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What is “Basketball, Stat”

I’ve always been obsessed in finding value that others don’t see. Perhaps it has to do with feeling that I played this role in teams growing up, but I’ve always believed that the “stars” in teams, whether or not it’s a sports team, receive much more credit than they should (win or lose), and then other “role players” are routinely undervalued.

For me, the example of this that I couldn’t get away from when thinking about the NBA was Shane Battier. I’ve spent so much of my NBA fandom thinking about Shane Battier, it’s quite frankly embarrassing. What was it about him that caused him to never make an All-Star team, yet still end his career in the top 60 of Volume Over Replacement Player (VORP)? He unquestionably made his team better when he was on the floor, but he never put up the counting stats to gain that recognition. To me, he epitomized the case of finding value in the role player that others may not recognize. Did he have a skill set that would allow him to lead a 20-win team into being a 40-win team? Probably not. But he had the more important value of allowing a 40-win team to break through 50-wins and above, by enabling stars around him to succeed.

And that leads us to one of the most interesting questions of basketball: How do you quantify value? The biggest misunderstanding of the analytics movement in sports is that “basketball people” feel like they’re being told what basketball is by “analytics people.” As someone who played the game (granted only at a high school level, but I’ve always played basketball) and still loves the analytics side, I think most people that are involved in analytics would tell you that they’re just trying to answer questions objectively. Are there elements of basketball, teams, and human interactions and behaviors that can’t be predicted and quantified? Absolutely!!! But anyone who is competitive is going to be looking for every edge they can find. For scouts, that might be finding a player with potential to develop skills that others don’t see. For the analytics community, that’s trying to use data to find information that isn’t visible to the naked eye.

Analytics are like a bikini, they show you a lot, but they don’t show you everything.

— Bob Myers, GM of the Golden State Warriors

So I absolutely agree with every skeptic that says that the game isn’t played on paper or that you can’t just shoot threes and layups and expect to win. Analytics isn’t about telling people what to do and how to do it. It’s about asking questions trying to learn more than your opponent to gain an edge.

And that’s what I hope to do here: ask questions. I hope that’s something that you can appreciate and enjoy. So with that, welcome to Basketball, Stat.

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Early Season SRS and Ratings

https://www.podbean.com/media/share/pb-z366m-c6018f

Solo pod this week! Just wanted to take a look at some notable early season performances and talk about what I’d expect to continue and what will change. I talked about the Heat, 76ers, Lakers, Jazz, Suns, Raptors, Rockets, Kings, and Bulls.

 

Twitter: https://twitter.com/RobertAntle

Instagram: https://www.instagram.com/robertantle/

E-mail: robert.antle@gmail.com

First games and watching film with Mike De La Rosa

https://www.podbean.com/media/share/pb-xamyr-c4a625

This week we’re joined by Mike De La Rosa who is the video coordinator for Thinking Basketball with Ben Taylor. Mike and I talked about our initial impressions from the first 2 days of NBA basketball and also what it’s like breaking down film for Thinking Basketball and some of his takeaways for specific players. You can find Mike in his work here in the following places:

Twitter: https://twitter.com/MikeDeLaRosaNBA

Thinking Basketball: https://www.youtube.com/channel/UC3HPbvB6f58X_7SMIp6OPYw

 

Twitter: https://twitter.com/RobertAntle

Instagram: https://www.instagram.com/robertantle/

E-mail: robert.antle@gmail.com

Former Philadelphia 76ers Director of Analytics – Aaron Barzilai

https://www.podbean.com/media/share/pb-mniy8-c36c7b

This week we’re joined by Aaron Barzilai, founder of Her Hoop Stats and former Director of Analytics for the Philadelphia 76ers. We talk about his path to the NBA and different aspects of what it was like to work in an NBA front office. We also talked about his most recent endeavor: Her Hoop Stats, where he’s looking to provide basketball analytics for the women’s game.

Her Hoop Stats website: https://herhoopstats.com/

Her Hoop Stats Twitter: https://twitter.com/herhoopstats

Aaron Barzilai Twitter: https://twitter.com/basketballvalue

 

Twitter: https://twitter.com/RobertAntle

Instagram: https://www.instagram.com/robertantle/

E-mail: robert.antle@gmail.com

Predictive win %, luck adjustments, SPR, and more with Nathan Walker

https://www.podbean.com/media/share/pb-rhxu9-c2952d

This week Nathan Walker joins the show to talk about his luck-adjusted net rating model that was used in Jacob Goldstein’s PIPM metric, as well as some of the other things he’s been working on.

 

You can find Nathan and his work here:

Twitter: https://twitter.com/bbstats

Patreon: https://www.patreon.com/bbstats/posts

 

Twitter: https://twitter.com/RobertAntle

Instagram: https://www.instagram.com/robertantle/

Blog: https://basketballstat.home.blog/

Best lineups for the 2019-20 season with Mike Bossetti

https://www.podbean.com/media/share/pb-bih2j-c1d559

This week Mike Bossetti joins me to discuss what we think will be the best 5-man lineups for the upcoming season.

You can find Mike’s work on:

Twitter: https://twitter.com/mikebosports

Nylon Calculus: https://fansided.com/author/mbossetti/

 

Twitter: https://twitter.com/RobertAntle

Instagram: https://www.instagram.com/robertantle/

Blog: https://basketballstat.home.blog/

PIPM with Jacob Goldstein

https://www.podbean.com/media/share/pb-8a7mm-c14144

This week we’re joined by Jacob Goldstein! Jacob created PIPM (Player Impact Plus Minus) so we brought him on to talk about how he created the stat and what all goes into it. You can find his work online at https://www.bball-index.com/ and his writing at https://fansided.com/author/jgoldstein/.

 

Twitter: https://twitter.com/RobertAntle

Instagram: https://www.instagram.com/robertantle/

Blog: https://basketballstat.home.blog/

5 Most Intriguing Players for the 2019-20 Season with Stevie Cozens and Sam Shin

https://www.podbean.com/media/share/pb-tgqxa-c05f46

This week is a departure from the stats series I’ve been doing. I asked two of my friends from SBC, Stevie Cozens and Sam Shin, to join me and we each go through our 5 most intriguing players for the 2019-20 season. It’s a much longer conversation as we get into pretty detailed discussion on 15 different players, but it was a lot of fun to do.

Twitter: https://twitter.com/RobertAntle

Instagram: https://www.instagram.com/robertantle/

Blog: https://basketballstat.home.blog/

CARMELO

https://www.podbean.com/media/share/pb-m2gmv-bf484e

This week we’re looking at a statistic by FiveThirtyEight called CARMELO. This stat is a fun projection system used to predict a player’s career arc developed by Nate Silver. I didn’t do a deep dive article into how the stat is calculated since not all of the information is available, but I still took a look at everything that goes into coming up with the projections. 

 

CARMELO ratings: https://projects.fivethirtyeight.com/carmelo/

Historical Elo ratings: https://projects.fivethirtyeight.com/complete-history-of-the-nba/

Article on initial CARMELO ratings: https://fivethirtyeight.com/features/how-were-predicting-nba-player-career/

 

Twitter: https://twitter.com/RobertAntle

Instagram: https://www.instagram.com/robertantle/

Blog: https://basketballstat.home.blog/

BPM: Box Plus/Minus

https://www.podbean.com/media/share/pb-i7pan-bec260

This week on the show, we’re looking at a box score stat called Box Plus/Minus (BPM)! Like with each of these, we’ll talk a bit about the history of the stat, how it’s calculated, and we’ll take a look at some player comparisons from past seasons.

 

Here’s a link to the article I wrote on BPM: https://basketballstat.home.blog/2019/08/27/box-plus-minus-bpm/

Here’s a link to Dan Myers’ description of the stat on basketball-reference.com: https://www.basketball-reference.com/about/bpm.html

 

Twitter: https://twitter.com/RobertAntle

Instagram: https://www.instagram.com/robertantle/

Blog: https://basketballstat.home.blog/

RAPM: Regularized Adjusted Plus/Minus

https://www.podbean.com/media/share/pb-jyiyy-bddbbb

This week, we’re going to look at our first stat in the plus/minus family called Regularized Adjusted Plus/Minus (RAPM). Like with each of these, we’ll talk a bit about the history of the stat, how it’s calculated, and we’ll take a look at some player comparisons from the most recent seasons.

 

My article breaking down RAPM: https://basketballstat.home.blog/2019/08/14/regularized-adjusted-plus-minus-rapm/

Dan Rosenbaum article on APM from 2004: http://www.82games.com/comm30.htm

Lecture by Jeremias Engelmann on RAPM: https://www.youtube.com/watch?v=OuC0YZTADcE

RAPM values I used: https://basketball-analytics.gitlab.io/rapm-data/

 

Twitter: https://twitter.com/RobertAntle

Instagram: https://www.instagram.com/robertantle/

Blog: https://basketballstat.home.blog/