Basketball Analytics (the book) is available

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steveshea
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Basketball Analytics (the book) is available

Post by steveshea » Wed Nov 06, 2013 12:29 am

Basketball Analytics
Objective and Efficient Strategies for Understanding How Teams Win

is available for purchase. See http://www.BasketballAnalyticsBook.com for a preview of the book and a link to purchase.


Let me highlight some of the main points of the book.

The book is divided into three sections: Player Evaluation, Team Evaluation, and Team Construction. In Player Evaluation, we define new measures of offensive and defensive production. We define Efficient Offensive Production, an adjustment of points and assists based on the player's offensive efficiency. In this way, we hope to have defined an offensive metric that takes into account both quality and quantity of production. Our defensive measure uses opponents' turnover, offensive rebound, and effective field goal percentages to approximate the defensive stops gained by a player's team when he is on the court as compared to what the team would have done otherwise.

In Team Evaluation, we introduce lineup entropy and player involvement metrics. Measuring a team's lineup entropy is like taking the body's temperature. If the entropy is particularly high or low relative to the team's usual base rate, it generally indicates something is not quite right. An example of anomalous play could be that the team rested one or more of it's star players. Easily and objectively identifying these atypical situations can be useful when doing a large scale study on team performance. Player involvement looks at the degree of balance in each team's offense. For example, we looked at the Laker's offensive balance last season (and in the clutch last season). We compared that balance to the balance in Nash's Suns and Howard's Magic in previous seasons. Given the differences, it shouldn't have been a surprise when Howard began complaining about his touches with the Lakers.

In Team Construction, we discuss the typical value of draft picks and the strategy of rebuilding through the draft. We present the biggest draft steals and draft blunders of the last 35 years. We rank teams based on the production of their picks, and rank the draft classes.

We also touch on unpredictability in the game, the idea of redefining player positions, and visualizing basketball metrics.

If anyone has specific questions for me or my coauthor Chris, please message me here, or email us at authors@BasketballAnalyticsBook.com.

Thanks.

steveshea
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Re: Basketball Analytics (the book) is available

Post by steveshea » Wed Nov 06, 2013 2:23 pm

For those that are interested, the book is now available through amazon

http://www.amazon.com/Basketball-Analyt ... +analytics

(Better price and shipping options)

Thanks

sbs
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Re: Basketball Analytics (the book) is available

Post by sbs » Wed Nov 06, 2013 6:39 pm

Damn, I bought it last night from createspace. $5 more and shipped to me 10 days later.

steveshea
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Re: Basketball Analytics (the book) is available

Post by steveshea » Wed Nov 06, 2013 8:56 pm

sbs wrote:Damn, I bought it last night from createspace. $5 more and shipped to me 10 days later.

Thanks for purchasing the book. We were told it would take longer to appear on Amazon, and we did not want to hold up the public release too much longer. I also did not anticipate the slow shipping options from createspace. Thanks again for your interest in our book, and I hope you enjoy it when it arrives.

Crow
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Re: Basketball Analytics (the book) is available

Post by Crow » Thu Nov 07, 2013 8:01 pm

Steve,

Would you be willing to post the table of contents page or a sample chapter? I am curious but could use more of a taste of the book's style and depth.

steveshea
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Joined: Wed Oct 23, 2013 8:17 pm

Re: Basketball Analytics (the book) is available

Post by steveshea » Thu Nov 07, 2013 9:10 pm

Let me start by posting the table of contents (Sorry about the formatting). The ToC can also be found at www.BasketballAnalyticsBook.com. I will follow up shortly with an excerpt from the book.


About the Authors v
Preface vii

Introduction 1

Searching for the I in Team: Player Evaluation

Chapter 1 Top Down vs. Bottom Up Metrics 9
Chapter 2 Surveying Player Value Metrics 13
Chapter 3 Measuring Efficient Offensive Production 25
Chapter 4 Defensive Stops Gained and Player Approximate Value 51
Chapter 5 Visualizing NBA Analytics 62
Chapter 6 Player Development 71

All for One: Team Evaluation

Chapter 7 Lineup Entropy 77
Chapter 8 Introducing Player Involvement Metrics 87
Chapter 9 New Positions? 102
Chapter 10 A Commentary on the Unpredictability of Outcomes 111

If You Build It: Team Construction

Chapter 11 Draft Pick Value Chart 117
Chapter 12 Rebuilding Through the Draft (or Not) 134
Chapter 13 The Super Team Era 141

Conclusions 146

Team-by-Team Advanced Metrics 149

Appendix 210

Index 225

steveshea
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Re: Basketball Analytics (the book) is available

Post by steveshea » Thu Nov 07, 2013 9:31 pm

Below are several excerpts from Chapter 7 of our book. That chapter is on Lineup Entropy. It doesn't present the "sexy" metrics such as those approximating an individual player's overall value. (We do that earlier in the book with our metrics: Efficient Offensive Production, Defensive Stops Gained, and Approximate Value) Instead, it presents a tool for analysis (that may only appeal to the true stat nerds). I hope you enjoy.


7
Lineup Entropy

Miles plays basketball for the Predictables. In the first half of each game this season, the Predictables played Miles, Jimmy, Jason, Jacob, and Jordan. In the second half, the team played Michael, Marcus, Marvin, Mario and Mo. In the same season, Gavin played for the Unpredictables. At every stoppage of play, the coach of the Unpredictables randomly selected five players from the team of ten to play. If you were told at some time in the season, Miles was on the court, how difficult would it be to predict his teammates? Now, consider the same question for Gavin.

Teams in the NBA live between the predictable and unpredictable extremes described above. Exactly where each team stands on the spectrum of possibilities is important for our statistical analysis. Recall our classification of top down and bottom up metrics. A top down metric is one that measures a player’s contributions by looking at the contributions of entire units on the court. For example, a player’s plus-minus is the points scored by the player’s team while he is on the court minus the points scored by the opponent in the same time frame. Top down metrics, and in particular plus-minus, have a major weakness. They are biased towards players that play with good teammates.

...(omitting discussions, charts and examples regarding plus-minus, plus-minus per 48, and net plus-minus per 48)...

Statistically, the Unpredictables’ mixing of lineups is ideal for trying to distinguish the contributions of the individual from that of his teammates. The Predictables’ strategy is the worst possible statistically. While we do not see either extreme reached in the NBA, there are situations where players play a significant amount of time together. For example, Mario Chalmers played 1922 of his 2068 minutes with LeBron. In other words, Chalmers played 93% of his time with LeBron. Our metrics (and everyone else’s) say LeBron is pretty good. There is an obvious opportunity for Chalmers’ plus-minus numbers and net plus-minus numbers to be inflated. This is why Chalmers is sixth in net plus-minus when other Miami players such as Bosh and Battier drop out of the top 20.

If we want to consider top down metrics, we need to pay attention to the amount of time that players play with each of their teammates. For a specific player, we can look at their proportion of time with each teammate. However, to do this for all players would be tedious at best. It also does not give us a method to quickly identify players that might be particularly prone to team pull biases. This chapter is about taking all of the percentages of a player’s playing time with each teammate and combining them into one number that well represents the degree to which that player mixes with different teammates. This will be called Lineup Entropy. Here, the more teammates the player plays with and the more evenly distributed the time with those teammates, the better.

To be clear, our new Lineup Entropy is not the sexy new metric everyone likes to read about. In this Chapter, we will not be producing top 20 lists with Jordan, Magic and LeBron. Instead, Lineup Entropy is about laying a proper foundation for and assigning confidence to top down metrics. It is about better understanding where stats such as adjusted plus-minus or net plus-minus might be misleading. Let us give an analogy that Boston sports fans will appreciate. Like stapling Curt Schilling's ankle before the famed bloody sock game in the 2004 ALCS, Lineup Entropy is ugly and essential.

What is Shannon’s Entropy?

To quantify the diversity in lineups of an individual, we will use Shannon’s entropy from information theory. Our use of entropy here is motivated by its application as the measure of diversity in other settings. It has been used by entomologists (bug scientists) to study the variety of insects in a swamp and economists to study concentration in industries . We now explain Shannon’s entropy formula and how it can be used for these types of applications.

...(omitting explanation of, motivation for, and formal definitions of Shannon's Entropy and Lineup Entropy (LE))...

Our intuition told us that plus-minus per 48 was overrating players like Chalmers, Perkins, Sefolosha, and Haslem because these players played for excellent teams. These players also had very low LE. As a point of reference, Chalmers’ LE of 2.97 is in the 5th percentile of our data. In other words, 95% of players mix at a higher rate than Chalmers did in 2012-13. We knew these players played for good teams. Low LE tells us that these players are playing almost all of their minutes with a select few players on that good team.

...

Team Lineup Entropy

We can also define Lineup Entropy for the entire team. The team number allows an efficient and objective means of monitoring a team’s situation. Allow us to give a somewhat depressing analogy. If an individual is not feeling well, he or a doctor might take his temperature. The temperature alone cannot determine the cause of the illness. However, it can be a means of classification, an indication that something is amiss. In a more serious scenario, a doctor might monitor a patient’s respiratory or heart rate. The rates themselves do not give an answer, but abnormal numbers again can indicate malfunction of some sort. Team Lineup Entropy (TLE) is a measure of the degree of mixing of linemates for the entire team. Teams will have different baseline TLEs as TLE can be a reflection of coaching style and the make-up of the team. For example, the Spurs rely on second string units often as coach Gregg Popovich is excellent at resting his aging core of Duncan, Ginobili and Parker. Like monitoring a patient’s heart rate, when TLE hits the extremes or varies a great deal from the team’s baseline, it reflects a serious shift in the team and suggests that something has gone wrong. One possible cause is an injury to a star player.

...

Crow
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Re: Basketball Analytics (the book) is available

Post by Crow » Fri Nov 08, 2013 8:04 pm

Thanks for the additional information. I saw the back cover link at your site but I guess I missed the table of contents link below it.
Good to have another book out there. Hope you get useful feedback.

EvanZ
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Re: Basketball Analytics (the book) is available

Post by EvanZ » Fri Nov 08, 2013 11:48 pm

Steve, regarding lineup entropy, it sounds a bit like a visualization I did a while back on team units:

http://www.d3coder.com/thecity/2012/03/ ... lustering/

Here's one example of the Rockets (from a couple seasons ago) with 3 distinct clusters:

Image

Here's Charlotte/New Orleans with perhaps not surprisingly more diffuse clusters (if any):

Image

steveshea
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Re: Basketball Analytics (the book) is available

Post by steveshea » Sat Nov 09, 2013 12:03 am

EvanZ wrote:Steve, regarding lineup entropy, it sounds a bit like a visualization I did a while back on team units:
Thanks for the link. I hadn't seen these. Team Lineup Entropy and Individual Lineup Entropy will each be a single number. If I'm understanding the images, then it appears that the images with a dense red block for a particular lineup will be the the team's with a lower Lineup Entropy and the players corresponding to those rows/columns will be the individuals with the lowest individual entropy. It is a nice visualization of the dynamic we're quantifying. Thanks again.

EvanZ
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Re: Basketball Analytics (the book) is available

Post by EvanZ » Sat Nov 09, 2013 1:24 am

Yeah, that's pretty much what I was thinking. More mixing of players = higher entropy. Less mixing = lower entropy.

steveshea
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Re: Basketball Analytics (the book) is available

Post by steveshea » Sat Nov 09, 2013 11:24 pm

Amazon's "Look Inside" feature is now up.

http://www.amazon.com/Basketball-Analyt ... 492923176/

Through that feature, you should be able to access (at least a large portion) of the Preface and Introduction. You should also be able to view the tables in the Appendix. See, for example, Table A1 which produces the top 100 in our new statistic Efficient Offensive Production (EOP) for 2012-13. James, Durant, Bryant, and Westbrook led that board. We also provided the same list for 1991-92 and 1984-85. Jordan, Hardaway, Stockton and Malone were the leaders in 91-92. Thomas, Bird, Jordan and Magic led 84-85.

Statman
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Re: Basketball Analytics (the book) is available

Post by Statman » Mon Nov 11, 2013 5:55 am

steveshea wrote:Amazon's "Look Inside" feature is now up.

http://www.amazon.com/Basketball-Analyt ... 492923176/

Through that feature, you should be able to access (at least a large portion) of the Preface and Introduction. You should also be able to view the tables in the Appendix. See, for example, Table A1 which produces the top 100 in our new statistic Efficient Offensive Production (EOP) for 2012-13. James, Durant, Bryant, and Westbrook led that board. We also provided the same list for 1991-92 and 1984-85. Jordan, Hardaway, Stockton and Malone were the leaders in 91-92. Thomas, Bird, Jordan and Magic led 84-85.
I'll be getting a copy. I love to see what others are working on, and do my part to help put at least a tiny amount of $$ in their pockets. I hope you sell tons, and bring more attention to the type of work many of us are trying to do.

steveshea
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Re: Basketball Analytics (the book) is available

Post by steveshea » Mon Nov 11, 2013 5:44 pm

Statman wrote:I'll be getting a copy. I love to see what others are working on, and do my part to help put at least a tiny amount of $$ in their pockets. I hope you sell tons, and bring more attention to the type of work many of us are trying to do.
Thanks for the support. If you have any questions, comments or suggestions while reading, please let us know. We provide complete definitions for all of the new metrics in the book, so the reader will have the tools to recreate or expand on our analysis. Still, gathering and organizing the data can be time consuming. So, if you would like to see more data on a particular stat than we display in the book, we can easily pass that along. Also, if you want to see the output for a slight modification of one of our formulas, we may be able to help there.

Thanks again.

fpliii
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Re: Basketball Analytics (the book) is available

Post by fpliii » Wed Nov 13, 2013 7:42 pm

Just ordered on Amazon, looking forward to checking it out. :)

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