple as asking Excel to tell you “OK” if the sum of all players in a row is equal to five. Also, be sure to keep track of the score while recording your data. This is incredibly useful in situations where you realize your final score doesn’t add up and you want to find the exact possession where you may have entered the wrong point value. I recommend creating two columns that keep a running tally of the “Pts Offense” and “Pts Against.”

3.6 Unique Indicator Variables

The picture below shows how I tagged each lineup with an individual rotation indicator variable.

To create a unique indicator variable, I used Excel’s sumproduct function. This function, in context of my database, adds up each of the numbers of the players in the lineup. I had originally used just the players’ numbers (with no added decimals) in the sumproduct function, but ran across the problem of overlap among certain combinations of players. For example, the sum of players 0, 5, 13, 25, and 34 would have the exact same rotation indicator variable15 as the players 3, 5, 10, 25, and 34. This was only one of numerous instances that this problem arose. What I did to combat this problem was add a random three digit decimal to the end of the players’ numbers. At first, I tried to increase the three digit numbers in a specific order, but ran into overlap a second time. Choosing random numbers was my solution to removing overlap. There are more statistically sound ways to make sure there is a 0% possibility of overlap and I will look to apply those methods at a later date.

14 It is amazing how easy it is to put four or six players on the court at one time. 15 Rotation indicator = 77

Rot OK CP OppOK 2 0OK 3 2OK 5 2OK 5 2

0.001 1.020 3.040 4.050 5.006 10.007 13.080 14.090 23.001 25.110 30.120 34.130 42.1401 1 1 0 0 0 0 0 0 1 1 0 0

Rotation Indicator59.291

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3.7 Defense Tracking

This aspect of the data tracking process requires hours of practice and a keen eye. In the Big West conference, there are only four types of defenses that teams play.

1) Man-to-Man (Each player on defense is matched up with a player on offense. This type of defense is used by every team in the country.)

2) 2-3 Zone (Each player on defense is responsible for a specific zone on the court. There

are typically two guards around the free throw line, a center in the middle of the paint, and two forwards in the lower corners. This defense is used most often by teams with a very tall center16.)

3) 1-2-2 Zone (Like the 2-3 zone, each player is responsible for a zone on the court. This

alignment however has a forward and a center protecting the hoop, two wings near the free throw line, and a guard pressuring near the top of the key and dropping down to help in the middle of the defense.)

16 A good example of this type of team is UC Irvine. They have a 7’8” center who is very effective as a rim protector.

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4) 1-3-1 Zone (The 1-3-1 zone operates similarly to the other zones, but there is usually a long-armed, athletic wing at the very top of the formation. He is there trying to pressure the ball as it gets thrown from one side of the court to the other side. There are three players lined up in the middle of the court trying to prevent a pass to the corners nearest the basket. The player on the bottom of the formation is responsible for running and contesting shots in the corners.)

Tracking defensive alignments is a tedious process. There are no services that will track this data for you. A trick that I found very useful was to watch how the defense responds to a cutting player. The initial cut will let you know if the defense is in Man-to-Man or in a type of zone. If a defender follows the cutting player everywhere around the court, you can be certain that the defense is in man. If the defender of the cutter does not follow the cutter and another player picks up the player at the end of the cut, the defense is probably in zone. The only way to get good at tracking defense is by watching film and picking up on tendencies of teams.17

3.8 Summary of Data Collection/Tips and Tricks

Tracking all of this information in real time is not an easy process. Offensive rebounds, quick lineup changes, and changing defenses are not easy variables to monitor and log. When the data collector cannot be at a game, using a school’s play by play18 makes the logging of points and substitutions easy. In the play by play, every substitution is listed and every score is logged. The only thing you need to do is record the information in a numerical format. Once you have documented every point and lineup change, all you have to do is watch the film and mark which defense the teams are in during their possessions. Play by plays are not always one hundred percent accurate, but most are near perfect. Understanding how the game flows will help you correct errors and understand when you need to make an adjustment.

17 There are even teams, such as CSU Northridge, that will change their defense halfway through a play. Their coach would actually make a loud whistle noise and his players would switch from their zone defense to man-to-man. 18 This is usually found on a school’s athletics website.

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4 Analysis

Once you have all of your data recorded and everything is compiled into a single spreadsheet, you will want to create a PivotTable to analyze and produce summary statistics.

4.1 How to build a PivotTable

1) Highlight everything on the spreadsheet that contains all of your recorded data. (Ctrl+a) 2) Insert -> Pivot Table (Choose new worksheet) 3) Filters (Drag the variables that you want to filter your data by):

- Possession - Win/Loss - Location - Half - All Player numbers - All Defenses - Team

4) Columns: (Don’t Touch) 5) Rows: Rotation Indicator 6) Values:

- Counts of rotation indicators - Sum of +/- - Sum of Pts Offense - Sum of Pts Against

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The great thing about pivot tables is how easy it makes the process of analyzing your data.

• You can sort your data by highest scoring lineups to lowest scoring lineups. • You can use Excel’s conditional formatting option to color coordinate high and low

performing lineups. • You can filter out players who are no longer of interest in your rotations.19 • NOTE: I ran into the issue of figuring out who was in the lineups listed in the PivotTable.

o To solve this issue, I copied all of the data from my database sheet into a separate sheet that I called Rotation Match. In this sheet, I sorted all of the data in order of the value of the rotation indicator variable.20

o Going back to the PivotTable sheet, I used a vlookup function to pull the 0 and 1 values from the Rotation Match sheet. Example syntax: =VLOOKUP($G18,'Rotation Match'!$G$2:$S$1310,2)

(Full database spreadsheet layout)

(Rotation Match spreadsheet)

19 This is especially useful if a player becomes ineligible during a season or you lose a player to injury. Instead of panicking about figuring out how to use only relevant data, you can just filter the player out of your table. 20 Make sure the variable Rotation Indicator Variable is in the column next to the player numbers.

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4.2 Summary of Analyses

Sort all of our lineups based on overall efficiency. How do these lineups compare to the team average? (Sort your lineups by most frequently used and divide their plus/minus values by the amount of times each of the lineups were used.)

How has the team performed against zone defense? (In the PivotTable, set your opponent’s defense filter of man-to-man to zero. The resulting observations should be all of your team’s possessions against a type of zone defense.21)

21 If you want to be more specific, you can filter your opponent’s defense filter to only include the defense you are interested in.

0.410.33

0.230.12 0.11 0.09 0.06

-0.07 -0.07-0.15

-0.30-0.40-0.30-0.20-0.100.000.100.200.300.400.50

Overall Plus/Minus

Plus Minus per possession Average Plus Minus

0.89

-0.07 -0.11-0.38 -0.43 -0.46

-0.70 -0.73 -0.74

-1.50

-1.00

-0.50

0.00

0.50

1.00

77.293 92.401 77.298 67.287 102.407 74.254 121.403 75.361 72.288

Plus/Minus against Zone

Plus Minus per possession Average Plus Minus

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Show only the summary statistics starting from a certain point in time to now. (Our head coach made an adjustment to his coaching strategy midway through our season. All you have to do is filter which games were played in the time frame after the adjustment in coaching strategy.)

- It will be beneficial to label each of your data files in chronological order. I chose to list each of mine by what date the game was played on and who the game was against.

o (Ex. 2015.3.7 CP. UC Santa Barbara 2.xlsx)

Are there any lineups that we use frequently that are not effective?

Our second highest used lineup has a large, negative plus/minus value of -26. This statistic should be very surprising to followers of our team. The players featured in this lineup are all very good players. The lineup included a senior leader, a dynamic slasher, a prominent point guard, and our two staple big men. What seemed to bother this lineup was a lack of spacing on the basketball court. The skills of the players overlapped and the lineup efficiency suffered.

Which lineup is the most effective in our 1-3-1 defense?

Based on our defensive tracking data, lineup 92.401 proved to be our most effective 1-3-1 lineup. Lineup 121.403 was more efficient in regards to ratio of plus/minus to frequency of usage, but it had nearly 20 fewer observations. In this analysis, I chose to reward lineups who were used more frequently because getting defensive stops does not get easier with an increase in attempts. My efficiency statistic was calculated by dividing plus/minus by the frequency of the lineup. To weight the efficiency, I multiplied the percent usage by the efficiency statistic to get a better indicator of how well the lineup performed.

Row Labels Count of Rotation Indicator Count weight Sum of P.M Efficiency Weighted Efficiency Sum of Pts Offense Sum of Pts Against 0 1 3 4 5 10 13 14 23 25 30 34 4292.401 30 24% 8 0.267 0.065 29 21 92.401 1 0 1 0 0 0 0 0 0 1 1 1 067.287 20 16% 1 0.050 0.008 30 29 67.287 1 0 1 0 1 0 0 0 0 1 0 1 0

102.407 17 14% 3 0.176 0.024 19 16 102.41 0 0 1 0 0 1 0 0 0 1 1 1 075.361 14 11% 3 0.214 0.024 11 8 75.361 1 0 1 0 0 0 1 0 0 1 0 1 097.406 13 10% 5 0.385 0.040 15 10 97.406 0 0 1 0 1 0 0 0 0 1 1 1 0

121.403 11 9% 7 0.636 0.056 12 5 121.4 0 0 0 0 1 1 0 0 0 0 1 1 191.284 10 8% 2 0.200 0.016 10 8 91.284 1 0 0 0 1 1 0 0 0 0 0 1 174.254 9 7% -12 -1.333 -0.097 6 18 74.254 1 0 0 0 1 1 0 0 0 1 0 1 0

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How can we put ourselves in the best position to win?

(Substitution patterns and scoring trends)

March 12, 2015 was the date of Cal Poly’s first round Big West Tournament game. They were scheduled to play UC Santa Barbara, a team that they were defeated by five days prior. Days before our tournament game, I decided to try my data tracking method on our opponent. I used all of UC Santa Barbara’s conference games as my data source and was able to gain an understanding of UCSB’s substitution patterns and the scoring tendencies of their team.

Based on the results in the substitution patterns table, I was able to interpret that UC Santa Barbara was a team that put the majority of their effort in getting ahead early in the game. Also, I was able to interpret that their starting lineup was essentially their only efficient scoring lineup. Over the course of conference play, their starters were +25 in the first 20 possessions of the game and +34 in the entire first half. The same group of players, however, were only +7 in the second half of games. The reason this kind of information is valuable is because starting lineups tend to play the majority of the minutes in a game. Understanding the opponent’s production patterns can definitely give your team a strategic advantage. The summary statistics told us that if you can manage to keep the game close throughout the first half, you can take advantage of their lower levels of efficiency at the end of game.

Possessions 1-20 Start - first half of first halfRotation Usage Plus Minus SB Opp % Played 0 1 2 3 11 12 13 15 21 24 31 4476.152 63 25 64 39 53% 0 0 1 1 0 0 0 1 0 1 1 074.851 11 1 7 6 9% 0 1 0 1 0 0 0 1 0 1 1 064.302 5 -3 2 5 4% 0 1 1 0 0 0 0 1 1 1 0 074.211 5 -4 5 9 4% 0 1 1 0 0 0 0 1 0 1 1 076.888 5 -1 2 3 4% 0 0 0 1 0 0 1 1 1 1 0 0

120 Possessions

Possessions 21-37 2nd half of first half - half timeRotation Usage Plus Minus SB Opp % Played 0 1 2 3 11 12 13 15 21 24 31 4476.152 21 9 25 16 21% 0 0 1 1 0 0 0 1 0 1 1 064.852 17 -6 11 17 17% 0 0 1 1 0 0 1 1 0 0 1 062.911 16 -2 16 18 16% 0 1 1 0 0 0 1 1 0 0 1 056.24 6 1 5 4 6% 0 0 1 1 1 0 0 1 0 1 0 0

86.797 6 -4 4 8 6% 0 0 0 1 0 0 1 1 0 1 1 0102 Possessions

Possessions 38-60 Half time - first half of second half Most likely to see Childress, Taylor, Brewe, BeelerRotation Usage Plus Minus SB Opp % Played 0 1 2 3 11 12 13 15 21 24 31 4476.152 59 4 59 55 43% 0 0 1 1 0 0 0 1 0 1 1 074.851 18 -8 10 18 13% 0 1 0 1 0 0 0 1 0 1 1 066.245 12 -3 9 12 9% 0 0 1 0 1 0 1 1 0 1 0 051.984 9 1 12 11 7% 0 1 0 1 1 0 0 1 1 0 0 0

114.006 5 -2 2 4 4% 0 1 0 0 0 0 1 0 0 1 1 1138 Possessions

Possessions 61-Finish 2nd half of 2nd Half - End of GameRotation Usage Plus Minus SB Opp % Played 0 1 2 3 11 12 13 15 21 24 31 4476.152 22 3 14 11 18% 0 0 1 1 0 0 0 1 0 1 1 057.898 10 5 11 6 8% 0 0 1 1 0 0 1 1 0 1 0 045.952 8 5 8 3 7% 0 1 1 1 0 0 0 1 0 1 0 074.851 8 3 10 7 7% 0 1 0 1 0 0 0 1 0 1 1 054.187 7 3 4 1 6% 0 0 1 1 1 0 1 0 0 1 0 056.597 7 1 4 3 6% 0 1 0 1 0 0 1 1 0 1 0 0

122 Possessions482 Total

Most Likely to see Smith in during this time

Most Likely to see Smith in during this time

Most Likely to see Brewe in during this time

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Game Results:

Actual substitutions:

As predicted, Santa Barbara’s starters were the only efficient scoring group of players on their team. They were played 46 percent of the game together and finished with a plus/minus of +8. An interesting observation was that the starting lineup followed their second half trend of dropping in efficiency. They dropped from +7 in the first half all the way down to +1 in the second half. Also, the pattern of UCSB’s substitutions were very similar to the predictions made prior to the game. The only inconsistency was the increase in playing time of player number 1 over player number 13. This inconsistency could be due to the absence of player number 13 from the previous game.

Cal Poly was in a position to win this game. With two minutes to go, Cal Poly was down 50 to 52 and had possession of the ball. They had two attempts to make a shot and were unsuccessful. Santa Barbara had also missed a shot and turned the ball over in this timespan. With 50 seconds to go, Santa Barbara had possession of the ball and Cal Poly needed to get a defensive stop. Our best defensive lineup22, according to the data, was inserted into the game and successfully forced a turnover. With 30 seconds remaining, Cal Poly had a shot to tie or win the game.

22 Rotation Indicator: 92.401

Rotation Indicator Count of Rotation Indicator Sum of +/- Sum of Pts Offense Sum of Pts Against 0 1 2 3 11 12 13 15 21 24 31 4452.906 4 -1 2 3 52.906 0 1 1 1 0 0 0 1 0 0 1 063.194 4 -3 3 6 63.194 0 0 1 1 1 0 0 1 0 0 1 074.851 14 -2 11 13 74.851 0 1 0 1 0 0 0 1 0 1 1 074.947 7 1 4 3 74.947 0 1 0 0 0 0 1 1 1 1 0 076.152 32 8 29 21 76.152 0 0 1 1 0 0 0 1 0 1 1 081.143 1 0 2 2 81.143 0 1 0 1 0 0 0 0 1 1 1 094.092 3 2 2 0 94.092 0 1 0 1 0 0 0 0 1 1 0 1

103.361 3 1 1 0 103.361 0 1 1 0 0 0 0 0 0 1 1 1106.038 2 -2 0 2 106.038 0 0 0 1 0 0 1 0 1 1 0 1

Grand Total 70 4 54 50

Possessions 1-20Rotation Usage Plus Minus SB Opp % Played 0 1 2 3 11 12 13 15 21 24 31 44

74.947 6 1 4 3 30% 0 1 0 0 0 0 1 1 1 1 0 076.152 11 1 6 5 55% 0 0 1 1 0 0 0 1 0 1 1 0

103.361 3 1 1 0 15% 0 1 1 0 0 0 0 0 0 1 1 1Possessions 20 3

Possessions 21-37Rotation Usage Plus Minus SB Opp % Played 0 1 2 3 11 12 13 15 21 24 31 44

52.906 4 -1 2 3 24% 0 1 1 1 0 0 0 1 0 0 1 063.194 4 -3 3 6 24% 0 0 1 1 1 0 0 1 0 0 1 074.947 1 0 0 6% 0 1 0 0 0 0 1 1 1 1 0 076.152 8 6 11 5 47% 0 0 1 1 0 0 0 1 0 1 1 0

Possessions 17 2

Possessions 38-60Rotation Usage Plus Minus SB Opp % Played 0 1 2 3 11 12 13 15 21 24 31 44

74.851 5 3 7 4 22% 0 1 0 1 0 0 0 1 0 1 1 076.152 12 -1 10 11 52% 0 0 1 1 0 0 0 1 0 1 1 081.143 1 0 2 2 4% 0 1 0 1 0 0 0 0 1 1 1 094.092 3 2 2 0 13% 0 1 0 1 0 0 0 0 1 1 0 1

106.038 2 -2 0 2 9% 0 0 0 1 0 0 1 0 1 1 0 1Possessions 23 2

Possessions 61-FinishRotation Usage Plus Minus SB Opp % Played 0 1 2 3 11 12 13 15 21 24 31 44

74.851 9 -5 4 9 90% 0 1 0 1 0 0 0 1 0 1 1 076.152 1 2 2 10% 0 0 1 1 0 0 0 1 0 1 1 0

Possessions 10 -3Total 70

Plus/Minus

Half time - first half of second half

2nd half of 2nd Half - End of Game

2nd half of first half - half time

Start - first half of first half

Plus/Minus

Plus/Minus

Plus/Minus

Prediction: Most Likely to see Smith in during this time

Prediction: Most Likely to see Brewe in during this time

Prediction: Most likely to see Childress, Taylor, Brewe, Beeler

Prediction: Most Likely to see Smith in during this time

Page 14: Lineup Efficiency

14

Cal Poly did not end up winning this basketball game.23 Despite limiting Santa Barbara’s first half scoring and controlling the pace of the game, Santa Barbara prevailed. The key thing to remember, however, is that Cal Poly was in a position to win this game.

5 Conclusion

Basketball analytics are revolutionizing how teams are able to form game strategies. What I have done is break the game of basketball down to its purest form of scoring and defending. My data tracking methods and simple analyses have enabled our team to gain an advanced understanding of team tendencies, strengths, and weaknesses.

Analytics will never be able to take the place of a coach, replace basketball instinct, or guarantee the success of a team. It can, however, help coaches understand pieces of the game where intuition cannot provide the answer. It is my hope that my framework for tracking a team’s production can help basketball programs understand the basics of advanced metrics and provide the building blocks for finding strategic advantages.

23 Final score was 54-50.

0

10

20

30

40

50

60

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70

Cal Poly vs. UCSB3/12/2015

SB CP

Page 15: Lineup Efficiency

15

6 Acknowledgements

I would like to thank the following professors for their assistance on this project: Pratish Patel, Larry Gorman, Ziemowit Bednarek, Peter Chi, and Gary Hughes. I am very gracious for all of your the suggestions and input.

Also, thank you to the Cal Poly Men’s Basketball program for giving me the resources to pursue this project.

7 Sample Report

Once I had a firm understanding of how to create visual representations the data I was collecting, I decided to create a scouting report for our last regular season game against UC Santa Barbara. There was not enough time to create a full report for our Big West tournament game. Included are examples of tables and graphs that can help visualize the performance of lineups. Also, there are interpretations of the visuals to help coaches understand what they was looking at.

Page 16: Lineup Efficiency

Cal Poly vs. Santa Barbara

Page 17: Lineup Efficiency

Game Recap and Notes:

- 1st Half Possessions: 38 vs. 52 2nd Half Possessions (Pace of game was more in their favor toward the end) - SB used their 1-2-2 on 28 possessions:

o We were only able to convert 19 points on 28 possessions. o If we were to looking to get at least 1 point per possession then we converted on 68% of that goal. o Lineups facing the 1-2-2 unfortunately ended up allowing 27 points on those same 28 possessions.

- Not sure if this was purely a random shift in momentum, but #0 (Hunter Ford) was the addition to SB’s rotation that pushed them to the lead. (Reference: Ford, Vincent, Harmon, Al, Bryson lineup) He was also featured in other lineups that performed fairly well.

o He is a bottom level guy on Synergy’s report, but his addition on the court (29% of the game and a +13 while he was on the court) was a difference maker in our previous game.

- SB seemed prepared for 13. We attempted to run the defense on 19 possessions and they were able to score 19 points.

o SB converted every time 13 dropped too low. (Three middle guys sank too low into the key)

0

10

20

30

40

50

60

1 9 17 25 33 41 49 57 65 73 81 89

CP vs. SB 1.1.2015CP SB

Page 18: Lineup Efficiency

CP Breakdown vs. Santa Barbara

Total Possessions

Played (+/-) CP

Score SB

Score 0 1 3 4 5 10 13 14 23 25 30 34 42 41.144 12 1 4 3 1 1 0 0 1 1 0 0 0 1 0 0 0 59.291 3 -2 0 2 1 1 1 0 0 0 0 0 0 1 1 0 0 63.277 5 -4 0 4 1 0 1 0 1 0 0 0 0 1 1 0 0 67.287 8 -8 5 13 1 0 1 0 1 0 0 0 0 1 0 1 0 73.283 4 -2 4 6 0 0 1 0 1 1 0 0 0 1 1 0 0 77.293 6 3 3 0 0 0 1 0 1 1 0 0 0 1 0 1 0 77.298 11 -4 4 8 1 0 1 0 0 1 0 0 0 0 1 1 0 91.284 2 0 0 0 1 0 0 0 1 1 0 0 0 0 0 1 1 92.401 23 4 16 12 1 0 1 0 0 0 0 0 0 1 1 1 0 102.407 3 2 2 0 0 0 1 0 0 1 0 0 0 1 1 1 0 119.437 1 -2 0 2 0 0 1 0 0 1 0 0 0 0 1 1 1 121.403 12 7 7 0 0 0 0 0 1 1 0 0 0 0 1 1 1

Vs. Zone

Possessions Played (+/-)

CP Score

SB Score 0 1 3 4 5 10 13 14 23 25 30 34 42

59.291 2 -2 0 2 1 1 1 0 0 0 0 0 0 1 1 0 0 63.277 2 -1 0 1 1 0 1 0 1 0 0 0 0 1 1 0 0 67.287 3 -7 2 9 1 0 1 0 1 0 0 0 0 1 0 1 0 73.283 1 0 3 3 0 0 1 0 1 1 0 0 0 1 1 0 0 77.293 5 3 3 0 0 0 1 0 1 1 0 0 0 1 0 1 0 77.298 8 -4 4 8 1 0 1 0 0 1 0 0 0 0 1 1 0

92.401 7 3 7 4 1 0 1 0 0 0 0 0 0 1 1 1 0

Grand Total 28 -8 19 27

Page 19: Lineup Efficiency

Vs.

Man Possessions

Played (+/-) CP Score

SB Score 0 1 3 4 5 10 13 14 23 25 30 34 42

41.144 12 1 4 3 1 1 0 0 1 1 0 0 0 1 0 0 0 59.291 1 0 0 1 1 1 0 0 0 0 0 0 1 1 0 0 63.277 3 -3 0 3 1 0 1 0 1 0 0 0 0 1 1 0 0 67.287 5 -1 3 4 1 0 1 0 1 0 0 0 0 1 0 1 0 73.283 3 -2 1 3 0 0 1 0 1 1 0 0 0 1 1 0 0 77.293 1 0 0 0 0 0 1 0 1 1 0 0 0 1 0 1 0 77.298 3 0 0 1 0 1 0 0 1 0 0 0 0 1 1 0 91.284 2 0 0 0 1 0 0 0 1 1 0 0 0 0 0 1 1 92.401 16 1 9 8 1 0 1 0 0 0 0 0 0 1 1 1 0 102.407 3 2 2 0 0 0 1 0 0 1 0 0 0 1 1 1 0 119.437 1 -2 0 2 0 0 1 0 0 1 0 0 0 0 1 1 1 121.403 12 7 7 0 0 0 0 0 1 1 0 0 0 0 1 1 1 Grand Total 62 3 26 23

CP 13 Possessions

Played (+/-) CP

Score SB

Score 0 1 3 4 5 10 13 14 23 25 30 34 42 41.144 2 2 2 0 1 1 0 0 1 1 0 0 0 1 0 0 0 59.291 2 -2 0 2 1 1 1 0 0 0 0 0 0 1 1 0 0 63.277 3 -2 0 2 1 0 1 0 1 0 0 0 0 1 1 0 0 67.287 6 -4 5 9 1 0 1 0 1 0 0 0 0 1 0 1 0 73.283 1 0 3 3 0 0 1 0 1 1 0 0 0 1 1 0 0 91.284 1 0 0 1 0 0 0 1 1 0 0 0 0 0 1 1 92.401 3 -2 1 3 1 0 1 0 0 0 0 0 0 1 1 1 0 121.403 1 2 2 0 0 0 0 0 1 1 0 0 0 0 1 1 1 Grand Total 19 -6 13 19

Page 20: Lineup Efficiency

Santa Barbara Rotations

• Santa Barbara’s scoring was well distributed among their lineups. • Starting line-up’s +/- was (-7) in our previous game. (This could be attributed to us having ideal matchups against

that lineup.

-3

-2

-1

0

1

2

3

1 2 3 4 5 6 7 8 9 10 11 38 39 40 41

Vincent, Harmon, Al, Brewe, Bryson

CP SB

-3

-2

-1

0

1

2

3

29 30 31 32 33 34 35 36 37

Vincent, Harmon, Taylor, Al, Brewe

CP SB

-3

-2

-1

0

1

2

69 70 71 72 73 74 75 76 77 78 79 80 81 82

Ford, Vincent, Harmon, Al, Bryson

CP SB

-2

-1

0

1

2

3

21 22 23 24 25

Childress, Taylor, Al, Green, Beeler

CP SB

Page 21: Lineup Efficiency

Cal Poly Rotations

• In the 12 possessions Ant was in, SB did not score a basket. We were also able to capitalize on offense and score 7 points in those 12 possessions.

0

0.5

1

1.5

2

2.5

3

19 20 21 22 23 54 55 56 57 58 59 60

Reese, Ridge, Mike, Brian, Ant

Pts O Opp

0

0.5

1

1.5

2

65 66 67 79 85 87

Maliik, Reese, Ridge, Joel, Brian

Pts O Opp

-3

-2

-1

0

1

2

68 69 70 71 72 73 74 75 76 77 78

Dave, Maliik, Ridge, Mike, Brian

Pts O Opp

-3

-2

-1

0

1

2

3

61 62 63 64

Liik, Reese, Ridge, Joel, Mike

Pts O Opp

Page 22: Lineup Efficiency

Cal Poly Rotations: Starter Comparison

*Old Starters +/-: +33 *New Starters +/-: +13 (Bigger 3PT threat)

(Strongest overall defensive presence: +8 in 13)

-3

-2

-1

0

1

2

3

1 2 3 4 5 6 28 29 30 31 32 33 34 35 39 40 41 42 43 44 45 46 47

Dave, Maliik, Joel, Mike, Brian

Pts O Opp

-3

-2

-1

0

1

2

3

CSU

F1CS

UF1

CSU

F1CS

UF1

CSU

F1CS

UF1

CSU

F1CS

UF1

UCR

1U

CR1

UCR

1U

CR1

UCR

1U

CR1

UCR

1U

CI1

UCI

1U

CI1

UCI

1

Dave, Maliik, Joel, Mike, Brian +/-

-3

-2

-1

0

1

2

3

CSU

F2

CSU

LB2

CSU

LB2

CSU

LB2

CSU

N2

CSU

N2

CSU

N2

CSU

N2

UCD

2

UCD

2

UCD

2

UCI

2

UCI

2

UCI

2

UCI

2

UCR

2

UCR

2

UCR

2

UCS

B1

Maliik, Ridge, Joel, Mike, Brian +/-

0

0.5

1

1.5

2

1 2 3

Maliik, Ridge, Joel, Mike, Brian (Current Starters)

Pts O Opp

Page 23: Lineup Efficiency

Santa Barbara Rotation Breakdown

Rotation Indicator 0 1 2 3 11 13 15 21 24 31 44

Times Used

Sum of +/-

CP Pts

SB Pts

% of Total Points

44.707 1 0 1 1 0 0 1 0 1 0 0 14 10 6 16 32% 52.406 0 0 1 1 1 0 1 1 0 0 0 9 2 7 9 18% 52.703 1 0 1 0 1 0 1 0 1 0 0 4 1 0 1 2% 55.706 0 0 1 1 1 0 1 0 1 0 0 4 4 0 4 8% 62.406 0 0 1 1 0 0 1 0 1 1 0 2 0 0 0 0% 65.706 0 0 1 1 0 0 1 1 1 0 0 15 -7 16 9 18% 71.701 1 1 1 0 0 0 0 0 1 0 1 1 -1 1 0 0% 75.706 0 0 1 1 0 0 1 0 1 1 0 2 -1 1 0 0% 78.401 1 1 1 0 0 0 0 0 0 1 1 4 0 0 0 0% 81.701 1 0 1 0 1 0 0 0 1 0 1 3 3 3 6 12% 84.306 0 0 0 1 1 0 1 0 1 1 0 6 1 2 3 6% 91.404 0 0 1 1 1 0 0 0 0 1 1 7 0 0 0 0% 94.306 0 0 0 1 0 0 1 1 1 1 0 2 -2 2 0%

102.002 0 1 0 0 1 0 1 0 0 1 1 7 -3 5 2 4% 102.7 0 1 1 0 0 0 0 0 1 1 1 3 0 0 0 0%

104.704 0 0 1 1 0 0 0 0 1 1 1 4 0 0 0 0% 113.304 0 0 1 1 1 0 0 0 0 1 1 3 -2 2 0 0%

• UCSB’s most frequent line-ups correspond to the graphs provided. (Except for the line-up that had a cumulative +/- of zero…)

Page 24: Lineup Efficiency

UCSB Roster

No. Name Pos. Cl. Ht. Wt. Hometown/High School 0 Hunter Ford Guard Sophomore 6-3 180 Roseville, Calif./Oakmont HS 1 Eric Childress Guard Sophomore 6-0 175 Hawthorne, Calif./Leuzinger HS 2 Gabe Vincent Guard Freshman 6-3 190 Stockton, Calif./St. Mary's 3 Zalmico Harmon Guard Senior 6-0 185 Washington, D.C./Ballou High School 5 Tide Osifeso Guard Freshman 5-10 145 Rancho Cucamonga, Calif./Los Osos, Calif. 11 T.J. Taylor Guard Junior 5-9 160 Oakland, Calif./Oakland HS 12 Alex Hart Forward Sophomore 6-10 215 Kelowna, British Columbia, Canada/Immaculata High School 13 DaJuan Smith Guard Junior 6-3 175 Abbeville, La./Abbeville HS 14 Ami Lakoju Forward Freshman 6-8 265 Harlem, N.Y./St. Luke's School 15 Alan Williams Center Senior 6-8 265 Phoenix, Ariz/North HS 20 Logan Louks Guard Junior 6-2 175 Danville, Calif./San Ramon Valley HS 21 Mitch Brewe Forward Junior 6-8 242 Seattle, Wash./Seattle Preparatory School 23 Sam Walters Guard Freshman 6-2 170 Soquel, Calif./Soquel HS 24 Michael Bryson Guard Junior 6-4 201 Sacramento, Calif./Foothill HS 25 Justin Burks Forward/Guard Freshman 6-6 210 Las Vegas, Nev./Arbor View HS 31 John Green Guard Junior 6-5 180 Oakland, Calif./Westwind Prep Academy (Phoenix) 32 J.D. Slajchert Forward Freshman 6-6 215 Oak Park, Calif./Phillips Exeter Academy 43 Joey Goodreault Guard Freshman 6-3 175 Orinda, Calif./Miramonte HS 44 Sam Beeler Forward Junior 6-10 210 Poway, Calif./Poway HS

Page 25: Lineup Efficiency

** Last note:

- I am still unsure why this lineup doesn’t work, but it has not performed well over the course of the season…

• The only game that this group was used in, that resulted in a win, was the very first

conference game against Hawaii.

-3

-2

-1

0

1

2

3

Haw

aii1

CSU

F1U

CR1

UCR

1U

CI1

UCD

1U

CD1

UCD

1U

CD1

UCD

1U

CD1

UCD

1U

CD1

UCD

1U

CD1

UCD

1U

CD1

UCD

1U

CD1

UCD

1Ha

wai

i2Ha

wai

i2Ha

wai

i2Ha

wai

i2Ha

wai

i2

Dave, Maliik, Ridge, Joel, Brian

CP Opponent


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