Skip to main content

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 the starting 5 of Murray, DeRozan, Will Barton, Aaron Gordon, and LaMarcus Aldridge, with a win co-efficient of 0.802. As previously stated, DeRozan and Aldridge are on other teams (the Chicago Bulls and the Brooklyn Nets, respectively). Gordon and Barton both play for the Denver Nuggets, after a 2021 trade sent Gordon from Orlando to Denver.

Using the current stats of the players for the 2021-22 season, we can determine the win co-efficient of both the Spurs’ current line-up and their theoretical counterparts.

 


The proposed line-up has a win co-efficient of 0.728, down from the previous win co-efficient 0.802. This can likely be explained by LaMarcus Aldridge taking a reduced role in Brooklyn. The Spurs current line-up has a win co-efficient of 0.596, up from the previous iteration’s 0.579.but still a ways away from being a consistent playoff team and championship contender.

The Spurs also have a group of promising young prospects who could potentially make them championship contenders in the future, with players like Devin Vassell, Josh Primo, and Lonnie Walker IV set to break into the starting line-up and serve as the difference makers that could change the Spurs’ future in the years to come.

Comments

  1. Nice to see the follow up and the validation of your methodology. It will be interesting to see the trades that the Spurs can do in the next window and see if the same players bring value given the new lineup.
    Very nicely done!!!

    ReplyDelete

Post a Comment

Popular posts from this blog

Gone Too Soon: The Story Of Dražen Petrović

In the 1989-90 season, the Portland Trail Blazers bought out Dražen Petrović’s contract with Real Madrid and convinced him to join the NBA. This would mark the start of a trailblazing career that was tragically cut short.        Dražen Petrović was born in Šibenik, Croatia on the 22 nd of October, 1964. At the age of 15, he was already in the first team of his hometown club, and by the age of 18, Petrović had blossomed into a star for Šibenik. After serving in the military for a year, he moved to Cibona in 1984, where he would play till 1988. At Cibona, Petrović shined. He once scored 112 points in a Yugoslavian League game ( 40/60 FG, 10/20 3Pts, 22/22 FT), which is possibly the most efficient performance in any European league ever. He averaged 37.7 points in the Yugoslavian first division and 33.8 points in European competitions in his 4 years at Cibona, cementing his status as a European star. In 1988, at the age of 23, he moved to Real Madrid, where he stayed ...

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...

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 ...