4-Down Episode 14: Introducing Scott Sereday


The 48MoH crew is happy to announce the newest member of our team, Scott Sereday.

Scott may be a new name to some of you, but many will recognize him from his contributions to the APBRmetrics community and his own site, Basketball-Analysis.  He’s currently enrolled at Columbia pursuing an M.A. in statistics.

(Here’s an easter egg to round out the edges. You can be the judge whether that is virtue or vice on display, but it’s not bad for someone who stands 5’11”.)

In addition to our 4-Down podcast with Scott (located at the bottom of this post), we conducted a short interview to give you a better sense of his work and what you should expect from him in the coming months.

48MoH: Greetings, Scott. Tell us about your statistical work.

SS: I currently run a site called basketball-analysis.com. My work includes expected championships added, clutch adjusted plus-minus, estimations of assisted % and efficiencies using box score statistics. In addition to this, I’ve done lots of analysis of the play-by-play data available at basketballvalue.com and the utilization of statistical methods such as regression analysis.

48MoH: I’m already lost. Let’s try this a different way. What, in your estimation, is the value of mining data for new ways to see the game?

SS: Well, as humans we typically do a good job of innately telling if something is helpful or harmful towards achieving an ultimate goal. But determining magnitude is a very crude process. I think we can all agree that Tim Duncan creates more wins than Kwame Brown. But without quantitative analysis, how can we tell how many wins this difference amounts to? Additionally, subtle statistical patterns are typically not noticeable to the naked eye. I believe it was Bill James who once stated that one would be unlikely to visually distinguish between a .300 hitter and a .270 hitter.

48MoH: Quantitative analysts such as yourself often talk about the box score as if it’s an embarrassing relic of the past. Why is the box score such a limited (misleading?) rubric for player evaluation?

SS: Many have said before that basketball is behind other sports such as baseball in terms of tracking statistics and the availability of more detailed statistics to the public. It’s clear to see that a large portion of defensive plays are excluded from the box score. The play-by-play sheds some light on other statistics, such as detailed shooting and situational statistics, but even that misses some telling offensive statistics and struggles to improve the picture of individual defenders.

48MoH: To ask the previous question a little differently, why is play-by-play data useful in helping us understand what happens on the court?

SS: Play-by-play details help determine nontraditional – but still crucially important – statistics such as how many assists are on close field goals and how dependent a player is on other players to help create his shots. For example, Erick Dampier appeared very efficient over the last few years. But the large portion of his scores are assisted close shots, which rely more on the passer than on the scorer, and need to be made at a higher rate than other attempts because of the inherent risk of the entry pass. Dampier shot over 63% from the field over the last 4 years. Sounds great, but consider that over 60% of his made field goals are assisted close attempts. I estimate league average FG% for this type of shot to be around 70%, which put his numbers in a more appropriate perspective.

48MoH: There is another side to this, that of the statistical absolutist. This sort of person — one whose dogged adherence to hard data suggests that findings never err — often turns people off to the work of other quants. You still see value to what we might call a coach’s or scout’s gut-knowledge, right?

SS: I think the problem is that both sides often try to oversimplify things so they can imagine that they have a complete understanding of everything involved. People who rely heavily on observational intuition may try to say that statistics don’t mean anything or that any statistical analysis they don’t understand is nonsense. But they often take this approach in order to make their opinions seem more valid to themselves. Similarly, people who rely too heavily on statistical analysis often make the mistake of assuming that everything that goes into that data can be fully digested by a simple study. If I beat you in a hand of poker, it doesn’t mean that I am a better poker player than you, it just means I am slightly more likely to be a better poker player than you. Both sides need to remember that my victory isn’t meaningless, but we have to be careful not to draw too many hard-and-fast conclusions from it. The numbers don’t err, they just might not mean what we think they mean.

48MoH: One thing I like about your work is that you recognize your community is not above correction. For example, you’ve recently pointed out that it isn’t always the case, as models such as PER indicate, that fouls represent a negative value. In some circumstances, fouls are good. What do you think are the major shortcomings of the current quantitative analyst crowd?

SS: Man, this is a tough one. I guess it depends on what aspect you want to look into. In terms of running a team, it’s kind of tough to tell because NBA teams are secretive about their data. I’m sure there is also much data that is not easily available to everyone.

In another aspect, I think it’s interesting that you don’t see any quant guys as talking heads on TV or radio. This might be because their message may not be as readily received by the common fan or because they aren’t as good at making knee-jerk reactions.

48MoH: What first attracted you to the APBR community — perhaps a particular thinker or article? Not that I’m a quant, but I remember, years ago, getting my mind around the very simple distinction between rebound average and rebound rate. That alone was enough to bend my ear toward statistical refinement.

SS: I’ve always been someone who wanted proof before accepting something as fact. Just because everyone else held a certain viewpoint was never enough to me. So when I was young, I tried developing my own formulas even when I had no idea anyone else had already been doing the same. Some inspirational names include Dean Oliver, Ed Kupfer and Dan Rosenbaum.

48MoH: What can our readers expect from you this season? Anything fun waiting in the wings?

SS: In addition to applying some of the work I do on basketball-analysis.com to player statistics for 48MoH, I will develop new measurements, such as adjusting offensive ratings to use more detailed statistics to create a more appropriate representation of players like Dampier. (See my articles on estimating offensive efficiencies by shot type: created and assisted and shot location). I will manipulate the play-by-play data to come up with specific clutch statistics or fast break statistics that I believe are useful and interesting. I also hope to come up with improved defensive metrics.

I even have some thoughts on ways to improve the NBA. Some of these studies will take longer to complete, but look for me to write at least weekly regarding lighter subject matter like evaluations of Spurs personnel and other observations pertaining to the basketball world. Also, look for a statistical season preview in the coming weeks.

48MoH: Well, that sounds fun for me and exhausting for you. Thanks for your time, Scott. We’re all excited to read your insights and, hopefully, watch the Spurs with more intelligence because of it.

And here, dear readers, is your big bag of podcast:

  • ITGuy

    No Comments

  • BlaseE

    The podcast isn’t showing up on itunes on my phone.

  • http://48minutesofhell.com Andrew A. McNeill


    The podcast hasn’t been sent to iTunes yet. I’ll do that shortly.

  • Bryan

    I haven’t had a chance to listen to the podcast yet, but looking forward to Scott’s work here at 48MoH. Advanced stats have peaked my interest in the last couple of years, and I’m looking forward to advanced stats that relate directly to the team I love the most.

    Glad to have you here, Scott… with the exception of the dunking highlight reel. Mostly because I could never get my 6’3″ frame to dunk.

  • http://www.48minutesofhell.com Timothy Varner

    Yeah, it should be a good year at 48MoH. We’ll also have bodies at all home games and a dozen or so road games. We’re looking forward to the start of the season. But Scott’s contribution should add another facet that will help us better understand and enjoy the Spurs.

  • Big Whit

    Welcome Board

  • Jim Henderson

    Advanced stats are obviously quite useful in evaluating player & team performance. In particular, I’ll be very interested to see the ideas that Scott comes up with to better account for defensive performance/value, which I believe is an area that’s important to as much as possible fully account for, but has unfortunately thus far been lacking in its coverage by other available online statistical data bases. Blocked shots, steals, & defensive rebound stats don’t tell the whole story. I’ll look forward to your posts in the future, Scott. Welcome.

  • Jim Henderson
  • Kevin

    Does Scott have any history with the Spurs (analysis-wise, fan-wise, ect.)… or was he just a free-agent-stats-guy grab for the 48MoH team?

  • ITGuy

    Thanks for the clip Jim, I can’t wait to see the results of all the hard work GHill is putting in.

    Go Spurs Go!!

  • http://48minutesofhell.com Andrew A. McNeill


    In the podcast, I ask Scott something along those lines. While he’s not necessarily a Spurs fan (he’s from Jersey and grew up a 76ers fan), his favorite player for a long time was David Robinson. So I think that’s something everyone here can get behind.

  • metalandganja

    Sounds like a great addition to the site. Looking forward to your work, Scott!

  • ThatBigGuy

    We’re already the smartest fans in the NBA and now you add an APBRmetrics expert to the mix? That popping sound you just heard is Mavs fans’ heads bursting.

    I love us.

  • rob

    Welcome aboard. I’m sure all of us will be looking for insightful, detailed break downs on the games and players.

    Here’s one stat question that I’ve been curious to know. By most indications, Richard Jefferson is perceived as a bust for this team. His past achievements never came to fruition during his first year. Is there any data suggesting that to be a one time event in his career or more in the realm of expectation while playing for the Spurs?

  • DorieStreet

    New to aspect of basketball analysis. Will coverage of the Spurs in this realm include a ‘preseason capsule’ on what to expect, or just begin once first game tipoff is underway?

  • McShane

    I’m teaching AP Stats this year and would be very pleased if there were a post in the near future which I could use in my class…

  • Bushka

    5’11 Dunking in a car park on a portable ring? Colour me impressed.

    Anyone willing to go to those lengths is obviously a hoops junkie.

    If his first post isn’t on the Bonner Factor I’ll be devastated.

  • Hobson13

    Well, Team USA completed their romp through FIBA competition with wiping out the Turkish National team in front of their 15,000 lunatic fans. Team USA played unreal defense. It was almost like watching the 2001 Baltimore Ravens team defense (for those who also watch NFL). Dang, fellas its almost NBA time. NBA Finals – check. NBA draft – check. NBA Free Agency – check. FIBA World Championships – check. Next up is NBA training camp. I’m super stoked.

    P.S. Come on FO, get off your arses and sign Louis Amundson. Can’t believe this cat still hasn’t found a team. Unreal!

  • ITGuy

    No!! please, no more Bonner in-depth analysis.

    Go Spurs Go!!

  • http://sonicscentral.com/apbrmetrics/viewforum.php?f=1 DSMok1

    Good to see Scott get a writing gig! I hope this larger audience helps you!

    Glad to see you’ve got your man, Tim.

  • Art

    Basketball analytics in 1959 by famous computer scientist Donald Knuth, http://www.adafruit.com/blog/2010/09/13/the-electronic-coach/

  • McShane


    Has to be Dr. Hanna

  • Gebo

    Excuse me for being an old cynic, but I am sceptical of video showing someone dunking on an adjustable basket. My suggestion would be to measure the image of Scott at the basket, measure the image of the basket, and do the math. That aside, I think statistical analysis is yet another way to look at and to try to understand the game. All to the good. Write long and prosper, Scott. Hey, you kids, get off my lawn!

  • Alger Hiss

    I went back and listened to this again, having read some of his work. Seems like he’s grasping at straws a bit. Fouling is bad, drawing fouls is good…gee, thanks for the incredible insight!