Skip to main content

Statistics to Help the Spurs


Every sports fan, diehard or casual, has watched Moneyball, the movie about the use of statistics in baseball. While sports has become more receptive to the use of statistics to identify players, many fans still do not like to use or misuse statistics to back up their opinions. As an avid NBA fan, I too love to concoct fictitious trades to help make my team better. Through the use of statistics, I am going to try to make well informed decisions regarding player acquisitions for the San Antonio Spurs, my favourite NBA team. To tackle this problem, I used a linear regression model.

To create the model, I first collected box score data for the Spurs’ 2019-20 season. This data was then used to create a model that will give a composite score, which predicts a team’s record. According to the model, a score closer to 1 indicates a better record, while a score closer to 0 indicates a worse record. Using Basketball Reference, I identified 8 players who the Spurs could feasibly acquire and who would improve their record. Then, I extrapolated all of the thirteen players’ stats to 48-minute stats, this is useful because it focuses on if the main 5 played all 48 minutes of a game, which delivers a better estimate of the team’s quality. As it is almost impossible to completely change a team’s roster within one season, I limited the number of new additions to either 1 or 2 players.

The 13 players involved in this are the San Antonio Spurs main 5 (Dejounte Murray, Bryn Forbes, DeMar DeRozan, Trey Lyles and LaMarcus Aldridge) and 8 other players who I thought could join the Spurs. The players are Malik Monk, E’Twaun Moore, Otto Porter Jr., Will Barton, Aaron Gordon, Dario Saric, Mo Bamba, and Montrezl Harrell.

When the model was run, the top team consisted of Murray, DeRozan, Aldridge, Gordon, and Barton. The aforementioned team achieved a score of 0.802, while the Spurs current main 5 achieved a score of 0.579. DeRozan and Murray were present in all of the top 10 results, and Aldridge was in 7 out of the 10. Will Barton was in five of the top 10 results, and Aaron Gordon was in 7 of the top 10. Mo Bamba was in 4 of the top 10, though this can be attributed to his amazing blocks numbers in limited playing time. Most of the best results substituted Bryn Forbes and Trey Lyles for Gordon or Barton. These are the 10 best lineups, with their Win Co-Efficient.

 



 On the other hand, the 10 least productive lineups according to the model all featured Bryn Forbes, LaMarcus Aldridge and Trey Lyles. These lineups mostly failed due to a lack of passing, and all the lineups had two Lou Williams-type ‘Professional Scorers’ (Bryn Forbes and Malik Monk/E’Twaun Moore) and three big men. Otto Porter Jr., who makes two appearances in the 10 least productive lineups, also possesses sub-par passing ability. In fact, none of these lineups had more than 18.5 assists. They are listed below along with their Win Co-Efficient.

 


A quick study shows that the Spurs should likely let Bryn Forbes play in his natural sixth man role, where he can be best utilized as a gunner and 3-point specialist, and trade for Aaron Gordon or Will Barton. This will allow the Spurs to strengthen positions of need without gutting the rest of their roster. The data shows that the Spurs, while unlikely to win a championship in the near future, can contend for a spot in the Western Conference playoffs with a few savvy trades, and can keep their record 22-year playoff streak alive.

In an upcoming article, I will also be detailing the trades that the Spurs could make to acquire the players I identified as reasonable targets.

Comments

  1. RamanJuly 5, 2020 at 11:34 PM

    Cool - this is interesting. Can I use this to determine my fantasy team too?

    ReplyDelete
    Replies
    1. Vidyaratnam GanapathyJuly 12, 2020 at 2:15 PM

      Thanks. You can certainly use it if you want to. Thanks for the feedback.

      Delete
  2. HiJuly 12, 2020 at 6:17 PM

    Some intense dissection here!

    ReplyDelete
  3. VidyamurthyJuly 12, 2020 at 7:38 PM

    Although I do not follow basketball much, I am quite familiar with mathematical modeling. The article very clearly explains the modeling technique and the conclusions. Additionally, there are hints on the strengths and weakness of the modeling approach.

    The analysis is thorough and written in a lucid style.

    Very impressive!!

    ReplyDelete
  4. UnknownOctober 22, 2020 at 11:08 AM

    The article was very informative and give clear picture of the scenario. Excellent narration. I am proud of you Ratnam.

    ReplyDelete

Post a Comment

Popular posts from this blog

An Analysis of Car and Driver Impact on Formula 1 Success - Part 2

  In the previous part of this project, I looked at the variables I was using, and some of the trends that I identified through a preliminary analysis. Part 2 of this project is dedicated to:  - The research questions I formulated  - The statistical analyses that I used for each question  - The interpretation of my analysis  - What conclusions I was able to draw to answer each research question Research Questions Based on my preliminary analysis of the variables that I was working with, I came up with more questions that I was interested in exploring, in addition to my original goal of figuring out whether the car or driver was more crucial to Formula 1 success. One of the first things that piqued my interest was how the different points systems affected overall scoring. While it was immediately clear that the change in point scoring systems from 10 points for a win to 25 points for a win resulted in drastic changes to the point totals, my hypothesis was that the change from sys
Post a Comment
Read more

The Car Driver Quandary

  If you were a Formula 1 team principal, would you rather have the best driver or the best car? For those of you who are regular readers of the blog, firstly, thank you for your support. It means a lot to me. Secondly, this might seem like a weird hook, considering my previous articles have all been about basketball. While that is true, I am actually a big fan of multiple sports, including football (soccer to American readers), baseball, Formula 1, and yes, basketball. Instead of creating a different blog for each sport, I figured it would be better to write about multiple different sports on the same blog. The dilemma of car or driver popped into my head while I was watching the US Grand Prix this past weekend. While Max Verstappen was comfortably on his way to another victory, the 50 th of his career, there were many interesting battles throughout the rest of the field. This got me thinking about the age-old question of whether having the best driver or the best car brings more
1 comment
Read more

Statistics To Help the Spurs – A Retrospective

A little while ago, I made a post about how the San Antonio Spurs could get better using statistics. A season and change later, it’s time to re-examine the players involved in that piece and see how the Spurs are playing. As of the new year of 2022, the Spurs are 14-20, with the 5 players averaging the most minutes per game being Dejounte Murray, Keldon Johnson, Derrick White, Jakob Poeltl, and Doug McDermott. Murray is the only player who was in the starting 5 from last season. White, Johnson, Poeltl were all on the roster and have moved into bigger roles, while McDermott is a free agent signing in his first season with the Spurs. The previous starting 5 was Murray, Bryn Forbes, DeMar DeRozan, LaMarcus Aldridge, and Trey Lyles. 3 of those players (DeRozan, Aldridge, and Lyles) have moved on. Forbes, after winning an NBA championship with the Bucks, returned to the Spurs this season. The 2019-20 Spurs line-up had a win co-efficient of 0.579. The top line-up that I looked at was t
1 comment
Read more