I found myself wondering about pace and if teams try to dictate it. It's a long held belief that teams can or should employ strategies around the pace of play to help them in games where they might be outmatched. Without any evidence to prove it, this feels like the most commonly mentioned strategy by fans and talking heads. Any time a mediocre offensive team goes up against a good offensive team you hear, they need to keep the pace slow to have a chance. Gotta limit the scoring opportunities for the better offensive team and muddy the game up. I decided to see if I could find any evidence that this is happening.
I've discussed my smart shooting metric here before, and tried to use it as a way to compare players to other players and try to figure out who is taking advantage of their skillsets effectively. Today, I decided to take a derivative of that equation to look at how teams rank up against each other. I decided to not include any usage data here and did not include any weight for the 3 points like previously done. Additionally, I did not include any Free Throw shooting in this, out of pure laziness. It wasn't immediately in the data I had, so I didn't bother with it. This is calculated by taking the percentage of shots taken from each BBR shooting zone and multiplying it by the shooting percentage in that zone minus league average for that zone. Additionally, I used the opponent shooting for each team and did the same calculation to see if teams were doing a decent job at forcing opponents into bad shots. I know there are a multitude of fallacies in the way I'm analyzing this data, but it was just meant as a quick visual and discussion starter.
The NW Division is the worst in the NBA. In a down year, luck is very important. It's better than the NFC East in the NFL, but only because the Thunder have kept a decent record in place. So far this season, it's not just the NW division but the entire western conference has been down. The 8th place team (Utah) actually has under a .500 record whereas the Eastern conference has 11 of 15 teams at or above .500. 10 of the 15 teams in the West have an SRS less than 0 while only 5 in the East do. I'm not convinced at this point that the West will get its feet under itself, and overtake the East. When looking at BBR's adjusted net rating, the top 3 teams in the league are Spurs, Warriors and Thunder. The next 9 teams are Eastern Conference teams. 5 of the top 15 are western teams.
Sometimes you take a look at numbers and they paint a picture. Sometime's that picture is so clear that it matches up with exactly what your eyes tell you while watching NBA games. It's even rarer that what you see while watching your favorite player on your favorite team matches up with statistics. As an unabashed fan of the Oklahoma City Thunder, I will tell anyone that will listen that I'd trust Kevin Durant to make a shot to save my life 100 times out of 100. He's simply amazing. This year has been the year of Curry. (By the way green curry > red curry every day) He's had an unbelievable start to the season, historic in every imaginable way. What has not really been discussed is KD quietly humming along behind him. Steph is sitting at a TS% of 68.8 and KD is at 66.4. All time, there has only been 49 seasons where a player averaged 25+ points and >.600 TS%. Historic stuff.
Things have turned around in Oklahoma City. The fan base has stopped considering the benefits of tanking and the team is playing in a way that just seems more enjoyable. They are having fun in this recent stretch of games and it is reflected in the way we as fans feel about the team. They are on a 4 game win streak, and arbitrarily 8-12 in their last 10. On the season they are only 5-5 in road games. That's not good enough to get things done, but wins at Utah and Memphis during the current 4 game streak is nice. It's impossible to draw any conclusions from that since the 2 losses in the last 10 were at Atlanta and Miami. Both games were close, but couldn't close out. Things feel better currently but we won't know if things are changing for a while. At Cleveland will be a good test, especially considering Kyrie will most likely be back.
131 picks made so far this season. 69-62 overall ~ 52.6%
31 games picked in December so far. 20-11 overall ~ 64.5% 24 games picked this week. 16-8 overall. ~ 67% Not sure if the results are strengthening or just on an aberrational run. In previous posts I've mentioned that part of my motivation for the work being done here is to learn. Learn about basketball, learn about data acquisition, analysis, and the assorted tools that can or should be used to do the work. Insofar, I have solely used Microsoft Excel and VBA to perform work that I have done. Its what I know best and by far has the lowest knowledge entry point. What you'll see in this post is charts created in an open source statistical package called R. R is the premium statistical analysis package in use today for one paramount reason, it's free. I've done work in SPSS and Spotfire before. They are both far easier in terms of usability, but don't hold a candle to the accessibility to your run of the mill NBA fan looking to do work completely over their head.
One of the simplest, and thus most confusing ways of projecting wins is the pythagorean wins calculation. This is purely based off of points scored verse allowed. For those curious it is calculated with this equation.
G * (Tm PTS14 / (Tm PTS14 + Opp PTS14)) This over the course of a season does a really good job at predicting the win loss record for teams with an error of around 3 wins. This is a fairly simple equation obviously, but I'm curious if a comparison of actual wins to projected wins can tell us anything. Currently, 7 teams projected wins is the exact same of their actual record, 10 have won more than projected and, 13 are worse. **Keep in mind over the course of a full season there is an understood error of +/- 3 games. ** With the above caveat in mind I'm going to make some declarations based on this data. UPDATED!
Today, I was running through some statistics such as WS/48, VORP and my Layman's metric just seeing how they all compared up to this point in the season. Eventually, I put player's ranks on their respective team's for each metric then selected out the top 5 on average. (There was filtering based on minutes played >100 on the season). I did not do any selection based on positions. Here are the top 5 man units for each team based on average ranking of the three metrics listed. In the last post about the smart shooting metric I kind of fell off topic in my analysis of the data. I'm happy with the results despite the initial error in the data. Typing out thoughts on the data led me in a direction I didn't anticipate. Putting the post together I fell into a trap of comparing player's shooting basically to their amount of shooting. Ended up highlighting players who were shooting too much or too little.
This usage realization spurred me to take the total attempts factors out of the equations and see what the results looked like then. It had become obvious that the shot attempts were weighting the results in a way I didn't necessarily want. Removing those factors results in a (mostly) new top 20. |
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